Earth Science Guided Reading and Study Workbook Chapter 3
Special Report: Global Warming of 1.5 ºC
Ch 03
Impacts of 1.5ºC global warming on natural and human systems
Why is it necessary and even vital to maintain the global temperature increase below 1.5°C versus higher levels? Adaptation will be less difficult. Our world will suffer less negative impacts on intensity and frequency of extreme events, on resources, ecosystems, biodiversity, food security, cities, tourism, and carbon removal.
FAQ 3.1: What are the Impacts of 1.5°C and 2°C of Warming?
Summary : The impacts of climate change are being felt in every inhabited continent and in the oceans. However, they are not spread uniformly across the globe, and different parts of the world experience impacts differently. An average warming of 1.5°C across the whole globe raises the risk of heatwaves and heavy rainfall events, amongst many other potential impacts. Limiting warming to 1.5°C rather than 2°C can help reduce these risks, but the impacts the world experiences will depend on the specific greenhouse gas emissions 'pathway' taken. The consequences of temporarily overshooting 1.5°C of warming and returning to this level later in the century, for example, could be larger than if temperature stabilizes below 1.5°C. The size and duration of an overshoot will also affect future impacts.
Human activity has warmed the world by about 1°C since pre-industrial times, and the impacts of this warming have already been felt in many parts of the world. This estimate of the increase in global temperature is the average of many thousands of temperature measurements taken over the world's land and oceans. Temperatures are not changing at the same speed everywhere, however: warming is strongest on continents and is particularly strong in the Arctic in the cold season and in mid-latitude regions in the warm season. This is due to self-amplifying mechanisms, for instance due to snow and ice melt reducing the reflectivity of solar radiation at the surface, or soil drying leading to less evaporative cooling in the interior of continents. This means that some parts of the world have already experienced temperatures greater than 1.5°C above pre-industrial levels.
Extra warming on top of the approximately 1°C we have seen so far would amplify the risks and associated impacts, with implications for the world and its inhabitants. This would be the case even if the global warming is held at 1.5°C, just half a degree above where we are now, and would be further amplified at 2°C of global warming. Reaching 2°C instead of 1.5°C of global warming would lead to substantial warming of extreme hot days in all land regions. It would also lead to an increase in heavy rainfall events in some regions, particularly in the high latitudes of the Northern Hemisphere, potentially raising the risk of flooding. In addition, some regions, such as the Mediterranean, are projected to become drier at 2°C versus 1.5°C of global warming. The impacts of any additional warming would also include stronger melting of ice sheets and glaciers, as well as increased sea level rise, which would continue long after the stabilization of atmospheric CO2 concentrations.
Change in climate means and extremes have knock-on effects for the societies and ecosystems living on the planet. Climate change is projected to be a poverty multiplier, which means that its impacts are expected to make the poor poorer and the total number of people living in poverty greater. The 0.5°C rise in global temperatures that we have experienced in the past 50 years has contributed to shifts in the distribution of plant and animal species, decreases in crop yields and more frequent wildfires. Similar changes can be expected with further rises in global temperature.
Essentially, the lower the rise in global temperature above pre-industrial levels, the lower the risks to human societies and natural ecosystems. Put another way, limiting warming to 1.5°C can be understood in terms of 'avoided impacts' compared to higher levels of warming. Many of the impacts of climate change assessed in this report have lower associated risks at 1.5°C compared to 2°C.
Thermal expansion of the oceans means sea level will continue to rise even if the increase in global temperature is limited to 1.5°C, but this rise would be lower than in a 2°C warmer world. Ocean acidification, the process by which excess CO2 dissolves into oceans and makes them more acidic, is expected to be less damaging in a world where CO2 emissions are reduced and warming is stabilized at 1.5°C compared to 2°C. The persistence of coral reefs is greater in a 1.5°C world than that of a 2°C world, too.
The impacts of climate change that we experience in future will be affected by factors other than the change in temperature. The consequences of 1.5°C of warming will additionally depend on the specific greenhouse gas emissions 'pathway' that is followed and the extent to which adaptation can reduce vulnerability. This IPCC Special Report uses a number of 'pathways' to explore different possibilities for limiting global warming to 1.5°C above pre-industrial levels. One type of pathway sees global temperature stabilize at, or just below, 1.5°C. Another sees global temperature temporarily exceed 1.5°C before declining later in the century (known as an 'overshoot' pathway).
Such pathways would have different associated impacts, so it is important to distinguish between them for planning adaptation and mitigation strategies. For example, impacts from an overshoot pathway could be larger than impacts from a stabilization pathway. The size and duration of an overshoot would also have consequences for the impacts the world experiences. For instance, pathways that overshoot 1.5°C run a greater risk of passing through 'tipping points', thresholds beyond which certain impacts can no longer be avoided even if temperatures are brought back down later on. The collapse of the Greenland and Antarctic ice sheets on the time scale of centuries and millennia is one example of a tipping point.
FAQ 3.1, Figure 1: Temperature change is not uniform across the globe. Projected changes are shown for the average temperature of the annual hottest day (top) and the annual coldest night (bottom) with 1.5°C of global warming (left) and 2°C of global warming (right) compared to pre-industrial levels.
This chapter builds on findings of AR5 and assesses new scientific evidence of changes in the climate system and the associated impacts on natural and human systems, with a specific focus on the magnitude and pattern of risks linked for global warming of 1.5°C above temperatures in the pre-industrial period. Chapter 3 explores observed impacts and projected risks to a range of natural and human systems, with a focus on how risk levels change from 1.5°C to 2°C of global warming. The chapter also revisits major categories of risk (Reasons for Concern, RFC) based on the assessment of new knowledge that has become available since AR5.
1.5°C and 2°C Warmer Worlds
The global climate has changed relative to the pre-industrial period, and there are multiple lines of evidence that these changes have had impacts on organisms and ecosystems, as well as on human systems and well-being ( high confidence ). The increase in global mean surface temperature (GMST), which reached 0.87°C in 2006–2015 relative to 1850–1900, has increased the frequency and magnitude of impacts (high confidence), strengthening evidence of how an increase in GMST of 1.5°C or more could impact natural and human systems (1.5°C versus 2°C). {3.3, 3.4, 3.5, 3.6, Cross-Chapter Boxes 6, 7 and 8 in this chapter}
Human-induced global warming has already caused multiple observed changes in the climate system ( high confidence ). Changes include increases in both land and ocean temperatures, as well as more frequent heatwaves in most land regions (high confidence). There is also (high confidence) global warming has resulted in an increase in the frequency and duration of marine heatwaves. Further, there is substantial evidence that human-induced global warming has led to an increase in the frequency, intensity and/or amount of heavy precipitation events at the global scale (medium confidence), as well as an increased risk of drought in the Mediterranean region (medium confidence). {3.3.1, 3.3.2, 3.3.3, 3.3.4, Box 3.4}
Trends in intensity and frequency of some climate and weather extremes have been detected over time spans during which about 0.5°C of global warming occurred (medium confidence). This assessment is based on several lines of evidence, including attribution studies for changes in extremes since 1950. {3.2, 3.3.1, 3.3.2, 3.3.3, 3.3.4}
Several regional changes in climate are assessed to occur with global warming up to 1.5°C as compared to pre-industrial levels, including warming of extreme temperatures in many regions (high confidence), increases in frequency, intensity and/or amount of heavy precipitation in several regions (high confidence), and an increase in intensity or frequency of droughts in some regions (medium confidence). {3.3.1, 3.3.2, 3.3.3, 3.3.4, Table 3.2}
There is no single '1.5°C warmer world' (high confidence). In addition to the overall increase in GMST, it is important to consider the size and duration of potential overshoots in temperature. Furthermore, there are questions on how the stabilization of an increase in GMST of 1.5°C can be achieved, and how policies might be able to influence the resilience of human and natural systems, and the nature of regional and subregional risks. Overshooting poses large risks for natural and human systems, especially if the temperature at peak warming is high, because some risks may be long-lasting and irreversible, such as the loss of some ecosystems (high confidence). The rate of change for several types of risks may also have relevance, with potentially large risks in the case of a rapid rise to overshooting temperatures, even if a decrease to 1.5°C can be achieved at the end of the 21st century or later (medium confidence). If overshoot is to be minimized, the remaining equivalent CO2 budget available for emissions is very small, which implies that large, immediate and unprecedented global efforts to mitigate greenhouse gases are required (high confidence). {3.2, 3.6.2, Cross-Chapter Box 8 in this chapter}
Robust 1global differences in temperature means and extremes are expected if global warming reaches 1.5°C versus 2°C above the pre-industrial levels ( high confidence) . For oceans, regional surface temperature means and extremes are projected to be higher at 2°C compared to 1.5°C of global warming (high confidence). Temperature means and extremes are also projected to be higher at 2°C compared to 1.5°C in most land regions, with increases being 2–3 times greater than the increase in GMST projected for some regions (high confidence). Robust increases in temperature means and extremes are also projected at 1.5°C compared to present-day values (high confidence) {3.3.1, 3.3.2}. There are decreases in the occurrence of cold extremes, but substantial increases in their temperature, in particular in regions with snow or ice cover (high confidence) {3.3.1}.
Climate models project robust 2 differences in regional climate between present-day and global warming up to 1.5°C 3 , and between 1.5°C and 2°C 4 (high confidence), depending on the variable and region in question (high confidence). Large, robust and widespread differences are expected for temperature extremes (high confidence). Regarding hot extremes, the strongest warming is expected to occur at mid-latitudes in the warm season (with increases of up to 3°C for 1.5°C of global warming, i.e., a factor of two) and at high latitudes in the cold season (with increases of up to 4.5°C at 1.5°C of global warming, i.e., a factor of three) (high confidence). The strongest warming of hot extremes is projected to occur in central and eastern North America, central and southern Europe, the Mediterranean region (including southern Europe, northern Africa and the Near East), western and central Asia, and southern Africa (medium confidence). The number of exceptionally hot days are expected to increase the most in the tropics, where interannual temperature variability is lowest; extreme heatwaves are thus projected to emerge earliest in these regions, and they are expected to already become widespread there at 1.5°C global warming (high confidence). Limiting global warming to 1.5°C instead of 2°C could result in around 420 million fewer people being frequently exposed to extreme heatwaves, and about 65 million fewer people being exposed to exceptional heatwaves, assuming constant vulnerability (medium confidence). {3.3.1, 3.3.2, Cross-Chapter Box 8 in this chapter}
Limiting global warming to 1.5°C would limit risks of increases in heavy precipitation events on a global scale and in several regions compared to conditions at 2°C global warming (medium confidence). The regions with the largest increases in heavy precipitation events for 1.5°C to 2°C global warming include: several high-latitude regions (e.g. Alaska/western Canada, eastern Canada/ Greenland/Iceland, northern Europe and northern Asia); mountainous regions (e.g.,Tibetan Plateau); eastern Asia (including China and Japan); and eastern North America (medium confidence). Tropical cyclones are projected to decrease in frequency but with an increase in the number of very intense cyclones (limited evidence, low confidence). Heavy precipitation associated with tropical cyclones is projected to be higher at 2°C compared to 1.5°C of global warming (medium confidence). Heavy precipitation, when aggregated at a global scale, is projected to be higher at 2°C than at 1.5°C of global warming (medium confidence) {3.3.3, 3.3.6}
Limiting global warming to 1.5°C is expected to substantially reduce the probability of extreme drought, precipitation deficits, and risks associated with water availability (i.e., water stress) in some regions (medium confidence). In particular, risks associated with increases in drought frequency and magnitude are projected to be substantially larger at 2°C than at 1.5°C in the Mediterranean region (including southern Europe, northern Africa and the Near East) and southern Africa (medium confidence). {3.3.3, 3.3.4, Box 3.1, Box 3.2}
Risks to natural and human systems are expected to be lower at 1.5°C than at 2°C of global warming (high confidence). This difference is due to the smaller rates and magnitudes of climate change associated with a 1.5°C temperature increase, including lower frequencies and intensities of temperature-related extremes. Lower rates of change enhance the ability of natural and human systems to adapt, with substantial benefits for a wide range of terrestrial, freshwater, wetland, coastal and ocean ecosystems (including coral reefs) (high confidence), as well as food production systems, human health, and tourism (medium confidence), together with energy systems and transportation (low confidence). {3.3.1, 3.4}
Exposure to multiple and compound climate-related risks is projected to increase between 1.5°C and 2°C of global warming with greater proportions of people both exposed and susceptible to poverty in Africa and Asia (high confidence). For global warming from 1.5°C to 2°C, risks across energy, food, and water sectors could overlap spatially and temporally, creating new – and exacerbating current – hazards, exposures, and vulnerabilities that could affect increasing numbers of people and regions (medium confidence). Small island states and economically disadvantaged populations are particularly at risk (high confidence). {3.3.1, 3.4.5.3, 3.4.5.6, 3.4.11, 3.5.4.9, Box 3.5}
Global warming of 2°C would lead to an expansion of areas with significant increases in runoff, as well as those affected by flood hazard, compared to conditions at 1.5°C (medium confidence). Global warming of 1.5°C would also lead to an expansion of the global land area with significant increases in runoff (medium confidence) and an increase in flood hazard in some regions (medium confidence) compared to present-day conditions. {3.3.5}
The probability of a sea-ice-free Arctic Ocean 5 during summer is substantially higher at 2°C compared to 1.5°C of global warming (medium confidence). Model simulations suggest that at least one sea-ice-free Arctic summer is expected every 10 years for global warming of 2°C, with the frequency decreasing to one sea-ice-free Arctic summer every 100 years under 1.5°C (medium confidence). An intermediate temperature overshoot will have no long- term consequences for Arctic sea ice coverage, and hysteresis is not expected (high confidence). {3.3.8, 3.4.4.7}
Global mean sea level rise (GMSLR) is projected to be around 0.1 m (0.04 – 0.16 m) less by the end of the 21st century in a 1.5°C warmer world compared to a 2°C warmer world (medium confidence). Projected GMSLR for 1.5°C of global warming has an indicative range of 0.26 – 0.77m, relative to 1986–2005, (medium confidence). A smaller sea level rise could mean that up to 10.4 million fewer people (based on the 2010 global population and assuming no adaptation) would be exposed to the impacts of sea level rise globally in 2100 at 1.5°C compared to at 2°C. A slower rate of sea level rise enables greater opportunities for adaptation (medium confidence). There is high confidence that sea level rise will continue beyond 2100. Instabilities exist for both the Greenland and Antarctic ice sheets, which could result in multi-meter rises in sea level on time scales of century to millennia. There is (medium confidence) that these instabilities could be triggered at around 1.5°C to 2°C of global warming. {3.3.9, 3.4.5, 3.6.3}
The ocean has absorbed about 30% of the anthropogenic carbon dioxide, resulting in ocean acidification and changes to carbonate chemistry that are unprecedented for at least the last 65 million years (high confidence). Risks have been identified for the survival, calcification, growth, development and abundance of a broad range of marine taxonomic groups, ranging from algae to fish, with substantial evidence of predictable trait-based sensitivities (high confidence). There are multiple lines of evidence that ocean warming and acidification corresponding to 1.5°C of global warming would impact a wide range of marine organisms and ecosystems, as well as sectors such as aquaculture and fisheries (high confidence). {3.3.10, 3.4.4}
Larger risks are expected for many regions and systems for global warming at 1.5°C, as compared to today, with adaptation required now and up to 1.5°C. However, risks would be larger at 2°C of warming and an even greater effort would be needed for adaptation to a temperature increase of that magnitude (high confidence). {3.4, Box 3.4, Box 3.5, Cross-Chapter Box 6 in this chapter}
Future risks at 1.5°C of global warming will depend on the mitigation pathway and on the possible occurrence of a transient overshoot (high confidence). The impacts on natural and human systems would be greater if mitigation pathways temporarily overshoot 1.5°C and return to 1.5°C later in the century, as compared to pathways that stabilize at 1.5°C without an overshoot (high confidence). The size and duration of an overshoot would also affect future impacts (e.g., irreversible loss of some ecosystems) (high confidence). Changes in land use resulting from mitigation choices could have impacts on food production and ecosystem diversity. {3.6.1, 3.6.2, Cross-Chapter Boxes 7 and 8 in this chapter}
Climate Change Risks for Natural and Human systems
Terrestrial and Wetland Ecosystems
Risks of local species losses and, consequently, risks of extinction are much less in a 1.5°C versus a 2°C warmer world (high confidence). The number of species projected to lose over half of their climatically determined geographic range at 2°C global warming (18% of insects, 16% of plants, 8% of vertebrates) is projected to be reduced to 6% of insects, 8% of plants and 4% of vertebrates at 1.5°C warming (medium confidence). Risks associated with other biodiversity-related factors, such as forest fires, extreme weather events, and the spread of invasive species, pests and diseases, would also be lower at 1.5°C than at 2°C of warming (high confidence), supporting a greater persistence of ecosystem services.
{3.4.3, 3.5.2}
Constraining global warming to 1.5°C, rather than to 2°C and higher, is projected to have many benefits for terrestrial and wetland ecosystems and for the preservation of their services to humans (high confidence). Risks for natural and managed ecosystems are higher on drylands compared to humid lands. The global terrestrial land area projected to be affected by ecosystem transformations (13%, interquartile range 8–20%) at 2°C is approximately halved at 1.5°C global warming to 4% (interquartile range 2–7%) (medium confidence). Above 1.5°C, an expansion of desert terrain and vegetation would occur in the Mediterranean biome (medium confidence), causing changes unparalleled in the last 10,000 years (medium confidence). {3.3.2.2, 3.4.3.2, 3.4.3.5, 3.4.6.1, 3.5.5.10, Box 4.2}
Many impacts are projected to be larger at higher latitudes, owing to mean and cold-season warming rates above the global average (medium confidence). High-latitude tundra and boreal forest are particularly at risk, and woody shrubs are already encroaching into tundra (high confidence) and will proceed with further warming. Constraining warming to 1.5°C would prevent the thawing of an estimated permafrost area of 1.5 to 2.5 million km2 over centuries compared to thawing under 2°C (medium confidence). {3.3.2, 3.4.3, 3.4.4}
Ocean Ecosystems
Ocean ecosystems are already experiencing large-scale changes, and critical thresholds are expected to be reached at 1.5°C and higher levels of global warming (high confidence). In the transition to 1.5°C of warming, changes to water temperatures are expected to drive some species (e.g., plankton, fish) to relocate to higher latitudes and cause novel ecosystems to assemble (high confidence). Other ecosystems (e.g., kelp forests, coral reefs) are relatively less able to move, however, and are projected to experience high rates of mortality and loss (very high confidence). For example, multiple lines of evidence indicate that the majority (70–90%) of warm water (tropical) coral reefs that exist today will disappear even if global warming is constrained to 1.5°C (very high confidence). {3.4.4, Box 3.4}
Current ecosystem services from the ocean are expected to be reduced at 1.5°C of global warming, with losses being even greater at 2°C of global warming (high confidence). The risks of declining ocean productivity, shifts of species to higher latitudes, damage to ecosystems (e.g., coral reefs, and mangroves, seagrass and other wetland ecosystems), loss of fisheries productivity (at low latitudes), and changes to ocean chemistry (e.g., acidification, hypoxia and dead zones) are projected to be substantially lower when global warming is limited to 1.5°C (high confidence). {3.4.4, Box 3.4}
Water Resources
The projected frequency and magnitude of floods and droughts in some regions are smaller under 1.5°C than under 2°C of warming (medium confidence). Human exposure to increased flooding is projected to be substantially lower at 1.5°C compared to 2°C of global warming, although projected changes create regionally differentiated risks (medium confidence). The differences in the risks among regions are strongly influenced by local socio-economic conditions (medium confidence). {3.3.4, 3.3.5, 3.4.2}
Risks of water scarcity are projected to be greater at 2°C than at 1.5°C of global warming in some regions (medium confidence). Depending on future socio-economic conditions, limiting global warming to 1.5°C, compared to 2°C, may reduce the proportion of the world population exposed to a climate change-induced increase in water stress by up to 50%, although there is considerable variability between regions (medium confidence). Regions with particularly large benefits could include the Mediterranean and the Caribbean (medium confidence). Socio-economic drivers, however, are expected to have a greater influence on these risks than the changes in climate (medium confidence). {3.3.5, 3.4.2, Box 3.5}
Land Use, Food Security and Food Production Systems
Limiting global warming to 1.5°C, compared with 2°C, is projected to result in smaller net reductions in yields of maize, rice, wheat, and potentially other cereal crops, particularly in sub-Saharan Africa, Southeast Asia, and Central and South America; and in the CO2-dependent nutritional quality of rice and wheat (high confidence). A loss of 7–10% of rangeland livestock globally is projected for approximately 2°C of warming, with considerable economic consequences for many communities and regions (medium confidence). {3.4.6, 3.6, Box 3.1, Cross-Chapter Box 6 in this chapter}
Reductions in projected food availability are larger at 2°C than at 1.5°C of global warming in the Sahel, southern Africa, the Mediterranean, central Europe and the Amazon (medium confidence). This suggests a transition from medium to high risk of regionally differentiated impacts on food security between 1.5°C and 2°C (medium confidence). Future economic and trade environments and their response to changing food availability (medium confidence) are important potential adaptation options for reducing hunger risk in low- and middle-income countries. {Cross-Chapter Box 6 in this chapter}
Fisheries and aquaculture are important to global food security but are already facing increasing risks from ocean warming and acidification (medium confidence). These risks are projected to increase at 1.5°C of global warming and impact key organisms such as fin fish and bivalves (e.g., oysters), especially at low latitudes (medium confidence). Small-scale fisheries in tropical regions, which are very dependent on habitat provided by coastal ecosystems such as coral reefs, mangroves, seagrass and kelp forests, are expected to face growing risks at 1.5°C of warming because of loss of habitat (medium confidence). Risks of impacts and decreasing food security are projected to become greater as global warming reaches beyond 1.5°C and both ocean warming and acidification increase, with substantial losses likely for coastal livelihoods and industries (e.g., fisheries and aquaculture) (medium to high confidence). {3.4.4, 3.4.5, 3.4.6, Box 3.1, Box 3.4, Box 3.5, Cross-Chapter Box 6 in this chapter}
Land use and land-use change emerge as critical features of virtually all mitigation pathways that seek to limit global warming to 1.5°C (high confidence). Most least-cost mitigation pathways to limit peak or end-of-century warming to 1.5°C make use of carbon dioxide removal (CDR), predominantly employing significant levels of bioenergy with carbon capture and storage (BECCS) and/or afforestation and reforestation (AR) in their portfolio of mitigation measures (high confidence). {Cross-Chapter Box 7 in this chapter}
Large-scale deployment of BECCS and/or AR would have a far-reaching land and water footprint (high confidence). Whether this footprint would result in adverse impacts, for example on biodiversity or food production, depends on the existence and effectiveness of measures to conserve land carbon stocks, measures to limit agricultural expansion in order to protect natural ecosystems, and the potential to increase agricultural productivity (medium agreement). In addition, BECCS and/or AR would have substantial direct effects on regional climate through biophysical feedbacks, which are generally not included in Integrated Assessments Models (high confidence). {3.6.2, Cross-Chapter Boxes 7 and 8 in this chapter}
The impacts of large-scale CDR deployment could be greatly reduced if a wider portfolio of CDR options were deployed, if a holistic policy for sustainable land management were adopted, and if increased mitigation efforts were employed to strongly limit the demand for land, energy and material resources, including through lifestyle and dietary changes (medium confidence). In particular, reforestation could be associated with significant co-benefits if implemented in a manner than helps restore natural ecosystems (high confidence). {Cross-Chapter Box 7 in this chapter}
Human Health, Well-Being, Cities and Poverty
Any increase in global temperature (e.g., +0.5°C) is projected to affect human health, with primarily negative consequences (high confidence). Lower risks are projected at 1.5°C than at 2°C for heat-related morbidity and mortality (very high confidence), and for ozone-related mortality if emissions needed for ozone formation remain high (high confidence). Urban heat islands often amplify the impacts of heatwaves in cities (high confidence). Risks for some vector-borne diseases, such as malaria and dengue fever are projected to increase with warming from 1.5°C to 2°C, including potential shifts in their geographic range (high confidence). Overall for vector- borne diseases, whether projections are positive or negative depends on the disease, region and extent of change (high confidence). Lower risks of undernutrition are projected at 1.5°C than at 2°C (medium confidence). Incorporating estimates of adaptation into projections reduces the magnitude of risks (high confidence). {3.4.7, 3.4.7.1, 3.4.8, 3.5.5.8}
Global warming of 2°C is expected to pose greater risks to urban areas than global warming of 1.5°C (medium confidence). The extent of risk depends on human vulnerability and the effectiveness of adaptation for regions (coastal and non-coastal), informal settlements and infrastructure sectors (such as energy, water and transport) (high confidence). {3.4.5, 3.4.8}
Poverty and disadvantage have increased with recent warming (about 1°C) and are expected to increase for many populations as average global temperatures increase from 1°C to 1.5°C and higher (medium confidence). Outmigration in agricultural- dependent communities is positively and statistically significantly associated with global temperature (medium confidence). Our understanding of the links of 1.5°C and 2°C of global warming to human migration are limited and represent an important knowledge gap. {3.4.10, 3.4.11, 5.2.2, Table 3.5}
Key Economic Sectors and Services
Risks to global aggregated economic growth due to climate change impacts are projected to be lower at 1.5°C than at 2°C by the end of this century (medium confidence). {3.5.2, 3.5.3} The largest reductions in economic growth at 2°C compared to 1.5°C of warming are projected for low- and middle-income countries and regions (the African continent, Southeast Asia, India, Brazil and Mexico) (low to medium confidence). Countries in the tropics and Southern Hemisphere subtropics are projected to experience the largest impacts on economic growth due to climate change should global warming increase from 1.5°C to 2°C (medium confidence). {3.5} Global warming has already affected tourism, with increased risks projected under 1.5°C of warming in specific geographic regions and for seasonal tourism including sun, beach and snow sports destinations (very high confidence). Risks will be lower for tourism markets that are less climate sensitive, such as gaming and large hotel-based activities (high confidence). Risks for coastal tourism, particularly in subtropical and tropical regions, will increase with temperature-related degradation (e.g., heat extremes, storms) or loss of beach and coral reef assets (high confidence). {3.3.6, 3.4.4.12, 3.4.9.1, Box 3.4}
Small Islands, and Coastal and Low-lying areas
Small islands are projected to experience multiple inter- related risks at 1.5°C of global warming that will increase with warming of 2°C and higher levels (high confidence). Climate hazards at 1.5°C are projected to be lower compared to those at 2°C (high confidence). Long-term risks of coastal flooding and impacts on populations, infrastructures and assets (high confidence), freshwater stress (medium confidence), and risks across marine ecosystems (high confidence) and critical sectors (medium confidence) are projected to increase at 1.5°C compared to present-day levels and increase further at 2°C, limiting adaptation opportunities and increasing loss and damage (medium confidence). Migration in small islands (internally and internationally) occurs for multiple reasons and purposes, mostly for better livelihood opportunities (high confidence) and increasingly owing to sea level rise (medium confidence). {3.3.2.2, 3.3.6–9, 3.4.3.2, 3.4.4.2, 3.4.4.5, 3.4.4.12, 3.4.5.3, 3.4.7.1, 3.4.9.1, 3.5.4.9, Box 3.4, Box 3.5}
Impacts associated with sea level rise and changes to the salinity of coastal groundwater, increased flooding and damage to infrastructure, are projected to be critically important in vulnerable environments, such as small islands, low-lying coasts and deltas, at global warming of 1.5°C and 2°C (high confidence). Localized subsidence and changes to river discharge can potentially exacerbate these effects. Adaptation is already happening (high confidence) and will remain important over multi-centennial time scales. {3.4.5.3, 3.4.5.4, 3.4.5.7, 5.4.5.4, Box
3.5}
Existing and restored natural coastal ecosystems may be effective in reducing the adverse impacts of rising sea levels and intensifying storms by protecting coastal and deltaic regions (medium confidence). Natural sedimentation rates are expected to be able to offset the effect of rising sea levels, given the slower rates of sea level rise associated with 1.5°C of warming (medium confidence). Other feedbacks, such as landward migration of wetlands and the adaptation of infrastructure, remain important (medium confidence). {3.4.4.12, 3.4.5.4, 3.4.5.7}
Increased Reasons for Concern
There are multiple lines of evidence that since AR5 the assessed levels of risk increased for four of the five Reasons for Concern (RFCs) for global warming levels of up to 2°C (high confidence). The risk transitions by degrees of global warming are now: from high to very high between 1.5°C and 2°C for RFC1 (Unique and threatened systems) (high confidence); from moderate to high risk between 1°C and 1.5°C for RFC2 (Extreme weather events) (medium confidence); from moderate to high risk between 1.5°C and 2°C for RFC3 (Distribution of impacts) (high confidence); from moderate to high risk between 1.5°C and 2.5°C for RFC4 (Global aggregate impacts) (medium confidence); and from moderate to high risk between 1°C and 2.5°C for RFC5 (Large-scale singular events) (medium confidence). {3.5.2}
- The category 'Unique and threatened systems' (RFC1) display a transition from high to very high risk which is now located between 1.5°C and 2°C of global warming as opposed to at 2.6°C of global warming in AR5, owing to new and multiple lines of evidence for changing risks for coral reefs, the Arctic and biodiversity in general (high confidence). {3.5.2.1}
- In 'Extreme weather events' (RFC2), the transition from moderate to high risk is now located between 1.0°C and 1.5°C of global warming, which is very similar to the AR5 assessment but is projected with greater confidence (medium confidence). The impact literature contains little information about the potential for human society to adapt to extreme weather events, and hence it has not been possible to locate the transition from 'high' to 'very high' risk within the context of assessing impacts at 1.5°C versus 2°C of global warming. There is thus low confidence in the level at which global warming could lead to very high risks associated with extreme weather events in the context of this report. {3.5}
- With respect to the 'Distribution of impacts' (RFC3) a transition from moderate to high risk is now located between 1.5°C and 2°C of global warming, compared with between 1.6°C and 2.6°C global warming in AR5, owing to new evidence about regionally differentiated risks to food security, water resources, drought, heat exposure and coastal submergence (high confidence). {3.5}
- In 'global aggregate impacts' (RFC4) a transition from moderate to high levels of risk is now located between 1.5°C and 2 .5°C of global warming, as opposed to at 3.6°C of warming in AR5, owing to new evidence about global aggregate economic impacts and risks to Earth's biodiversity (medium confidence). {3.5}
- Finally, 'large-scale singular events' (RFC5), moderate risk is now located at 1°C of global warming and high risk is located at 2.5°C of global warming, as opposed to at 1.6°C (moderate risk) and around 4°C (high risk) in AR5, because of new observations and models of the West Antarctic ice sheet (medium confidence). {3.3.9, 3.5.2, 3.6.3}
This chapter should be cited as:
Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5ºC Global Warming on Natural and Human Systems. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In Press.
Chapter 3 uses relevant definitions of a potential 1.5°C warmer world from Chapters 1 and 2 and builds directly on their assessment of gradual versus overshoot scenarios. It interacts with information presented in Chapter 2 via the provision of specific details relating to the mitigation pathways (e.g., land-use changes) and their implications for impacts. Chapter 3 also includes information needed for the assessment and implementation of adaptation options (presented in Chapter 4), as well as the context for considering the interactions of climate change with sustainable development and for the assessment of impacts on sustainability, poverty and inequalities at the household to subregional level (presented in Chapter 5).
This chapter is necessarily transdisciplinary in its coverage of the climate system, natural and managed ecosystems, and human systems and responses, owing to the integrated nature of the natural and human experience. While climate change is acknowledged as a centrally important driver, it is not the only driver of risks to human and natural systems, and in many cases, it is the interaction between these two broad categories of risk that is important (Chapter 1).
The flow of the chapter, linkages between sections, a list of chapter-and cross-chapter boxes, and a content guide for reading according to focus or interest are given in Figure 3.1. Key definitions used in the chapter are collected in the Glossary. Confidence language is used throughout this chapter and likelihood statements (e.g., likely, very likely) are provided when there is high confidence in the assessment.
Figure 3.1
Chapter 3 structure and quick guide
Chapter 3 structure and quick guide
The underlying literature assessed in Chapter 3 is broad and includes a large number of recent publications specific to assessments for 1.5°C of warming. The chapter also utilizes information covered in prior IPCC special reports, for example the Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; IPCC, 2012)1, and many chapters from the IPCC WGII Fifth Assessment Report (AR5) that assess impacts on natural and managed ecosystems and humans, as well as adaptation options (IPCC, 2014b)2. For this reason, the chapter provides information based on a broad range of assessment methods. Details about the approaches used are presented in Section 3.2.
Section 3.3 gives a general overview of recent literature on observed climate change impacts as the context for projected future risks. With a few exceptions, the focus here is the analysis of transient responses at 1.5°C and 2°C of global warming, with simulations of short-term stabilization scenarios (Section 3.2) also assessed in some cases. In general, long-term equilibrium stabilization responses could not be assessed owing to a lack of data and analysis. A detailed analysis of detection and attribution is not provided but will be the focus of the next IPCC assessment report (AR6). Furthermore, possible interventions in the climate system through radiation modification measures, which are not tied to reductions of greenhouse gas emissions or concentrations, are not assessed in this chapter.
Understanding the observed impacts and projected risks of climate change is crucial to comprehending how the world is likely to change under global warming of 1.5°C above temperatures in the pre-industrial period (with reference to 2°C). Section 3.4 explores the new literature and updates the assessment of impacts and projected risks for a large number of natural and human systems. By also exploring adaptation opportunities, where the literature allows, the section prepares the reader for discussions in subsequent chapters about opportunities to tackle both mitigation and adaptation. The section is mostly globally focused because of limited research on regional risks and adaptation options at 1.5°C and 2°C. For example, the risks of 1.5°C and 2°C of warming in urban areas, as well as the risks of health outcomes under these two warming scenarios (e.g. climate-related diseases, air quality impacts and mental health problems), were not considered because of a lack of projections of how these risks might change in a 1.5°C or 2°C warmer world. In addition, the complexity of many interactions of climate change with drivers of poverty, along with a paucity of relevant studies, meant it was not possible to detect and attribute many dimensions of poverty and disadvantage to climate change. Even though there is increasing documentation of climate-related impacts on places where indigenous people live and where subsistence-oriented communities are found, relevant projections of the risks associated with warming of 1.5°C and 2°C are necessarily limited.
To explore avoided impacts and reduced risks at 1.5°C compared with at 2°C of global warming, the chapter adopts the AR5 'Reasons for Concern' aggregated projected risk framework (Section 3.5). Updates in terms of the aggregation of risks are informed by the most recent literature and the assessments offered in Sections 3.3 and 3.4, with a focus on the impacts at 2°C of warming that could potentially be avoided if warming were constrained to 1.5°C. Economic benefits that would be obtained (Section 3.5.3), climate change 'hotspots' that could be avoided or reduced (Section 3.5.4 as guided by the assessments of Sections 3.3, 3.4 and 3.5), and tipping points that could be circumvented (Section 3.5.5) at 1.5°C compared to higher degrees of global warming are all examined. The latter assessments are, however, constrained to regional analyses, and hence this particular section does not include an assessment of specific losses and damages.
Section 3.6 provides an overview on specific aspects of the mitigation pathways considered compatible with 1.5°C of global warming, including some scenarios involving temperature overshoot above 1.5°C global warming during the 21st century. Non-CO2 implications and projected risks of mitigation pathways, such as changes to land use and atmospheric compounds, are presented and explored. Finally, implications for sea ice, sea level and permafrost beyond the end of the century are assessed.
The exhaustive assessment of literature specific to global warming of 1.5°C above the pre-industrial period, presented across all the sections in Chapter 3, highlights knowledge gaps resulting from the heterogeneous information available across systems, regions and sectors. Some of these gaps are described in Section 3.7.
3.2
How are Risks at 1.5°C and Higher Levels of Global Warming Assessed in this Chapter?
The methods that are applied for assessing observed and projected changes in climate and weather are presented in Section 3.2.1, while those used for assessing the observed impacts on and projected risks to natural and managed systems, and to human settlements, are described in Section 3.2.2. Given that changes in climate associated with 1.5°C of global warming were not the focus of past IPCC reports, dedicated approaches based on recent literature that are specific to the present report are also described. Background on specific methodological aspects (climate model simulations available for assessments at 1.5°C global warming, attribution of observed changes in climate and their relevance for assessing projected changes at 1.5°C and 2°C global warming, and the propagation of uncertainties from climate forcing to impacts on ecosystems) are provided in the Supplementary Material 3.SM.
3.2.1
How are Changes in Climate and Weather at 1.5°C versus Higher Levels of Warming Assessed?
Evidence for the assessment of changes to climate at 1.5°C versus 2°C can be drawn both from observations and model projections. Global mean surface temperature (GMST) anomalies were about +0.87°C (±0.10°C likely range) above pre-industrial industrial (1850–1900) values in the 2006-–2015 decade, with a recent warming of about 0.2°C (±0.10°C) per decade (Chapter 1). Human-induced global warming reached approximately 1°C (±0.2°C likely range) in 2017 (Chapter 1). While some of the observed trends may be due to internal climate variability, methods of detection and attribution can be applied to assess which part of the observed changes may be attributed to anthropogenic forcing (Bindoff et al., 2013b)3. Hence, evidence from attribution studies can be used to assess changes in the climate system that are already detectable at lower levels of global warming and would thus continue to change with a further 0.5°C or 1°C of global warming (see Supplementary Material 3.SM.1 and Sections 3.3.1, 3.3.2, 3.3.3, 3.3.4 and 3.3.11). A recent study identified significant changes in extremes for a 0.5°C difference in global warming based on the historical record (Schleussner et al., 2017)4. It should also be noted that attributed changes in extremes since 1950 that were reported in the IPCC AR5 report (IPCC, 2013)5 generally correspond to changes in global warming of about 0.5°C (see 3.SM.1)
Climate model simulations are necessary for the investigation of the response of the climate system to various forcings, in particular to forcings associated with higher levels of greenhouse gas concentrations. Model simulations include experiments with global and regional climate models, as well as impact models – driven with output from climate models – to evaluate the risk related to climate change for natural and human systems (Supplementary Material 3.SM.1). Climate model simulations were generally used in the context of particular 'climate scenarios' from previous IPCC reports (e.g., IPCC, 2007, 2013)6. This means that emissions scenarios (IPCC, 2000)7 were used to drive climate models, providing different projections for given emissions pathways. The results were consequently used in a 'storyline' framework, which presents the development of climate in the course of the 21st century and beyond for a given emissions pathway. Results were assessed for different time slices within the model projections such as 2016–2035 ('near term', which is slightly below a global warming of 1.5°C according to most scenarios, Kirtman et al., 2013)8, 2046–2065 (mid-21st century, Collins et al., 2013)9, and 2081–2100 (end of 21st century, Collins et al., 2013)10. Given that this report focuses on climate change for a given mean global temperature response (1.5°C or 2°C), methods of analysis had to be developed and/or adapted from previous studies in order to provide assessments for the specific purposes here.
A major challenge in assessing climate change under 1.5°C, or 2°C (and higher levels), of global warming pertains to the definition of a '1.5°C or 2°C climate projection' (see also Cross-Chapter Box 8 in this chapter). Resolving this challenge includes the following considerations:
- The need to distinguish between (i) transient climate responses (i.e., those that 'pass through' 1.5°C or 2°C of global warming), (ii) short-term stabilization responses (i.e., scenarios for the late 21st century that result in stabilization at a mean global warming of 1.5°C or 2°C by 2100), and (iii) long-term equilibrium stabilization responses (i.e., those occurring after several millennia once climate (temperature) equilibrium at 1.5°C or 2°C is reached). These responses can be very different in terms of climate variables and the inertia associated with a given climate forcing. A striking example is sea level rise (SLR). In this case, projected increases within the 21st century are minimally dependent on the scenario considered, yet they stabilize at very different levels for a long-term warming of 1.5°C versus 2°C (Section 3.3.9).
- The '1.5°C or 2°C emissions scenarios' presented in Chapter 2 are targeted to hold warming below 1.5°C or 2°C with a certain probability (generally two-thirds) over the course, or at the end, of the 21st century. These scenarios should be seen as the operationalization of 1.5°C or 2°C warmer worlds. However, when these emission scenarios are used to drive climate models, some of the resulting simulations lead to warming above these respective thresholds (typically with a probability of one-third, see Chapter 2 and Cross-Chapter Box 8 in this chapter). This is due both to discrepancies between models and to internal climate variability. For this reason, the climate outcome for any of these scenarios, even those excluding an overshoot (see next point, C.), include some probability of reaching a global climate warming of more than 1.5°C or 2°C. Hence, a comprehensive assessment of climate risks associated with '1.5°C or 2°C climate scenarios' needs to include consideration of higher levels of warming (e.g., up to 2.5°C to 3°C, see Chapter 2 and Cross-Chapter Box 8 in this chapter).
- Most of the '1.5°C scenarios', and some of the '2°C emissions scenarios' presented in Chapter 2 include a temperature overshoot during the course of the 21st century. This means that median temperature projections under these scenarios exceed the target warming levels over the course of the century (typically 0.5°C–1°C higher than the respective target levels at most), before warming returns to below 1.5°C or 2°C by 2100. During the overshoot phase, impacts would therefore correspond to higher transient temperature increases than 1.5°C or 2°C. For this reason, impacts of transient responses at these higher warming levels are also partly addressed in Cross-Chapter Box 8 in this chapter (on a 1.5°C warmer world), and some analyses for changes in extremes are also presented for higher levels of warming in Section 3.3 (Figures 3.5, 3.6, 3.9, 3.10, 3.12 and 3.13). Most importantly, different overshoot scenarios may have very distinct impacts depending on (i) the peak temperature of the overshoot, (ii) the length of the overshoot period, and (iii) the associated rate of change in global temperature over the time period of the overshoot. While some of these issues are briefly addressed in Sections 3.3 and 3.6, and in the Cross-Chapter Box 8, the definition of overshoot and related questions will need to be more comprehensively addressed in the IPCC AR6 report.
- The levels of global warming that are the focus of this report (1.5ºC and 2ºC) are measured relative to the pre-industrial period. This definition requires an agreement on the exact reference time period (for 0°C of warming) and the time frame over which the global warming is assessed, typically 20 to 30 years in length. As discussed in Chapter 1, a climate with 1.5ºC global warming is one in which temperatures averaged over a multi-decade time scale are 1.5°C above those in the pre-industrial reference period. Greater detail is provided in Cross-Chapter Box 8 in this chapter. Inherent to this is the observation that the mean temperature of a '1.5°C warmer world' can be regionally and temporally much higher (for e.g., with regional annual temperature extremes involving warming of more than 6°C; see Section 3.3 and Cross-Chapter Box 8 in this chapter).
- The interference of factors unrelated to greenhouse gases with mitigation pathways can strongly affect regional climate. For example, biophysical feedbacks from changes in land use and irrigation (e.g., Hirsch et al., 2017; Thiery et al., 2017)11, or projected changes in short-lived pollutants (e.g., Z. Wang et al., 2017)12, can have large influences on local temperatures and climate conditions. While these effects are not explicitly integrated into the scenarios developed in Chapter 2, they may affect projected changes in climate under 1.5°C of global warming. These issues are addressed in more detail in Section 3.6.2.2.
The assessment presented in the current chapter largely focuses on the analysis of transient responses in climate at 1.5°C versus 2°C and higher levels of global warming (see point A. above and Section 3.3). It generally uses the empirical scaling relationship (ESR) approach (Seneviratne et al., 2018c)13, also termed the 'time sampling' approach (James et al., 2017)14, which consists of sampling the response at 1.5°C and other levels of global warming from all available global climate model scenarios for the 21st century (e.g., Schleussner et al., 2016b; Seneviratne et al., 2016; Wartenburger et al., 2017)15. The ESR approach focuses more on the derivation of a continuous relationship, while the term 'time sampling' is more commonly used when comparing a limited number of warming levels (e.g., 1.5°C versus 2°C). A similar approach in the case of regional climate model (RCM) simulations consists of sampling the RCM model output corresponding to the time frame at which the driving general circulation model (GCM) reaches the considered temperature level, for example, as done within IMPACT2C (Jacob and Solman, 2017), see16description in Vautard et al. (2014)17. As an alternative to the ESR or time sampling approach, pattern scaling may be used. Pattern scaling is a statistical approach that describes relationships of specific climate responses as a function of global temperature change. Some assessments presented in this chapter are based on this method. The disadvantage of pattern scaling, however, is that the relationship may not perfectly emulate the models' responses at each location and for each global temperature level (James et al., 2017)18. Expert judgement is a third methodology that can be used to assess probable changes at 1.5°C or 2°C of global warming by combining changes that have been attributed to the observed time period (corresponding to warming of 1°C or less if assessed over a shorter period) with known projected changes at 3°C or 4°C above pre-industrial temperatures (Supplementary Material 3.SM.1). In order to assess effects induced by a 0.5°C difference in global warming, the historical record can be used at first approximation as a proxy, meaning that conditions are compared for two periods that have a 0.5ºC difference in GMST warming (such as 1991–2010 and 1960–1979, e.g., Schleussner et al., 2017)19. This in particular also applies to attributed changes in extremes since 1950 that were reported in the IPCC AR5 report (IPCC, 201320; see also 3.SM.1). Using observations, however, it is not possible to account for potential non-linear changes that could occur above 1°C of global warming or as 1.5°C of warming is reached.
In some cases, assessments of short-term stabilization responses are also presented, derived using a subset of model simulations that reach a given temperature limit by 2100, or driven by sea surface temperature (SST) values consistent with such scenarios. This includes new results from the 'Half a degree additional warming, prognosis and projected impacts' (HAPPI) project (Section 1.5.2; Mitchell et al., 2017)21. Notably, there is evidence that for some variables (e.g., temperature and precipitation extremes), responses after short-term stabilization (i.e., approximately equivalent to the RCP2.6 scenario) are very similar to the transient response of higher-emissions scenarios (Seneviratne et al., 2016, 2018c; Wartenburger et al., 2017; Tebaldi and Knutti, 2018)22. This is, however, less the case for mean precipitation (e.g., Pendergrass et al., 2015)23, for which other aspects of the emissions scenarios appear relevant.
For the assessment of long-term equilibrium stabilization responses, this chapter uses results from existing simulations where available (e.g., for sea level rise), although the available data for this type of projection is limited for many variables and scenarios and will need to be addressed in more depth in the IPCC AR6 report.
Supplementary Material 3.SM.1 of this chapter includes further details of the climate models and associated simulations that were used to support the present assessment, as well as a background on detection and attribution approaches of relevance to assessing changes in climate at 1.5°C of global warming.
3.2.2
How are Potential Impacts on Ecosystems Assessed at 1.5°C versus Higher Levels of Warming?
Considering that the impacts observed so far are for a global warming lower than 1.5°C (generally up to the 2006–2015 decade, i.e., for a global warming of 0.87°C or less; see above), direct information on the impacts of a global warming of 1.5°C is not yet available. The global distribution of observed impacts shown in AR5 (Cramer et al., 2014)24, however, demonstrates that methodologies now exist which are capable of detecting impacts on systems strongly influenced by factors (e.g., urbanization and human pressure in general) or where climate may play only a secondary role in driving impacts. Attribution of observed impacts to greenhouse gas forcing is more rarely performed, but a recent study (Hansen and Stone, 2016)25 shows that most of the detected temperature-related impacts that were reported in AR5 (Cramer et al., 2014)26 can be attributed to anthropogenic climate change, while the signals for precipitation-induced responses are more ambiguous.
One simple approach for assessing possible impacts on natural and managed systems at 1.5°C versus 2°C consists of identifying impacts of a global 0.5°C of warming in the observational record (e.g., Schleussner et al., 2017)27 assuming that the impacts would scale linearly for higher levels of warming (although this may not be appropriate). Another approach is to use conclusions from analyses of past climates combined with modelling of the relationships between climate drivers and natural systems (Box 3.3). A more complex approach relies on laboratory or field experiments (Dove et al., 2013; Bonal et al., 2016)28, which provide useful information on the causal effect of a few factors, which can be as diverse as climate, greenhouse gases (GHG), management practices, and biological and ecological variables, on specific natural systems that may have unusual physical and chemical characteristics (e.g., Fabricius et al., 2011; Allen et al., 2017)29. This last approach can be important in helping to develop and calibrate impact mechanisms and models through empirical experimentation and observation.
Risks for natural and human systems are often assessed with impact models where climate inputs are provided by representative concentration pathway (RCP)-based climate projections. The number of studies projecting impacts at 1.5°C or 2°C of global warming has increased in recent times (see Section 3.4), even if the four RCP scenarios used in AR5 are not strictly associated with these levels of global warming. Several approaches have been used to extract the required climate scenarios, as described in Section 3.2.1. As an example, Schleussner et al. (2016b)30 applied a time sampling (or ESR) approach, described in Section 3.2.1, to estimate the differential effect of 1.5°C and 2°C of global warming on water availability and impacts on agriculture using an ensemble of simulations under the RCP8.5 scenario. As a further example using a different approach, Iizumi et al. (2017)31 derived a 1.5°C scenario from simulations with a crop model using an interpolation between the no-change (approximately 2010) conditions and the RCP2.6 scenario (with a global warming of 1.8°C in 2100), and they derived the corresponding 2°C scenario from RCP2.6 and RCP4.5 simulations in 2100. The Inter-Sectoral Impact Model Integration and Intercomparison Project Phase 2 (ISIMIP2; Frieler et al., 2017)32 extended this approach to investigate a number of sectoral impacts on terrestrial and marine ecosystems. In most cases, risks are assessed by impact models coupled offline to climate models after bias correction, which may modify long-term trends (Grillakis et al., 2017)33.
Assessment of local impacts of climate change necessarily involves a change in scale, such as from the global scale to that of natural or human systems (Frieler et al., 2017; Reyer et al., 2017d; Jacob et al., 2018)34. An appropriate method of downscaling (Supplementary Material 3.SM.1) is crucial for translating perspectives on 1.5°C and 2°C of global warming to scales and impacts relevant to humans and ecosystems. A major challenge associated with this requirement is the correct reproduction of the variance of local to regional changes, as well as the frequency and amplitude of extreme events (Vautard et al., 2014)35. In addition, maintaining physical consistency between downscaled variables is important but challenging (Frost et al., 2011)36.
Another major challenge relates to the propagation of the uncertainties at each step of the methodology, from the global forcings to the global climate and from regional climate to impacts at the ecosystem level, considering local disturbances and local policy effects. The risks for natural and human systems are the result of complex combinations of global and local drivers, which makes quantitative uncertainty analysis difficult. Such analyses are partly done using multimodel approaches, such as multi-climate and multi-impact models (Warszawski et al., 2013, 2014; Frieler et al., 2017)37. In the case of crop projections, for example, the majority of the uncertainty is caused by variation among crop models rather than by downscaling outputs of the climate models used (Asseng et al., 2013)38. Error propagation is an important issue for coupled models. Dealing correctly with uncertainties in a robust probabilistic model is particularly important when considering the potential for relatively small changes to affect the already small signal associated with 0.5°C of global warming (Supplementary Material 3.SM.1). The computation of an impact per unit of climatic change, based either on models or on data, is a simple way to present the probabilistic ecosystem response while taking into account the various sources of uncertainties (Fronzek et al., 2011)39.
In summary, in order to assess risks at 1.5°C and higher levels of global warming, several things need to be considered. Projected climates under 1.5°C of global warming differ depending on temporal aspects and emission pathways. Considerations include whether global temperature is (i) temporarily at this level (i.e., is a transient phase on its way to higher levels of warming), (ii) arrives at 1.5°C, with or without overshoot, after stabilization of greenhouse gas concentrations, or (iii) is at this level as part of long-term climate equilibrium (complete only after several millennia). Assessments of impacts of 1.5°C of warming are generally based on climate simulations for these different possible pathways. Most existing data and analyses focus on transient impacts (i). Fewer data are available for dedicated climate model simulations that are able to assess pathways consistent with (ii), and very few data are available for the assessment of changes at climate equilibrium (iii). In some cases, inferences regarding the impacts of further warming of 0.5°C above present-day temperatures (i.e., 1.5°C of global warming) can also be drawn from observations of similar sized changes (0.5°C) that have occurred in the past, such as during the last 50 years. However, impacts can only be partly inferred from these types of observations, given the strong possibility of non-linear changes, as well as lag effects for some climate variables (e.g., sea level rise, snow and ice melt). For the impact models, three challenges are noted about the coupling procedure: (i) the bias correction of the climate model, which may modify the simulated response of the ecosystem, (ii) the necessity to downscale the climate model outputs to reach a pertinent scale for the ecosystem without losing physical consistency of the downscaled climate fields, and (iii) the necessity to develop an integrated study of the uncertainties.
3.3
Global and Regional Climate Changes and Associated Hazards
This section provides the assessment of changes in climate at 1.5°C of global warming relative to changes at higher global mean temperatures. Section 3.3.1 provides a brief overview of changes to global climate. Sections 3.3.2–3.3.11 provide assessments for specific aspects of the climate system, including regional assessments for temperature (Section 3.3.2) and precipitation (Section 3.3.3) means and extremes. Analyses of regional changes are based on the set of regions displayed in Figure 3.2. A synthesis of the main conclusions of this section is provided in Section 3.3.11. The section builds upon assessments from the IPCC AR5 WGI report (Bindoff et al., 2013a; Christensen et al., 2013; Collins et al., 2013; Hartmann et al., 2013; IPCC, 2013)40 and Chapter 3 of the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX; Seneviratne et al., 2012)41, as well as a substantial body of new literature related to projections of climate at 1.5°C and 2°C of warming above the pre-industrial period (e.g., Vautard et al., 2014; Fischer and Knutti, 2015; Schleussner et al., 2016b, 2017; Seneviratne et al., 2016, 2018c; Déqué et al., 2017; Maule et al., 2017; Mitchell et al., 2017, 2018a; Wartenburger et al., 2017; Zaman et al., 2017; Betts et al., 2018; Jacob et al., 2018; Kharin et al., 2018; Wehner et al., 2018b)42. The main assessment methods are as already detailed in Section 3.2.
Figure 3.2
Regions used for regional analyses provided in Section 3.3. The choice of regions is based on the IPCC Fifth Assessment Report (AR5, Chapter 14, Christensen et al., 2013 and Annex 1: Atlas) and the Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX, Chapter 3, Seneviratne et […]
Regions used for regional analyses provided in Section 3.3. The choice of regions is based on the IPCC Fifth Assessment Report (AR5, Chapter 14, Christensen et al., 201343 and Annex 1: Atlas) and the Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX, Chapter 3, Seneviratne et al., 2012)44, with seven additional regions in the Arctic, Antarctic and islands not included in the IPCC SREX report (indicated with asterisks). Analyses for regions with asterisks are provided in the Supplementary Material 3.SM.2
3.3.1
Global Changes in Climate
There is high confidence that the increase in global mean surface temperature (GMST) has reached 0.87°C (±0.10°C likely range) above pre-industrial values in the 2006–2015 decade (Chapter 1). AR5 assessed that the globally averaged temperature (combined over land and ocean) displayed a warming of about 0.85°C [0.65°C to 1.06°C] during the period 1880–2012, with a large fraction of the detected global warming being attributed to anthropogenic forcing (Bindoff et al., 2013a; Hartmann et al., 2013; Stocker et al., 2013)45. While new evidence has highlighted that sampling biases and the choice of approaches used to estimate GMST (e.g., using water versus air temperature over oceans and using model simulations versus observations-based estimates) can affect estimates of GMST increase (Richardson et al., 2016;46 see also Supplementary Material 3.SM.2), the present assessment is consistent with that of AR5 regarding a detectable and dominant effect of anthropogenic forcing on observed trends in global temperature (also confirmed in Ribes et al., 2017)47. As highlighted in Chapter 1, human-induced warming reached approximately 1°C (±0.2°C likely range) in 2017. More background on recent observed trends in global climate is provided in the Supplementary Material 3.SM.2.
A global warming of 1.5°C implies higher mean temperatures compared to during pre-industrial times in almost all locations, both on land and in oceans (high confidence) (Figure 3.3). In addition, a global warming of 2°C versus 1.5°C results in robust differences in the mean temperatures in almost all locations, both on land and in the ocean (high confidence). The land–sea contrast in warming is important and implies particularly large changes in temperature over land, with mean warming of more than 1.5°C in most land regions (high confidence; see Section 3.3.2 for more details). The largest increase in mean temperature is found in the high latitudes of the Northern Hemisphere (high confidence; Figure 3.3, see Section 3.3.2 for more details). Projections for precipitation are more uncertain, but they highlight robust increases in mean precipitation in the Northern Hemisphere high latitudes at 1.5ºC global warming versus pre-industrial conditions, as well as at 2ºC global warming versus pre-industrial conditions (high confidence) (Figure 3.3). There are consistent but less robust signals when comparing changes in mean precipitation at 2ºC versus 1.5°C of global warming. Hence, it is assessed that there is medium confidence in an increase of mean precipitation in high-latitudes at 2ºC versus 1.5ºC of global warming (Figure 3.3). For droughts, changes in evapotranspiration and precipitation timing are also relevant (see Section 3.3.4). Figure 3.4 displays changes in temperature extremes (the hottest daytime temperature of the year, TXx, and the coldest night-time temperature of the year, TNn) and heavy precipitation (the annual maximum 5-day precipitation, Rx5day). These analyses reveal distinct patterns of changes, with the largest changes in TXx occurring on mid-latitude land and the largest changes in TNn occurring at high latitudes (both on land and in oceans). Differences in TXx and TNn compared to pre-industrial climate are robust at both global warming levels. Differences in TXx and TNn at 2°C versus 1.5°C of global warming are robust across most of the globe. Changes in heavy precipitation are less robust, but particularly strong increases are apparent at high latitudes as well as in the tropics at both 1.5°C and 2°C of global warming compared to pre-industrial conditions. The differences in heavy precipitation at 2ºC versus 1.5ºC global warming are generally not robust at grid-cell scale, but they display consistent increases in most locations (Figure 3.4). However, as addressed in Section 3.3.3, statistically significant differences are found in several large regions and when aggregated over the global land area. We thus assess that there is high confidence regarding global-scale differences in temperature means and extremes at 2°C versus 1.5°C global warming, and medium confidence regarding global-scale differences in precipitation means and extremes. Further analyses, including differences at 1.5°C and 2°C global warming versus 1°C (i.e., present-day) conditions are provided in the Supplementary Material 3.SM.2.
Figure 3.3
Projected changes in mean temperature (top) and mean precipitation (bottom) at 1.5°C (left) and 2°C (middle) of global warming compared to the pre-industrial period (1861–1880), and the difference between 1.5°C and 2°C of global warming (right).
Cross-hatching highlights areas where at least two-thirds of the models agree on the sign of change as a measure of robustness (18 or more out of 26). Values were assessed from the transient response over a 10-year period at a given warming level, based on Representative Concentration Pathway (RCP)8.5 Coupled Model Intercomparison Project Phase 5 (CMIP5) model simulations (adapted from Seneviratne et al., 201648 and Wartenburger et al., 201749, see Supplementary Material 3.SM.2 for more details). Note that the responses at 1.5°C of global warming are similar for RCP2.6 simulations (see Supplementary Material 3.SM.2). Differences compared to 1°C of global warming are provided in the Supplementary Material 3.SM.2.
Figure 3.4
Projected changes in extremes at 1.5°C (left) and 2°C (middle) of global warming compared to the pre-industrial period (1861–1880), and the difference between 1.5°C and 2°C of global warming (right).
Cross-hatching highlights areas where at least two-thirds of the models agree on the sign of change as a measure of robustness (18 or more out of 26): T: temperature of annual hottest day (maximum temperature), TXx (top), and temperature of annual coldest night (minimum temperature), TNn (middle), and annual maximum 5-day precipitation, Rx5day (bottom). The underlying methodology and data basis are the same as for Figure 3.3 (see Supplementary Material 3.SM.2 for more details). Note that the responses at 1.5°C of global warming are similar for Representative Concentration Pathway (RCP) 2.6 simulations (see Supplementary Material 3.SM.2). Differences compared to 1°C of global warming are provided in the Supplementary Material 3.SM.2.
These projected changes at 1.5°C and 2°C of global warming are consistent with the attribution of observed historical global trends in temperature and precipitation means and extremes (Bindoff et al., 2013a)50, as well as with some observed changes under the recent global warming of 0.5°C (Schleussner et al., 2017)51. These comparisons are addressed in more detail in Sections 3.3.2 and 3.3.3. Attribution studies have shown that there is high confidence that anthropogenic forcing has had a detectable influence on trends in global warming (virtually certain since the mid-20th century), in land warming on all continents except Antarctica (likely since the mid-20th century), in ocean warming since 1970 (very likely), and in increases in hot extremes and decreases in cold extremes since the mid-20th century (very likely) (Bindoff et al., 2013a)52. In addition, there is medium confidence that anthropogenic forcing has contributed to increases in mean precipitation at high latitudes in the Northern Hemisphere since the mid-20th century and to global-scale increases in heavy precipitation in land regions with sufficient observations over the same period (Bindoff et al., 2013a)53. Schleussner et al. (2017)54 showed, through analyses of recent observed tendencies, that changes in temperature extremes and heavy precipitation indices are detectable in observations for the 1991–2010 period compared with those for 1960–1979, with a global warming of approximately 0.5°C occurring between these two periods (high confidence). The observed tendencies over that time frame are thus consistent with attributed changes since the mid-20th century (high confidence).
The next sections assess changes in several different types of climate-related hazards. It should be noted that the different types of hazards are considered in isolation but some regions are projected to be affected by collocated and/or concomitant changes in several types of hazards (high confidence). Two examples are sea level rise and heavy precipitation in some regions, possibly leading together to more flooding, and droughts and heatwaves, which can together increase the risk of fire occurrence. Such events, also called compound events, may substantially increase risks in some regions (e.g., AghaKouchak et al., 2014; Van Den Hurk et al., 2015; Martius et al., 2016; Zscheischler et al., 2018)55. A detailed assessment of physically-defined compound events was not possible as part of this report, but aspects related to overlapping multi-sector risks are highlighted in Sections 3.4 and 3.5.
3.3.2
Regional Temperatures on Land, Including Extremes
3.3.2.1
Observed and attributed changes in regional temperature means and extremes
While the quality of temperature measurements obtained through ground observational networks tends to be high compared to that of measurements for other climate variables (Seneviratne et al., 2012)56, it should be noted that some regions are undersampled. Cowtan and Way (2014)57 highlighted issues regarding undersampling, which is most problematic at the poles and over Africa, and which may lead to biases in estimated changes in GMST (see also Supplementary Material 3.SM.2 and Chapter 1). This undersampling also affects the confidence of assessments regarding regional observed and projected changes in both mean and extreme temperature. Despite this partly limited coverage, the attribution chapter of AR5 (Bindoff et al., 2013a)58 and recent papers (e.g., Sun et al., 2016; Wan et al., 2018)59 assessed that, over every continental region and in many sub-continental regions, anthropogenic influence has made a substantial contribution to surface temperature increases since the mid-20th century.
Based on the AR5 and SREX, as well as recent literature (see Supplementary Material 3.SM), there is high confidence (very likely) that there has been an overall decrease in the number of cold days and nights and an overall increase in the number of warm days and nights at the global scale on land. There is also high confidence (likely) that consistent changes are detectable on the continental scale in North America, Europe and Australia. There is high confidence that these observed changes in temperature extremes can be attributed to anthropogenic forcing (Bindoff et al., 2013a)60. As highlighted in Section 3.2, the observational record can be used to assess past changes associated with a global warming of 0.5°C. Schleussner et al. (2017)61 used this approach to assess observed changes in extreme indices for the 1991–2010 versus the 1960–1979 period, which corresponds to just about a 0.5°C GMST difference in the observed record (based on the Goddard Institute for Space Studies Surface Temperature Analysis (GISTEMP) dataset, Hansen et al., 2010)62. They found that substantial changes due to 0.5°C of warming are apparent for indices related to hot and cold extremes, as well as for the Warm Spell Duration Indicator (WSDI). In particular, they identified that one-quarter of the land has experienced an intensification of hot extremes (maximum temperature on the hottest day of the year, TXx) by more than 1°C and a reduction in the intensity of cold extremes by at least 2.5°C (minimum temperature on the coldest night of the year, TNn). In addition, the same study showed that half of the global land mass has experienced changes in WSDI of more than six days, as well as an emergence of extremes outside the range of natural variability (Schleussner et al., 2017)63. Analyses from Schleussner et al. (2017)64 for temperature extremes are provided in the Supplementary Material 3.SM, Figure 3.SM.6. It should be noted that assessments of attributed changes in the IPCC SREX and AR5 reports were generally provided since 1950, for time frames also approximately corresponding to a 0.5°C global warming (3.SM).
3.3.2.2
Projected changes in regional temperature means and extremes at 1.5°C versus 2°C of global warming
There are several lines of evidence available for providing a regional assessment of projected changes in temperature means and extremes at 1.5°C versus 2°C of global warming (see Section 3.2). These include: analyses of changes in extremes as a function of global warming based on existing climate simulations using the empirical scaling relationship (ESR) and variations thereof (e.g., Schleussner et al., 2017; Dosio and Fischer, 2018; Seneviratne et al., 2018c65; see Section 3.2 for details about the methodology); dedicated simulations of 1.5°C versus 2°C of global warming, for instance based on the Half a degree additional warming, prognosis and projected impacts (HAPPI) experiment (Mitchell et al., 2017)66 or other model simulations (e.g., Dosio et al., 2018; Kjellström et al., 2018)67; and analyses based on statistical pattern scaling approaches (e.g., Kharin et al., 2018)68. These different lines of evidence lead to qualitatively consistent results regarding changes in temperature means and extremes at 1.5°C of global warming compared to the pre-industrial climate and 2°C of global warming.
There are statistically significant differences in temperature means and extremes at 1.5°C versus 2°C of global warming, both in the global average (Schleussner et al., 2016b; Dosio et al., 2018; Kharin et al., 2018)69, as well as in most land regions (high confidence) (Wartenburger et al., 2017; Seneviratne et al., 2018c; Wehner et al., 2018b)70. Projected temperatures over oceans display significant increases in means and extremes between 1.5°C and 2°C of global warming (Figures 3.3 and 3.4). A general background on the available evidence on regional changes in temperature means and extremes at 1.5°C versus 2°C of global warming is provided in the Supplementary Material 3.SM.2. As an example, Figure 3.5 shows regionally-based analyses for the IPCC SREX regions (see Figure 3.2) of changes in the temperature of hot extremes as a function of global warming (corresponding analyses for changes in the temperature of cold extremes are provided in the Supplementary Material 3.SM.2). As demonstrated in these analyses, the mean response of the intensity of temperature extremes in climate models to changes in the global mean temperature is approximately linear and independent of the considered emissions scenario (Seneviratne et al., 2016; Wartenburger et al., 2017)71. Nonetheless, in the case of changes in the number of days exceeding a given threshold, changes are approximately exponential, with higher increases for rare events (Fischer and Knutti, 2015; Kharin et al., 2018)72; see also Figure 3.6. This behaviour is consistent with a linear increase in absolute temperature for extreme threshold exceedances (Whan et al., 2015)73.
As mentioned in Section 3.3.1, there is an important land–sea warming contrast, with stronger warming on land (see also Christensen et al., 2013; Collins et al., 2013; Seneviratne et al., 2016)74, which implies that regional warming on land is generally more than 1.5°C even when mean global warming is at 1.5°C. As highlighted in Seneviratne et al. (2016)75, this feature is generally stronger for temperature extremes (Figures 3.4 and 3.5; Supplementary Material 3.SM.2 ). For differences in regional temperature extremes at a mean global warming of 1.5°C versus 2°C, that is, a difference of 0.5ºC in global warming, this implies differences of as much as 1°C–1.5°C in some locations, which are two to three times larger than the differences in global mean temperature. For hot extremes, the strongest warming is found in central and eastern North America, central and southern Europe, the Mediterranean, western and central Asia, and southern Africa (Figures 3.4 and 3.5) (medium confidence). These regions are all characterized by a strong soil-moisture–temperature coupling and projected increased dryness (Vogel et al., 2017)76, which leads to a reduction in evaporative cooling in the projections. Some of these regions also show a wide range of responses to temperature extremes, in particular central Europe and central North America, owing to discrepancies in the representation of the underlying processes in current climate models (Vogel et al., 2017)77. For mean temperature and cold extremes, the strongest warming is found in the northern high-latitude regions (high confidence). This is due to substantial ice-snow-albedo-temperature feedbacks (Figure 3.3 and Figure 3.4, middle) related to the known 'polar amplification' mechanism (e.g., IPCC, 2013; Masson-Delmotte et al., 2013)78.
Figure 3.7 displays maps of changes in the number of hot days (NHD) at 1.5°C and 2°C of GMST increase. Maps of changes in the number of frost days (FD) can be found in Supplementary Material 3.SM.2. These analyses reveal clear patterns of changes between the two warming levels, which are consistent with analysed changes in heatwave occurrence (e.g., Dosio et al., 2018)79. For the NHD, the largest differences are found in the tropics (high confidence), owing to the low interannual temperature variability there (Mahlstein et al., 2011)80, although absolute changes in hot temperature extremes tended to be largest at mid-latitudes (high confidence) (Figures 3.4 and 3.5). Extreme heatwaves are thus projected to emerge earliest in the tropics and to become widespread in these regions already at 1.5°C of global warming (high confidence). These results are consistent with other recent assessments. Coumou and Robinson (2013)81 found that 20% of the global land area, centred in low-latitude regions, is projected to experience highly unusual monthly temperatures during Northern Hemisphere summers at 1.5°C of global warming, with this number nearly doubling at 2°C of global warming.
Figure 3.8 features an objective identification of 'hotspots' / key risks in temperature indices subdivided by region, based on the ESR approach applied to Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations (Wartenburger et al., 2017)82. Note that results based on the HAPPI multimodel experiment (Mitchell et al., 2017)83 are similar (Seneviratne et al., 2018c)84. The considered regions follow the classification used in Figure 3.2 and also include the global land areas. Based on these analyses, the following can be stated: significant changes in responses are found in all regions for most temperature indices, with the exception of i) the diurnal temperature range (DTR) in most regions, ii) ice days (ID), frost days (FD) and growing season length (GSL) (mostly in regions where differences are zero, because, e.g., there are no ice or frost days), iii) the minimum yearly value of the maximum daily temperature (TXn) in very few regions. In terms of the sign of the changes, warm extremes display an increase in intensity, frequency and duration (e.g., an increase in the temperature of the hottest day of the year (TXx) in all regions, an increase in the proportion of days with a maximum temperature above the 90th percentile of Tmax (TX90p) in all regions, and an increase in the length of the WSDI in all regions), while cold extremes display a decrease in intensity, frequency and duration (e.g., an increase in the temperature of the coldest night of the year (TNn) in all regions, a decrease in the proportion of days with a minimum temperature below the 10th percentile of Tmin (TN10p), and a decrease in the cold spell duration index (CSDI) in all regions). Hence, while warm extremes are intensified, cold extremes become less intense in affected regions.
Overall, large increases in hot extremes occur in many densely inhabited regions (Figure 3.5), for both warming scenarios compared to pre-industrial and present-day climate, as well as for 2°C versus 1.5°C GMST warming. For instance, Dosio et al. (2018)85 concluded, based on a modelling study, that 13.8% of the world population would be exposed to 'severe heatwaves' at least once every 5 years under 1.5°C of global warming, with a threefold increase (36.9%) under 2°C of GMST warming, corresponding to a difference of about 1.7 billion people between the two global warming levels. They also concluded that limiting global warming to 1.5°C would result in about 420 million fewer people being frequently exposed to extreme heatwaves, and about 65 million fewer people being exposed to 'exceptional heatwaves' compared to conditions at 2ºC GMST warming. However, changes in vulnerability were not considered in their study. For this reason, we assess that there is medium confidence in their conclusions.
In summary, there is high confidence that there are robust and statistically significant differences in the projected temperature means and extremes at 1.5°C versus 2°C of global warming, both in the global average and in nearly all land regions6 (likely). Further, the observational record reveals that substantial changes due to a 0.5°C GMST warming are apparent for indices related to hot and cold extremes, as well as for the WSDI (likely). A global warming of 2°C versus 1.5°C would lead to more frequent and more intense hot extremes in all land regions7 , as well as longer warm spells, affecting many densely inhabited regions (very likely). The strongest increases in the frequency of hot extremes are projected for the rarest events (very likely). On the other hand, cold extremes would become less intense and less frequent, and cold spells would be shorter (very likely). Temperature extremes on land would generally increase more than the global average temperature (very likely). Temperature increases of extreme hot days in mid-latitudes are projected to be up to two times the increase in GMST, that is, 3ºC at 1.5ºC GMST warming (high confidence). The highest levels of warming for extreme hot days are expected to occur in central and eastern North America, central and southern Europe, the Mediterranean, western and central Asia, and southern Africa (medium confidence). These regions have a strong soil-moisture-temperature coupling in common as well as increased dryness and, consequently, a reduction in evaporative cooling. However, there is a substantial range in the representation of these processes in models, in particular in central Europe and central North America (medium confidence). The coldest nights in high latitudes warm by as much as 1.5°C for a 0.5°C increase in GMST, corresponding to a threefold stronger warming (high confidence). NHD shows the largest differences between 1.5°C and 2°C in the tropics, because of the low interannual temperature variability there (high confidence); extreme heatwaves are thus projected to emerge earliest in these regions, and they are expected to become widespread already at 1.5°C of global warming (high confidence). Limiting global warming to 1.5°C instead of 2°C could result in around 420 million fewer people being frequently exposed to extreme heatwaves, and about 65 million fewer people being exposed to exceptional heatwaves, assuming constant vulnerability (medium confidence).
Figure 3.5
Projected changes in annual maximum daytime temperature (TXx) as a function of global warming for IPCC Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions (see Figure 3.2), based on an empirical scaling relationship applied to Coupled Model Intercomparison Project Phase 5 (CMIP5) data (adapted from Seneviratne et al., 201686 and Wartenburger et al., 2017)87 together with projected changes from the Half a degree additional warming, prognosis and projected impacts (HAPPI) multimodel experiment (Mitchell et al., 201788; based on analyses in Seneviratne et al., 2018c)89 (bar plots on regional analyses and central plot, respectively).
For analyses for other regions from Figure 3.2 (with asterisks), see Supplementary Material 3.SM.2. (The stippling indicates significance of the differences in changes between 1.5°C and 2°C of global warming based on all model simulations, using a two-sided paired Wilcoxon test (P = 0.01, after controlling the false discovery rate according to Benjamini and Hochberg, 1995)90. See Supplementary Material 3.SM.2 for details.
Figure 3.6
Probability ratio (PR) of exceeding extreme temperature thresholds.
(a) PR of exceeding the 99th (blue) and 99.9th (red) percentile of pre-industrial daily temperatures at a given warming level, averaged across land (from Fischer and Knutti, 2015)91. (b) PR for the hottest daytime temperature of the year (TXx). (c) PR for the coldest night of the year (TNn) for different event probabilities (with RV indicating return values) in the current climate (1°C of global warming). Shading shows the interquartile (25–75%) range (from Kharin et al., 2018)92.
Figure 3.7
Projected changes in the number of hot days (NHD; 10% warmest days) at 1.5°C (left) and at 2°C (middle) of global warming compared to the pre-industrial period (1861–1880), and the difference between 1.5°C and 2°C of warming (right).
Cross-hatching highlights areas where at least two-thirds of the models agree on the sign of change as a measure of robustness (18 or more out of 26). The underlying methodology and the data basis are the same as for Figure 3.2 (see Supplementary Material 3.SM.2 for more details). Differences compared to 1°C global warming are provided in the Supplementary Material 3.SM.2.
Figure 3.8
Significance of differences in regional mean temperature and range of temperature indices between the 1.5°C and 2°C global mean temperature targets (rows).
Definitions of indices: T: mean temperature; CSDI: cold spell duration index; DTR: diurnal temperature range; FD: frost days; GSL: growing season length; ID: ice days; SU: summer days; TN10p: proportion of days with a minimum temperature (TN) lower than the 10th percentile of TN; TN90p: proportion of days with TN higher than the 90th percentile of TN; TNn: minimum yearly value of TN; TNx: maximum yearly value of TN; TR: tropical nights; TX10p: proportion of days with a maximum temperature (TX) lower than the 10th percentile of TX; TX90p: proportion of days with TX higher than the 90th percentile of TX; TXn: minimum yearly value of TX; TXx: maximum yearly value of TX; WSDI: warm spell duration index. Columns indicate analysed regions and global land (see Figure 3.2 for definitions). Significant differences are shown in red shading, with increases indicated with + and decreases indicated with –, while non-significant differences are shown in grey shading. White shading indicates when an index is the same at the two global warming levels (i.e., zero changes). Note that decreases in CSDI, FD, ID, TN10p and TX10p are linked to increased temperatures on cold days or nights. Significance was tested using a two-sided paired Wilcoxon test (P=0.01, after controlling the false discovery rate according to Benjamini and Hochberg, 1995)93 (adapted from Wartenburger et al., 2017)94.
3.3.3
Regional Precipitation, Including Heavy Precipitation and Monsoons
This section addresses regional changes in precipitation on land, with a focus on heavy precipitation and consideration of changes to the key features of monsoons.
3.3.3.1
Observed and attributed changes in regional precipitation
Observed global changes in the water cycle, including precipitation, are more uncertain than observed changes in temperature (Hartmann et al., 2013; Stocker et al., 2013)95. There is high confidence that mean precipitation over the mid-latitude land areas of the Northern Hemisphere has increased since 1951 (Hartmann et al., 2013)96. For other latitudinal zones, area-averaged long-term positive or negative trends have low confidence because of poor data quality, incomplete data or disagreement amongst available estimates (Hartmann et al., 2013)97. There is, in particular, low confidence regarding observed trends in precipitation in monsoon regions, according to the SREX report (Seneviratne et al., 2012)98 and AR5 (Hartmann et al., 2013)99, as well as more recent publications (Singh et al., 2014; Taylor et al., 2017; Bichet and Diedhiou, 2018;100 see Supplementary Material 3.SM.2).
For heavy precipitation, AR5 (Hartmann et al., 2013)101 assessed that observed trends displayed more areas with increases than decreases in the frequency, intensity and/or amount of heavy precipitation (likely). In addition, for land regions where observational coverage is sufficient for evaluation, it was assessed that there is medium confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century (Bindoff et al., 2013a)102.
Regarding changes in precipitation associated with global warming of 0.5°C, the observed record suggests that increases in precipitation extremes can be identified for annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day) for GMST changes of this magnitude (Supplementary Material 3.SM.2, Figure 3.SM.7; Schleussner et al., 2017)103. It should be noted that assessments of attributed changes in the IPCC SREX and AR5 reports were generally provided since 1950, for time frames also approximately corresponding to a 0.5°C global warming (3.SM).
3.3.3.2
Projected changes in regional precipitation at 1.5°C versus 2°C of global warming
Figure 3.3 in Section 3.3.1 summarizes the projected changes in mean precipitation at 1.5°C and 2°C of global warming. Both warming levels display robust differences in mean precipitation compared to the pre-industrial period. Regarding differences at 2°C vs 1.5°C global warming, some regions are projected to display changes in mean precipitation at 2°C compared with that at 1.5°C of global warming in the CMIP5 multimodel average, such as decreases in the Mediterranean area, including southern Europe, the Arabian Peninsula and Egypt, or increases in high latitudes. The results, however, are less robust across models than for mean temperature. For instance, Déqué et al. (2017)104 investigated the impact of 2°C of global warming on precipitation over tropical Africa and found that average precipitation does not show a significant response, owing to two phenomena: (i) the number of days with rain decreases whereas the precipitation intensity increases, and (ii) the rainy season occurs later during the year, with less precipitation in early summer and more precipitation in late summer. The results from Déqué et al. (2017)105 regarding insignificant differences between 1.5°C and 2°C scenarios for tropical Africa are consistent with the results presented in Figure 3.3. For Europe, recent studies (Vautard et al., 2014; Jacob et al., 2018; Kjellström et al., 2018)106 have shown that 2°C of global warming was associated with a robust increase in mean precipitation over central and northern Europe in winter but only over northern Europe in summer, and with decreases in mean precipitation in central/southern Europe in summer. Precipitation changes reaching 20% have been projected for the 2°C scenario (Vautard et al., 2014)107 and are overall more pronounced than with 1.5°C of global warming (Jacob et al., 2018; Kjellström et al., 2018)108.
Regarding changes in heavy precipitation, Figure 3.9 displays projected changes in the 5-day maximum precipitation (Rx5day) as a function of global temperature increase, using a similar approach as in Figure 3.5. Further analyses are available in Supplementary Material 3.SM.2. These analyses show that projected changes in heavy precipitation are more uncertain than those for temperature extremes. However, the mean response of model simulations is generally robust and linear (see also Fischer et al., 2014; Seneviratne et al., 2016)109. As observed for temperature extremes, this response is also mostly independent of the considered emissions scenario (e.g., RCP2.6 versus RCP8.5; see also Section 3.2). This feature appears to be specific to heavy precipitation, possibly due to a stronger coupling with temperature, as the scaling of projections of mean precipitation changes with global warming shows some scenario dependency (Pendergrass et al., 2015)110.
Robust changes in heavy precipitation compared to pre-industrial conditions are found at both 1.5°C and 2°C global warming (Figure 3.4). This is also consistent with results for, for example, the European continent, although different indices for heavy precipitation changes have been analysed. Based on regional climate simulations, Vautard et al. (2014)111 found a robust increase in heavy precipitation everywhere in Europe and in all seasons, except southern Europe in summer at 2°C versus 1971–2000. Their findings are consistent with those of Jacob et al. (2014)112, who used more recent downscaled climate scenarios (EURO-CORDEX) and a higher resolution (12 km), but the change is not so pronounced in Teichmann et al. (2018)113. There is consistent agreement in the direction of change in heavy precipitation at 1.5°C of global warming over much of Europe, compared to 1971–2000 (Jacob et al., 2018)114.
Differences in heavy precipitation are generally projected to be small between 1.5°C and 2°C GMST warming (Figure 3.4 and 3.9 and Supplementary Material 3.SM.2, Figure 3.SM.10). Some regions display substantial increases, for instance southern Asia, but generally in less than two-thirds of the CMIP5 models (Figure 3.4, Supplementary Material 3.SM.2, Figure 3.SM.10). Wartenburger et al. (2017)115 suggested that there are substantial differences in heavy precipitation in eastern Asia at 1.5°C versus 2°C. Overall, while there is variation among regions, the global tendency is for heavy precipitation to increase at 2°C compared with at 1.5°C (see e.g., Fischer and Knutti, 2015116 and Kharin et al., 2018117 , as illustrated in Figure 3.10 from this chapter; see also Betts et al., 2018)118.
AR5 assessed that the global monsoon, aggregated over all monsoon systems, is likely to strengthen, with increases in its area and intensity, while the monsoon circulation weakens (Christensen et al., 2013)119. A few publications provide more recent evaluations of projections of changes in monsoons for high-emission scenarios (e.g., Jiang and Tian, 2013; Jones and Carvalho, 2013; Sylla et al., 2015, 2016120; Supplementary Material 3.SM.2 ). However, scenarios at 1.5°C or 2°C global warming would involve a substantially smaller radiative forcing than those assessed in AR5 and these more recent studies, and there appears to be no specific assessment of changes in monsoon precipitation at 1.5°C versus 2°C of global warming in the literature. Consequently, the current assessment is that there is low confidence regarding changes in monsoons at these lower global warming levels, as well as regarding differences in monsoon responses at 1.5°C versus 2°C.
Similar to Figure 3.8, Figure 3.11 features an objective identification of 'hotspots' / key risks outlined in heavy precipitation indices subdivided by region, based on the approach by Wartenburger et al. (2017)121. The considered regions follow the classification used in Figure 3.2 and also include global land areas. Hotspots displaying statistically significant changes in heavy precipitation at 1.5°C versus 2°C global warming are located in high-latitude (Alaska/western Canada, eastern Canada/Greenland/Iceland, northern Europe, northern Asia) and high-elevation (e.g., Tibetan Plateau) regions, as well as in eastern Asia (including China and Japan) and in eastern North America. Results are less consistent for other regions. Note that analyses for meteorological drought (lack of precipitation) are provided in Section 3.3.4.
In summary, observations and projections for mean and heavy precipitation are less robust than for temperature means and extremes (high confidence). Observations show that there are more areas with increases than decreases in the frequency, intensity and/or amount of heavy precipitation (high confidence). Several large regions display statistically significant differences in heavy precipitation at 1.5°C versus 2°C GMST warming, with stronger increases at 2°C global warming, and there is a global tendency towards increases in heavy precipitation on land at 2°C compared with 1.5°C warming (high confidence). Overall, regions that display statistically significant changes in heavy precipitation between 1.5°C and 2°C of global warming are located in high latitudes (Alaska/western Canada, eastern Canada/Greenland/Iceland, northern Europe, northern Asia) and high elevation (e.g., Tibetan Plateau), as well as in eastern Asia (including China and Japan) and in eastern North America (medium confidence). There is low confidence in projected changes in heavy precipitation in other regions.
Figure 3.9
Projected changes in annual 5-day maximum precipitation (Rx5day) as a function of global warming for IPCC Special Report on the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions (see Figure 3.2), based on an empirical scaling relationship applied to Coupled Model Intercomparison Project Phase 5 (CMIP5) data together with projected changes from the HAPPI multimodel experiment (bar plots on regional analyses and central plot).
The underlying methodology and data basis are the same as for Figure 3.5 (see Supplementary Material 3.SM.2 for more details).
Figure 3.10
Probability ratio (PR) of exceeding (heavy precipitation) thresholds.
(a) PR of exceeding the 99th (blue) and 99.9th (red) percentile of pre-industrial daily precipitation at a given warming level, averaged across land (from Fischer and Knutti, 2015)122. (b) PR for precipitation extremes (RX1day) for different event probabilities (with RV indicating return values) in the current climate (1°C of global warming). Shading shows the interquartile (25–75%) range (from Kharin et al., 2018)123.
Figure 3.11
Significance of differences in regional mean precipitation and range of precipitation indices between the 1.5°C and 2°C global mean temperature targets (rows).
Definition of indices: PRCPTOT: mean precipitation; CWD: consecutive wet days; R10mm: number of days with precipitation >10 mm; R1mm: number of days with precipitation >1 mm; R20mm: number of days with precipitation >20 mm; R95ptot: proportion of rain falling as 95th percentile or higher; R99ptot: proportion of rain falling as 99th percentile or higher; RX1day: intensity of maximum yearly 1-day precipitation; RX5day: intensity of maximum yearly 5-day precipitation; SDII: Simple Daily Intensity Index. Columns indicate analysed regions and global land (see Figure 3.2 for definitions). Significant differences are shown in light blue (wetting tendency) or brown (drying tendency) shading, with increases indicated with '+' and decreases indicated with '–', while non-significant differences are shown in grey shading. The underlying methodology and the data basis are the same as in Figure 3.8 (see Supplementary Material 3.SM.2 for more details).
3.3.4
Drought and Dryness
3.3.4.1
Observed and attributed changes
The IPCC AR5 assessed that there was low confidence in the sign of drought trends since 1950 at the global scale, but that there was high confidence in observed trends in some regions of the world, including drought increases in the Mediterranean and West Africa and drought decreases in central North America and northwest Australia (Hartmann et al., 2013; Stocker et al., 2013)124. AR5 assessed that there was low confidence in the attribution of global changes in droughts and did not provide assessments for the attribution of regional changes in droughts (Bindoff et al., 2013a)125.
The recent literature does not suggest that the SREX and AR5 assessment of drought trends should be revised, except in the Mediterranean region. Recent publications based on observational and modelling evidence suggest that human emissions have substantially increased the probability of drought years in the Mediterranean region (Gudmundsson and Seneviratne, 2016; Gudmundsson et al., 2017)126. Based on this evidence, there is medium confidence that enhanced greenhouse forcing has contributed to increased drying in the Mediterranean region (including southern Europe, northern Africa and the Near East) and that this tendency will continue to increase under higher levels of global warming.
3.3.4.2
Projected changes in drought and dryness at 1.5°C versus 2°C
There is medium confidence in projections of changes in drought and dryness. This is partly consistent with AR5, which assessed these projections as being 'likely (medium confidence)' (Collins et al., 2013; Stocker et al., 2013)143. However, given this medium confidence, the current assessment does not include a likelihood statement, thereby maintaining consistency with the IPCC uncertainty guidance document (Mastrandrea et al., 2010)144 and the assessment of the IPCC SREX report (Seneviratne et al., 2012)145. The technical summary of AR5 (Stocker et al., 2013)146 assessed that soil moisture drying in the Mediterranean, southwestern USA and southern African regions was consistent with projected changes in the Hadley circulation and increased surface temperatures, and it concluded that there was high confidence in likely surface drying in these regions by the end of this century under the RCP8.5 scenario. However, more recent assessments have highlighted uncertainties in dryness projections due to a range of factors, including variations between the drought and dryness indices considered, and the effects of enhanced CO2 concentrations on plant water-use efficiency (Orlowsky and Seneviratne, 2013; Roderick et al., 2015)147. Overall, projections of changes in drought and dryness for high-emissions scenarios (e.g., RCP8.5, corresponding to about 4°C of global warming) are uncertain in many regions, although a few regions display consistent drying in most assessments (e.g., Seneviratne et al., 2012; Orlowsky and Seneviratne, 2013)148. Uncertainty is expected to be even larger for conditions with a smaller signal-to-noise ratio, such as for global warming levels of 1.5°C and 2°C.
Some published literature is now available on the evaluation of differences in drought and dryness occurrence at 1.5°C and 2°C of global warming for (i) precipitation minus evapotranspiration (P–E, a general measure of water availability; Wartenburger et al., 2017; Greve et al., 2018)149, (ii) soil moisture anomalies (Lehner et al., 2017; Wartenburger et al., 2017),150, (iii) consecutive dry days (CDD) (Schleussner et al., 2016b; Wartenburger et al., 2017)151, (iv) the 12-month standardized precipitation index (Wartenburger et al. 2017)152 , (v) the Palmer drought severity index (Lehner et al., 2017)153, and (vi) annual mean runoff (Schleussner et al., 2016b154 , see also next section). These analyses have produced consistent findings overall, despite the known sensitivity of drought assessments to chosen drought indices (see above paragraph). These analyses suggest that increases in drought, dryness or precipitation deficits are projected at 1.5°C or 2°C global warming in some regions compared to the pre-industrial or present-day conditions, as well as between these two global warming levels, although there is substantial variability in signals depending on the considered indices or climate models (Lehner et al., 2017; Schleussner et al., 2017; Greve et al., 2018)155 (medium confidence). Generally, the clearest signals are found for the Mediterranean region (medium confidence).
Greve et al. (2018156 , Figure 3.12) derives the sensitivity of regional changes in precipitation minus evapotranspiration to global temperature changes. The simulations analysed span the full range of available emission scenarios, and the sensitivities are derived using a modified pattern scaling approach. The applied approach assumes linear dependencies on global temperature changes while thoroughly addressing associated uncertainties via resampling methods. Northern high-latitude regions display robust responses tending towards increased wetness, while subtropical regions display a tendency towards drying but with a large range of responses. While the internal variability and the scenario choice play an important role in the overall spread of the simulations, the uncertainty stemming from the climate model choice usually dominates, accounting for about half of the total uncertainty in most regions (Wartenburger et al., 2017; Greve et al., 2018)157. The sign of projections, that is, whether there might be increases or decreases in water availability under higher global warming levels, is particularly uncertain in tropical and mid-latitude regions. An assessment of the implications of limiting the global mean temperature increase to values below (i) 1.5°C or (ii) 2°C shows that constraining global warming to the 1.5°C target might slightly influence the mean response but could substantially reduce the risk of experiencing extreme changes in regional water availability (Greve et al., 2018)158.
Figure 3.12
Summary of the likelihood of increases/decreases in precipitation minus evapotranspiration (P–E) in Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations considering all scenarios and a representative subset of 14 climate models (one from each modelling centre).
Panel plots show the uncertainty distribution of the sensitivity of P–E to global temperature change, averaged for most IPCC Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions (see Figure 3.2) outlined in the map (from Greve et al., 2018)159.
The findings from the analysis for the mean response by Greve et al. (2018)160 are qualitatively consistent with results from Wartenburger et al. (2017)161, who used an ESR (Section 3.2) rather than a pattern scaling approach for a range of drought and dryness indices. They are also consistent with a study by Lehner et al. (2017)162, who assessed changes in droughts based on soil moisture changes and the Palmer-Drought Severity Index. Notably, these two publications do not provide a specific assessment of changes in the tails of the drought and dryness distribution. The conclusions of Lehner et al. (2017)163 are that (i) 'risks of consecutive drought years show little change in the US Southwest and Central Plains, but robust increases in Europe and the Mediterranean', and that (ii) 'limiting warming to 1.5°C may have benefits for future drought risk, but such benefits are regional, and in some cases highly uncertain'.
Figure 3.13 features projected changes in CDD as a function of global temperature increase, using a similar approach as for Figures 3.5 (based on Wartenburger et al., 2017)164. The figure also include results from the HAPPI experiment (Mitchell et al., 2017)165. Again, the CMIP5-based ESR estimates and the results of the HAPPI experiment agree well. Note that the responses vary widely among the considered regions.
Similar to Figures 3.8 and 3.11, Figure 3.14 features an objective identification of 'hotspots' / key risks in dryness indices subdivided by region, based on the approach by Wartenburger et al. (2017)166. This analysis reveals the following hotspots of drying (i.e. increases in CDD and/or decreases in P–E, soil moisture anomalies (SMA) and 12-month Standardized Precipitation Index (SPI12), with at least one of the indices displaying statistically significant drying): the Mediterranean region (MED; including southern Europe, northern Africa, and the Near East), northeastern Brazil (NEB) and southern Africa.
Figure 3.13
Projected changes in consecutive dry days (CDD) as a function of global warming for IPCC Special Report on Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) regions, based on an empirical scaling relationship applied to Coupled Model Intercomparison Project Phase 5 (CMIP5) data together with projected changes from the HAPPI multimodel experiment (bar plots on regional analyses and central plot, respectively).
The underlying methodology and the data basis are the same as for Figure 3.5 (see Supplementary Material 3.SM.2 for more details).
Figure 3.14
Significance of differences in regional drought and dryness indices between the 1.5°C and 2°C global mean temperature targets (rows).
Definition of indices: CDD: consecutive dry days; P–E: precipitation minus evapotranspiration; SMA: soil moisture anomalies; SPI12: 12-month Standarized Precipitation Index. Columns indicate analysed regions and global land (see Figure 3.2 for definitions). Significant differences are shown in light blue/brown shading (increases indicated with +, decreases indicated with –; light blue shading indicates decreases in dryness (decreases in CDD, or increases in P–E, SMA or SPI12) and light brown shading indicates increases in dryness (increases in CDD, or decreases in P–E, SMA or SPI12). Non—significant differences are shown in grey shading. The underlying methodology and the data basis are the same as for Figure 3.7 (see Supplementary Material 3.SM.2 for more details).
Consistent with this analysis, the available literature particularly supports robust increases in dryness and decreases in water availability in southern Europe and the Mediterranean with a shift from 1.5°C to 2°C of global warming (medium confidence) (Figure 3.13; Schleussner et al., 2016b; Lehner et al., 2017; Wartenburger et al., 2017; Greve et al., 2018; Samaniego et al., 2018167 ). This region is already displaying substantial drying in the observational record (Seneviratne et al., 2012; Sheffield et al., 2012; Greve et al., 2014; Gudmundsson and Seneviratne, 2016; Gudmundsson et al., 2017)168, which provides additional evidence supporting this tendency and suggests that it will be a hotspot of dryness change at global warming levels beyond 1.5°C (see also Box 3.2). The other identified hotspots, southern Africa and northeastern Brazil, also consistently display drying trends under higher levels of forcing in other publications (e.g., Orlowsky and Seneviratne, 2013)169, although no published studies could be found reporting observed drying trends in these regions. There are substantial increases in the risk of increased dryness (medium confidence) in both the Mediterranean region and Southern Africa at 2°C versus 1.5°C of global warming because these regions display significant changes in two dryness indicators (CDD and SMA) between these two global warming levels (Figure 3.14); the strongest effects are expected for extreme droughts (medium confidence) (Figure 3.12). There is low confidence elsewhere, owing to a lack of consistency in analyses with different models or different dryness indicators. However, in many regions there is medium confidence that most extreme risks of changes in dryness are avoided if global warming is constrained at 1.5°C instead of 2°C (Figure 3.12).
In summary, in terms of drought and dryness, limiting global warming to 1.5°C is expected to substantially reduce the probability of extreme changes in water availability in some regions compared to changes under 2°C of global warming (medium confidence). For shift from 1.5°C to 2°C of GMST warming, the available studies and analyses suggest strong increases in the probability of dryness and reduced water availability in the Mediterranean region (including southern Europe, northern Africa and the Near East) and in southern Africa (medium confidence). Based on observations and modelling experiments, a drying trend is already detectable in the Mediterranean region, that is, at global warming of less than 1°C (medium confidence).
3.3.5
Runoff and Fluvial Flooding
3.3.5.1
Observed and attributed changes in runoff and river flooding
There has been progress since AR5 in identifying historical changes in streamflow and continental runoff. Using the available streamflow data, Dai (2016)184 showed that long‐term (1948–2012) flow trends are statistically significant only for 27.5% of the world's 200 major rivers, with negative trends outnumbering the positive ones. Although streamflow trends are mostly not statistically significant, they are consistent with observed regional precipitation changes. From 1950 to 2012, precipitation and runoff have increased over southeastern South America, central and northern Australia, the central and northeastern United States, central and northern Europe, and most of Russia, and they have decreased over most of Africa, East and South Asia, eastern coastal Australia, the southeastern and northwestern United States, western and eastern Canada, the Mediterranean region and some regions of Brazil (Dai, 2016)185.
A large part of the observed regional trends in streamflow and runoff might have resulted from internal multi-decadal and multi-year climate variations, especially the Pacific decadal variability (PDV), the Atlantic Multi-Decadal Oscillation (AMO) and the El Niño–Southern Oscillation (ENSO), although the effect of anthropogenic greenhouse gases and aerosols could also be important (Hidalgo et al., 2009; Gu and Adler, 2013, 2015; Chiew et al., 2014; Luo et al., 2016; Gudmundsson et al., 2017)186. Additionally, other human activities can influence the hydrological cycle, such as land-use/land-cover change, modifications in river morphology and water table depth, construction and operation of hydropower plants, dikes and weirs, wetland drainage, and agricultural practices such as water withdrawal for irrigation. All of these activities can also have a large impact on runoff at the river basin scale, although there is less agreement over their influence on global mean runoff (Gerten et al., 2008; Sterling et al., 2012; Hall et al., 2014; Betts et al., 2015; Arheimer et al., 2017)187. Some studies suggest that increases in global runoff resulting from changes in land cover or land use (predominantly deforestation) are counterbalanced by decreases resulting from irrigation (Gerten et al., 2008; Sterling et al., 2012)188. Likewise, forest and grassland fires can modify the hydrological response at the watershed scale when the burned area is significant (Versini et al., 2013; Springer et al., 2015; Wine and Cadol, 2016)189.
Few studies have explored observed changes in extreme streamflow and river flooding since the IPCC AR5. Mallakpour and Villarini (2015)190 analysed changes of flood magnitude and frequency in the central United States by considering stream gauge daily records with at least 50 years of data ending no earlier than 2011. They showed that flood frequency has increased, whereas there was limited evidence of a decrease in flood magnitude in this region. Stevens et al. (2016)191 found a rise in the number of reported floods in the United Kingdom during the period 1884–2013, with flood events appearing more frequently towards the end of the 20th century. A peak was identified in 2012, when annual rainfall was the second highest in over 100 years. Do et al. (2017)192 computed the trends in annual maximum daily streamflow data across the globe over the 1966–2005 period. They found decreasing trends for a large number of stations in western North America and Australia, and increasing trends in parts of Europe, eastern North America, parts of South America, and southern Africa.
In summary, streamflow trends since 1950 are not statistically significant in most of the world's largest rivers (high confidence), while flood frequency and extreme streamflow have increased in some regions (high confidence).
3.3.5.2
Projected changes in runoff and river flooding at 1.5°C versus 2°C of global warming
Global-scale assessments of projected changes in freshwater systems generally suggest that areas with either positive or negative changes in mean annual streamflow are smaller for 1.5°C than for 2°C of global warming (Betts et al., 2018; Döll et al., 2018)193. Döll et al. (2018)194 found that only 11% of the global land area (excluding Greenland and Antarctica) shows a statistically significantly larger hazard at 2°C than at 1.5°C. Significant decreases are found for 13% of the global land area for both global warming levels, while significant increases are projected to occur for 21% of the global land area at 1.5°C, and rise to between 26% (Döll et al., 2018)195 and approximately 50% (Betts et al., 2018)196 at 2°C.
At the regional scale, projected runoff changes generally follow the spatial extent of projected changes in precipitation (see Section 3.3.3). Emerging literature includes runoff projections for different warming levels. For 2°C of global warming, an increase in runoff is projected for much of the high northern latitudes, Southeast Asia, East Africa, northeastern Europe, India, and parts of, Austria, China, Hungary, Norway, Sweden, the northwest Balkans and Sahel (Schleussner et al., 2016b; Donnelly et al., 2017; Döll et al., 2018; Zhai et al., 2018)197. Additionally, decreases are projected in the Mediterranean region, southern Australia, Central America, and central and southern South America (Schleussner et al., 2016b; Donnelly et al., 2017; Döll et al., 2018)198. Differences between 1.5°C and 2°C would be most prominent in the Mediterranean, where the median reduction in annual runoff is expected to be about 9% (likely range 4.5–15.5%) at 1.5°C, while at 2°C of warming runoff could decrease by 17% (likely range 8–25%) (Schleussner et al., 2016b)199. Consistent with these projections, Döll et al. (2018)200 found that statistically insignificant changes in the mean annual streamflow around the Mediterranean region became significant when the global warming scenario was changed from 1.5°C to 2°C, with decreases of 10–30% between these two warming levels. Donnelly et al. (2017)201 found an intense decrease in runoff along both the Iberian and Balkan coasts with an increase in warming level.
Basin-scale projections of river runoff at different warming levels are available for many regions. Betts et al. (2018)202 assessed runoff changes in 21 of the world's major river basins at 1.5°C and 2°C of global warming (Figure 3.15). They found a general tendency towards increased runoff, except in the Amazon, Orange, Danube and Guadiana basins where the range of projections indicate decreased mean flows (Figure 3.13). In the case of the Amazon, mean flows are projected to decline by up to 25% at 2°C global warming Betts et al., 2018, Gosling et al., (2017)204 analysed the impact of global warming of 1°C, 2°C and 3°C above pre-industrial levels on river runoff at the catchment scale, focusing on eight major rivers in different continents: Upper Amazon, Darling, Ganges, Lena, Upper Mississippi, Upper Niger, Rhine and Tagus. Their results show that the sign and magnitude of change with global warming for the Upper Amazon, Darling, Ganges, Upper Niger and Upper Mississippi is unclear, while the Rhine and Tagus may experience decreases in projected runoff and the Lena may experience increases. Donnelly et al. (2017)205 analysed the mean flow response to different warming levels for six major European rivers: Glomma, Wisla, Lule, Ebro, Rhine and Danube. Consistent with the increases in mean runoff projected for large parts of northern Europe, the Glomma, Wisla and Lule rivers could experience increased discharges with global warming while discharges from the Ebro could decrease, in part due to a decrease in runoff in southern Europe. In the case of the Rhine and Danube rivers, Donnelly et al. (2017)206 did not find clear results. Mean annual runoff of the Yiluo River catchment in northern China is projected to decrease by 22% at 1.5°C and by 21% at 2°C, while the mean annual runoff for the Beijiang River catchment in southern China is projected to increase by less than 1% at 1.5°C and 3% at 2°C in comparison to the studied baseline period (L. Liu et al., 2017)207. Chen et al. (2017)208 assessed the future changes in water resources in the Upper Yangtze River basin for the same warming levels and found a slight decrease in the annual discharge at 1.5°C but a slight increase at 2°C. Montroull et al. (2018)209 studied the hydrological impacts of the main rivers (Paraguay, Paraná, Iguazú and Uruguay) in La Plata basin in South America under 1.5°C and 2°C of global warming and for two emissions scenarios. The Uruguay basin shows increases in streamflow for all scenarios/warming targets except for the combination of RCP8.5/1.5°C of warming. The increase is approximately 15% above the 1981–2000 reference period for 2°C of global warming and the RCP4.5 scenario. For the other three rivers the sign of the change in mean streamflow depends strongly on the RCP and GCM used.
Marx et al. (2018)210 analysed how hydrological low flows in Europe are affected under different global warming levels (1.5°C, 2°C and 3°C). The Alpine region showed the strongest low flow increase, from 22% at 1.5°C to 30% at 2°C, because of the relatively large snow melt contribution, while in the Mediterranean low flows are expected to decrease because of the decreases in annual precipitation projected for that region. Döll et al. (2018)211 found that extreme low flows in the tropical Amazon, Congo and Indonesian basins could decrease by 10% at 1.5°C, whereas they could increase by 30% in the southwestern part of Russia under the same warming level. At 2°C, projected increases in extreme low flows are exacerbated in the higher northern latitudes and in eastern Africa, India and Southeast Asia, while projected decreases intensify in the Amazon basin, western United States, central Canada, and southern and western Europe, although not in the Congo basin or Indonesia, where models show less agreement.
Figure 3.15
Runoff changes in twenty-one of the world's major river basins at 1.5°C (blue) and 2°C (orange) of global warming, simulated by the Joint UK Land Environment Simulator (JULES) ecosystem–hydrology model under the ensemble of six climate projections.
Boxes show the 25th and 75th percentile changes, whiskers show the range, circles show the four projections that do not define the ends of the range, and crosses show the ensemble means. Numbers in square brackets show the ensemble-mean flow in the baseline (millimetres of rain equivalent) (Source: Betts et al., 2018)212.
Recent analyses of projections in river flooding and extreme runoff and flows are available for different global warming levels. At the global scale, Alfieri et al. (2017)213 assessed the frequency and magnitude of river floods and their impacts under 1.5°C, 2°C and 4°C global warming scenarios. They found that flood events with an occurrence interval longer than the return period of present-day flood protections are projected to increase in all continents under all considered warming levels, leading to a widespread increment in the flood hazard. Döll et al. (2018)214 found that high flows are projected to increase significantly on 11% and 21% of the global land area at 1.5°C and 2°C, respectively. Significantly increased high flows are expected to occur in South and Southeast Asia and Central Africa at 1.5°C, with this effect intensifying and including parts of South America at 2°C.
Regarding the continental scale, Donnelly et al. (2017)215 and Thober et al. (2018)216 explored climate change impacts on European high flows and/or floods under 1.5°C, 2°C and 3°C of global warming. Thober et al. (2018)217 identified the Mediterranean region as a hotspot of change, with significant decreases in high flows of −11% and –13% at 1.5°C and 2°C, respectively, mainly resulting from reduced precipitation (Box 3.2). In northern regions, high flows are projected to rise by 1% and 5% at 1.5°C and 2°C, respectively, owing to increasing precipitation, although floods could decrease by 6% in both scenarios because of less snowmelt. Donnelly et al. (2017)218 found that high runoff levels could rise in intensity, robustness and spatial extent over large parts of continental Europe with an increasing warming level. At 2°C, flood magnitudes are expected to increase significantly in Europe south of 60°N, except for some regions (Bulgaria, Poland and southern Spain); in contrast, they are projected to decrease at higher latitudes (e.g., in most of Finland, northwestern Russia and northern Sweden), with the exception of southern Sweden and some coastal areas in Norway where flood magnitudes may increase (Roudier et al., 2016)219. At the basin scale, Mohammed et al. (2017)220 found that floods are projected to be more frequent and flood magnitudes greater at 2°C than at 1.5°C in the Brahmaputra River in Bangladesh. In coastal regions, increases in heavy precipitation associated with tropical cyclones (Section 3.3.6) combined with increased sea levels (Section 3.3.9) may lead to increased flooding (Section 3.4.5).
In summary, there is medium confidence that global warming of 2°C above the pre-industrial period would lead to an expansion of the area with significant increases in runoff, as well as the area affected by flood hazard, compared to conditions at 1.5°C of global warming. A global warming of 1.5°C would also lead to an expansion of the global land area with significant increases in runoff (medium confidence) and to an increase in flood hazard in some regions (medium confidence) compared to present-day conditions.
3.3.6
Tropical Cyclones and Extratropical Storms
Most recent studies on observed trends in the attributes of tropical cyclones have focused on the satellite era starting in 1979 (Rienecker et al., 2011)221, but the study of observed trends is complicated by the heterogeneity of constantly advancing remote sensing techniques and instrumentation during this period (e.g., Landsea, 2006; Walsh et al., 2016)222. Numerous studies leading up to and after AR5 have reported a decreasing trend in the global number of tropical cyclones and/or the globally accumulated cyclonic energy (Emanuel, 2005; Elsner et al., 2008; Knutson et al., 2010; Holland and Bruyère, 2014; Klotzbach and Landsea, 2015; Walsh et al., 2016)223. A theoretical physical basis for such a decrease to occur under global warming was recently provided by Kang and Elsner (2015)224. However, using a relatively short (20 year) and relatively homogeneous remotely sensed record, Klotzbach (2006)225 reported no significant trends in global cyclonic activity, consistent with more recent findings of Holland and Bruyère (2014)226. Such contradictions, in combination with the fact that the almost four-decade-long period of remotely sensed observations remains relatively short to distinguish anthropogenically induced trends from decadal and multi-decadal variability, implies that there is only low confidence regarding changes in global tropical cyclone numbers under global warming over the last four decades.
Studies in the detection of trends in the occurrence of very intense tropical cyclones (category 4 and 5 hurricanes on the Saffir-Simpson scale) over recent decades have yielded contradicting results. Most studies have reported increases in these systems (Emanuel, 2005; Webster et al., 2005; Klotzbach, 2006; Elsner et al., 2008; Knutson et al., 2010; Holland and Bruyère, 2014; Walsh et al., 2016)227, in particular for the North Atlantic, North Indian and South Indian Ocean basins (e.g., Singh et al., 2000; Singh, 2010; Kossin et al., 2013; Holland and Bruyère, 2014; Walsh et al., 2016)228. In the North Indian Ocean over the Arabian Sea, an increase in the frequency of extremely severe cyclonic storms has been reported and attributed to anthropogenic warming (Murakami et al., 2017)229. However, to the east over the Bay of Bengal, tropical cyclones and severe tropical cyclones have exhibited decreasing trends over the period 1961–2010, although the ratio between severe tropical cyclones and all tropical cyclones is increasing (Mohapatra et al., 2017)230. Moreover, studies that have used more homogeneous records, but were consequently limited to rather short periods of 20 to 25 years, have reported no statistically significant trends or decreases in the global number of these systems (Kamahori et al., 2006; Klotzbach and Landsea, 2015)231. Likewise, CMIP5 model simulations of the historical period have not produced anthropogenically induced trends in very intense tropical cyclones (Bender et al., 2010; Knutson et al., 2010, 2013; Camargo, 2013; Christensen et al., 2013)232, consistent with the findings of Klotzbach and Landsea (2015)233. There is consequently low confidence in the conclusion that the number of very intense cyclones is increasing globally.
General circulation model (GCM) projections of the changing attributes of tropical cyclones under high levels of greenhouse gas forcing (3°C to 4°C of global warming) consistently indicate decreases in the global number of tropical cyclones (Knutson et al., 2010, 2015; Sugi and Yoshimura, 2012; Christensen et al., 2013; Yoshida et al., 2017)234. A smaller number of studies based on statistical downscaling methodologies contradict these findings, however, and indicate increases in the global number of tropical cyclones under climate change (Emanuel, 2017)235. Most studies also indicate increases in the global number of very intense tropical cyclones under high levels of global warming (Knutson et al., 2015; Sugi et al., 2017)236, consistent with dynamic theory (Kang and Elsner, 2015)237, although a few studies contradict this finding (e.g., Yoshida et al., 2017)238. Hence, it is assessed that under 3°C to 4°C of warming that the global number of tropical cyclones would decrease whilst the number of very intense cyclones would increase (medium confidence).
To date, only two studies have directly explored the changing tropical cyclone attributes under 1.5°C versus 2°C of global warming. Using a high resolution global atmospheric model, Wehner et al. (2018a)239 concluded that the differences in tropical cyclone statistics under 1.5°C versus 2°C stabilization scenarios, as defined by the HAPPI protocols (Mitchell et al., 2017)240 are small. Consistent with the majority of studies performed for higher degrees of global warming, the total number of tropical cyclones is projected to decrease under global warming, whilst the most intense (categories 4 and 5) cyclones are projected to occur more frequently. These very intense storms are projected to be associated with higher peak wind speeds and lower central pressures under 2°C versus 1.5°C of global warming. The accumulated cyclonic energy is projected to decrease globally from 1.5°C to 2°C, in association with a decrease in the global number of tropical cyclones under progressively higher levels of global warming. It is also noted that heavy rainfall associated with tropical cyclones was assessed in the IPCC SREX as likely to increase under increasing global warming (Seneviratne et al., 2012)241. Two recent articles suggest that there is high confidence that the current level of global warming (i.e., about 1°C, see Section 3.3.1) increased the heavy precipitation associated with the 2017 Hurricane Harvey by about 15% or more (Risser and Wehner, 2017; van Oldenborgh et al., 2017)242. Hence, it can be inferred, under the assumption of linear dynamics, that further increases in heavy precipitation would occur under 1.5°C, 2°C and higher levels of global warming (medium confidence). Using a high resolution regional climate model explored the effects of different degrees of global warming on tropical cyclones over the southwest Indian Ocean, using transient simulations that downscaled a number of RCP8.5 GCM projections. Decreases in tropical cyclone frequencies are projected under both 1.5°C and 2°C of global warming. The decreases in cyclone frequencies under 2°C of global warming are somewhat larger than under 1.5°C, but no further decreases are projected under 3°C. This suggests that 2°C of warming, at least in these downscaling simulations, represents a type of stabilization level in terms of tropical cyclone formation over the southwest Indian Ocean and landfall over southern Africa (Muthige et al., 2018)244. There is thus limited evidence that the global number of tropical cyclones will be lower under 2°C compared to 1.5°C of global warming, but with an increase in the number of very intense cyclones (low confidence).
The global response of the mid-latitude atmospheric circulation to 1.5°C and 2°C of warming was investigated using the HAPPI ensemble with a focus on the winter season (Li et al., 2018)245. Under 1.5°C of global warming a weakening of storm activity over North America, an equatorward shift of the North Pacific jet exit and an equatorward intensification of the South Pacific jet are projected. Under an additional 0.5°C of warming a poleward shift of the North Atlantic jet exit and an intensification on the flanks of the Southern Hemisphere storm track are projected to become more pronounced. The weakening of the Mediterranean storm track that is projected under low mitigation emerges in the 2°C warmer world (Li et al., 2018)246. AR5 assessed that under high greenhouse gas forcing (3°C or 4°C of global warming) there is low confidence in projections of poleward shifts of the Northern Hemisphere storm tracks, while there is high confidence that there would be a small poleward shift of the Southern Hemisphere storm tracks (Stocker et al., 2013)247. In the context of this report, the assessment is that there is limited evidence and low confidence in whether any projected signal for higher levels of warming would be clearly manifested under 2°C of global warming.
3.3.7
Ocean Circulation and Temperature
It is virtually certain that the temperature of the upper layers of the ocean (0–700 m in depth) has been increasing, and that the global mean for sea surface temperature (SST) has been changing at a rate just behind that of GMST. The surfaces of three ocean basins has warmed over the period 1950–2016 (by 0.11°C, 0.07°C and 0.05°C per decade for the Indian, Atlantic and Pacific Oceans, respectively; Hoegh-Guldberg et al., 2014248), with the greatest changes occurring at the highest latitudes. Isotherms (i.e., lines of equal temperature) of sea surface temperature (SST) are shifting to higher latitudes at rates of up to 40 km per year (Burrows et al., 2014; García Molinos et al., 2015)249. Long-term patterns of variability make detecting signals due to climate change complex, although the recent acceleration of changes to the temperature of the surface layers of the ocean has made the climate signal more distinct (Hoegh-Guldberg et al., 2014)250. There is also evidence of significant increases in the frequency of marine heatwaves in the observational record (Oliver et al., 2018)251, consistent with changes in mean ocean temperatures (high confidence). Increasing climate extremes in the ocean are associated with the general rise in global average surface temperature, as well as more intense patterns of climate variability (e.g., climate change intensification of ENSO) (Section 3.5.2.5). Increased heat in the upper layers of the ocean is also driving more intense storms and greater rates of inundation in some regions, which, together with sea level rise, are already driving significant impacts to sensitive coastal and low-lying areas (Section 3.3.6).
Increasing land–sea temperature gradients have the potential to strengthen upwelling systems associated with the eastern boundary currents (Benguela, Canary, Humboldt and Californian Currents; Bakun, 1990)252. Observed trends support the conclusion that a general strengthening of longshore winds has occurred (Sydeman et al., 2014)253, but the implications of trends detected in upwelling currents themselves are unclear (Lluch-Cota et al., 2014)254. Projections of the scale of changes between 1°C and 1.5°C of global warming and between 1.5°C and 2°C are only informed by the changes during the past increase in GMST of 0.5°C (low confidence). However, evidence from GCM projections of future climate change indicates that a general strengthening of the Benguela, Canary and Humboldt upwelling systems under enhanced anthropogenic forcing (D. Wang et al., 2015)255 is projected to occur (medium confidence). This strengthening is projected to be stronger at higher latitudes. In fact, evidence from regional climate modelling is supportive of an increase in long-shore winds at higher latitudes, whereas long-shore winds may decrease at lower latitudes as a consequence of the poleward displacement of the subtropical highs under climate change (Christensen et al., 2007; Engelbrecht et al., 2009)256.
It is more likely than not that the Atlantic Meridional Overturning Circulation (AMOC) has been weakening in recent decades, given the detection of the cooling of surface waters in the North Atlantic and evidence that the Gulf Stream has slowed since the late 1950s (Rahmstorf et al., 2015b; Srokosz and Bryden, 2015; Caesar et al., 2018)257. There is only limited evidence linking the current anomalously weak state of AMOC to anthropogenic warming (Caesar et al., 2018)258. It is very likely that the AMOC will weaken over the 21st century. The best estimates and ranges for the reduction based on CMIP5 simulations are 11% (1– 24%) in RCP2.6 and 34% (12– 54%) in RCP8.5 (AR5). There is no evidence indicating significantly different amplitudes of AMOC weakening for 1.5°C versus 2°C of global warming.
3.3.8
Sea Ice
Summer sea ice in the Arctic has been retreating rapidly in recent decades. During the period 1997 to 2014, for example, the monthly mean sea ice extent during September (summer) decreased on average by 130,000 km² per year (Serreze and Stroeve, 2015)259. This is about four times as fast as the September sea ice loss during the period 1979 to 1996. Sea ice thickness has also decreased substantially, with an estimated decrease in ice thickness of more than 50% in the central Arctic (Lindsay and Schweiger, 2015)260. Sea ice coverage and thickness also decrease in CMIP5 simulations of the recent past, and are projected to decrease in the future (Collins et al., 2013)261. However, the modelled sea ice loss in most CMIP5 models is much smaller than observed losses. Compared to observations, the simulations are less sensitive to both global mean temperature rise (Rosenblum and Eisenman, 2017)262 and anthropogenic CO2 emissions (Notz and Stroeve, 2016)263. This mismatch between the observed and modelled sensitivity of Arctic sea ice implies that the multi-model-mean responses of future sea ice evolution probably underestimates the sea ice loss for a given amount of global warming. To address this issue, studies estimating the future evolution of Arctic sea ice tend to bias correct the model simulations based on the observed evolution of Arctic sea ice in response to global warming. Based on such bias correction, pre-AR5 and post-AR5 studies generally agree that for 1.5°C of global warming relative to pre-industrial levels, the Arctic Ocean will maintain a sea ice cover throughout summer in most years (Collins et al., 2013; Notz and Stroeve, 2016; Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018; Sigmond et al., 2018)264. For 2°C of global warming, chances of a sea ice-free Arctic during summer are substantially higher (Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018; Screen et al., 2018; Sigmond et al., 2018)265. Model simulations suggest that there will be at least one sea ice-free Arctic8 summer after approximately 10 years of stabilized warming at 2°C, as compared to one sea ice-free summer after 100 years of stabilized warming at 1.5°C above pre-industrial temperatures (Jahn, 2018; Screen et al., 2018; Sigmond et al., 2018)266. For a specific given year under stabilized warming of 2°C, studies based on large ensembles of simulations with a single model estimate the likelihood of ice-free conditions as 35% without a bias correction of the underlying model (Sanderson et al., 2017; Jahn, 2018)267; as between 10% and >99% depending on the observational record used to correct the sensitivity of sea ice decline to global warming in the underlying model (Niederdrenk and Notz, 2018)268; and as 19% based on a procedure to correct for biases in the climatological sea ice coverage in the underlying model (Sigmond et al., 2018)269. The uncertainty of the first year of the occurrence of an ice-free Arctic Ocean arising from internal variability is estimated to be about 20 years (Notz, 2015; Jahn et al., 2016)270.
The more recent estimates of the warming necessary to produce an ice-free Arctic Ocean during summer are lower than the ones given in AR5 (about 2.6°C–3.1°C of global warming relative to pre-industrial levels or 1.6°C–2.1°C relative to present–day conditions), which were similar to the estimate of 3°C of global warming relative to pre-industrial levels (or 2°C relative to present-day conditions) by Mahlstein and Knutti (2012)271 based on bias-corrected CMIP3 models. Rosenblum and Eisenman (2016)272 explained why the sensitivity estimated by Mahlstein and Knutti (2012)273 might be too low, estimating instead that September sea ice in the Arctic would disappear at 2°C of global warming relative to pre-industrial levels (or about 1°C relative to present-day conditions), in line with the other recent estimates. Notz and Stroeve (2016)274 used the observed correlation between September sea ice extent and cumulative CO2 emissions to estimate that the Arctic Ocean would become nearly free of sea ice during September with a further 1000 Gt of emissions, which also implies a sea ice loss at about 2°C of global warming. Some of the uncertainty in these numbers stems from the possible impact of aerosols (Gagne et al., 2017)275 and of volcanic forcing (Rosenblum and Eisenman, 2016)276. During winter, little Arctic sea ice is projected to be lost for either 1.5°C or 2°C of global warming (Niederdrenk and Notz, 2018)277.
A substantial number of pre-AR5 studies found that there is no indication of hysteresis behaviour of Arctic sea ice under decreasing temperatures following a possible overshoot of a long-term temperature target (Holland et al., 2006; Schröder and Connolley, 2007; Armour et al., 2011; Sedláček et al., 2011; Tietsche et al., 2011; Boucher et al., 2012; Ridley et al., 2012)278. In particular, the relationship between Arctic sea ice coverage and GMST was found to be indistinguishable between a warming scenario and a cooling scenario. These results have been confirmed by post-AR5 studies (Li et al., 2013; Jahn, 2018)279, which implies high confidence that an intermediate temperature overshoot has no long-term consequences for Arctic sea ice coverage.
In the Antarctic, sea ice shows regionally contrasting trends, such as a strong decrease in sea ice coverage near the Antarctic peninsula but increased sea ice coverage in the Amundsen Sea (Hobbs et al., 2016)280. Averaged over these contrasting regional trends, there has been a slow long-term increase in overall sea ice coverage in the Southern Ocean, although with comparably low ice coverage from September 2016 onwards. Collins et al. (2013)281 assessed low confidence in Antarctic sea ice projections because of the wide range of model projections and an inability of almost all models to reproduce observations such as the seasonal cycle, interannual variability and the long-term slow increase. No existing studies have robustly assessed the possible future evolution of Antarctic sea ice under low-warming scenarios.
In summary, the probability of a sea-ice-free Arctic Ocean during summer is substantially higher at 2°C compared to 1.5°C of global warming relative to pre-industrial levels, and there is medium confidence that there will be at least one sea ice-free Arctic summer after about 10 years of stabilized warming at 2°C, while about 100 years are required at 1.5°C. There is high confidence that an intermediate temperature overshoot has no long-term consequences for Arctic sea ice coverage with regrowth on decadal time scales.
3.3.9
Sea Level
Sea level varies over a wide range of temporal and spatial scales, which can be divided into three broad categories. These are global mean sea level (GMSL), regional variation about this mean, and the occurrence of sea-level extremes associated with storm surges and tides. GMSL has been rising since the late 19th century from the low rates of change that characterized the previous two millennia (Church et al., 2013)282. Slowing in the reported rate over the last two decades (Cazenave et al., 2014)283 may be attributable to instrumental drift in the observing satellite system (Watson et al., 2015)284 and increased volcanic activity (Fasullo et al., 2016)285. Accounting for the former results in rates (1993 to mid-2014) between 2.6 and 2.9 mm yr–1 (Watson et al., 2015)286. The relative contributions from thermal expansion, glacier and ice-sheet mass loss, and freshwater storage on land are relatively well understood (Church et al., 2013; Watson et al., 2015)287 and their attribution is dominated by anthropogenic forcing since 1970 (15 ± 55% before 1950, 69 ± 31% after 1970) (Slangen et al., 2016)288.
There has been a significant advance in the literature since AR5, which has included the development of semi-empirical models (SEMs) into a broader emulation-based approach (Kopp et al., 2014; Mengel et al., 2016; Nauels et al., 2017)289 that is partially based on the results from more detailed, process-based modelling Church et al. (2013)290 assigned low confidence to SEMs because these models assume that the relation between climate forcing and GMSL is the same in the past (calibration) and future (projection). Probable future changes in the relative contributions of thermal expansion, glaciers and (in particular) ice sheets invalidate this assumption. However, recent emulation-based studies overcame this shortcoming by considering individual GMSL contributors separately, and they are therefore employed in this assessment. In this subsection, the process-based literature of individual contributors to GMSL is considered for scenarios close to 1.5°C and 2°C of global warming before emulation-based approaches are assessed.
A limited number of processes-based studies are relevant to GMSL in 1.5°C and 2°C worlds. Marzeion et al. (2018)291 used a global glacier model with temperature-scaled scenarios based on RCP2.6 to investigate the difference between 1.5°C and 2°C of global warming and found little difference between scenarios in the glacier contribution to GMSL for the year 2100 (54–97 mm relative to present-day levels for 1.5°C and 63–112 mm for 2°C, using a 90% confidence interval). This arises because glacier melt during the remainder of the century is dominated by the response to warming from pre-industrial to present-day levels, which is in turn a reflection of the slow response times of glaciers. Fürst et al. (2015)292 made projections of the Greenland ice sheet's contribution to GMSL using an ice-flow model forced by the regional climate model Modèle Atmosphérique Régional (MAR; considered by Church et al. (2013)293 to be the 'most realistic' such model). They projected an RCP2.6 range of 24–60 mm (1 standard deviation) by the end of the century (relative to the year 2000 and consistent with the assessment of Church et al. (2013)294; however, their projections do not allow the difference between 1.5°C and 2°C worlds to be evaluated.
The Antarctic ice sheet can contribute both positively, through increases in outflow (solid ice lost directly to the ocean), and negatively, through increases in snowfall (owing to the increased moisture-bearing capacity of a warmer atmosphere), to future GMSL rise. Frieler et al. (2015)295 suggested a range of 3.5–8.7% °C–1 for this effect, which is consistent with AR5. Observations from the Amundsen Sea sector of Antarctica suggest an increase in outflow (Mouginot et al., 2014)296 over recent decades associated with grounding line retreat (Rignot et al., 2014)297 and the influx of relatively warm Circumpolar Deepwater (Jacobs et al., 2011)298. Literature on the attribution of these changes to anthropogenic forcing is still in its infancy (Goddard et al., 2017; Turner et al., 2017a)299. RCP2.6-based projections of Antarctic outflow (Levermann et al., 2014; Golledge et al., 2015; DeConto and Pollard, 2016300, who include snowfall changes) are consistent with the AR5 assessment of Church et al. (2013)301 for end-of-century GMSL for RCP2.6, and do not support substantial additional GMSL rise by Marine Ice Sheet Instability or associated instabilities (see Section 3.6). While agreement is relatively good, concerns about the numerical fidelity of these models still exist, and this may affect the quality of their projections (Drouet et al., 2013; Durand and Pattyn, 2015)302. An assessment of Antarctic contributions beyond the end of the century, in particular related to the Marine Ice Sheet Instability, can be found in Section 3.6.
While some literature on process-based projections of GMSL for the period up to 2100 is available, it is insufficient for distinguishing between emissions scenarios associated with 1.5°C and 2°C warmer worlds. This literature is, however, consistent with the assessment by Church et al. (2013)303 of a likely range of 0.28–0.61 m in 2100 (relative to 1986–2005), suggesting that the AR5 assessment is still appropriate. Recent emulation-based studies show convergence towards this AR5 assessment (Table 3.1) and offer the advantage of allowing a comparison between 1.5°C and 2°C warmer worlds. Table 3.1 features a compilation of recent emulation-based and SEM studies.
Table 3.1:
Compilation of recent projections for sea level at 2100 (in cm) for Representative Concentration Pathway (RCP)2.6, and 1.5°C and 2°C scenarios. Upper and lower limits are shown for the 17-84% and 5-95% confidence intervals quoted in the original papers.
| Study | Baseline | RCP2.6 | 1.5°C | 2°C | |||
|---|---|---|---|---|---|---|---|
| 67% | 90% | 67% | 90% | 67% | 90% | ||
| AR5 | 1986–2005 | 28–61 | |||||
| Kopp et al. (2014)304 | 2000 | 37–65 | 29–82 | ||||
| Jevrejeva et al. (2016)305 | 1986–2005 | 29–58 | |||||
| Kopp et al. (2016)306 | 2000 | 28–51 | 24–61 | ||||
| Mengel et al. (2016)307 | 1986–2005 | 28–56 | |||||
| Nauels et al. (2017)308 | 1986–2005 | 35–56 | |||||
| Goodwin et al. (2017)309 | 1986–2005 | 31–59 45–70 45–72 | |||||
| Schaeffer et al. (2012)310 | 2000 | 52–96 | 54–99 | 56–105 | |||
| Schleussner et al. (2016b)311 | 2000 | 26–53 | 36–65 | ||||
| Bittermann et al. (2017)312 | 2000 | 29–46 | 39–61 | ||||
| Jackson et al. (2018)313 | 1986–2005 | 30–58 40–77 | 20–67 28–93 | 35–64 47–93 | 24–74 32–117 | ||
| Sanderson et al. (2017)314 | 50–80 | 60–90 | |||||
| Nicholls et al. (2018)315 | 1986–2005 | 24–54 | 31–65 | ||||
| Rasmussen et al. (2018)316 | 2000 | 35–64 | 28–82 | 39–76 | 28–96 | ||
| Goodwin et al. (2018)317 | 1986–2005 | 26–62 | 30–69 | ||||
There is little consensus between the reported ranges of GMSL rise (Table 3.1). Projections vary in the range 0.26–0.77 m and 0.35–0.93 m for 1.5°C and 2°C respectively for the 17–84% confidence interval (0.20–0.99 m and 0.24–1.17 m for the 5–95% confidence interval). There is, however, medium agreement that GMSL in 2100 would be 0.04–0.16 m higher in a 2°C warmer world compared to a 1.5°C warmer world based on the 17–84% confidence interval (0.00–0.24 m based on 5–95% confidence interval) with a value of around 0.1m. There is medium confidence in this assessment because of issues associated with projections of the Antarctic contribution to GMSL that are employed in emulation-based studies (see above) and the issues previously identified with SEMs (Church et al., 2013)318.
Translating projections of GMSL to the scale of coastlines and islands requires two further steps. The first step accounts for regional changes associated with changing water and ice loads (such as Earth's gravitational field and rotation, and vertical land movement), as well as spatial differences in ocean heat uptake and circulation. The second step maps regional sea level to changes in the return periods of particular flood events to account for effects not included in global climate models, such as tides, storm surges, and wave setup and runup. Kopp et al. (2014)319 presented a framework to do this and gave an example application for nine sites located in the US, Japan, northern Europe and Chile. Of these sites, seven (all except those in northern Europe) were found to experience at least a quadrupling in the number of years in the 21st century with 1-in-100-year floods under RCP2.6 compared to under no future sea level rise. Rasmussen et al. (2018)320 used this approach to investigate the difference between 1.5°C and 2°C warmer worlds up to 2200. They found that the reduction in the frequency of 1-in-100-year floods in a 1.5°C compared to a 2°C warmer world would be greatest in the eastern USA and Europe, with ESL event frequency amplification being reduced by about a half and with smaller reductions for small island developing states (SIDS). This last result contrasts with the finding of Vitousek et al. (2017)321 that regions with low variability in extreme water levels (such as SIDS in the tropics) are particularly sensitive to GMSL rise, such that a doubling of frequency may be expected for even small (0.1–0.2 m) rises. Schleussner et al. (2011)322 emulated the AMOC based on a subset of CMIP-class climate models. When forced using global temperatures appropriate for the CP3-PD scenario (1°C of warming in 2100 relative to 2000 or about 2°C of warming relative to pre-industrial) the emulation suggests an 11% median reduction in AMOC strength at 2100 (relative to 2000) with an associated 0.04 m dynamic sea level rise along the New York City coastline.
In summary, there is medium confidence that GMSL rise will be about 0.1 m (within a 0.00–0.20 m range based on 17–84% confidence-interval projections) less by the end of the 21st century in a 1.5°C compared to a 2°C warmer world. Projections for 1.5°C and 2°C global warming cover the ranges 0.2–0.8 m and 0.3–1.00 m relative to 1986–2005, respectively (medium confidence). Sea level rise beyond 2100 is discussed in Section 3.6; however, recent literature strongly supports the assessment by Church et al. (2013)323 that sea level rise will continue well beyond 2100 (high confidence).
3.3.10
Ocean Chemistry
Ocean chemistry includes pH, salinity, oxygen, CO2, and a range of other ions and gases, which are in turn affected by precipitation, evaporation, storms, river runoff, coastal erosion, up-welling, ice formation, and the activities of organisms and ecosystems (Stocker et al., 2013)345. Ocean chemistry is changing alongside increasing global temperature, with impacts projected at 1.5°C and, more so, at 2°C of global warming (Doney et al., 2014)346 (medium to high confidence). Projected changes in the upper layers of the ocean include altered pH, oxygen content and sea level. Despite its many component processes, ocean chemistry has been relatively stable for long periods of time prior to the industrial period (Hönisch et al., 2012)347. Ocean chemistry is changing under the influence of human activities and rising greenhouse gases (virtually certain; Rhein et al., 2013; Stocker et al., 2013)348. About 30% of CO2 emitted by human activities, for example, has been absorbed by the upper layers of the ocean, where it has combined with water to produce a dilute acid that dissociates and drives ocean acidification (high confidence) (Cao et al., 2007; Stocker et al., 2013)349. Ocean pH has decreased by 0.1 pH units since the pre-industrial period, a shift that is unprecedented in the last 65 Ma (high confidence) (Ridgwell and Schmidt, 2010)350 or even 300 Ma of Earth's history (medium confidence) (Hönisch et al., 2012)351.
Ocean acidification is a result of increasing CO2 in the atmosphere (very high confidence) and is most pronounced where temperatures are lowest (e.g., polar regions) or where CO2-rich water is brought to the ocean surface by upwelling (Feely et al., 2008)352. Acidification can also be influenced by effluents from natural or disturbed coastal land use (Salisbury et al., 2008)353, plankton blooms (Cai et al., 2011)354, and the atmospheric deposition of acidic materials (Omstedt et al., 2015)355. These sources may not be directly attributable to climate change, but they may amplify the impacts of ocean acidification (Bates and Peters, 2007; Duarte et al., 2013)356. Ocean acidification also influences the ionic composition of seawater by changing the organic and inorganic speciation of trace metals (e.g., 20-fold increases in free ion concentrations of metals such as aluminium) – with changes expected to have impacts although they are currently poorly documented and understood (low confidence) (Stockdale et al., 2016)357.
Oxygen varies regionally and with depth; it is highest in polar regions and lowest in the eastern basins of the Atlantic and Pacific Oceans and in the northern Indian Ocean (Doney et al., 2014; Karstensen et al., 2015; Schmidtko et al., 2017)358. Increasing surface water temperatures have reduced oxygen in the ocean by 2% since 1960, with other variables such as ocean acidification, sea level rise, precipitation, wind and storm patterns playing roles (Schmidtko et al., 2017)359. Changes to ocean mixing and metabolic rates, due to increased temperature and greater supply of organic carbon to deep areas, has increased the frequency of 'dead zones', areas where oxygen levels are so low that they no longer support oxygen dependent life (Diaz and Rosenberg, 2008)360. The changes are complex and include both climate change and other variables (Altieri and Gedan, 2015)361, and are increasing in tropical as well as temperate regions (Altieri et al., 2017)362.
Ocean salinity is changing in directions that are consistent with surface temperatures and the global water cycle (i.e., precipitation versus evaporation). Some regions, such as northern oceans and the Arctic, have decreased in salinity, owing to melting glaciers and ice sheets, while others have increased in salinity, owing to higher sea surface temperatures and evaporation (Durack et al., 2012)363. These changes in salinity (i.e., density) are also potentially contributing to large-scale changes in water movement (Section 3.3.8).
3.3.11
Global Synthesis
Table 3.2 features a summary of the assessments of global and regional climate changes and associated hazards described in this chapter, based on the existing literature. For more details about observation and attribution in ocean and cryosphere systems, please refer to the upcoming IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) due to be released in 2019.
Table 3.2
Summary of assessments of global and regional climate changes and associated hazards
Confidence and likelihood statements are quoted from the relevant chapter text and are omitted where no assessment was made, in which case the IPCC Fifth Assessment Report (AR5) assessment is given where available. GMST: global mean surface temperature, AMOC: Atlantic Meridional Overturning Circulation, GMSL: global mean sea level.
| Observed change (recent past versus pre-industrial) | Attribution of observed change to human-induced forcing (present-day versus pre-industrial) | Projected change at 1.5°C of global warming compared to pre-industrial (1.5°C versus 0°C) | Projected change at 2°C of global warming compared to pre-industrial (2°C versus 0°C) | Differences between 2°C and 1.5°C of global warming | |
|---|---|---|---|---|---|
| GMST anomaly | GMST anomalies were 0.87°C (±0.10°C likely range) above pre-industrial (1850–1900) values in the 2006–2015 decade, with a recent warming of about 0.2°C (±0.10°C) per decade (high confidence) [Chapter 1] | The observed 0.87°C GMST increase in the 2006–2015 decade compared to pre-industrial (1850–1900) conditions was mostly human-induced (high confidence) Human-induced warming reached about 1°C (±0.2°C likely range) above pre-industrial levels in 2017 [Chapter 1] | 1.5°C | 2°C | 0.5°C |
| Temperature extremes | Overall decrease in the number of cold days and nights and overall increase in the number of warm days and nights at the global scale on land (very likely) Continental-scale increase in intensity and frequency of hot days and nights, and decrease in intensity and frequency of cold days and nights, in North America, Europe and Australia (very likely) Increases in frequency or duration of warm spell lengths in large parts of Europe, Asia and Australia (high confidence (likely)), as well as at the global scale (medium confidence) [Section 3.3.2] | Anthropogenic forcing has contributed to the observed changes in frequency and intensity of daily temperature extremes on the global scale since the mid-20th century (very likely) [Section 3.3.2] | Global-scale increased intensity and frequency of hot days and nights, and decreased intensity and frequency of cold days and nights (very likely) Warming of temperature extremes highest over land, including many inhabited regions (high confidence), with increases of up to 3°C in the mid-latitude warm season and up to 4.5°C in the high-latitude cold season (high confidence) Largest increase in frequency of unusually hot extremes in tropical regions (high confidence) [Section 3.3.2] | Global-scale increased intensity and frequency of hot days and nights, and decreased intensity and frequency of cold days and nights (very likely) Warming of temperature extremes highest over land, including many inhabited regions (high confidence), with increases of up to 4°C in the mid-latitude warm season and up to 6°C in the high-latitude cold season (high confidence) Largest increase in frequency of unusually hot extremes in tropical regions (high confidence) [Section 3.3.2] | Global-scale increased intensity and frequency of hot days and nights, and decreased intensity and frequency of cold days and nights (high confidence) Global-scale increase in length of warm spells and decrease in length of cold spells (high confidence) Strongest increase in frequency for the rarest and most extreme events (high confidence) Particularly large increases in hot extremes in inhabited regions (high confidence) [Section 3.3.2] |
| Heavy precipitation | More areas with increases than decreases in the frequency, intensity and/or amount of heavy precipitation (likely) [Section 3.3.3] | Human influence contributed to the global-scale tendency towards increases in the frequency, intensity and/or amount of heavy precipitation events (medium confidence) [Section 3.3.3; AR5 Chapter 10 (Bindoff et al., 2013a) 364] | Increases in frequency, intensity and/or amount heavy precipitation when averaged over global land, with positive trends in several regions (high confidence) [Section 3.3.3] | Increases in frequency, intensity and/or amount heavy precipitation when averaged over global land, with positive trends in several regions (high confidence) [Section 3.3.3] | Higher frequency, intensity and/or amount of heavy precipitation when averaged over global land, with positive trends in several regions (medium confidence) Several regions are projected to experience increases in heavy precipitation at 2°C versus 1.5°C (medium confidence), in particular in high-latitude and mountainous regions, as well as in eastern Asia and eastern North America (medium confidence) [Section 3.3.3] |
| Drought and dryness | High confidence in dryness trends in some regions, especially drying in the Mediterranean region (including southern Europe, northern Africa and the Near East) Low confidence in drought and dryness trends at the global scale [Section 3.3.4] | Medium confidence in attribution of drying trends in southern Europe (Mediterranean region) Low confidence elsewhere, in part due to large interannual variability and longer duration (and thus lower frequency) of drought events, as well as to dependency on the dryness index definition applied [Section 3.3.4] | Medium confidence in drying trends in the Mediterranean region Low confidence elsewhere, in part due to large interannual variability and longer duration (and thus lower frequency) of drought events, as well as to dependency on the dryness index definition applied Increases in drought, dryness or precipitation deficits projected in some regions compared to the pre-industrial or present-day conditions, but substantial variability in signals depending on considered indices or climate model (medium confidence) [Section 3.3.4] | Medium confidence in drying trends in the Mediterranean region and Southern Africa Low confidence elsewhere, in part due to large interannual variability and longer duration (and thus lower frequency) of drought events, as well as to dependency on the dryness index definition applied Increases in drought, dryness or precipitation deficits projected in some regions compared to the pre-industrial or present-day conditions, but substantial variability in signals depending on considered indices or climate model (medium confidence). [Section 3.3.4] | Medium confidence in stronger drying trends in the Mediterranean region and Southern Africa Low confidence elsewhere, in part due to large interannual variability and longer duration (and thus lower frequency) of drought events, as well as to dependency on the dryness index definition applied [Section 3.3.4] |
| Runoff and river flooding | Streamflow trends mostly not statistically significant (high confidence) Increase in flood frequency and extreme streamflow in some regions (high confidence) [Section 3.3.5] | Not assessed in this report | Expansion of the global land area with a significant increase in runoff (medium confidence) Increase in flood hazard in some regions (medium confidence) [Section 3.3.5] | Expansion of the global land area with a significant increase in runoff (medium confidence) Increase in flood hazard in some regions (medium confidence) [Section 3.3.5] | Expansion of the global land area with significant increase in runoff (medium confidence) Expansion in the area affected by flood hazard (medium confidence) [Section 3.3.5] |
| Tropical and extra-tropical cyclones | Low confidence in the robustness of observed changes [Section 3.3.6] | Not meaningful to assess given low confidence in changes, due to large interannual variability, heterogeneity of the observational record and contradictory findings regarding trends in the observational record | Increases in heavy precipitation associated with tropical cyclones (medium confidence) | Further increases in heavy precipitation associated with tropical cyclones (medium confidence) | Heavy precipitation associated with tropical cyclones is projected to be higher at 2°C compared to 1.5°C global warming (medium confidence). Limited evidence that the global number of tropical cyclones will be lower under 2°C of global warming compared to under 1.5°C of warming, but an increase in the number of very intense cyclones (low confidence) |
| Ocean circulation and temperature | Observed warming of the upper ocean, with slightly lower rates than global warming (virtually certain) Increased occurrence of marine heatwaves (high confidence) AMOC has been weakening over recent decades (more likely than not) [Section 3.3.7] | Limited evidence attributing the weakening of AMOC in recent decades to anthropogenic forcing [Section 3.3.7] | Further increases in ocean temperatures, including more frequent marine heatwaves (high confidence) AMOC will weaken over the 21st century and substantially so under high levels (more than 2°C) of global warming (very likely) [Section 3.3.7] | ||
| Sea ice | Continuing the trends reported in AR5, the annual Arctic sea ice extent decreased over the period 1979–2012. The rate of this decrease was very likely between 3.5 and 4.1% per decade (0.45 to 0.51 million km2 per decade) [AR5 Chapter 4 (Vaughan et al., 2013) 365] | Anthropogenic forcings are very likely to have contributed to Arctic sea ice loss since 1979 [AR5 Chapter 10 | At least one sea-ice-free Arctic summer after about 100 years of stabilized warming (medium confidence)[Section 3.3.8] | At least one sea-ice-free Arctic summer after about 10 years of stabilized warming (medium confidence)[Section 3.3.8] | Probability of sea-ice-free Arctic summer greatly reduced at 1.5°C versus 2°C of global warming (medium confidence) [Section 3.3.8] |
| Intermediate temperature overshoot has no long-term consequences for Arctic sea ice cover (high confidence) [3.3.8] | |||||
| Sea level | It is likely that the rate of GMSL rise has continued to increase since the early 20th century, with estimates that range from 0.000 [–0.002 to 0.002] mm yr–2 to 0.013 [0.007 to 0.019] mm yr–2 [AR5 Chapter 13 | It is very likely that there is a substantial contribution from anthropogenic forcings to the global mean sea level rise since the 1970s [AR5 Chapter 10 (Bindoff et al., 2013a)368] | Not assessed in this report | Not assessed in this report | GMSL rise will be about 0.1 m (0.00–0.20 m) less at 1.5°C versus 2°C global warming (medium confidence)[Section 3.3.9] |
| Ocean chemistry | Ocean acidification due to increased CO2 has resulted in a 0.1 pH unit decrease since the pre-industrial period, which is unprecedented in the last 65 Ma (high confidence) [Section 3.3.10] | The oceanic uptake of anthropogenic CO2 has resulted in acidification of surface waters (very high confidence). [Section 3.3.10] | Ocean chemistry is changing with global temperature increases, with impacts projected at 1.5°C and, more so, at 2°C of warming (high confidence) [Section 3.3.10] | ||
3.4
Observed Impacts and Projected Risks in Natural and Human Systems
3.4.1
Introduction
In Section 3.4, new literature is explored and the assessment of impacts and projected risks is updated for a large number of natural and human systems. This section also includes an exploration of adaptation opportunities that could be important steps towards reducing climate change, thereby laying the ground for later discussions on opportunities to tackle both mitigation and adaptation while at the same time recognising the importance of sustainable development and reducing the inequities among people and societies facing climate change.
Working Group II (WGII) of the IPCC Fifth Assessment Report (AR5) provided an assessment of the literature on the climate risk for natural and human systems across a wide range of environments, sectors and greenhouse gas scenarios, as well as for particular geographic regions (IPCC, 2014a, b)369. The comprehensive assessment undertaken by AR5 evaluated the evidence of changes to natural systems, and the impact on human communities and industry. While impacts varied substantially among systems, sectors and regions, many changes over the past 50 years could be attributed to human driven climate change and its impacts. In particular, AR5 attributed observed impacts in natural ecosystems to anthropogenic climate change, including changes in phenology, geographic and altitudinal range shifts in flora and fauna, regime shifts and increased tree mortality, all of which can reduce ecosystem functioning and services thereby impacting people. AR5 also reported increasing evidence of changing patterns of disease and invasive species, as well as growing risks for communities and industry, which are especially important with respect to sea level rise and human vulnerability.
One of the important themes that emerged from AR5 is that previous assessments may have under-estimated the sensitivity of natural and human systems to climate change. A more recent analysis of attribution to greenhouse gas forcing at the global scale (Hansen and Stone, 2016)370 confirmed that many impacts related to changes in regional atmospheric and ocean temperature can be confidently attributed to anthropogenic forcing, while attribution to anthropogenic forcing of changes related to precipitation are by comparison less clear. Moreover, there is no strong direct relationship between the robustness of climate attribution and that of impact attribution (Hansen and Stone, 2016)371. The observed changes in human systems are amplified by the loss of ecosystem services (e.g., reduced access to safe water) that are supported by biodiversity (Oppenheimer et al., 2014)372. Limited research on the risks of warming of 1.5°C and 2°C was conducted following AR5 for most key economic sectors and services, for livelihoods and poverty, and for rural areas. For these systems, climate is one of many drivers that result in adverse outcomes. Other factors include patterns of demographic change, socio-economic development, trade and tourism. Further, consequences of climate change for infrastructure, tourism, migration, crop yields and other impacts interact with underlying vulnerabilities, such as for individuals and communities engaged in pastoralism, mountain farming and artisanal fisheries, to affect livelihoods and poverty (Dasgupta et al., 2014)373.
Incomplete data and understanding of these lower-end climate scenarios have increased the need for more data and an improved understanding of the projected risks of warming of 1.5°C and 2°C for reference. In this section, the available literature on the projected risks, impacts and adaptation options is explored, supported by additional information and background provided in Supplementary Material 3.SM.3.1, 3.SM.3.2, 3.SM.3.4, and 3.SM.3.5. A description of the main assessment methods of this chapter is given in Section 3.2.2.
3.4.2
Freshwater Resources (Quantity and Quality)
3.4.2.1
Water availability
Working Group II of AR5 concluded that about 80% of the world's population already suffers from serious threats to its water security, as measured by indicators including water availability, water demand and pollution (Jiménez Cisneros et al., 2014)374. UNESCO (2011)375 concluded that climate change can alter the availability of water and threaten water security.
Although physical changes in streamflow and continental runoff that are consistent with climate change have been identified (Section 3.3.5), water scarcity in the past is still less well understood because the scarcity assessment needs to take into account various factors, such as the operations of water supply infrastructure and human water use behaviour (Mehran et al., 2017)376, as well as green water, water quality and environmental flow requirements (J. Liu et al., 2017)377. Over the past century, substantial growth in populations, industrial and agricultural activities, and living standards have exacerbated water stress in many parts of the world, especially in semi-arid and arid regions such as California in the USA (AghaKouchak et al., 2015378; Mehran et al., 2015)379. Owing to changes in climate and water consumption behaviour, and particularly effects of the spatial distribution of population growth relative to water resources, the population under water scarcity increased from 0.24 billion (14% of the global population) in the 1900s to 3.8 billion (58%) in the 2000s. In that last period (2000s), 1.1 billion people (17% of the global population) who mostly live in South and East Asia, North Africa and the Middle East faced serious water shortage and high water stress (Kummu et al., 2016)380.
Over the next few decades, and for increases in global mean temperature less than about 2°C, AR5 concluded that changes in population will generally have a greater effect on water resource availability than changes in climate. Climate change, however, will regionally exacerbate or offset the effects of population pressure (Jiménez Cisneros et al., 2014)381.
The differences in projected changes to levels of runoff under 1.5°C and 2°C of global warming, particularly those that are regional, are described in Section 3.3.5. Constraining warming to 1.5°C instead of 2°C might mitigate the risks for water availability, although socio-economic drivers could affect water availability more than the risks posed by variation in warming levels, while the risks are not homogeneous among regions (medium confidence) (Gerten et al., 2013; Hanasaki et al., 2013; Arnell and Lloyd-Hughes, 2014; Schewe et al., 2014; Karnauskas et al., 2018)382. Assuming a constant population in the models used in his study, Gerten et al. (2013)383 determined that an additional 8% of the world population in 2000 would be exposed to new or aggravated water scarcity at 2°C of global warming. This value was almost halved – with 50% greater reliability – when warming was constrained to 1.5°C. People inhabiting river basins, particularly in the Middle East and Near East, are projected to become newly exposed to chronic water scarcity even if global warming is constrained to less than 2°C. Many regions, especially those in Europe, Australia and southern Africa, appear to be affected at 1.5°C if the reduction in water availability is computed for non-water-scarce basins as well as for water-scarce regions. Out of a contemporary population of approximately 1.3 billion exposed to water scarcity, about 3% (North America) to 9% (Europe) are expected to be prone to aggravated scarcity at 2°C of global warming (Gerten et al., 2013)384. Under the Shared Socio-Economic Pathway (SSP)2 population scenario, about 8% of the global population is projected to experience a severe reduction in water resources under warming of 1.7°C in 2021–2040, increasing to 14% of the population under 2.7°C in 2043–2071, based on the criteria of discharge reduction of either >20% or >1 standard deviation (Schewe et al., 2014)385. Depending on the scenarios of SSP1–5, exposure to the increase in water scarcity in 2050 will be globally reduced by 184–270 million people at about 1.5°C of warming compared to the impacts at about 2°C. However, the variation between socio-economic levels is larger than the variation between warming levels (Arnell and Lloyd-Hughes, 2014)386.
On many small islands (e.g., those constituting SIDS), freshwater stress is expected to occur as a result of projected aridity change. Constraining warming to 1.5°C, however, could avoid a substantial fraction of water stress compared to 2°C, especially across the Caribbean region, particularly on the island of Hispaniola (Dominican Republic and Haiti) (Karnauskas et al., 2018)387. Hanasaki et al. (2013)388 concluded that the projected range of changes in global irrigation water withdrawal (relative to the baseline of 1971–2000), using human configuration fixing non-meteorological variables for the period around 2000, are 1.1–2.3% and 0.6–2.0% lower at 1.5°C and 2°C, respectively. In the same study, Hanasaki et al. (2013)389 highlighted the importance of water use scenarios in water scarcity assessments, but neither quantitative nor qualitative information regarding water use is available.
When the impacts on hydropower production at 1.5°C and 2°C are compared, it is found that mean gross potential increases in northern, eastern and western Europe, and decreases in southern Europe (Jacob et al., 2018; Tobin et al., 2018)390. The Baltic and Scandinavian countries are projected to experience the most positive impacts on hydropower production. Greece, Spain and Portugal are expected to be the most negatively impacted countries, although the impacts could be reduced by limiting warming to 1.5°C (Tobin et al., 2018)391. In Greece, Spain and Portugal, warming of 2°C is projected to decrease hydropower potential below 10%, while limiting global warming to 1.5°C would keep the reduction to 5% or less. There is, however, substantial uncertainty associated with these results due to a large spread between the climate models (Tobin et al., 2018)392.
Due to a combination of higher water temperatures and reduced summer river flows, the usable capacity of thermoelectric power plants using river water for cooling is expected to reduce in all European countries (Jacob et al., 2018; Tobin et al., 2018)393, with the magnitude of decreases being about 5% for 1.5°C and 10% for 2°C of global warming for most European countries (Tobin et al., 2018)394. Greece, Spain and Bulgaria are projected to have the largest reduction at 2°C of warming (Tobin et al., 2018)395.
Fricko et al. (2016)396 assessed the direct water use of the global energy sector across a broad range of energy system transformation pathways in order to identify the water impacts of a 2°C climate policy. This study revealed that there would be substantial divergence in water withdrawal for thermal power plant cooling under conditions in which the distribution of future cooling technology for energy generation is fixed, whereas adopting alternative cooling technologies and water resources would make the divergence considerably smaller.
3.4.2.2
Extreme hydrological events (floods and droughts)
Working Group II of AR5 concluded that socio-economic losses from flooding since the mid-20th century have increased mainly because of greater exposure and vulnerability (high confidence) (Jiménez Cisneros et al., 2014)397. There was low confidence due to limited evidence, however, that anthropogenic climate change has affected the frequency and magnitude of floods. WGII AR5 also concluded that there is no evidence that surface water and groundwater drought frequency has changed over the last few decades, although impacts of drought have increased mostly owing to increased water demand (Jiménez Cisneros et al., 2014)398.
Since AR5, the number of studies related to fluvial flooding and meteorological drought based on long-term observed data has been gradually increasing. There has also been progress since AR5 in identifying historical changes in streamflow and continental runoff (Section 3.3.5). As a result of population and economic growth, increased exposure of people and assets has caused more damage due to flooding. However, differences in flood risks among regions reflect the balance among the magnitude of the flood, the populations, their vulnerabilities, the value of assets affected by flooding, and the capacity to cope with flood risks, all of which depend on socio-economic development conditions, as well as topography and hydro-climatic conditions (Tanoue et al., 2016)399. AR5 concluded that there was low confidence in the attribution of global changes in droughts (Bindoff et al., 2013b)400. However, recent publications based on observational and modelling evidence assessed that human emissions have substantially increased the probability of drought years in the Mediterranean region (Section 3.3.4).
WGII AR5 assessed that global flood risk will increase in the future, partly owing to climate change (low to medium confidence), with projected changes in the frequency of droughts longer than 12 months being more uncertain because of their dependence on accumulated precipitation over long periods (Jiménez Cisneros et al., 2014)401.
Increases in the risks associated with runoff at the global scale (medium confidence), and in flood hazard in some regions (medium confidence), can be expected at global warming of 1.5°C, with an overall increase in the area affected by flood hazard at 2°C (medium confidence) (Section 3.3.5). There are studies, however, that indicate that socio-economic conditions will exacerbate flood impacts more than global climate change, and that the magnitude of these impacts could be larger in some regions (Arnell and Lloyd-Hughes, 2014; Winsemius et al., 2016; Alfieri et al., 2017; Arnell et al., 2018; Kinoshita et al., 2018)402. Assuming constant population sizes, countries representing 73% of the world population will experience increasing flood risk, with an average increase of 580% at 4°C compared to the impact simulated over the baseline period 1976–2005. This impact is projected to be reduced to a 100% increase at 1.5°C and a 170% increase at 2°C (Alfieri et al., 2017)403. Alfieri et al. (2017)404 additionally concluded that the largest increases in flood risks would be found in the US, Asia, and Europe in general, while decreases would be found in only a few countries in eastern Europe and Africa. Overall, Alfieri et al., (2017)405 reported that the projected changes are not homogeneously distributed across the world land surface. Alfieri et al. (2018)406 studied the population affected by flood events using three case studies in European states, specifically central and western Europe, and found that the population affected could be limited to 86% at 1.5°C of warming compared to 93% at 2°C. Under the SSP2 population scenario, Arnell et al. (2018)407 found that 39% (range 36–46%) of impacts on populations exposed to river flooding globally could be avoided at 1.5°C compared to 2°C of warming.
Under scenarios SSP1–5, Arnell and Lloyd-Hughes (2014)408 found that the number of people exposed to increased flooding in 2050 under warming of about 1.5°C could be reduced by 26–34 million compared to the number exposed to increased flooding associated with 2°C of warming. Variation between socio-economic levels, however, is projected to be larger than variation between the two levels of global warming. Kinoshita et al. (2018)409 found that a serious increase in potential flood fatality (5.7%) is projected without any adaptation if global warming increases from 1.5°C to 2°C, whereas the projected increase in potential economic loss (0.9%) is relatively small. Nevertheless, their study indicates that socio-economic changes make a larger contribution to the potentially increased consequences of future floods, and about half of the increase in potential economic losses could be mitigated by autonomous adaptation.
There is limited information about the global and regional projected risks posed by droughts at 1.5°C and 2°C of global warming. However, hazards by droughts at 1.5°C could be reduced compared to the hazards at 2°C in some regions, in particular in the Mediterranean region and southern Africa (Section 3.3.4). Under constant socio-economic conditions, the population exposed to drought at 2°C of warming is projected to be larger than at 1.5°C (low to medium confidence) (Smirnov et al., 2016; Sun et al., 2017; Arnell et al., 2018; Liu et al., 2018)410. Under the same scenario, the global mean monthly number of people expected to be exposed to extreme drought at 1.5°C in 2021–2040 is projected to be 114.3 million, compared to 190.4 million at 2°C in 2041–2060 (Smirnov et al., 2016)411. Under the SSP2 population scenario, Arnell et al. (2018)412 projected that 39% (range 36–51%) of impacts on populations exposed to drought could be globally avoided at 1.5°C compared to 2°C warming.
Liu et al. (2018)413 studied the changes in population exposure to severe droughts in 27 regions around the globe for 1.5°C and 2°C of warming using the SSP1 population scenario compared to the baseline period of 1986–2005 based on the Palmer Drought Severity Index (PDSI). They concluded that the drought exposure of urban populations in most regions would be decreased at 1.5°C (350.2 ± 158.8 million people) compared to 2°C (410.7 ± 213.5 million people). Liu et al. (2018)414 also suggested that more urban populations would be exposed to severe droughts at 1.5ºC in central Europe, southern Europe, the Mediterranean, West Africa, East and West Asia, and Southeast Asia, and that number of affected people would increase further in these regions at 2°C. However, it should be noted that the PDSI is known to have limitations (IPCC SREX, Seneviratne et al., 2012)415, and drought projections strongly depend on considered indices (Section 3.3.4); thus only medium confidence is assigned to these projections. In the Haihe River basin in China, a study has suggested that the proportion of the population exposed to droughts is projected to be reduced by 30.4% at 1.5°C but increased by 74.8% at 2°C relative to the baseline value of 339.65 million people in the 1986–2005 period, when assessing changes in droughts using the Standardized Precipitation-Evaporation Index, using a Penman–Monteith estimate of potential evaporation (Sun et al., 2017)416 .
Alfieri et al. (2018)417 estimated damage from flooding in Europe for the baseline period (1976–2005) at 5 billion euro of losses annually, with projections of relative changes in flood impacts that will rise with warming levels, from 116% at 1.5°C to 137% at 2°C.
Kinoshita et al. (2018)418 studied the increase of potential economic loss under SSP3 and projected that the smaller loss at 1.5°C compared to 2°C (0.9%) is marginal, regardless of whether the vulnerability is fixed at the current level or not. By analysing the differences in results with and without flood protection standards, Winsemius et al. (2016)419 showed that adaptation measures have the potential to greatly reduce present-day and future flood damage. They concluded that increases in flood-induced economic impacts (% gross domestic product, GDP) in African countries are mainly driven by climate change and that Africa's growing assets would become increasingly exposed to floods. Hence, there is an increasing need for long-term and sustainable investments in adaptation in Africa.
3.4.2.3
Groundwater
Working Group II of AR5 concluded that the detection of changes in groundwater systems, and attribution of those changes to climatic changes, are rare, owing to a lack of appropriate observation wells and an overall small number of studies (Jiménez Cisneros et al., 2014)420.
Since AR5, the number of studies based on long-term observed data continues to be limited. The groundwater-fed lakes in northeastern central Europe have been affected by climate and land-use changes, and they showed a predominantly negative lake-level trend in 1999–2008 (Kaiser et al., 2014)421.
WGII AR5 concluded that climate change is projected to reduce groundwater resources significantly in most dry subtropical regions (high confidence) (Jiménez Cisneros et al., 2014)422.
In some regions, groundwater is often intensively used to supplement the excess demand, often leading to groundwater depletion. Climate change adds further pressure on water resources and exaggerates human water demands by increasing temperatures over agricultural lands (Wada et al., 2017)423. Very few studies have projected the risks of groundwater depletion under 1.5°C and 2°C of global warming. Under 2°C of warming, impacts posed on groundwater are projected to be greater than at 1.5°C (low confidence) (Portmann et al., 2013; Salem et al., 2017)424.
Portmann et al. (2013)425 indicated that 2% (range 1.1–2.6%) of the global land area is projected to suffer from an extreme decrease in renewable groundwater resources of more than 70% at 2°C, with a clear mitigation at 1.5°C. These authors also projected that 20% of the global land surface would be affected by a groundwater reduction of more than 10% at 1.5°C of warming, with the percentage of land impacted increasing at 2°C. In a groundwater-dependent irrigated region in northwest Bangladesh, the average groundwater level during the major irrigation period (January–April) is projected to decrease in accordance with temperature rise (Salem et al., 2017)426.
3.4.2.4
Water quality
Working Group II of AR5 concluded that most observed changes to water quality from climate change are from isolated studies, mostly of rivers or lakes in high-income countries, using a small number of variables (Jiménez Cisneros et al., 2014)427. AR5 assessed that climate change is projected to reduce raw water quality, posing risks to drinking water quality with conventional treatment (medium to high confidence) (Jiménez Cisneros et al., 2014)428.
Since AR5, studies have detected climate change impacts on several indices of water quality in lakes, watersheds and regions (e.g., Patiño et al., 2014; Aguilera et al., 2015; Watts et al., 2015; Marszelewski and Pius, 2016; Capo et al., 2017)429. The number of studies utilising RCP scenarios at the regional or watershed scale have gradually increased since AR5 (e.g., Boehlert et al., 2015; Teshager et al., 2016; Marcinkowski et al., 2017)430. Few studies, have explored projected impacts on water quality under 1.5°C versus 2°C of warming, however, the differences are unclear (low confidence) (Bonte and Zwolsman, 2010431; Hosseini et al., 2017)432. The daily probability of exceeding the chloride standard for drinking water taken from Lake IJsselmeer (Andijk, the Netherlands) is projected to increase by a factor of about five at 2°C relative to the present-day warming level of 1°C since 1990 (Bonte and Zwolsman, 2010)433. Mean monthly dissolved oxygen concentrations and nutrient concentrations in the upper Qu'Appelle River (Canada) in 2050–2055 are projected to decrease less at about 1.5°C of warming (RCP2.6) compared to concentrations at about 2°C (RCP4.5) (Hosseini et al., 2017)434. In three river basins in Southeast Asia (Sekong, Sesan and Srepok), about 2°C of warming (corresponding to a 1.05°C increase in the 2030s relative to the baseline period 1981–2008, RCP8.5), impacts posed by land-use change on water quality are projected to be greater than at 1.5°C (corresponding to a 0.89°C increase in the 2030s relative to the baseline period 1981–2008, RCP4.5) (Trang et al., 2017)435. Under the same warming scenarios, Trang et al. (2017)436 projected changes in the annual nitrogen (N) and phosphorus (P) yields in the 2030s, as well as with combinations of two land-use change scenarios: (i) conversion of forest to grassland, and (ii) conversion of forest to agricultural land. The projected changes in N (P) yield are +7.3% (+5.1%) under a 1.5°C scenario and –6.6% (–3.6%) under 2°C, whereas changes under the combination of land-use scenarios are (i) +5.2% (+12.6%) at 1.5°C and +8.8% (+11.7%) at 2°C, and (ii) +7.5% (+14.9%) at 1.5°C and +3.7% (+8.8%) at 2°C (Trang et al., 2017)437.
3.4.2.5
Soil erosion and sediment load
Working Group II of AR5 concluded that there is little or no observational evidence that soil erosion and sediment load have been altered significantly by climate change (low to medium confidence) (Jiménez Cisneros et al., 2014)438. As the number of studies on climate change impacts on soil erosion has increased where rainfall is an important driver (Lu et al., 2013)439, studies have increasingly considered other factors, such as rainfall intensity (e.g., Shi and Wang, 2015; Li and Fang, 2016)440, snow melt, and change in vegetation cover resulting from temperature rise (Potemkina and Potemkin, 2015)441, as well as crop management practices (Mullan et al., 2012)442. WGII AR5 concluded that increases in heavy rainfall and temperature are projected to change soil erosion and sediment yield, although the extent of these changes is highly uncertain and depends on rainfall seasonality, land cover, and soil management practices (Jiménez Cisneros et al., 2014)443.
While the number of published studies of climate change impacts on soil erosion have increased globally since 2000 (Li and Fang, 2016)444, few articles have addressed impacts at 1.5°C and 2°C of global warming. The existing studies have found few differences in projected risks posed on sediment load under 1.5°C and 2°C (low confidence) (Cousino et al., 2015; Shrestha et al., 2016)445. The differences between average annual sediment load under 1.5°C and 2°C of warming are not clear, owing to complex interactions among climate change, land cover/surface and soil management (Cousino et al., 2015; Shrestha et al., 2016)446. Averages of annual sediment loads are projected to be similar under 1.5°C and 2°C of warming, in particular in the Great Lakes region in the USA and in the Lower Mekong region in Southeast Asia (Cross-Chapter Box 6 in this chapter, Cousino et al., 2015; Shrestha et al., 2016)447.
3.4.3
Terrestrial and Wetland Ecosystems
3.4.3.1
Biome shifts
Latitudinal and elevational shifts of biomes (major ecosystem types) in boreal, temperate and tropical regions have been detected (Settele et al., 2014)448 and new studies confirm these changes (e.g., shrub encroachment on tundra; Larsen et al., 2014)449. Attribution studies indicate that anthropogenic climate change has made a greater contribution to these changes than any other factor (medium confidence) (Settele et al., 2014)450.
An ensemble of seven Dynamic Vegetation Models driven by projected climates from 19 alternative general circulation models (GCMs) (Warszawski et al., 2013)451 shows 13% (range 8–20%) of biomes transforming at 2°C of global warming, but only 4% (range 2–7%) doing so at 1°C, suggesting that about 6.5% may be transformed at 1.5°C; these estimates indicate a doubling of the areal extent of biome shifts between 1.5°C and 2°C of warming (medium confidence) (Figure 3.16a). A study using the single ecosystem model LPJmL (Gerten et al., 2013)452 illustrated that biome shifts in the Arctic, Tibet, Himalayas, southern Africa and Australia would be avoided by constraining warming to 1.5°C compared with 2°C (Figure 3.16b). Seddon et al. (2016)453 quantitatively identified ecologically sensitive regions to climate change in most of the continents from tundra to tropical rainforest. Biome transformation may in some cases be associated with novel climates and ecological communities (Prober et al., 2012)454.
Figure 3.16a
(a) Fraction of global natural vegetation (including managed forests) at risk of severe ecosystem change as a function of global mean temperature change for all ecosystems, models, global climate change models and Representative Concentration Pathways (RCPs).
The colours represent the different ecosystem models, which are also horizontally separated for clarity. Results are collated in unit-degree bins, where the temperature for a given year is the average over a 30-year window centred on that year. The boxes span the 25th and 75th percentiles across the entire ensemble. The short, horizontal stripes represent individual (annual) data points, the curves connect the mean value per ecosystem model in each bin. The solid (dashed) curves are for models with (without) dynamic vegetation composition changes. Source: (Warszawski et al., 2013)455
Figure 3.16b
(b) Threshold level of global temperature anomaly above pre-industrial levels that leads to significant local changes in terrestrial ecosystems.
Regions with severe (coloured) or moderate (greyish) ecosystem transformation; delineation refers to the 90 biogeographic regions. All values denote changes found in >50% of the simulations. Source: (Gerten et al., 2013)456. Regions coloured in dark red are projected to undergo severe transformation under a global warming of 1.5°C while those coloured in light red do so at 2°C; other colours are used when there is no severe transformation unless global warming exceeds 2°C.
3.4.3.2
Changes in phenology
Advancement in spring phenology of 2.8 ± 0.35 days per decade has been observed in plants and animals in recent decades in most Northern Hemisphere ecosystems (between 30°N and 72°N), and these shifts have been attributed to changes in climate (high confidence) (Settele et al., 2014)457. The rates of change are particularly high in the Arctic zone owing to the stronger local warming (Oberbauer et al., 2013)458, whereas phenology in tropical forests appears to be more responsive to moisture stress (Zhou et al., 2014)459. While a full review cannot be included here, trends consistent with this earlier finding continue to be detected, including in the flowering times of plants (Parmesan and Hanley, 2015)460, in the dates of egg laying and migration in birds (newly reported in China; Wu and Shi, 2016)461, in the emergence dates of butterflies (Roy et al., 2015)462, and in the seasonal greening-up of vegetation as detected by satellites (i.e., in the normalized difference vegetation index, NDVI; Piao et al., 2015)463.
The potential for decoupling species–species interactions owing to differing phenological responses to climate change is well established (Settele et al., 2014)464, for example for plants and their insect pollinators (Willmer, 2012; Scaven and Rafferty, 2013)465. Mid-century projections of plant and animal phenophases in the UK clearly indicate that the timing of phenological events could change more for primary consumers (6.2 days earlier on average) than for higher trophic levels (2.5–2.9 days earlier on average) (Thackeray et al., 2016)466. This indicates the potential for phenological mismatch and associated risks for ecosystem functionality in the future under global warming of 2.1°C–2.7°C above pre-industrial levels. Further, differing responses could alter community structure in temperate forests (Roberts et al., 2015)467. Specifically, temperate forest phenology is projected to advance by 14.3 days in the near term (2010–2039) and 24.6 days in the medium term (2040–2069), so as a first approximation the difference between 2°C and 1.5°C of global warming is about 10 days (Roberts et al., 2015)468. This phenological plasticity is not always adaptive and must be interpreted cautiously (Duputié et al., 2015)469, and considered in the context of accompanying changes in climate variability (e.g., increased risk of frost damage for plants or earlier emergence of insects resulting in mortality during cold spells). Another adaptive response of some plants is range expansion with increased vigour and altered herbivore resistance in their new range, analogous to invasive plants (Macel et al., 2017)470.
In summary, limiting warming to 1.5°C compared with 2°C may avoid advance in spring phenology (high confidence) by perhaps a few days (medium confidence) and hence decrease the risks of loss of ecosystem functionality due to phenological mismatch between trophic levels, and also of maladaptation coming from the sensitivity of many species to increased climate variability. Nevertheless, this difference between 1.5°C and 2°C of warming might be limited for plants that are able to expand their range.
3.4.3.3
Changes in species range, abundance and extinction
AR5 (Settele et al., 2014)471 concluded that the geographical ranges of many terrestrial and freshwater plant and animal species have moved over the last several decades in response to warming: approximately 17 km poleward and 11 m up in altitude per decade. Recent trends confirm this finding; for example, the spatial and interspecific variance in bird populations in Europe and North America since 1980 were found to be well predicted by trends in climate suitability (Stephens et al., 2016)472. Further, a recent meta-analysis of 27 studies concerning a total of 976 species (Wiens, 2016)473 found that 47% of local extinctions (extirpations) reported across the globe during the 20th century could be attributed to climate change, with significantly more extinctions occurring in tropical regions, in freshwater habitats and for animals. IUCN (2018)474 lists 305 terrestrial animal and plant species from Pacific Island developing nations as being threatened by climate change and severe weather. Owing to lags in the responses of some species to climate change, shifts in insect pollinator ranges may result in novel assemblages with unknown implications for biodiversity and ecosystem function (Rafferty, 2017)475.
Warren et al. (2013)476 simulated climatically determined geographic range loss under 2°C and 4°C of global warming for 50,000 plant and animal species, accounting for uncertainty in climate projections and for the potential ability of species to disperse naturally in an attempt to track their geographically shifting climate envelope. This earlier study has now been updated and expanded to incorporate 105,501 species, including 19,848 insects, and new findings indicate that warming of 2°C by 2100 would lead to projected bioclimatic range losses of >50% in 18% (6–35%) of the 19,848 insects species, 8% (4–16%) of the 12,429 vertebrate species, and 16% (9–28%) of the 73,224 plant species studied (Warren et al., 2018a)477. At 1.5°C of warming, these values fall to 6% (1–18%) of the insects, 4% (2–9%) of the vertebrates and 8% (4–15%) of the plants studied. Hence, the number of insect species projected to lose over half of their geographic range is reduced by two-thirds when warming is limited to 1.5°C compared with 2°C, while the number of vertebrate and plant species projected to lose over half of their geographic range is halved (Warren et al., 2018a)478 (medium confidence). These findings are consistent with estimates made from an earlier study suggesting that range losses at 1.5°C were significantly lower for plants than those at 2°C of warming (Smith et al., 2018)479. It should be noted that at 1.5°C of warming, and if species' ability to disperse naturally to track their preferred climate geographically is inhibited by natural or anthropogenic obstacles, there would still remain 10% of the amphibians, 8% of the reptiles, 6% of the mammals, 5% of the birds, 10% of the insects and 8% of the plants which are projected to lose over half their range, while species on average lose 20–27% of their range (Warren et al., 2018a)480. Given that bird and mammal species can disperse more easily than amphibians and reptiles, a small proportion can expand their range as climate changes, but even at 1.5°C of warming the total range loss integrated over all birds and mammals greatly exceeds the integrated range gain (Warren et al., 2018a)481.
A number of caveats are noted for studies projecting changes to climatic range. This approach, for example, does not incorporate the effects of extreme weather events and the role of interactions between species. As well, trophic interactions may locally counteract the range expansion of species towards higher altitudes (Bråthen et al., 2018)482. There is also the potential for highly invasive species to become established in new areas as the climate changes (Murphy and Romanuk, 2014)483, but there is no literature that quantifies this possibility for 1.5°C of global warming.
Pecl et al. (2017)484 summarized at the global level the consequences of climate-change-induced species redistribution for economic development, livelihoods, food security, human health and culture. These authors concluded that even if anthropogenic greenhouse gas emissions stopped today, the effort for human systems to adapt to the most crucial effects of climate-driven species redistribution will be far-reaching and extensive. For example, key insect crop pollinator families (Apidae, Syrphidae and Calliphoridae; i.e., bees, hoverflies and blowflies) are projected to retain significantly greater geographic ranges under 1.5°C of global warming compared with 2°C (Warren et al., 2018a)485. In some cases, when species (such as pest and disease species) move into areas which have become climatically suitable they may become invasive or harmful to human or natural systems (Settele et al., 2014)486. Some studies are beginning to locate 'refugial' areas where the climate remains suitable in the future for most of the species currently present. For example, Smith et al., (2018)487 estimated that 5.5–14% more of the globe's terrestrial land area could act as climatic refugia for plants under 1.5°C of warming compared to 2°C.
There is no literature that directly estimates the proportion of species at increased risk of global (as opposed to local) commitment to extinction as a result of climate change, as this is inherently difficult to quantify. However, it is possible to compare the proportions of species at risk of very high range loss; for example, a discernibly smaller number of terrestrial species are projected to lose over 90% of their range at 1.5°C of global warming compared with 2°C (Figure 2 in Warren et al., 2018a)488. A link between very high levels of range loss and greatly increased extinction risk may be inferred (Urban, 2015)489. Hence, limiting global warming to 1.5°C compared with 2°C would be expected to reduce both range losses and associated extinction risks in terrestrial species (high confidence).
3.4.3.4
Changes in ecosystem function, biomass and carbon stocks
Working Group II of AR5 (Settele et al., 2014)490 concluded that there is high confidence that net terrestrial ecosystem productivity at the global scale has increased relative to the pre-industrial era and that rising CO2 concentrations are contributing to this trend through stimulation of photosynthesis. There is, however, no clear and consistent signal of a climate change contribution. In northern latitudes, the change in productivity has a lower velocity than the warming, possibly because of a lack of resource and vegetation acclimation mechanisms (M. Huang et al., 2017)491. Biomass and soil carbon stocks in terrestrial ecosystems are currently increasing (high confidence), but they are vulnerable to loss of carbon to the atmosphere as a result of projected increases in the intensity of storms, wildfires, land degradation and pest outbreaks (Settele et al., 2014; Seidl et al., 2017)492. These losses are expected to contribute to a decrease in the terrestrial carbon sink. Anderegg et al. (2015)493 demonstrated that total ecosystem respiration at the global scale has increased in response to increases in night-time temperature (1 PgC yr–1°C–1, P=0.02).
The increase in total ecosystem respiration in spring and autumn, associated with higher temperatures, may convert boreal forests from carbon sinks to carbon sources (Hadden and Grelle, 2016)494. In boreal peatlands, for example, increased temperature may diminish carbon storage and compromise the stability of the peat (Dieleman et al., 2016)495. In addition, J. Yang et al. (2015)496 showed that fires reduce the carbon sink of global terrestrial ecosystems by 0.57 PgC yr–1 in ecosystems with large carbon stores, such as peatlands and tropical forests. Consequently, for adaptation purposes, it is necessary to enhance carbon sinks, especially in forests which are prime regulators within the water, energy and carbon cycles (Ellison et al., 2017)497. Soil can also be a key compartment for substantial carbon sequestration (Lal, 2014; Minasny et al., 2017)498, depending on the net biome productivity and the soil quality (Bispo et al., 2017)499.
AR5 assessed that large uncertainty remains regarding the land carbon cycle behaviour of the future (Ciais et al., 2013)500, with most, but not all, CMIP5 models simulating continued terrestrial carbon uptake under all four RCP scenarios (Jones et al., 2013)501. Disagreement between models outweighs differences between scenarios even up to the year 2100 (Hewitt et al., 2016; Lovenduski and Bonan, 2017)502. Increased atmospheric CO2 concentrations are expected to drive further increases in the land carbon sink (Ciais et al., 2013; Schimel et al., 2015)503, which could persist for centuries (Pugh et al., 2016)504. Nitrogen, phosphorus and other nutrients will limit the terrestrial carbon cycle response to both elevated CO2 and altered climate (Goll et al., 2012; Yang et al., 2014; Wieder et al., 2015; Zaehle et al., 2015; Ellsworth et al., 2017)505. Climate change may accelerate plant uptake of carbon (Gang et al., 2015)506 but also increase the rate of decomposition (Todd-Brown et al., 2014; Koven et al., 2015; Crowther et al., 2016)507. Ahlström et al. (2012)508 found a net loss of carbon in extra-tropical regions and the largest spread across model results in the tropics. The projected net effect of climate change is to reduce the carbon sink expected under CO2 increase alone (Settele et al., 2014)509. Friend et al. (2014)510 found substantial uptake of carbon by vegetation under future scenarios when considering the effects of both climate change and elevated CO2.
There is limited published literature examining modelled land carbon changes specifically under 1.5°C of warming, but existing CMIP5 models and published data are used in this report to draw some conclusions. For systems with significant inertia, such as vegetation or soil carbon stores, changes in carbon storage will depend on the rate of change of forcing and thus depend on the choice of scenario (Jones et al., 2009; Ciais et al., 2013; Sihi et al., 2017)511. To avoid legacy effects of the choice of scenario, this report focuses on the response of gross primary productivity (GPP) – the rate of photosynthetic carbon uptake – by the models, rather than by changes in their carbon store.
Figure 3.17 shows different responses of the terrestrial carbon cycle to climate change in different regions. The models show a consistent response of increased GPP in temperate latitudes of approximately 2 GtC yr–1 °C–1. Similarly, Gang et al. (2015)512 projected a robust increase in the net primary productivity (NPP) of temperate forests. However, Ahlström et al. (2012)513 showed that this effect could be offset or reversed by increases in decomposition. Globally, most models project that GPP will increase or remain approximately unchanged (Hashimoto et al., 2013)514. This projection is supported by findings by Sakalli et al. (2017)515 for Europe using Euro-CORDEX regional models under a 2°C global warming for the period 2034–2063, which indicated that storage will increase by 5% in soil and by 20% in vegetation. However, using the same models Jacob et al. (2018)516 showed that limiting warming to 1.5°C instead of 2°C avoids an increase in ecosystem vulnerability (compared to a no-climate change scenario) of 40–50%.
At the global level, linear scaling is acceptable for net primary production, biomass burning and surface runoff, and impacts on terrestrial carbon storage are projected to be greater at 2°C than at 1.5°C (Tanaka et al., 2017)517. If global CO2 concentrations and temperatures stabilize, or peak and decline, then both land and ocean carbon sinks – which are primarily driven by the continued increase in atmospheric CO2 – will also decline and may even become carbon sources (Jones et al., 2016)518. Consequently, if a given amount of anthropogenic CO2 is removed from the atmosphere, an equivalent amount of land and ocean anthropogenic CO2 will be released to the atmosphere (Cao and Caldeira, 2010)519.
In conclusion, ecosystem respiration is expected to increase with increasing temperature, thus reducing soil carbon storage. Soil carbon storage is expected to be larger if global warming is restricted to 1.5°C, although some of the associated changes will be countered by enhanced gross primary production due to elevated CO2 concentrations (i.e., the 'fertilization effect') and higher temperatures, especially at mid-and high latitudes (medium confidence).
Figure 3.17
The response of terrestrial productivity (gross primary productivity, GPP) to climate change, globally (top left) and for three latitudinal regions: 30°S–30°N; 30–60°N and 60–90°N.
Data come from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive (http://cmip-pcmdi.llnl.gov/cmip5/). Seven Earth System Models were used: Norwegian Earth System Model (NorESM-ME, yellow); Community Earth System Model (CESM, red); Institute Pierre Simon Laplace (IPLS)-CM5-LR (dark blue); Geophysical Fluid Dynamics Laboratory (GFDL, pale blue); Max Plank Institute-Earth System Model (MPI-ESM, pink); Hadley Centre New Global Environmental Model 2-Earth System (HadGEM2-ES, orange); and Canadian Earth System Model 2 (CanESM2, green). Differences in GPP between model simulations with ('1pctCO2') and without ('esmfixclim1') the effects of climate change are shown. Data are plotted against the global mean temperature increase above pre-industrial levels from simulations with a 1% per year increase in CO2 ('1pctCO2').
3.4.3.5
Regional and ecosystem-specific risks
A large number of threatened systems, including mountain ecosystems, highly biodiverse tropical wet and dry forests, deserts, freshwater systems and dune systems, were assessed in AR5. These include Mediterranean areas in Europe, Siberian, tropical and desert ecosystems in Asia, Australian rainforests, the Fynbos and succulent Karoo areas of South Africa, and wetlands in Ethiopia, Malawi, Zambia and Zimbabwe. In all these systems, it has been shown that impacts accrue with greater warming, and thus impacts at 2°C are expected to be greater than those at 1.5°C (medium confidence).
The High Arctic region, with tundra-dominated landscapes, has warmed more than the global average over the last century (Section 3.3; Settele et al., 2014)520. The Arctic tundra biome is experiencing increasing fire disturbance and permafrost degradation (Bring et al., 2016; DeBeer et al., 2016; Jiang et al., 2016; Yang et al., 2016)521. Both of these processes facilitate the establishment of woody species in tundra areas. Arctic terrestrial ecosystems are being disrupted by delays in winter onset and mild winters associated with global warming (high confidence) (Cooper, 2014)522. Observational constraints suggest that stabilization at 1.5°C of warming would avoid the thawing of approximately 1.5 to 2.5 million km2 of permafrost (medium confidence) compared with stabilization at 2°C (Chadburn et al., 2017)523, but the time scale for release of thawed carbon as CO2 or CH4 should be many centuries (Burke et al., 2017)524. In northern Eurasia, the growing season length is projected to increase by about 3–12 days at 1.5°C and 6–16 days at 2°C of warming (medium confidence) (Zhou et al., 2018)525. Aalto et al. (2017)526 predicted a 72% reduction in cryogenic land surface processes in northern Europe for RCP2.6 in 2040–2069 (corresponding to a global warming of approximately 1.6°C), with only slightly larger losses for RCP4.5 (2°C of global warming).
Projected impacts on forests as climate change occurs include increases in the intensity of storms, wildfires and pest outbreaks (Settele et al., 2014)527, potentially leading to forest dieback (medium confidence). Warmer and drier conditions in particular facilitate fire, drought and insect disturbances, while warmer and wetter conditions increase disturbances from wind and pathogens (Seidl et al., 2017)528. Particularly vulnerable regions are Central and South America, Mediterranean Basin, South Africa, South Australia where the drought risk will increase (see Figure 3.12). Including disturbances in simulations may influence productivity changes in European forests in response to climate change (Reyer et al., 2017b)529. There is additional evidence for the attribution of increased forest fire frequency in North America to anthropogenic climate change during 1984–2015, via the mechanism of increasing fuel aridity almost doubling the western USA forest fire area compared to what would have been expected in the absence of climate change (Abatzoglou and Williams, 2016)530. This projection is in line with expected fire risks, which indicate that fire frequency could increase over 37.8% of the global land area during 2010–2039 (Moritz et al., 2012)531, corresponding to a global warming level of approximately 1.2°C, compared with over 61.9% of the global land area in 2070–2099, corresponding to a warming of approximately 3.5°C9.The values in Table 26-1 in a recent paper by Romero-Lankao et al. (2014)532 also indicate significantly lower wildfire risks in North America for near-term warming (2030–2040, considered a proxy for 1.5°C of warming) than at 2°C (high confidence).
The Amazon tropical forest has been shown to be close to its climatic limits (Hutyra et al., 2005)533, but this threshold may move under elevated CO2 (Good et al., 2011)534. Future changes in rainfall, especially dry season length, will determine responses of the Amazon forest (Good et al., 2013)535. The forest may be especially vulnerable to combined pressure from multiple stressors, namely changes in climate and continued anthropogenic disturbance (Borma et al., 2013; Nobre et al., 2016)536. Modelling (Huntingford et al., 2013)537 and observational constraints (Cox et al., 2013)538 suggest that large-scale forest dieback is less likely than suggested under early coupled modelling studies (Cox et al., 2000; Jones et al., 2009)539. Nobre et al. (2016)540 estimated a climatic threshold of 4°C of warming and a deforestation threshold of 40%.
In many places around the world, the savanna boundary is moving into former grasslands. Woody encroachment, including increased tree cover and biomass, has increased over the past century, owing to changes in land management, rising CO2 levels, and climate variability and change (often in combination) (Settele et al., 2014)541. For plant species in the Mediterranean region, shifts in phenology, range contraction and health decline have been observed with precipitation decreases and temperature increases (medium confidence) (Settele et al., 2014)542. Recent studies using independent complementary approaches have shown that there is a regional-scale threshold in the Mediterranean region between 1.5°C and 2°C of warming (Guiot and Cramer, 2016; Schleussner et al., 2016b)543. Further, Guiot and Cramer (2016)544 concluded that biome shifts unprecedented in the last 10,000 years can only be avoided if global warming is constrained to 1.5°C (medium confidence) – whilst 2°C of warming will result in a decrease of 12–15% of the Mediterranean biome area. The Fynbos biome in southwestern South Africa is vulnerable to the increasing impact of fires under increasing temperatures and drier winters. It is projected to lose about 20%, 45% and 80% of its current suitable climate area under 1°C, 2°C and 3°C of global warming, respectively, compared to 1961–1990 (high confidence) (Engelbrecht and Engelbrecht, 2016)545. In Australia, an increase in the density of trees and shrubs at the expense of grassland species is occurring across all major ecosystems and is projected to be amplified (NCCARF, 2013)546. Regarding Central America, Lyra et al. (2017)547 showed that the tropical rainforest biomass would be reduced by about 40% under global warming of 3°C, with considerable replacement by savanna and grassland. With a global warming of close to 1.5°C in 2050, a biomass decrease of 20% is projected for tropical rainforests of Central America (Lyra et al., 2017)548. If a linear response is assumed, this decrease may reach 30% (medium confidence).
Freshwater ecosystems are considered to be among the most threatened on the planet (Settele et al., 2014)549. Although peatlands cover only about 3% of the land surface, they hold one-third of the world's soil carbon stock (400 to 600 Pg) (Settele et al., 2014)550. When drained, this carbon is released to the atmosphere. At least 15% of peatlands have drained, mostly in Europe and South east Asia, and are responsible for 5% of human derived CO2 emissions (Green and Page, 2017)551. Moreover, in the Congo basin (Dargie et al., 2017)552 and in the Amazonian basin (Draper et al., 2014)553, the peatlands store the equivalent carbon as that of a tropical forest. However, stored carbon is vulnerable to land-use change and future risk of drought, for example in northeast Brazil (high confidence) (Figure 3.12, Section 3.3.4.2). At the global scale, these peatlands are undergoing rapid major transformations through drainage and burning in preparation for oil palm and other crops or through unintentional burning (Magrin et al., 2014)554. Wetland salinization, a widespread threat to the structure and ecological functioning of inland and coastal wetlands, is occurring at a high rate and large geographic scale (Section 3.3.6; Herbert et al., 2015)555. Settele et al. (2014)556 found that rising water temperatures are projected to lead to shifts in freshwater species distributions and worsen water quality. Some of these ecosystems respond non-linearly to changes in temperature. For example, Johnson and Poiani (2016)557 found that the wetland function of the Prairie Pothole region in North America is projected to decline at temperatures beyond a local warming of 2°C–3°C above present-day values (1°C local warming, corresponding to 0.6°C of global warming). If the ratio of local to global warming remains similar for these small levels of warming, this would indicate a global temperature threshold of 1.2°C–1.8°C of warming. Hence, constraining global warming to approximately 1.5°C would maintain the functioning of prairie pothole ecosystems in terms of their productivity and biodiversity, although a 20% increase of precipitation could offset 2°C of global warming (high confidence) (Johnson and Poiani, 2016)558.
3.4.3.6
Summary of implications for ecosystem services
In summary, constraining global warming to 1.5°C rather than 2°C has strong benefits for terrestrial and wetland ecosystems and their services (high confidence). These benefits include avoidance or reduction of changes such as biome transformations, species range losses, increased extinction risks (all high confidence) and changes in phenology (high confidence), together with projected increases in extreme weather events which are not yet factored into these analyses (Section 3.3). All of these changes contribute to disruption of ecosystem functioning and loss of cultural, provisioning and regulating services provided by these ecosystems to humans. Examples of such services include soil conservation (avoidance of desertification), flood control, water and air purification, pollination, nutrient cycling, sources of food, and recreation.
3.4.4
Ocean Ecosystems
The ocean plays a central role in regulating atmospheric gas concentrations, global temperature and climate. It also provides habitat to a large number of organisms and ecosystems that provide goods and services worth trillions of USD per year (e.g., Costanza et al., 2014; Hoegh-Guldberg et al., 2015)559. Together with local stresses (Halpern et al., 2015)560, climate change poses a major threat to an increasing number of ocean ecosystems (e.g., warm water or tropical coral reefs: virtually certain, WGII AR5) and consequently to many coastal communities that depend on marine resources for food, livelihoods and a safe place to live. Previous sections of this report have described changes in the ocean, including rapid increases in ocean temperature down to a depth of at least 700 m (Section 3.3.7). In addition, anthropogenic carbon dioxide has decreased ocean pH and affected the concentration of ions in seawater such as carbonate (Sections 3.3.10 and 3.4.4.5), both over a similar depth range. Increased ocean temperatures have intensified storms in some regions (Section 3.3.6), expanded the ocean volume and increased sea levels globally (Section 3.3.9), reduced the extent of polar summer sea ice (Section 3.3.8), and decreased the overall solubility of the ocean for oxygen (Section 3.3.10). Importantly, changes in the response to climate change rarely operate in isolation. Consequently, the effect of global warming of 1.5°C versus 2°C must be considered in the light of multiple factors that may accumulate and interact over time to produce complex risks, hazards and impacts on human and natural systems.
3.4.4.1
Observed impacts
Physical and chemical changes to the ocean resulting from increasing atmospheric CO2 and other GHGs are already driving significant changes to ocean systems (very high confidence) and will continue to do so at 1.5°C, and more so at 2°C, of global warming above pre-industrial temperatures (Section 3.3.11). These changes have been accompanied by other changes such as ocean acidification, intensifying storms and deoxygenation (Levin and Le Bris, 2015)561. Risks are already significant at current greenhouse gas concentrations and temperatures, and they vary significantly among depths, locations and ecosystems, with impacts being singular, interactive and/or cumulative (Boyd et al., 2015)562.
3.4.4.2
Warming and stratification of the surface ocean
As atmospheric greenhouse gases have increased, the global mean surface temperature (GMST) has reached about 1°C above the pre-industrial period, and oceans have rapidly warmed from the ocean surface to the deep sea (high confidence) (Sections 3.3.7; Hughes and Narayanaswamy, 2013; Levin and Le Bris, 2015; Yasuhara and Danovaro, 2016; Sweetman et al., 2017)563. Marine organisms are already responding to these changes by shifting their biogeographical ranges to higher latitudes at rates that range from approximately 0 to 40 km yr–1 (Burrows et al., 2014; Chust, 2014; Bruge et al., 2016; Poloczanska et al., 2016)564, which has consequently affected the structure and function of the ocean, along with its biodiversity and foodwebs (high confidence). Movements of organisms does not necessarily equate to the movement of entire ecosystems. For example, species of reef-building corals have been observed to shift their geographic ranges, yet this has not resulted in the shift of entire coral ecosystems (high confidence) (Woodroffe et al., 2010; Yamano et al., 2011)565. In the case of 'less mobile' ecosystems (e.g., coral reefs, kelp forests and intertidal communities), shifts in biogeographical ranges may be limited, with mass mortalities and disease outbreaks increasing in frequency as the exposure to extreme temperatures increases (very high confidence) (Hoegh-Guldberg, 1999; Garrabou et al., 2009; Rivetti et al., 2014; Maynard et al., 2015; Krumhansl et al., 2016; Hughes et al., 2017b; see also Box 3.4)566. These trends are projected to become more pronounced at warming of 1.5°C, and more so at 2°C, above the pre-industrial period (Hoegh-Guldberg et al., 2007; Donner, 2009; Frieler et al., 2013; Horta E Costa et al., 2014; Vergés et al., 2014, 2016; Zarco-Perello et al., 2017)567 and are likely to result in decreases in marine biodiversity at the equator but increases in biodiversity at higher latitudes (Cheung et al., 2009; Burrows et al., 2014)568.
While the impacts of species shifting their ranges are mostly negative for human communities and industry, there are instances of short-term gains. Fisheries, for example, may expand temporarily at high latitudes in the Northern Hemisphere as the extent of summer sea ice recedes and NPP increases (medium confidence) (Cheung et al., 2010; Lam et al., 2016; Weatherdon et al., 2016)569. High-latitude fisheries are not only influenced by the effect of temperature on NPP but are also strongly influenced by the direct effects of changing temperatures on fish and fisheries (Section 3.4.4.9; Barange et al., 2014; Pörtner et al., 2014; Cheung et al., 2016b; Weatherdon et al., 2016570). Temporary gains in the productivity of high-latitude fisheries are offset by a growing number of examples from low and mid-latitudes where increases in sea temperature are driving decreases in NPP, owing to the direct effects of elevated temperatures and/or reduced ocean mixing from reduced ocean upwelling, that is, increased stratification (low-medium confidence) (Cheung et al., 2010; Ainsworth et al., 2011; Lam et al., 2012, 2014, 2016; Bopp et al., 2013; Boyd et al., 2014; Chust et al., 2014; Hoegh-Guldberg et al., 2014; Poloczanska et al., 2014; Pörtner et al., 2014; Signorini et al., 2015)571. Reduced ocean upwelling has implications for millions of people and industries that depend on fisheries for food and livelihoods (Bakun et al., 2015; FAO, 2016; Kämpf and Chapman, 2016)572, although there is low confidence in the projection of the size of the consequences at 1.5°C. It is also important to appreciate these changes in the context of large-scale ocean processes such as the ocean carbon pump. The export of organic carbon to deeper layers of the ocean increases as NPP changes in the surface ocean, for example, with implications for foodwebs and oxygen levels (Boyd et al., 2014; Sydeman et al., 2014; Altieri and Gedan, 2015; Bakun et al., 2015; Boyd, 2015)573.
3.4.4.3
Storms and coastal runoff
Storms, wind, waves and inundation can have highly destructive impacts on ocean and coastal ecosystems, as well as the human communities that depend on them (IPCC, 2012; Seneviratne et al., 2012)574. The intensity of tropical cyclones across the world's oceans has increased, although the overall number of tropical cyclones has remained the same or decreased (medium confidence) (Section 3.3.6; Elsner et al., 2008; Holland and Bruyère, 2014)575. The direct force of wind and waves associated with larger storms, along with changes in storm direction, increases the risks of physical damage to coastal communities and to ecosystems such as mangroves (low to medium confidence) (Long et al., 2016; Primavera et al., 2016; Villamayor et al., 2016; Cheal et al., 2017)576 and tropical coral reefs (De'ath et al., 2012; Bozec et al., 2015; Cheal et al., 2017)577. These changes are associated with increases in maximum wind speed, wave height and the inundation, although trends in these variables vary from region to region (Section 3.3.5). In some cases, this can lead to increased exposure to related impacts, such as flooding, reduced water quality and increased sediment runoff (medium-high confidence) (Brodie et al., 2012; Wong et al., 2014; Anthony, 2016578; AR5, Table 5.1).
Sea level rise also amplifies the impacts of storms and wave action (Section 3.3.9), with robust evidence that storm surges and damage are already penetrating farther inland than a few decades ago, changing conditions for coastal ecosystems and human communities. This is especially true for small islands (Box 3.5) and low-lying coastal communities, where issues such as storm surges can transform coastal areas (Section 3.4.5; Brown et al., 2018a)579. Changes in the frequency of extreme events, such as an increase in the frequency of intense storms, have the potential (along with other factors, such as disease, food web changes, invasive organisms and heat stress-related mortality; Burge et al., 2014; Maynard et al., 2015; Weatherdon et al., 2016; Clements et al., 2017)580 to overwhelm the capacity for natural and human systems to recover following disturbances. This has recently been seen for key ecosystems such as tropical coral reefs (Box 3.4), which have changed from coral-dominated ecosystems to assemblages dominated by other organisms such as seaweeds, with changes in associated organisms and ecosystem services (high confidence) (De'ath et al., 2012; Bozec et al., 2015; Cheal et al., 2017; Hoegh-Guldberg et al., 2017; Hughes et al., 2017a, b)581. The impacts of storms are amplified by sea level rise (Section 3.4.5), leading to substantial challenges today and in the future for cities, deltas and small island states in particular (Sections 3.4.5.2 to 3.4.5.4), as well as for coastlines and their associated ecosystems (Sections 3.4.5.5 to 3.4.5.7).
3.4.4.4
Ocean circulation
The movement of water within the ocean is essential to its biology and ecology, as well to the circulation of heat, water and nutrients around the planet (Section 3.3.7). The movement of these factors drives local and regional climates, as well as primary productivity and food production. Firmly attributing recent changes in the strength and direction of ocean currents to climate change, however, is complicated by long-term patterns and variability (e.g., Pacific decadal oscillation, PDO; Signorini et al., 2015)582 and a lack of records that match the long-term nature of these changes in many cases (Lluch-Cota et al., 2014)583. An assessment of the literature since AR5 (Sydeman et al., 2014)584, however, concluded that (overall) upwelling-favourable winds have intensified in the California, Benguela and Humboldt upwelling systems, but have weakened in the Iberian system and have remained neutral in the Canary upwelling system in over 60 years of records (1946–2012) (medium confidence). These conclusions are consistent with a growing consensus that wind-driven upwelling systems are likely to intensify under climate change in many upwelling systems (Sydeman et al., 2014; Bakun et al., 2015; Di Lorenzo, 2015)585, with potentially positive and negative consequences (Bakun et al., 2015)586.
Changes in ocean circulation can have profound impacts on marine ecosystems by connecting regions and facilitating the entry and establishment of species in areas where they were unknown before (e.g., 'tropicalization' of temperate ecosystems; Wernberg et al., 2012; Vergés et al., 2014, 2016; Zarco-Perello et al., 2017)587, as well as the arrival of novel disease agents (low-medium confidence) (Burge et al., 2014; Maynard et al., 2015; Weatherdon et al., 2016)588. For example, the herbivorous sea urchin Centrostephanus rodgersii has been reached Tasmania from the Australian mainland, where it was previously unknown, owing to a strengthening of the East Australian Current (EAC) that connects the two regions (high confidence) (Ling et al., 2009)589 . As a consequence, the distribution and abundance of kelp forests has rapidly decreased, with implications for fisheries and other ecosystem services (Ling et al., 2009)590. These risks to marine ecosystems are projected to become greater at 1.5°C, and more so at 2°C (medium confidence) (Cheung et al., 2009; Pereira et al., 2010; Pinsky et al., 2013; Burrows et al., 2014)591.
Changes to ocean circulation can have even larger influence in terms of scale and impacts. Weakening of the Atlantic Meridional Overturning Circulation (AMOC), for example, is projected to be highly disruptive to natural and human systems as the delivery of heat to higher latitudes via this current system is reduced (Collins et al., 2013)592. Evidence of a slowdown of AMOC has increased since AR5 (Smeed et al., 2014; Rahmstorf et al., 2015a, b; Kelly et al., 2016)593, yet a strong causal connection to climate change is missing (low confidence) (Section 3.3.7).
3.4.4.5
Ocean acidification
Ocean chemistry encompasses a wide range of phenomena and chemical species, many of which are integral to the biology and ecology of the ocean (Section 3.3.10; Gattuso et al., 2014, 2015; Hoegh-Guldberg et al., 2014; Pörtner et al., 2014)594. While changes to ocean chemistry are likely to be of central importance, the literature on how climate change might influence ocean chemistry over the short and long term is limited (medium confidence). By contrast, numerous risks from the specific changes associated with ocean acidification have been identified (Dove et al., 2013; Kroeker et al., 2013; Pörtner et al., 2014; Gattuso et al., 2015; Albright et al., 2016)595, with the consensus that resulting changes to the carbonate chemistry of seawater are having, and are likely to continue to have, fundamental and substantial impacts on a wide variety of organisms (high confidence). Organisms with shells and skeletons made out of calcium carbonate are particularly at risk, as are the early life history stages of a large number of organisms and processes such as de-calcification, although there are some taxa that have not shown high-sensitivity to changes in CO2, pH and carbonate concentrations (Dove et al., 2013; Fang et al., 2013; Kroeker et al., 2013; Pörtner et al., 2014; Gattuso et al., 2015)596. Risks of these impacts also vary with latitude and depth, with the greatest changes occurring at high latitudes as well as deeper regions. The aragonite saturation horizon (i.e., where concentrations of calcium and carbonate fall below the saturation point for aragonite, a key crystalline form of calcium carbonate) is decreasing with depth as anthropogenic CO2 penetrates deeper into the ocean over time. Under many models and scenarios, the aragonite saturation is projected to reach the surface by 2030 onwards, with a growing list of impacts and consequences for ocean organisms, ecosystems and people (Orr et al., 2005; Hauri et al., 2016)597.
Further, it is difficult to reliably separate the impacts of ocean warming and acidification. As ocean waters have increased in sea surface temperature (SST) by approximately 0.9°C they have also decreased by 0.2 pH units since 1870–1899 ('pre-industrial'; Table 1 in Gattuso et al., 2015; Bopp et al., 2013)598. As CO2 concentrations continue to increase along with other GHGs, pH will decrease while sea temperature will increase, reaching 1.7°C and a decrease of 0.2 pH units (by 2100 under RCP4.5) relative to the pre-industrial period. These changes are likely to continue given the negative correlation of temperature and pH. Experimental manipulation of CO2, temperature and consequently acidification indicate that these impacts will continue to increase in size and scale as CO2 and SST continue to increase in tandem (Dove et al., 2013; Fang et al., 2013; Kroeker et al., 2013)599.
While many risks have been defined through laboratory and mesocosm experiments, there is a growing list of impacts from the field (medium confidence) that include community-scale impacts on bacterial assemblages and processes (Endres et al., 2014)600, coccolithophores (K.J.S. Meier et al., 2014)601, pteropods and polar foodwebs (Bednaršek et al., 2012, 2014)602, phytoplankton (Moy et al., 2009; Riebesell et al., 2013; Richier et al., 2014)603, benthic ecosystems (Hall-Spencer et al., 2008; Linares et al., 2015)604, seagrass (Garrard et al., 2014)605, and macroalgae (Webster et al., 2013; Ordonez et al., 2014)606, as well as excavating sponges, endolithic microalgae and reef-building corals (Dove et al., 2013; Reyes-Nivia et al., 2013; Fang et al., 2014)607, and coral reefs (Box 3.4; Fabricius et al., 2011; Allen et al., 2017)608. Some ecosystems, such as those from bathyal areas (i.e., 200–3000 m below the surface), are likely to undergo very large reductions in pH by the year 2100 (0.29 to 0.37 pH units), yet evidence of how deep-water ecosystems will respond is currently limited despite the potential planetary importance of these areas (low to medium confidence) (Hughes and Narayanaswamy, 2013; Sweetman et al., 2017)609.
3.4.4.6
Deoxygenation
Oxygen levels in the ocean are maintained by a series of processes including ocean mixing, photosynthesis, respiration and solubility (Boyd et al., 2014, 2015; Pörtner et al., 2014; Breitburg et al., 2018)610. Concentrations of oxygen in the ocean are declining (high confidence) owing to three main factors related to climate change: (i) heat-related stratification of the water column (less ventilation and mixing), (ii) reduced oxygen solubility as ocean temperature increases, and (iii) impacts of warming on biological processes that produce or consume oxygen such as photosynthesis and respiration (high confidence) (Bopp et al., 2013; Pörtner et al., 2014; Altieri and Gedan, 2015; Deutsch et al., 2015; Schmidtko et al., 2017; Shepherd et al., 2017; Breitburg et al., 2018)611. Further, a range of processes (Section 3.4.11) are acting synergistically, including factors not related to climate change, such as runoff and coastal eutrophication (e.g., from coastal farming and intensive aquaculture). These changes can lead to increased phytoplankton productivity as a result of the increased concentration of dissolved nutrients. Increased supply of organic carbon molecules from coastal run-off can also increase the metabolic activity of coastal microbial communities (Altieri and Gedan, 2015; Bakun et al., 2015; Boyd, 2015)612. Deep sea areas are likely to experience some of the greatest challenges, as abyssal seafloor habitats in areas of deep-water formation are projected to experience decreased water column oxygen concentrations by as much as 0.03 mL L–1 by 2100 (Levin and Le Bris, 2015; Sweetman et al., 2017)613.
The number of 'dead zones' (areas where oxygenated waters have been replaced by hypoxic conditions) has been growing strongly since the 1990s (Diaz and Rosenberg, 2008; Altieri and Gedan, 2015; Schmidtko et al., 2017)614. While attribution can be difficult because of the complexity of the processes involved, both related and unrelated to climate change, some impacts associated to deoxygenation (low-medium confidence) include the expansion of oxygen minimum zones (OMZ) (Turner et al., 2008; Carstensen et al., 2014; Acharya and Panigrahi, 2016; Lachkar et al., 2018)615, physiological impacts (Pörtner et al., 2014)616, and mortality and/or displacement of oxygen dependent organisms such as fish (Hamukuaya et al., 1998; Thronson and Quigg, 2008; Jacinto, 2011)617 and invertebrates (Hobbs and Mcdonald, 2010; Bednaršek et al., 2016; Seibel, 2016; Altieri et al., 2017)618. In addition, deoxygenation interacts with ocean acidification to present substantial separate and combined challenges for fisheries and aquaculture (medium confidence) (Hamukuaya et al., 1998; Bakun et al., 2015; Rodrigues et al., 2015; Feely et al., 2016; S. Li et al., 2016; Asiedu et al., 2017a; Clements and Chopin, 2017; Clements et al., 2017; Breitburg et al., 2018)619. Deoxygenation is expected to have greater impacts as ocean warming and acidification increase (high confidence), with impacts being larger and more numerous than today (e.g., greater challenges for aquaculture and fisheries from hypoxia), and as the number of hypoxic areas continues to increase. Risks from deoxygenation are virtually certain to increase as warming continues, although our understanding of risks at 1.5°C versus 2°C is incomplete (medium confidence). Reducing coastal pollution, and consequently the penetration of organic carbon into deep benthic habitats, is expected to reduce the loss of oxygen in coastal waters and hypoxic areas in general (high confidence) (Breitburg et al., 2018)620.
3.4.4.7
Loss of sea ice
Sea ice is a persistent feature of the planet's polar regions (Polyak et al., 2010)621 and is central to marine ecosystems, people (e.g., food, culture and livelihoods) and industries (e.g., fishing, tourism, oil and gas, and shipping). Summer sea ice in the Arctic, however, has been retreating rapidly in recent decades (Section 3.3.8), with an assessment of the literature revealing that a fundamental transformation is occurring in polar organisms and ecosystems, driven by climate change (high confidence) (Larsen et al., 2014)622. These changes are strongly affecting people in the Arctic who have close relationships with sea ice and associated ecosystems, and these people are facing major adaptation challenges as a result of sea level rise, coastal erosion, the accelerated thawing of permafrost, changing ecosystems and resources, and many other issues (Ford, 2012; Ford et al., 2015)623.
There is considerable and compelling evidence that a further increase of 0.5°C beyond the present-day average global surface temperature will lead to multiple levels of impact on a variety of organisms, from phytoplankton to marine mammals, with some of the most dramatic changes occurring in the Arctic Ocean and western Antarctic Peninsula (Turner et al., 2014, 2017b; Steinberg et al., 2015; Piñones and Fedorov, 2016)624.
The impacts of climate change on sea ice are part of the focus of the IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC), due to be released in 2019, and hence are not covered comprehensively here. However, there is a range of responses to the loss of sea ice that are occurring and which increase at 1.5°C and further so with 2°C of global warming. Some of these changes are described briefly here. Photosynthetic communities, such macroalgae, phytoplankton and microalgae dwelling on the underside of floating sea ice are changing, owing to increased temperatures, light and nutrient levels. As sea ice retreats, mixing of the water column increases, and phototrophs have increased access to seasonally high levels of solar radiation (medium confidence) (Dalpadado et al., 2014; W.N. Meier et al., 2014)625. These changes are expected to stimulate fisheries productivity in high-latitude regions by mid-century (high confidence) (Cheung et al., 2009, 2010, 2016b; Lam et al., 2014)626, with evidence that this is already happening for several high-latitude fisheries in the Northern Hemisphere, such as the Bering Sea, although these 'positive' impacts may be relatively short-lived (Hollowed and Sundby, 2014; Sundby et al., 2016)627. In addition to the impact of climate change on fisheries via impacts on net primary productivity (NPP), there are also direct effects of temperature on fish, which may in turn have a range of impacts (Pörtner et al., 2014)628. Sea ice in Antarctica is undergoing changes that exceed those seen in the Arctic (Maksym et al., 2011; Reid et al., 2015)629, with increases in sea ice coverage in the western Ross Sea being accompanied by strong decreases in the Bellingshausen and Amundsen Seas (Hobbs et al., 2016)630. While Antarctica is not permanently populated, the ramifications of changes to the productivity of vast regions, such as the Southern Ocean, have substantial implications for ocean foodwebs and fisheries globally.
3.4.4.8
Sea level rise
Mean sea level is increasing (Section 3.3.9), with substantial impacts already being felt by coastal ecosystems and communities (Wong et al., 2014)631 (high confidence). These changes are interacting with other factors, such as strengthening storms, which together are driving larger storm surges, infrastructure damage, erosion and habitat loss (Church et al., 2013; Stocker et al., 2013; Blankespoor et al., 2014)632. Coastal wetland ecosystems such as mangroves, sea grasses and salt marshes are under pressure from rising sea level (medium confidence) (Section 3.4.5; Di Nitto et al., 2014; Ellison, 2014; Lovelock et al., 2015; Mills et al., 2016; Nicholls et al., 2018)633, as well as from a wide range of other risks and impacts unrelated to climate change, with the ongoing loss of wetlands recently estimated at approximately 1% per annum across a large number of countries (Blankespoor et al., 2014; Alongi, 2015)634. While some ecosystems (e.g., mangroves) may be able to shift shoreward as sea levels increase, coastal development (e.g., buildings, seawalls and agriculture) often interrupts shoreward shifts, as well as reducing sediment supplies down some rivers (e.g., dams) due to coastal development (Di Nitto et al., 2014; Lovelock et al., 2015; Mills et al., 2016)635.
Responses to sea level rise challenges for ocean and coastal systems include reducing the impact of other stresses, such as those arising from tourism, fishing, coastal development, reduced sediment supply and unsustainable aquaculture/agriculture, in order to build ecological resilience (Hossain et al., 2015; Sutton-Grier and Moore, 2016; Asiedu et al., 2017a)636. The available literature largely concludes that these impacts will intensify under a 1.5°C warmer world but will be even higher at 2°C, especially when considered in the context of changes occurring beyond the end of the current century. In some cases, restoration of coastal habitats and ecosystems may be a cost-effective way of responding to changes arising from increasing levels of exposure to rising sea levels, intensifying storms, coastal inundation and salinization (Section 3.4.5 and Box 3.5; Arkema et al., 2013)637, although limitations of these strategies have been identified (e.g., Lovelock et al., 2015; Weatherdon et al., 2016)638.
3.4.4.9
Projected risks and adaptation options for oceans under global warming of 1.5°C or 2°C above pre-industrial levels
A comprehensive discussion of risk and adaptation options for all natural and human systems is not possible in the context and length of this report, and hence the intention here is to illustrate key risks and adaptation options for ocean ecosystems and sectors. This assessment builds on the recent expert consensus of Gattuso et al. (2015)639 by assessing new literature from 2015–2017 and adjusting the levels of risk from climate change in the light of literature since 2014. The original expert group's assessment (Supplementary Material 3.SM.3.2) was used as input for this new assessment, which focuses on the implications of global warming of 1.5°C as compared to 2°C. A discussion of potential adaptation options is also provided, the details of which will be further explored in later chapters of this special report. The section draws on the extensive analysis and literature presented in the Supplementary Material of this report (3.SM.3.2, 3.SM.3.3) and has a summary in Figures 3.18 and 3.20 which outline the added relative risks of climate change.
3.4.4.10
Framework organisms (tropical corals, mangroves and seagrass)
Marine organisms ('ecosystem engineers'), such as seagrass, kelp, oysters, salt marsh species, mangroves and corals, build physical structures or frameworks (i.e., sea grass meadows, kelp forests, oyster reefs, salt marshes, mangrove forests and coral reefs) which form the habitat for a large number of species (Gutiérrez et al., 2012)640. These organisms in turn provide food, livelihoods, cultural significance, and services such as coastal protection to human communities (Bell et al., 2011, 2018; Cinner et al., 2012; Arkema et al., 2013; Nurse et al., 2014; Wong et al., 2014; Barbier, 2015; Bell and Taylor, 2015; Hoegh-Guldberg et al., 2015; Mycoo, 2017; Pecl et al., 2017)641.
Risks of climate change impacts for seagrass and mangrove ecosystems were recently assessed by an expert group led by Short et al. (2016)642. Impacts of climate change were assessed to be similar across a range of submerged and emerged plants. Submerged plants such as sea-grass were affected mostly by temperature extremes (Arias-Ortiz et al., 2018)643, and indirectly by turbidity, while emergent communities such as mangroves and salt marshes were most susceptible to sea level variability and temperature extremes, which is consistent with other evidence (Di Nitto et al., 2014; Sierra-Correa and Cantera Kintz, 2015; Osorio et al., 2016; Sasmito et al., 2016)644, especially in the context of human activities that reduce sediment supply (Lovelock et al., 2015)645 or interrupt the shoreward movement of mangroves though the construction of coastal infrastructure. This in turn leads to 'coastal squeeze' where coastal ecosystems are trapped between changing ocean conditions and coastal infrastructure (Mills et al., 2016)646. Projections of the future distribution of seagrasses suggest a poleward shift, which raises concerns that low-latitude seagrass communities may contract as a result of increasing stress levels (Valle et al., 2014)647.
Climate change (e.g., sea level rise, heat stress, storms) presents risk for coastal ecosystems such as seagrass (high confidence) and reef-building corals (very high confidence) (Figure 3.18, Supplementary Material 3.SM.3.2), with evidence of increasing concern since AR5 and the conclusion that tropical corals may be even more vulnerable to climate change than indicated in assessments made in 2014 (Hoegh-Guldberg et al., 2014; Gattuso et al., 2015)648. The current assessment also considered the heatwave-related loss of 50% of shallow-water corals across hundreds of kilometres of the world's largest continuous coral reef system, the Great Barrier Reef. These large-scale impacts, plus the observation of back-to-back bleaching events on the Great Barrier Reef (predicted two decades ago, Hoegh-Guldberg, 1999)649 and arriving sooner than predicted (Hughes et al., 2017b, 2018)650, suggest that the research community may have underestimated climate risks for coral reefs (Figure 3.18). The general assessment of climate risks for mangroves prior to this special report was that they face greater risks from deforestation and unsustainable coastal development than from climate change (Alongi, 2008; Hoegh-Guldberg et al., 2014; Gattuso et al., 2015)651. Recent large-scale die-offs (Duke et al., 2017; Lovelock et al., 2017)652, however, suggest that risks from climate change may have been underestimated for mangroves as well. With the events of the last past three years in mind, risks are now considered to be undetectable to moderate (i.e., moderate risks now start at 1.3°C as opposed to 1.8°C; medium confidence). Consequently, when average global warming reaches 1.3°C above pre-industrial levels, the risk of climate change to mangroves are projected to be moderate (Figure 3.18) while tropical coral reefs will have reached a high level of risk as examplified by increasing damage from heat stress since the early 1980s. At global warming of 1.8°C above pre-industrial levels, seagrasses are projected to reach moderate to high levels of risk (e.g., damage resulting from sea level rise, erosion, extreme temperatures, and storms), while risks to mangroves from climate change are projected to remain moderate (e.g., not keeping up with sea level rise, and more frequent heat stress mortality) although there is low certainty as to when or if this important ecosystem is likely to transition to higher levels of additional risk from climate change (Figure 3.18).
Warm water (tropical) coral reefs are projected to reach a very high risk of impact at 1.2°C (Figure 3.18), with most available evidence suggesting that coral-dominated ecosystems will be non-existent at this temperature or higher (high confidence). At this point, coral abundance will be near zero at many locations and storms will contribute to 'flattening' the three-dimensional structure of reefs without recovery, as already observed for some coral reefs (Alvarez-Filip et al., 2009)653. The impacts of warming, coupled with ocean acidification, are expected to undermine the ability of tropical coral reefs to provide habitat for thousand of species, which together provide a range of ecosystem services (e.g., food, livelihoods, coastal protection, cultural services) that are important for millions of people (high confidence) (Burke et al., 2011)654.
Strategies for reducing the impact of climate change on framework organisms include reducing stresses not directly related to climate change (e.g., coastal pollution, overfishing and destructive coastal development) in order to increase their ecological resilience in the face of accelerating climate change impacts (World Bank, 2013; Ellison, 2014; Anthony et al., 2015; Sierra-Correa and Cantera Kintz, 2015; Kroon et al., 2016; O'Leary et al., 2017)655, as well as protecting locations where organisms may be more robust (Palumbi et al., 2014)656 or less exposed to climate change (Bongaerts et al., 2010; van Hooidonk et al., 2013; Beyer et al., 2018)657. This might involve cooler areas due to upwelling, or involve deep-water locations that experience less extreme conditions and impacts. Given the potential value of such locations for promoting the survival of coral communities under climate change, efforts to prevent their loss resulting from other stresses are important (Bongaerts et al., 2010, 2017; Chollett et al., 2010, 2014; Chollett and Mumby, 2013; Fine et al., 2013; van Hooidonk et al., 2013; Cacciapaglia and van Woesik, 2015; Beyer et al., 2018)658. A full understanding of the role of refugia in reducing the loss of ecosystems has yet to be developed (low to medium confidence). There is also interest in ex situ conservation approaches involving the restoration of corals via aquaculture (Shafir et al., 2006; Rinkevich, 2014)659 or the use of 'assisted evolution' to help corals adapt to changing sea temperatures (van Oppen et al., 2015, 2017)660, although there are numerous challenges that must be surpassed if these approaches are to be cost-effective responses to preserving coral reefs under rapid climate change (low confidence) (Hoegh-Guldberg, 2012, 2014a; Bayraktarov et al., 2016)661.
High levels of adaptation are expected to be required to prevent impacts on food security and livelihoods in coastal populations (medium confidence). Integrating coastal infrastructure with changing ecosystems such as mangroves, seagrasses and salt marsh, may offer adaptation strategies as they shift shoreward as sea levels rise (high confidence). Maintaining the sediment supply to coastal areas would also assist mangroves in keeping pace with sea level rise (Shearman et al., 2013; Lovelock et al., 2015; Sasmito et al., 2016)662. For this reason, habitat for mangroves can be strongly affected by human actions such as building dams which reduce the sediment supply and hence the ability of mangroves to escape 'drowning' as sea level rises (Lovelock et al., 2015)663. In addition, integrated coastal zone management should recognize the importance and economic expediency of using natural ecosystems such as mangroves and tropical coral reefs to protect coastal human communities (Arkema et al., 2013; Temmerman et al., 2013; Ferrario et al., 2014; Hinkel et al., 2014; Elliff and Silva, 2017)664. Adaptation options include developing alternative livelihoods and food sources, ecosystem-based management/adaptation such as ecosystem restoration, and constructing coastal infrastructure that reduces the impacts of rising seas and intensifying storms (Rinkevich, 2015; Weatherdon et al., 2016; Asiedu et al., 2017a; Feller et al., 2017)665. Clearly, these options need to be carefully assessed in terms of feasibility, cost and scalability, as well as in the light of the coastal ecosystems involved (Bayraktarov et al., 2016)666.
3.4.4.11
Ocean foodwebs (pteropods, bivalves, krill and fin fish)
Ocean foodwebs are vast interconnected systems that transfer solar energy and nutrients from phytoplankton to higher trophic levels, including apex predators and commercially important species such as tuna. Here, we consider four representative groups of marine organisms which are important within foodwebs across the ocean, and which illustrate the impacts and ramifications of 1.5°C or higher levels of warming.
The first group of organisms, pteropods, are small pelagic molluscs that suspension feed and produce a calcium carbonate shell. They are highly abundant in temperate and polar waters where they are an important link in the foodweb between phytoplankton and a range of other organisms including fish, whales and birds. The second group, bivalve molluscs (e.g., clams, oysters and mussels), are filter-feeding invertebrates. These invertebrate organisms underpin important fisheries and aquaculture industries, from polar to tropical regions, and are important food sources for a range of organisms including humans. The third group of organisms considered here is a globally significant group of invertebrates known as euphausiid crustaceans (krill), which are a key food source for many marine organisms and hence a major link between primary producers and higher trophic levels (e.g., fish, mammals and sea birds). Antarctic krill,Euphausia superba, are among the most abundant species in terms of mass and are consequently an essential component of polar foodwebs (Atkinson et al., 2009)667. The last group, fin fishes, is vitally important components of ocean foodwebs, contribute to the income of coastal communities, industries and nations, and are important to the foodsecurity and livelihood of hundreds of millions of people globally (FAO, 2016)668. Further background for this section is provided in Supplementary Material 3.SM.3.2.
There is a moderate risk to ocean foodwebs under present-day conditions (medium to high confidence) (Figure 3.18). Changing water chemistry and temperature are already affecting the ability of pteropods to produce their shells, swim and survive (Bednaršek et al., 2016)669. Shell dissolution, for example, has increased by 19–26% in both nearshore and offshore populations since the pre-industrial period (Feely et al., 2016)670. There is considerable concern as to whether these organisms are declining further, especially given the central importance in ocean foodwebs (David et al., 2017)671. Reviewing the literature reveals that pteropods are projected to face high risks of impact at average global temperatures 1.5°C above pre-industrial levels and increasing risks of impacts at 2°C (medium confidence).
As GMST increases by 1.5°C and more, the risk of impacts from ocean warming and acidification are expected to be moderate to high, except in the case of bivalves (mid-latitudes) where the risks of impacts are projected to be high to very high (Figure 3.18). Ocean warming and acidification are already affecting the life history stages of bivalve molluscs (e.g., Asplund et al., 2014; Mackenzie et al., 2014; Waldbusser et al., 2014; Zittier et al., 2015; Shi et al., 2016; Velez et al., 2016; Q. Wang et al., 2016; Castillo et al., 2017; Lemasson et al., 2017; Ong et al., 2017; X. Zhao et al., 2017)672. Impacts on adult bivalves include decreased growth, increased respiration and reduced calcification, whereas larval stages tend to show greater developmental abnormalities and increased mortality after exposure to these conditions (medium to high confidence) (Q. Wang et al., 2016; Lemasson et al., 2017; Ong et al., 2017; X. Zhao et al., 2017)673. Risks are expected to accumulate at higher temperatures for bivalve molluscs, with very high risks expected at 1.8°C of warming or more. This general pattern applies to low-latitude fin fish, which are expected to experience moderate to high risks of impact at 1.3°C of global warming (medium confidence), and very high risks at 1.8°C at low latitudes (medium confidence) (Figure 3.18).
Large-scale changes to foodweb structure are occurring in all oceans. For example, record levels of sea ice loss in the Antarctic (Notz and Stroeve, 2016; Turner et al., 2017b)674 translate into a loss of habitat and hence reduced abundance of krill (Piñones and Fedorov, 2016)675, with negative ramifications for the seabirds and whales which feed on krill (Croxall, 1992; Trathan and Hill, 2016)676 (low-medium confidence). Other influences, such as high rates of ocean acidification coupled with shoaling of the aragonite saturation horizon, are likely to also play key roles (Kawaguchi et al., 2013; Piñones and Fedorov, 2016)677. As with many risks associated with impacts at the ecosystem scale, most adaptation options focus on the management of stresses unrelated to climate change but resulting from human activities, such as pollution and habitat destruction. Reducing these stresses will be important in efforts to maintain important foodweb components. Fisheries management at local to regional scales will be important in reducing stress on foodweb organisms, such as those discussed here, and in helping communities and industries adapt to changing foodweb structures and resources (see further discussion of fisheries per se below; Section 3.4.6.3). One strategy is to maintain larger population levels of fished species in order to provide more resilient stocks in the face of challenges that are increasingly driven by climate change (Green et al., 2014; Bell and Taylor, 2015)678.
3.4.4.12
Key ecosystem services (e.g., carbon uptake, coastal protection, and tropical coral reef recreation)
The ocean provides important services, including the regulation of atmospheric composition via gas exchange across the boundary between ocean and atmosphere, and the storage of carbon in vegetation and soils associated with ecosystems such as mangroves, salt marshes and coastal peatlands. These services involve a series of physicochemical processes which are influenced by ocean chemistry, circulation, biology, temperature and biogeochemical components, as well as by factors other than climate (Boyd, 2015)679. The ocean is also a net sink for CO2 (another important service), absorbing approximately 30% of human emissions from the burning of fossil fuels and modification of land use (IPCC, 2013)680. Carbon uptake by the ocean is decreasing (Iida et al., 2015)681, and there is increasing concern from observations and models regarding associated changes to ocean circulation (Sections 3.3.7 and 3.4.4., Rahmstorf et al., 2015b)682;. Biological components of carbon uptake by the ocean are also changing, with observations of changing net primary productivity (NPP) in equatorial and coastal upwelling systems (medium confidence) (Lluch-Cota et al., 2014; Sydeman et al., 2014; Bakun et al., 2015)683, as well as subtropical gyre systems (low confidence) (Signorini et al., 2015)684. There is general agreement that NPP will decline as ocean warming and acidification increase (medium confidence) (Bopp et al., 2013; Boyd et al., 2014; Pörtner et al., 2014; Boyd, 2015)685.
Projected risks of impacts from reductions in carbon uptake, coastal protection and services contributing to coral reef recreation suggest a transition from moderate to high risks at 1.5°C and higher (low confidence). At 2°C, risks of impacts associated with changes to carbon uptake are high (high confidence), while the risks associated with reduced coastal protection and recreation on tropical coral reefs are high, especially given the vulnerability of this ecosystem type, and others (e.g., seagrass and mangroves), to climate change (medium confidence) (Figure 3.18). Coastal protection is a service provided by natural barriers such as mangroves, seagrass meadows, coral reefs, and other coastal ecosystems, and it is important for protecting human communities and infrastructure against the impacts associated with rising sea levels, larger waves and intensifying storms (high confidence) (Gutiérrez et al., 2012; Kennedy et al., 2013; Ferrario et al., 2014; Barbier, 2015; Cooper et al., 2016; Hauer et al., 2016; Narayan et al., 2016)686. Both natural and human coastal protection have the potential to reduce these impacts (Fu and Song, 2017)687. Tropical coral reefs, for example, provide effective protection by dissipating about 97% of wave energy, with 86% of the energy being dissipated by reef crests alone (Ferrario et al., 2014; Narayan et al., 2016)688. Mangroves similarly play an important role in coastal protection, as well as providing resources for coastal communities, but they are already under moderate risk of not keeping up with sea level rise due to climate change and to contributing factors, such as reduced sediment supply or obstacles to shoreward shifts (Saunders et al., 2014; Lovelock et al., 2015)689. This implies that coastal areas currently protected by mangroves may experience growing risks over time.
Tourism is one of the largest industries globally (Rosselló-Nadal, 2014; Markham et al., 2016; Spalding et al., 2017)690. A substantial part of the global tourist industry is associated with tropical coastal regions and islands, where tropical coral reefs and related ecosystems play important roles (Section 3.4.9.1) (medium confidence). Coastal tourism can be a dominant money earner in terms of foreign exchange for many countries, particularly small island developing states (SIDS) (Section 3.4.9.1, Box 3.5; Weatherdon et al., 2016; Spalding et al., 2017)691. The direct relationship between increasing global temperatures, intensifying storms, elevated thermal stress, and the loss of tropical coral reefs has raised concern about the risks of climate change for local economies and industries based on tropical coral reefs. Risks to coral reef recreational services from climate change are considered here, as well as in Box 3.5, Section 3.4.9 and Supplementary Material 3.SM.3.2.
Adaptations to the broad global changes in carbon uptake by the ocean are limited and are discussed later in this report with respect to changes in NPP and implications for fishing industries. These adaptation options are broad and indirect, and the only other solution at large scale is to reduce the entry of CO2 into the ocean. Strategies for adapting to reduced coastal protection involve (a) avoidance of vulnerable areas and hazards, (b) managed retreat from threatened locations, and/or (c) accommodation of impacts and loss of services (Bell, 2012; André et al., 2016; Cooper et al., 2016; Mills et al., 2016; Raabe and Stumpf, 2016; Fu and Song, 2017)692. Within these broad options, there are some strategies that involve direct human intervention, such as coastal hardening and the construction of seawalls and artificial reefs (Rinkevich, 2014, 2015; André et al., 2016; Cooper et al., 2016; Narayan et al., 2016)693, while others exploit opportunities for increasing coastal protection by involving naturally occurring oyster banks, coral reefs, mangroves, seagrass and other ecosystems (UNEP-WCMC, 2006; Scyphers et al., 2011; Zhang et al., 2012; Ferrario et al., 2014; Cooper et al., 2016)694. Natural ecosystems, when healthy, also have the ability to repair themselves after being damaged, which sets them apart from coastal hardening and other human structures that require constant maintenance (Barbier, 2015; Elliff and Silva, 2017)695. In general, recognizing and restoring coastal ecosystems may be more cost-effective than installing human structures, in that creating and maintaining structures is typically expensive (Temmerman et al., 2013; Mycoo, 2017)696.
Recent studies have increasingly stressed the need for coastal protection to be considered within the context of coastal land management, including protecting and ensuring that coastal ecosystems are able to undergo shifts in their distribution and abundance as climate change occurs (Clausen and Clausen, 2014; Martínez et al., 2014; Cui et al., 2015; André et al., 2016; Mills et al., 2016)697. Facilitating these changes will require new tools in terms of legal and financial instruments, as well as integrated planning that involves not only human communities and infrastructure, but also associated ecosystem responses and values (Bell, 2012; Mills et al., 2016)698. In this regard, the interactions between climate change, sea level rise and coastal disasters are increasingly being informed by models (Bosello and De Cian, 2014)699 with a widening appreciation of the role of natural ecosystems as an alternative to hardened coastal structures (Cooper et al., 2016)700. Adaptation options for tropical coral reef recreation include: (i) protecting and improving biodiversity and ecological function by minimizing the impact of stresses unrelated to climate change (e.g., pollution and overfishing), (ii) ensuring adequate levels of coastal protection by supporting and repairing ecosystems that protect coastal regions, (iii) ensuring fair and equitable access to the economic opportunities associated with recreational activities, and (iv) seeking and protecting supplies of water for tourism, industry and agriculture alongside community needs.
Figure 3.18
Summary of additional risks of impacts from ocean warming (and associated climate change factors such ocean acidification) for a range of ocean organisms, ecosystems and sectors at 1.0°C, 1.5°C and 2.0°C of warming of the average sea surface temperature (SST) relative to the pre-industrial period.
The grey bar represents the range of GMST for the most recent decade: 2006–2015. The assessment of changing risk levels and associated confidence were primarily derived from the expert judgement of Gattuso et al. (2015701) and the lead authors and relevant contributing authors of Chapter 3 (SR1.5), while additional input was received from the many reviewers of the ocean systems section of SR1.5. Notes: (i) The analysis shown here is not intended to be comprehensive. The examples of organisms, ecosystems and sectors included here are intended to illustrate the scale, types and projection of risks for representative natural and human ocean systems. (ii) The evaluation of risks by experts did not consider genetic adaptation, acclimatization or human risk reduction strategies (mitigation and societal adaptation). (iii) As discussed elsewhere (Sections 3.3.10 and 3.4.4.5, Box 3.4; Gattuso et al., 2015702), ocean acidification is also having impacts on organisms and ecosystems as carbon dioxide increases in the atmosphere. These changes are part of the responses reported here, although partitioning the effects of the two drivers is difficult at this point in time and hence was not attempted. (iv) Confidence levels for location of transition points between levels of risk (L = low, M = moderate, H = high and VH = very high) are assessed and presented here as in the accompanying study by Gattuso et al. (2015703). Three transitions in risk were possible: W–Y (white to yellow), Y–R (yellow to red), and R–P (red to purple), with the colours corresponding to the level of additional risk posed by climate change. The confidence levels for these transitions were assessed, based on level of agreement and extent of evidence, and appear as letters associated with each transition (see key in diagram).
3.4.5
Coastal and Low-Lying Areas, and Sea Level Rise
Sea level rise (SLR) is accelerating in response to climate change (Section 3.3.9; Church et al., 2013)737 and will produce significant impacts (high confidence). In this section, impacts and projections of SLR are reported at global and city scales (Sections 3.4.5.1 and 3.4.5.2) and for coastal systems (Sections 3.4.5.3 to 3.4.5.6). For some sectors, there is a lack of precise evidence of change at 1.5°C and 2°C of global warming. Adaptation to SLR is discussed in Section 3.4.5.7.
3.4.5.1
Global / sub-global scale
Sea level rise (SLR) and other oceanic climate changes are already resulting in salinization, flooding, and erosion and in the future are projected to affect human and ecological systems, including health, heritage, freshwater availability, biodiversity, agriculture, fisheries and other services, with different impacts seen worldwide (high confidence). Owing to the commitment to SLR, there is an overlapping uncertainty in projections at 1.5°C and 2°C (Schleussner et al., 2016b; Sanderson et al., 2017; Goodwin et al., 2018; Mengel et al., 2018; Nicholls et al., 2018; Rasmussen et al., 2018)738 and about 0.1 m difference in global mean sea level (GMSL) rise between 1.5°C and 2°C worlds in the year 2100 (Section 3.3.9, Table 3.3). Exposure and impacts at 1.5°C and 2°C differ at different time horizons (Schleussner et al., 2016b; Brown et al., 2018a, b; Nicholls et al., 2018; Rasmussen et al., 2018)739. However, these are distinct from impacts associated with higher increases in temperature (e.g., 4ºC or more, as discussed in Brown et al., 2018a)740 over centennial scales. The benefits of climate change mitigation reinforce findings of earlier IPCC reports (e.g., Wong et al., 2014)741.
Table 3.3 shows the land and people exposed to SLR (assuming there is no adaptation or protection at all) using the Dynamic Interactive Vulnerability Assessment (DIVA) model (extracted from Brown et al., 2018a742 and Goodwin et al., 2018743; see also Supplementary Material 3.SM, Table 3.SM.4). Thus, exposure increases even with temperature stabilization. The exposed land area is projected to at least double by 2300 using a RCP8.5 scenario compared with a mitigation scenario (Brown et al., 2018a)744. In the 21st century, land area exposed to sea level rise (assuming there is no adaptation or protection at all) is projected to be at least an order of magnitude larger than the cumulative land loss due to submergence (which takes into account defences) (Brown et al., 2016, 2018a)745 regardless of the SLR scenario applied. Slower rates of rise due to climate change mitigation may provide a greater opportunity for adaptation (medium confidence), which could substantially reduce impacts.
In agreement with the assessment in WGII AR5 Section 5.4.3.1 (Wong et al., 2014)746, climate change mitigation may reduce or delay coastal exposure and impacts (very high confidence). Adaptation has the potential to substantially reduce risk through a portfolio of available options (Sections 5.4.3.1 and 5.5 of Wong et al., 2014; Sections 6.4.2.3 and 6.6 of Nicholls et al., 2007)747. At 1.5°C in 2100, 31–69 million people (2010 population values) worldwide are projected to be exposed to flooding, assuming no adaptation or protection at all, compared with 32–79 million people (2010 population values) at 2°C in 2100 (Supplementary Material 3.SM, Table 3.SM.4; Rasmussen et al., 2018748). As a result, up to 10.4 million more people would be exposed to sea level rise at 2°C compared with 1.5°C in 2100 (medium confidence). With a 1.5°C stabilization scenario in 2100, 62.7 million people per year are at risk from flooding, with this value increasing to 137.6 million people per year in 2300 (50th percentile average across SSP1–5, no socio-economic change after 2100). These projections assume that no upgrade to current protection levels occurs (Nicholls et al., 2018)749. The number of people at risk increases by approximately 18% in 2030 if a 2°C scenario is used and by 266% in 2300 if an RCP8.5 scenario is considered (Nicholls et al., 2018)750. Through prescribed IPCC Special Report on Emissions Scenarios (SRES) SLR scenarios, Arnell et al. (2016)751 also found that the number of people exposed to flooding increased substantially at warming levels higher than 2°C, assuming no adaptation beyond current protection levels. Additionally, impacts increased in the second half of the 21st century.
Coastal flooding is projected to cost thousands of billions of USD annually, with damage costs under constant protection estimated at 0.3–5.0% of global gross domestic product (GDP) in 2100 under an RCP2.6 scenario (Hinkel et al., 2014)752. Risks are projected to be highest in South and Southeast Asia, assuming there is no upgrade to current protection levels, for all levels of climate warming (Arnell et al., 2016; Brown et al., 2016)753. Countries with at least 50 million people exposed to SLR (assuming no adaptation or protection at all) based on a 1,280 Pg C emissions scenario (approximately a 1.5°C temperature rise above today's level) include China, Bangladesh, Egypt, India, Indonesia, Japan, Philippines, United States and Vietnam (Clark et al., 2016)754. Rasmussen et al. (2018)755 and Brown et al. (2018a)756 project that similar countries would have high exposure to SLR in the 21st century using 1.5°C and 2°C scenarios. Thus, there is high confidence that SLR will have significant impacts worldwide in this century and beyond.
3.4.5.2
Cities
Observations of the impacts of SLR in cities are difficult to record because multiple drivers of change are involved. There are observations of ongoing and planned adaptation to SLR and extreme water levels in some cities (Araos et al., 2016; Nicholls et al., 2018)757, whilst other cities have yet to prepare for these impacts (high confidence) (see Section 3.4.8 and Cross-Chapter Box 9 in Chapter 4). There are limited observations and analyses of how cities will cope with higher and/or multi-centennial SLR, with the exception of Amsterdam, New York and London (Nicholls et al., 2018)758.
Coastal urban areas are projected to see more extreme water levels due to rising sea levels, which may lead to increased flooding and damage of infrastructure from extreme events (unless adaptation is undertaken), plus salinization of groundwater. These impacts may be enhanced through localized subsidence (Wong et al., 2014)759, which causes greater relative SLR. At least 136 megacities (port cities with a population greater than 1 million in 2005) are at risk from flooding due to SLR (with magnitudes of rise possible under 1.5°C or 2°C in the 21st century, as indicated in Section 3.3.9) unless further adaptation is undertaken (Hanson et al., 2011; Hallegatte et al., 2013)760. Many of these cities are located in South and Southeast Asia (Hallegatte et al., 2013; Cazenave and Cozannet, 2014; Clark et al., 2016; Jevrejeva et al., 2016)761. Jevrejeva et al. (2016)762 projected that more than 90% of global coastlines could experience SLR greater than 0.2 m with 2°C of warming by 2040 (RCP8.5). However, for scenarios where 2°C is stabilized or occurs later in time, this figure is likely to differ because of the commitment to SLR. Raising existing dikes helps protect against SLR, substantially reducing risks, although other forms of adaptation exist. By 2300, dike heights under a non-mitigation scenario (RCP8.5) could be more than 2 m higher (on average for 136 megacities) than under climate change mitigation scenarios at 1.5°C or 2°C (Nicholls et al., 2018)763. Thus, rising sea levels commit coastal cities to long-term adaptation (high confidence).
3.4.5.3
Small islands
Qualitative physical observations of SLR (and other stresses) include inundation of parts of low-lying islands, land degradation due to saltwater intrusion in Kiribati and Tuvalu (Wairiu, 2017)764, and shoreline change in French Polynesia (Yates et al., 2013)765, Tuvalu (Kench et al., 2015, 2018)766 and Hawaii (Romine et al., 2013)767. Observations, models and other evidence indicate that unconstrained Pacific atolls have kept pace with SLR, with little reduction in size or net gain in land (Kench et al., 2015, 2018; McLean and Kench, 2015; Beetham et al., 2017)768. Whilst islands are highly vulnerable to SLR (high confidence), they are also reactive to change. Small islands are impacted by multiple climatic stressors, with SLR being a more important stressor to some islands than others (Sections 3.4.10, 4.3.5.6, 5.2.1, 5.5.3.3, Boxes 3.5, 4.3 and 5.3).
Observed adaptation to multiple drivers of coastal change, including SLR, includes retreat (migration), accommodation and defence. Migration (internal and international) has always been important on small islands (Farbotko and Lazrus, 2012; Weir et al., 2017)769, with changing environmental and weather conditions being just one factor in the choice to migrate (Sections 3.4.10, 4.3.5.6 and 5.3.2; Campbell and Warrick, 2014)770. Whilst flooding may result in migration or relocation, for example in Vunidogoloa, Fiji (McNamara and Des Combes, 2015; Gharbaoui and Blocher, 2016)771 and the Solomon Islands (Albert et al., 2017)772, in situ adaptation may be tried or preferred, for example stilted housing or raised floors in Tubigon, Bohol, Philippines (Jamero et al., 2017)773, raised roads and floors in Batasan and Ubay, Philippines (Jamero et al., 2018)774, and raised platforms for faluw in Leang, Federated States of Micronesia (Nunn et al., 2017)775. Protective features, such as seawalls or beach nourishment, are observed to locally reduce erosion and flood risk but can have other adverse implications (Sovacool, 2012; Mycoo, 2014, 2017; Nurse et al., 2014; AR5 Section 29.6.22)776.
There is a lack of precise, quantitative studies of projected impacts of SLR at 1.5°C and 2°C. Small islands are projected to be at risk and very sensitive to coastal climate change and other stressors (high confidence) (Nurse et al., 2014; Benjamin and Thomas, 2016; Ourbak and Magnan, 2017; Brown et al., 2018a; Nicholls et al., 2018; Rasmussen et al., 2018;777 AR5 Sections 29.3 and 29.4), such as oceanic warming, SLR (resulting in salinization, flooding and erosion), cyclones and mass coral bleaching and mortality (Section 3.4.4, Boxes 3.4 and 3.5). These impacts can have significant socio-economic and ecological implications, such as on health, agriculture and water resources, which in turn have impacts on livelihoods (Sovacool, 2012; Mycoo, 2014, 2017; Nurse et al., 2014)778. Combinations of drivers causing adverse impacts are important. For example, Storlazzi et al. (2018)779 found that the impacts of SLR and wave-induced flooding (within a temperature horizon equivalent of 1.5°C), could affect freshwater availability on Roi-Namur, Marshall Islands, but is also dependent on other extreme weather events. Freshwater resources may also be affected by a 0.40 m rise in sea level (which may be experienced with a 1.5°C warming) in other Pacific atolls (Terry and Chui, 2012)780. Whilst SLR is a major hazard for atolls, islands reaching higher elevations are also threatened given that there is often a lot of infrastructure located near the coast (high confidence) (Kumar and Taylor, 2015; Nicholls et al., 2018)781. Tens of thousands of people on small islands are exposed to SLR (Rasmussen et al., 2018)782. Giardino et al. (2018)783 found that hard defence structures on the island of Ebeye in the Marshall Islands were effective in reducing damage due to SLR at 1.5°C and 2°C. Additionally, damage was also reduced under mitigation scenarios compared with non-mitigation scenarios. In Jamaica and St Lucia, SLR and extreme sea levels are projected to threaten transport system infrastructure at 1.5°C unless further adaptation is undertaken (Monioudi et al., 2018)784. Slower rates of SLR will provide a greater opportunity for adaptation to be successful (medium confidence), but this may not be substantial enough on islands with a very low mean elevation. Migration and/or relocation may be an adaptation option (Section 3.4.10). Thomas and Benjamin (2017)785 highlight three areas of concern in the context of loss and damage at 1.5°C: a lack of data, gaps in financial assessments, and a lack of targeted policies or mechanisms to address these issues (Cross-Chapter Box 12 in Chapter 5). Small islands are projected to remain vulnerable to SLR (high confidence).
3.4.5.4
Deltas and estuaries
Observations of SLR and human influence are felt through salinization, which leads to mixing in deltas and estuaries, aquifers, leading to flooding (also enhanced by precipitation and river discharge), land degradation and erosion. Salinization is projected to impact freshwater sources and pose risks to ecosystems and human systems (Section 5.4; Wong et al., 2014)786. For instance, in the Delaware River estuary on the east coast of the USA, upward trends of salinity (measured since the 1900s), accounting for the effects of streamflow and seasonal variations, have been detected and SLR is a potential cause (Ross et al., 2015)787.
Z. Yang et al. (2015)788 found that future climate scenarios for the USA (A1B 1.6°C and B1 2°C in the 2040s) had a greater effect on salinity intrusion than future land-use/land-cover change in the Snohomish River estuary in Washington state (USA). This resulted in a shift in the salinity both upstream and downstream in low flow conditions. Projecting impacts in deltas needs an understanding of both fluvial discharge and SLR, making projections complex because the drivers operate on different temporal and spatial scales (Zaman et al., 2017; Brown et al., 2018b)789. The mean annual flood depth when 1.5°C is first projected to be reached in the Ganges-Brahmaputra delta may be less than the most extreme annual flood depth seen today, taking into account SLR, surges, tides, bathymetry and local river flows (Brown et al., 2018b)790. Further, increased river salinity and saline intrusion in the Ganges-Brahmaputra-Meghna is likely with 2°C of warming (Zaman et al., 2017)791. Salinization could impact agriculture and food security (Cross-Chapter Box 6 in this chapter). For 1.5°C or 2°C stabilization conditions in 2200 or 2300 plus surges, a minimum of 44% of the Bangladeshi Ganges-Brahmaputra, Indian Bengal, Indian Mahanadi and Ghanese Volta delta land area (without defences) would be exposed unless sedimentation occurs (Brown et al., 2018b)792. Other deltas are similarly vulnerable. SLR is only one factor affecting deltas, and assessment of numerous geophysical and anthropogenic drivers of geomorphic change is important (Tessler et al., 2018)793. For example, dike building to reduce flooding and dam building (Gupta et al., 2012)794 restricts sediment movement and deposition, leading to enhanced subsidence, which can occur at a greater rate than SLR (Auerbach et al., 2015; Takagi et al., 2016)795. Although dikes remain essential for reducing flood risk today, promoting sedimentation is an advisable strategy (Brown et al., 2018b)796 which may involve nature-based solutions. Transformative decisions regarding the extent of sediment restrictive infrastructure may need to be considered over centennial scales (Brown et al., 2018b)797. Thus, in a 1.5°C or 2°C warmer world, deltas, which are home to millions of people, are expected to be highly threatened from SLR and localized subsidence (high confidence).
3.4.5.5
Wetlands
Observations indicate that wetlands, such as saltmarshes and mangrove forests, are disrupted by changing conditions (Sections 3.4.4.8; Wong et al., 2014; Lovelock et al., 2015)798, such as total water levels and sediment availability. For example, saltmarshes in Connecticut and New York, USA, measured from 1900 to 2012, have accreted with SLR but have lost marsh surface relative to tidal datums, leading to increased marsh flooding and further accretion (Hill and Anisfeld, 2015)799. This change stimulated marsh carbon storage and aided climate change mitigation.
Salinization may lead to shifts in wetland communities and their ecosystem functions (Herbert et al., 2015)800. Some projections of wetland change, with magnitudes (but not necessarily rates or timing) of SLR analogous to 1.5°C and 2°C of global warming, indicate a net loss of wetlands in the 21st century (e.g., Blankespoor et al., 2014; Cui et al., 2015; Arnell et al., 2016; Crosby et al., 2016)801, whilst others report a net gain with wetland transgression (e.g. Raabe and Stumpf, 2016802 in the Gulf of Mexico). However, the feedback between wetlands and sea level is complex, with parameters such as a lack of accommodation space restricting inland migration, or sediment supply and feedbacks between plant growth and geomorphology (Kirwan and Megonigal, 2013; Ellison, 2014; Martínez et al., 2014; Spencer et al., 2016)803 still being explored. Reducing global warming from 2°C to 1.5°C will deliver long-term benefits, with natural sedimentation rates more likely keep up with SLR. It remains unclear how wetlands will respond and under what conditions (including other climate parameters) to a global temperature rise of 1.5°C and 2°C. However, they have great potential to aid and benefit climate change mitigation and adaptation (medium confidence) (Sections 4.3.2.2 and 4.3.2.3).
3.4.5.6
Other coastal settings
Numerous impacts have not been quantified at 1.5°C or 2°C but remain important. This includes systems identified in WGII AR5 (AR5 – Section 5.4 of Wong et al., 2014)804, such as beaches, barriers, sand dunes, rocky coasts, aquifers, lagoons and coastal ecosystems (for the last system, see Section 3.4.4.12). For example, SLR potentially affects erosion and accretion, and therefore sediment movement, instigating shoreline change (Section 5.4.2.1 of Wong et al., 2014)805, which could affect land-based ecosystems. Global observations indicate no overall clear effect of SLR on shoreline change (Le Cozannet et al., 2014)806, as it is highly site specific (e.g., Romine et al., 2013)807. Infrastructure and geological constraints reduce shoreline movement, causing coastal squeeze. In Japan, for example, SLR is projected to cause beach losses under an RCP2.6 scenario, which will worsen under RCP8.5 (Udo and Takeda, 2017)808. Further, compound flooding (the combined risk of flooding from multiple sources) has increased significantly over the past century in major coastal cities (Wahl et al., 2015)809 and is likely to increase with further development and SLR at 1.5°C and 2°C unless adaptation is undertaken. Thus, overall SLR will have a wide range of adverse effects on coastal zones (medium confidence).
3.4.5.7
Adapting to coastal change
Adaptation to coastal change from SLR and other drivers is occurring today (high confidence) (see Cross-Chapter Box 9 in Chapter 4), including migration, ecosystem-based adaptation, raising infrastructure and defences, salt-tolerant food production, early warning systems, insurance and education (Section 5.4.2.1 of Wong et al., 2014)810. Climate change mitigation will reduce the rate of SLR this century, decreasing the need for extensive and, in places, immediate adaptation. Adaptation will reduce impacts in human settings (high confidence) (Hinkel et al., 2014; Wong et al., 2014)811, although there is less certainty for natural ecosystems (Sections 4.3.2 and 4.3.3.3). While some ecosystems (e.g., mangroves) may be able to move shoreward as sea levels increase, coastal development (e.g., coastal building, seawalls and agriculture) often interrupt these transitions (Saunders et al., 2014)812. Options for responding to these challenges include reducing the impact of other stresses such as those arising from tourism, fishing, coastal development and unsustainable aquaculture/agriculture. In some cases, restoration of coastal habitats and ecosystems can be a cost-effective way of responding to changes arising from increasing levels of exposure from rising sea levels, changes in storm conditions, coastal inundation and salinization (Arkema et al., 2013; Temmerman et al., 2013; Ferrario et al., 2014; Hinkel et al., 2014; Spalding et al., 2014; Elliff and Silva, 2017)813.
Since AR5, planned and autonomous adaptation and forward planning have become more widespread (Araos et al., 2016; Nicholls et al., 2018)814, but continued efforts are required as many localities are in the early stages of adapting or are not adapting at all (Cross-Chapter Box 9 in Chapter 4; Araos et al., 2016)815. This is region and sub-sector specific, and also linked to non-climatic factors (Ford et al., 2015; Araos et al., 2016; Lesnikowski et al., 2016)816. Adaptation pathways (e.g., Ranger et al., 2013; Barnett et al., 2014; Rosenzweig and Solecki, 2014; Buurman and Babovic, 2016)817 assist long-term planning but are not widespread practices despite knowledge of long-term risks (Section 4.2.2). Furthermore, human retreat and migration are increasingly being considered as an adaptation response (Hauer et al., 2016; Geisler and Currens, 2017)818, with a growing emphasis on green adaptation. There are few studies on the adaptation limits to SLR where transformation change may be required (AR5 Section 5.5 of Wong et al., 2014819; Nicholls et al., 2015820). Sea level rise poses a long-term threat (Section 3.3.9), and adaptation will remain essential at the centennial scale under 1.5°C and 2°C of warming (high confidence).
Table 3.3
Land and people exposed to sea level rise (SLR), assuming no protection at all. Extracted from Brown et al. (2018a) and Goodwin et al. (2018). SSP: Shared Socio- Economic Pathway; wrt: with respect to; *: Population held constant at 2100 level.
| Climate scenario | Impact factor, assuming there is no adaptation or protection at all (50th, [5th-95th percentiles]) | Year | |||
|---|---|---|---|---|---|
| 2050 | 2100 | 2200 | 2300 | ||
| 1.5°C | Temperature rise wrt 1850–1900 (°C) | 1.71 (1.44–2.16) | 1.60 (1.26–2.33) | 1.41 (1.15–2.10) | 1.32 (1.12–1.81) |
| SLR (m) wrt 1986–2005 | 0.20 (0.14–0.29) | 0.40 (0.26–0.62) | 0.73 (0.47–1.25) | 1.00 (0.59–1.55) | |
| Land exposed (x103 km2) | 574 [558–597] | 620 [575–669] | 666 [595–772] | 702 [666–853] | |
| People exposed, SSP1–5 (millions) | 127.9–139.0 [123.4–134.0, | 102.7–153.5 [94.8–140.7, | — | 133.8–207.1 [112.3–169.6, 165.2–263.4]* | |
| 2°C | Temperature rise wrt 1850–1900 (° C) | 1.76 (1.51–2.16) | 2.03 (1.72–2.64) | 1.90 (1.66–2.57) | 1.80 (1.60–2.20) |
| SLR (m) wrt 1986-2005 | 0.20 (0.14–0.29) | 0.46 (0.30–0.69) | 0.90 (0.58–1.50) | 1.26 (0.74–1.90) | |
| Land exposed (x103 km2) | 575 [558–598] | 637 [585–686] | 705 [618–827] | 767 [642–937] | |
| People exposed, SSP1–5 (millions) | 128.1–139.2 [123.6–134.2, | 105.5–158.1 [97.0–144.1, | — | 148.3–233.0 [120.3–183.4, 186.4–301.8]* | |
3.4.6
Food, Nutrition Security and Food Production Systems (Including Fisheries and Aquaculture)
3.4.6.1
Crop production
Quantifying the observed impacts of climate change on food security and food production systems requires assumptions about the many non-climate variables that interact with climate change variables. Implementing specific strategies can partly or greatly alleviate the climate change impacts on these systems (Wei et al., 2017)852, whilst the degree of compensation is mainly dependent on the geographical area and crop type (Rose et al., 2016)853. Despite these uncertainties, recent studies confirm that observed climate change has already affected crop suitability in many areas, resulting in changes in the production levels of the main agricultural crops. These impacts are evident in many areas of the world, ranging from Asia (C. Chen et al., 2014; Sun et al., 2015; He and Zhou, 2016)854 to America (Cho and McCarl, 2017)855 and Europe (Ramirez-Cabral et al., 2016)856, and they particularly affect the typical local crops cultivated in specific climate conditions (e.g., Mediterranean crops like olive and grapevine, Moriondo et al., 2013a, b)857.
Temperature and precipitation trends have reduced crop production and yields, with the most negative impacts being on wheat and maize (Lobell et al., 2011)858, whilst the effects on rice and soybean yields are less clear and may be positive or negative (Kim et al., 2013; van Oort and Zwart, 2018)859. Warming has resulted in positive effects on crop yield in some high-latitude areas (Jaggard et al., 2007; Supit et al., 2010; Gregory and Marshall, 2012; C. Chen et al., 2014; Sun et al., 2015; He and Zhou, 2016; Daliakopoulos et al., 2017)860, and may make it possible to have more than one harvest per year (B. Chen et al., 2014; Sun et al., 2015)861. Climate variability has been found to explain more than 60% of the of maize, rice, wheat and soybean yield variations in the main global breadbaskets areas (Ray et al., 2015)862, with the percentage varying according to crop type and scale (Moore and Lobell, 2015; Kent et al., 2017)863. Climate trends also explain changes in the length of the growing season, with greater modifications found in the northern high-latitude areas (Qian et al., 2010; Mueller et al., 2015)864.
The rise in tropospheric ozone has already reduced yields of wheat, rice, maize and soybean by 3–16% globally (Van Dingenen et al., 2009)865. In some studies, increases in atmospheric CO2 concentrations were found to increase yields by enhancing radiation and water use efficiencies (Elliott et al., 2014; Durand et al., 2018)866. In open-top chamber experiments with a combination of elevated CO2 and 1.5°C of warming, maize and potato yields were observed to increase by 45.7% and 11%, respectively (Singh et al., 2013; Abebe et al., 2016)867. However, observations of trends in actual crop yields indicate that reductions as a result of climate change remain more common than crop yield increases, despite increased atmospheric CO2 concentrations (Porter et al., 2014)868. For instance, McGrath and Lobell (2013)869 indicated that production stimulation at increased atmospheric CO2 concentrations was mostly driven by differences in climate and crop species, whilst yield variability due to elevated CO2 was only about 50–70% of the variability due to climate. Importantly, the faster growth rates induced by elevated CO2 have been found to coincide with lower protein content in several important C3 cereal grains (Myers et al., 2014)870, although this may not always be the case for C4 grains, such as sorghum, under drought conditions (De Souza et al., 2015)871. Elevated CO2 concentrations of 568–590 ppm (a range that corresponds approximately to RCP6 in the 2080s and hence a warming of 2.3°C–3.3°C (van Vuuren et al., 2011a872, AR5 WGI Table 12.2 ) alone reduced the protein, micronutrient and B vitamin content of the 18 rice cultivars grown most widely in Southeast Asia, where it is a staple food source, by an amount sufficient to create nutrition-related health risks for 600 million people (Zhu et al., 2018)873. Overall, the effects of increased CO2 concentrations alone during the 21st century are therefore expected to have a negative impact on global food security (medium confidence).
Crop yields in the future will also be affected by projected changes in temperature and precipitation. Studies of major cereals showed that maize and wheat yields begin to decline with 1°C–2°C of local warming and under nitrogen stress conditions at low latitudes (high confidence) (Porter et al., 2014; Rosenzweig et al., 2014)874. A few studies since AR5 have focused on the impacts on cropping systems for scenarios where the global mean temperature increase is within 1.5°C. Schleussner et al. (2016b)875 projected that constraining warming to 1.5°C rather than 2°C would avoid significant risks of declining tropical crop yield in West Africa, Southeast Asia, and Central and South America. Ricke et al. (2016)876 highlighted that cropland stability declines rapidly between 1°C and 3°C of warming, whilst Bassu et al. (2014)877 found that an increase in air temperature negatively influences the modelled maize yield response by –0.5 t ha−1°C–1 and Challinor et al. (2014)878 reported similar effect for tropical regions. Niang et al. (2014)879 projected significantly lower risks to crop productivity in Africa at 1.5°C compared to 2°C of warming. Lana et al. (2017)880 indicated that the impact of temperature increases on crop failure of maize hybrids would be much greater as temperatures increase by 2°C compared to 1.5°C (high confidence). J. Huang et al. (2017)881 found that limiting warming to 1.5°C compared to 2°C would reduce maize yield losses over drylands. Although Rosenzweig et al. (2017, 2018)882 did not find a clear distinction between yield declines or increases in some breadbasket regions between the two temperature levels, they generally did find projections of decreasing yields in breadbasket regions when the effects of CO2 fertilization were excluded. Iizumi et al. (2017)883 found smaller reductions in maize and soybean yields at 1.5°C than at 2°C of projected warming, higher rice production at 2°C than at 1.5°C, and no clear differences for wheat on a global mean basis. These results are largely consistent with those of other studies (Faye et al., 2018; Ruane et al., 2018)884. In the western Sahel and southern Africa, moving from 1.5°C to 2°C of warming has been projected to result in a further reduction of the suitability of maize, sorghum and cocoa cropping areas and yield losses, especially for C3 crops, with rainfall change only partially compensating these impacts (Läderach et al., 2013; World Bank, 2013; Sultan and Gaetani, 2016)885.
A significant reduction has been projected for the global production of wheat (by 6.0 ± 2.9%), rice (by 3.2 ± 3.7%), maize (by 7.4 ± 4.5%), and soybean, (by 3.1%) for each degree Celsius increase in global mean temperature (Asseng et al., 2015; C. Zhao et al., 2017)886. Similarly, Li et al. (2017)887 indicated a significant reduction in rice yields for each degree Celsius increase, by about 10.3%, in the greater Mekong subregion (medium confidence; Cross-Chapter Box 6: Food Security in this chapter). Large rice and maize yield losses are to be expected in China, owing to climate extremes (medium confidence) (Wei et al., 2017; Zhang et al., 2017)888.
While not often considered, crop production is also negatively affected by the increase in both direct and indirect climate extremes. Direct extremes include changes in rainfall extremes (Rosenzweig et al., 2014)889, increases in hot nights (Welch et al., 2010; Okada et al., 2011)890, extremely high daytime temperatures (Schlenker and Roberts, 2009; Jiao et al., 2016; Lesk et al., 2016)891, drought (Jiao et al., 2016; Lesk et al., 2016)892, heat stress (Deryng et al., 2014893, Betts et al., 2018)894, flooding (Betts et al., 2018; Byers et al., 2018)895, and chilling damage (Jiao et al., 2016)896, while indirect effects include the spread of pests and diseases (Jiao et al., 2014; van Bruggen et al., 2015)897, which can also have detrimental effects on cropping systems.
Taken together, the findings of studies on the effects of changes in temperature, precipitation, CO2 concentration and extreme weather events indicate that a global warming of 2°C is projected to result in a greater reduction in global crop yields and global nutrition than global warming of 1.5°C (high confidence; Section 3.6).
3.4.6.2
Livestock production
Studies of climate change impacts on livestock production are few in number. Climate change is expected to directly affect yield quantity and quality (Notenbaert et al., 2017)898, as well as indirectly impacting the livestock sector through feed quality changes and spread of pests and diseases (Kipling et al., 2016)899 (high confidence). Increased warming and its extremes are expected to cause changes in physiological processes in livestock (i.e., thermal distress, sweating and high respiratory rates) (Mortola and Frappell, 2000)900 and to have detrimental effects on animal feeding, growth rates (André et al., 2011; Renaudeau et al., 2011; Collier and Gebremedhin, 2015)901 and reproduction (De Rensis et al., 2015)902. Wall et al. (2010)903 observed reduced milk yields and increased cow mortality as the result of heat stress on dairy cow production over some UK regions.
Further, a reduction in water supply might increase cattle water demand (Masike and Urich, 2008)904. Generally, heat stress can be responsible for domestic animal mortality increase and economic losses (Vitali et al., 2009)905, affecting a wide range of reproductive parameters (e.g., embryonic development and reproductive efficiency in pigs, Barati et al., 2008906; ovarian follicle development and ovulation in horses, Mortensen et al., 2009)907. Much attention has also been dedicated to ruminant diseases (e.g., liver fluke, Fox et al., 2011908; blue-tongue virus, Guis et al., 2012909; foot-and-mouth disease (FMD), Brito et al. (2017)910; and zoonotic diseases, Njeru et al., 2016; Simulundu et al., 2017)911.
Climate change impacts on livestock are expected to increase. In temperate climates, warming is expected to lengthen the forage growing season but decrease forage quality, with important variations due to rainfall changes (Craine et al., 2010; Hatfield et al., 2011; Izaurralde et al., 2011)912. Similarly, a decrease in forage quality is expected for both natural grassland in France (Graux et al., 2013)913 and sown pastures in Australia (Perring et al., 2010)914. Water resource availability for livestock is expected to decrease owing to increased runoff and reduced groundwater resources. Increased temperature will likely induce changes in river discharge and the amount of water in basins, leading human and livestock populations to experience water stress, especially in the driest areas (i.e., sub-Saharan Africa and South Asia) (medium confidence) (Palmer et al., 2008)915. Elevated temperatures are also expected to increase methane production (Knapp et al., 2014; M.A. Lee et al., 2017)916. Globally, a decline in livestock of 7–10% is expected at about 2°C of warming, with associated economic losses between $9.7 and $12.6 billion (Boone et al., 2018)917.
3.4.6.3
Fisheries and aquaculture production
Global fisheries and aquaculture contribute a total of 88.6 and 59.8 million tonnes of fish and other products annually (FAO, 2016)918, and play important roles in the food security of a large number of countries (McClanahan et al., 2015; Pauly and Charles, 2015)919 as well as being essential for meeting the protein demand of a growing global population (Cinner et al., 2012, 2016; FAO, 2016; Pendleton et al., 2016)920. A steady increase in the risks associated with bivalve fisheries and aquaculture at mid-latitudes is coincident with increases in temperature, ocean acidification, introduced species, disease and other drivers (Lacoue-Labarthe et al., 2016; Clements and Chopin, 2017; Clements et al., 2017; Parker et al., 2017)921. Sea level rise and storm intensification pose a risk to hatcheries and other infrastructure (Callaway et al., 2012; Weatherdon et al., 2016)922, whilst others risks are associated with the invasion of parasites and pathogens (Asplund et al., 2014; Castillo et al., 2017)923. Specific human strategies have reduced these risks, which are expected to be moderate under RCP2.6 and very high under RCP8.5 (Gattuso et al., 2015)924. The risks related to climate change for fin fish (Section 3.4.4) are producing a number of challenges for small-scale fisheries (e.g., Kittinger, 2013; Pauly and Charles, 2015; Bell et al., 2018)925. Recent literature from 2015 to 2017 has described growing threats from rapid shifts in the biogeography of key species (Poloczanska et al., 2013, 2016; Burrows et al., 2014; García Molinos et al., 2015)926 and the ongoing rapid degradation of key ecosystems such as coral reefs, seagrass and mangroves (Section 3.4.4, Box 3.4). The acceleration of these changes, coupled with non-climate stresses (e.g., pollution, overfishing and unsustainable coastal development), are driving many small-scale fisheries well below the sustainable harvesting levels required to maintain these resources as a source of food (McClanahan et al., 2009, 2015; Cheung et al., 2010; Pendleton et al., 2016)927. As a result, future scenarios surrounding climate change and global population growth increasingly project shortages of fish protein for many regions, such as the Pacific Ocean (Bell et al., 2013, 2018)928 and Indian Ocean (McClanahan et al., 2015)929. Mitigation of these risks involves marine spatial planning, fisheries repair, sustainable aquaculture, and the development of alternative livelihoods (Kittinger, 2013; McClanahan et al., 2015; Song and Chuenpagdee, 2015; Weatherdon et al., 2016)930. Other threats concern the increasing incidence of alien species and diseases (Kittinger et al., 2013; Weatherdon et al., 2016)931.
Risks of impacts related to climate change on low-latitude small-scale fin fisheries are moderate today but are expected to reach very high levels by 1.1°C of global warming. Projections for mid-to high-latitude fisheries include increases in fishery productivity in some cases (Cheung et al., 2013; Hollowed et al., 2013; Lam et al., 2014; FAO, 2016)932. These projections are associated with the biogeographical shift of species towards higher latitudes (Fossheim et al., 2015)933, which brings benefits as well as challenges (e.g., increased production yet a greater risk of disease and invasive species; low confidence). Factors underpinning the expansion of fisheries production to high-latitude locations include warming, increased light levels and mixing due to retreating sea ice (Cheung et al., 2009)934, which result in substantial increases in primary productivity and fish harvesting in the North Pacific and North Atlantic (Hollowed and Sundby, 2014)935.
Present-day risks for mid-latitude bivalve fisheries and aquaculture become undetectible up to 1.1°C of global warming, moderate at 1.3°C, and moderate to high up to 1.9°C (Figure 3.18). For instance, Cheung et al. (2016a)936, simulating the loss in fishery productivity at 1.5°C, 2°C and 3.5°C above the pre-industrial period, found that the potential global catch for marine fisheries will likely decrease by more than three million metric tonnes for each degree of warming. Low-latitude fin-fish fisheries have higher risks of impacts, with risks being moderate under present-day conditions and becoming high above 0.9°C and very high at 2°C of global warming. High-latitude fisheries are undergoing major transformations, and while production is increasing, present-day risk is moderate and is projected to remain moderate at 1.5°C and 2°C (Figure 3.18).
Adaptation measures can be applied to shellfish, large pelagic fish resources and biodiversity, and they include options such as protecting reproductive stages and brood stocks from periods of high ocean acidification (OA), stock selection for high tolerance to OA (high confidence) (Ekstrom et al., 2015; Rodrigues et al., 2015; Handisyde et al., 2016; Lee, 2016; Weatherdon et al., 2016; Clements and Chopin, 2017)937, redistribution of highly migratory resources (e.g., Pacific tuna) (high confidence), governance instruments such as international fisheries agreements (Lehodey et al., 2015; Matear et al., 2015)938, protection and regeneration of reef habitats, reduction of coral reef stresses, and development of alternative livelihoods (e.g., aquaculture; Bell et al., 2013, 2018)939.
3.4.7
Human Health
Climate change adversely affects human health by increasing exposure and vulnerability to climate-related stresses, and decreasing the capacity of health systems to manage changes in the magnitude and pattern of climate-sensitive health outcomes (Cramer et al., 2014; Hales et al., 2014)992. Changing weather patterns are associated with shifts in the geographic range, seasonality and transmission intensity of selected climate-sensitive infectious diseases (e.g., Semenza and Menne, 2009)993, and increasing morbidity and mortality are associated with extreme weather and climate events (e.g., K.R. Smith et al., 2014)994. Health detection and attribution studies conducted since AR5 have provided evidence, using multistep attribution, that climate change is negatively affecting adverse health outcomes associated with heatwaves, Lyme disease in Canada, and Vibrio emergence in northern Europe (Mitchell, 2016; Mitchell et al., 2016; Ebi et al., 2017)995. The IPCC AR5 concluded there is high to very high confidence that climate change will lead to greater risks of injuries, disease and death, owing to more intense heatwaves and fires, increased risks of undernutrition, and consequences of reduced labour productivity in vulnerable populations (K.R. Smith et al., 2014)996.
3.4.7.1
Projected risk at 1.5°C and 2°C of global warming
The projected risks to human health of warming of 1.5°C and 2°C, based on studies of temperature-related morbidity and mortality, air quality and vector borne diseases assessed in and since AR5, are summarized in Supplementary Material 3.SM, Tables 3.SM.8, 3.SM.9 and 3.SM.10 (based on Ebi et al., 2018)997. Other climate-sensitive health outcomes, such as diarrheal diseases, mental health issues and the full range of sources of poor air quality, were not considered because of the lack of projections of how risks could change at 1.5°C and 2°C. Few projections were available for specific temperatures above pre-industrial levels; Supplementary Material 3.SM, Table 3.SM.7 provides the conversions used to translate risks projected for particular time slices to those for specific temperature changes (Ebi et al., 2018)998.
Temperature-related morbidity and mortality: The magnitude of projected heat-related morbidity and mortality is greater at 2°C than at 1.5°C of global warming (very high confidence)(Doyon et al., 2008; Jackson et al., 2010; Hanna et al., 2011; Huang et al., 2012; Petkova et al., 2013; Hajat et al., 2014; Hales et al., 2014; Honda et al., 2014; Vardoulakis et al., 2014; Garland et al., 2015; Huynen and Martens, 2015; Li et al., 2015; Schwartz et al., 2015; L. Wang et al., 2015; Guo et al., 2016; T. Li et al., 2016; Chung et al., 2017; Kendrovski et al., 2017; Mishra et al., 2017; Arnell et al., 2018; Mitchell et al., 2018b)999. The number of people exposed to heat events is projected to be greater at 2°C than at 1.5°C (Russo et al., 2016; Mora et al., 2017; Byers et al., 2018; Harrington and Otto, 2018; King et al., 2018)1000. The extent to which morbidity and mortality are projected to increase varies by region, presumably because of differences in acclimatization, population vulnerability, the built environment, access to air conditioning and other factors (Russo et al., 2016; Mora et al., 2017; Byers et al., 2018; Harrington and Otto, 2018; King et al., 2018)1001. Populations at highest risk include older adults, children, women, those with chronic diseases, and people taking certain medications (very high confidence). Assuming adaptation takes place reduces the projected magnitude of risks (Hales et al., 2014; Huynen and Martens, 2015; T. Li et al., 2016)1002.
In some regions, cold-related mortality is projected to decrease with increasing temperatures, although increases in heat-related mortality generally are projected to outweigh any reductions in cold-related mortality with warmer winters, with the heat-related risks increasing with greater degrees of warming (Huang et al., 2012; Hajat et al., 2014; Vardoulakis et al., 2014; Gasparrini et al., 2015; Huynen and Martens, 2015; Schwartz et al., 2015)1003.
Occupational health: Higher ambient temperatures and humidity levels place additional stress on individuals engaging in physical activity. Safe work activity and worker productivity during the hottest months of the year would be increasingly compromised with additional climate change (medium confidence) (Dunne et al., 2013; Kjellstrom et al., 2013, 2018; Sheffield et al., 2013; Habibi Mohraz et al., 2016)1004. Patterns of change may be complex; for example, at 1.5°C, there could be about a 20% reduction in areas experiencing severe heat stress in East Asia, compared to significant increases in low latitudes at 2°C (Lee and Min, 2018)1005. The costs of preventing workplace heat-related illnesses through worker breaks suggest that the difference in economic loss between 1.5°C and 2°C could be approximately 0.3% of global gross domestic product (GDP) in 2100 (Takakura et al., 2017)1006. In China, taking into account population growth and employment structure, high temperature subsidies for employees working on extremely hot days are projected to increase from 38.6 billion yuan yr–1 in 1979–2005 to 250 billion yuan yr–1 in the 2030s (about 1.5°C) (Zhao et al., 2016)1007.
Air quality: Because ozone formation is temperature dependent, projections focusing only on temperature increase generally conclude that ozone-related mortality will increase with additional warming, with the risks higher at 2°C than at 1.5°C (high confidence) (Supplementary Material 3.SM, Table 3.SM.9; Heal et al., 2013; Tainio et al., 2013; Likhvar et al., 2015; Silva et al., 2016; Dionisio et al., 2017; J.Y. Lee et al., 2017)1008. Reductions in precursor emissions would reduce future ozone concentrations and associated mortality. Mortality associated with exposure to particulate matter could increase or decrease in the future, depending on climate projections and emissions assumptions (Supplementary Material 3.SM, Table 3.SM.8; Tainio et al., 2013; Likhvar et al., 2015; Silva et al., 2016)1009.
Malaria: Recent projections of the potential impacts of climate change on malaria globally and for Asia, Africa, and South America (Supplementary Material 3.SM, Table 3.SM.10) confirm that weather and climate are among the drivers of the geographic range, intensity of transmission, and seasonality of malaria, and that the relationships are not necessarily linear, resulting in complex patterns of changes in risk with additional warming (very high confidence) (Ren et al., 2016; Song et al., 2016; Semakula et al., 2017)1010. Projections suggest that the burden of malaria could increase with climate change because of a greater geographic range of the Anopheles vector, longer season, and/or increase in the number of people at risk, with larger burdens at higher levels of warming, but with regionally variable patterns (medium to high confidence). Vector populations are projected to shift with climate change, with expansions and reductions depending on the degree of local warming, the ecology of the mosquito vector, and other factors (Ren et al., 2016)1011.
Aedes (mosquito vector for dengue fever, chikungunya, yellow fever and Zika virus): Projections of the geographic distribution of Aedes aegypti and Ae. albopictus (principal vectors) or of the prevalence of dengue fever generally conclude that there will be an increase in the number of mosquitos and a larger geographic range at 2°C than at 1.5°C, and they suggest that more individuals will be at risk of dengue fever, with regional differences (high confidence) (Fischer et al., 2011, 2013; Colón-González et al., 2013, 2018; Bouzid et al., 2014; Ogden et al., 2014a; Mweya et al., 2016)1012. The risks increase with greater warming. Projections suggest that climate change is projected to expand the geographic range of chikungunya, with greater expansions occurring at higher degrees of warming (Tjaden et al., 2017)1013.
Other vector-borne diseases: Increased warming in North America and Europe could result in geographic expansions of regions (latitudinally and altitudinally) climatically suitable for West Nile virus transmission, particularly along the current edges of its transmission areas, and extension of the transmission season, with the magnitude and pattern of changes varying by location and level of warming (Semenza et al., 2016)1014. Most projections conclude that climate change could expand the geographic range and seasonality of Lyme and other tick-borne diseases in parts of North America and Europe (Ogden et al., 2014b; Levi et al., 2015)1015. The projected changes are larger with greater warming and under higher greenhouse gas emissions pathways. Projections of the impacts of climate change on leishmaniosis and Chagas disease indicate that climate change could increase or decrease future health burdens, with greater impacts occurring at higher degrees of warming (González et al., 2014; Ceccarelli and Rabinovich, 2015)1016.
In summary, warming of 2°C poses greater risks to human health than warming of 1.5°C, often with the risks varying regionally, with a few exceptions (high confidence). There is very high confidence that each additional unit of warming could increase heat-related morbidity and mortality, and that adaptation would reduce the magnitude of impacts. There is high confidence that ozone-related mortality could increase if precursor emissions remain the same, and that higher temperatures could affect the transmission of some infectious diseases, with increases and decreases projected depending on the disease (e.g., malaria, dengue fever, West Nile virus and Lyme disease), region and degree of temperature change.
3.4.8
Urban Areas
There is new literature on urban climate change and its differential impacts on and risks for infrastructure sectors – energy, water, transport and buildings – and vulnerable populations, including those living in informal settlements (UCCRN, 2018)1017. However, there is limited literature on the risks of warming of 1.5°C and 2°C in urban areas. Heat-related extreme events (Matthews et al., 2017)1018, variability in precipitation (Yu et al., 2018)1019 and sea level rise can directly affect urban areas (Section 3.4.5, Bader et al., 2018; Dawson et al., 2018)1020. Indirect risks may arise from interactions between urban and natural systems.
Future warming and urban expansion could lead to more extreme heat stress (Argüeso et al., 2015; Suzuki-Parker et al., 2015)1021. At 1.5°C of warming, twice as many megacities (such as Lagos, Nigeria and Shanghai, China) could become heat stressed, exposing more than 350 million more people to deadly heat by 2050 under midrange population growth. Without considering adaptation options, such as cooling from more reflective roofs, and overall characteristics of urban agglomerations in terms of land use, zoning and building codes (UCCRN, 2018)1022, Karachi (Pakistan) and Kolkata (India) could experience conditions equivalent to the deadly 2015 heatwaves on an annual basis under 2°C of warming (Akbari et al., 2009; Oleson et al., 2010; Matthews et al., 2017)1023. Warming of 2°C is expected to increase the risks of heatwaves in China's urban agglomerations (Yu et al., 2018)1024. Stabilizing at 1.5°C of warming instead of 2°C could decrease mortality related to extreme temperatures in key European cities, assuming no adaptation and constant vulnerability (Jacob et al., 2018; Mitchell et al., 2018a)1025. Holding temperature change to below 2°C but taking urban heat islands (UHI) into consideration, projections indicate that there could be a substantial increase in the occurrence of deadly heatwaves in cities. The urban impacts of these heatwaves are expected to be similar at 1.5°C and 2°C and substantially larger than under the present climate (Matthews et al., 2017; Yu et al., 2018)1026. Increases in the intensity of UHI could exacerbate warming of urban areas, with projections ranging from a 6% decrease to a 30% increase for a doubling of CO2 (McCarthy et al., 2010)1027. Increases in population and city size, in the context of a warmer climate, are projected to increase UHI (Georgescu et al., 2012; Argüeso et al., 2014; Conlon et al., 2016; Kusaka et al., 2016; Grossman-Clarke et al., 2017)1028.
For extreme heat events, an additional 0.5°C of warming implies a shift from the upper bounds of observed natural variability to a new global climate regime (Schleussner et al., 2016b)1029, with distinct implications for the urban poor (Revi et al., 2014; Jean-Baptiste et al., 2018; UCCRN, 2018)1030. Adverse impacts of extreme events could arise in tropical coastal areas of Africa, South America and Southeast Asia (Schleussner et al., 2016b)1031. These urban coastal areas in the tropics are particularly at risk given their large informal settlements and other vulnerable urban populations, as well as vulnerable assets, including businesses and critical urban infrastructure (energy, water, transport and buildings) (McGranahan et al., 2007; Hallegatte et al., 2013; Revi et al., 2014; UCCRN, 2018)1032. Mediterranean water stress is projected to increase from 9% at 1.5°C to 17% at 2°C compared to values in 1986–2005 period. Regional dry spells are projected to expand from 7% at 1.5°C to 11% at 2°C for the same reference period. Sea level rise is expected to be lower at 1.5°C than 2°C, lowering risks for coastal metropolitan agglomerations (Schleussner et al., 2016b)1033.
Climate models are better at projecting implications of greenhouse gas forcing on physical systems than at assessing differential risks associated with achieving a specific temperature target (James et al., 2017)1034. These challenges in managing risks are amplified when combined with the scale of urban areas and assumptions about socio-economic pathways (Krey et al., 2012; Kamei et al., 2016; Yu et al., 2016; Jiang and Neill, 2017)1035.
In summary, in the absence of adaptation, in most cases, warming of 2°C poses greater risks to urban areas than warming of 1.5°C, depending on the vulnerability of the location (coastal or non-coastal) (high confidence), businesses, infrastructure sectors (energy, water and transport), levels of poverty, and the mix of formal and informal settlements.
3.4.9
Key Economic Sectors and Services
Climate change could affect tourism, energy systems and transportation through direct impacts on operations (e.g., sea level rise) and through impacts on supply and demand, with the risks varying significantly with geographic region, season and time. Projected risks also depend on assumptions with respect to population growth, the rate and pattern of urbanization, and investments in infrastructure. Table 3.SM.11 in Supplementary Material 3.SM summarizes the cited publications.
3.4.9.1
Tourism
The implications of climate change for the global tourism sector are far-reaching and are impacting sector investments, destination assets (environment and cultural), operational and transportation costs, and tourist demand patterns (Scott et al., 2016a; Scott and Gössling, 2018)1036. Since AR5, observed impacts on tourism markets and destination communities continue to be not well analysed, despite the many analogue conditions (e.g., heatwaves, major hurricanes, wild fires, reduced snow pack, coastal erosion and coral reef bleaching) that are anticipated to occur more frequently with climate change. There is some evidence that observed impacts on tourism assets, such as environmental and cultural heritage, are leading to the development of 'last chance to see' tourism markets, where travellers visit destinations before they are substantially degraded by climate change impacts or to view the impacts of climate change on landscapes (Lemelin et al., 2012; Stewart et al., 2016; Piggott-McKellar and McNamara, 2017)1037.
There is limited research on the differential risks of a 1.5° versus 2°C temperature increase and resultant environmental and socio-economic impacts in the tourism sector. The translation of these changes in climate resources for tourism into projections of tourism demand remains geographically limited to Europe. Based on analyses of tourist comfort, summer and spring /autumn tourism in much of western Europe may be favoured by 1.5°C of warming, but with negative effects projected for Spain and Cyprus (decreases of 8% and 2%, respectively, in overnight stays) and most coastal regions of the Mediterranean (Jacob et al., 2018)1038. Similar geographic patterns of potential tourism gains (central and northern Europe) and reduced summer favourability (Mediterranean countries) are projected under 2°C (Grillakis et al., 2016)1039. Considering potential changes in natural snow only, winter overnight stays at 1.5°C are projected to decline by 1–2% in Austria, Italy and Slovakia, with an additional 1.9 million overnight stays lost under 2°C of warming (Jacob et al., 2018)1040. Using an econometric analysis of the relationship between regional tourism demand and climate conditions, Ciscar et al. (2014)1041 projected that a 2°C warmer world would reduce European tourism by 5% (€15 billion yr–1), with losses of up to 11% (€6 billion yr–1) for southern Europe and a potential gain of €0.5 billion yr–1 in the UK.
There is growing evidence that the magnitude of projected impacts is temperature dependent and that sector risks could be much greater with higher temperature increases and resultant environmental and socio-economic impacts (Markham et al., 2016; Scott et al., 2016a; Jones, 2017; Steiger et al., 2017)1042. Studies from 27 countries consistently project substantially decreased reliability of ski areas that are dependent on natural snow, increased snowmaking requirements and investment in snowmaking systems, shortened and more variable ski seasons, a contraction in the number of operating ski areas, altered competitiveness among and within regional ski markets, and subsequent impacts on employment and the value of vacation properties (Steiger et al., 2017)1043. Studies that omit snowmaking do not reflect the operating realities of most ski areas and overestimate impacts at 1.5°C–2°C. In all regional markets, the extent and timing of these impacts depend on the magnitude of climate change and the types of adaptive responses by the ski industry, skiers and destination communities. The decline in the number of former Olympic Winter Games host locations that could remain climatically reliable for future Olympic and Paralympic Winter Games has been projected to be much greater under scenarios warmer than 2°C (Scott et al., 2015; Jacob et al., 2018)1044.
The tourism sector is also affected by climate-induced changes in environmental assets critical for tourism, including biodiversity, beaches, glaciers and other features important for environmental and cultural heritage. Limited analyses of projected risks associated with 1.5°C versus 2°C are available (Section 3.4.4.12). A global analysis of sea level rise (SLR) risk to 720 UNESCO Cultural World Heritage sites projected that about 47 sites might be affected under 1°C of warming, with this number increasing to 110 and 136 sites under 2°C and 3°C, respectively (Marzeion and Levermann, 2014)1045. Similar risks to vast worldwide coastal tourism infrastructure and beach assets remain unquantified for most major tourism destinations and small island developing states (SIDS) that economically depend on coastal tourism. One exception is the projection that an eventual 1 m SLR could partially or fully inundate 29% of 900 coastal resorts in 19 Caribbean countries, with a substantially higher proportion (49–60%) vulnerable to associated coastal erosion (Scott and Verkoeyen, 2017)1046.
A major barrier to understanding the risks of climate change for tourism, from the destination community scale to the global scale, has been the lack of integrated sectoral assessments that analyse the full range of potential compounding impacts and their interactions with other major drivers of tourism (Rosselló-Nadal, 2014; Scott et al., 2016b)1047. When applied to 181 countries, a global vulnerability index including 27 indicators found that countries with the lowest risk are located in western and northern Europe, central Asia, Canada and New Zealand, while the highest sector risks are projected for Africa, the Middle East, South Asia and SIDS in the Caribbean, Indian and Pacific Oceans (Scott and Gössling, 2018)1048. Countries with the highest risks and where tourism represents a significant proportion of the national economy (i.e., more than 15% of GDP) include many SIDS and least developed countries. Sectoral climate change risk also aligns strongly with regions where tourism growth is projected to be the strongest over the coming decades, including sub-Saharan Africa and South Asia, pointing to an important potential barrier to tourism development. The transnational implications of these impacts on the highly interconnected global tourism sector and the contribution of tourism to achieving the 2030 sustainable development goals (SDGs) remain important uncertainties.
In summary, climate is an important factor influencing the geography and seasonality of tourism demand and spending globally (very high confidence). Increasing temperatures are projected to directly impact climate-dependent tourism markets, including sun, beach and snow sports tourism, with lesser risks for other tourism markets that are less climate sensitive (high confidence). The degradation or loss of beach and coral reef assets is expected to increase risks for coastal tourism, particularly in subtropical and tropical regions (high confidence).
3.4.9.2
Energy systems
Climate change is projected to lead to an increased demand for air conditioning in most tropical and subtropical regions (Arent et al., 2014; Hong and Kim, 2015)1049 (high confidence). Increasing temperatures will decrease the thermal efficiency of fossil, nuclear, biomass and solar power generation technologies, as well as buildings and other infrastructure (Arent et al., 2014)1050. For example, in Ethiopia, capital expenditures through 2050 might either decrease by approximately 3% under extreme wet scenarios or increase by up to 4% under a severe dry scenario (Block and Strzepek, 2012)1051.
Impacts on energy systems can affect gross domestic product (GDP). The economic damage in the United States from climate change is estimated to be, on average, roughly 1.2% cost of GDP per year per 1°C increase under RCP8.5 (Hsiang et al., 2017)1052. Projections of GDP indicate that negative impacts of energy demand associated with space heating and cooling in 2100 will be greatest (median: –0.94% change in GDP) under 4°C (RCP8.5) compared with under 1.5°C (median: –0.05%), depending on the socio-economic conditions (Park et al., 2018)1053. Additionally, projected total energy demands for heating and cooling at the global scale do not change much with increases in global mean surface temperature (GMST) of up to 2°C. A high degree of variability is projected between regions (Arnell et al., 2018)1054.
Evidence for the impact of climate change on energy systems since AR5 is limited. Globally, gross hydropower potential is projected to increase (by 2.4% under RCP2.6 and by 6.3% under RCP8.5 for the 2080s), with the most growth expected in Central Africa, Asia, India and northern high latitudes (van Vliet et al., 2016)1055. Byers et al. (2018)1056 found that energy impacts at 2°C increase, including more cooling degree days, especially in tropical regions, as well as increased hydro-climatic risk to thermal and hydropower plants predominantly in Europe, North America, South and Southeast Asia and southeast Brazil. Donk et al. (2018)1057 assessed future climate impacts on hydropower in Suriname and projected a decrease of approximately 40% in power capacity for a global temperature increase in the range of 1.5°C. At minimum and maximum increases in global mean temperature of 1.35°C and 2°C, the overall stream flow in Florida, USA is projected to increase by an average of 21%, with pronounced seasonal variations, resulting in increases in power generation in winter (+72%) and autumn (+15%) and decreases in summer (–14%; Chilkoti et al., 2017)1058. Greater changes are projected at higher temperature increases. In a reference scenario with global mean temperatures rising by 1.7°C from 2005 to 2050, U.S. electricity demand in 2050 was 1.6–6.5% higher than in a control scenario with constant temperatures (McFarland et al., 2015)1059. Decreased electricity generation of –15% is projected for Brazil starting in 2040, with values expected to decline to –28% later in the century (de Queiroz et al., 2016)1060. In large parts of Europe, electricity demand is projected to decrease, mainly owing to reduced heating demand (Jacob et al., 2018)1061.
In Europe, no major differences in large-scale wind energy resources or in inter-or intra-annual variability are projected for 2016–2035 under RCP8.5 and RCP4.5 (Carvalho et al., 2017)1062. However, in 2046–2100, wind energy density is projected to decrease in eastern Europe (–30%) and increase in Baltic regions (+30%). Intra-annual variability is expected to increase in northern Europe and decrease in southern Europe. Under RCP4.5 and RCP8.5, the annual energy yield of European wind farms as a whole, as projected to be installed by 2050, will remain stable (±5 yield for all climate models). However, wind farm yields are projected to undergo changes of up to 15% in magnitude at country and local scales and of 5% at the regional scale (Tobin et al., 2015, 2016)1063. Hosking et al. (2018)1064 assessed wind power generation over Europe for 1.5°C of warming and found the potential for wind energy to be greater than previously assumed in northern Europe. Additionally, Tobin et al. (2018)1065 assessed impacts under 1.5°C and 2°C of warming on wind, solar photovoltaic and thermoelectric power generation across Europe. These authors found that photovoltaic and wind power might be reduced by up to 10%, and hydropower and thermoelectric generation might decrease by up to 20%, with impacts being limited at 1.5°C of warming but increasing as temperature increases (Tobin et al., 2018)1066.
3.4.9.3
Transportation
Road, air, rail, shipping and pipeline transportation can be impacted directly or indirectly by weather and climate, including increases in precipitation and temperature; extreme weather events (flooding and storms); SLR; and incidence of freeze–thaw cycles (Arent et al., 2014)1067. Much of the published research on the risks of climate change for the transportation sector has been qualitative.
The limited new research since AR5 supports the notion that increases in global temperatures will impact the transportation sector. Warming is projected to result in increased numbers of days of ice-free navigation and a longer shipping season in cold regions, thus affecting shipping and reducing transportation costs (Arent et al., 2014)1068. In the North Sea Route, large-scale commercial shipping might not be possible until 2030 for bulk shipping and until 2050 for container shipping under RCP8.5. A 0.05% increase in mean temperature is projected from an increase in short-lived pollutants, as well as elevated CO2 and non-CO2 emissions, associated with additional economic growth enabled by the North Sea Route. (Yumashev et al., 2017)1069. Open water vessel transit has the potential to double by mid-century, with a two to four month longer season (Melia et al., 2016)1070.
3.4.10
Livelihoods and Poverty, and the Changing Structure of Communities
Multiple drivers and embedded social processes influence the magnitude and pattern of livelihoods and poverty, as well as the changing structure of communities related to migration, displacement and conflict (Adger et al., 2014)1071. In AR5, evidence of a climate change signal was limited, with more evidence of impacts of climate change on the places where indigenous people live and use traditional ecological knowledge (Olsson et al., 2014)1072.
3.4.10.1
Livelihoods and poverty
At approximately 1.5°C of global warming (2030), climate change is expected to be a poverty multiplier that makes poor people poorer and increases the poverty head count (Hallegatte et al., 2016; Hallegatte and Rozenberg, 2017)1073. Poor people might be heavily affected by climate change even when impacts on the rest of population are limited. Climate change alone could force more than 3 million to 16 million people into extreme poverty, mostly through impacts on agriculture and food prices (Hallegatte et al., 2016; Hallegatte and Rozenberg, 2017)1074. Unmitigated warming could reshape the global economy later in the century by reducing average global incomes and widening global income inequality (Burke et al., 2015b)1075. The most severe impacts are projected for urban areas and some rural regions in sub-Saharan Africa and Southeast Asia.
3.4.10.2
The changing structure of communities: migration, displacement and conflict
Migration: In AR5, the potential impacts of climate change on migration and displacement were identified as an emerging risk (Oppenheimer et al., 2014)1076. The social, economic and environmental factors underlying migration are complex and varied; therefore, detecting the effect of observed climate change or assessing its possible magnitude with any degree of confidence is challenging (Cramer et al., 2014)1077.
No studies have specifically explored the difference in risks between 1.5°C and 2°C of warming on human migration. The literature consistently highlights the complexity of migration decisions and the difficulties in attributing causation (e.g., Nicholson, 2014; Baldwin and Fornalé, 2017; Bettini, 2017; Constable, 2017; Islam and Shamsuddoha, 2017; Suckall et al., 2017)1078. The studies on migration that have most closely explored the probable impacts of 1.5°C and 2°C have mainly focused on the direct effects of temperature and precipitation anomalies on migration or the indirect effects of these climatic changes through changing agriculture yield and livelihood sources (Mueller et al., 2014; Piguet and Laczko, 2014; Mastrorillo et al., 2016; Sudmeier-Rieux et al., 2017)1079.
Temperature has had a positive and statistically significant effect on outmigration over recent decades in 163 countries, but only for agriculture-dependent countries (R. Cai et al., 2016)1080. A 1°C increase in average temperature in the International Migration Database of the Organisation for Economic Co-operation and Development (OECD) was associated with a 1.9% increase in bilateral migration flows from 142 sending countries and 19 receiving countries, and an additional millimetre of average annual precipitation was associated with an increase in migration by 0.5% (Backhaus et al., 2015)1081. In another study, an increase in precipitation anomalies from the long-term mean, was strongly associated with an increase in outmigration, whereas no significant effects of temperature anomalies were reported (Coniglio and Pesce, 2015)1082.
Internal and international migration have always been important for small islands (Farbotko and Lazrus, 2012; Weir et al., 2017)1083. There is rarely a single cause for migration (Constable, 2017)1084. Numerous factors are important, including work, education, quality of life, family ties, access to resources, and development (Bedarff and Jakobeit, 2017; Speelman et al., 2017; Nicholls et al., 2018)1085. Depending on the situation, changing weather, climate or environmental conditions might each be a factor in the choice to migrate (Campbell and Warrick, 2014)1086.
Displacement: At 2°C of warming, there is a potential for significant population displacement concentrated in the tropics (Hsiang and Sobel, 2016)1087. Tropical populations may have to move distances greater than 1000 km if global mean temperature rises by 2°C from 2011–2030 to the end of the century. A disproportionately rapid evacuation from the tropics could lead to a concentration of population in tropical margins and the subtropics, where population densities could increase by 300% or more (Hsiang and Sobel, 2016)1088.
Conflict: A recent study has called for caution in relating conflict to climate change, owing to sampling bias (Adams et al., 2018)1089. Insufficient consideration of the multiple drivers of conflict often leads to inconsistent associations being reported between climate change and conflict (e.g., Hsiang et al., 2013; Hsiang and Burke, 2014; Buhaug, 2015, 2016; Carleton and Hsiang, 2016; Carleton et al., 2016)1090. There also are inconsistent relationships between climate change, migration and conflict (e.g., Theisen et al., 2013; Buhaug et al., 2014; Selby, 2014; Christiansen, 2016; Brzoska and Fröhlich, 2016; Burrows and Kinney, 2016; Reyer et al., 2017c; Waha et al., 2017)1091. Across world regions and from the international to micro level, the relationship between drought and conflict is weak under most circumstances (Buhaug, 2016; von Uexkull et al., 2016)1092. However, drought significantly increases the likelihood of sustained conflict for particularly vulnerable nations or groups, owing to the dependence of their livelihood on agriculture. This is particularly relevant for groups in the least developed countries (von Uexkull et al., 2016)1093, in sub-Saharan Africa (Serdeczny et al., 2016; Almer et al., 2017)1094 and in the Middle East (Waha et al., 2017)1095. Hsiang et al. (2013)1096 reported causal evidence and convergence across studies that climate change is linked to human conflicts across all major regions of the world, and across a range of spatial and temporal scales. A 1°C increase in temperature or more extreme rainfall increases the frequency of intergroup conflicts by 14% (Hsiang et al., 2013)1097. If the world warms by 2°C–4°C by 2050, rates of human conflict could increase. Some causal associations between violent conflict and socio-political instability were reported from local to global scales and from hour to millennium time frames (Hsiang and Burke, 2014)1098. A temperature increase of one standard deviation increased the risk of interpersonal conflict by 2.4% and intergroup conflict by 11.3% (Burke et al., 2015a)1099. Armed-conflict risks and climate-related disasters are both relatively common in ethnically fractionalized countries, indicating that there is no clear signal that environmental disasters directly trigger armed conflicts (Schleussner et al., 2016a)1100.
In summary, average global temperatures that extend beyond 1.5°C are projected to increase poverty and disadvantage in many populations globally (medium confidence). By the mid-to late 21st century, climate change is projected to be a poverty multiplier that makes poor people poorer and increases poverty head count, and the association between temperature and economic productivity is not linear (high confidence). Temperature has a positive and statistically significant effect on outmigration for agriculture-dependent communities (medium confidence).
3.4.11
Interacting and Cascading Risks
The literature on compound as well as interacting and cascading risks at warming of 1.5°C and 2°C is limited. Spatially compound risks, often referred to as hotspots, involve multiple hazards from different sectors overlapping in location (Piontek et al., 2014)1101. Global exposures were assessed for 14 impact indicators, covering water, energy and land sectors, from changes including drought intensity and water stress index, cooling demand change and heatwave exposure, habitat degradation, and crop yields using an ensemble of climate and impact models (Byers et al., 2018)1102. Exposures are projected to approximately double between 1.5°C and 2°C, and the land area affected by climate risks is expected to increase as warming progresses. For populations vulnerable to poverty, the exposure to climate risks in multiple sectors could be an order of magnitude greater (8–32 fold) in the high poverty and inequality scenarios (SSP3; 765–1,220 million) compared to under sustainable socio-economic development (SSP1; 23–85 million). Asian and African regions are projected to experience 85–95% of global exposure, with 91–98% of the exposed and vulnerable population (depending on SSP/GMT combination), approximately half of which are in South Asia. Figure 3.19 shows that moderate and large multi-sector impacts are prevalent at 1.5°C where vulnerable people live, predominantly in South Asia (mostly Pakistan, India and China), but that impacts spread to sub-Saharan Africa, the Middle East and East Asia at higher levels of warming. Beyond 2°C and at higher risk thresholds, the world's poorest populations are expected to be disproportionately impacted, particularly in cases (SSP3) of great inequality in Africa and southern Asia. Table 3.4 shows the number of exposed and vulnerable people at 1.5°C and 2°C of warming, with 3°C shown for context, for selected multi-sector risks.
Figure 3.19
Multi-sector risk maps for 1.5°C (top), 2°C (middle), and locations where 2°C brings impacts not experienced at 1.5°C (2°C–1.5°C; bottom).
The maps in the left column show the full range of the multi-sector risk (MSR) score (0–9), with scores ≤5.0 shown with a transparency gradient and scores >5.0 shown with a colour gradient. Score must be >4.0 to be considered 'multi-sector'. The maps in the right column overlay the 2050 vulnerable populations (low income) under Shared Socio-Economic Pathway (SSP)2 (greyscale) with the multi-sector risk score >5.0 (colour gradient), thus indicating the concentrations of exposed and vulnerable populations to risks in multiple sectors. Source: Byers et al. (2018)1103.
Table 3.4
Number of exposed and vulnerable people at 1.5°C, 2°C, and 3°C for selected multi-sector risks under shared socioeconomic pathways (SSPs).
Source: Byers et al. (2018)1104
| SSP2 (SSP1 to SSP3 range), millions | 1.5°C | 2°C | 3°C | |||
|---|---|---|---|---|---|---|
| Indicator | Exposed | Exposed and vulnerable | Exposed | Exposed and vulnerable | Exposed | Exposed and vulnerable |
| Water stress index | 3340 (3032–3584) | 496 (103–1159) | 3658 (3080–3969) | 586 (115–1347) | 3920 (3202–4271) | 662 (146–1480) |
| Heatwave event exposure | 3960 (3546–4508) | 1187 (410–2372) | 5986 (5417–6710) | 1581 (506–3218) | 7909 (7286–8640) | 1707 (537–3575) |
| Hydroclimate risk to power production | 334 (326–337) | 30 (6–76) | 385 (374–389) | 38 (9–94) | 742 (725–739) | 72 (16–177) |
| Crop yield change | 35 (32–36) | 8 (2–20) | 362 (330–396) | 81 (24–178) | 1817 (1666–1992) | 406 (118–854) |
| Habitat degradation | 91 (92–112) | 10 (4–31) | 680 (314–706) | 102 (23–234) | 1357 (809–1501) | 248 (75–572) |
| Multi-sector exposure | ||||||
| Two indicators | 1129 (1019–1250) | 203 (42–487) | 2726 (2132–2945) | 562 (117–1220) | 3500 (3212–3864) | 707 (212–1545) |
| Three indicators | 66 (66–68) | 7 (0.9–19) | 422 (297–447) | 54 (8–138) | 1472 (1177–1574) | 237 (48–538) |
| Four indicators | 5 (0.3–5.7) | 0.3 (0–1.2) | 11 (5–14) | 0.5 (0–2) | 258 (104–280) | 33 (4–86) |
3.4.12
Summary of Projected Risks at 1.5°C and 2°C of Global Warming
The information presented in Section 3.4 is summarised below in Table 3.5, which illustrates the growing evidence of increasing risks across a broad range of natural and human systems at 1.5°C and 2°C of global warming.
Table 3.5
Summary of projected risks to natural and human systems at 1.5°C and 2°C of global warming, and of the potential to adapt to these risks
Table summarizes the chapter text and with references supporting table entries found in the main chapter text. Risk magnitude is provided either as assessed levels of risk (very high: vh, high: h, medium: m, or low: l) or as quantitative examples of risk levels taken from the literature. Further compilations of quantified levels of risk taken from the literature may be found Tables 3.SM1-5 in the Supplementary Material. Similarly, potential to adapt is assessed from the literature by expert judgement as either high (h), medium (m), or low (l). Confidence in each assessed level/quantification of risk, or in each assessed adaptation potential, is indicated as very high (VH), high (H), medium (M), or low (L). Note that the use of l, m, h and vh here is distinct from the use of L, M, H and VH in Figures 3.18, 3.20 and 3.21.
| Sector | Physical climate change drivers | Nature of risk | Global risks at 1.5°C of global warming above pre-industrial | Global risks at 2°C of global warming above pre-industrial | Change in risk when moving from 1.5°C to 2°C of warming | Confidence in risk statements | Regions where risks are particularly high with 2°C of global warming | Regions where the change in risk when moving from 1.5°C to 2°C are particularly high | Regions with little or no information | RFC* | Adaptation potential at 1.5°C | Adaptation potential at 2°C | Confidence in assigning adaptation potential |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Freshwater | Precipitation, temperature, snowmelt | Water Stress | Around half compared to the risks at 2°C1 | Additional 8% of the world population in 2000 exposed to new or aggravated water scarcity1 | Up to 100% increase | M | Europe, Australia, southern Africa | 3 | l | l | M | ||
| Fluvial flood | 100% increase in the population affected compared to the impact simulated over the baseline period 1976–20052 | 170% increase in the population affected compared to the impact simulated over the baseline period 1976–20052 | 70% increase | M | USA, Asia, Europe | Africa, Oceania | 2 | l/m | l/m | M | |||
| Drought | 350.2 ± 158.8 million, changes in urban population exposure to severe drought at the globe scale3 | 410.7 ± 213.5 million, changes in urban population exposure to severe drought at the globe scale3 | 60.5 ± 84.1 million (±84.1 based on the SSP1 scenario) (based on PDSI estimate) | M | Central Europe, southern Europe, Mediterranean, West Africa, East and West Asia, Southeast Asia (based on PDSI estimate#) | 2 | l/m | l/m | L | ||||
| Terrestrial ecosystems | Temperature, precipitation | Species range loss | 6% insects, 4% vertebrates, 8% plants, lose >50% range4 | 18% insects, 8% vertebrates, 16% plants lose >50% range4 | Double or triple | M | Amazon, Europe, southern Africa | 1,4 | m | l | H | ||
| Loss of ecosystem functioning and services | m | h | M | 4 | |||||||||
| Shifts of biomes (major ecosystem types) | About 7% transformed5 | 13% (range 8–20%) transformed5 | About double | M | Arctic, Tibet, Himalayas, South Africa, Australia | 4 | |||||||
| Heat and cold stress, warming, precipitation drought | Wildfire | h | h | Increased risk | M | Canada, USA and Mediterranean | Mediterranean | Central and South America, Australia, Russia, China, Africa | 1, 2, 4, 5 | l | l | M | |
| Ocean | Warming and stratification of the surface ocean | Loss of framework species (coral reefs) | vh | vh | Greater rate of loss: from 70–90% loss at 1.5°C to 99% loss at 2°C and above | H/very H | Tropical/subtropical countries | Tropical/subtropical countries | Southern Red Sea, Somalia, Yemen, deep water coral reefs | 1,2 | h | l | H |
| Loss of framework species (seagrass) | m | h | Increase in risk | M | Tropical/subtropical countries | Tropical/subtropical countries | Southern Red Sea, Somalia, Yemen, Myanmar | 1,2 | m | l | M/H | ||
| Loss of framework species (mangroves) | m | m | Uncertain and depends on other human activities | M/H | Tropical/subtropical countries | Tropical/subtropical countries | Southern Red Sea, Somalia, Yemen, Myanmar | 1,3 | m | l | L/M | ||
| Disruption of marine foodwebs | h | vh | Large increase in risk | M | Global | Global | Deep sea | 4 | m | l | M/H | ||
| Range migration of marine species and ecosystems | m | h | Large increase in risk | H | Global | Global | Deep sea | 1 | m | l | H | ||
| Loss of fin fish and fisheries | h | h/vh | Large increase in risk | H | Global | Global | Deep sea, up-welling systems | 4 | m | m/l | M/H | ||
| Ocean acidification and elevated sea temperatures | Loss of coastal ecosystems and protection | m | h | Increase in risk | M | Low-latitude tropical/subtropical countries | Low-latitude tropical/subtropical countries | Most regions – risks not well defined | 1 | m | m/l | M | |
| Loss of bivalves and bivalve fisheries | m/h | h/vh | Large increase in risk | H | Temperate countries with upwelling | Temperate countries with upwelling | Most regions – risks not well defined | 4 | m/h | l/m | M/H | ||
| Changes to physiology and ecology of marine species | l/m | m | Increase in risk | H | Global | Global | Most regions – risks not well defined | 4 | l | l | M/H | ||
| Reduced bulk ocean circulation and de-oxygenation | Increased hypoxic dead zones | l | l/m | Large increase in risk | L/M | Temperate countries with upwelling | Temperate countries with upwelling | Deep sea | 4 | m | l | M | |
| Changes to upwelling productivity | l | m | Increase in risk | L/M | Most upwelling regions | Most upwelling regions | Some upwelling systems | 4 | l | l | M | ||
| Intensified storms, precipitation plus sea level rise | Loss of coastal ecosystems | h | h/vh | Large increase in risk | H | Tropical/subtropical countries | Tropical/subtropical countries | 1, 4 | m | l | M | ||
| Inundation and destruction of human/coastal infrastructure and livelihoods | h | h/vh | Large increase in risk | H | Global | Global | 1, 5 | m/h | m | M/L | |||
| Loss of sea ice | Loss of habitat | h | vh | Large increase in risk | H | Polar regions | Polar regions | 1 | l | very l | H | ||
| Increased productivity but changing fisheries | l/m | m/h | Large increase in risk | very H | Polar regions | Polar regions | 1, 4 | l | m/l | H | |||
| Coastal | Sea level rise, increased storminess | Area exposed (assuming no defences) | 562–575th km2 when 1.5°C first reached6,7,8 | 590–613th km2 when 2°C first reached6,7,8 | Increasing; 25–38th km2 when temperatures are first reached, 10–17th km2 in 2100 increasing to 16–230th km2 in 23006,7,8 | M/H (dependent on population datasets) | Asia, small islands | Asia, small islands | Small islands | 2, 3 | m | m | M |
| Population exposed (assuming no defences) | 128–143 million when 1.5°C first reached | 141–151 million when 2°C first reached | Increasing; 8–13 million when temperatures are first reached, 0–6 million people in 2100, increasing to 35–95 million people in 23006 | M/H (dependent on population datasets) | Asia, small islands | Asia, small islands | Small islands | 2, 3 | m | m | M | ||
| People at risk accounting for defences (modelled in 1995) | 2–28 million people yr–1 if defences are not upgraded from the modelled 1995 baseline9 | 15–53 million people yr–1 if defences are not upgraded from the modelled 1995 baseline9 | Increasing with time, but highly dependent on adaptation9 | M/H (dependent on adaptation) | Asia, small islands, potentially African nations | Asia, small islands | Small islands | 2, 3, 4 | m | m | M | ||
| Food security and food production systems | Heat and cold stress, warming, precipitation, drought | Changes in ecosystem production | m/h | h | Large increase | M/H | Global | North America, Central and South America, Mediterranean basin, South Africa, Australia, Asia | 2, 4, 5 | h | m/h | M/H | |
| Heat and cold stress, warming, precipitation drought | Shift and composition change of biomes (major ecosystem types) | m/h | h | Moderate increase | L/M | Global | Global, tropical areas, Mediterranean | Africa, Asia | 1, 2, 3, 4 | l/m | l | L/M | |
| Human health | Temperature | Heat-related morbidity and mortality | m | m/h | Risk increased | VH | All regions at risk | All regions | Africa | 2, 3, 4 | h | h | H |
| Occupational heat stress | m | m/h | Risk increased | M | Tropical regions | Tropical regions | Africa | 2, 3, 4 | h | m | M | ||
| Air quality | Ozone-related mortality | m (if precursor emissions remain the same) | m/h (if precursor emissions remain the same) | Risk increased | H | High income and emerging economies | High income and emerging economies | Africa, parts of Asia | 2, 3, 4 | l | l | M | |
| Temperature, precipitation | Undernutrition | m | m/h | Risk increased | H | Low-income countries in Africa and Asia | Low-income countries in Africa and Asia | Small islands | 2, 3, 4 | m | l | M | |
| Key economic sectors | Temperature | Tourism (sun, beach, and snow sports) | m/h | h | Risk increased | VH | Coastal tourism, particularly in subtropical and tropical regions | Coastal tourism, particularly in subtropical and tropical regions | Africa | 1, 2, 3 | m | l | H |
3.4.13
Synthesis of Key Elements of Risk
Some elements of the assessment in Section 3.4 were synthesized into Figure 3.18 and 3.20, indicating the overall risk for a representative set of natural and human systems from increases in global mean surface temperature (GMST) and anthropogenic climate change. The elements included are supported by a substantive enough body of literature providing at least medium confidence in the assessment. The format for Figures 3.18 and 3.20 match that of Figure 19.4 of WGII AR5 Chapter 19 (Oppenheimer et al., 2014)1105 indicating the levels of additional risk as colours: undetectable (white) to moderate (detected and attributed; yellow), from moderate to high (severe and widespread; red), and from high to very high (purple), the last of which indicates significant irreversibility or persistence of climate-related hazards combined with a much reduced capacity to adapt. Regarding the transition from undetectable to moderate, the impact literature assessed in AR5 focused on describing and quantifying linkages between weather and climate patterns and impact outcomes, with limited detection and attribution to anthropogenic climate change (Cramer et al., 2014)1106. A more recent analysis of attribution to greenhouse gas forcing at the global scale (Hansen and Stone, 2016)1107 confirmed that the impacts related to changes in regional atmospheric and ocean temperature can be confidently attributed to anthropogenic forcing, while attribution to anthropogenic forcing of those impacts related to precipitation is only weakly evident or absent. Moreover, there is no strong direct relationship between the robustness of climate attribution and that of impact attribution (Hansen and Stone, 2016)1108.
The current synthesis is complementary to the synthesis in Section 3.5.2 that categorizes risks into 'Reasons for Concern' (RFCs), as described in Oppenheimer et al. (2014)1109. Each element, or burning ember, presented here (Figures 3.18, 3.20) maps to one or more RFCs (Figure 3.21). It should be emphasized that risks to the elements assessed here are only a subset of the full range of risks that contribute to the RFCs. Figures 3.18 and 3.20 are not intended to replace the RFCs but rather to indicate how risks to particular elements of the Earth system accrue with global warming, through the visual burning embers format, with a focus on levels of warming of 1.5°C and 2°C. Key evidence assessed in earlier parts of this chapter is summarized to indicate the transition points between the levels of risk. In this regard, the assessed confidence in assigning the transitions between risk levels are as follows: L=Low, M=Medium, H=High, and VH=Very high levels of confidence. A detailed account of the procedures involved is provided in the Supplementary Material (3.SM.3.2 and 3.SM.3.3).
In terrestrial ecosystems (feeding into RFC1 and RFC4), detection and attribution studies show that impacts of climate change on terrestrial ecosystems began to take place over the past few decades, indicating a transition from no risk (white areas in Figure 3.20) to moderate risk below recent temperatures (high confidence) (Section 3.4.3). Risks to unique and threatened terrestrial ecosystems are generally projected to be higher under warming of 2°C compared to 1.5°C (Section 3.5.2.1), while at the global scale severe and widespread risks are projected to occur by 2°C of warming. These risks are associated with biome shifts and species range losses (Sections 3.4.3 and 3.5.2.4); however, because many systems and species are projected to be unable to adapt to levels of warming below 2°C, the transition to high risk (red areas in Figure 3.20) is located below 2°C (high confidence). With 3°C of warming, however, biome shifts and species range losses are expected to escalate to very high levels, and the systems are projected to have very little capacity to adapt (Figure 3.20) (high confidence) (Section 3.4.3).
In the Arctic (related to RFC1), the increased rate of summer sea ice melt was detected and attributed to climate change by the year 2000 (corresponding to warming of 0.7°C), indicating moderate risk. At 1.5°C of warming an ice-free Arctic Ocean is considered unlikely, whilst by 2°C of warming it is considered likely and this unique ecosystem is projected to be unable to adapt. Hence, a transition from high to very high risk is expected between 1.5°C and 2°C of warming.
For warm-water coral reefs, there is high confidence in the transitions between risk levels, especially in the growing impacts in the transition of warming from non-detectable (0.2°C to 0.4°C), and then successively higher levels risk until high and very high levels of risks by 1.2°C (Section 3.4.4 and Box 3.4). This assessment considered the heatwave-related loss of 50% of shallow water corals across hundreds of kilometres of the world's largest continuous coral reef system, the Great Barrier Reef, as well as losses at other sites globally. The major increase in the size and loss of coral reefs over the past three years, plus sequential mass coral bleaching and mortality events on the Great Barrier Reef, (Hoegh-Guldberg, 1999; Hughes et al., 2017b, 2018)1110, have reinforced the scale of climate-change related risks to coral reefs. General assessments of climate-related risks for mangroves prior to this special report concluded that they face greater risks from deforestation and unsustainable coastal development than from climate change (Alongi, 2008; Hoegh-Guldberg et al., 2014; Gattuso et al., 2015)1111. Recent climate-related die-offs (Duke et al., 2017; Lovelock et al., 2017)1112, however, suggest that climate change risks may have been underestimated for mangroves as well, and risks have thus been assessed as undetectable to moderate, with the transition now starting at 1.3°C as opposed to 1.8°C as assessed in 2015 (Gattuso et al., 2015)1113. Risks of impacts related to climate change on small-scale fisheries at low latitudes, many of which are dependent on ecosystems such as coral reefs and mangroves, are moderate today but are expected to reach high levels of risk around 0.9°C– 1.1°C (high confidence) (Section 3.4.4.10).
The transition from undetectable to moderate risk (related to RFCs 3 and 4), shown as white to yellow in Figure 3.20, is based on AR5 WGII Chapter 7, which indicated with high confidence that climate change impacts on crop yields have been detected and attributed to climate change, and the current assessment has provided further evidence to confirm this (Section 3.4.6). Impacts have been detected in the tropics (AR5 WGII Chapters 7 and 18), and regional risks are projected to become high in some regions by 1.5°C of warming, and in many regions by 2.5°C, indicating a transition from moderate to high risk between 1.5°C and 2.5°C of warming (medium confidence).
Impacts from fluvial flooding (related to RFCs 2, 3 and 4) depend on the frequency and intensity of the events, as well as the extent of exposure and vulnerability of society (i.e., socio-economic conditions and the effect of non-climate stressors). Moderate risks posed by 1.5°C of warming are expected to continue to increase with higher levels of warming (Sections 3.3.5 and 3.4.2), with projected risks being threefold the current risk in economic damages due to flooding in 19 countries for warming of 2°C, indicating a transition to high risk at this level (medium confidence). Because few studies have assessed the potential to adapt to these risks, there was insufficient evidence to locate a transition to very high risk (purple).
Climate-change induced sea level rise (SLR) and associated coastal flooding (related to RFCs 2, 3 and 4) have been detectable and attributable since approximately 1970 (Slangen et al., 2016)1114, during which time temperatures have risen by 0.3°C (medium confidence) (Section 3.3.9). Analysis suggests that impacts could be more widespread in sensitive systems such as small islands (high confidence) (Section 3.4.5.3) and increasingly widespread by the 2070s (Brown et al., 2018a) as temperatures rise from 1.5°C to 2°C1115, even when adaptation measures are considered, suggesting a transition to high risk (Section 3.4.5). With 2.5°C of warming, adaptation limits are expected to be exceeded in sensitive areas, and hence a transition to very high risk is projected. Additionally, at this temperature, sea level rise could have adverse effects for centuries, posing significant risk to low-lying areas (high confidence) (Sections 3.4.5.7 and 3.5.2.5).
For heat-related morbidity and mortality (related to RFCs 2, 3 and 4), detection and attribution studies show heat-related mortality in some locations increasing with climate change (high confidence) (Section 3.4.7; Ebi et al., 2017)1116. The projected risks of heat-related morbidity and mortality are generally higher under warming of 2°C than 1.5°C (high confidence), with projections of greater exposure to high ambient temperatures and increased morbidity and mortality (Section 3.4.7). Risk levels will depend on the rate of warming and the (related) level of adaptation, so a transition in risk from moderate (yellow) to high (red) is located between 1°C and 3°C (medium confidence).
For tourism (related to RFCs 3 and 4), changing weather patterns, extreme weather and climate events, and sea level rise are affecting many – but not all – global tourism investments, as well as environmental and cultural destination assets (Section 3.4.4.12), with 'last chance to see' tourism markets developing based on observed impacts on environmental and cultural heritage (Section 3.4.9.1), indicating a transition from undetectable to moderate risk between 0°C and 1.5°C of warming (high confidence). Based on limited analyses, risks to the tourism sector are projected to be larger at 2°C than at 1.5°C, with impacts on climate-sensitive sun, beach and snow sports tourism markets being greatest. The degradation or loss of coral reef systems is expected to increase the risks to coastal tourism in subtropical and tropical regions. A transition in risk from moderate to high levels of added risk from climate change is projcted to occur between 1.5°C and 3°C (medium confidence).
Climate change is already having large scale impacts on ecosystems, human health and agriculture, which is making it much more difficult to reach goals to eradicate poverty and hunger, and to protect health and life on land (Sections 5.1 and 5.2.1 in Chapter 5), suggesting a transition from undetectable to moderate risk for recent temperatures at 0.5°C of warming (medium confidence). Based on the limited analyses available, there is evidence and agreement that the risks to sustainable development are considerably less at 1.5°C than 2°C (Section 5.2.2), including impacts on poverty and food security. It is easier to achieve many of the sustainable development goals (SDGs) at 1.5°C, suggesting that a transition to higher risk will not begin yet at this level. At 2°C and higher levels of warming (e.g., RCP8.5), however, there are high risks of failure to meet SDGs such as eradicating poverty and hunger, providing safe water, reducing inequality and protecting ecosystems, and these risks are projected to become severe and widespread if warming increases further to about 3°C (medium confidence) (Section 5.2.3).
Disclosure statement: The selection of elements depicted in Figures 3.18 and 3.20 is not intended to be fully comprehensive and does not necessarily include all elements for which there is a substantive body of literature, nor does it necessarily include all elements which are of particular interest to decision-makers.
Figure 3.20
The dependence of risks and/or impacts associated with selected elements of human and natural systems on the level of climate change, adapted from Figure 3.21 and from AR5 WGII Chapter 19, Figure 19.4, and highlighting the nature of this dependence between 0°C and 2ºC warming above pre-industrial levels.
The selection of impacts and risks to natural, managed and human systems is illustrative and is not intended to be fully comprehensive. Following the approach used in AR5, literature was used to make expert judgements to assess the levels of global warming at which levels of impact and/or risk are undetectable (white), moderate (yellow), high (red) or very high (purple). The colour scheme thus indicates the additional risks due to climate change. The transition from red to purple, introduced for the first time in AR4, is defined by a very high risk of severe impacts and the presence of significant irreversibility or persistence of climate-related hazards combined with limited ability to adapt due to the nature of the hazard or impact. Comparison of the increase of risk across RFCs indicates the relative sensitivity of RFCs to increases in GMST. As was done previously, this assessment takes autonomous adaptation into account, as well as limits to adaptation independently of development pathway. The levels of risk illustrated reflect the judgements of the authors of Chapter 3 and Gattuso et al. (20151117; for three marine elements). The grey bar represents the range of GMST for the most recent decade: 2006–2015.
3.5
Avoided Impacts and Reduced Risks at 1.5°C Compared with 2°C of Global Warming
3.5.1
Introduction
Oppenheimer et al. (2014, AR5 WGII Chapter 19)1118 provided a framework that aggregates projected risks from global mean temperature change into five categories identified as 'Reasons for Concern'. Risks are classified as moderate, high or very high and coloured yellow, red or purple, respectively, in Figure 19.4 of that chapter (AR5 WGII Chapter 19 for details and findings). The framework's conceptual basis and the risk judgements made by Oppenheimer et al. (2014)1119 were recently reviewed, and most judgements were confirmed in the light of more recent literature (O'Neill et al., 2017)1120. The approach of Oppenheimer et al. (2014)1121 was adopted, with updates to the aggregation of risk informed by the most recent literature, for the analysis of avoided impacts at 1.5°C compared to 2°C of global warming presented in this section.
The regional economic benefits that could be obtained by limiting the global temperature increase to 1.5°C of warming, rather than 2°C or higher levels, are discussed in Section 3.5.3 in the light of the five RFCs explored in Section 3.5.2. Climate change hotspots that could be avoided or reduced by achieving the 1.5°C target are summarized in Section 3.5.4. The section concludes with a discussion of regional tipping points that could be avoided at 1.5°C compared to higher degrees of global warming (Section 3.5.5).
3.5.2
Aggregated Avoided Impacts and Reduced Risks at 1.5°C versus 2°C of Global Warming
A brief summary of the accrual of RFCs with global warming, as assessed in WGII AR5, is provided in the following sections, which leads into an update of relevant literature published since AR5. The new literature is used to confirm the levels of global warming at which risks are considered to increase from undetectable to moderate, from moderate to high, and from high to very high. Figure 3.21 modifies Figure 19.4 from AR5 WGII, and the following text in this subsection provides justification for the modifications. O'Neill et al. (2017)1122 presented a very similar assessment to that of WGII AR5, but with further discussion of the potential to create 'embers' specific to socio-economic scenarios in the future. There is insufficient literature to do this at present, so the original, simple approach has been used here. As the focus of the present assessment is on the consequences of global warming of 1.5°C–2°C above the pre-industrial period, no assessment for global warming of 3°C or more is included in the figure (i.e., analysis is discontinued at 2.5°C).
Figure 3.21
The dependence of risks and/or impacts associated with the Reasons for Concern (RFCs) on the level of climate change, updated and adapted from WGII AR5 Ch 19, Figure 19.4 and highlighting the nature of this dependence between 0°C and 2°C warming above pre-industrial levels.
As in the AR5, literature was used to make expert judgements to assess the levels of global warming at which levels of impact and/or risk are undetectable (white), moderate (yellow), high (red) or very high (purple). The colour scheme thus indicates the additional risks due to climate change. The transition from red to purple, introduced for the first time in AR4, is defined by very high risk of severe impacts and the presence of significant irreversibility, or persistence of climate-related hazards combined with a limited ability to adapt due to the nature of the hazard or impact. Comparison of the increase of risk across RFCs indicates the relative sensitivity of RFCs to increases in GMST. As was done previously, this assessment takes autonomous adaptation into account, as well as limits to adaptation (RFC 1, 3, 5) independently of development pathway. The rate and timing of impacts were taken into account in assessing RFC 1 and 5. The levels of risk illustrated reflect the judgements of the Ch 3 authors. RFC1 Unique and threatened systems: ecological and human systems that have restricted geographic ranges constrained by climate related conditions and have high endemism or other distinctive properties. Examples include coral reefs, the Arctic and its indigenous people, mountain glaciers and biodiversity hotspots. RFC2 Extreme weather events: risks/impacts to human health, livelihoods, assets and ecosystems from extreme weather events such as heat waves, heavy rain, drought and associated wildfires, and coastal flooding. RFC3 Distribution of impacts: risks/impacts that disproportionately affect particular groups due to uneven distribution of physical climate change hazards, exposure or vulnerability. RFC4 Global aggregate impacts: global monetary damage, global scale degradation and loss of ecosystems and biodiversity. RFC5 Large-scale singular events: are relatively large, abrupt and sometimes irreversible changes in systems that are caused by global warming. Examples include disintegration of the Greenland and Antarctic ice sheets. The grey bar represents the range of GMST for the most recent decade: 2006–2015.
3.5.2.1
RFC 1 – Unique and threatened systems
WGII AR5 Chapter 19 found that some unique and threatened systems are at risk from climate change at current temperatures, with increasing numbers of systems at potential risk of severe consequences at global warming of 1.6°C above pre-industrial levels. It was also observed that many species and ecosystems have a limited ability to adapt to the very large risks associated with warming of 2.6°C or more, particularly Arctic sea ice and coral reef systems (high confidence). In the AR5 analysis, a transition from white to yellow indicated that the onset of moderate risk was located below present-day global temperatures (medium confidence); a transition from yellow to red indicated that the onset of high risk was located at 1.6°C, and a transition from red to purple indicated that the onset of very high risk was located at about 2.6°C. This WGII AR5 analysis already implied that there would be a significant reduction in risks to unique and threatened systems if warming were limited to 1.5°C compared with 2°C. Since AR5, evidence of present-day impacts in these systems has continued to grow (Sections 3.4.2, 3.4.4 and 3.4. 5), whilst new evidence has also accumulated for reduced risks at 1.5°C compared to 2°C of warming in Arctic ecosystems (Section 3.3.9), coral reefs (Section 3.4.4) and some other unique ecosystems (Section 3.4.3), as well as for biodiversity.
New literature since AR5 has provided a closer focus on the comparative levels of risk to coral reefs at 1.5°C versus 2°C of global warming. As assessed in Section 3.4.4 and Box 3.4, reaching 2°C will increase the frequency of mass coral bleaching and mortality to a point at which it will result in the total loss of coral reefs from the world's tropical and subtropical regions. Restricting overall warming to 1.5°C will still see a downward trend in average coral cover (70–90% decline by mid-century) but will prevent the total loss of coral reefs projected with warming of 2°C (Frieler et al., 2013)1123. The remaining reefs at 1.5°C will also benefit from increasingly stable ocean conditions by the mid-to-late 21st century. Limiting global warming to 1.5°C during the course of the century may, therefore, open the window for many ecosystems to adapt or reassort geographically. This indicates a transition in risk in this system from high to very high (high confidence) at 1.5°C of warming and contributes to a lowering of the transition from high to very high (Figure 3.21) in this RFC1 compared to in AR5. Further details of risk transitions for ocean systems are described in Figure 3.18.
Substantial losses of Arctic Ocean summer ice were projected in WGI AR5 for global warming of 1.6°C, with a nearly ice-free Arctic Ocean being projected for global warming of more than 2.6°C. Since AR5, the importance of a threshold between 1°C and 2°C has been further emphasized in the literature, with sea ice projected to persist throughout the year for a global warming of less than 1.5°C, yet chances of an ice-free Arctic during summer being high at 2°C of warming (Section 3.3.8). Less of the permafrost in the Arctic is projected to thaw under 1.5°C of warming (17–44%) compared with under 2°C (28–53%) (Section 3.3.5.2; Chadburn et al., 2017)1124, which is expected to reduce risks to both social and ecological systems in the Arctic. This indicates a transition in the risk in this system from high to very high between 1.5°C and 2°C of warming and contributes to a lowering of the transition from high to very high in this RFC1 compared to in AR5.
AR5 identified a large number of threatened systems, including mountain ecosystems, highly biodiverse tropical wet and dry forests, deserts, freshwater systems and dune systems. These include Mediterranean areas in Europe, Siberian, tropical and desert ecosystems in Asia, Australian rainforests, the Fynbos and succulent Karoo areas of South Africa, and wetlands in Ethiopia, Malawi, Zambia and Zimbabwe. In all these systems, impacts accrue with greater warming and impacts at 2°C are expected to be greater than those at 1.5°C (medium confidence). One study since AR5 has shown that constraining global warming to 1.5°C would maintain the functioning of prairie pothole ecosystems in North America in terms of their productivity and biodiversity, whilst warming of 2°C would not do so (Johnson et al., 2016)1125. The large proportion of insects projected to lose over half their range at 2°C of warming (25%) compared to at 1.5°C (9%) also suggests a significant loss of functionality in these threatened systems at 2°C of warming, owing to the critical role of insects in nutrient cycling, pollination, detritivory and other important ecosystem processes (Section 3.4.3).
Unique and threatened systems in small island states and in systems fed by glacier meltwater were also considered to contribute to this RFC in AR5, but there is little new information about these systems that pertains to 1.5°C or 2°C of global warming. Taken together, the evidence suggests that the transition from high to very high risk in unique and threatened systems occurs at a lower level of warming, between 1.5°C and 2°C (high confidence), than in AR5, where this transition was located at 2.6°C. The transition from moderate to high risk relocates very slightly from 1.6°C to 1.5°C (high confidence). There is also high confidence in the location of the transition from low to moderate risk below present-day global temperatures.
3.5.2.2
RFC 2 – Extreme weather events
Reduced risks in terms of the likelihood of occurrence of extreme weather events are discussed in this sub-subsection for 1.5°C as compared to 2°C of global warming, for those extreme events where evidence is currently available based on the assessments of Section 3.3. AR5 assigned a moderate level of risk from extreme weather events at recent temperatures (1986–2005) owing to the attribution of heat and precipitation extremes to climate change, and a transition to high risk beginning below 1.6°C of global warming based on the magnitude, likelihood and timing of projected changes in risk associated with extreme events, indicating more severe and widespread impacts. The AR5 analysis already suggested a significant benefit of limiting warming to 1.5°C, as doing so might keep risks closer to the moderate level. New literature since AR5 has provided greater confidence in a reduced level of risks due to extreme weather events at 1.5°C versus 2°C of warming for some types of extremes (Section 3.3 and below; Figure 3.21).
Temperature: It is expected that further increases in the number of warm days/nights and decreases in the number of cold days/nights, and an increase in the overall temperature of hot and cold extremes would occur under 1.5°C of global warming relative to pre-industrial levels (high confidence) compared to under the present-day climate (1°C of warming), with further changes occurring towards 2°C of global warming (Section 3.3). As assessed in Sections 3.3.1 and 3.3.2, impacts of 0.5°C of global warming can be identified for temperature extremes at global scales, based on observations and the analysis of climate models. At 2°C of global warming, it is likely that temperature increases of more than 2°C would occur over most land regions in terms of extreme temperatures (up to 4°C–6°C depending on region and considered extreme index) (Section 3.3.2, Table 3.2). Regional increases in temperature extremes can be robustly limited if global warming is constrained to 1.5°C, with regional warmings of up to 3°C–4.5°C (Section 3.3.2, Table 3.2). Benefits obtained from this general reduction in extremes depend to a large extent on whether the lower range of increases in extremes at 1.5°C is sufficient for critical thresholds to be exceeded, within the context of wide-ranging aspects such as crop yields, human health and the sustainability of ecosystems.
Heavy precipitation: AR5 assessed trends in heavy precipitation for land regions where observational coverage was sufficient for assessment. It concluded with medium confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation over the second half of the 20th century, for a global warming of approximately 0.5°C (Section 3.3.3). A recent observation-based study likewise showed that a 0.5°C increase in global mean temperature has had a detectable effect on changes in precipitation extremes at the global scale (Schleussner et al., 2017)1126, thus suggesting that there would be detectable differences in heavy precipitation at 1.5°C and 2°C of global warming. These results are consistent with analyses of climate projections, although they also highlight a large amount of regional variation in the sensitivity of changes in heavy precipitation (Section 3.3.3).
Droughts: When considering the difference between precipitation and evaporation (P–E) as a function of global temperature changes, the subtropics generally display an overall trend towards drying, whilst the northern high latitudes display a robust response towards increased wetting (Section 3.3.4, Figure 3.12). Limiting global mean temperature increase to 1.5°C as opposed to 2°C could substantially reduce the risk of reduced regional water availability in some regions (Section 3.3.4). Regions that are projected to benefit most robustly from restricted warming include the Mediterranean and southern Africa (Section 3.3.4).
Fire: Increasing evidence that anthropogenic climate change has already caused significant increases in fire area globally (Section 3.4.3) is in line with projected fire risks. These risks are projected to increase further under 1.5°C of global warming relative to the present day (Section 3.4.3). Under 1.2°C of global warming, fire frequency has been estimated to increase by over 37.8% of global land areas, compared to 61.9% of global land areas under 3.5°C of warming. For in-depth discussion and uncertainty estimates, see Meehl et al. (2007), Moritz et al. (2012) and Romero-Lankao et al. (2014)1127.
Regarding extreme weather events (RFC2), the transition from moderate to high risk is located between 1°C and 1.5°C of global warming (Figure 3.21), which is very similar to the AR5 assessment but is assessed with greater confidence (medium confidence). The impact literature contains little information about the potential for human society to adapt to extreme weather events, and hence it has not been possible to locate the transition from high to very high risk within the context of assessing impacts at 1.5°C and 2°C of global warming. There is thus low confidence in the level at which global warming could lead to very high risks associated with extreme weather events in the context of this report.
3.5.2.3
RFC 3 – Distribution of impacts
Risks due to climatic change are unevenly distributed and are generally greater at lower latitudes and for disadvantaged people and communities in countries at all levels of development. AR5 located the transition from undetectable to moderate risk below recent temperatures, owing to the detection and attribution of regionally differentiated changes in crop yields (medium to high confidence; Figure 3.20), and new literature has continued to confirm this finding. Based on the assessment of risks to regional crop production and water resources, AR5 located the transition from moderate to high risk between 1.6°C and 2.6°C above pre-industrial levels. Cross-Chapter Box 6 in this chapter highlights that at 2°C of warming, new literature shows that risks of food shortage are projected to emerge in the African Sahel, the Mediterranean, central Europe, the Amazon, and western and southern Africa, and that these are much larger than the corresponding risks at 1.5°C. This suggests a transition from moderate to high risk of regionally differentiated impacts between 1.5°C and 2°C above pre-industrial levels for food security (medium confidence) (Figure 3.20). Reduction in the availability of water resources at 2°C is projected to be greater than 1.5°C of global warming, although changes in socio-economics could have a greater influence (Section 3.4.2), with larger risks in the Mediterranean (Box 3.2); estimates of the magnitude of the risks remain similar to those cited in AR5. Globally, millions of people may be at risk from sea level rise (SLR) during the 21st century (Hinkel et al., 2014; Hauer et al., 2016)1128, particularly if adaptation is limited. At 2°C of warming, more than 90% of global coastlines are projected to experience SLR greater than 0.2 m, suggesting regional differences in the risks of coastal flooding. Regionally differentiated multi-sector risks are already apparent at 1.5°C of warming, being more prevalent where vulnerable people live, predominantly in South Asia (mostly Pakistan, India and China), but these risks are projected to spread to sub-Saharan Africa, the Middle East and East Asia as temperature rises, with the world's poorest people disproportionately impacted at 2°C of warming (Byers et al., 2018)1129. The hydrological impacts of climate change in Europe are projected to increase in spatial extent and intensity across increasing global warming levels of 1.5°C, 2°C and 3°C (Donnelly et al., 2017)1130. Taken together, a transition from moderate to high risk is now located between 1.5°C and 2°C above pre-industrial levels, based on the assessment of risks to food security, water resources, drought, heat exposure and coastal submergence (high confidence; Figure 3.21).
3.5.2.4
RFC 4 – Global aggregate impacts
Oppenheimer et al. (2014)1131 explained the inclusion of non-economic metrics related to impacts on ecosystems and species at the global level, in addition to economic metrics in global aggregate impacts. The degradation of ecosystem services by climate change and ocean acidification have generally been excluded from previous global aggregate economic analyses.
Global economic impacts : WGII AR5 found that overall global aggregate impacts become moderate at 1°C–2°C of warming, and the transition to moderate risk levels was therefore located at 1.6°C above pre-industrial levels. This was based on the assessment of literature using model simulations which indicated that the global aggregate economic impact will become significantly negative between 1°C and 2°C of warming (medium confidence), whilst there will be a further increase in the magnitude and likelihood of aggregate economic risks at 3°C of warming (low confidence).
Since AR5, three studies have emerged using two entirely different approaches which indicate that economic damages are projected to be higher by 2100 if warming reaches 2°C than if it is constrained to 1.5°C. The study by Warren et al. (2018c)1132 used the integrated assessment model PAGE09 to estimate that avoided global economic damages of 22% (10–26%) accrue from constraining warming to 1.5°C rather than 2°C, 90% (77–93%) from 1.5°C rather than 3.66°C, and 87% (74–91%) from 2°C rather than 3.66°C. In the second study, Pretis et al. (2018)1133 identified several regions where economic damages are projected to be greater at 2°C compared to 1.5°C of warming, further estimating that projected damages at 1.5°C remain similar to today's levels of economic damage. The third study, by M. Burke et al. (2018)1134 used an empirical, statistical approach and found that limiting warming to 1.5°C instead of 2°C would save 1.5–2.0% of the gross world product (GWP) by mid-century and 3.5% of the GWP by end-of-century (see Figure 2A in M. Burke et al., 2018)1135. Based on a 3% discount rate, this corresponds to 8.1–11.6 trillion USD and 38.5 trillion USD in avoided damages by mid-and end-of-century, respectively, agreeing closely with the estimate by Warren et al. (2018c)1136 of 15 trillion USD. Under the no-policy baseline scenario, temperature rises by 3.66°C by 2100, resulting in a global gross domestic product (GDP) loss of 2.6% (5–95% percentile range 0.5–8.2%), compared with 0.3% (0.1–0.5%) by 2100 under the 1.5°C scenario and 0.5% (0.1–1.0%) in the 2°C scenario. Limiting warming to 1.5°C rather than 2°C by 2060 has also been estimated to result in co-benefits of 0.5–0.6% of the world GDP, owing to reductions in air pollution (Shindell et al., 2018)1137, which is similar to the avoided damages identified for the USA (Box 3.6).
Two studies focusing only on the USA found that economic damages are projected to be higher by 2100 if warming reaches 2°C than if it is constrained to 1.5°C. Hsiang et al. (2017)1138 found a mean difference of 0.35% GDP (range 0.2–0.65%), while Yohe (2017)1139 identified a GDP loss of 1.2% per degree of warming, hence approximately 0.6% for half a degree. Further, the avoided risks compared to a no-policy baseline are greater in the 1.5°C case (4%, range 2–7%) compared to the 2°C case (3.5%, range 1.8–6.5%). These analyses suggest that the point at which global aggregates of economic impacts become negative is below 2°C (medium confidence), and that there is a possibility that it is below 1.5°C of warming.
Oppenheimer et al. (2014)1140 noted that the global aggregated damages associated with large-scale singular events has not been explored, and reviews of integrated modelling exercises have indicated a potential underestimation of global aggregate damages due to the lack of consideration of the potential for these events in many studies. Since AR5, further analyses of the potential economic consequences of triggering these large-scale singular events have indicated a two to eight fold larger economic impact associated with warming of 3°C than estimated in most previous analyses, with the extent of increase depending on the number of events incorporated. Lemoine and Traeger (2016)1141 included only three known singular events whereas Y. Cai et al. (2016)1142 included five.
Biome shifts, species range loss, increased risks of species extinction and risks of loss of ecosystem functioning and services: 13% (range 8–20%) of Earth's land area is projected to undergo biome shifts at 2°C of warming compared to approximately 7% at 1.5°C (medium confidence) (Section 3.4.3; Warszawski et al., 2013)1143, implying a halving of biome transformations. Overall levels of species loss at 2°C of warming are similar to values found in previous studies for plants and vertebrates (Warren et al., 2013, 2018a)1144, but insects have been found to be more sensitive to climate change, with 18% (6–35%) projected to lose over half their range at 2°C of warming compared to 6% (1–18%) under 1.5°C of warming, corresponding to a difference of 66% (Section 3.4.3). The critical role of insects in ecosystem functioning therefore suggests that there will be impacts on global ecosystem functioning already at 2°C of warming, whilst species that lose large proportions of their range are considered to be at increased risk of extinction (Section 3.4.3.3). Since AR5, new literature has indicated that impacts on marine fish stocks and fisheries are lower under 1.5°C–2°C of global warming relative to pre-industrial levels compared to under higher warming scenarios (Section 3.4.6), especially in tropical and polar systems.
In AR5, the transition from undetectable to moderate impacts was considered to occur between 1.6°C and 2.6°C of global warming reflecting impacts on the economy and on biodiversity globally, whereas high risks were associated with 3.6°C of warming to reflect the high risks to biodiversity and accelerated effects on the global economy. New evidence suggests moderate impacts on the global aggregate economy and global biodiversity by 1.5°C of warming, suggesting a lowering of the temperature level for the transition to moderate risk to 1.5°C (Figure 3.21). Further, recent literature points to higher risks than previously assessed for the global aggregate economy and global biodiversity by 2°C of global warming, suggesting that the transition to a high risk level is located between 1.5°C and 2.5°C of warming (Figure 3.21), as opposed to at 3.6°C as previously assessed (medium confidence).
3.5.2.5
RFC 5 – Large-scale singular events
Large-scale singular events are components of the global Earth system that are thought to hold the risk of reaching critical tipping points under climate change, and that can result in or be associated with major shifts in the climate system. These components include:
• the cryosphere: West Antarctic ice sheet, Greenland ice sheet
• the thermohaline circulation: slowdown of the Atlantic Meridional Overturning Circulation (AMOC)
• the El Niño–Southern Oscillation (ENSO) as a global mode of climate variability
• role of the Southern Ocean in the global carbon cycle
AR5 assessed that the risks associated with these events become moderate between 0.6°C and 1.6°C above pre-industrial levels, based on early warning signs, and that risk was expected to become high between 1.6°C and 4.6°C based on the potential for commitment to large irreversible sea level rise from the melting of land-based ice sheets (low to medium confidence). The increase in risk between 1.6°C and 2.6°C above pre-industrial levels was assessed to be disproportionately large. New findings since AR5 are described in detail below.
Greenland and West Antarctic ice sheets and marine ice sheet instability (MISI): Various feedbacks between the Greenland ice sheet and the wider climate system, most notably those related to the dependence of ice melt on albedo and surface elevation, make irreversible loss of the ice sheet a possibility. Church et al. (2013)1145 assessed this threshold to be at 2°C of warming or higher levels relative to pre-industrial temperature. Robinson et al. (2012)1146 found a range for this threshold of 0.8°C–3.2°C (95% confidence). The threshold of global temperature increase that may initiate irreversible loss of the West Antarctic ice sheet and marine ice sheet instability (MISI) is estimated to lie be between 1.5°C and 2°C. The time scale for eventual loss of the ice sheets varies between millennia and tens of millennia and assumes constant surface temperature forcing during this period. If temperature were to decline subsequently the ice sheets might regrow, although the amount of cooling required is likely to be highly dependent on the duration and rate of the previous retreat. The magnitude of global sea level rise that could occur over the next two centuries under 1.5°C–2°C of global warming is estimated to be in the order of several tenths of a metre according to most studies (low confidence) (Schewe et al., 2011; Church et al., 2013; Levermann et al., 2014; Marzeion and Levermann, 2014; Fürst et al., 2015; Golledge et al., 2015)1147, although a smaller number of investigations (Joughin et al., 2014; Golledge et al., 2015; DeConto and Pollard, 2016)1148 project increases of 1–2 m. This body of evidence suggests that the temperature range of 1.5°C–2°C may be regarded as representing moderate risk, in that it may trigger MISI in Antarctica or irreversible loss of the Greenland ice sheet and it may be associated with sea level rise by as much as 1–2 m over a period of two centuries.
Thermohaline circulation (slowdown of AMOC): It is more likely than not that the AMOC has been weakening in recent decades, given the detection of cooling of surface waters in the North Atlantic and evidence that the Gulf Stream has slowed since the late 1950s (Rahmstorf et al., 2015b; Srokosz and Bryden, 2015; Caesar et al., 2018)1149. There is limited evidence linking the recent weakening of the AMOC to anthropogenic warming (Caesar et al., 2018)1150. It is very likely that the AMOC will weaken over the 21st century. Best estimates and ranges for the reduction based on CMIP5 simulations are 11% (1–24%) in RCP2.6 and 34% (12–54%) in RCP8.5 (AR5). There is no evidence indicating significantly different amplitudes of AMOC weakening for 1.5°C versus 2°C of global warming, or of a shutdown of the AMOC at these global temperature thresholds. Associated risks are classified as low to moderate.
El Niño–Southern Oscillation (ENSO): Extreme El Niño events are associated with significant warming of the usually cold eastern Pacific Ocean, and they occur about once every 20 years (Cai et al., 2015)1151. Such events reorganize the distribution of regions of organized convection and affect weather patterns across the globe. Recent research indicates that the frequency of extreme El Niño events increases linearly with the global mean temperature, and that the number of such events might double (one event every ten years) under 1.5°C of global warming (G. Wang et al., 2017)1152. This pattern is projected to persist for a century after stabilization at 1.5°C, thereby challenging the limits to adaptation, and thus indicates high risk even at the 1.5°C threshold. La Niña event (the opposite or balancing event to El Niño) frequency is projected to remain similar to that of the present day under 1.5°C–2°C of global warming.
Role of the Southern Ocean in the global carbon cycle: The critical role of the Southern Ocean as a net sink of carbon might decline under global warming, and assessing this effect under 1.5°C compared to 2°C of global warming is a priority. Changes in ocean chemistry (e.g., oxygen content and ocean acidification), especially those associated with the deep sea, are associated concerns (Section 3.3.10).
For large-scale singular events (RFC5), moderate risk is now located at 1°C of warming and high risk is located at 2.5°C (Figure 3.21), as opposed to at 1.6°C (moderate risk) and around 4°C (high risk) in AR5, because of new observations and models of the West Antarctic ice sheet (medium confidence), which suggests that the ice sheet may be in the early stages of marine ice sheet instability (MISI). Very high risk is assessed as lying above 5°C because the growing literature on process-based projections of the West Antarctic ice sheet predominantly supports the AR5 assessment of an MISI contribution of several additional tenths of a metre by 2100.
3.5.3
Regional Economic Benefit Analysis for the 1.5°C versus 2°C Global Goals
This section reviews recent literature that has estimated the economic benefits of constraining global warming to 1.5°C compared to 2°C. The focus here is on evidence pertaining to specific regions, rather than on global aggregated benefits (Section 3.5.2.4). At 2°C of global warming, lower economic growth is projected for many countries than at 1.5C of global warming, with low-income countries projected to experience the greatest losses (low to medium confidence) (M. Burke et al., 2018; Pretis et al., 2018)1153. A critical issue for developing countries in particular is that advantages in some sectors are projected to be offset by increasing mitigation costs (Rogelj et al., 2013; M. Burke et al., 2018)1154, with food production being a key factor. That is, although restraining the global temperature increase to 2°C is projected to reduce crop losses under climate change relative to higher levels of warming, the associated mitigation costs may increase the risk of hunger in low-income countries (low confidence) (Hasegawa et al., 2016)1155. It is likely that the even more stringent mitigation measures required to restrict global warming to 1.5°C (Rogelj et al., 2013)1156 will further increase these mitigation costs and impacts. International trade in food might be a key response measure for alleviating hunger in developing countries under 1.5°C and 2°C stabilization scenarios (IFPRI, 2018)1157.
Although warming is projected to be the highest in the Northern Hemisphere under 1.5°C or 2°C of global warming, regions in the tropics and Southern Hemisphere subtropics are projected to experience the largest impacts on economic growth (low to medium confidence) (Gallup et al., 1999; M. Burke et al., 2018; Pretis et al., 2018)1158. Despite the uncertainties associated with climate change projections and econometrics (e.g., M. Burke et al., 2018)1159, it is more likely than not that there will be large differences in economic growth under 1.5°C and 2°C of global warming for developing versus developed countries (M. Burke et al., 2018; Pretis et al., 2018)1160. Statistically significant reductions in gross domestic product (GDP) per capita growth are projected across much of the African continent, Southeast Asia, India, Brazil and Mexico (low to medium confidence). Countries in the western parts of tropical Africa are projected to benefit most from restricting global warming to 1.5°C, as opposed to 2°C, in terms of future economic growth (Pretis et al., 2018)1161. An important reason why developed countries in the tropics and subtropics are projected to benefit substantially from restricting global warming to 1.5°C is that present-day temperatures in these regions are above the threshold thought to be optimal for economic production (M. Burke et al., 2015b, 2018)1162.
The world's largest economies are also projected to benefit from restricting warming to 1.5°C as opposed to 2°C (medium confidence), with the likelihood of such benefits being realized estimated at 76%, 85% and 81% for the USA, China and Japan, respectively (M. Burke et al., 2018)1163. Two studies focusing only on the USA found that economic damages are projected to be higher by 2100 if warming reaches 2°C than if it is constrained to 1.5°C. Yohe (2017)1164 found a mean difference of 0.35% GDP (range 0.2–0.65%), while Hsiang et al. (2017)1165 identified a GDP loss of 1.2% per degree of warming, hence approximately 0.6% for half a degree. Overall, no statistically significant changes in GDP are projected to occur over most of the developed world under 1.5°C of global warming in comparison to present-day conditions, but under 2°C of global warming impacts on GDP are projected to be generally negative (low confidence) (Pretis et al., 2018)1166.
A caveat to the analyses of Pretis et al. (2018)1167 and M. Burke et al. (2018)1168 is that the effects of sea level rise were not included in the estimations of damages or future economic growth, implying a potential underestimation of the benefits of limiting warming to 1.5°C for the case where significant sea level rise is avoided at 1.5°C but not at 2°C.
3.5.4
Reducing Hotspots of Change for 1.5°C and 2°C of Global Warming
This subsection integrates Sections 3.3 and 3.4 in terms of climate-change-induced hotspots that occur through interactions across the physical climate system, ecosystems and socio-economic human systems, with a focus on the extent to which risks can be avoided or reduced by achieving the 1.5°C global warming goal (as opposed to the 2°C goal). Findings are summarized in Table 3.6.
3.5.4.1
Arctic sea ice
Ice-free Arctic Ocean summers are very likely at levels of global warming higher than 2°C (Notz and Stroeve, 2016; Rosenblum and Eisenman, 2016; Screen and Williamson, 2017; Niederdrenk and Notz, 2018)1169. Some studies even indicate that the entire Arctic Ocean summer period will become ice free under 2°C of global warming, whilst others more conservatively estimate this probability to be in the order of 50% (Section 3.3.8; Sanderson et al., 2017)1170. The probability of an ice-free Arctic in September at 1.5°C of global warming is low and substantially lower than for the case of 2°C of global warming (high confidence) (Section 3.3.8; Screen and Williamson, 2017; Jahn, 2018; Niederdrenk and Notz, 2018)1171. There is, however, a single study that questions the validity of the 1.5°C threshold in terms of maintaining summer Arctic Ocean sea ice (Niederdrenk and Notz, 2018)1172. In contrast to summer, little ice is projected to be lost during winter for either 1.5°C or 2°C of global warming (medium confidence) (Niederdrenk and Notz, 2018)1173. The losses in sea ice at 1.5°C and 2°C of warming will result in habitat losses for organisms such as seals, polar bears, whales and sea birds (e.g., Larsen et al., 2014)1174. There is high agreement and robust evidence that photosynthetic species will change because of sea ice retreat and related changes in temperature and radiation (Section 3.4.4.7), and this is very likely to benefit fisheries productivity in the Northern Hemisphere spring bloom system (Section 3.4.4.7).
3.5.4.2
Arctic land regions
In some Arctic land regions, the warming of cold extremes and the increase in annual minimum temperature at 1.5°C are stronger than the global mean temperature increase by a factor of two to three, meaning 3°C–4.5°C of regional warming at 1.5°C of global warming (e.g., northern Europe in Supplementary Material 3.SM, Figure 3.SM.5 see also Section 3.3.2.2 and Seneviratne et al., 2016)1175. Moreover, over much of the Arctic, a further increase of 0.5°C in the global surface temperature, from 1.5°C to 2°C, may lead to further temperature increases of 2°C–2.5°C (Figure 3.3). As a consequence, biome (major ecosystem type) shifts are likely in the Arctic, with increases in fire frequency, degradation of permafrost, and tree cover likely to occur at 1.5°C of warming and further amplification of these changes expected under 2°C of global warming (e.g., Gerten et al., 2013; Bring et al., 2016)1176. Rising temperatures, thawing permafrost and changing weather patterns are projected to increasingly impact people, infrastructure and industries in the Arctic (W.N. Meier et al., 2014)1177 with these impacts larger at 2°C than at 1.5°C of warming (medium confidence).
3.5.4.3
Alpine regions
Alpine regions are generally regarded as climate change hotspots given that rich biodiversity has evolved in their cold and harsh climate, but with many species consequently being vulnerable to increases in temperature. Under regional warming, alpine species have been found to migrate upwards on mountain slopes (Reasoner and Tinner, 2009)1178, an adaptation response that is obviously limited by mountain height and habitability. Moreover, many of the world's alpine regions are important from a water security perspective through associated glacier melt, snow melt and river flow (see Section 3.3.5.2 for a discussion of these aspects). Projected biome shifts are likely to be severe in alpine regions already at 1.5°C of warming and to increase further at 2°C (Gerten et al., 2013, Figure 1b1179; B. Chen et al., 2014)1180.
3.5.4.4
Southeast Asia
Southeast Asia is a region highly vulnerable to increased flooding in the context of sea level rise (Arnell et al., 2016; Brown et al., 2016, 2018a)1181. Risks from increased flooding are projected to rise from 1.5°C to 2°C of warming (medium confidence), with substantial increases projected beyond 2°C (Arnell et al., 2016)1182. Southeast Asia displays statistically significant differences in projected changes in heavy precipitation, runoff and high flows at 1.5°C versus 2°C of warming, with stronger increases occurring at 2°C (Section 3.3.3; Wartenburger et al., 2017; Döll et al., 2018; Seneviratne et al., 2018c)1183; thus, this region is considered a hotspot in terms of increases in heavy precipitation between these two global temperature levels (medium confidence) (Schleussner et al., 2016b; Seneviratne et al., 2016)1184. For Southeast Asia, 2°C of warming by 2040 could lead to a decline by one-third in per capita crop production associated with general decreases in crop yields (Nelson et al., 2010)1185. However, under 1.5°C of warming, significant risks for crop yield reduction in the region are avoided (Schleussner et al., 2016b)1186. These changes pose significant risks for poor people in both rural regions and urban areas of Southeast Asia (Section 3.4.10.1), with these risks being larger at 2°C of global warming compared to 1.5°C (medium confidence).
3.5.4.5
Southern Europe and the Mediterranean
The Mediterranean is regarded as a climate change hotspot, both in terms of projected stronger warming of the regional land-based hot extremes compared to the mean global temperature increase (e.g., Seneviratne et al., 2016)1187 and in terms of of robust increases in the probability of occurrence of extreme droughts at 2°C vs 1.5°C global warming (Section 3.3.4). Low river flows are projected to decrease in the Mediterranean under 1.5°C of global warming (Marx et al., 2018)1188, with associated significant decreases in high flows and floods (Thober et al., 2018)1189, largely in response to reduced precipitation. The median reduction in annual runoff is projected to almost double from about 9% (likely range 4.5–15.5%) at 1.5°C to 17% (likely range 8–25%) at 2°C (Schleussner et al., 2016b)1190. Similar results were found by Döll et al. (2018)1191. Overall, there is high confidence that strong increases in dryness and decreases in water availability in the Mediterranean and southern Europe would occur from 1.5°C to 2°C of global warming. Sea level rise is expected to be lower for 1.5°C versus 2°C, lowering risks for coastal metropolitan agglomerations. The risks (assuming current adaptation) related to water deficit in the Mediterranean are high for global warming of 2°C but could be substantially reduced if global warming were limited to 1.5°C (Section 3.3.4; Guiot and Cramer, 2016; Schleussner et al., 2016b; Donnelly et al., 2017)1192.
3.5.4.6
West Africa and the Sahel
West Africa and the Sahel are likely to experience increases in the number of hot nights and longer and more frequent heatwaves even if the global temperature increase is constrained to 1.5°C, with further increases expected at 2°C of global warming and beyond (e.g., Weber et al., 2018)1193. Moreover, daily rainfall intensity and runoff is expected to increase (low confidence) towards 2°C and higher levels of global warming (Schleussner et al., 2016b; Weber et al., 2018)1194, with these changes also being relatively large compared to the projected changes at 1.5°C of warming. Moreover, increased risks are projected in terms of drought, particularly for the pre-monsoon season (Sylla et al., 2015)1195, with both rural and urban populations affected, and more so at 2°C of global warming as opposed to 1.5°C (Liu et al., 2018)1196. Based on a World Bank (2013)1197 study for sub-Saharan Africa, a 1.5°C warming by 2030 might reduce the present maize cropping areas by 40%, rendering these areas no longer suitable for current cultivars. Substantial negative impacts are also projected for sorghum suitability in the western Sahel (Läderach et al., 2013; Sultan and Gaetani, 2016)1198. An increase in warming to 2°C by 2040 would result in further yield losses and damages to crops (i.e., maize, sorghum, wheat, millet, groundnut and cassava). Schleussner et al. (2016b)1199 found consistently reduced impacts on crop yield for West Africa under 2°C compared to 1.5°C of global warming. There is medium confidence that vulnerabilities to water and food security in the African Sahel will be higher at 2°C compared to 1.5°C of global warming (Cheung et al., 2016a; Betts et al., 2018)1200, and at 2°C these vulnerabilities are expected to be worse (high evidence) (Sultan and Gaetani, 2016; Lehner et al., 2017; Betts et al., 2018; Byers et al., 2018; Rosenzweig et al., 2018)1201 . Under global warming of more than 2°C, the western Sahel might experience the strongest drying and experience serious food security issues (Ahmed et al., 2015; Parkes et al., 2018)1202.
3.5.4.7
Southern Africa
The southern African region is projected to be a climate change hotspot in terms of both hot extremes (Figures 3.5 and 3.6) and drying (Figure 3.12). Indeed, temperatures have been rising in the subtropical regions of southern Africa at approximately twice the global rate over the last five decades (Engelbrecht et al., 2015)1203. Associated elevated warming of the regional land-based hot extremes has occurred (Section 3.3; Seneviratne et al., 2016)1204. Increases in the number of hot nights, as well as longer and more frequent heatwaves, are projected even if the global temperature increase is constrained to 1.5°C (high confidence), with further increases expected at 2°C of global warming and beyond (high confidence) (Weber et al., 2018)1205.
Moreover, southern Africa is likely to generally become drier with reduced water availability under low mitigation (Niang et al., 2014; Engelbrecht et al., 2015; Karl et al., 2015; James et al., 2017)1206, with this particular risk being prominent under 2°C of global warming and even under 1.5°C (Gerten et al., 2013)1207. Risks are significantly reduced, however, under 1.5°C of global warming compared to under higher levels (Schleussner et al., 2016b)1208. There are consistent and statistically significant increases in projected risks of increased meteorological drought in southern Africa at 2°C versus 1.5°C of warming (medium confidence). Despite the general rainfall reductions projected for southern Africa, daily rainfall intensities are expected to increase over much of the region (medium confidence), and increasingly so with higher levels of global warming. There is medium confidence that livestock in southern Africa will experience increased water stress under both 1.5°C and 2°C of global warming, with negative economic consequences (e.g., Boone et al., 2018)1209. The region is also projected to experience reduced maize, sorghum and cocoa cropping area suitability, as well as yield losses under 1.5°C of warming, with further decreases occurring towards 2°C of warming (World Bank, 2013)1210. Generally, there is high confidence that vulnerability to decreases in water and food availability is reduced at 1.5°C versus 2°C for southern Africa (Betts et al., 2018)1211, whilst at 2°C these are expected to be higher (high confidence) (Lehner et al., 2017; Betts et al., 2018; Byers et al., 2018; Rosenzweig et al., 2018)1212.
3.5.4.8
Tropics
Worldwide, the largest increases in the number of hot days are projected to occur in the tropics (Figure 3.7). Moreover, the largest differences in the number of hot days for 1.5°C versus 2°C of global warming are projected to occur in the tropics (Mahlstein et al., 2011)1213. In tropical Africa, increases in the number of hot nights, as well as longer and more frequent heatwaves, are projected under 1.5°C of global warming, with further increases expected under 2°C of global warming (Weber et al., 2018)1214. Impact studies for major tropical cereals reveal that yields of maize and wheat begin to decline with 1°C to 2°C of local warming in the tropics. Schleussner et al. (2016b)1215 project that constraining warming to 1.5°C rather than 2°C would avoid significant risks of tropical crop yield declines in West Africa, Southeast Asia, and Central and South America. There is limited evidence and thus low confidence that these changes may result in significant population displacement from the tropics to the subtropics (e.g., Hsiang and Sobel, 2016)1216.
3.5.4.9
Small islands
It is widely recognized that small islands are very sensitive to climate change impacts such as sea level rise, oceanic warming, heavy precipitation, cyclones and coral bleaching (high confidence) (Nurse et al., 2014; Ourbak and Magnan, 2017)1217 . Even at 1.5°C of global warming, the compounding impacts of changes in rainfall, temperature, tropical cyclones and sea level are likely to be significant across multiple natural and human systems. There are potential benefits to small island developing states (SIDS) from avoided risks at 1.5°C versus 2°C, especially when coupled with adaptation efforts. In terms of sea level rise, by 2150, roughly 60,000 fewer people living in SIDS will be exposed in a 1.5°C world than in a 2°C world (Rasmussen et al., 2018)1218. Constraining global warming to 1.5°C may significantly reduce water stress (by about 25%) compared to the projected water stress at 2°C, for example in the Caribbean region (Karnauskas et al., 2018)1219, and may enhance the ability of SIDS to adapt (Benjamin and Thomas, 2016)1220. Up to 50% of the year is projected to be very warm in the Caribbean at 1.5°C, with a further increase by up to 70 days at 2°C versus 1.5°C (Taylor et al., 2018)1221. By limiting warming to 1.5°C instead of 2°C in 2050, risks of coastal flooding (measured as the flood amplification factors for 100-year flood events) are reduced by 20–80% for SIDS (Rasmussen et al., 2018)1222. A case study of Jamaica with lessons for other Caribbean SIDS demonstrated that the difference between 1.5°C and 2°C is likely to challenge livestock thermoregulation, resulting in persistent heat stress for livestock (Lallo et al., 2018)1223.
3.5.4.10
Fynbos and shrub biomes
The Fynbos and succulent Karoo biomes of South Africa are threatened systems that were assessed in AR5. Similar shrublands exist in the semi-arid regions of other continents, with the Sonora-Mojave creosotebush-white bursage desert scrub ecosystem in the USA being a prime example. Impacts accrue across these systems with greater warming, with impacts at 2°C likely to be greater than those at 1.5°C (medium confidence). Under 2°C of global warming, regional warming in drylands is projected to be 3.2°C–4°C, and under 1.5°C of global warming, mean warming in drylands is projected to still be about 3°C. The Fynbos biome in southwestern South Africa is vulnerable to the increasing impact of fires under increasing temperatures and drier winters (high confidence). The Fynbos biome is projected to lose about 20%, 45% and 80% of its current suitable climate area relative to its present-day area under 1°C, 2°C and 3°C of warming, respectively (Engelbrecht and Engelbrecht, 2016)1224, demonstrating the value of climate change mitigation in protecting this rich centre of biodiversity.
Table 3.6
Emergence and intensity of climate change hotspots under different degrees of global warming.
| Region and/or Phenomenon | Warming of 1.5°C or less | Warming of 1.5°C–2°C | Warming of 2°C–3°C |
|---|---|---|---|
| Arctic sea ice | Arctic summer sea ice is likely to be maintained Habitat losses for organisms such as polar bears, Benefits for Arctic fisheries | The risk of an ice-free Arctic in summer is about 50% or higher Habitat losses for organisms such as polar bears, whales,seals and sea birds may be critical if summers are ice free Benefits for Arctic fisheries | The Arctic is very likely to be ice free in summer Critical habitat losses for organisms such as polar bears, whales, seals and sea birds Benefits for Arctic fisheries |
| Arctic land regions | Cold extremes warm by a factor of 2–3, reaching up to 4.5°C (high confidence) Biome shifts in the tundra and permafrost deterioration are likely | Cold extremes warm by as much as 8°C (high confidence) Larger intrusions of trees and shrubs in the tundra than under 1.5°C of warming are likely; larger but constrained losses in permafrost are likely | Drastic regional warming is very likely A collapse in permafrost may occur (low confidence); a drastic biome shift from tundra to boreal forest is possible (low confidence) |
| Alpine regions | Severe shifts in biomes are likely | Even more severe shifts are likely | Critical losses in alpine habitats are likely |
| Southeast Asia | Risks for increased flooding related to sea level rise Increases in heavy precipitation events Significant risks of crop yield reductions are avoided | Higher risks of increased flooding related to sea level rise (medium confidence) Stronger increases in heavy precipitation events (medium confidence) One-third decline in per capita crop production (medium confidence) | Substantial increases in risks related to flooding from sea level rise Substantial increase in heavy precipitation and high-flow events Substantial reductions in crop yield |
| Mediterranean | Increase in probability of extreme drought (medium confidence) Medium confidence in reduction in runoff of about 9% (likely range 4.5–15.5%) Risk of water deficit (medium confidence) | Robust increase in probability of extreme drought (medium confidence) Medium confidence in further reductions (about 17%) in runoff (likely range 8–28%) Higher risks of water deficit (medium confidence) | Robust and large increases in extreme drought. Substantial reductions in precipitation and in runoff (medium confidence) Very high risks of water deficit (medium confidence) |
| West Africa and the Sahel | Increases in the number of hot nights and longer and more frequent heatwaves are likely Reduced maize and sorghum production is likely, with area suitable for maize production reduced by as much as 40% Increased risks of undernutrition | Further increases in number of hot nights and longer and more frequent heatwaves are likely Negative impacts on maize and sorghum production likely larger than at 1.5°C; medium confidence that vulnerabilities to food security in the African Sahel will be higher at 2°C compared to 1.5°C Higher risks of undernutrition | Substantial increases in the number of hot nights and heatwave duration and frequency (very likely) Negative impacts on crop yield may result in major regional food insecurities (medium confidence) High risks of undernutrition |
| Southern Africa | Reductions in water availability (medium confidence) Increases in number of hot nights and longer and more frequent heatwaves (high confidence) High risks of increased mortality from heatwaves High risk of undernutrition in communities dependent on dryland agriculture and livestock | Larger reductions in rainfall and water availability (medium confidence) Further increases in number of hot nights and longer and more frequent heatwaves (high confidence), associated increases in risks of increased mortality from heatwaves compared to 1.5°C warming (high confidence) Higher risks of undernutrition in communities dependent on dryland agriculture and livestock | Large reductions in rainfall and water availability (medium confidence) Drastic increases in the number of hot nights, hot days and heatwave duration and frequency to impact substantially on agriculture, livestock and human health and mortality (high confidence) Very high risks of undernutrition in communities dependent on dryland agriculture and livestock |
| Tropics | Increases in the number of hot days and hot nights as well as longer and more frequent heatwaves (high confidence) Risks to tropical crop yields in West Africa, Southeast Asia and Central and South America are significantly less than under 2°C of warming | The largest increase in hot days under 2°C compared to 1.5°C is projected for the tropics. Risks to tropical crop yields in West Africa, Southeast Asia and Central and South America could be extensive | Oppressive temperatures and accumulated heatwave duration very likely to directly impact human health, mortality and productivity Substantial reductions in crop yield very likely |
| Small islands | Land of 60,000 less people exposed by 2150 on SIDS compared to impacts under 2°C of global warming Risks for coastal flooding reduced by 20–80% for SIDS compared to 2°C of global warming Freshwater stress reduced by 25% Increase in the number of warm days for SIDS in the tropics Persistent heat stress in cattle avoided Loss of 70–90% of coral reefs | Tens of thousands of people displaced owing to inundation of SIDSHigh risks for coastal floodingFreshwater stress reduced by 25% compared to 2°C of global warming Freshwater stress from projected aridity Further increase of about 70 warm days per year Persistent heat stress in cattle in SIDS Loss of most coral reefs and weaker remaining structures owing to ocean acidification | Substantial and widespread impacts through inundation of SIDS, coastal flooding, freshwater stress, persistent heat stress and loss of most coral reefs (very likely) |
| Fynbos biome | About 30% of suitable climate area lost (medium confidence) | Increased losses (about 45%) of suitable climate area (medium confidence) | Up to 80% of suitable climate area lost (medium confidence) |
3.5.5
Avoiding Regional Tipping Points by Achieving More Ambitious Global Temperature Goals
Tipping points refer to critical thresholds in a system that, when exceeded, can lead to a significant change in the state of the system, often with an understanding that the change is irreversible. An understanding of the sensitivities of tipping points in the physical climate system, as well as in ecosystems and human systems, is essential for understanding the risks associated with different degrees of global warming. This subsection reviews tipping points across these three areas within the context of the different sensitivities to 1.5°C versus 2°C of global warming. Sensitivities to less ambitious global temperature goals are also briefly reviewed. Moreover, an analysis is provided of how integrated risks across physical, natural and human systems may accumulate to lead to the exceedance of thresholds for particular systems. The emphasis in this section is on the identification of regional tipping points and their sensitivity to 1.5°C and 2°C of global warming, whereas tipping points in the global climate system, referred to as large-scale singular events, were already discussed in Section 3.5.2. A summary of regional tipping points is provided in Table 3.7.
3.5.5.1
Arctic sea ice
Collins et al. (2013)1225 discussed the loss of Artic sea ice in the context of potential tipping points. Climate models have been used to assess whether a bifurcation exists that would lead to the irreversible loss of Arctic sea ice (Armour et al., 2011; Boucher et al., 2012; Ridley et al., 2012)1226 and to test whether the summer sea ice extent can recover after it has been lost (Schröder and Connolley, 2007; Sedláček et al., 2011; Tietsche et al., 2011)1227. These studies did not find evidence of bifurcation or indicate that sea ice returns within a few years of its loss, leading Collins et al. (2013)1228 to conclude that there is little evidence for a tipping point in the transition from perennial to seasonal ice cover. No evidence has been found for irreversibility or tipping points, suggesting that year-round sea ice will return given a suitable climate (medium confidence) (Schröder and Connolley, 2007; Sedláček et al., 2011; Tietsche et al., 2011)1229.
3.5.5.2
Tundra
Tree growth in tundra-dominated landscapes is strongly constrained by the number of days with mean air temperature above 0°C. A potential tipping point exists where the number of days below 0°C decreases to the extent that the tree fraction increases significantly. Tundra-dominated landscapes have warmed more than the global average over the last century (Settele et al., 2014)1230, with associated increases in fires and permafrost degradation (Bring et al., 2016; DeBeer et al., 2016; Jiang et al., 2016; Yang et al., 2016)1231. These processes facilitate conditions for woody species establishment in tundra areas, and for the eventual transition of the tundra to boreal forest. The number of investigations into how the tree fraction may respond in the Arctic to different degrees of global warming is limited, and studies generally indicate that substantial increases will likely occur gradually (e.g., Lenton et al., 2008)1232. Abrupt changes are only plausible at levels of warming significantly higher than 2°C (low confidence) and would occur in conjunction with a collapse in permafrost (Drijfhout et al., 2015)1233.
3.5.5.3
Permafrost
Widespread thawing of permafrost potentially makes a large carbon store (estimated to be twice the size of the atmospheric store; Dolman et al., 2010)1234 vulnerable to decomposition, which could lead to further increases in atmospheric carbon dioxide and methane and hence to further global warming. This feedback loop between warming and the release of greenhouse gas from thawing tundra represents a potential tipping point. However, the carbon released to the atmosphere from thawing permafrost is projected to be restricted to 0.09–0.19 Gt C yr–1 at 2°C of global warming and to 0.08–0.16 Gt C yr–1 at 1.5°C (E.J. Burke et al., 2018)1235, which does not indicate a tipping point (medium confidence). At higher degrees of global warming, in the order of 3°C, a different type of tipping point in permafrost may be reached. A single model projection (Drijfhout et al., 2015)1236 suggested that higher temperatures may induce a smaller ice fraction in soils in the tundra, leading to more rapidly warming soils and a positive feedback mechanism that results in permafrost collapse (low confidence). The disparity between the multi-millennial time scales of soil carbon accumulation and potentially rapid decomposition in a warming climate implies that the loss of this carbon to the atmosphere would be essentially irreversible (Collins et al., 2013)1237.
3.5.5.4
Asian monsoon
At a fundamental level, the pressure gradient between the Indian Ocean and Asian continent determines the strength of the Asian monsoon. As land masses warm faster than the oceans, a general strengthening of this gradient, and hence of monsoons, may be expected under global warming (e.g., Lenton et al., 2008)1238. Additional factors such as changes in albedo induced by aerosols and snow-cover change may also affect temperature gradients and consequently pressure gradients and the strength of the monsoon. In fact, it has been estimated that an increase of the regional land mass albedo to 0.5 over India would represent a tipping point resulting in the collapse of the monsoon system (Lenton et al., 2008)1239. The overall impacts of the various types of radiative forcing under different emissions scenarios are more subtle, with a weakening of the monsoon north of about 25°N in East Asia but a strengthening south of this latitude projected by Jiang and Tian (2013)1240 under high and modest emissions scenarios. Increases in the intensity of monsoon precipitation are likely under low mitigation (AR5). Given that scenarios of 1.5°C or 2°C of global warming would include a substantially smaller radiative forcing than those assessed in the study by Jiang and Tian (2013)1241, there is low confidence regarding changes in monsoons at these low global warming levels, as well as regarding the differences between responses at 1.5°C versus 2°C of warming.
3.5.5.5
West African monsoon and the Sahel
Earlier work has identified 3°C of global warming as the tipping point leading to a significant strengthening of the West African monsoon and subsequent wettening (and greening) of the Sahel and Sahara (Lenton et al., 2008)1242. AR5 (Niang et al., 2014)1243, as well as more recent research through the Coordinated Regional Downscaling Experiment for Africa (CORDEX–AFRICA), provides a more uncertain view, however, in terms of the rainfall futures of the Sahel under low mitigation futures. Even if a wetter Sahel should materialize under 3°C of global warming (low confidence), it should be noted that there would be significant offsets in the form of strong regional warming and related adverse impacts on crop yield, livestock mortality and human health under such low mitigation futures (Engelbrecht et al., 2015; Sylla et al., 2016; Weber et al., 2018)1244.
3.5.5.6
Rainforests
A large portion of rainfall over the world's largest rainforests is recirculated (e.g., Lenton et al., 2008)1245, which raises the concern that deforestation may trigger a threshold in reduced forest cover, leading to pronounced forest dieback. For the Amazon, this deforestation threshold has been estimated to be 40% (Nobre et al., 2016)1246. Global warming of 3°C–4°C may also, independent of deforestation, represent a tipping point that results in a significant dieback of the Amazon forest, with a key forcing mechanism being stronger El Niño events bringing more frequent droughts to the region (Nobre et al., 2016)1247. Increased fire frequencies under global warming may interact with and accelerate deforestation, particularly during periods of El Niño-induced droughts (Lenton et al., 2008; Nobre et al., 2016)1248. Global warming of 3°C is projected to reduce the extent of tropical rainforest in Central America, with biomass being reduced by about 40%, which can lead to a large replacement of rainforest by savanna and grassland (Lyra et al., 2017)1249. Overall, modelling studies (Huntingford et al., 2013; Nobre et al., 2016)1250 and observational constraints (Cox et al., 2013)1251 suggest that pronounced rainforest dieback may only be triggered at 3°C–4°C (medium confidence), although pronounced biomass losses may occur at 1.5°C– 2°C of global warming.
3.5.5.7
Boreal forests
Boreal forests are likely to experience stronger local warming than the global average (WGII AR5; Collins et al., 2013)1252. Increased disturbance from fire, pests and heat-related mortality may affect, in particular, the southern boundary of boreal forests (medium confidence) (Gauthier et al., 2015)1253, with these impacts accruing with greater warming and thus impacts at 2°C would be expected to be greater than those at 1.5°C (medium confidence). A tipping point for significant dieback of the boreal forests is thought to exist, where increased tree mortality would result in the creation of large regions of open woodlands and grasslands, which would favour further regional warming and increased fire frequencies, thus inducing a powerful positive feedback mechanism (Lenton et al., 2008; Lenton, 2012)1254. This tipping point has been estimated to exist between 3°C and 4°C of global warming (low confidence) (Lucht et al., 2006; Kriegler et al., 2009)1255, but given the complexities of the various forcing mechanisms and feedback processes involved, this is thought to be an uncertain estimate.
3.5.5.8
Heatwaves, unprecedented heat and human health
Increases in ambient temperature are linearly related to hospitalizations and deaths once specific thresholds are exceeded (so there is not a tipping point per se). It is plausible that coping strategies will not be in place for many regions, with potentially significant impacts on communities with low adaptive capacity, effectively representing the occurrence of a local/regional tipping point. In fact, even if global warming is restricted to below 2°C, there could be a substantial increase in the occurrence of deadly heatwaves in cities if urban heat island effects are considered, with impacts being similar at 1.5°C and 2°C but substantially larger than under the present climate (Matthews et al., 2017)1256. At 1.5°C of warming, twice as many megacities (such as Lagos, Nigeria, and Shanghai, China) than at present are likely to become heat stressed, potentially exposing more than 350 million more people to deadly heat stress by 2050. At 2°C of warming, Karachi (Pakistan) and Kolkata (India) could experience conditions equivalent to their deadly 2015 heatwaves on an annual basis (medium confidence). These statistics imply a tipping point in the extent and scale of heatwave impacts. However, these projections do not integrate adaptation to projected warming, for instance cooling that could be achieved with more reflective roofs and urban surfaces in general (Akbari et al., 2009; Oleson et al., 2010)1257.
3.5.5.9
Agricultural systems: key staple crops
A large number of studies have consistently indicated that maize crop yield will be negatively affected under increased global warming, with negative impacts being higher at 2°C of warming than at 1.5°C (e.g., Niang et al., 2014; Schleussner et al., 2016b; J. Huang et al., 2017; Iizumi et al., 2017)1258. Under 2°C of global warming, losses of 8–14% are projected in global maize production (Bassu et al., 2014)1259. Under global warming of more than 2°C, regional losses are projected to be about 20% if they co-occur with reductions in rainfall (Lana et al., 2017)1260. These changes may be classified as incremental rather than representing a tipping point. Large-scale reductions in maize crop yield, including the potential collapse of this crop in some regions, may exist under 3°C or more of global warming (low confidence) (e.g., Thornton et al., 2011)1261.
3.5.5.10
Agricultural systems: livestock in the tropics and subtropics
The potential impacts of climate change on livestock (Section 3.4.6), in particular the direct impacts through increased heat stress, have been less well studied than impacts on crop yield, especially from the perspective of critical thresholds being exceeded. A case study from Jamaica revealed that the difference in heat stress for livestock between 1.5°C and 2°C of warming is likely to exceed the limits for normal thermoregulation and result in persistent heat stress for these animals (Lallo et al., 2018)1262. It is plausible that this finding holds for livestock production in both tropical and subtropical regions more generally (medium confidence) (Section 3.4.6). Under 3°C of global warming, significant reductions in the areas suitable for livestock production could occur (low confidence), owing to strong increases in regional temperatures in the tropics and subtropics (high confidence). Thus, regional tipping points in the viability of livestock production may well exist, but little evidence quantifying such changes exists.
Table 3.7
Summary of enhanced risks in the exceedance of regional tipping points under different global temperature goals
| Tipping point | Warming of 1.5°C or less | Warming of 1.5°C–2°C | Warming of up to 3°C |
|---|---|---|---|
| Arctic sea ice | Arctic summer sea ice is likely to be maintained Sea ice changes reversible under suitable climate | The risk of an ice-free Arctic in summer is about 50% or higher Sea ice changes reversible under suitable climate restoration | Arctic is very likely to be ice free in summer Sea ice changes reversible under suitable climate |
| Tundra | Decrease in number of growing degree days below 0°CAbrupt increases in tree cover are unlikely | Further decreases in number of growing degree days below 0°C Abrupt increased in tree cover are unlikely | Potential for an abrupt increase in tree fraction (low confidence) |
| Permafrost | 17–44% reduction in permafrost Approximately 2 million km2 more permafrost maintained than under 2°C of global warming (medium confidence) Irreversible loss of stored carbon | 28–53% reduction in permafrost Irreversible loss of stored carbon | Potential for permafrost collapse (low confidence) |
| Asian monsoon | Low confidence in projected changes | Low confidence in projected changes | Increases in the intensity of monsoon precipitation likely |
| West African monsoon and the Sahel | Uncertain changes; unlikely that a tipping point is reached | Uncertain changes; unlikely that tipping point is reached | Strengthening of monsoon with wettening and greening of the Sahel and Sahara (low confidence) Negative associated impacts through increases in extreme temperature events |
| Rainforests | Reduced biomass, deforestation and fire increases pose uncertain risks to forest dieback | Larger biomass reductions than under 1.5°C of warming; deforestation and fire increases pose uncertain risk to forest dieback | Reduced extent of tropical rainforest in Central America and large replacement of rainforest by savanna and grassland Potential tipping point leading to pronounced forest dieback (medium confidence) |
| Boreal forests | Increased tree mortality at southern boundary of boreal forest (medium confidence) | Further increases in tree mortality at southern boundary of boreal forest (medium confidence) | Potential tipping point at 3°C–4°C for significant dieback of boreal forest (low confidence) |
| Heatwaves, unprecedented heat and human health | Substantial increase in occurrence of potentially deadly heatwaves (likely)More than 350 million more people exposed to deadly heat by 2050 under a midrange population growth scenario (likely) | Substantial increase in potentially deadly heatwaves (likely)Annual occurrence of heatwaves similar to the deadly 2015 heatwaves in India and Pakistan (medium confidence) | Substantial increase in potentially deadly heatwaves very likely |
| Agricultural systems: key staple crops | Global maize crop reductions of about 10% | Larger reductions in maize crop production than under 1.5°C of about 15% | Drastic reductions in maize crop globally and in Africa (high confidence) Potential tipping point for collapse of maize crop in some regions |
| Livestock in the tropics and subtropics | Increased heat stress | Onset of persistent heat stress (medium confidence) | Persistent heat stress likely |
3.6
Implications of Different 1.5°C and 2°C Pathways
This section provides an overview on specific aspects of the mitigation pathways considered compatible with 1.5°C of global warming. Some of these aspects are also addressed in more detail in Cross-Chapter Boxes 7 and 8 in this chapter.
3.6.1
Gradual versus Overshoot in 1.5°C Scenarios
All 1.5°C scenarios from Chapter 2 include some overshoot above 1.5°C of global warming during the 21st century (Chapter 2 and Cross-Chapter Box 8 in this chapter). The level of overshoot may also depend on natural climate variability. An overview of possible outcomes of 1.5°C-consistent mitigation scenarios for changes in the physical climate at the time of overshoot and by 2100 is provided in Cross-Chapter Box 8 on '1.5°C warmer worlds'. Cross-Chapter Box 8 also highlights the implications of overshoots.
3.6.2
Non-CO2 Implications and Projected Risks of Mitigation Pathways
3.6.2.1
Risks arising from land-use changes in mitigation pathways
In mitigation pathways, land-use change is affected by many different mitigation options. First, mitigation of non-CO2 emissions from agricultural production can shift agricultural production between regions via trade of agricultural commodities. Second, protection of carbon-rich ecosystems such as tropical forests constrains the area for agricultural expansion. Third, demand-side mitigation measures, such as less consumption of resource-intensive commodities (animal products) or reductions in food waste, reduce pressure on land (Popp et al., 2017; Rogelj et al., 2018)1272. Finally, carbon dioxide removal (CDR) is a key component of most, but not all, mitigation pathways presented in the literature to date which constrain warming to 1.5°C or 2°C. Carbon dioxide removal measures that require land include bioenergy with carbon capture and storage (BECCS), afforestation and reforestation (AR), soil carbon sequestration, direct air capture, biochar and enhanced weathering (see Cross-Chapter Box 7 in this chapter). These potential methods are assessed in Section 4.3.7.
In cost-effective integrated assessment modelling (IAM) pathways recently developed to be consistent with limiting warming to 1.5°C, use of CDR in the form of BECCS and AR are fundamental elements (Chapter 2; Popp et al., 2017; Hirsch et al., 2018; Rogelj et al., 2018; Seneviratne et al., 2018c)1273. The land-use footprint of CDR deployment in 1.5°C-consistent pathways can be substantial (Section 2.3.4, Figure 2.11), even though IAMs predominantly rely on second-generation biomass and assume future productivity increases in agriculture.
A body of literature has explored potential consequences of large-scale use of CDR. In this case, the corresponding land footprint by the end of the century could be extremely large, with estimates including: up to 18% of the land surface being used (Wiltshire and Davies-Barnard, 2015)1274; vast acceleration of the loss of primary forest and natural grassland (Williamson, 2016)1275 leading to increased greenhouse gas emissions (P. Smith et al., 2013, 2015)1276; and potential loss of up to 10% of the current forested lands to biofuels (Yamagata et al., 2018)1277. Other estimates reach 380–700 Mha or 21–64% of current arable cropland (Section 4.3.7). Boysen et al. (2017)1278 found that in a scenario in which emissions reductions were sufficient only to limit warming to 2.5°C, use of CDR to further limit warming to 1.7°C would result in the conversion of 1.1–1.5 Gha of land – implying enormous losses of both cropland and natural ecosystems. Newbold et al. (2015)1279 found that biodiversity loss in the Representative Concentration Pathway (RCP)2.6 scenario could be greater than that in RCP4.5 and RCP6, in which there is more climate change but less land-use change. Risks to biodiversity conservation and agricultural production are therefore projected to result from large-scale bioenergy deployment pathways (P. Smith et al., 2013; Tavoni and Socolow, 2013)1280. One study explored an extreme mitigation strategy encouraging biofuel expansion sufficient to limit warming to 1.5°C and found that this would be more disruptive to land use and crop prices than the impacts of a 2°C warmer world which has a larger climate signal and lower mitigation requirement (Ruane et al., 2018)1281. However, it should again be emphasized that many of the pathways explored in Chapter 2 of this report follow strategies that explore how to reduce these issues. Chapter 4 provides an assessment of the land footprint of various CDR technologies (Section 4.3.7).
The degree to which BECCS has these large land-use footprints depends on the source of the bioenergy used and the scale at which BECCS is deployed. Whether there is competition with food production and biodiversity depends on the governance of land use, agricultural intensification, trade, demand for food (in particular meat), feed and timber, and the context of the whole supply chain (Section 4.3.7, Fajardy and Mac Dowell, 2017; Booth, 2018; Sterman et al., 2018)1282.
The more recent literature reviewed in Chapter 2 explores pathways which limit warming to 2°C or below and achieve a balance between sources and sinks of CO2 by using BECCS that relies on second-generation (or even third-generation) biofuels, changes in diet or more generally, management of food demand, or CDR options such as forest restoration (Chapter 2; Bajželj et al., 2014)1283. Overall, this literature explores how to reduce the issues of competition for land with food production and with natural ecosystems (in particular forests) (Cross-Chapter Box 1 in Chapter 1; van Vuuren et al., 2009; Haberl et al., 2010, 2013; Bajželj et al., 2014; Daioglou et al., 2016; Fajardy and Mac Dowell, 2017)1284.
Some IAMs manage this transition by effectively protecting carbon stored on land and focusing on the conversion of pasture area into both forest area and bioenergy cropland. Some IAMs explore 1.5°C-consistent pathways with demand-side measures such as dietary changes and efficiency gains such as agricultural changes (Sections 2.3.4 and 2.4.4), which lead to a greatly reduced CDR deployment and consequently land-use impacts (van Vuuren et al., 2018)1285. In reality, however, whether this CDR (and bioenergy in general) has large adverse impacts on environmental and societal goals depends in large part on the governance of land use (Section 2.3.4; Obersteiner et al., 2016; Bertram et al., 2018; Humpenöder et al., 2018)1286.
Rates of sequestration of 3.3 GtC ha–1 require 970 Mha of afforestation and reforestation (Smith et al., 2015)1287. Humpenöder et al. (2014)1288 estimated that in least-cost pathways afforestation would cover 2800 Mha by the end of the century to constrain warming to 2°C. Hence, the amount of land considered if least-cost mitigation is implemented by afforestation and reforestation could be up to three to five times greater than that required by BECCS, depending on the forest management used. However, not all of the land footprint of CDR is necessarily to be in competition with biodiversity protection. Where reforestation is the restoration of natural ecosystems, it benefits both carbon sequestration and conservation of biodiversity and ecosystem services (Section 4.3.7) and can contribute to the achievement of the Aichi targets under the Convention on Biological Diversity (CBD) (Leadley et al., 2016)1289. However, reforestation is often not defined in this way (Section 4.3.8; Stanturf et al., 2014)1290 and the ability to deliver biodiversity benefits is strongly dependent on the precise nature of the reforestation, which has different interpretations in different contexts and can often include agroforestry rather than restoration of pristine ecosystems (Pistorious and Kiff, 2017)1291. However, 'natural climate solutions', defined as conservation, restoration, and improved land management actions that increase carbon storage and/or avoid greenhouse gas emissions across global forests, wetlands, grasslands and agricultural lands, are estimated to have the potential to provide 37% of the cost-effective CO2 mitigation needed by southern Europe and the Mediterranean by 2030 – in order to have a >66% chance of holding warming to below 2°C (Griscom et al., 2017)1292.
Any reductions in agricultural production driven by climate change and/or land management decisions related to CDR may (e.g., Nelson et al., 2014a; Dalin and Rodríguez-Iturbe, 2016)1293 or may not (Muratori et al., 2016)1294 affect food prices. However, these studies did not consider the deployment of second-generation (instead of first-generation) bioenergy crops, for which the land footprint can be much smaller.
Irrespective of any mitigation-related issues, in order for ecosystems to adapt to climate change, land use would also need to be carefully managed to allow biodiversity to disperse to areas that become newly climatically suitable for it (Section 3.4.1) and to protect the areas where the future climate will still remain suitable. This implies a need for considerable expansion of the protected area network (Warren et al., 2018b)1295, either to protect existing natural habitat or to restore it (perhaps through reforestation, see above). At the same time, adaptation to climate change in the agricultural sector (Rippke et al., 2016)1296 can require transformational as well as new approaches to land-use management; in order to meet the rising food demand of a growing human population, it is projected that additional land will need to be brought into production unless there are large increases in agricultural productivity (Tilman et al., 2011)1297. However, future rates of deforestation may be underestimated in the existing literature (Mahowald et al., 2017a)1298, and reforestation may therefore be associated with significant co-benefits if implemented to restore natural ecosystems (high confidence).
3.6.2.2
Biophysical feedbacks on regional climate associated with land-use changes
Changes in the biophysical characteristics of the land surface are known to have an impact on local and regional climates through changes in albedo, roughness, evapotranspiration and phenology, which can lead to a change in temperature and precipitation. This includes changes in land use through agricultural expansion/intensification (e.g., Mueller et al., 2016)1299, reforestation/revegetation endeavours (e.g., Feng et al., 2016; Sonntag et al., 2016; Bright et al., 2017)1300 and changes in land management (e.g., Luyssaert et al., 2014; Hirsch et al., 2017)1301 that can involve double cropping (e.g., Jeong et al., 2014; Mueller et al., 2015; Seifert and Lobell, 2015)1302, irrigation (e.g., Lobell et al., 2009; Sacks et al., 2009; Cook et al., 2011; Qian et al., 2013; de Vrese et al., 2016; Pryor et al., 2016; Thiery et al., 2017)1303, no-till farming and conservation agriculture (e.g., Lobell et al., 2006; Davin et al., 2014)1304, and wood harvesting (e.g., Lawrence et al., 2012)1305. Hence, the biophysical impacts of land-use changes are an important topic to assess in the context of low-emissions scenarios (e.g., van Vuuren et al., 2011b)1306, in particular for 1.5°C warming levels (see also Cross-Chapter Box 7 in this chapter).
The magnitude of the biophysical impacts is potentially large for temperature extremes. Indeed, changes induced both by modifications in moisture availability and irrigation and by changes in surface albedo tend to be larger (i.e., stronger cooling) for hot extremes than for mean temperatures (e.g., Seneviratne et al., 2013; Davin et al., 2014; Wilhelm et al., 2015; Hirsch et al., 2017; Thiery et al., 2017)1307. The reasons for reduced moisture availability are related to a strong contribution of moisture deficits to the occurrence of hot extremes in mid-latitude regions (Mueller and Seneviratne, 2012; Seneviratne et al., 2013)1308. In the case of surface albedo, cooling associated with higher albedo (e.g., in the case of no-till farming) is more effective at cooling hot days because of the higher incoming solar radiation for these days (Davin et al., 2014)1309. The overall effect of either irrigation or albedo has been found to be at the most in the order of about 1°C–2°C regionally for temperature extremes. This can be particularly important in the context of low-emissions scenarios because the overall effect is in this case of similar magnitude to the response to the greenhouse gas forcing (Figure 3.22; Hirsch et al., 2017; Seneviratne et al., 2018a, c)1310.
In addition to the biophysical feedbacks from land-use change and land management on climate, there are potential consequences for particular ecosystem services. This includes climate change-induced changes in crop yield (e.g., Schlenker and Roberts, 2009; van der Velde et al., 2012; Asseng et al., 2013, 2015; Butler and Huybers, 2013; Lobell et al., 2014)1311 which may be further exacerbated by competing demands for arable land between reforestation mitigation activities, crop growth for BECCS (Chapter 2), increasing food production to support larger populations, and urban expansion (see review by Smith et al., 2010)1312. In particular, some land management practices may have further implications for food security, for instance throughincreases or decreases in yield when tillage is ceased in some regions (Pittelkow et al., 2014)1313.
We note that the biophysical impacts of land use in the context of mitigation pathways constitute an emerging research topic. This topic, as well as the overall role of land-use change in climate change projections and socio-economic pathways, will be addressed in depth in the upcoming IPCC Special Report on Climate Change and Land Use due in 2019.
Figure 3.22
Regional temperature scaling with carbon dioxide (CO2) concentration (ppm) from 1850 to 2099 for two different regions defined in the Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) for central Europe (CEU) (a) and central North America (CNA) (b).
Solid lines correspond to the regional average annual maximum daytime temperature (TXx) anomaly, and dashed lines correspond to the global mean temperature anomaly, where all temperature anomalies are relative to 1850–1870 and units are degrees Celsius. The black line in all panels denotes the three-member control ensemble mean, with the grey shaded regions corresponding to the ensemble range. The coloured lines represent the three-member ensemble means of the experiments corresponding to albedo +0.02 (cyan), albedo +0.04 (purple), albedo + 0.08 (orange), albedo +0.10 (red), irrigation (blue), and irrigation with albedo +0.10 (green). Adapted from Hirsch et al. (2017)1314.
3.6.2.3
Atmospheric compounds (aerosols and methane)
There are multiple pathways that could be used to limit anthropogenic climate change, and the details of the pathways will influence the impacts of climate change on humans and ecosystems. Anthropogenic-driven changes in aerosols cause important modifications to the global climate (Bindoff et al., 2013a; Boucher et al., 2013b; P. Wu et al., 2013; Sarojini et al., 2016; H. Wang et al., 2016)1315. Enforcement of strict air quality policies may lead to a large decrease in cooling aerosol emissions in the next few decades. These aerosol emission reductions may cause a warming comparable to that resulting from the increase in greenhouse gases by mid-21st century under low CO2 pathways (Kloster et al., 2009; Acosta Navarro et al., 2017)1316. Further background is provided in Sections 2.2.2 and 2.3.1; Cross Chapter Box 1 in Chapter 1). Because aerosol effects on the energy budget are regional, strong regional changes in precipitation from aerosols may occur if aerosol emissions are reduced for air quality reasons or as a co-benefit from switches to sustainable energy sources (H. Wang et al., 2016)1317. Thus, regional impacts, especially on precipitation, are very sensitive to 1.5°C-consistent pathways (Z. Wang et al., 2017)1318.
Pathways which rely heavily on reductions in methane (CH4) instead of CO2 will reduce warming in the short term because CH4 is such a stronger and shorter-lived greenhouse gas than CO2, but will lead to stronger warming in the long term because of the much longer residence time of CO2 (Myhre et al., 2013; Pierrehumbert, 2014)1319. In addition, the dominant loss mechanism for CH4 is atmospheric photo-oxidation. This conversion modifies ozone formation and destruction in the troposphere and stratosphere, therefore modifying the contribution of ozone to radiative forcing, as well as feedbacks on the oxidation rate of methane itself (Myhre et al., 2013)1320. Focusing on pathways and policies which both improve air quality and reduce impacts of climate change can provide multiple co-benefits (Shindell et al., 2017)1321. These pathways are discussed in detail in Sections 4.3.7 and 5.4.1 and in Cross-Chapter Box 12 in Chapter 5.
Atmospheric aerosols and gases can also modify the land and ocean uptake of anthropogenic CO2; some compounds enhance uptake while others reduce it (Section 2.6.2; Ciais et al., 2013)1322. While CO2 emissions tend to encourage greater uptake of carbon by the land and the ocean (Ciais et al., 2013)1323, CH4 emissions can enhance ozone pollution, depending on nitrogen oxides, volatile organic compounds and other organic species concentrations, and ozone pollution tends to reduce land productivity (Myhre et al., 2013; B. Wang et al., 2017)1324. Aside from inhibiting land vegetation productivity, ozone may also alter the CO2, CH4 and nitrogen (N2O) exchange at the land–atmosphere interface and transform the global soil system from a sink to a source of carbon (B. Wang et al., 2017)1325. Aerosols and associated nitrogen-based compounds tend to enhance the uptake of CO2 in land and ocean systems through deposition of nutrients and modification of climate (Ciais et al., 2013; Mahowald et al., 2017b)1326.
3.6.3
Implications Beyond the End of the Century
3.6.3.1
Sea ice
Sea ice is often cited as a tipping point in the climate system (Lenton, 2012)1356. Detailed modelling of sea ice (Schröder and Connolley, 2007; Sedláček et al., 2011; Tietsche et al., 2011)1357, however, suggests that summer sea ice can return within a few years after its artificial removal for climates in the late 20th and early 21st centuries. Further studies (Armour et al., 2011; Boucher et al., 2012; Ridley et al., 2012)1358 modelled the removal of sea ice by raising CO2 concentrations and studied subsequent regrowth by lowering CO2. These studies suggest that changes in Arctic sea ice are neither irreversible nor exhibit bifurcation behaviour. It is therefore plausible that the extent of Arctic sea ice may quickly re-equilibrate to the end-of-century climate under an overshoot scenario.
3.6.3.2
Sea level
Policy decisions related to anthropogenic climate change will have a profound impact on sea level, not only for the remainder of this century but for many millennia to come (Clark et al., 2016)1359. On these long time scales, 50 m of sea level rise (SLR) is possible (Clark et al., 2016)1360. While it is virtually certain that sea level will continue to rise well beyond 2100, the amount of rise depends on future cumulative emissions (Church et al., 2013)1361 as well as their profile over time (Bouttes et al., 2013; Mengel et al., 2018)1362. Marzeion et al. (2018)1363 found that 28–44% of present-day glacier volume is unsustainable in the present-day climate and that it would eventually melt over the course of a few centuries, even if there were no further climate change. Some components of SLR, such as thermal expansion, are only considered reversible on centennial time scales (Bouttes et al., 2013; Zickfeld et al., 2013)1364, while the contribution from ice sheets may not be reversible under any plausible future scenario (see below).
Based on the sensitivities summarized by Levermann et al. (2013)1365, the contributions of thermal expansion (0.20–0.63 m °C–1) and glaciers (0.21 m °C–1 but falling at higher degrees of warming mostly because of the depletion of glacier mass, with a possible total loss of about 0.6 m) amount to 0.5–1.2 m and 0.6–1.7 m in 1.5°C and 2°C warmer worlds, respectively. The bulk of SLR on greater than centennial time scales will therefore be caused by contributions from the continental ice sheets of Greenland and Antarctica, whose existence is threatened on multi-millennial time scales.
For Greenland, where melting from the ice sheet's surface is important, a well-documented instability exists where the surface of a thinning ice sheet encounters progressively warmer air temperatures that further promote melting and thinning. A useful indicator associated with this instability is the threshold at which annual mass loss from the ice sheet by surface melt exceeds mass gain by snowfall. Previous estimates put this threshold at about 1.9°C to 5.1°C above pre-industrial temperatures (Gregory and Huybrechts, 2006)1366. More recent analyses, however, suggest that this threshold sits between 0.8°C and 3.2°C, with a best estimate at 1.6°C (Robinson et al., 2012)1367. The continued decline of the ice sheet after this threshold has been passed is highly dependent on the future climate and varies between about 80% loss after 10,000 years to complete loss after as little as 2000 years (contributing about 6 m to SLR). Church et al. (2013)1368 were unable to quantify a likely range for this threshold. They assigned medium confidence to a range greater than 2°C but less than 4°C, and had low confidence in a threshold of about 1°C. There is insufficient new literature to change this assessment.
The Antarctic ice sheet, in contrast, loses the mass gained by snowfall as outflow and subsequent melt to the ocean, either directly from the underside of floating ice shelves or indirectly by the melting of calved icebergs. The long-term existence of this ice sheet will also be affected by a potential instability (the marine ice sheet instability, MISI), which links outflow (or mass loss) from the ice sheet to water depth at the grounding line (i.e., the point at which grounded ice starts to float and becomes an ice shelf) so that retreat into deeper water (the bedrock underlying much of Antarctica slopes downwards towards the centre of the ice sheet) leads to further increases in outflow and promotes yet further retreat (Schoof, 2007)1369. More recently, a variant on this mechanism was postulated in which an ice cliff forms at the grounding line and retreats rapidly though fracture and iceberg calving (DeConto and Pollard, 2016)1370. There is a growing body of evidence (Golledge et al., 2015; DeConto and Pollard, 2016)1371 that large-scale retreat may be avoided in emissions scenarios such as Representative Concentration Pathway (RCP)2.6 but that higher-emissions RCP scenarios could lead to the loss of the West Antarctic ice sheet and sectors in East Antarctica, although the duration (centuries or millennia) and amount of mass loss during such a collapse is highly dependent on model details and no consensus exists yet. Schoof (2007)1372 suggested that retreat may be irreversible, although a rigorous test has yet to be made. In this context, overshoot scenarios, especially of higher magnitude or longer duration, could increase the risk of such irreversible retreat.
Church et al. (2013)1373 noted that the collapse of marine sectors of the Antarctic ice sheet could lead to a global mean sea level (GMSL) rise above the likely range, and that there was medium confidence that this additional contribution 'would not exceed several tenths of a metre during the 21st century'.
The multi-centennial evolution of the Antarctic ice sheet has been considered in papers by DeConto and Pollard (2016)1374 and Golledge et al. (2015)1375. Both suggest that RCP2.6 is the only RCP scenario leading to long-term contributions to GMSL of less than 1.0 m. The long-term committed future of Antarctica and the GMSL contribution at 2100 are complex and require further detailed process-based modelling; however, a threshold in this contribution may be located close to 1.5°C to 2°C of global warming.
In summary, there is medium confidence that a threshold in the long-term GMSL contribution of both the Greenland and Antarctic ice sheets lies around 1.5°C to 2°C of global warming relative to pre-industrial; however, the GMSL associated with these two levels of global warming cannot be differentiated on the basis of the existing literature.
3.6.3.3
Permafrost
The slow rate of permafrost thaw introduces a lag between the transient degradation of near-surface permafrost and contemporary climate, so that the equilibrium response is expected to be 25–38% greater than the transient response simulated in climate models (Slater and Lawrence, 2013)1376. The long-term, equilibrium Arctic permafrost loss to global warming was analysed by Chadburn et al. (2017)1377. They used an empirical relation between recent mean annual air temperatures and the area underlain by permafrost coupled to Coupled Model Intercomparison Project Phase 5 (CMIP5) stabilization projections to 2300 for RCP2.6 and RCP4.5. Their estimate of the sensitivity of permafrost to warming is 2.9–5.0 million km2 °C–1 (1 standard deviation confidence interval), which suggests that stabilizing climate at 1.5°C as opposed to 2°C would reduce the area of eventual permafrost loss by 1.5 to 2.5 million km2 (stabilizing at 56–83% as opposed to 43–72% of 1960–1990 levels). This work, combined with the assessment of Collins et al. (2013)1378 on the link between global warming and permafrost loss, leads to the assessment that permafrost extent would be appreciably greater in a 1.5°C warmer world compared to in a 2°C warmer world (low to medium confidence).
Most scientific literature specific to global warming of 1.5°C is only just emerging. This has led to differences in the amount of information available and gaps across the various sections of this chapter. In general, the number of impact studies that specifically focused on 1.5°C lags behind climate-change projections in general, due in part to the dependence of the former on the latter. There are also insufficient studies focusing on regional changes, impacts and consequences at 1.5°C and 2°C of global warming.
The following gaps have been identified with respect to tools, methodologies and understanding in the current scientific literature specific to Chapter 3. The gaps identified here are not comprehensive but highlight general areas for improved understanding, especially regarding global warming at 1.5°C compared to 2°C and higher levels.
3.7.1
Gaps in Methods and Tools
- Regional and global climate model simulations for low-emissions scenarios such as a 1.5°C warmer world.
- Robust probabilistic models which separate the relatively small signal between 1.5°C versus 2°C from background noise, and which handle the many uncertainties associated with non-linearities, innovations, overshoot, local scales, and latent or lagging responses in climate.
- Projections of risks under a range of climate and development pathways required to understand how development choices affect the magnitude and pattern of risks, and to provide better estimates of the range of uncertainties.
- More complex and integrated socio-ecological models for predicting the response of terrestrial as well as coastal and oceanic ecosystems to climate and models which are more capable of separating climate effects from those associated with human activities.
- Tools for informing local and regional decision-making, especially when the signal is ambiguous at 1.5°C and/or reverses sign at higher levels of global warming.
3.7.2
Gaps in Understanding
3.7.2.1
Earth systems and 1.5°C of global warming
- The cumulative effects of multiple stresses and risks (e.g., increased storm intensity interacting with sea level rise and the effect on coastal people; feedbacks on wetlands due to climate change and human activities).
- Feedbacks associated with changes in land use/cover for low-emissions scenarios, for example feedback from changes in forest cover, food production, biofuel production, bio-energy with carbon capture and storage (BECCS), and associated unquantified biophysical impacts.
- The distinct impacts of different overshoot scenarios, depending on (i) the peak temperature of the overshoot, (ii) the length of the overshoot period, and (iii) the associated rate of change in global temperature over the time period of the overshoot.
3.7.2.2
Physical and chemical characteristics of a 1.5°C warmer world
- Critical thresholds for extreme events (e.g., drought and inundation) between 1.5°C and 2°C of warming for different climate models and projections. All aspects of storm intensity and frequency as a function of climate change, especially for 1.5°C and 2°C warmer worlds, and the impact of changing storminess on storm surges, damage, and coastal flooding at regional and local scales.
- The timing and implications of the release of stored carbon in Arctic permafrost in a 1.5°C warmer world and for climate stabilization by the end of the century.
- Antarctic ice sheet dynamics, global sea level, and links between seasonal and year-long sea ice in both polar regions.
3.7.2.3
Terrestrial and freshwater systems
- The dynamics between climate change, freshwater resources and socio-economic impacts for lower levels of warming.
- How the health of vegetation is likely to change, carbon storage in plant communities and landscapes, and phenomena such as the fertilization effect.
- The risks associated with species' maladaptation in response to climatic changes (e.g., effects of late frosts). Questions associated with issues such as the consequences of species advancing their spring phenology in response to warming, as well as the interaction between climate change, range shifts and local adaptation in a 1.5°C warmer world.
- The biophysical impacts of land use in the context of mitigation pathways.
3.7.2.4
Ocean Systems
- Deep sea processes and risks to deep sea habitats and ecosystems.
- How changes in ocean chemistry in a 1.5°C warmer world, including decreasing ocean oxygen content, ocean acidification and changes in the activity of multiple ion species, will affect natural and human systems.
- How ocean circulation is changing towards 1.5°C and 2°C warmer worlds, including vertical mixing, deep ocean processes, currents, and their impacts on weather patterns at regional to local scales.
- The impacts of changing ocean conditions at 1.5°C and 2°C of warming on foodwebs, disease, invading species, coastal protection, fisheries and human well-being, especially as organisms modify their biogeographical ranges within a changing ocean.
- Specific linkages between food security and changing coastal and ocean resources.
3.7.2.5
Human systems
- The impacts of global and regional climate change at 1.5°C on food distribution, nutrition, poverty, tourism, coastal infrastructure and public health, particularly for developing nations.
- Health and well-being risks in the context of socio-economic and climate change at 1.5°C, especially in key areas such as occupational health, air quality and infectious disease.
- Micro-climates at urban/city scales and their associated risks for natural and human systems, within cities and in interaction with surrounding areas. For example, current projections do not integrate adaptation to projected warming by considering cooling that could be achieved through a combination of revised building codes, zoning and land use to build more reflective roofs and urban surfaces that reduce urban heat island effects.
- Implications of climate change at 1.5°C on livelihoods and poverty, as well as on rural communities, indigenous groups and marginalized people.
- The changing levels of risk in terms of extreme events, including storms and heatwaves, especially with respect to people being displaced or having to migrate away from sensitive and exposed systems such as small islands, low-lying coasts and deltas.
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Footnotes
- Robust is used here to mean that at least two thirds of climate models show the same sign of changes at the grid point scale, and that differences in large regions are statistically significant. ↩
- Robust is used here to mean that at least two thirds of climate models show the same sign of changes at the grid point scale, and that differences in large regions are statistically significant. ↩
- Projected changes in impacts between different levels of global warming are determined with respect to changes in global mean near-surface air temperature. ↩
- Projected changes in impacts between different levels of global warming are determined with respect to changes in global mean near-surface air temperature. ↩
- Ice free is defined for the Special Report as when the sea ice extent is less than 106 km2. Ice coverage less than this is considered to be equivalent to an ice-free Arctic Ocean for practical purposes in all recent studies. ↩
- Using the SREX definition of regions (Figure 3.2) ↩
- Using the SREX definition of regions (Figure 3.2) ↩
- Ice free is defined for the Special Report as when the sea ice extent is less than 106 km2. Ice coverage less than this is considered to be equivalent to an ice-free Arctic Ocean for practical purposes in all recent studies. ↩
- The approximate temperatures are derived from Figure 10.5a in Meehl et al. (2007), which indicates an ensemble average projection of 0.7°C or 3°C above 1980–1999 temperatures, which were already 0.5°C above pre-industrial values ↩
- Part of this discussion is based on (Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres, 122(4), 2269–2288, doi:10.1002/2016jd026125.
↩
References
- IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of IPCC Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, USA, 594 pp. ↩
- IPCC, 2014b: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 688 pp. ↩
- Bindoff, N.L. et al., 2013b: Detection and Attribution of Climate Change: from Global to Regional – Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–25. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. ↩
- IPCC, 2007: Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II, III to the Fourth Assessment Report of the International Panel on Climate Change. [Core Writing Team, R.K. Pachauri, and A. Reisinger (eds.)]. IPCC, Geneva, Switzerland, 104 pp.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. ↩ - IPCC, 2000: Special Report on Emissions Scenarios. [Nakicenovic, N. and R. Swart (eds.)]. A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 599 pp. ↩
- Kirtman, B. et al., 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 953–1028. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres, 122(4), 2269–2288, doi:10.1002/2016jd026125.
Thiery, W. et al., 2017: Present-day irrigation mitigates heat extremes. Journal of Geophysical Research: Atmospheres, 122(3), 1403–1422, doi:10.1002/2016jd025740. ↩ - Wang, Z. et al., 2017: Scenario dependence of future changes in climate extremes under 1.5°C and 2°C global warming.. Scientific Reports, 7, 46432, doi:10.1038/srep46432. ↩
- Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩
- James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. Wiley Interdisciplinary Reviews: Climate Change, 8(2), e457, doi:10.1002/wcc.457. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩ - Jacob, D. and S. Solman, 2017: IMPACT2C – An introduction. Climate Services, 7, 1–2, doi:10.1016/j.cliser.2017.07.006. ↩
- Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters, 9(3), 034006, doi:10.1088/1748-9326/9/3/034006. ↩
- James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. Wiley Interdisciplinary Reviews: Climate Change, 8(2), e457, doi:10.1002/wcc.457. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. ↩
- Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450.
Tebaldi, C. and R. Knutti, 2018: Evaluating the accuracy of climate change pattern emulation for low warming targets. Environmental Research Letters, 13(5), 055006, doi:10.1088/1748-9326/aabef2. ↩ - Pendergrass, A.G., F. Lehner, B.M. Sanderson, and Y. Xu, 2015: Does extreme precipitation intensity depend on the emissions scenario? Geophysical Research Letters, 42(20), 8767–8774, doi:10.1002/2015gl065854. ↩
- Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037. ↩
- Hansen, G. and D. Stone, 2016: Assessing the observed impact of anthropogenic climate change. Nature Climate Change, 6(5), 532–537, doi:10.1038/nclimate2896. ↩
- Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Bonal, D., B. Burban, C. Stahl, F. Wagner, and B. Hérault, 2016: The response of tropical rainforests to drought – lessons from recent research and future prospects. Annals of Forest Science, 73(1), 27–44, doi:10.1007/s13595-015-0522-5. ↩ - Fabricius, K.E. et al., 2011: Losers and winners in coral reefs acclimatized to elevated carbon dioxide concentrations. Nature Climate Change, 1(3), 165–169, doi:10.1038/nclimate1122.
Allen, R., A. Foggo, K. Fabricius, A. Balistreri, and J.M. Hall-Spencer, 2017: Tropical CO2 seeps reveal the impact of ocean acidification on coral reef invertebrate recruitment. Marine Pollution Bulletin, 124(2), 607–613, doi:10.1016/j.marpolbul.2016.12.031. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Iizumi, T. et al., 2017: Responses of crop yield growth to global temperature and socioeconomic changes. Scientific Reports, 7(1), 7800, doi:10.1038/s41598-017-08214-4. ↩
- Frieler, K. et al., 2017: Assessing the impacts of 1.5°C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development, 10, 4321–4345, doi:10.5194/gmd-10-4321-2017. ↩
- Grillakis, M.G., A.G. Koutroulis, I.N. Daliakopoulos, and I.K. Tsanis, 2017: A method to preserve trends in quantile mapping bias correction of climate modeled temperature. Earth System Dynamics, 8, 889–900, doi:10.5194/esd-8-889-2017. ↩
- Frieler, K. et al., 2017: Assessing the impacts of 1.5°C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development, 10, 4321–4345, doi:10.5194/gmd-10-4321-2017.
Reyer, C.P.O. et al., 2017d: Turn down the heat: regional climate change impacts on development. Regional Environmental Change, 17(6), 1563–1568, doi:10.1007/s10113-017-1187-4.
Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩ - Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters, 9(3), 034006, doi:10.1088/1748-9326/9/3/034006. ↩
- Frost, A.J. et al., 2011: A comparison of multi-site daily rainfall downscaling techniques under Australian conditions. Journal of Hydrology, 408(1), 1–18, doi:10.1016/j.jhydrol.2011.06.021. ↩
- Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environmental Research Letters, 8(4), 044018, doi:10.1088/1748-9326/8/4/044018.
Warszawski, L. et al., 2014: The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework. Proceedings of the National Academy of Sciences, 111(9), 3228–32, doi:10.1073/pnas.1312330110.
Frieler, K. et al., 2017: Assessing the impacts of 1.5°C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development, 10, 4321–4345, doi:10.5194/gmd-10-4321-2017. ↩ - Asseng, S. et al., 2013: Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3(9), 827–832, doi:10.1038/nclimate1916. ↩
- Fronzek, S., T.R. Carter, and M. Luoto, 2011: Evaluating sources of uncertainty in modelling the impact of probabilistic climate change on sub-arctic palsa mires. Natural Hazards and Earth System Science, 11(11), 2981–2995, doi:10.5194/nhess-11-2981-2011. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952.
Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. ↩ - Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters, 9(3), 034006, doi:10.1088/1748-9326/9/3/034006.
Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560–564, doi:10.1038/nclimate2617.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542.
Déqué, M. et al., 2017: A multi-model climate response over tropical Africa at +2°C. Climate Services, 7, 87–95, doi:10.1016/j.cliser.2016.06.002.
Maule, C.F., T. Mendlik, and O.B. Christensen, 2017: The effect of the pathway to a two degrees warmer world on the regional temperature change of Europe. Climate Services, 7, 3–11, doi:10.1016/j.cliser.2016.07.002.
Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017.
Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Zaman, A.M. et al., 2017: Impacts on river systems under 2°C warming: Bangladesh Case Study. Climate Services, 7, 96–114, doi:10.1016/j.cliser.2016.10.002.
Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452.
Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710.
Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813.
Mitchell, D. et al., 2018a: The myriad challenges of the Paris Agreement. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20180066, doi:10.1098/rsta.2018.0066.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450.
Wehner, M.F. et al., 2018b: Changes in extremely hot days under stabilized 1.5 and 2.0°C global warming scenarios as simulated by the HAPPI multi-model ensemble. Earth System Dynamics, 9(1), 299–311, doi:10.5194/esd-9-299-2018. ↩ - Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308. ↩
- Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952.
Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩ - Richardson, M., K. Cowtan, E. Hawkins, and M.B. Stolpe, 2016: Reconciled climate response estimates from climate models and the energy budget of Earth. Nature Climate Change, 6(10), 931–935, doi:10.1038/nclimate3066. ↩
- Ribes, A., F.W. Zwiers, J.-M. Azaïs, and P. Naveau, 2017: A new statistical approach to climate change detection and attribution. Climate Dynamics, 48(1), 367–386, doi:10.1007/s00382-016-3079-6. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- AghaKouchak, A., L. Cheng, O. Mazdiyasni, and A. Farahmand, 2014: Global warming and changes in risk of concurrent climate extremes: Insights from the 2014 California drought. Geophysical Research Letters, 41(24), 8847–8852, doi:10.1002/2014gl062308.
Van Den Hurk, B., E. Van Meijgaard, P. De Valk, K.J. Van Heeringen, and J. Gooijer, 2015: Analysis of a compounding surge and precipitation event in the Netherlands. Environmental Research Letters, 10(3), 035001, doi:10.1088/1748-9326/10/3/035001.
Martius, O., S. Pfahl, and C. Chevalier, 2016: A global quantification of compound precipitation and wind extremes. Geophysical Research Letters, 43(14), 7709–7717, doi:10.1002/2016gl070017.
Zscheischler, J. et al., 2018: Future climate risk from compound events. Nature Climate Change, 8(6), 469–477, doi:10.1038/s41558-018-0156-3. ↩ - Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Cowtan, K. and R.G. Way, 2014: Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society, 140(683), 1935–1944, doi:10.1002/qj.2297. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Sun, Y., X. Zhang, G. Ren, F.W. Zwiers, and T. Hu, 2016: Contribution of urbanization to warming in China. Nature Climate Change, 6(7), 706–709, doi:10.1038/nclimate2956.
Wan, H., X. Zhang, and F. Zwiers, 2018: Human influence on Canadian temperatures. Climate Dynamics, 1–16, doi:10.1007/s00382-018-4145-z. ↩ - Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Reviews of Geophysics, 48(4), RG4004, doi:10.1029/2010rg000345. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320.
Dosio, A. and E.M. Fischer, 2018: Will half a degree make a difference? Robust projections of indices of mean and extreme climate in Europe under 1.5°C, 2°C, and 3°C global warming. Geophysical Research Letters, 45, 935–944, doi:10.1002/2017gl076222.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩ - Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017. ↩
- Dosio, A., L. Mentaschi, E.M. Fischer, and K. Wyser, 2018: Extreme heat waves under 1.5°C and 2°C global warming. Environmental Research Letters, 13(5), 054006, doi:10.1088/1748-9326/aab827.
Kjellström, E. et al., 2018: European climate change at global mean temperature increases of 1.5 and 2°C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models. Earth System Dynamics, 9(2), 459–478, doi:10.5194/esd-9-459-2018. ↩ - Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Dosio, A., L. Mentaschi, E.M. Fischer, and K. Wyser, 2018: Extreme heat waves under 1.5°C and 2°C global warming. Environmental Research Letters, 13(5), 054006, doi:10.1088/1748-9326/aab827.
Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩ - Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450.
Wehner, M.F. et al., 2018b: Changes in extremely hot days under stabilized 1.5 and 2.0°C global warming scenarios as simulated by the HAPPI multi-model ensemble. Earth System Dynamics, 9(1), 299–311, doi:10.5194/esd-9-299-2018. ↩ - Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩ - Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560–564, doi:10.1038/nclimate2617.
Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩ - Whan, K. et al., 2015: Impact of soil moisture on extreme maximum temperatures in Europe. Weather and Climate Extremes, 9, 57–67, doi:10.1016/j.wace.2015.05.001. ↩
- Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩ - Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Vogel, M.M. et al., 2017: Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophysical Research Letters, 44(3), 1511–1519, doi:10.1002/2016gl071235. ↩
- Vogel, M.M. et al., 2017: Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophysical Research Letters, 44(3), 1511–1519, doi:10.1002/2016gl071235. ↩
- IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
Masson-Delmotte, V. et al., 2013: Information from Paleoclimate Archives. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 383–464. ↩ - Dosio, A., L. Mentaschi, E.M. Fischer, and K. Wyser, 2018: Extreme heat waves under 1.5°C and 2°C global warming. Environmental Research Letters, 13(5), 054006, doi:10.1088/1748-9326/aab827. ↩
- Mahlstein, I., R. Knutti, S. Solomon, and R.W. Portmann, 2011: Early onset of significant local warming in low latitude countries. Environmental Research Letters, 6(3), 034009, doi:10.1088/1748-9326/6/3/034009. ↩
- Coumou, D. and A. Robinson, 2013: Historic and future increase in the global land area affected by monthly heat extremes. Environmental Research Letters, 8(3), 034018, doi:10.1088/1748-9326/8/3/034018. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017. ↩
- Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩
- Dosio, A., L. Mentaschi, E.M. Fischer, and K. Wyser, 2018: Extreme heat waves under 1.5°C and 2°C global warming. Environmental Research Letters, 13(5), 054006, doi:10.1088/1748-9326/aab827. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017. ↩
- Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩
- Benjamini, Y. and Y. Hochberg, 1995: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289–300, http://www.jstor.org/stable/2346101. ↩
- Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560–564, doi:10.1038/nclimate2617. ↩
- Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩
- Benjamini, Y. and Y. Hochberg, 1995: Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289–300, http://www.jstor.org/stable/2346101. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩ - Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254. ↩
- Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254. ↩
- Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254. ↩
- Singh, D., M. Tsiang, B. Rajaratnam, and N.S. Diffenbaugh, 2014: Observed changes in extreme wet and dry spells during the South Asian summer monsoon season. Nature Climate Change, 4(6), 456–461, doi:10.1038/nclimate2208.
Taylor, C.M. et al., 2017: Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature, 544(7651), 475–478, doi:10.1038/nature22069.
Bichet, A. and A. Diedhiou, 2018: West African Sahel has become wetter during the last 30 years, but dry spells are shorter and more frequent. Climate Research, 75(2), 155–162, doi:10.3354/cr01515. ↩ - Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Déqué, M. et al., 2017: A multi-model climate response over tropical Africa at +2°C. Climate Services, 7, 87–95, doi:10.1016/j.cliser.2016.06.002. ↩
- Déqué, M. et al., 2017: A multi-model climate response over tropical Africa at +2°C. Climate Services, 7, 87–95, doi:10.1016/j.cliser.2016.06.002. ↩
- Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters, 9(3), 034006, doi:10.1088/1748-9326/9/3/034006.
Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710.
Kjellström, E. et al., 2018: European climate change at global mean temperature increases of 1.5 and 2°C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models. Earth System Dynamics, 9(2), 459–478, doi:10.5194/esd-9-459-2018. ↩ - Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters, 9(3), 034006, doi:10.1088/1748-9326/9/3/034006. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710.
Kjellström, E. et al., 2018: European climate change at global mean temperature increases of 1.5 and 2°C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models. Earth System Dynamics, 9(2), 459–478, doi:10.5194/esd-9-459-2018. ↩ - Fischer, E.M., J. Sedláček, E. Hawkins, and R. Knutti, 2014: Models agree on forced response pattern of precipitation and temperature extremes. Geophysical Research Letters, 41(23), 8554–8562, doi:10.1002/2014gl062018.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩ - Pendergrass, A.G., F. Lehner, B.M. Sanderson, and Y. Xu, 2015: Does extreme precipitation intensity depend on the emissions scenario? Geophysical Research Letters, 42(20), 8767–8774, doi:10.1002/2015gl065854. ↩
- Vautard, R. et al., 2014: The European climate under a 2°C global warming. Environmental Research Letters, 9(3), 034006, doi:10.1088/1748-9326/9/3/034006. ↩
- Jacob, D. et al., 2014: EURO-CORDEX: new high-resolution climate change projections for European impact research. Regional Environmental Change, 14(2), 563–578, doi:10.1007/s10113-013-0499-2. ↩
- Teichmann, C. et al., 2018: Avoiding extremes: Benefits of staying below +1.5°C compared to +2.0°C and +3.0°C global warming. Atmosphere, 9(4), 1–19, doi:10.3390/atmos9040115. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560–564, doi:10.1038/nclimate2617. ↩
- Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308. ↩
- Jiang, D.B. and Z.P. Tian, 2013: East Asian monsoon change for the 21st century: Results of CMIP3 and CMIP5 models. Chinese Science Bulletin, 58(12), 1427–1435, doi:10.1007/s11434-012-5533-0.
Jones, C. and L.M. Carvalho, 2013: Climate change in the South American monsoon system: Present climate and CMIP5 projections. Journal of Climate, 26(17), 6660–6678, doi:10.1175/jcli-d-12-00412.1.
Sylla, M.B. et al., 2015: Projected Changes in the Annual Cycle of High-Intensity Precipitation Events over West Africa for the Late Twenty-First Century. Journal of Climate, 28(16), 6475–6488, doi:10.1175/jcli-d-14-00854.1.
Sylla, M.B., N. Elguindi, F. Giorgi, and D. Wisser, 2016: Projected robust shift of climate zones over West Africa in response to anthropogenic climate change for the late 21st century. Climatic Change, 134(1), 241–253, doi:10.1007/s10584-015-1522-z. ↩ - Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Fischer, E.M. and R. Knutti, 2015: Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change, 5(6), 560–564, doi:10.1038/nclimate2617. ↩
- Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩
- Hartmann, D.L. et al., 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 159–254.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩ - Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Gudmundsson, L. and S.I. Seneviratne, 2016: Anthropogenic climate change affects meteorological drought risk in Europe. Environmental Research Letters, 11(4), 044005, doi:10.1088/1748-9326/11/4/044005.
Gudmundsson, L., S.I. Seneviratne, and X. Zhang, 2017: Anthropogenic climate change detected in European renewable freshwater resources. Nature Climate Change, 7(11), 813–816, doi:10.1038/nclimate3416. ↩ - Paeth, H. et al., 2010: Meteorological characteristics and potential causes of the 2007 flood in sub-Saharan Africa. International Journal of Climatology, 31(13), 1908–1926, doi:10.1002/joc.2199.
Taylor, C.M. et al., 2017: Frequency of extreme Sahelian storms tripled since 1982 in satellite observations. Nature, 544(7651), 475–478, doi:10.1038/nature22069. ↩ - Weber, T. et al., 2018: Analysing regional climate change in Africa in a 1.5°C, 2°C and 3°C global warming world. Earth's Future, 6, 1–13, doi:10.1002/2017ef000714. ↩
- Kharin, V. et al., 2018: Risks from Climate Extremes Change Differently from 1.5°C to 2.0°C Depending on Rarity. Earth's Future, 6(5), 704–715, doi:10.1002/2018ef000813. ↩
- Mahlstein, I., R. Knutti, S. Solomon, and R.W. Portmann, 2011: Early onset of significant local warming in low latitude countries. Environmental Research Letters, 6(3), 034009, doi:10.1088/1748-9326/6/3/034009. ↩
- Diedhiou, A. et al., 2018: Changes in climate extremes over West and Central Africa at 1.5°C and 2°C global warming. Environmental Research Letters, 13(6), 065020, doi:10.1088/1748-9326/aac3e5. ↩
- Diedhiou, A. et al., 2018: Changes in climate extremes over West and Central Africa at 1.5°C and 2°C global warming. Environmental Research Letters, 13(6), 065020, doi:10.1088/1748-9326/aac3e5. ↩
- Salem, G.S.A., S. Kazama, S. Shahid, and N.C. Dey, 2017: Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh. Hydrological Research Letters, 11(1), 85–91, doi:10.3178/hrl.11.85.
Parkes, B., D. Defrance, B. Sultan, P. Ciais, and X. Wang, 2018: Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era. Earth System Dynamics, 9(1), 119–134, doi:10.5194/esd-9-119-2018. ↩ - Sultan, B. and M. Gaetani, 2016: Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation. Frontiers in Plant Science, 7, 1262, doi:10.3389/fpls.2016.01262.
Palazzo, A. et al., 2017: Linking regional stakeholder scenarios and shared socioeconomic pathways: Quantified West African food and climate futures in a global context. Global Environmental Change, 45, 227–242, doi:10.1016/j.gloenvcha.2016.12.002. ↩ - Roudier, P., B. Sultan, P. Quirion, and A. Berg, 2011: The impact of future climate change on West African crop yields: What does the recent literature say? Global Environmental Change, 21(3), 1073–1083, doi:10.1016/j.gloenvcha.2011.04.007. ↩
- Klutse, N.A.B. et al., 2018: Potential Impact of 1.5°C and 2°C global warming on extreme rainfall over West Africa. Environmental Research Letters, 13(5), 055013, doi:10.1088/1748-9326/aab37b. ↩
- Mba, W.P. et al., 2018: Consequences of 1.5°C and 2°C global warming levels for temperature and precipitation changes over Central Africa. Environmental Research Letters, 13(5), 055011, doi:10.1088/1748-9326/aab048. ↩
- Engelbrecht, F.A. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environmental Research Letters, 10(8), 085004, doi:10.1088/1748-9326/10/8/085004.
Maúre, G. et al., 2018: The southern African climate under 1.5°C and 2°C of global warming as simulated by CORDEX regional climate models. Environmental Research Letters, 13(6), 065002, doi:10.1088/1748-9326/aab190. ↩ - Engelbrecht, F.A. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environmental Research Letters, 10(8), 085004, doi:10.1088/1748-9326/10/8/085004.
Dosio, A., 2017: Projection of temperature and heat waves for Africa with an ensemble of CORDEX Regional Climate Models. Climate Dynamics, 49(1), 493–519, doi:10.1007/s00382-016-3355-5.
Maúre, G. et al., 2018: The southern African climate under 1.5°C and 2°C of global warming as simulated by CORDEX regional climate models. Environmental Research Letters, 13(6), 065002, doi:10.1088/1748-9326/aab190. ↩ - Maúre, G. et al., 2018: The southern African climate under 1.5°C and 2°C of global warming as simulated by CORDEX regional climate models. Environmental Research Letters, 13(6), 065002, doi:10.1088/1748-9326/aab190. ↩
- Kling, H., P. Stanzel, and M. Preishuber, 2014: Impact modelling of water resources development and climate scenarios on Zambezi River discharge. Journal of Hydrology: Regional Studies, 1, 17–43, doi:10.1016/j.ejrh.2014.05.002. ↩
- Osima, S. et al., 2018: Projected climate over the Greater Horn of Africa under 1.5°C and 2°C global warming. Environmental Research Letters, 13(6), 065004, doi:10.1088/1748-9326/aaba1b. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩ - Mastrandrea, M.D. et al., 2010: Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties. Intergovernmental Panel on Climate Change (IPCC), Geneva, Switzerland, 7 pp. ↩
- Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩
- Orlowsky, B. and S.I. Seneviratne, 2013: Elusive drought: uncertainty in observed trends and short- and long-term CMIP5 projections. Hydrology and Earth System Sciences, 17(5), 1765–1781, doi:10.5194/hess-17-1765-2013.
Roderick, M., G. Peter, and F.G. D., 2015: On the assessment of aridity with changes in atmospheric CO2. Water Resources Research, 51(7), 5450–5463, doi:10.1002/2015wr017031. ↩ - Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230.
Orlowsky, B. and S.I. Seneviratne, 2013: Elusive drought: uncertainty in observed trends and short- and long-term CMIP5 projections. Hydrology and Earth System Sciences, 17(5), 1765–1781, doi:10.5194/hess-17-1765-2013. ↩ - Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩ - Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩ - Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117.
Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320.
Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩ - Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩ - Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩
- Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩
- Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117. ↩
- Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117.
Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Greve, P., L. Gudmundsson, and S.I. Seneviratne, 2018: Regional scaling of annual mean precipitation and water availability with global temperature change. Earth System Dynamics, 9(1), 227–240, doi:10.5194/esd-9-227-2018.
Samaniego, L. et al., 2018: Anthropogenic warming exacerbates European soil moisture droughts. Nature Climate Change, 8(5), 421–426, doi:10.1038/s41558-018-0138-5. ↩ - Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230.
Sheffield, J., E.F. Wood, and M.L. Roderick, 2012: Little change in global drought over the past 60 years. Nature, 491(7424), 435–438, doi:10.1038/nature11575.
Greve, P. et al., 2014: Global assessment of trends in wetting and drying over land. Nature Geoscience, 7(10), 716–721, doi:10.1038/ngeo2247.
Gudmundsson, L. and S.I. Seneviratne, 2016: Anthropogenic climate change affects meteorological drought risk in Europe. Environmental Research Letters, 11(4), 044005, doi:10.1088/1748-9326/11/4/044005.
Gudmundsson, L., S.I. Seneviratne, and X. Zhang, 2017: Anthropogenic climate change detected in European renewable freshwater resources. Nature Climate Change, 7(11), 813–816, doi:10.1038/nclimate3416. ↩ - Orlowsky, B. and S.I. Seneviratne, 2013: Elusive drought: uncertainty in observed trends and short- and long-term CMIP5 projections. Hydrology and Earth System Sciences, 17(5), 1765–1781, doi:10.5194/hess-17-1765-2013. ↩
- Holmgren, K. et al., 2016: Mediterranean Holocene climate, environment and human societies. Quaternary Science Reviews, 136, 1–4, doi:10.1016/j.quascirev.2015.12.014. ↩
- Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230.
Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Greve, P. and S.I. Seneviratne, 2015: Assessment of future changes in water availability and aridity. Geophysical Research Letters, 42(13), 5493–5499, doi:10.1002/2015gl064127. ↩ - Mathbout, S., J.A. Lopez-bustins, J. Martin-vide, and F.S. Rodrigo, 2017: Spatial and temporal analysis of drought variability at several time scales in Syria during 1961–2012. Atmospheric Research, 1–39, doi:10.1016/j.atmosres.2017.09.016. ↩
- Kelley, C.P., S. Mohtadi, M.A. Cane, R. Seager, and Y. Kushnir, 2015: Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proceedings of the National Academy of Sciences, 112(11), 3241–6, doi:10.1073/pnas.1421533112. ↩
- Cook, B.I., K.J. Anchukaitis, R. Touchan, D.M. Meko, and E.R. Cook, 2016: Spatiotemporal drought variability in the Mediterranean over the last 900 years. Journal of Geophysical Research: Atmospheres, 121(5), 2060–2074, doi:10.1002/2015jd023929. ↩
- Trigo, R.M., C.M. Gouveia, and D. Barriopedro, 2010: The intense 2007–2009 drought in the Fertile Crescent: Impacts and associated atmospheric circulation. Agricultural and Forest Meteorology, 150(9), 1245–1257, doi:10.1016/j.agrformet.2010.05.006.
Kelley, C.P., S. Mohtadi, M.A. Cane, R. Seager, and Y. Kushnir, 2015: Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proceedings of the National Academy of Sciences, 112(11), 3241–6, doi:10.1073/pnas.1421533112. ↩ - Yazdanpanah, M., M. Thompson, and J. Linnerooth-Bayer, 2016: Do Iranian Policy Makers Truly Understand And Dealing with the Risk of Climate Change Regarding Water Resource Management? IDRiM, 367–368. ↩
- Saeidi, M., F. Moradi, and M. Abdoli, 2017: Impact of drought stress on yield, photosynthesis rate, and sugar alcohols contents in wheat after anthesis in semiarid region of Iran. Arid Land Research and Management, 31(2), 1–15, doi:10.1080/15324982.2016.1260073. ↩
- Kaniewski, D., J.J.J. Guiot, and E. Van Campo, 2015: Drought and societal collapse 3200 years ago in the Eastern Mediterranean: A review. Wiley Interdisciplinary Reviews: Climate Change, 6(4), 369–382, doi:10.1002/wcc.345. ↩
- Kelley, C.P., S. Mohtadi, M.A. Cane, R. Seager, and Y. Kushnir, 2015: Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proceedings of the National Academy of Sciences, 112(11), 3241–6, doi:10.1073/pnas.1421533112. ↩
- Koutroulis, A.G., M.G. Grillakis, I.N. Daliakopoulos, I.K. Tsanis, and D. Jacob, 2016: Cross sectoral impacts on water availability at +2°C and +3°C for east Mediterranean island states: The case of Crete. Journal of Hydrology, 532, 16–28, doi:10.1016/j.jhydrol.2015.11.015. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩
- Greve, P. et al., 2014: Global assessment of trends in wetting and drying over land. Nature Geoscience, 7(10), 716–721, doi:10.1038/ngeo2247. ↩
- Guiot, J. and W. Cramer, 2016: Climate change: The 2015 Paris Agreement thresholds and Mediterranean basin ecosystems. Science, 354(6311), 4528–4532, doi:10.1126/science.aah5015. ↩
- Dai, A., 2016: Historical and Future Changes in Streamflow and Continental Runoff. In: Terrestrial Water Cycle and Climate Change: Natural and Human-Induced Impacts [Tang, Q. and T. Oki (eds.)]. American Geophysical Union (AGU), Washington DC, USA, pp. 17–37, doi:10.1002/9781118971772.ch2. ↩
- Dai, A., 2016: Historical and Future Changes in Streamflow and Continental Runoff. In: Terrestrial Water Cycle and Climate Change: Natural and Human-Induced Impacts [Tang, Q. and T. Oki (eds.)]. American Geophysical Union (AGU), Washington DC, USA, pp. 17–37, doi:10.1002/9781118971772.ch2. ↩
- Hidalgo, H.G. et al., 2009: Detection and Attribution of Streamflow Timing Changes to Climate Change in the Western United States. Journal of Climate, 22(13), 3838–3855, doi:10.1175/2009jcli2470.1.
Gu, G. and R.F. Adler, 2013: Interdecadal variability/long-term changes in global precipitation patterns during the past three decades: global warming and/or pacific decadal variability? Climate Dynamics, 40(11–12), 3009–3022, doi:10.1007/s00382-012-1443-8.
Chiew, F.H.S. et al., 2014: Observed hydrologic non-stationarity in far south-eastern Australia: Implications for modelling and prediction. Stochastic Environmental Research and Risk Assessment, 28(1), 3–15, doi:10.1007/s00477-013-0755-5.
Gu, G. and R.F. Adler, 2015: Spatial patterns of global precipitation change and variability during 1901–2010. Journal of Climate, 28(11), 4431–4453, doi:10.1175/jcli-d-14-00201.1.
Luo, K., F. Tao, J.P. Moiwo, D. Xiao, and J. Zhang, 2016: Attribution of hydrological change in Heihe River Basin to climate and land use change in the past three decades. Scientific Reports, 6(1), 33704, doi:10.1038/srep33704.
Gudmundsson, L., S.I. Seneviratne, and X. Zhang, 2017: Anthropogenic climate change detected in European renewable freshwater resources. Nature Climate Change, 7(11), 813–816, doi:10.1038/nclimate3416. ↩ - Gerten, D., S. Rost, W. von Bloh, and W. Lucht, 2008: Causes of change in 20th century global river discharge. Geophysical Research Letters, 35(20), L20405, doi:10.1029/2008gl035258.
Sterling, S.M., A. Ducharne, and J. Polcher, 2012: The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3(4), 385–390, doi:10.1038/nclimate1690.
Hall, J. et al., 2014: Understanding flood regime changes in Europe: a state-of-the-art assessment. Hydrology and Earth System Sciences, 18(7), 2735–2772, doi:10.5194/hess-18-2735-2014.
Betts, R.A. et al., 2015: Climate and land use change impacts on global terrestrial ecosystems and river flows in the HadGEM2-ES Earth system model using the representative concentration pathways. Biogeosciences, 12(5), 1317–1338, doi:10.5194/bg-12-1317-2015.
Arheimer, B., C. Donnelly, and G. Lindström, 2017: Regulation of snow-fed rivers affects flow regimes more than climate change. Nature Communications, 8(1), 62, doi:10.1038/s41467-017-00092-8. ↩ - Gerten, D., S. Rost, W. von Bloh, and W. Lucht, 2008: Causes of change in 20th century global river discharge. Geophysical Research Letters, 35(20), L20405, doi:10.1029/2008gl035258.
Sterling, S.M., A. Ducharne, and J. Polcher, 2012: The impact of global land-cover change on the terrestrial water cycle. Nature Climate Change, 3(4), 385–390, doi:10.1038/nclimate1690. ↩ - Versini, P.-A., M. Velasco, A. Cabello, and D. Sempere-Torres, 2013: Hydrological impact of forest fires and climate change in a Mediterranean basin. Natural Hazards, 66(2), 609–628, doi:10.1007/s11069-012-0503-z.
Springer, J., R. Ludwig, and S. Kienzle, 2015: Impacts of Forest Fires and Climate Variability on the Hydrology of an Alpine Medium Sized Catchment in the Canadian Rocky Mountains. Hydrology, 2(1), 23–47, doi:10.3390/hydrology2010023.
Wine, M.L. and D. Cadol, 2016: Hydrologic effects of large southwestern USA wildfires significantly increase regional water supply: fact or fiction? Environmental Research Letters, 11(8), 085006, doi:10.1088/1748-9326/11/8/085006. ↩ - Mallakpour, I. and G. Villarini, 2015: The changing nature of flooding across the central United States. Nature Climate Change, 5(3), 250–254, doi:10.1038/nclimate2516. ↩
- Stevens, A.J., D. Clarke, and R.J. Nicholls, 2016: Trends in reported flooding in the UK: 1884–2013. Hydrological Sciences Journal, 61(1), 50–63, doi:10.1080/02626667.2014.950581. ↩
- Do, H.X., S. Westra, and M. Leonard, 2017: A global-scale investigation of trends in annual maximum streamflow. Journal of Hydrology, 552, 28–43, doi:10.1016/j.jhydrol.2017.06.015. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452.
Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩ - Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩
- Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7.
Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792.
Zhai, R., F. Tao, and Z. Xu, 2018: Spatial-temporal changes in runoff and terrestrial ecosystem water retention under 1.5 and 2°C warming scenarios across China. Earth System Dynamics, 9(2), 717–738, doi:10.5194/esd-9-717-2018. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7.
Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩
- Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Gosling, S.N. et al., 2017: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1°C, 2°C and 3°C. Climatic Change, 141(3), 577–595, doi:10.1007/s10584-016-1773-3. ↩
- Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩
- Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩
- Liu, L., H. Xu, Y. Wang, and T. Jiang, 2017: Impacts of 1.5 and 2°C global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China. Climatic Change, 145, 145–158, doi:10.1007/s10584-017-2072-3. ↩
- Chen, J. et al., 2017: Assessing changes of river discharge under global warming of 1.5°C and 2°C in the upper reaches of the Yangtze River Basin: Approach by using multiple-GCMs and hydrological models. Quaternary International, 453, 1–11, doi:10.1016/j.quaint.2017.01.017. ↩
- Montroull, N.B., R.I. Saurral, and I.A. Camilloni, 2018: Hydrological impacts in La Plata basin under 1.5, 2 and 3°C global warming above the pre-industrial level. International Journal of Climatology, 38(8), 3355–3368, doi:10.1002/joc.5505. ↩
- Marx, A. et al., 2018: Climate change alters low flows in Europe under global warming of 1.5, 2, and 3°C. Hydrology and Earth System Sciences, 22(2), 1017–1032, doi:10.5194/hess-22-1017-2018. ↩
- Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485. ↩
- Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩
- Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩
- Thober, T. et al., 2018: Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003, doi:10.1088/1748-9326/aa9e35. ↩
- Thober, T. et al., 2018: Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003, doi:10.1088/1748-9326/aa9e35. ↩
- Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩
- Roudier, P. et al., 2016: Projections of future floods and hydrological droughts in Europe under a +2°C global warming. Climatic Change, 135(2), 341–355, doi:10.1007/s10584-015-1570-4. ↩
- Mohammed, K. et al., 2017: Extreme flows and water availability of the Brahmaputra River under 1.5 and 2°C global warming scenarios. Climatic Change, 145(1–2), 159–175, doi:10.1007/s10584-017-2073-2. ↩
- Rienecker, M.M. et al., 2011: MERRA: NASA's Modern-Era Retrospective Analysis for Research and Applications. Journal of Climate, 24(14), 3624–3648, doi:10.1175/jcli-d-11-00015.1. ↩
- Landsea, C.W., 2006: Can We Detect Trends in Extreme Tropical Cyclones? Science, 313(5786), 452–454, doi:10.1126/science.1128448.
Walsh, K.J.E. et al., 2016: Tropical cyclones and climate change. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 65–89, doi:10.1002/wcc.371. ↩ - Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436(7051), 686–688, doi:10.1038/nature03906.
Elsner, J.B., J.P. Kossin, and T.H. Jagger, 2008: The increasing intensity of the strongest tropical cyclones. Nature, 455(7209), 92–95, doi:10.1038/nature07234.
Knutson, T.R. et al., 2010: Tropical cyclones and climate change. Nature Geoscience, 3(3), 157–163, doi:10.1038/ngeo779.
Holland, G. and C.L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dynamics, 42(3–4), 617–627, doi:10.1007/s00382-013-1713-0.
Klotzbach, P.J. and C.W. Landsea, 2015: Extremely Intense Hurricanes: Revisiting Webster et al. (2005) after 10 Years. Journal of Climate, 28(19), 7621–7629, doi:10.1175/jcli-d-15-0188.1.
Walsh, K.J.E. et al., 2016: Tropical cyclones and climate change. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 65–89, doi:10.1002/wcc.371. ↩ - Kang, N.-Y. and J.B. Elsner, 2015: Trade-off between intensity and frequency of global tropical cyclones. Nature Climate Change, 5(7), 661–664, doi:10.1038/nclimate2646. ↩
- Klotzbach, P.J., 2006: Trends in global tropical cyclone activity over the past twenty years (1986–2005). Geophysical Research Letters, 33(10), L10805, doi:10.1029/2006gl025881. ↩
- Holland, G. and C.L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dynamics, 42(3–4), 617–627, doi:10.1007/s00382-013-1713-0. ↩
- Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436(7051), 686–688, doi:10.1038/nature03906.
Webster, P.J., G.J. Holland, J.A. Curry, and H.-R. Chang, 2005: Changes in Tropical Cyclone Number, Duration, and Intensity in a Warming Environment. Science, 309(5742), 1844–1846, doi:10.1126/science.1116448.
Klotzbach, P.J., 2006: Trends in global tropical cyclone activity over the past twenty years (1986–2005). Geophysical Research Letters, 33(10), L10805, doi:10.1029/2006gl025881.
Elsner, J.B., J.P. Kossin, and T.H. Jagger, 2008: The increasing intensity of the strongest tropical cyclones. Nature, 455(7209), 92–95, doi:10.1038/nature07234.
Knutson, T.R. et al., 2010: Tropical cyclones and climate change. Nature Geoscience, 3(3), 157–163, doi:10.1038/ngeo779.
Holland, G. and C.L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dynamics, 42(3–4), 617–627, doi:10.1007/s00382-013-1713-0.
Walsh, K.J.E. et al., 2016: Tropical cyclones and climate change. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 65–89, doi:10.1002/wcc.371. ↩ - Singh, O.P., T.M. Ali Khan, and M.S. Rahman, 2000: Changes in the frequency of tropical cyclones over the North Indian Ocean. Meteorology and Atmospheric Physics, 75(1–2), 11–20, doi:10.1007/s007030070011.
Singh, O.P., 2010: Recent Trends in Tropical Cyclone Activity in the North Indian Ocean. In: Indian Ocean Tropical Cyclones and Climate Change [Charabi, Y. (ed.)]. Springer Netherlands, Dordrecht, pp. 51–54, doi:10.1007/978-90-481-3109-9_8.
Kossin, J.P. et al., 2013: Trend Analysis with a New Global Record of Tropical Cyclone Intensity. Journal of Climate, 26(24), 9960–9976, doi:10.1175/jcli-d-13-00262.1.
Holland, G. and C.L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dynamics, 42(3–4), 617–627, doi:10.1007/s00382-013-1713-0.
Walsh, K.J.E. et al., 2016: Tropical cyclones and climate change. Wiley Interdisciplinary Reviews: Climate Change, 7(1), 65–89, doi:10.1002/wcc.371. ↩ - Murakami, H., G.A. Vecchi, and S. Underwood, 2017: Increasing frequency of extremely severe cyclonic storms over the Arabian Sea. Nature Climate Change, 7(12), 885–889, doi:10.1038/s41558-017-0008-6. ↩
- Mohapatra, M., A.K. Srivastava, S. Balachandran, and B. Geetha, 2017: Inter-annual Variation and Trends in Tropical Cyclones and Monsoon Depressions Over the North Indian Ocean. In: Observed Climate Variability and Change over the Indian Region [Rajeevan, M.N. and S. Nayak (eds.)]. Springer Singapore, Singapore, pp. 89–106, doi:10.1007/978-981-10-2531-0_6. ↩
- Kamahori, H., N. Yamazaki, N. Mannoji, and K. Takahashi, 2006: Variability in Intense Tropical Cyclone Days in the Western North Pacific. SOLA, 2, 104–107, doi:10.2151/sola.2006-027.
Klotzbach, P.J. and C.W. Landsea, 2015: Extremely Intense Hurricanes: Revisiting Webster et al. (2005) after 10 Years. Journal of Climate, 28(19), 7621–7629, doi:10.1175/jcli-d-15-0188.1. ↩ - Bender, M.A. et al., 2010: Modeled Impact of Anthropogenic Warming on the Frequency of Intense Atlantic Hurricanes. Science, 327(5964), 454–458, doi:10.1126/science.1180568.
Knutson, T.R. et al., 2010: Tropical cyclones and climate change. Nature Geoscience, 3(3), 157–163, doi:10.1038/ngeo779.
Camargo, S.J., 2013: Global and Regional Aspects of Tropical Cyclone Activity in the CMIP5 Models. Journal of Climate, 26(24), 9880–9902, doi:10.1175/jcli-d-12-00549.1.
Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
Knutson, T.R. et al., 2013: Dynamical Downscaling Projections of Twenty-First-Century Atlantic Hurricane Activity: CMIP3 and CMIP5 Model-Based Scenarios. Journal of Climate, 26(17), 6591–6617, doi:10.1175/jcli-d-12-00539.1. ↩ - Klotzbach, P.J. and C.W. Landsea, 2015: Extremely Intense Hurricanes: Revisiting Webster et al. (2005) after 10 Years. Journal of Climate, 28(19), 7621–7629, doi:10.1175/jcli-d-15-0188.1. ↩
- Knutson, T.R. et al., 2010: Tropical cyclones and climate change. Nature Geoscience, 3(3), 157–163, doi:10.1038/ngeo779.
Sugi, M. and J. Yoshimura, 2012: Decreasing trend of tropical cyclone frequency in 228-year high-resolution AGCM simulations. Geophysical Research Letters, 39(19), L19805, doi:10.1029/2012gl053360.
Christensen, J.H. et al., 2013: Climate Phenomena and their Relevance for Future Regional Climate Change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1217–1308.
Knutson, T.R. et al., 2015: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty-First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. Journal of Climate, 28(18), 7203–7224, doi:10.1175/jcli-d-15-0129.1.
Yoshida, K., M. Sugi, M. Ryo, H. Murakami, and I. Masayoshi, 2017: Future Changes in Tropical Cyclone Activity in High-Resolution Large-Ensemble Simulations. Geophysical Research Letters, 44(19), 9910–9917, doi:10.1002/2017gl075058. ↩ - Emanuel, K., 2017: A fast intensity simulator for tropical cyclone risk analysis. Natural Hazards, 88(2), 779–796, doi:10.1007/s11069-017-2890-7. ↩
- Knutson, T.R. et al., 2015: Global Projections of Intense Tropical Cyclone Activity for the Late Twenty-First Century from Dynamical Downscaling of CMIP5/RCP4.5 Scenarios. Journal of Climate, 28(18), 7203–7224, doi:10.1175/jcli-d-15-0129.1.
Sugi, M., H. Murakami, and K. Yoshida, 2017: Projection of future changes in the frequency of intense tropical cyclones. Climate Dynamics, 49(1), 619–632, doi:10.1007/s00382-016-3361-7. ↩ - Kang, N.-Y. and J.B. Elsner, 2015: Trade-off between intensity and frequency of global tropical cyclones. Nature Climate Change, 5(7), 661–664, doi:10.1038/nclimate2646. ↩
- Yoshida, K., M. Sugi, M. Ryo, H. Murakami, and I. Masayoshi, 2017: Future Changes in Tropical Cyclone Activity in High-Resolution Large-Ensemble Simulations. Geophysical Research Letters, 44(19), 9910–9917, doi:10.1002/2017gl075058. ↩
- Wehner, M.F., K.A. Reed, B. Loring, D. Stone, and H. Krishnan, 2018a: Changes in tropical cyclones under stabilized 1.5 and 2.0°C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols. Earth System Dynamics, 9(1), 187–195, doi:10.5194/esd-9-187-2018. ↩
- Mitchell, D. et al., 2017: Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design. Geoscientific Model Development, 10, 571–583, doi:10.5194/gmd-10-571-2017. ↩
- Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Risser, M.D. and M.F. Wehner, 2017: Attributable Human-Induced Changes in the Likelihood and Magnitude of the Observed Extreme Precipitation during Hurricane Harvey. Geophysical Research Letters, 44(24), 12457–12464, doi:10.1002/2017gl075888.
van Oldenborgh, G.J. et al., 2017: Attribution of extreme rainfall from Hurricane Harvey, August 2017. Environmental Research Letters, 12(12), 124009, doi:10.1088/1748-9326/aa9ef2. ↩ - Muthige, M.S. et al., 2018: Projected changes in tropical cyclones over the South West Indian Ocean under different extents of global warming. Environmental Research Letters, 13(6), 065019, doi:10.1088/1748-9326/aabc60. ↩
- Muthige, M.S. et al., 2018: Projected changes in tropical cyclones over the South West Indian Ocean under different extents of global warming. Environmental Research Letters, 13(6), 065019, doi:10.1088/1748-9326/aabc60. ↩
- Li, C. et al., 2018: Midlatitude atmospheric circulation responses under 1.5 and 2.0°C warming and implications for regional impacts. Earth System Dynamics, 9(2), 359–382, doi:10.5194/esd-9-359-2018. ↩
- Li, C. et al., 2018: Midlatitude atmospheric circulation responses under 1.5 and 2.0°C warming and implications for regional impacts. Earth System Dynamics, 9(2), 359–382, doi:10.5194/esd-9-359-2018. ↩
- Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩
- Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731. ↩
- Burrows, M.T. et al., 2014: Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492–495, doi:10.1038/nature12976.
García Molinos, J. et al., 2015: Climate velocity and the future global redistribution of marine biodiversity. Nature Climate Change, 6(1), 83–88, doi:10.1038/nclimate2769. ↩ - Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731. ↩
- Oliver, E.C.J. et al., 2018: Longer and more frequent marine heatwaves over the past century. Nature Communications, 9(1), 1324, doi:10.1038/s41467-018-03732-9. ↩
- Bakun, A., 1990: Global climate change and intensification of coastal ocean upwelling. Science, 247(4939), 198–201, doi:10.1126/science.247.4939.198. ↩
- Sydeman, W.J. et al., 2014: Climate change and wind intensification in coastal upwelling ecosystems. Science, 345(6192), 77–80, doi:10.1126/science.1251635. ↩
- Lluch-Cota, S.E. et al., 2014: Cross-chapter box on uncertain trends in major upwelling ecosystems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E. Kissel, A. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 149–151. ↩
- Wang, D., T.C. Gouhier, B.A. Menge, and A.R. Ganguly, 2015: Intensification and spatial homogenization of coastal upwelling under climate change. Nature, 518(7539), 390–394, doi:10.1038/nature14235. ↩
- Christensen, J.H. et al., 2007: Regional Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 847–940.
Engelbrecht, F.A., J.L. McGregor, and C.J. Engelbrecht, 2009: Dynamics of the Conformal-Cubic Atmospheric Model projected climate-change signal over southern Africa. International Journal of Climatology, 29(7), 1013–1033, doi:10.1002/joc.1742. ↩ - Rahmstorf, S. et al., 2015b: Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nature Climate Change, 5(5), 475–480, doi:10.1038/nclimate2554.
Srokosz, M.A. and H.L. Bryden, 2015: Observing the Atlantic Meridional Overturning Circulation yields a decade of inevitable surprises. Science, 348(6241), 1255575, doi:10.1126/science.1255575.
Caesar, L., S. Rahmstorf, A. Robinson, G. Feulner, and V. Saba, 2018: Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191–196, doi:10.1038/s41586-018-0006-5. ↩ - Caesar, L., S. Rahmstorf, A. Robinson, G. Feulner, and V. Saba, 2018: Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191–196, doi:10.1038/s41586-018-0006-5. ↩
- Serreze, M.C. and J. Stroeve, 2015: Arctic sea ice trends, variability and implications for seasonal ice forecasting. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2045), 20140159, doi:10.1098/rsta.2014.0159. ↩
- Lindsay, R. and A. Schweiger, 2015: Arctic sea ice thickness loss determined using subsurface, aircraft, and satellite observations. The Cryosphere, 9(1), 269–283, doi:10.5194/tc-9-269-2015. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Rosenblum, E. and I. Eisenman, 2017: Sea Ice Trends in Climate Models Only Accurate in Runs with Biased Global Warming. Journal of Climate, 30(16), 6265–6278, doi:10.1175/jcli-d-16-0455.1. ↩
- Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi:10.1126/science.aag2345. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136.
Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi:10.1126/science.aag2345.
Screen, J.A. and D. Williamson, 2017: Ice-free Arctic at 1.5°C? Nature Climate Change, 7(4), 230–231, doi:10.1038/nclimate3248.
Jahn, A., 2018: Reduced probability of ice-free summers for 1.5°C compared to 2°C warming. Nature Climate Change, 8(5), 409–413, doi:10.1038/s41558-018-0127-8.
Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159.
Sigmond, M., J.C. Fyfe, and N.C. Swart, 2018: Ice-free Arctic projections under the Paris Agreement. Nature Climate Change, 8, 404–408, doi:10.1038/s41558-018-0124-y. ↩ - Screen, J.A. and D. Williamson, 2017: Ice-free Arctic at 1.5°C? Nature Climate Change, 7(4), 230–231, doi:10.1038/nclimate3248.
Jahn, A., 2018: Reduced probability of ice-free summers for 1.5°C compared to 2°C warming. Nature Climate Change, 8(5), 409–413, doi:10.1038/s41558-018-0127-8.
Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159.
Screen, J.A. et al., 2018: Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nature Geoscience, 11(3), 155–163, doi:10.1038/s41561-018-0059-y.
Sigmond, M., J.C. Fyfe, and N.C. Swart, 2018: Ice-free Arctic projections under the Paris Agreement. Nature Climate Change, 8, 404–408, doi:10.1038/s41558-018-0124-y. ↩ - Jahn, A., 2018: Reduced probability of ice-free summers for 1.5°C compared to 2°C warming. Nature Climate Change, 8(5), 409–413, doi:10.1038/s41558-018-0127-8.
Screen, J.A. et al., 2018: Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models. Nature Geoscience, 11(3), 155–163, doi:10.1038/s41561-018-0059-y.
Sigmond, M., J.C. Fyfe, and N.C. Swart, 2018: Ice-free Arctic projections under the Paris Agreement. Nature Climate Change, 8, 404–408, doi:10.1038/s41558-018-0124-y. ↩ - Sanderson, B.M. et al., 2017: Community climate simulations to assess avoided impacts in 1.5°C and 2°C futures. Earth System Dynamics, 8(3), 827–847, doi:10.5194/esd-8-827-2017.
Jahn, A., 2018: Reduced probability of ice-free summers for 1.5°C compared to 2°C warming. Nature Climate Change, 8(5), 409–413, doi:10.1038/s41558-018-0127-8. ↩ - Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159. ↩
- Sigmond, M., J.C. Fyfe, and N.C. Swart, 2018: Ice-free Arctic projections under the Paris Agreement. Nature Climate Change, 8, 404–408, doi:10.1038/s41558-018-0124-y. ↩
- Notz, D., 2015: How well must climate models agree with observations? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2052), 20140164, doi:10.1098/rsta.2014.0164.
Jahn, A., J.E. Kay, M.M. Holland, and D.M. Hall, 2016: How predictable is the timing of a summer ice-free Arctic? Geophysical Research Letters, 43(17), 9113–9120, doi:10.1002/2016gl070067. ↩ - Mahlstein, I. and R. Knutti, 2012: September Arctic sea ice predicted to disappear near 2°C global warming above present. Journal of Geophysical Research: Atmospheres, 117(D6), D06104, doi:10.1029/2011jd016709. ↩
- Rosenblum, E. and I. Eisenman, 2016: Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes. Journal of Climate, 29(24), 9179–9188, doi:10.1175/jcli-d-16-0391.1. ↩
- Mahlstein, I. and R. Knutti, 2012: September Arctic sea ice predicted to disappear near 2°C global warming above present. Journal of Geophysical Research: Atmospheres, 117(D6), D06104, doi:10.1029/2011jd016709. ↩
- Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi:10.1126/science.aag2345. ↩
- Gagne, M.-E., J.C. Fyfe, N.P. Gillett, I. Polyakov, and G.M. Flato, 2017: Aerosol-driven increase in Arctic sea ice over the middle of the twentieth century. Geophysical Research Letters, 44(14), 7338–7346, doi:10.1002/2016gl071941. ↩
- Rosenblum, E. and I. Eisenman, 2016: Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes. Journal of Climate, 29(24), 9179–9188, doi:10.1175/jcli-d-16-0391.1. ↩
- Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159. ↩
- Holland, M.M., C.M. Bitz, and B. Tremblay, 2006: Future abrupt reductions in the summer Arctic sea ice. Geophysical Research Letters, 33(23), L23503, doi:10.1029/2006gl028024.
Schröder, D. and W.M. Connolley, 2007: Impact of instantaneous sea ice removal in a coupled general circulation model. Geophysical Research Letters, 34(14), L14502, doi:10.1029/2007gl030253.
Armour, K.C., I. Eisenman, E. Blanchard-Wrigglesworth, K.E. McCusker, and C.M. Bitz, 2011: The reversibility of sea ice loss in a state-of-the-art climate model. Geophysical Research Letters, 38(16), L16705, doi:10.1029/2011gl048739.
Sedláček, J., O. Martius, and R. Knutti, 2011: Influence of subtropical and polar sea-surface temperature anomalies on temperatures in Eurasia. Geophysical Research Letters, 38(12), L12803, doi:10.1029/2011gl047764.
Tietsche, S., D. Notz, J.H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Arctic summer sea ice. Geophysical Research Letters, 38(2), L02707, doi:10.1029/2010gl045698.
Boucher, O. et al., 2012: Reversibility in an Earth System model in response to CO2 concentration changes. Environmental Research Letters, 7(2), 024013, doi:10.1088/1748-9326/7/2/024013.
Ridley, J.K., J.A. Lowe, and H.T. Hewitt, 2012: How reversible is sea ice loss? The Cryosphere, 6(1), 193–198, doi:10.5194/tc-6-193-2012. ↩ - Li, C., D. Notz, S. Tietsche, and J. Marotzke, 2013: The Transient versus the Equilibrium Response of Sea Ice to Global Warming. Journal of Climate, 26(15), 5624–5636, doi:10.1175/jcli-d-12-00492.1.
Jahn, A., 2018: Reduced probability of ice-free summers for 1.5°C compared to 2°C warming. Nature Climate Change, 8(5), 409–413, doi:10.1038/s41558-018-0127-8. ↩ - Hobbs, W.R. et al., 2016: A review of recent changes in Southern Ocean sea ice, their drivers and forcings. Global and Planetary Change, 143, 228–250, doi:10.1016/j.gloplacha.2016.06.008. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Cazenave, A. et al., 2014: The rate of sea-level rise. Nature Climate Change, 4(5), 358–361, doi:10.1038/nclimate2159. ↩
- Watson, C.S. et al., 2015: Unabated global mean sea-level rise over the satellite altimeter era. Nature Climate Change, 5(6), 565–568, doi:10.1038/nclimate2635. ↩
- Fasullo, J.T., R.S. Nerem, and B. Hamlington, 2016: Is the detection of accelerated sea level rise imminent? Scientific Reports, 6, 1–7, doi:10.1038/srep31245. ↩
- Watson, C.S. et al., 2015: Unabated global mean sea-level rise over the satellite altimeter era. Nature Climate Change, 5(6), 565–568, doi:10.1038/nclimate2635. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216.
Watson, C.S. et al., 2015: Unabated global mean sea-level rise over the satellite altimeter era. Nature Climate Change, 5(6), 565–568, doi:10.1038/nclimate2635. ↩ - Slangen, A.B.A. et al., 2016: Anthropogenic forcing dominates global mean sea-level rise since 1970. Nature Climate Change, 6(7), 701–705, doi:10.1038/nclimate2991. ↩
- Kopp, R.E. et al., 2014: Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth's Future, 2(8), 383–406, doi:10.1002/2014ef000239.
Mengel, M. et al., 2016: Future sea level rise constrained by observations and long-term commitment. Proceedings of the National Academy of Sciences, 113(10), 2597–2602, doi:10.1073/pnas.1500515113.
Nauels, A., M. Meinshausen, M. Mengel, K. Lorbacher, and T.M.L. Wigley, 2017: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0. Geoscientific Model Development, 10(6), 2495–2524, doi:10.5194/gmd-10-2495-2017. ↩ - Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Marzeion, B., G. Kaser, F. Maussion, and N. Champollion, 2018: Limited influence of climate change mitigation on short-term glacier mass loss. Nature Climate Change, 8, 305–308, doi:10.1038/s41558-018-0093-1. ↩
- Fürst, J.J., H. Goelzer, and P. Huybrechts, 2015: Ice-dynamic projections of the Greenland ice sheet in response to atmospheric and oceanic warming. The Cryosphere, 9(3), 1039–1062, doi:10.5194/tc-9-1039-2015. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Frieler, K. et al., 2015: Consistent evidence of increasing Antarctic accumulation with warming. Nature Climate Change, 5(4), 348–352, doi:10.1038/nclimate2574. ↩
- Mouginot, J., E. Rignot, and B. Scheuchl, 2014: Sustained increase in ice discharge fromthe Amundsen Sea Embayment, West Antarctica, from1973 to 2013. Geophysical Research Letters, 41(5), 1576–1584, doi:10.1002/2013gl059069. ↩
- Rignot, E., J. Mouginot, M. Morlighem, H. Seroussi, and B. Scheuchl, 2014: Widespread, rapid grounding line retreat of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica, from 1992 to 2011. Geophysical Research Letters, 41(10), 3502–3509, doi:10.1002/2014gl060140. ↩
- Jacobs, S.S., A. Jenkins, C.F. Giulivi, and P. Dutrieux, 2011: Stronger ocean circulation and increased melting under Pine Island Glacier ice shelf. Nature Geoscience, 4(8), 519–523, doi:10.1038/ngeo1188. ↩
- Goddard, P.B., C.O. Dufour, J. Yin, S.M. Griffies, and M. Winton, 2017: CO2-Induced Ocean Warming of the Antarctic Continental Shelf in an Eddying Global Climate Model. Journal of Geophysical Research: Oceans, 122(10), 8079–8101, doi:10.1002/2017jc012849.
Turner, J. et al., 2017a: Atmosphere-ocean-ice interactions in the Amundsen Sea Embayment, West Antarctica. Reviews of Geophysics, 55(1), 235–276, doi:10.1002/2016rg000532. ↩ - Levermann, A. et al., 2014: Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models. Earth System Dynamics, 5(2), 271–293, doi:10.5194/esd-5-271-2014.
Golledge, N.R. et al., 2015: The multi-millennial Antarctic commitment to future sea-level rise. Nature, 526(7573), 421–425, doi:10.1038/nature15706.
DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596), 591–7, doi:10.1038/nature17145. ↩ - Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Drouet, A.S. et al., 2013: Grounding line transient response in marine ice sheet models. The Cryosphere, 7(2), 395–406, doi:10.5194/tc-7-395-2013.
Durand, G. and F. Pattyn, 2015: Reducing uncertainties in projections of Antarctic ice mass loss. The Cryosphere, 9(6), 2043–2055, doi:10.5194/tc-9-2043-2015. ↩ - Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Kopp, R.E. et al., 2014: Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth's Future, 2(8), 383–406, doi:10.1002/2014ef000239. ↩
- Jevrejeva, S., L.P. Jackson, R.E.M. Riva, A. Grinsted, and J.C. Moore, 2016: Coastal sea level rise with warming above 2°C. Proceedings of the National Academy of Sciences, 113(47), 13342–13347, doi:10.1073/pnas.1605312113. ↩
- Kopp, R.E. et al., 2016: Temperature-driven global sea-level variability in the Common Era. Proceedings of the National Academy of Sciences, 113(11), E1434–E1441, doi:10.1073/pnas.1517056113. ↩
- Mengel, M. et al., 2016: Future sea level rise constrained by observations and long-term commitment. Proceedings of the National Academy of Sciences, 113(10), 2597–2602, doi:10.1073/pnas.1500515113. ↩
- Nauels, A., M. Meinshausen, M. Mengel, K. Lorbacher, and T.M.L. Wigley, 2017: Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0. Geoscientific Model Development, 10(6), 2495–2524, doi:10.5194/gmd-10-2495-2017. ↩
- Goodwin, P., I.D. Haigh, E.J. Rohling, and A. Slangen, 2017: A new approach to projecting 21st century sea-level changes and extremes. Earth's Future, 5(2), 240–253, doi:10.1002/2016ef000508. ↩
- Schaeffer, M., W. Hare, S. Rahmstorf, and M. Vermeer, 2012: Long-term sea-level rise implied by 1.5°C and 2°C warming levels. Nature Climate Change, 2(12), 867–870, doi:10.1038/nclimate1584. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Bittermann, K., S. Rahmstorf, R.E. Kopp, and A.C. Kemp, 2017: Global mean sea-level rise in a world agreed upon in Paris. Environmental Research Letters, 12(12), 124010, doi:10.1088/1748-9326/aa9def. ↩
- Jackson, L.P., A. Grinsted, and S. Jevrejeva, 2018: 21st Century Sea-Level Rise in Line with the Paris Accord. Earth's Future, 6(2), 213–229, doi:10.1002/2017ef000688. ↩
- Sanderson, B.M. et al., 2017: Community climate simulations to assess avoided impacts in 1.5°C and 2°C futures. Earth System Dynamics, 8(3), 827–847, doi:10.5194/esd-8-827-2017. ↩
- Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
- Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Goodwin, P., S. Brown, I.D. Haigh, R.J. Nicholls, and J.M. Matter, 2018: Adjusting Mitigation Pathways to Stabilize Climate at 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 601–615, doi:10.1002/2017ef000732. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Kopp, R.E. et al., 2014: Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth's Future, 2(8), 383–406, doi:10.1002/2014ef000239. ↩
- Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Vitousek, S. et al., 2017: Doubling of coastal flooding frequency within decades due to sea-level rise. Scientific Reports, 7(1), 1399, doi:10.1038/s41598-017-01362-7. ↩
- Schleussner, C.-F., K. Frieler, M. Meinshausen, J. Yin, and A. Levermann, 2011: Emulating Atlantic overturning strength for low emission scenarios: Consequences for sea-level rise along the North American east coast. Earth System Dynamics, 2(2), 191–200, doi:10.5194/esd-2-191-2011. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Knutti, R. and J. Sedláček, 2012: Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, 3(4), 369–373, doi:10.1038/nclimate1716. ↩
- Fischer, H. et al., 2018: Palaeoclimate constraints on the impact of 2°C anthropogenic warming and beyond. Nature Geoscience, 11, 1–12, doi:10.1038/s41561-018-0146-0. ↩
- Masson-Delmotte, V. et al., 2013: Information from Paleoclimate Archives. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 383–464. ↩
- Fischer, H. et al., 2018: Palaeoclimate constraints on the impact of 2°C anthropogenic warming and beyond. Nature Geoscience, 11, 1–12, doi:10.1038/s41561-018-0146-0. ↩
- Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A Reconstruction of Regional and Global Temperature for the Past 11,300 Years. Science, 339(6124), 1198–1201, doi:10.1126/science.1228026. ↩
- Hoffman, J.S., P.U. Clark, A.C. Parnell, and F. He, 2017: Regional and global sea-surface temperatures during the last interglaciation. Science, 355(6322), 276–279, doi:10.1126/science.aai8464. ↩
- Capron, E., A. Govin, R. Feng, B.L. Otto-Bliesner, and E.W. Wolff, 2017: Critical evaluation of climate syntheses to benchmark CMIP6/PMIP4 127 ka Last Interglacial simulations in the high-latitude regions. Quaternary Science Reviews, 168, 137–150, doi:10.1016/j.quascirev.2017.04.019. ↩
- Marcott, S.A., J.D. Shakun, P.U. Clark, and A.C. Mix, 2013: A Reconstruction of Regional and Global Temperature for the Past 11,300 Years. Science, 339(6124), 1198–1201, doi:10.1126/science.1228026. ↩
- Brigham-Grette, J. et al., 2013: Pliocene Warmth, Polar Amplification, and Stepped Pleistocene Cooling Recorded in NE Arctic Russia. Science, 340(6139), 1421–1427, doi:10.1126/science.1233137. ↩
- Yu, Z., J. Loisel, D. Brosseau, D. Beilman, and S. Hunt, 2010: Global peatland dynamics since the Last Glacial Maximum. Geophysical Research Letters, 37(13), L13402, doi:10.1029/2010gl043584. ↩
- Frank, D.C. et al., 2010: Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate. Nature, 463, 527, doi:10.1038/nature08769. ↩
- Galaasen, E.V. et al., 2014: Rapid Reductions in North Atlantic Deep Water During the Peak of the Last Interglacial Period. Science, 343(6175), 1129–1132, doi:10.1126/science.1248667. ↩
- Dowsett, H. et al., 2016: The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction. Climate of the Past, 12(7), 1519–1538, doi:10.5194/cp-12-1519-2016. ↩
- Williams, J.W., B. Shuman, and P.J. Bartlein, 2009: Rapid responses of the prairie-forest ecotone to early Holocene aridity in mid-continental North America. Global and Planetary Change, 66(3), 195–207, doi:10.1016/j.gloplacha.2008.10.012.
Haywood, A.M., H.J. Dowsett, and A.M. Dolan, 2016: Integrating geological archives and climate models for the mid-Pliocene warm period. Nature Communications, 7, 10646, doi:10.1038/ncomms10646. ↩ - de Vernal, A., R. Gersonde, H. Goosse, M.-S. Seidenkrantz, and E.W. Wolff, 2013: Sea ice in the paleoclimate system: the challenge of reconstructing sea ice from proxies – an introduction. Quaternary Science Reviews, 79, 1–8, doi:10.1016/j.quascirev.2013.08.009. ↩
- Dutton, A. et al., 2015: Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science, 349(6244), doi:10.1126/science.aaa4019. ↩
- DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596), 591–7, doi:10.1038/nature17145. ↩
- Dutton, A. et al., 2015: Sea-level rise due to polar ice-sheet mass loss during past warm periods. Science, 349(6244), doi:10.1126/science.aaa4019. ↩
- Kopp, R.E., F.J. Simons, J.X. Mitrovica, A.C. Maloof, and M. Oppenheimer, 2013: A probabilistic assessment of sea level variations within the last interglacial stage. Geophysical Journal International, 193(2), 711–716, doi:10.1093/gji/ggt029. ↩
- Fischer, H. et al., 2018: Palaeoclimate constraints on the impact of 2°C anthropogenic warming and beyond. Nature Geoscience, 11, 1–12, doi:10.1038/s41561-018-0146-0. ↩
- Fischer, H. et al., 2018: Palaeoclimate constraints on the impact of 2°C anthropogenic warming and beyond. Nature Geoscience, 11, 1–12, doi:10.1038/s41561-018-0146-0. ↩
- Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩
- Doney, S., L. Bopp, and M. Long, 2014: Historical and Future Trends in Ocean Climate and Biogeochemistry. Oceanography, 27(1), 108–119, doi:10.5670/oceanog.2014.14. ↩
- Hönisch, B. et al., 2012: The geological record of ocean acidification. Science, 335(6072), 1058–1063, doi:10.1126/science.1208277. ↩
- Rhein, M., S.R. Rintoul, S. Aoki, E. Campos, and D. Chambers, 2013: Observations: Ocean. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 255–316.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩ - Cao, L., K. Caldeira, and A.K. Jain, 2007: Effects of carbon dioxide and climate change on ocean acidification and carbonate mineral saturation. Geophysical Research Letters, 34(5), L05607, doi:10.1029/2006gl028605.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115. ↩ - Ridgwell, A. and D.N. Schmidt, 2010: Past constraints on the vulnerability of marine calcifiers to massive carbon dioxide release. Nature Geoscience, 3(3), 196–200, doi:10.1038/ngeo755. ↩
- Hönisch, B. et al., 2012: The geological record of ocean acidification. Science, 335(6072), 1058–1063, doi:10.1126/science.1208277. ↩
- Feely, R.A., C.L. Sabine, J.M. Hernandez-Ayon, D. Ianson, and B. Hales, 2008: Evidence for Upwelling of Corrosive "Acidified" Water onto the Continental Shelf. Science, 320(5882), 1490–1492, doi:10.1126/science.1155676. ↩
- Salisbury, J., M. Green, C. Hunt, and J. Campbell, 2008: Coastal acidification by rivers: A threat to shellfish? Eos, 89(50), 513, doi:10.1029/2008eo500001. ↩
- Cai, W.-J. et al., 2011: Acidification of subsurface coastal waters enhanced by eutrophication. Nature Geoscience, 4(11), 766–770, doi:10.1038/ngeo1297. ↩
- Omstedt, A., M. Edman, B. Claremar, and A. Rutgersson, 2015: Modelling the contributions to marine acidification from deposited SOx, NOx, and NHx in the Baltic Sea: Past and present situations. Continental Shelf Research, 111, 234–249, doi:10.1016/j.csr.2015.08.024. ↩
- Bates, N.R. and A.J. Peters, 2007: The contribution of atmospheric acid deposition to ocean acidification in the subtropical North Atlantic Ocean. Marine Chemistry, 107(4), 547–558, doi:10.1016/j.marchem.2007.08.002.
Duarte, C.M. et al., 2013: Is Ocean Acidification an Open-Ocean Syndrome? Understanding Anthropogenic Impacts on Seawater pH. Estuaries and Coasts, 36(2), 221–236, doi:10.1007/s12237-013-9594-3. ↩ - Stockdale, A., E. Tipping, S. Lofts, and R.J.G. Mortimer, 2016: Effect of Ocean Acidification on Organic and Inorganic Speciation of Trace Metals. Environmental Science & Technology, 50(4), 1906–1913, doi:10.1021/acs.est.5b05624. ↩
- Doney, S., L. Bopp, and M. Long, 2014: Historical and Future Trends in Ocean Climate and Biogeochemistry. Oceanography, 27(1), 108–119, doi:10.5670/oceanog.2014.14.
Karstensen, J. et al., 2015: Open ocean dead zones in the tropical North Atlantic Ocean. Biogeosciences, 12(8), 2597–2605, doi:10.5194/bg-12-2597-2015.
Schmidtko, S., L. Stramma, and M. Visbeck, 2017: Decline in global oceanic oxygen content during the past five decades. Nature, 542(7641), 335–339, doi:10.1038/nature21399. ↩ - Schmidtko, S., L. Stramma, and M. Visbeck, 2017: Decline in global oceanic oxygen content during the past five decades. Nature, 542(7641), 335–339, doi:10.1038/nature21399. ↩
- Diaz, R.J. and R. Rosenberg, 2008: Spreading Dead Zones and Consequences for Marine Ecosystems. Science, 321(5891), 926–929, doi:10.1126/science.1156401. ↩
- Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biology, 21(4), 1395–1406, doi:10.1111/gcb.12754. ↩
- Altieri, A.H. et al., 2017: Tropical dead zones and mass mortalities on coral reefs. Proceedings of the National Academy of Sciences, 114(14), 3660–3665, doi:10.1073/pnas.1621517114. ↩
- Durack, P.J., S.E. Wijffels, and R.J. Matear, 2012: Ocean Salinities Reveal Strong Global Water Cycle Intensification During 1950 to 2000. Science, 336(6080), 455–458, doi:10.1126/science.1212222. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Vaughan, D.G. et al., 2013: Observations: Cryosphere. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V.B. And, P.M. Midgley, and Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 317–382. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952. ↩
- IPCC, 2014a: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.
IPCC, 2014b: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 688 pp. ↩ - Hansen, G. and D. Stone, 2016: Assessing the observed impact of anthropogenic climate change. Nature Climate Change, 6(5), 532–537, doi:10.1038/nclimate2896. ↩
- Hansen, G. and D. Stone, 2016: Assessing the observed impact of anthropogenic climate change. Nature Climate Change, 6(5), 532–537, doi:10.1038/nclimate2896. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Dasgupta, P. et al., 2014: Rural areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 613–657. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- UNESCO, 2011: The Impact of Global Change on Water Resources: The Response of UNESCO'S International Hydrology Programme. United Nations Educational Scientific and Cultural Organization (UNESCO) International Hydrological Programme (IHP), Paris, France, 20 pp. ↩
- Mehran, A. et al., 2017: Compounding Impacts of Human-Induced Water Stress and Climate Change on Water Availability. Scientific Reports, 7(1), 1–9, doi:10.1038/s41598-017-06765-0. ↩
- Liu, J. et al., 2017: Water scarcity assessments in the past, present, and future. Earth's Future, 5(6), 545–559, doi:10.1002/2016ef000518. ↩
- AghaKouchak, A., D. Feldman, M. Hoerling, T. Huxman, and J. Lund, 2015: Water and climate: Recognize anthropogenic drought. Nature, 524(7566), 409–411, doi:10.1038/524409a. ↩
- Mehran, A., O. Mazdiyasni, and A. Aghakouchak, 2015: A hybrid framework for assessing socioeconomic drought: Linking climate variability, local resilience, and demand. Journal of Geophysical Research: Atmospheres, 120(15), 7520–7533, doi:10.1002/2015jd023147. ↩
- Kummu, M. et al., 2016: The world's road to water scarcity: shortage and stress in the 20th century and pathways towards sustainability. Scientific Reports, 6(1), 38495, doi:10.1038/srep38495. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032.
Hanasaki, N. et al., 2013: A global water scarcity assessment under Shared Socio-economic Pathways – Part 2: Water availability and scarcity. Hydrology and Earth System Sciences, 17(7), 2393–2413, doi:10.5194/hess-17-2393-2013.
Arnell, N.W. and B. Lloyd-Hughes, 2014: The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Change, 122(1–2), 127–140, doi:10.1007/s10584-013-0948-4.
Schewe, J. et al., 2014: Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences, 111(9), 3245–3250, doi:10.1073/pnas.1222460110.
Karnauskas, K.B. et al., 2018: Freshwater Stress on Small Island Developing States: Population Projections and Aridity Changes at 1.5°C and 2°C. Regional Environmental Change, 1–10, doi:10.1007/s10113-018-1331-9. ↩ - Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Schewe, J. et al., 2014: Multimodel assessment of water scarcity under climate change. Proceedings of the National Academy of Sciences, 111(9), 3245–3250, doi:10.1073/pnas.1222460110. ↩
- Arnell, N.W. and B. Lloyd-Hughes, 2014: The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Change, 122(1–2), 127–140, doi:10.1007/s10584-013-0948-4. ↩
- Karnauskas, K.B. et al., 2018: Freshwater Stress on Small Island Developing States: Population Projections and Aridity Changes at 1.5°C and 2°C. Regional Environmental Change, 1–10, doi:10.1007/s10113-018-1331-9. ↩
- Hanasaki, N. et al., 2013: A global water scarcity assessment under Shared Socio-economic Pathways – Part 2: Water availability and scarcity. Hydrology and Earth System Sciences, 17(7), 2393–2413, doi:10.5194/hess-17-2393-2013. ↩
- Hanasaki, N. et al., 2013: A global water scarcity assessment under Shared Socio-economic Pathways – Part 2: Water availability and scarcity. Hydrology and Earth System Sciences, 17(7), 2393–2413, doi:10.5194/hess-17-2393-2013. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710.
Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩ - Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩
- Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710.
Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩ - Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩
- Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩
- Fricko, O. et al., 2016: Energy sector water use implications of a 2°C climate policy. Environmental Research Letters, 11(3), 034011, doi:10.1088/1748-9326/11/3/034011. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Tanoue, M., Y. Hirabayashi, H. Ikeuchi, E. Gakidou, and T. Oki, 2016: Global-scale river flood vulnerability in the last 50 years. Scientific Reports, 6(1), 36021, doi:10.1038/srep36021. ↩
- Bindoff, N.L. et al., 2013b: Detection and Attribution of Climate Change: from Global to Regional – Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1–25. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Arnell, N.W. and B. Lloyd-Hughes, 2014: The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Change, 122(1–2), 127–140, doi:10.1007/s10584-013-0948-4.
Winsemius, H.C. et al., 2016: Global drivers of future river flood risk. Nature Climate Change, 6(4), 381–385, doi:10.1038/nclimate2893.
Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485.
Arnell, N.W., J.A. Lowe, B. Lloyd-Hughes, and T.J. Osborn, 2018: The impacts avoided with a 1.5°C climate target: a global and regional assessment. Climatic Change, 147(1–2), 61–76, doi:10.1007/s10584-017-2115-9.
Kinoshita, Y., M. Tanoue, S. Watanabe, and Y. Hirabayashi, 2018: Quantifying the effect of autonomous adaptation to global river flood projections: Application to future flood risk assessments. Environmental Research Letters, 13(1), 014006, doi:10.1088/1748-9326/aa9401. ↩ - Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485. ↩
- Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485. ↩
- Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485. ↩
- Alfieri, L., F. Dottori, R. Betts, P. Salamon, and L. Feyen, 2018: Multi-Model Projections of River Flood Risk in Europe under Global Warming. Climate, 6(1), 6, doi:10.3390/cli6010006. ↩
- Arnell, N.W., J.A. Lowe, B. Lloyd-Hughes, and T.J. Osborn, 2018: The impacts avoided with a 1.5°C climate target: a global and regional assessment. Climatic Change, 147(1–2), 61–76, doi:10.1007/s10584-017-2115-9. ↩
- Arnell, N.W. and B. Lloyd-Hughes, 2014: The global-scale impacts of climate change on water resources and flooding under new climate and socio-economic scenarios. Climatic Change, 122(1–2), 127–140, doi:10.1007/s10584-013-0948-4. ↩
- Kinoshita, Y., M. Tanoue, S. Watanabe, and Y. Hirabayashi, 2018: Quantifying the effect of autonomous adaptation to global river flood projections: Application to future flood risk assessments. Environmental Research Letters, 13(1), 014006, doi:10.1088/1748-9326/aa9401. ↩
- Smirnov, O. et al., 2016: The relative importance of climate change and population growth for exposure to future extreme droughts. Climatic Change, 138(1–2), 41–53, doi:10.1007/s10584-016-1716-z.
Sun, H. et al., 2017: Exposure of population to droughts in the Haihe River Basin under global warming of 1.5 and 2.0°C scenarios. Quaternary International, 453, 74–84, doi:10.1016/j.quaint.2017.05.005.
Arnell, N.W., J.A. Lowe, B. Lloyd-Hughes, and T.J. Osborn, 2018: The impacts avoided with a 1.5°C climate target: a global and regional assessment. Climatic Change, 147(1–2), 61–76, doi:10.1007/s10584-017-2115-9.
Liu, W. et al., 2018: Global drought and severe drought-affected populations in 1.5 and 2°C warmer worlds. Earth System Dynamics, 9(1), 267–283, doi:10.5194/esd-9-267-2018. ↩ - Smirnov, O. et al., 2016: The relative importance of climate change and population growth for exposure to future extreme droughts. Climatic Change, 138(1–2), 41–53, doi:10.1007/s10584-016-1716-z. ↩
- Arnell, N.W., J.A. Lowe, B. Lloyd-Hughes, and T.J. Osborn, 2018: The impacts avoided with a 1.5°C climate target: a global and regional assessment. Climatic Change, 147(1–2), 61–76, doi:10.1007/s10584-017-2115-9. ↩
- Liu, W. et al., 2018: Global drought and severe drought-affected populations in 1.5 and 2°C warmer worlds. Earth System Dynamics, 9(1), 267–283, doi:10.5194/esd-9-267-2018. ↩
- Liu, W. et al., 2018: Global drought and severe drought-affected populations in 1.5 and 2°C warmer worlds. Earth System Dynamics, 9(1), 267–283, doi:10.5194/esd-9-267-2018. ↩
- Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩
- Sun, H. et al., 2017: Exposure of population to droughts in the Haihe River Basin under global warming of 1.5 and 2.0°C scenarios. Quaternary International, 453, 74–84, doi:10.1016/j.quaint.2017.05.005. ↩
- Alfieri, L., F. Dottori, R. Betts, P. Salamon, and L. Feyen, 2018: Multi-Model Projections of River Flood Risk in Europe under Global Warming. Climate, 6(1), 6, doi:10.3390/cli6010006. ↩
- Kinoshita, Y., M. Tanoue, S. Watanabe, and Y. Hirabayashi, 2018: Quantifying the effect of autonomous adaptation to global river flood projections: Application to future flood risk assessments. Environmental Research Letters, 13(1), 014006, doi:10.1088/1748-9326/aa9401. ↩
- Winsemius, H.C. et al., 2016: Global drivers of future river flood risk. Nature Climate Change, 6(4), 381–385, doi:10.1038/nclimate2893. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Kaiser, K. et al., 2014: Detection and attribution of lake-level dynamics in north-eastern central Europe in recent decades. Regional Environmental Change, 14(4), 1587–1600, doi:10.1007/s10113-014-0600-5. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Wada, Y. et al., 2017: Human-water interface in hydrological modelling: current status and future directions. Hydrology and Earth System Sciences, 215194, 4169–4193, doi:10.5194/hess-21-4169-2017. ↩
- Portmann, F.T., P. Döll, S. Eisner, and M. Flörke, 2013: Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environmental Research Letters, 8(2), 024023, doi:10.1088/1748-9326/8/2/024023.
Salem, G.S.A., S. Kazama, S. Shahid, and N.C. Dey, 2017: Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh. Hydrological Research Letters, 11(1), 85–91, doi:10.3178/hrl.11.85. ↩ - Portmann, F.T., P. Döll, S. Eisner, and M. Flörke, 2013: Impact of climate change on renewable groundwater resources: assessing the benefits of avoided greenhouse gas emissions using selected CMIP5 climate projections. Environmental Research Letters, 8(2), 024023, doi:10.1088/1748-9326/8/2/024023. ↩
- Salem, G.S.A., S. Kazama, S. Shahid, and N.C. Dey, 2017: Impact of temperature changes on groundwater levels and irrigation costs in a groundwater-dependent agricultural region in Northwest Bangladesh. Hydrological Research Letters, 11(1), 85–91, doi:10.3178/hrl.11.85. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Patiño, R., D. Dawson, and M.M. VanLandeghem, 2014: Retrospective analysis of associations between water quality and toxic blooms of golden alga (Prymnesium parvum) in Texas reservoirs: Implications for understanding dispersal mechanisms and impacts of climate change. Harmful Algae, 33, 1–11, doi:10.1016/j.hal.2013.12.006.
Aguilera, R., R. Marcé, and S. Sabater, 2015: Detection and attribution of global change effects on river nutrient dynamics in a large Mediterranean basin. Biogeosciences, 12, 4085–4098, doi:10.5194/bg-12-4085-2015.
Watts, G. et al., 2015: Climate change and water in the UK – past changes and future prospects. Progress in Physical Geography, 39(1), 6–28, doi:10.1177/0309133314542957.
Marszelewski, W. and B. Pius, 2016: Long-term changes in temperature of river waters in the transitional zone of the temperate climate: a case study of Polish rivers. Hydrological Sciences Journal, 61(8), 1430–1442, doi:10.1080/02626667.2015.1040800.
Capo, E. et al., 2017: Tracking a century of changes in microbial eukaryotic diversity in lakes driven by nutrient enrichment and climate warming. Environmental Microbiology, 19(7), 2873–2892, doi:10.1111/1462-2920.13815. ↩ - Boehlert, B. et al., 2015: Climate change impacts and greenhouse gas mitigation effects on US water quality. Journal of Advances in Modeling Earth Systems, 7(3), 1326–1338, doi:10.1002/2014ms000400.
Teshager, A.D., P.W. Gassman, J.T. Schoof, and S. Secchi, 2016: Assessment of impacts of agricultural and climate change scenarios on watershed water quantity and quality, and crop production. Hydrology and Earth System Sciences, 20(8), 3325–3342, doi:10.5194/hess-20-3325-2016.
Marcinkowski, P. et al., 2017: Effect of Climate Change on Hydrology, Sediment and Nutrient Losses in Two Lowland Catchments in Poland. Water, 9(3), 156, doi:10.3390/w9030156. ↩ - Bonte, M. and J.J.G. Zwolsman, 2010: Climate change induced salinisation of artificial lakes in the Netherlands and consequences for drinking water production. Water Research, 44(15), 4411–4424, doi:10.1016/j.watres.2010.06.004. ↩
- Hosseini, N., J. Johnston, and K.-E. Lindenschmidt, 2017: Impacts of Climate Change on the Water Quality of a Regulated Prairie River. Water, 9(3), 199, doi:10.3390/w9030199. ↩
- Bonte, M. and J.J.G. Zwolsman, 2010: Climate change induced salinisation of artificial lakes in the Netherlands and consequences for drinking water production. Water Research, 44(15), 4411–4424, doi:10.1016/j.watres.2010.06.004. ↩
- Hosseini, N., J. Johnston, and K.-E. Lindenschmidt, 2017: Impacts of Climate Change on the Water Quality of a Regulated Prairie River. Water, 9(3), 199, doi:10.3390/w9030199. ↩
- Trang, N.T.T., S. Shrestha, M. Shrestha, A. Datta, and A. Kawasaki, 2017: Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok). Science of The Total Environment, 576, 586–598, doi:10.1016/j.scitotenv.2016.10.138. ↩
- Trang, N.T.T., S. Shrestha, M. Shrestha, A. Datta, and A. Kawasaki, 2017: Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok). Science of The Total Environment, 576, 586–598, doi:10.1016/j.scitotenv.2016.10.138. ↩
- Trang, N.T.T., S. Shrestha, M. Shrestha, A. Datta, and A. Kawasaki, 2017: Evaluating the impacts of climate and land-use change on the hydrology and nutrient yield in a transboundary river basin: A case study in the 3S River Basin (Sekong, Sesan, and Srepok). Science of The Total Environment, 576, 586–598, doi:10.1016/j.scitotenv.2016.10.138. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Lu, X.X. et al., 2013: Sediment loads response to climate change: A preliminary study of eight large Chinese rivers. International Journal of Sediment Research, 28(1), 1–14, doi:10.1016/s1001-6279(13)60013-x. ↩
- Shi, H. and G. Wang, 2015: Impacts of climate change and hydraulic structures on runoff and sediment discharge in the middle Yellow River. Hydrological Processes, 29(14), 3236–3246, doi:10.1002/hyp.10439.
Li, Z. and H. Fang, 2016: Impacts of climate change on water erosion: A review. Earth-Science Reviews, 163, 94–117, doi:10.1016/j.earscirev.2016.10.004. ↩ - Potemkina, T.G. and V.L. Potemkin, 2015: Sediment load of the main rivers of Lake Baikal in a changing environment (east Siberia, Russia). Quaternary International, 380–381, 342–349, doi:10.1016/j.quaint.2014.08.029. ↩
- Mullan, D., D. Favis-Mortlock, and R. Fealy, 2012: Addressing key limitations associated with modelling soil erosion under the impacts of future climate change. Agricultural and Forest Meteorology, 156, 18–30, doi:10.1016/j.agrformet.2011.12.004. ↩
- Jiménez Cisneros, B.E. et al., 2014: Freshwater Resources. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 229–269. ↩
- Li, Z. and H. Fang, 2016: Impacts of climate change on water erosion: A review. Earth-Science Reviews, 163, 94–117, doi:10.1016/j.earscirev.2016.10.004. ↩
- Cousino, L.K., R.H. Becker, and K.A. Zmijewski, 2015: Modeling the effects of climate change on water, sediment, and nutrient yields from the Maumee River watershed. Journal of Hydrology: Regional Studies, 4, 762–775, doi:10.1016/j.ejrh.2015.06.017.
Shrestha, B., T.A. Cochrane, B.S. Caruso, M.E. Arias, and T. Piman, 2016: Uncertainty in flow and sediment projections due to future climate scenarios for the 3S Rivers in the Mekong Basin. Journal of Hydrology, 540, 1088–1104, doi:10.1016/j.jhydrol.2016.07.019. ↩ - Cousino, L.K., R.H. Becker, and K.A. Zmijewski, 2015: Modeling the effects of climate change on water, sediment, and nutrient yields from the Maumee River watershed. Journal of Hydrology: Regional Studies, 4, 762–775, doi:10.1016/j.ejrh.2015.06.017.
Shrestha, B., T.A. Cochrane, B.S. Caruso, M.E. Arias, and T. Piman, 2016: Uncertainty in flow and sediment projections due to future climate scenarios for the 3S Rivers in the Mekong Basin. Journal of Hydrology, 540, 1088–1104, doi:10.1016/j.jhydrol.2016.07.019. ↩ - Cousino, L.K., R.H. Becker, and K.A. Zmijewski, 2015: Modeling the effects of climate change on water, sediment, and nutrient yields from the Maumee River watershed. Journal of Hydrology: Regional Studies, 4, 762–775, doi:10.1016/j.ejrh.2015.06.017.
Shrestha, B., T.A. Cochrane, B.S. Caruso, M.E. Arias, and T. Piman, 2016: Uncertainty in flow and sediment projections due to future climate scenarios for the 3S Rivers in the Mekong Basin. Journal of Hydrology, 540, 1088–1104, doi:10.1016/j.jhydrol.2016.07.019. ↩ - Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Larsen, J.N. et al., 2014: Polar regions. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1567–1612. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environmental Research Letters, 8(4), 044018, doi:10.1088/1748-9326/8/4/044018. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Seddon, A.W.R., M. Macias-Fauria, P.R. Long, D. Benz, and K.J. Willis, 2016: Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531(7593), 229–232, doi:10.1038/nature16986. ↩
- Prober, S.M., D.W. Hilbert, S. Ferrier, M. Dunlop, and D. Gobbett, 2012: Combining community-level spatial modelling and expert knowledge to inform climate adaptation in temperate grassy eucalypt woodlands and related grasslands. Biodiversity and Conservation, 21(7), 1627–1650, doi:10.1007/s10531-012-0268-4. ↩
- Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environmental Research Letters, 8(4), 044018, doi:10.1088/1748-9326/8/4/044018. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Oberbauer, S.F. et al., 2013: Phenological response of tundra plants to background climate variation tested using the International Tundra Experiment. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1624), 20120481, doi:10.1098/rstb.2012.0481. ↩
- Zhou, L. et al., 2014: Widespread decline of Congo rainforest greenness in the past decade. Nature, 508(7498), 86–90, doi:10.1038/nature13265. ↩
- Parmesan, C. and M.E. Hanley, 2015: Plants and climate change: complexities and surprises. Annals of Botany, 116(6, SI), 849–864, doi:10.1093/aob/mcv169. ↩
- Wu, J. and Y. Shi, 2016: Attribution index for changes in migratory bird distributions: The role of climate change over the past 50 years in China. Ecological Informatics, 31, 147–155, doi:10.1016/j.ecoinf.2015.11.013. ↩
- Roy, D.B. et al., 2015: Similarities in butterfly emergence dates among populations suggest local adaptation to climate. Global Change Biology, 21(9), 3313–3322, doi:10.1111/gcb.12920. ↩
- Piao, S. et al., 2015: Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21(4), 1601–1609, doi:10.1111/gcb.12795. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Willmer, P., 2012: Ecology: Pollinator-Plant Synchrony Tested by Climate Change. Current Biology, 22(4), R131–R132, doi:10.1016/j.cub.2012.01.009.
Scaven, V.L. and N.E. Rafferty, 2013: Physiological effects of climate warming on flowering plants and insect pollinators and potential consequences for their interactions. Current Zoology, 59(3), 418–426, doi:10.1093/czoolo/59.3.418. ↩ - Thackeray, S.J. et al., 2016: Phenological sensitivity to climate across taxa and trophic levels. Nature, 535(7611), 241–245, doi:10.1038/nature18608. ↩
- Roberts, A.M.I., C. Tansey, R.J. Smithers, and A.B. Phillimore, 2015: Predicting a change in the order of spring phenology in temperate forests. Global Change Biology, 21(7), 2603–2611, doi:10.1111/gcb.12896. ↩
- Roberts, A.M.I., C. Tansey, R.J. Smithers, and A.B. Phillimore, 2015: Predicting a change in the order of spring phenology in temperate forests. Global Change Biology, 21(7), 2603–2611, doi:10.1111/gcb.12896. ↩
- Duputié, A., A. Rutschmann, O. Ronce, and I. Chuine, 2015: Phenological plasticity will not help all species adapt to climate change. Global Change Biology, 21(8), 3062–3073, doi:10.1111/gcb.12914. ↩
- Macel, M., T. Dostálek, S. Esch, and A. Bucharová, 2017: Evolutionary responses to climate change in a range expanding plant. Oecologia, 184(2), 543–554, doi:10.1007/s00442-017-3864-x. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Stephens, P.A. et al., 2016: Consistent response of bird populations to climate change on two continents. Science, 352(6281), 84–87, doi:10.1126/science.aac4858. ↩
- Wiens, J.J., 2016: Climate-Related Local Extinctions Are Already Widespread among Plant and Animal Species. PLOS Biology, 14(12), e2001104, doi:10.1371/journal.pbio.2001104. ↩
- IUCN, 2018: The IUCN Red List of Threatened Species. International Union for Conservation of Nature (IUCN). Retrieved from: http://www.iucnredlist.org. ↩
- Rafferty, N.E., 2017: Effects of global change on insect pollinators: multiple drivers lead to novel communities. Current Opinion in Insect Science, 23, 1–6, doi:10.1016/j.cois.2017.06.009. ↩
- Warren, R. et al., 2013: Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nature Climate Change, 3(7), 678–682, doi:10.1038/nclimate1887. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Smith, P., J. Price, A. Molotoks, R. Warren, and Y. Malhi, 2018: Impacts on terrestrial biodiversity of moving from a 2°C to a 1.5°C target. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160456, doi:10.1098/rsta.2016.0456. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Bråthen, K., V. González, and N. Yoccoz, 2018: Gatekeepers to the effects of climate warming? Niche construction restricts plant community changes along a temperature gradient. Perspectives in Plant Ecology, Evolution and Systematics, 30, 71–81, doi:10.1016/j.ppees.2017.06.005. ↩
- Murphy, G.E.P. and T.N. Romanuk, 2014: A meta-analysis of declines in local species richness from human disturbances. Ecology and Evolution, 4(1), 91–103, doi:10.1002/ece3.909. ↩
- Pecl, G.T. et al., 2017: Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355(6332), eaai9214, doi:10.1126/science.aai9214. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Smith, P., J. Price, A. Molotoks, R. Warren, and Y. Malhi, 2018: Impacts on terrestrial biodiversity of moving from a 2°C to a 1.5°C target. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160456, doi:10.1098/rsta.2016.0456. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Urban, M.C., 2015: Accelerating extinction risk from climate change. Science, 348(6234), 571–573, doi:10.1126/science.aaa4984. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Huang, M. et al., 2017: Velocity of change in vegetation productivity over northern high latitudes. Nature Ecology & Evolution, 1(11), 1649–1654, doi:10.1038/s41559-017-0328-y. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359.
Seidl, R. et al., 2017: Forest disturbances under climate change. Nature Climate Change, 7, 395–402, doi:10.1038/nclimate3303. ↩ - Anderegg, W.R.L. et al., 2015: Tropical nighttime warming as a dominant driver of variability in the terrestrial carbon sink. Proceedings of the National Academy of Sciences, 112(51), 15591–15596, doi:10.1073/pnas.1521479112. ↩
- Hadden, D. and A. Grelle, 2016: Changing temperature response of respiration turns boreal forest from carbon sink into carbon source. Agricultural and Forest Meteorology, 223, 30–38, doi:10.1016/j.agrformet.2016.03.020. ↩
- Dieleman, C.M., Z. Lindo, J.W. McLaughlin, A.E. Craig, and B.A. Branfireun, 2016: Climate change effects on peatland decomposition and porewater dissolved organic carbon biogeochemistry. Biogeochemistry, 128(3), 385–396, doi:10.1007/s10533-016-0214-8. ↩
- Yang, J. et al., 2015: Century-scale patterns and trends of global pyrogenic carbon emissions and fire influences on terrestrial carbon balance. Global Biogeochemical Cycles, 29(9), 1549–1566, doi:10.1002/2015gb005160. ↩
- Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world. Global Environmental Change, 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002. ↩
- Lal, R., 2014: Soil Carbon Management and Climate Change. In: Soil Carbon [Hartemink, A.E. and K. McSweeney (eds.)]. Progress in Soil Science, Springer International Publishing, Cham, Switzerland, pp. 339–361, doi:10.1007/978-3-319-04084-4_35.
Minasny, B. et al., 2017: Soil carbon 4 per mille. Geoderma, 292, 59–86, doi:10.1016/j.geoderma.2017.01.002. ↩ - Bispo, A. et al., 2017: Accounting for Carbon Stocks in Soils and Measuring GHGs Emission Fluxes from Soils: Do We Have the Necessary Standards? Frontiers in Environmental Science, 5, 1–12, doi:10.3389/fenvs.2017.00041. ↩
- Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570. ↩
- Jones, C.D. et al., 2013: Twenty-First-Century Compatible CO2 Emissions and Airborne Fraction Simulated by CMIP5 Earth System Models under Four Representative Concentration Pathways. Journal of Climate, 26(13), 4398–4413, doi:10.1175/jcli-d-12-00554.1. ↩
- Hewitt, A.J. et al., 2016: Sources of Uncertainty in Future Projections of the Carbon Cycle. Journal of Climate, 29(20), 7203–7213, doi:10.1175/jcli-d-16-0161.1.
Lovenduski, N.S. and G.B. Bonan, 2017: Reducing uncertainty in projections of terrestrial carbon uptake. Environmental Research Letters, 12(4), 044020, doi:10.1088/1748-9326/aa66b8. ↩ - Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
Schimel, D., B.B. Stephens, and J.B. Fisher, 2015: Effect of increasing CO2 on the terrestrial carbon cycle. Proceedings of the National Academy of Sciences, 112(2), 436–441, doi:10.1073/pnas.1407302112. ↩ - Pugh, T.A.M., C. Müller, A. Arneth, V. Haverd, and B. Smith, 2016: Key knowledge and data gaps in modelling the influence of CO2 concentration on the terrestrial carbon sink. Journal of Plant Physiology, 203, 3–15, doi:10.1016/j.jplph.2016.05.001. ↩
- Goll, D.S. et al., 2012: Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling. Biogeosciences, 9(9), 3547–3569, doi:10.5194/bg-9-3547-2012.
Yang, X., P.E. Thornton, D.M. Ricciuto, and W.M. Post, 2014: The role of phosphorus dynamics in tropical forests – a modeling study using CLM-CNP. Biogeosciences, 11(6), 1667–1681, doi:10.5194/bg-11-1667-2014.
Wieder, W.R., C.C. Cleveland, W.K. Smith, and K. Todd-Brown, 2015: Future productivity and carbon storage limited by terrestrial nutrient availability. Nature Geoscience, 8, 441–444, doi:10.1038/ngeo2413.
Zaehle, S., C.D. Jones, B. Houlton, J.-F. Lamarque, and E. Robertson, 2015: Nitrogen Availability Reduces CMIP5 Projections of Twenty-First-Century Land Carbon Uptake. Journal of Climate, 28(6), 2494–2511, doi:10.1175/jcli-d-13-00776.1.
Ellsworth, D.S. et al., 2017: Elevated CO2 does not increase eucalypt forest productivity on a low-phosphorus soil. Nature Climate Change, 7(4), 279–282, doi:10.1038/nclimate3235. ↩ - Gang, C. et al., 2015: Projecting the dynamics of terrestrial net primary productivity in response to future climate change under the RCP2.6 scenario. Environmental Earth Sciences, 74(7), 5949–5959, doi:10.1007/s12665-015-4618-x. ↩
- Todd-Brown, K.E.O. et al., 2014: Changes in soil organic carbon storage predicted by Earth system models during the 21st century. Biogeosciences, 11(8), 2341–2356, doi:10.5194/bg-11-2341-2014.
Koven, C.D. et al., 2015: Controls on terrestrial carbon feedbacks by productivity versus turnover in the CMIP5 Earth System Models. Biogeosciences, 12(17), 5211–5228, doi:10.5194/bg-12-5211-2015.
Crowther, T.W. et al., 2016: Quantifying global soil carbon losses in response to warming. Nature, 540(7631), 104–108, doi:10.1038/nature20150. ↩ - Ahlström, A., G. Schurgers, A. Arneth, and B. Smith, 2012: Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environmental Research Letters, 7(4), 044008, doi:10.1088/1748-9326/7/4/044008. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Friend, A.D. et al., 2014: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proceedings of the National Academy of Sciences, 111(9), 3280–3285, doi:10.1073/pnas.1222477110. ↩
- Jones, C.D., J. Lowe, S. Liddicoat, and R. Betts, 2009: Committed terrestrial ecosystem changes due to climate change. Nature Geoscience, 2(7), 484–487, doi:10.1038/ngeo555.
Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
Sihi, D., P.W. Inglett, S. Gerber, and K.S. Inglett, 2017: Rate of warming affects temperature sensitivity of anaerobic peat decomposition and greenhouse gas production. Global Change Biology, 24(1), e259–e274, doi:10.1111/gcb.13839. ↩ - Gang, C. et al., 2015: Projecting the dynamics of terrestrial net primary productivity in response to future climate change under the RCP2.6 scenario. Environmental Earth Sciences, 74(7), 5949–5959, doi:10.1007/s12665-015-4618-x. ↩
- Ahlström, A., G. Schurgers, A. Arneth, and B. Smith, 2012: Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environmental Research Letters, 7(4), 044008, doi:10.1088/1748-9326/7/4/044008. ↩
- Hashimoto, H. et al., 2013: Structural Uncertainty in Model-Simulated Trends of Global Gross Primary Production. Remote Sensing, 5(3), 1258–1273, doi:10.3390/rs5031258. ↩
- Sakalli, A., A. Cescatti, A. Dosio, and M.U. Gücel, 2017: Impacts of 2°C global warming on primary production and soil carbon storage capacity at pan-European level. Climate Services, 7, 64–77, doi:10.1016/j.cliser.2017.03.006. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩
- Tanaka, A. et al., 2017: On the scaling of climate impact indicators with global mean temperature increase: a case study of terrestrial ecosystems and water resources. Climatic Change, 141(4), 775–782, doi:10.1007/s10584-017-1911-6. ↩
- Jones, C.D. et al., 2016: Simulating the Earth system response to negative emissions. Environmental Research Letters, 11(9), 095012, doi:10.1088/1748-9326/11/9/095012. ↩
- Cao, L. and K. Caldeira, 2010: Atmospheric carbon dioxide removal: long-term consequences and commitment. Environmental Research Letters, 5(2), 024011, doi:10.1088/1748-9326/5/2/024011. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Bring, A. et al., 2016: Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. Journal of Geophysical Research: Biogeosciences, 121(3), 621–649, doi:10.1002/2015jg003131.
DeBeer, C.M., H.S. Wheater, S.K. Carey, and K.P. Chun, 2016: Recent climatic, cryospheric, and hydrological changes over the interior of western Canada: a review and synthesis. Hydrology and Earth System Sciences, 20(4), 1573–1598, doi:10.5194/hess-20-1573-2016.
Jiang, Y. et al., 2016: Importance of soil thermal regime in terrestrial ecosystem carbon dynamics in the circumpolar north. Global and Planetary Change, 142, 28–40, doi:10.1016/j.gloplacha.2016.04.011.
Yang, Z. et al., 2016: Warming increases methylmercury production in an Arctic soil. Environmental Pollution, 214, 504–509, doi:10.1016/j.envpol.2016.04.069. ↩ - Cooper, E.J., 2014: Warmer Shorter Winters Disrupt Arctic Terrestrial Ecosystems. Annual Review of Ecology, Evolution, and Systematics, 45, 271–295, doi:10.1146/annurev-ecolsys-120213-091620. ↩
- Chadburn, S.E. et al., 2017: An observation-based constraint on permafrost loss as a function of global warming. Nature Climate Change, 1–6, doi:10.1038/nclimate3262. ↩
- Burke, E.J. et al., 2017: Quantifying uncertainties of permafrost carbon-climate feedbacks. Biogeosciences, 14(12), 3051–3066, doi:10.5194/bg-14-3051-2017. ↩
- Zhou, B., P. Zhai, Y. Chen, and R. Yu, 2018: Projected changes of thermal growing season over Northern Eurasia in a 1.5°C and 2°C warming world. Environmental Research Letters, 13(3), 035004, doi:10.1088/1748-9326/aaa6dc. ↩
- Aalto, J., S. Harrison, and M. Luoto, 2017: Statistical modelling predicts almost complete loss of major periglacial processes in Northern Europe by 2100. Nature Communications, 1–8, doi:10.1038/s41467-017-00669-3. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Seidl, R. et al., 2017: Forest disturbances under climate change. Nature Climate Change, 7, 395–402, doi:10.1038/nclimate3303. ↩
- Reyer, C.P.O. et al., 2017b: Are forest disturbances amplifying or canceling out climate change-induced productivity changes in European forests? Environmental Research Letters, 12(3), 034027, doi:10.1088/1748-9326/aa5ef1. ↩
- Abatzoglou, J.T. and A.P. Williams, 2016: Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences, 113(42), 11770–11775, doi:10.1073/pnas.1607171113. ↩
- Moritz, M.A. et al., 2012: Climate change and disruptions to global fire activity. Ecosphere, 3(6), art49, doi:10.1890/es11-00345.1. ↩
- Romero-Lankao, P. et al., 2014: North America. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1439–1498. ↩
- Hutyra, L.R. et al., 2005: Climatic variability and vegetation vulnerability in Amazônia. Geophysical Research Letters, 32(24), L24712, doi:10.1029/2005gl024981. ↩
- Good, P. et al., 2011: Quantifying Environmental Drivers of Future Tropical Forest Extent. Journal of Climate, 24(5), 1337–1349, doi:10.1175/2010jcli3865.1. ↩
- Good, P., C. Jones, J. Lowe, R. Betts, and N. Gedney, 2013: Comparing Tropical Forest Projections from Two Generations of Hadley Centre Earth System Models, HadGEM2-ES and HadCM3LC. Journal of Climate, 26(2), 495–511, doi:10.1175/jcli-d-11-00366.1. ↩
- Borma, L.S., C.A. Nobre, and M.F. Cardoso, 2013: 2.15 – Response of the Amazon Tropical Forests to Deforestation, Climate, and Extremes, and the Occurrence of Drought and Fire. In: Climate Vulnerability [Pielke, R.A. (ed.)]. Academic Press, Oxford, UK, pp. 153–163, doi:10.1016/b978-0-12-384703-4.00228-8.
Nobre, C.A. et al., 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–68, doi:10.1073/pnas.1605516113. ↩ - Huntingford, C. et al., 2013: Simulated resilience of tropical rainforests to CO2-induced climate change. Nature Geoscience, 6, 268–273, doi:10.1038/ngeo1741. ↩
- Cox, P.M. et al., 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494, 341–344, doi:10.1038/nature11882. ↩
- Cox, P.M., R.A. Betts, C.D. Jones, S.A. Spall, and I.J. Totterdell, 2000: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408, 184–187, doi:10.1038/35041539.
Jones, C.D., J. Lowe, S. Liddicoat, and R. Betts, 2009: Committed terrestrial ecosystem changes due to climate change. Nature Geoscience, 2(7), 484–487, doi:10.1038/ngeo555. ↩ - Nobre, C.A. et al., 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–68, doi:10.1073/pnas.1605516113. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Guiot, J. and W. Cramer, 2016: Climate change: The 2015 Paris Agreement thresholds and Mediterranean basin ecosystems. Science, 354(6311), 4528–4532, doi:10.1126/science.aah5015.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩ - Guiot, J. and W. Cramer, 2016: Climate change: The 2015 Paris Agreement thresholds and Mediterranean basin ecosystems. Science, 354(6311), 4528–4532, doi:10.1126/science.aah5015. ↩
- Engelbrecht, C.J. and F.A. Engelbrecht, 2016: Shifts in Köppen-Geiger climate zones over southern Africa in relation to key global temperature goals. Theoretical and Applied Climatology, 123(1–2), 247–261, doi:10.1007/s00704-014-1354-1. ↩
- NCCARF, 2013: Terrestrial Report Card 2013: Climate change impacts and adaptation on Australian biodiversity. National Climate Change Adaptation Research Facility (NCCARF), Gold Coast, Australia, 8 pp. ↩
- Lyra, A. et al., 2017: Projections of climate change impacts on central America tropical rainforest. Climatic Change, 141(1), 93–105, doi:10.1007/s10584-016-1790-2. ↩
- Lyra, A. et al., 2017: Projections of climate change impacts on central America tropical rainforest. Climatic Change, 141(1), 93–105, doi:10.1007/s10584-016-1790-2. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Green, S.M. and S. Page, 2017: Tropical peatlands: current plight and the need for responsible management. Geology Today, 33(5), 174–179, doi:10.1111/gto.12197. ↩
- Dargie, G.C. et al., 2017: Age, extent and carbon storage of the central Congo Basin peatland complex. Nature, 542(7639), 86–90, doi:10.1038/nature21048. ↩
- Draper, F.C. et al., 2014: The distribution and amount of carbon in the largest peatland complex in Amazonia. Environmental Research Letters, 9(12), 124017, doi:10.1088/1748-9326/9/12/124017. ↩
- Magrin, G.O. et al., 2014: Central and South America. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1499–1566. ↩
- Herbert, E.R. et al., 2015: A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands. Ecosphere, 6(10), 1–43, doi:10.1890/es14-00534.1. ↩
- Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Johnson, W.C. and K.A. Poiani, 2016: Climate Change Effects on Prairie Pothole Wetlands: Findings from a Twenty-five Year Numerical Modeling Project. Wetlands, 36(2), 273–285, doi:10.1007/s13157-016-0790-3. ↩
- Johnson, W.C. and K.A. Poiani, 2016: Climate Change Effects on Prairie Pothole Wetlands: Findings from a Twenty-five Year Numerical Modeling Project. Wetlands, 36(2), 273–285, doi:10.1007/s13157-016-0790-3. ↩
- Costanza, R. et al., 2014: Changes in the global value of ecosystem services. Global Environmental Change, 26(1), 152–158, doi:10.1016/j.gloenvcha.2014.04.002.
Hoegh-Guldberg, O. et al., 2015: Reviving the Ocean Economy: the case for action – 2015. WWF International, Gland, Switzerland, 60 pp. ↩ - Halpern, B.S. et al., 2015: Spatial and temporal changes in cumulative human impacts on the world's ocean. Nature Communications, 6(1), 7615, doi:10.1038/ncomms8615. ↩
- Levin, L.A. and N. Le Bris, 2015: The deep ocean under climate change. Science, 350(6262), 766–768, doi:10.1126/science.aad0126. ↩
- Boyd, P.W., S.T. Lennartz, D.M. Glover, and S.C. Doney, 2015: Biological ramifications of climate-change-mediated oceanic multi-stressors. Nature Climate Change, 5(1), 71–79, doi:10.1038/nclimate2441. ↩
- Hughes, D.J. and B.E. Narayanaswamy, 2013: Impacts of climate change on deep-sea habitats. In: MCCIP Science Review. Marine Climate Change Impacts Partnership (MCCIP), pp. 204–210, doi:10.14465/2013.arc17.155-166.
Levin, L.A. and N. Le Bris, 2015: The deep ocean under climate change. Science, 350(6262), 766–768, doi:10.1126/science.aad0126.
Yasuhara, M. and R. Danovaro, 2016: Temperature impacts on deep-sea biodiversity. Biological Reviews, 91(2), 275–287, doi:10.1111/brv.12169.
Sweetman, A.K. et al., 2017: Major impacts of climate change on deep-sea benthic ecosystems. Elementa: Science of the Anthropocene, 5, 4, doi:10.1525/elementa.203. ↩ - Burrows, M.T. et al., 2014: Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492–495, doi:10.1038/nature12976.
Chust, G., 2014: Are Calanus spp. shifting poleward in the North Atlantic? A habitat modelling approach. ICES Journal of Marine Science, 71(2), 241–253, doi:10.1093/icesjms/fst147.
Bruge, A., P. Alvarez, A. Fontán, U. Cotano, and G. Chust, 2016: Thermal Niche Tracking and Future Distribution of Atlantic Mackerel Spawning in Response to Ocean Warming. Frontiers in Marine Science, 3, 86, doi:10.3389/fmars.2016.00086.
Poloczanska, E.S. et al., 2016: Responses of Marine Organisms to Climate Change across Oceans. Frontiers in Marine Science, 3, 62, doi:10.3389/fmars.2016.00062. ↩ - Woodroffe, C.D. et al., 2010: Response of coral reefs to climate change: Expansion and demise of the southernmost pacific coral reef. Geophysical Research Letters, 37(15), L15602, doi:10.1029/2010gl044067.
Yamano, H., K. Sugihara, and K. Nomura, 2011: Rapid poleward range expansion of tropical reef corals in response to rising sea surface temperatures. Geophysical Research Letters, 38(4), L04601, doi:10.1029/2010gl046474. ↩ - Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the world's coral reefs. Marine and Freshwater Research, 50(8), 839–866, doi:10.1071/mf99078.
Garrabou, J. et al., 2009: Mass mortality in Northwestern Mediterranean rocky benthic communities: Effects of the 2003 heat wave. Global Change Biology, 15(5), 1090–1103, doi:10.1111/j.1365-2486.2008.01823.x.
Rivetti, I., S. Fraschetti, P. Lionello, E. Zambianchi, and F. Boero, 2014: Global warming and mass mortalities of benthic invertebrates in the Mediterranean Sea. PLOS ONE, 9(12), e115655, doi:10.1371/journal.pone.0115655.
Maynard, J. et al., 2015: Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change, 5(7), 688–694, doi:10.1038/nclimate2625.
Krumhansl, K.A. et al., 2016: Global patterns of kelp forest change over the past half-century. Proceedings of the National Academy of Sciences, 113(48), 13785–13790, doi:10.1073/pnas.1606102113.
Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707. ↩ - Hoegh-Guldberg, O. et al., 2007: Coral Reefs Under Rapid Climate Change and Ocean Acidification. Science, 318(5857), 1737–1742, doi:10.1126/science.1152509.
Donner, S.D., 2009: Coping with commitment: Projected thermal stress on coral reefs under different future scenarios. PLOS ONE, 4(6), e5712, doi:10.1371/journal.pone.0005712.
Frieler, K. et al., 2013: Limiting global warming to 2° C is unlikely to save most coral reefs. Nature Climate Change, 3(2), 165–170, doi:10.1038/nclimate1674.
Horta E Costa, B. et al., 2014: Tropicalization of fish assemblages in temperate biogeographic transition zones. Marine Ecology Progress Series, 504, 241–252, doi:10.3354/meps10749.
Vergés, A. et al., 2014: The tropicalization of temperate marine ecosystems: climate-mediated changes in herbivory and community phase shifts. Proceedings of the Royal Society B: Biological Sciences, 281(1789), 20140846, doi:10.1098/rspb.2014.0846.
Vergés, A. et al., 2016: Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proceedings of the National Academy of Sciences, 113(48), 13791–13796, doi:10.1073/pnas.1610725113.
Zarco-Perello, S., T. Wernberg, T.J. Langlois, and M.A. Vanderklift, 2017: Tropicalization strengthens consumer pressure on habitat-forming seaweeds. Scientific Reports, 7(1), 820, doi:10.1038/s41598-017-00991-2. ↩ - Cheung, W.W.L. et al., 2009: Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10(3), 235–251, doi:10.1111/j.1467-2979.2008.00315.x.
Burrows, M.T. et al., 2014: Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492–495, doi:10.1038/nature12976. ↩ - Cheung, W.W.L. et al., 2010: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16(1), 24–35, doi:10.1111/j.1365-2486.2009.01995.x.
Lam, V.W.Y., W.W.L. Cheung, G. Reygondeau, and U.R. Sumaila, 2016: Projected change in global fisheries revenues under climate change. Scientific Reports, 6(1), 32607, doi:10.1038/srep32607.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Barange, M. et al., 2014: Impacts of climate change on marine ecosystem production in societies dependent on fisheries. Nature Climate Change, 4(3), 211–216, doi:10.1038/nclimate2119.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Cheung, W.W.L. et al., 2016b: Structural uncertainty in projecting global fisheries catches under climate change. Ecological Modelling, 325, 57–66, doi:10.1016/j.ecolmodel.2015.12.018.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Cheung, W.W.L. et al., 2010: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16(1), 24–35, doi:10.1111/j.1365-2486.2009.01995.x.
Ainsworth, C.H. et al., 2011: Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries. ICES Journal of Marine Science, 68(6), 1217–1229, doi:10.1093/icesjms/fsr043.
Lam, V.W.Y., W.W.L. Cheung, W. Swartz, and U.R. Sumaila, 2012: Climate change impacts on fisheries in West Africa: implications for economic, food and nutritional security. African Journal of Marine Science, 34(1), 103–117, doi:10.2989/1814232x.2012.673294.
Bopp, L. et al., 2013: Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences, 10(10), 6225–6245, doi:10.5194/bg-10-6225-2013.
Boyd, P.W., S. Sundby, and H.-O. Pörtner, 2014: Cross-chapter box on net primary production in the ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 133–136.
Chust, G. et al., 2014: Biomass changes and trophic amplification of plankton in a warmer ocean. Global Change Biology, 20(7), 2124–2139, doi:10.1111/gcb.12562.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Lam, V.W.Y., W.W.L. Cheung, and U.R. Sumaila, 2014: Marine capture fisheries in the Arctic: Winners or losers under climate change and ocean acidification? Fish and Fisheries, 17, 335–357, doi:10.1111/faf.12106.
Poloczanska, E.S., O. Hoegh-Guldberg, W. Cheung, H.-O. Pörtner, and M. Burrows, 2014: Cross-chapter box on observed global responses of marine biogeography, abundance, and phenology to climate change. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 123–127.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Signorini, S.R., B.A. Franz, and C.R. McClain, 2015: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Frontiers in Marine Science, 2, 1–11, doi:10.3389/fmars.2015.00001.
Lam, V.W.Y., W.W.L. Cheung, G. Reygondeau, and U.R. Sumaila, 2016: Projected change in global fisheries revenues under climate change. Scientific Reports, 6(1), 32607, doi:10.1038/srep32607. ↩ - Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4.
FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 200 pp.
Kämpf, J. and P. Chapman, 2016: The Functioning of Coastal Upwelling Systems. In: Upwelling Systems of the World. Springer International Publishing, Cham, Switzerland, pp. 31–65, doi:10.1007/978-3-319-42524-5_2. ↩ - Boyd, P.W., S. Sundby, and H.-O. Pörtner, 2014: Cross-chapter box on net primary production in the ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 133–136.
Sydeman, W.J. et al., 2014: Climate change and wind intensification in coastal upwelling ecosystems. Science, 345(6192), 77–80, doi:10.1126/science.1251635.
Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biology, 21(4), 1395–1406, doi:10.1111/gcb.12754.
Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4.
Boyd, P.W., 2015: Toward quantifying the response of the oceans' biological pump to climate change. Frontiers in Marine Science, 2, 77, doi:10.3389/fmars.2015.00077. ↩ - IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of IPCC Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, USA, 594 pp.
Seneviratne, S.I. et al., 2012: Changes in Climate Extremes and their Impacts on the Natural Physical Environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109–230. ↩ - Elsner, J.B., J.P. Kossin, and T.H. Jagger, 2008: The increasing intensity of the strongest tropical cyclones. Nature, 455(7209), 92–95, doi:10.1038/nature07234.
Holland, G. and C.L. Bruyère, 2014: Recent intense hurricane response to global climate change. Climate Dynamics, 42(3–4), 617–627, doi:10.1007/s00382-013-1713-0. ↩ - Long, J., C. Giri, J. Primavera, and M. Trivedi, 2016: Damage and recovery assessment of the Philippines' mangroves following Super Typhoon Haiyan. Marine Pollution Bulletin, 109(2), 734–743, doi:10.1016/j.marpolbul.2016.06.080.
Primavera, J.H. et al., 2016: Preliminary assessment of post-Haiyan mangrove damage and short-term recovery in Eastern Samar, central Philippines. Marine Pollution Bulletin, 109(2), 744–750, doi:10.1016/j.marpolbul.2016.05.050.
Villamayor, B.M.R., R.N. Rollon, M.S. Samson, G.M.G. Albano, and J.H. Primavera, 2016: Impact of Haiyan on Philippine mangroves: Implications to the fate of the widespread monospecific Rhizophora plantations against strong typhoons. Ocean and Coastal Management, 132, 1–14, doi:10.1016/j.ocecoaman.2016.07.011.
Cheal, A.J., M.A. MacNeil, M.J. Emslie, and H. Sweatman, 2017: The threat to coral reefs from more intense cyclones under climate change. Global Change Biology, 23(4), 1511–1524, doi:10.1111/gcb.13593. ↩ - De'ath, G., K.E. Fabricius, H. Sweatman, and M. Puotinen, 2012: The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proceedings of the National Academy of Sciences, 109(44), 17995–9, doi:10.1073/pnas.1208909109.
Bozec, Y.-M.M., L. Alvarez-Filip, and P.J. Mumby, 2015: The dynamics of architectural complexity on coral reefs under climate change. Global Change Biology, 21(1), 223–235, doi:10.1111/gcb.12698.
Cheal, A.J., M.A. MacNeil, M.J. Emslie, and H. Sweatman, 2017: The threat to coral reefs from more intense cyclones under climate change. Global Change Biology, 23(4), 1511–1524, doi:10.1111/gcb.13593. ↩ - Brodie, J.E. et al., 2012: Terrestrial pollutant runoff to the Great Barrier Reef: An update of issues, priorities and management responses. Marine Pollution Bulletin, 65(4–9), 81–100, doi:10.1016/j.marpolbul.2011.12.012.
Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409.
Anthony, K.R.N., 2016: Coral Reefs Under Climate Change and Ocean Acidification: Challenges and Opportunities for Management and Policy. Annual Review of Environment and Resources, 41(1), 59–81, doi:10.1146/annurev-environ-110615-085610. ↩ - Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Burge, C.A. et al., 2014: Climate Change Influences on Marine Infectious Diseases: Implications for Management and Society. Annual Review of Marine Science, 6(1), 249–277, doi:10.1146/annurev-marine-010213-135029.
Maynard, J. et al., 2015: Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change, 5(7), 688–694, doi:10.1038/nclimate2625.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048.
Clements, J.C., D. Bourque, J. McLaughlin, M. Stephenson, and L.A. Comeau, 2017: Extreme ocean acidification reduces the susceptibility of eastern oyster shells to a polydorid parasite. Journal of Fish Diseases, 40(11), 1573–1585, doi:10.1111/jfd.12626. ↩ - De'ath, G., K.E. Fabricius, H. Sweatman, and M. Puotinen, 2012: The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proceedings of the National Academy of Sciences, 109(44), 17995–9, doi:10.1073/pnas.1208909109.
Bozec, Y.-M.M., L. Alvarez-Filip, and P.J. Mumby, 2015: The dynamics of architectural complexity on coral reefs under climate change. Global Change Biology, 21(1), 223–235, doi:10.1111/gcb.12698.
Cheal, A.J., M.A. MacNeil, M.J. Emslie, and H. Sweatman, 2017: The threat to coral reefs from more intense cyclones under climate change. Global Change Biology, 23(4), 1511–1524, doi:10.1111/gcb.13593.
Hoegh-Guldberg, O., E.S. Poloczanska, W. Skirving, and S. Dove, 2017: Coral Reef Ecosystems under Climate Change and Ocean Acidification. Frontiers in Marine Science, 4, 158, doi:10.3389/fmars.2017.00158.
Hughes, T.P. et al., 2017a: Coral reefs in the Anthropocene. Nature, 546(7656), 82–90, doi:10.1038/nature22901.
Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707. ↩ - Signorini, S.R., B.A. Franz, and C.R. McClain, 2015: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Frontiers in Marine Science, 2, 1–11, doi:10.3389/fmars.2015.00001. ↩
- Lluch-Cota, S.E. et al., 2014: Cross-chapter box on uncertain trends in major upwelling ecosystems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E. Kissel, A. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 149–151. ↩
- Sydeman, W.J. et al., 2014: Climate change and wind intensification in coastal upwelling ecosystems. Science, 345(6192), 77–80, doi:10.1126/science.1251635. ↩
- Sydeman, W.J. et al., 2014: Climate change and wind intensification in coastal upwelling ecosystems. Science, 345(6192), 77–80, doi:10.1126/science.1251635.
Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4.
Di Lorenzo, E., 2015: Climate science: The future of coastal ocean upwelling. Nature, 518(7539), 310–311, doi:10.1038/518310a. ↩ - Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4. ↩
- Wernberg, T. et al., 2012: An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nature Climate Change, 3(1), 78–82, doi:10.1038/nclimate1627.
Vergés, A. et al., 2014: The tropicalization of temperate marine ecosystems: climate-mediated changes in herbivory and community phase shifts. Proceedings of the Royal Society B: Biological Sciences, 281(1789), 20140846, doi:10.1098/rspb.2014.0846.
Vergés, A. et al., 2016: Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proceedings of the National Academy of Sciences, 113(48), 13791–13796, doi:10.1073/pnas.1610725113.
Zarco-Perello, S., T. Wernberg, T.J. Langlois, and M.A. Vanderklift, 2017: Tropicalization strengthens consumer pressure on habitat-forming seaweeds. Scientific Reports, 7(1), 820, doi:10.1038/s41598-017-00991-2. ↩ - Burge, C.A. et al., 2014: Climate Change Influences on Marine Infectious Diseases: Implications for Management and Society. Annual Review of Marine Science, 6(1), 249–277, doi:10.1146/annurev-marine-010213-135029.
Maynard, J. et al., 2015: Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change, 5(7), 688–694, doi:10.1038/nclimate2625.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Ling, S.D., C.R. Johnson, K. Ridgway, A.J. Hobday, and M. Haddon, 2009: Climate-driven range extension of a sea urchin: Inferring future trends by analysis of recent population dynamics. Global Change Biology, 15(3), 719–731, doi:10.1111/j.1365-2486.2008.01734.x. ↩
- Ling, S.D., C.R. Johnson, K. Ridgway, A.J. Hobday, and M. Haddon, 2009: Climate-driven range extension of a sea urchin: Inferring future trends by analysis of recent population dynamics. Global Change Biology, 15(3), 719–731, doi:10.1111/j.1365-2486.2008.01734.x. ↩
- Cheung, W.W.L. et al., 2009: Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10(3), 235–251, doi:10.1111/j.1467-2979.2008.00315.x.
Pereira, H.M. et al., 2010: Scenarios for global biodiversity in the 21st century. Science, 330(6010), 1496–1501, doi:10.1126/science.1196624.
Pinsky, M.L., B. Worm, M.J. Fogarty, J.L. Sarmiento, and S.A. Levin, 2013: Marine Taxa Track Local Climate Velocities. Science, 341(6151), 1239–1242, doi:10.1126/science.1239352.
Burrows, M.T. et al., 2014: Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492–495, doi:10.1038/nature12976. ↩ - Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Smeed, D.A. et al., 2014: Observed decline of the Atlantic meridional overturning circulation 2004–2012. Ocean Science, 10(1), 29–38, doi:10.5194/os-10-29-2014.
Rahmstorf, S. et al., 2015a: Corrigendum: Evidence for an exceptional twentieth-century slowdown in Atlantic Ocean overturning. Nature Climate Change, 5(10), 956–956, doi:10.1038/nclimate2781.
Rahmstorf, S. et al., 2015b: Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nature Climate Change, 5(5), 475–480, doi:10.1038/nclimate2554.
Kelly, K.A., K. Drushka, L.A. Thompson, D. Le Bars, and E.L. McDonagh, 2016: Impact of slowdown of Atlantic overturning circulation on heat and freshwater transports. Geophysical Research Letters, 43(14), 7625–7631, doi:10.1002/2016gl069789. ↩ - Gattuso, J.-P. et al., 2014: Cross-chapter box on ocean acidification. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 129–131.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Kroeker, K.J. et al., 2013: Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Global Change Biology, 19(6), 1884–1896, doi:10.1111/gcb.12179.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722.
Albright, R. et al., 2016: Ocean acidification: Linking science to management solutions using the Great Barrier Reef as a case study. Journal of Environmental Management, 182, 641–650, doi:10.1016/j.jenvman.2016.07.038. ↩ - Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Fang, J.K.H. et al., 2013: Sponge biomass and bioerosion rates increase under ocean warming and acidification. Global Change Biology, 19(12), 3581–3591, doi:10.1111/gcb.12334.
Kroeker, K.J. et al., 2013: Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Global Change Biology, 19(6), 1884–1896, doi:10.1111/gcb.12179.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Orr, J.C. et al., 2005: Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature, 437(7059), 681–686, doi:10.1038/nature04095.
Hauri, C., T. Friedrich, and A. Timmermann, 2016: Abrupt onset and prolongation of aragonite undersaturation events in the Southern Ocean. Nature Climate Change, 6(2), 172–176, doi:10.1038/nclimate2844. ↩ - Bopp, L. et al., 2013: Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences, 10(10), 6225–6245, doi:10.5194/bg-10-6225-2013.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Fang, J.K.H. et al., 2013: Sponge biomass and bioerosion rates increase under ocean warming and acidification. Global Change Biology, 19(12), 3581–3591, doi:10.1111/gcb.12334.
Kroeker, K.J. et al., 2013: Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Global Change Biology, 19(6), 1884–1896, doi:10.1111/gcb.12179. ↩ - Endres, S., L. Galgani, U. Riebesell, K.G. Schulz, and A. Engel, 2014: Stimulated bacterial growth under elevated pCO2: Results from an off-shore mesocosm study. PLOS ONE, 9(6), e99228, doi:10.1371/journal.pone.0099228. ↩
- Meier, K.J.S., L. Beaufort, S. Heussner, and P. Ziveri, 2014: The role of ocean acidification in Emiliania huxleyi coccolith thinning in the Mediterranean Sea. Biogeosciences, 11(10), 2857–2869, doi:10.5194/bg-11-2857-2014. ↩
- Bednaršek, N. et al., 2012: Extensive dissolution of live pteropods in the Southern Ocean. Nature Geoscience, 5(12), 881–885, doi:10.1038/ngeo1635.
Bednaršek, N. et al., 2014: Limacina helicina shell dissolution as an indicator of declining habitat suitability owing to ocean acidification in the California Current Ecosystem. Proceedings of the Royal Society B: Biological Sciences, 281(1785), 20140123, doi:10.1098/rspb.2014.0123. ↩ - Moy, A.D., W.R. Howard, S.G. Bray, and T.W. Trull, 2009: Reduced calcification in modern Southern Ocean planktonic foraminifera. Nature Geoscience, 2(4), 276–280, doi:10.1038/ngeo460.
Riebesell, U., J.-P. Gattuso, T.F. Thingstad, and J.J. Middelburg, 2013: Arctic ocean acidification: pelagic ecosystem and biogeochemical responses during a mesocosm study. Biogeosciences, 10(8), 5619–5626, doi:10.5194/bg-10-5619-2013.
Richier, S. et al., 2014: Phytoplankton responses and associated carbon cycling during shipboard carbonate chemistry manipulation experiments conducted around Northwest European shelf seas. Biogeosciences, 11(17), 4733–4752, doi:10.5194/bg-11-4733-2014. ↩ - Hall-Spencer, J.M. et al., 2008: Volcanic carbon dioxide vents show ecosystem effects of ocean acidification. Nature, 454(7200), 96–99, doi:10.1038/nature07051.
Linares, C. et al., 2015: Persistent natural acidification drives major distribution shifts in marine benthic ecosystems. Proceedings of the Royal Society B: Biological Sciences, 282(1818), 20150587, doi:10.1098/rspb.2015.0587. ↩ - Garrard, S.L. et al., 2014: Indirect effects may buffer negative responses of seagrass invertebrate communities to ocean acidification. Journal of Experimental Marine Biology and Ecology, 461, 31–38, doi:10.1016/j.jembe.2014.07.011. ↩
- Webster, N.S., S. Uthicke, E.S. Botté, F. Flores, and A.P. Negri, 2013: Ocean acidification reduces induction of coral settlement by crustose coralline algae. Global Change Biology, 19(1), 303–315, doi:10.1111/gcb.12008.
Ordonez, A. et al., 2014: Effects of Ocean Acidification on Population Dynamics and Community Structure of Crustose Coralline Algae. The Biological Bulletin, 226, 255–268, doi:10.1086/bblv226n3p255. ↩ - Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Reyes-Nivia, C., G. Diaz-Pulido, D. Kline, O.H. Guldberg, and S. Dove, 2013: Ocean acidification and warming scenarios increase microbioerosion of coral skeletons. Global Change Biology, 19(6), 1919–1929, doi:10.1111/gcb.12158.
Fang, J.K.H., C.H.L. Schönberg, M.A. Mello-Athayde, O. Hoegh-Guldberg, and S. Dove, 2014: Effects of ocean warming and acidification on the energy budget of an excavating sponge. Global Change Biology, 20(4), 1043–1054, doi:10.1111/gcb.12369. ↩ - Fabricius, K.E. et al., 2011: Losers and winners in coral reefs acclimatized to elevated carbon dioxide concentrations. Nature Climate Change, 1(3), 165–169, doi:10.1038/nclimate1122.
Allen, R., A. Foggo, K. Fabricius, A. Balistreri, and J.M. Hall-Spencer, 2017: Tropical CO2 seeps reveal the impact of ocean acidification on coral reef invertebrate recruitment. Marine Pollution Bulletin, 124(2), 607–613, doi:10.1016/j.marpolbul.2016.12.031. ↩ - Hughes, D.J. and B.E. Narayanaswamy, 2013: Impacts of climate change on deep-sea habitats. In: MCCIP Science Review. Marine Climate Change Impacts Partnership (MCCIP), pp. 204–210, doi:10.14465/2013.arc17.155-166.
Sweetman, A.K. et al., 2017: Major impacts of climate change on deep-sea benthic ecosystems. Elementa: Science of the Anthropocene, 5, 4, doi:10.1525/elementa.203. ↩ - Boyd, P.W., S. Sundby, and H.-O. Pörtner, 2014: Cross-chapter box on net primary production in the ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 133–136.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Boyd, P.W., S.T. Lennartz, D.M. Glover, and S.C. Doney, 2015: Biological ramifications of climate-change-mediated oceanic multi-stressors. Nature Climate Change, 5(1), 71–79, doi:10.1038/nclimate2441.
Breitburg, D. et al., 2018: Declining oxygen in the global ocean and coastal waters. Science, 359(6371), eaam7240, doi:10.1126/science.aam7240. ↩ - Bopp, L. et al., 2013: Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences, 10(10), 6225–6245, doi:10.5194/bg-10-6225-2013.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biology, 21(4), 1395–1406, doi:10.1111/gcb.12754.
Deutsch, C., A. Ferrel, B. Seibel, H.-O. Pörtner, and R.B. Huey, 2015: Climate change tightens a metabolic constraint on marine habitats. Science, 348(6239), 1132–1135, doi:10.1126/science.aaa1605.
Schmidtko, S., L. Stramma, and M. Visbeck, 2017: Decline in global oceanic oxygen content during the past five decades. Nature, 542(7641), 335–339, doi:10.1038/nature21399.
Shepherd, J.G., P.G. Brewer, A. Oschlies, and A.J. Watson, 2017: Ocean ventilation and deoxygenation in a warming world: introduction and overview. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375(2102), 20170240, doi:10.1098/rsta.2017.0240.
Breitburg, D. et al., 2018: Declining oxygen in the global ocean and coastal waters. Science, 359(6371), eaam7240, doi:10.1126/science.aam7240. ↩ - Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biology, 21(4), 1395–1406, doi:10.1111/gcb.12754.
Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4.
Boyd, P.W., 2015: Toward quantifying the response of the oceans' biological pump to climate change. Frontiers in Marine Science, 2, 77, doi:10.3389/fmars.2015.00077. ↩ - Levin, L.A. and N. Le Bris, 2015: The deep ocean under climate change. Science, 350(6262), 766–768, doi:10.1126/science.aad0126.
Sweetman, A.K. et al., 2017: Major impacts of climate change on deep-sea benthic ecosystems. Elementa: Science of the Anthropocene, 5, 4, doi:10.1525/elementa.203. ↩ - Diaz, R.J. and R. Rosenberg, 2008: Spreading Dead Zones and Consequences for Marine Ecosystems. Science, 321(5891), 926–929, doi:10.1126/science.1156401.
Altieri, A.H. and K.B. Gedan, 2015: Climate change and dead zones. Global Change Biology, 21(4), 1395–1406, doi:10.1111/gcb.12754.
Schmidtko, S., L. Stramma, and M. Visbeck, 2017: Decline in global oceanic oxygen content during the past five decades. Nature, 542(7641), 335–339, doi:10.1038/nature21399. ↩ - Turner, R.E., N.N. Rabalais, and D. Justic, 2008: Gulf of Mexico hypoxia: Alternate states and a legacy. Environmental Science & Technology, 42(7), 2323–2327, doi:10.1021/es071617k.
Carstensen, J., J.H. Andersen, B.G. Gustafsson, and D.J. Conley, 2014: Deoxygenation of the Baltic Sea during the last century. Proceedings of the National Academy of Sciences, 111(15), 5628–33, doi:10.1073/pnas.1323156111.
Acharya, S.S. and M.K. Panigrahi, 2016: Eastward shift and maintenance of Arabian Sea oxygen minimum zone: Understanding the paradox. Deep-Sea Research Part I: Oceanographic Research Papers, 115, 240–252, doi:10.1016/j.dsr.2016.07.004.
Lachkar, Z., M. Lévy, and S. Smith, 2018: Intensification and deepening of the Arabian Sea oxygen minimum zone in response to increase in Indian monsoon wind intensity. Biogeosciences, 15(1), 159–186, doi:10.5194/bg-15-159-2018. ↩ - Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484. ↩
- Hamukuaya, H., M. O'Toole, and P. Woodhead, 1998: Observations of severe hypoxia and offshore displacement of Cape hake over the Namibian shelf in 1994. South African Journal of Marine Science, 19, 57–59, doi:10.2989/025776198784126809.
Thronson, A. and A. Quigg, 2008: Fifty-five years of fish kills in coastal Texas. Estuaries and Coasts, 31(4), 802–813, doi:10.1007/s12237-008-9056-5.
Jacinto, G.S., 2011: Fish Kill in the Philippines – Déjà Vu. Science Diliman, 23, 1–3, http://journals.upd.edu.ph/index.php/sciencediliman/article/view/2835. ↩ - Hobbs, J.P.A. and C.A. Mcdonald, 2010: Increased seawater temperature and decreased dissolved oxygen triggers fish kill at the Cocos (Keeling) Islands, Indian Ocean. Journal of Fish Biology, 77(6), 1219–1229, doi:10.1111/j.1095-8649.2010.02726.x.
Bednaršek, N., C.J. Harvey, I.C. Kaplan, R.A. Feely, and J. Možina, 2016: Pteropods on the edge: Cumulative effects of ocean acidification, warming, and deoxygenation. Progress in Oceanography, 145, 1–24, doi:10.1016/j.pocean.2016.04.002.
Seibel, B.A., 2016: Cephalopod Susceptibility to Asphyxiation via Ocean Incalescence, Deoxygenation, and Acidification. Physiology, 31(6), 418–429, doi:10.1152/physiol.00061.2015.
Altieri, A.H. et al., 2017: Tropical dead zones and mass mortalities on coral reefs. Proceedings of the National Academy of Sciences, 114(14), 3660–3665, doi:10.1073/pnas.1621517114. ↩ - Hamukuaya, H., M. O'Toole, and P. Woodhead, 1998: Observations of severe hypoxia and offshore displacement of Cape hake over the Namibian shelf in 1994. South African Journal of Marine Science, 19, 57–59, doi:10.2989/025776198784126809.
Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4.
Rodrigues, L.C. et al., 2015: Sensitivity of Mediterranean Bivalve Mollusc Aquaculture to Climate Change, Ocean Acidification, and Other Environmental Pressures: Findings from a Producer Survey. Journal of Shellfish Research, 34(3), 1161–1176, doi:10.2983/035.034.0341.
Feely, R.A. et al., 2016: Chemical and biological impacts of ocean acidification along the west coast of North America. Estuarine, Coastal and Shelf Science, 183, 260–270, doi:10.1016/j.ecss.2016.08.043.
Li, S. et al., 2016: Interactive Effects of Seawater Acidification and Elevated Temperature on the Transcriptome and Biomineralization in the Pearl Oyster Pinctada fucata. Environmental Science & Technology, 50(3), 1157–1165, doi:10.1021/acs.est.5b05107.
Asiedu, B., J.-O. Adetola, and I. Odame Kissi, 2017a: Aquaculture in troubled climate: Farmers' perception of climate change and their adaptation. Cogent Food & Agriculture, 3(1), 1296400, doi:10.1080/23311932.2017.1296400.
Clements, J.C. and T. Chopin, 2017: Ocean acidification and marine aquaculture in North America: Potential impacts and mitigation strategies. Reviews in Aquaculture, 9(4), 326341, doi:10.1111/raq.12140.
Clements, J.C., D. Bourque, J. McLaughlin, M. Stephenson, and L.A. Comeau, 2017: Extreme ocean acidification reduces the susceptibility of eastern oyster shells to a polydorid parasite. Journal of Fish Diseases, 40(11), 1573–1585, doi:10.1111/jfd.12626.
Breitburg, D. et al., 2018: Declining oxygen in the global ocean and coastal waters. Science, 359(6371), eaam7240, doi:10.1126/science.aam7240. ↩ - Breitburg, D. et al., 2018: Declining oxygen in the global ocean and coastal waters. Science, 359(6371), eaam7240, doi:10.1126/science.aam7240. ↩
- Polyak, L. et al., 2010: History of sea ice in the Arctic. Quaternary Science Reviews, 29(15–16), 1757–1778, doi:10.1016/j.quascirev.2010.02.010. ↩
- Larsen, J.N. et al., 2014: Polar regions. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1567–1612. ↩
- Ford, J.D., 2012: Indigenous health and climate change. American Journal of Public Health, 102(7), 1260–1266, doi:10.2105/ajph.2012.300752.
Ford, J.D., G. McDowell, and T. Pearce, 2015: The adaptation challenge in the Arctic. Nature Climate Change, 5(12), 1046–1053, doi:10.1038/nclimate2723. ↩ - Turner, J. et al., 2014: Antarctic climate change and the environment: An update. Polar Record, 50(3), 237–259, doi:10.1017/s0032247413000296.
Steinberg, D.K. et al., 2015: Long-term (1993–2013) changes in macrozooplankton off the Western Antarctic Peninsula. Deep Sea Research Part I: Oceanographic Research Papers, 101, 54–70, doi:10.1016/j.dsr.2015.02.009.
Piñones, A. and A. Fedorov, 2016: Projected changes of Antarctic krill habitat by the end of the 21st century. Geophysical Research Letters, 43(16), 8580–8589, doi:10.1002/2016gl069656.
Turner, J. et al., 2017b: Unprecedented springtime retreat of Antarctic sea ice in 2016. Geophysical Research Letters, 44(13), 6868–6875, doi:10.1002/2017gl073656. ↩ - Dalpadado, P. et al., 2014: Productivity in the Barents Sea – Response to recent climate variability. PLOS ONE, 9(5), e95273, doi:10.1371/journal.pone.0095273.
Meier, W.N. et al., 2014: Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activity. Reviews of Geophysics, 52(3), 185–217, doi:10.1002/2013rg000431. ↩ - Cheung, W.W.L. et al., 2009: Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10(3), 235–251, doi:10.1111/j.1467-2979.2008.00315.x.
Cheung, W.W.L. et al., 2010: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16(1), 24–35, doi:10.1111/j.1365-2486.2009.01995.x.
Lam, V.W.Y., W.W.L. Cheung, and U.R. Sumaila, 2014: Marine capture fisheries in the Arctic: Winners or losers under climate change and ocean acidification? Fish and Fisheries, 17, 335–357, doi:10.1111/faf.12106.
Cheung, W.W.L. et al., 2016b: Structural uncertainty in projecting global fisheries catches under climate change. Ecological Modelling, 325, 57–66, doi:10.1016/j.ecolmodel.2015.12.018. ↩ - Hollowed, A.B. and S. Sundby, 2014: Change is coming to the northern oceans. Science, 344(6188), 1084–1085, doi:10.1126/science.1251166.
Sundby, S., K.F. Drinkwater, and O.S. Kjesbu, 2016: The North Atlantic Spring-Bloom System-Where the Changing Climate Meets the Winter Dark. Frontiers in Marine Science, 3, 28, doi:10.3389/fmars.2016.00028. ↩ - Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484. ↩
- Maksym, T., S.E. Stammerjohn, S. Ackley, and R. Massom, 2011: Antarctic Sea ice – A polar opposite? Oceanography, 24(3), 162–173, doi:10.5670/oceanog.2012.88.
Reid, P., S. Stammerjohn, R. Massom, T. Scambos, and J. Lieser, 2015: The record 2013 Southern Hemisphere sea-ice extent maximum. Annals of Glaciology, 56(69), 99–106, doi:10.3189/2015aog69a892. ↩ - Hobbs, W.R. et al., 2016: A review of recent changes in Southern Ocean sea ice, their drivers and forcings. Global and Planetary Change, 143, 228–250, doi:10.1016/j.gloplacha.2016.06.008. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216.
Stocker, T.F. et al., 2013: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 33–115.
Blankespoor, B., S. Dasgupta, and B. Laplante, 2014: Sea-Level Rise and Coastal Wetlands. Ambio, 43(8), 996–1005, doi:10.1007/s13280-014-0500-4. ↩ - Di Nitto, D. et al., 2014: Mangroves facing climate change: Landward migration potential in response to projected scenarios of sea level rise. Biogeosciences, 11(3), 857–871, doi:10.5194/bg-11-857-2014.
Ellison, J.C., 2014: Climate Change Adaptation: Management Options for Mangrove Areas. In: Mangrove Ecosystems of Asia: Status, Challenges and Management Strategies [Faridah-Hanum, I., A. Latiff, K.R. Hakeem, and M. Ozturk (eds.)]. Springer New York, New York, NY, USA, pp. 391–413, doi:10.1007/978-1-4614-8582-7_18.
Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538.
Mills, M. et al., 2016: Reconciling Development and Conservation under Coastal Squeeze from Rising Sea Level. Conservation Letters, 9(5), 361–368, doi:10.1111/conl.12213.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩ - Blankespoor, B., S. Dasgupta, and B. Laplante, 2014: Sea-Level Rise and Coastal Wetlands. Ambio, 43(8), 996–1005, doi:10.1007/s13280-014-0500-4.
Alongi, D.M., 2015: The Impact of Climate Change on Mangrove Forests. Current Climate Change Reports, 1(1), 30–39, doi:10.1007/s40641-015-0002-x. ↩ - Di Nitto, D. et al., 2014: Mangroves facing climate change: Landward migration potential in response to projected scenarios of sea level rise. Biogeosciences, 11(3), 857–871, doi:10.5194/bg-11-857-2014.
Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538.
Mills, M. et al., 2016: Reconciling Development and Conservation under Coastal Squeeze from Rising Sea Level. Conservation Letters, 9(5), 361–368, doi:10.1111/conl.12213. ↩ - Hossain, M.S., L. Hein, F.I. Rip, and J.A. Dearing, 2015: Integrating ecosystem services and climate change responses in coastal wetlands development plans for Bangladesh. Mitigation and Adaptation Strategies for Global Change, 20(2), 241–261, doi:10.1007/s11027-013-9489-4.
Sutton-Grier, A.E. and A. Moore, 2016: Leveraging Carbon Services of Coastal Ecosystems for Habitat Protection and Restoration. Coastal Management, 44(3), 259–277, doi:10.1080/08920753.2016.1160206.
Asiedu, B., J.-O. Adetola, and I. Odame Kissi, 2017a: Aquaculture in troubled climate: Farmers' perception of climate change and their adaptation. Cogent Food & Agriculture, 3(1), 1296400, doi:10.1080/23311932.2017.1296400. ↩ - Arkema, K.K. et al., 2013: Coastal habitats shield people and property from sea-level rise and storms. Nature Climate Change, 3(10), 913–918, doi:10.1038/nclimate1944. ↩
- Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Gutiérrez, J.L. et al., 2012: Physical Ecosystem Engineers and the Functioning of Estuaries and Coasts. In: Treatise on Estuarine and Coastal Science Vol. 7. Academic Press, Waltham, MA, USA, pp. 53–81, doi:10.1016/b978-0-12-374711-2.00705-1. ↩
- Bell, J.D., J.E. Johnson, and A.J. Hobday, 2011: Vulnerability of tropical pacific fisheries and aquaculture to climate change. Secretariat of the Pacific Community (SPC), Noumea, New Caledonia, 925 pp.
Cinner, J.E. et al., 2012: Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Global Environmental Change, 22(1), 12–20, doi:10.1016/j.gloenvcha.2011.09.018.
Arkema, K.K. et al., 2013: Coastal habitats shield people and property from sea-level rise and storms. Nature Climate Change, 3(10), 913–918, doi:10.1038/nclimate1944.
Nurse, L.A. et al., 2014: Small islands. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1613–1654.
Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409.
Barbier, E.B., 2015: Valuing the storm protection service of estuarine and coastal ecosystems. Ecosystem Services, 11, 32–38, doi:10.1016/j.ecoser.2014.06.010.
Bell, J. and M. Taylor, 2015: Building Climate-Resilient Food Systems for Pacific Islands. Program Report: 2015–15, WorldFish, Penang, Malaysia, 72 pp.
Hoegh-Guldberg, O. et al., 2015: Reviving the Ocean Economy: the case for action – 2015. WWF International, Gland, Switzerland, 60 pp.
Mycoo, M.A., 2017: Beyond 1.5°C: vulnerabilities and adaptation strategies for Caribbean Small Island Developing States. Regional Environmental Change, 1–13, doi:10.1007/s10113-017-1248-8.
Pecl, G.T. et al., 2017: Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science, 355(6332), eaai9214, doi:10.1126/science.aai9214.
Bell, J.D. et al., 2018: Adaptations to maintain the contributions of small-scale fisheries to food security in the Pacific Islands. Marine Policy, 88, 303–314, doi:10.1016/j.marpol.2017.05.019. ↩ - Short, F.T., S. Kosten, P.A. Morgan, S. Malone, and G.E. Moore, 2016: Impacts of climate change on submerged and emergent wetland plants. Aquatic Botany, 135, 3–17, doi:10.1016/j.aquabot.2016.06.006. ↩
- Arias-Ortiz, A. et al., 2018: A marine heatwave drives massive losses from the world's largest seagrass carbon stocks. Nature Climate Change, 8(4), 338–344, doi:10.1038/s41558-018-0096-y. ↩
- Di Nitto, D. et al., 2014: Mangroves facing climate change: Landward migration potential in response to projected scenarios of sea level rise. Biogeosciences, 11(3), 857–871, doi:10.5194/bg-11-857-2014.
Sierra-Correa, P.C. and J.R. Cantera Kintz, 2015: Ecosystem-based adaptation for improving coastal planning for sea-level rise: A systematic review for mangrove coasts. Marine Policy, 51, 385–393, doi:10.1016/j.marpol.2014.09.013.
Osorio, J.A., M.J. Wingfield, and J. Roux, 2016: A review of factors associated with decline and death of mangroves, with particular reference to fungal pathogens. South African Journal of Botany, 103, 295–301, doi:10.1016/j.sajb.2014.08.010.
Sasmito, S.D., D. Murdiyarso, D.A. Friess, and S. Kurnianto, 2016: Can mangroves keep pace with contemporary sea level rise? A global data review. Wetlands Ecology and Management, 24(2), 263–278, doi:10.1007/s11273-015-9466-7. ↩ - Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538. ↩
- Mills, M. et al., 2016: Reconciling Development and Conservation under Coastal Squeeze from Rising Sea Level. Conservation Letters, 9(5), 361–368, doi:10.1111/conl.12213. ↩
- Valle, M. et al., 2014: Projecting future distribution of the seagrass Zostera noltii under global warming and sea level rise. Biological Conservation, 170, 74–85, doi:10.1016/j.biocon.2013.12.017. ↩
- Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the world's coral reefs. Marine and Freshwater Research, 50(8), 839–866, doi:10.1071/mf99078. ↩
- Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707.
Hughes, T.P., J.T. Kerry, and T. Simpson, 2018: Large-scale bleaching of corals on the Great Barrier Reef. Ecology, 99(2), 501, doi:10.1002/ecy.2092. ↩ - Alongi, D.M., 2008: Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76(1), 1–13, doi:10.1016/j.ecss.2007.08.024.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Duke, N.C. et al., 2017: Large-scale dieback of mangroves in Australia's Gulf of Carpentaria: A severe ecosystem response, coincidental with an unusually extreme weather event. Marine and Freshwater Research, 68(10), 1816–1829, doi:10.1071/mf16322.
Lovelock, C.E., I.C. Feller, R. Reef, S. Hickey, and M.C. Ball, 2017: Mangrove dieback during fluctuating sea levels. Scientific Reports, 7(1), 1680, doi:10.1038/s41598-017-01927-6. ↩ - Alvarez-Filip, L., N.K. Dulvy, J.A. Gill, I.M. Cote, and A.R. Watkinson, 2009: Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proceedings of the Royal Society B: Biological Sciences, 276(1669), 3019–3025, doi:10.1098/rspb.2009.0339. ↩
- Burke, L., K. Reytar, M. Spalding, and A. Perry, 2011: Reefs at risk: Revisited. World Resources Institute, Washington DC, USA, 115 pp. ↩
- World Bank, 2013: Turn Down The Heat: Climate Extremes, Regional Impacts and the Case for Resilience. World Bank, Washington DC, USA, 255 pp., doi:10.1017/cbo9781107415324.004.
Ellison, J.C., 2014: Climate Change Adaptation: Management Options for Mangrove Areas. In: Mangrove Ecosystems of Asia: Status, Challenges and Management Strategies [Faridah-Hanum, I., A. Latiff, K.R. Hakeem, and M. Ozturk (eds.)]. Springer New York, New York, NY, USA, pp. 391–413, doi:10.1007/978-1-4614-8582-7_18.
Anthony, K.R.N. et al., 2015: Operationalizing resilience for adaptive coral reef management under global environmental change. Global Change Biology, 21(1), 48–61, doi:10.1111/gcb.12700.
Sierra-Correa, P.C. and J.R. Cantera Kintz, 2015: Ecosystem-based adaptation for improving coastal planning for sea-level rise: A systematic review for mangrove coasts. Marine Policy, 51, 385–393, doi:10.1016/j.marpol.2014.09.013.
Kroon, F.J., P. Thorburn, B. Schaffelke, and S. Whitten, 2016: Towards protecting the Great Barrier Reef from land-based pollution. Global Change Biology, 22(6), 1985–2002, doi:10.1111/gcb.13262.
O'Leary, J.K. et al., 2017: The Resilience of Marine Ecosystems to Climatic Disturbances. BioScience, 67(3), 208–220, doi:10.1093/biosci/biw161. ↩ - Palumbi, S.R., D.J. Barshis, N. Traylor-Knowles, and R.A. Bay, 2014: Mechanisms of reef coral resistance to future climate change. Science, 344(6186), 895–8, doi:10.1126/science.1251336. ↩
- Bongaerts, P., T. Ridgway, E.M. Sampayo, and O. Hoegh-Guldberg, 2010: Assessing the 'deep reef refugia' hypothesis: focus on Caribbean reefs. Coral Reefs, 29(2), 309–327, doi:10.1007/s00338-009-0581-x.
van Hooidonk, R., J.A. Maynard, and S. Planes, 2013: Temporary refugia for coral reefs in a warming world. Nature Climate Change, 3(5), 508–511, doi:10.1038/nclimate1829.
Beyer, H.L. et al., 2018: Risk-sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters, e12587, doi:10.1111/conl.12587. ↩ - Bongaerts, P., T. Ridgway, E.M. Sampayo, and O. Hoegh-Guldberg, 2010: Assessing the 'deep reef refugia' hypothesis: focus on Caribbean reefs. Coral Reefs, 29(2), 309–327, doi:10.1007/s00338-009-0581-x.
Chollett, I., P.J. Mumby, and J. Cortés, 2010: Upwelling areas do not guarantee refuge for coral reefs in a warming ocean. Marine Ecology Progress Series, 416, 47–56, doi:10.2307/24875251.
Chollett, I. and P.J. Mumby, 2013: Reefs of last resort: Locating and assessing thermal refugia in the wider Caribbean. Biological Conservation, 167(2013), 179–186, doi:10.1016/j.biocon.2013.08.010.
Fine, M., H. Gildor, and A. Genin, 2013: A coral reef refuge in the Red Sea. Global Change Biology, 19(12), 3640–3647, doi:10.1111/gcb.12356.
van Hooidonk, R., J.A. Maynard, and S. Planes, 2013: Temporary refugia for coral reefs in a warming world. Nature Climate Change, 3(5), 508–511, doi:10.1038/nclimate1829.
Chollett, I., S. Enríquez, and P.J. Mumby, 2014: Redefining Thermal Regimes to Design Reserves for Coral Reefs in the Face of Climate Change. PLOS ONE, 9(10), e110634, doi:10.1371/journal.pone.0110634.
Cacciapaglia, C. and R. van Woesik, 2015: Reef-coral refugia in a rapidly changing ocean. Global Change Biology, 21(6), 2272–2282, doi:10.1111/gcb.12851.
Bongaerts, P., C. Riginos, R. Brunner, N. Englebert, and S.R. Smith, 2017: Deep reefs are not universal refuges: reseeding potential varies among coral species. Science Advances, 3(2), e1602373, doi:10.1126/sciadv.1602373.
Beyer, H.L. et al., 2018: Risk-sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters, e12587, doi:10.1111/conl.12587. ↩ - Shafir, S., J. Van Rijn, and B. Rinkevich, 2006: Steps in the construction of underwater coral nursery, an essential component in reef restoration acts. Marine Biology, 149(3), 679–687, doi:10.1007/s00227-005-0236-6.
Rinkevich, B., 2014: Rebuilding coral reefs: Does active reef restoration lead to sustainable reefs? Current Opinion in Environmental Sustainability, 7, 28–36, doi:10.1016/j.cosust.2013.11.018. ↩ - van Oppen, M.J.H., J.K. Oliver, H.M. Putnam, and R.D. Gates, 2015: Building coral reef resilience through assisted evolution. Proceedings of the National Academy of Sciences, 112(8), 2307–2313, doi:10.1073/pnas.1422301112.
van Oppen, M.J.H. et al., 2017: Shifting paradigms in restoration of the world's coral reefs. Global Change Biology, 23(9), 3437–3448, doi:10.1111/gcb.13647. ↩ - Hoegh-Guldberg, O., 2012: The adaptation of coral reefs to climate change: Is the Red Queen being outpaced? Scientia Marina, 76(2), 403–408, doi:10.3989/scimar.03660.29a.
Hoegh-Guldberg, O., 2014a: Coral reef sustainability through adaptation: Glimmer of hope or persistent mirage? Current Opinion in Environmental Sustainability, 7, 127–133, doi:10.1016/j.cosust.2014.01.005.
Bayraktarov, E. et al., 2016: The cost and feasibility of marine coastal restoration. Ecological Applications, 26(4), 1055–1074, doi:10.1890/15-1077. ↩ - Shearman, P., J. Bryan, and J.P. Walsh, 2013: Trends in Deltaic Change over Three Decades in the Asia-Pacific Region. Journal of Coastal Research, 290, 1169–1183, doi:10.2112/jcoastres-d-12-00120.1.
Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538.
Sasmito, S.D., D. Murdiyarso, D.A. Friess, and S. Kurnianto, 2016: Can mangroves keep pace with contemporary sea level rise? A global data review. Wetlands Ecology and Management, 24(2), 263–278, doi:10.1007/s11273-015-9466-7. ↩ - Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538. ↩
- Arkema, K.K. et al., 2013: Coastal habitats shield people and property from sea-level rise and storms. Nature Climate Change, 3(10), 913–918, doi:10.1038/nclimate1944.
Temmerman, S. et al., 2013: Ecosystem-based coastal defence in the face of global change. Nature, 504(7478), 79–83, doi:10.1038/nature12859.
Ferrario, F. et al., 2014: The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nature Communications, 5, 3794, doi:10.1038/ncomms4794.
Hinkel, J. et al., 2014: Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111(9), 3292–7, doi:10.1073/pnas.1222469111.
Elliff, C.I. and I.R. Silva, 2017: Coral reefs as the first line of defense: Shoreline protection in face of climate change. Marine Environmental Research, 127, 148–154, doi:10.1016/j.marenvres.2017.03.007. ↩ - Rinkevich, B., 2015: Climate Change and Active Reef Restoration-Ways of Constructing the "Reefs of Tomorrow". Journal of Marine Science and Engineering, 3(1), 111–127, doi:10.3390/jmse3010111.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048.
Asiedu, B., J.-O. Adetola, and I. Odame Kissi, 2017a: Aquaculture in troubled climate: Farmers' perception of climate change and their adaptation. Cogent Food & Agriculture, 3(1), 1296400, doi:10.1080/23311932.2017.1296400.
Feller, I.C., D.A. Friess, K.W. Krauss, and R.R. Lewis, 2017: The state of the world's mangroves in the 21st century under climate change. Hydrobiologia, 803(1), 1–12, doi:10.1007/s10750-017-3331-z. ↩ - Bayraktarov, E. et al., 2016: The cost and feasibility of marine coastal restoration. Ecological Applications, 26(4), 1055–1074, doi:10.1890/15-1077. ↩
- Atkinson, A., V. Siegel, E.A. Pakhomov, M.J. Jessopp, and V. Loeb, 2009: A re-appraisal of the total biomass and annual production of Antarctic krill. Deep-Sea Research Part I: Oceanographic Research Papers, 56(5), 727–740, doi:10.1016/j.dsr.2008.12.007. ↩
- FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 200 pp. ↩
- Bednaršek, N., C.J. Harvey, I.C. Kaplan, R.A. Feely, and J. Možina, 2016: Pteropods on the edge: Cumulative effects of ocean acidification, warming, and deoxygenation. Progress in Oceanography, 145, 1–24, doi:10.1016/j.pocean.2016.04.002. ↩
- Feely, R.A. et al., 2016: Chemical and biological impacts of ocean acidification along the west coast of North America. Estuarine, Coastal and Shelf Science, 183, 260–270, doi:10.1016/j.ecss.2016.08.043. ↩
- David, C. et al., 2017: Community structure of under-ice fauna in relation to winter sea-ice habitat properties from the Weddell Sea. Polar Biology, 40(2), 247–261, doi:10.1007/s00300-016-1948-4. ↩
- Asplund, M.E. et al., 2014: Ocean acidification and host-pathogen interactions: Blue mussels, Mytilus edulis, encountering Vibrio tubiashii. Environmental Microbiology, 16(4), 1029–1039, doi:10.1111/1462-2920.12307.
Mackenzie, C.L. et al., 2014: Ocean Warming, More than Acidification, Reduces Shell Strength in a Commercial Shellfish Species during Food Limitation. PLOS ONE, 9(1), e86764, doi:10.1371/journal.pone.0086764.
Waldbusser, G.G. et al., 2014: Saturation-state sensitivity of marine bivalve larvae to ocean acidification. Nature Climate Change, 5(3), 273–280, doi:10.1038/nclimate2479.
Zittier, Z.M.C., C. Bock, G. Lannig, and H.O. Pörtner, 2015: Impact of ocean acidification on thermal tolerance and acid-base regulation of Mytilus edulis (L.) from the North Sea. Journal of Experimental Marine Biology and Ecology, 473, 16–25, doi:10.1016/j.jembe.2015.08.001.
Shi, W. et al., 2016: Ocean acidification increases cadmium accumulation in marine bivalves: a potential threat to seafood safety. Scientific Reports, 6(1), 20197, doi:10.1038/srep20197.
Velez, C., E. Figueira, A.M.V.M. Soares, and R. Freitas, 2016: Combined effects of seawater acidification and salinity changes in Ruditapes philippinarum. Aquatic Toxicology, 176, 141–150, doi:10.1016/j.aquatox.2016.04.016.
Wang, Q. et al., 2016: Effects of ocean acidification on immune responses of the Pacific oyster Crassostrea gigas. Fish & shellfish immunology, 49, 24–33, doi:10.1016/j.fsi.2015.12.025.
Castillo, N., L.M. Saavedra, C.A. Vargas, C. Gallardo-Escárate, and C. Détrée, 2017: Ocean acidification and pathogen exposure modulate the immune response of the edible mussel Mytilus chilensis. Fish and Shellfish Immunology, 70, 149–155, doi:10.1016/j.fsi.2017.08.047.
Lemasson, A.J., S. Fletcher, J.M. Hall-Spencer, and A.M. Knights, 2017: Linking the biological impacts of ocean acidification on oysters to changes in ecosystem services: A review. Journal of Experimental Marine Biology and Ecology, 492, 49–62, doi:10.1016/j.jembe.2017.01.019.
Ong, E.Z., M. Briffa, T. Moens, and C. Van Colen, 2017: Physiological responses to ocean acidification and warming synergistically reduce condition of the common cockle Cerastoderma edule. Marine Environmental Research, 130, 38–47, doi:10.1016/j.marenvres.2017.07.001.
Zhao, X. et al., 2017: Ocean acidification adversely influences metabolism, extracellular pH and calcification of an economically important marine bivalve, Tegillarca granosa. Marine Environmental Research, 125, 82–89, doi:10.1016/j.marenvres.2017.01.007. ↩ - Wang, Q. et al., 2016: Effects of ocean acidification on immune responses of the Pacific oyster Crassostrea gigas. Fish & shellfish immunology, 49, 24–33, doi:10.1016/j.fsi.2015.12.025.
Lemasson, A.J., S. Fletcher, J.M. Hall-Spencer, and A.M. Knights, 2017: Linking the biological impacts of ocean acidification on oysters to changes in ecosystem services: A review. Journal of Experimental Marine Biology and Ecology, 492, 49–62, doi:10.1016/j.jembe.2017.01.019.
Ong, E.Z., M. Briffa, T. Moens, and C. Van Colen, 2017: Physiological responses to ocean acidification and warming synergistically reduce condition of the common cockle Cerastoderma edule. Marine Environmental Research, 130, 38–47, doi:10.1016/j.marenvres.2017.07.001.
Zhao, X. et al., 2017: Ocean acidification adversely influences metabolism, extracellular pH and calcification of an economically important marine bivalve, Tegillarca granosa. Marine Environmental Research, 125, 82–89, doi:10.1016/j.marenvres.2017.01.007. ↩ - Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi:10.1126/science.aag2345.
Turner, J. et al., 2017b: Unprecedented springtime retreat of Antarctic sea ice in 2016. Geophysical Research Letters, 44(13), 6868–6875, doi:10.1002/2017gl073656. ↩ - Piñones, A. and A. Fedorov, 2016: Projected changes of Antarctic krill habitat by the end of the 21st century. Geophysical Research Letters, 43(16), 8580–8589, doi:10.1002/2016gl069656. ↩
- Croxall, J.P., 1992: Southern-Ocean Environmental-Changes – Effects on Seabird, Seal and Whale Populations. Philosophical Transactions of the Royal Society B: Biological Sciences, 338(1285), 319–328, doi:10.1098/rstb.1992.0152.
Trathan, P.N. and S.L. Hill, 2016: The Importance of Krill Predation in the Southern Ocean. In: Biology and Ecology of Antarctic Krill [Siegel, V. (ed.)]. Springer, Cham, Switzerland, pp. 321–350, doi:10.1007/978-3-319-29279-3_9. ↩ - Kawaguchi, S. et al., 2013: Risk maps for Antarctic krill under projected Southern Ocean acidification. Nature Climate Change, 3(9), 843–847, doi:10.1038/nclimate1937.
Piñones, A. and A. Fedorov, 2016: Projected changes of Antarctic krill habitat by the end of the 21st century. Geophysical Research Letters, 43(16), 8580–8589, doi:10.1002/2016gl069656. ↩ - Green, A.L. et al., 2014: Designing Marine Reserves for Fisheries Management, Biodiversity Conservation, and Climate Change Adaptation. Coastal Management, 42(2), 143–159, doi:10.1080/08920753.2014.877763.
Bell, J. and M. Taylor, 2015: Building Climate-Resilient Food Systems for Pacific Islands. Program Report: 2015–15, WorldFish, Penang, Malaysia, 72 pp. ↩ - Boyd, P.W., 2015: Toward quantifying the response of the oceans' biological pump to climate change. Frontiers in Marine Science, 2, 77, doi:10.3389/fmars.2015.00077. ↩
- IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. ↩
- Iida, Y. et al., 2015: Trends in pCO2 and sea-air CO2 flux over the global open oceans for the last two decades. Journal of Oceanography, 71(6), 637–661, doi:10.1007/s10872-015-0306-4. ↩
- Rahmstorf, S. et al., 2015b: Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nature Climate Change, 5(5), 475–480, doi:10.1038/nclimate2554. ↩
- Lluch-Cota, S.E. et al., 2014: Cross-chapter box on uncertain trends in major upwelling ecosystems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E. Kissel, A. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 149–151.
Sydeman, W.J. et al., 2014: Climate change and wind intensification in coastal upwelling ecosystems. Science, 345(6192), 77–80, doi:10.1126/science.1251635.
Bakun, A. et al., 2015: Anticipated Effects of Climate Change on Coastal Upwelling Ecosystems. Current Climate Change Reports, 1(2), 85–93, doi:10.1007/s40641-015-0008-4. ↩ - Signorini, S.R., B.A. Franz, and C.R. McClain, 2015: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends. Frontiers in Marine Science, 2, 1–11, doi:10.3389/fmars.2015.00001. ↩
- Bopp, L. et al., 2013: Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences, 10(10), 6225–6245, doi:10.5194/bg-10-6225-2013.
Boyd, P.W., S. Sundby, and H.-O. Pörtner, 2014: Cross-chapter box on net primary production in the ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 133–136.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Boyd, P.W., 2015: Toward quantifying the response of the oceans' biological pump to climate change. Frontiers in Marine Science, 2, 77, doi:10.3389/fmars.2015.00077. ↩ - Gutiérrez, J.L. et al., 2012: Physical Ecosystem Engineers and the Functioning of Estuaries and Coasts. In: Treatise on Estuarine and Coastal Science Vol. 7. Academic Press, Waltham, MA, USA, pp. 53–81, doi:10.1016/b978-0-12-374711-2.00705-1.
Kennedy, E. et al., 2013: Avoiding Coral Reef Functional Collapse Requires Local and Global Action. Current Biology, 23(10), 912–918, doi:10.1016/j.cub.2013.04.020.
Ferrario, F. et al., 2014: The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nature Communications, 5, 3794, doi:10.1038/ncomms4794.
Barbier, E.B., 2015: Valuing the storm protection service of estuarine and coastal ecosystems. Ecosystem Services, 11, 32–38, doi:10.1016/j.ecoser.2014.06.010.
Cooper, J.A.G., M.C. O'Connor, and S. McIvor, 2016: Coastal defences versus coastal ecosystems: A regional appraisal. Marine Policy, doi:10.1016/j.marpol.2016.02.021.
Hauer, M.E., J.M. Evans, and D.R. Mishra, 2016: Millions projected to be at risk from sea-level rise in the continental United States. Nature Climate Change, 6(7), 691–695, doi:10.1038/nclimate2961.
Narayan, S. et al., 2016: The effectiveness, costs and coastal protection benefits of natural and nature-based defences. PLOS ONE, 11(5), e0154735, doi:10.1371/journal.pone.0154735. ↩ - Fu, X. and J. Song, 2017: Assessing the economic costs of sea level rise and benefits of coastal protection: A spatiotemporal approach. Sustainability, 9(8), 1495, doi:10.3390/su9081495. ↩
- Ferrario, F. et al., 2014: The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nature Communications, 5, 3794, doi:10.1038/ncomms4794.
Narayan, S. et al., 2016: The effectiveness, costs and coastal protection benefits of natural and nature-based defences. PLOS ONE, 11(5), e0154735, doi:10.1371/journal.pone.0154735. ↩ - Saunders, M.I. et al., 2014: Interdependency of tropical marine ecosystems in response to climate change. Nature Climate Change, 4(8), 724–729, doi:10.1038/nclimate2274.
Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538. ↩ - Rosselló-Nadal, J., 2014: How to evaluate the effects of climate change on tourism. Tourism Management, 42, 334–340, doi:10.1016/j.tourman.2013.11.006.
Markham, A., E. Osipova, K. Lafrenz Samuels, and A. Caldas, 2016: World Heritage and Tourism in a Changing Climate. United Nations Environment Programme (UNEP), United Nations Educational, Scientific and Cultural Organization (UNESCO) and Union of Concerned Scientists, 108 pp.
Spalding, M.D. et al., 2017: Mapping the global value and distribution of coral reef tourism. Marine Policy, 82, 104–113, doi:10.1016/j.marpol.2017.05.014. ↩ - Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048.
Spalding, M.D. et al., 2017: Mapping the global value and distribution of coral reef tourism. Marine Policy, 82, 104–113, doi:10.1016/j.marpol.2017.05.014. ↩ - Bell, J., 2012: Planning for Climate Change and Sea Level Rise: Queensland's New Coastal Plan. Environmental and Planning Law Journal, 29(1), 61–74, http://ssrn.com/abstract=2611478.
André, C., D. Boulet, H. Rey-Valette, and B. Rulleau, 2016: Protection by hard defence structures or relocation of assets exposed to coastal risks: Contributions and drawbacks of cost-benefit analysis for long-term adaptation choices to climate change. Ocean and Coastal Management, 134, 173–182, doi:10.1016/j.ocecoaman.2016.10.003.
Cooper, J.A.G., M.C. O'Connor, and S. McIvor, 2016: Coastal defences versus coastal ecosystems: A regional appraisal. Marine Policy, doi:10.1016/j.marpol.2016.02.021.
Mills, M. et al., 2016: Reconciling Development and Conservation under Coastal Squeeze from Rising Sea Level. Conservation Letters, 9(5), 361–368, doi:10.1111/conl.12213.
Raabe, E.A. and R.P. Stumpf, 2016: Expansion of Tidal Marsh in Response to Sea-Level Rise: Gulf Coast of Florida, USA. Estuaries and Coasts, 39(1), 145–157, doi:10.1007/s12237-015-9974-y.
Fu, X. and J. Song, 2017: Assessing the economic costs of sea level rise and benefits of coastal protection: A spatiotemporal approach. Sustainability, 9(8), 1495, doi:10.3390/su9081495. ↩ - Rinkevich, B., 2014: Rebuilding coral reefs: Does active reef restoration lead to sustainable reefs? Current Opinion in Environmental Sustainability, 7, 28–36, doi:10.1016/j.cosust.2013.11.018.
Rinkevich, B., 2015: Climate Change and Active Reef Restoration-Ways of Constructing the "Reefs of Tomorrow". Journal of Marine Science and Engineering, 3(1), 111–127, doi:10.3390/jmse3010111.
André, C., D. Boulet, H. Rey-Valette, and B. Rulleau, 2016: Protection by hard defence structures or relocation of assets exposed to coastal risks: Contributions and drawbacks of cost-benefit analysis for long-term adaptation choices to climate change. Ocean and Coastal Management, 134, 173–182, doi:10.1016/j.ocecoaman.2016.10.003.
Cooper, J.A.G., M.C. O'Connor, and S. McIvor, 2016: Coastal defences versus coastal ecosystems: A regional appraisal. Marine Policy, doi:10.1016/j.marpol.2016.02.021.
Narayan, S. et al., 2016: The effectiveness, costs and coastal protection benefits of natural and nature-based defences. PLOS ONE, 11(5), e0154735, doi:10.1371/journal.pone.0154735. ↩ - UNEP-WCMC, 2006: In the front line: shoreline protection and other ecosystem services from mangroves and coral reefs. UNEP-WCMC Biodiversity Series 24, UNEP World Conservation Monitoring Centre (UNEP-WCMC), Cambridge, UK, 33 pp.
Scyphers, S.B., S.P. Powers, K.L. Heck, and D. Byron, 2011: Oyster reefs as natural breakwaters mitigate shoreline loss and facilitate fisheries. PLOS ONE, 6(8), e22396, doi:10.1371/journal.pone.0022396.
Zhang, K. et al., 2012: The role of mangroves in attenuating storm surges. Estuarine, Coastal and Shelf Science, 102–103, 11–23, doi:10.1016/j.ecss.2012.02.021.
Ferrario, F. et al., 2014: The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nature Communications, 5, 3794, doi:10.1038/ncomms4794.
Cooper, J.A.G., M.C. O'Connor, and S. McIvor, 2016: Coastal defences versus coastal ecosystems: A regional appraisal. Marine Policy, doi:10.1016/j.marpol.2016.02.021. ↩ - Barbier, E.B., 2015: Valuing the storm protection service of estuarine and coastal ecosystems. Ecosystem Services, 11, 32–38, doi:10.1016/j.ecoser.2014.06.010.
Elliff, C.I. and I.R. Silva, 2017: Coral reefs as the first line of defense: Shoreline protection in face of climate change. Marine Environmental Research, 127, 148–154, doi:10.1016/j.marenvres.2017.03.007. ↩ - Temmerman, S. et al., 2013: Ecosystem-based coastal defence in the face of global change. Nature, 504(7478), 79–83, doi:10.1038/nature12859.
Mycoo, M.A., 2017: Beyond 1.5°C: vulnerabilities and adaptation strategies for Caribbean Small Island Developing States. Regional Environmental Change, 1–13, doi:10.1007/s10113-017-1248-8. ↩ - Clausen, K.K. and P. Clausen, 2014: Forecasting future drowning of coastal waterbird habitats reveals a major conservation concern. Biological Conservation, 171, 177–185, doi:10.1016/j.biocon.2014.01.033.
Martínez, M.L., G. Mendoza-González, R. Silva-Casarín, and E. Mendoza-Baldwin, 2014: Land use changes and sea level rise may induce a "coastal squeeze" on the coasts of Veracruz, Mexico. Global Environmental Change, 29, 180–188, doi:10.1016/j.gloenvcha.2014.09.009.
Cui, L., Z. Ge, L. Yuan, and L. Zhang, 2015: Vulnerability assessment of the coastal wetlands in the Yangtze Estuary, China to sea-level rise. Estuarine, Coastal and Shelf Science, 156, 42–51, doi:10.1016/j.ecss.2014.06.015.
André, C., D. Boulet, H. Rey-Valette, and B. Rulleau, 2016: Protection by hard defence structures or relocation of assets exposed to coastal risks: Contributions and drawbacks of cost-benefit analysis for long-term adaptation choices to climate change. Ocean and Coastal Management, 134, 173–182, doi:10.1016/j.ocecoaman.2016.10.003.
Mills, M. et al., 2016: Reconciling Development and Conservation under Coastal Squeeze from Rising Sea Level. Conservation Letters, 9(5), 361–368, doi:10.1111/conl.12213. ↩ - Bell, J., 2012: Planning for Climate Change and Sea Level Rise: Queensland's New Coastal Plan. Environmental and Planning Law Journal, 29(1), 61–74, http://ssrn.com/abstract=2611478.
Mills, M. et al., 2016: Reconciling Development and Conservation under Coastal Squeeze from Rising Sea Level. Conservation Letters, 9(5), 361–368, doi:10.1111/conl.12213. ↩ - Bosello, F. and E. De Cian, 2014: Climate change, sea level rise, and coastal disasters. A review of modeling practices. Energy Economics, 46, 593–605, doi:10.1016/j.eneco.2013.09.002. ↩
- Cooper, J.A.G., M.C. O'Connor, and S. McIvor, 2016: Coastal defences versus coastal ecosystems: A regional appraisal. Marine Policy, doi:10.1016/j.marpol.2016.02.021. ↩
- Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Donner, S.D., W.J. Skirving, C.M. Little, M. Oppenheimer, and O. Hoegh-Guldberg, 2005: Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology, 11(12), 2251–2265, doi:10.1111/j.1365-2486.2005.01073.x.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
van Hooidonk, R. et al., 2016: Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific Reports, 6(1), 39666, doi:10.1038/srep39666.
Frieler, K. et al., 2017: Assessing the impacts of 1.5°C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b). Geoscientific Model Development, 10, 4321–4345, doi:10.5194/gmd-10-4321-2017.
Hughes, T.P. et al., 2017a: Coral reefs in the Anthropocene. Nature, 546(7656), 82–90, doi:10.1038/nature22901. ↩ - Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731. ↩
- Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707. ↩
- Reaka-Kudla, M.L., 1997: The Global Biodiversity of Coral Reefs: A comparison with Rain Forests. In: Biodiversity II: Understanding and Protecting Our Biological Resources [Reaka-Kudla, M., D.E. Wilson, and E.O. Wilson (eds.)]. Joseph Henry Press, Washington DC, USA, pp. 83–108, doi:10.2307/1791071. ↩
- Burke, L., K. Reytar, M. Spalding, and A. Perry, 2011: Reefs at risk: Revisited. World Resources Institute, Washington DC, USA, 115 pp.
Cinner, J.E. et al., 2012: Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Global Environmental Change, 22(1), 12–20, doi:10.1016/j.gloenvcha.2011.09.018.
Kennedy, E. et al., 2013: Avoiding Coral Reef Functional Collapse Requires Local and Global Action. Current Biology, 23(10), 912–918, doi:10.1016/j.cub.2013.04.020.
Pendleton, L. et al., 2016: Coral reefs and people in a high-CO2 world: Where can science make a difference to people? PLOS ONE, 11(11), 1–21, doi:10.1371/journal.pone.0164699. ↩ - Hoegh-Guldberg, O., E.S. Poloczanska, W. Skirving, and S. Dove, 2017: Coral Reef Ecosystems under Climate Change and Ocean Acidification. Frontiers in Marine Science, 4, 158, doi:10.3389/fmars.2017.00158. ↩
- Hoegh-Guldberg, O., E.S. Poloczanska, W. Skirving, and S. Dove, 2017: Coral Reef Ecosystems under Climate Change and Ocean Acidification. Frontiers in Marine Science, 4, 158, doi:10.3389/fmars.2017.00158. ↩
- Gardner, T.A., I.M. Côté, J.A. Gill, A. Grant, and A.R. Watkinson, 2005: Hurricanes and caribbean coral reefs: Impacts, recovery patterns, and role in long-term decline. Ecology, 86(1), 174–184, doi:10.1890/04-0141.
Bruno, J.F. and E.R. Selig, 2007: Regional decline of coral cover in the Indo-Pacific: Timing, extent, and subregional comparisons. PLOS ONE, 2(8), e711, doi:10.1371/journal.pone.0000711.
De'ath, G., K.E. Fabricius, H. Sweatman, and M. Puotinen, 2012: The 27-year decline of coral cover on the Great Barrier Reef and its causes. Proceedings of the National Academy of Sciences, 109(44), 17995–9, doi:10.1073/pnas.1208909109. ↩ - Burke, L., K. Reytar, M. Spalding, and A. Perry, 2011: Reefs at risk: Revisited. World Resources Institute, Washington DC, USA, 115 pp.
Halpern, B.S. et al., 2015: Spatial and temporal changes in cumulative human impacts on the world's ocean. Nature Communications, 6(1), 7615, doi:10.1038/ncomms8615.
Cheal, A.J., M.A. MacNeil, M.J. Emslie, and H. Sweatman, 2017: The threat to coral reefs from more intense cyclones under climate change. Global Change Biology, 23(4), 1511–1524, doi:10.1111/gcb.13593. ↩ - Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the world's coral reefs. Marine and Freshwater Research, 50(8), 839–866, doi:10.1071/mf99078.
Baker, A.C., P.W. Glynn, and B. Riegl, 2008: Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuarine, Coastal and Shelf Science, 80(4), 435–471, doi:10.1016/j.ecss.2008.09.003.
Spalding, M.D. and B.E. Brown, 2015: Warm-water coral reefs and climate change. Science, 350(6262), 769–771, doi:10.1126/science.aad0349.
Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707. ↩ - Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037. ↩
- Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Reyes-Nivia, C., G. Diaz-Pulido, D. Kline, O.H. Guldberg, and S. Dove, 2013: Ocean acidification and warming scenarios increase microbioerosion of coral skeletons. Global Change Biology, 19(6), 1919–1929, doi:10.1111/gcb.12158.
Reyes-Nivia, C., G. Diaz-Pulido, and S. Dove, 2014: Relative roles of endolithic algae and carbonate chemistry variability in the skeletal dissolution of crustose coralline algae. Biogeosciences, 11(17), 4615–4626, doi:10.5194/bg-11-4615-2014. ↩ - Wilson, S.K., N.A.J. Graham, M.S. Pratchett, G.P. Jones, and N.V.C. Polunin, 2006: Multiple disturbances and the global degradation of coral reefs: are reef fishes at risk or resilient? Global Change Biology, 12(11), 2220–2234, doi:10.1111/j.1365-2486.2006.01252.x.
Graham, N.A.J., 2014: Habitat complexity: Coral structural loss leads to fisheries declines. Current Biology, 24(9), R359–R361, doi:10.1016/j.cub.2014.03.069.
Graham, N.A.J., S. Jennings, M.A. MacNeil, D. Mouillot, and S.K. Wilson, 2015: Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature, 518(7537), 1–17, doi:10.1038/nature14140.
Cinner, J.E. et al., 2016: A framework for understanding climate change impacts on coral reef social-ecological systems. Regional Environmental Change, 16(4), 1133–1146, doi:10.1007/s10113-015-0832-z. ↩ - Pendleton, L. et al., 2016: Coral reefs and people in a high-CO2 world: Where can science make a difference to people? PLOS ONE, 11(11), 1–21, doi:10.1371/journal.pone.0164699. ↩
- Cheal, A.J., M.A. MacNeil, M.J. Emslie, and H. Sweatman, 2017: The threat to coral reefs from more intense cyclones under climate change. Global Change Biology, 23(4), 1511–1524, doi:10.1111/gcb.13593. ↩
- Gardner, T.A., I.M. Côté, J.A. Gill, A. Grant, and A.R. Watkinson, 2005: Hurricanes and caribbean coral reefs: Impacts, recovery patterns, and role in long-term decline. Ecology, 86(1), 174–184, doi:10.1890/04-0141.
Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Kennedy, E. et al., 2013: Avoiding Coral Reef Functional Collapse Requires Local and Global Action. Current Biology, 23(10), 912–918, doi:10.1016/j.cub.2013.04.020.
Webster, N.S., S. Uthicke, E.S. Botté, F. Flores, and A.P. Negri, 2013: Ocean acidification reduces induction of coral settlement by crustose coralline algae. Global Change Biology, 19(1), 303–315, doi:10.1111/gcb.12008.
Hoegh-Guldberg, O., 2014b: Coral reefs in the Anthropocene: persistence or the end of the line? Geological Society, London, Special Publications, 395(1), 167–183, doi:10.1144/sp395.17.
Anthony, K.R.N., 2016: Coral Reefs Under Climate Change and Ocean Acidification: Challenges and Opportunities for Management and Policy. Annual Review of Environment and Resources, 41(1), 59–81, doi:10.1146/annurev-environ-110615-085610. ↩ - Kline, D.I. et al., 2012: A short-term in situ CO2 enrichment experiment on Heron Island (GBR). Scientific Reports, 2, 413, doi:10.1038/srep00413.
Dove, S.G. et al., 2013: Future reef decalcification under a business-as-usual CO2 emission scenario. Proceedings of the National Academy of Sciences, 110(38), 15342–15347, doi:10.1073/pnas.1302701110.
Fang, J.K.H. et al., 2013: Sponge biomass and bioerosion rates increase under ocean warming and acidification. Global Change Biology, 19(12), 3581–3591, doi:10.1111/gcb.12334.
Reyes-Nivia, C., G. Diaz-Pulido, D. Kline, O.H. Guldberg, and S. Dove, 2013: Ocean acidification and warming scenarios increase microbioerosion of coral skeletons. Global Change Biology, 19(6), 1919–1929, doi:10.1111/gcb.12158.
Fang, J.K.H., C.H.L. Schönberg, M.A. Mello-Athayde, O. Hoegh-Guldberg, and S. Dove, 2014: Effects of ocean warming and acidification on the energy budget of an excavating sponge. Global Change Biology, 20(4), 1043–1054, doi:10.1111/gcb.12369.
Reyes-Nivia, C., G. Diaz-Pulido, and S. Dove, 2014: Relative roles of endolithic algae and carbonate chemistry variability in the skeletal dissolution of crustose coralline algae. Biogeosciences, 11(17), 4615–4626, doi:10.5194/bg-11-4615-2014. ↩ - Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the world's coral reefs. Marine and Freshwater Research, 50(8), 839–866, doi:10.1071/mf99078. ↩
- Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707. ↩
- Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the world's coral reefs. Marine and Freshwater Research, 50(8), 839–866, doi:10.1071/mf99078.
Donner, S.D., W.J. Skirving, C.M. Little, M. Oppenheimer, and O. Hoegh-Guldberg, 2005: Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology, 11(12), 2251–2265, doi:10.1111/j.1365-2486.2005.01073.x.
Donner, S.D., 2009: Coping with commitment: Projected thermal stress on coral reefs under different future scenarios. PLOS ONE, 4(6), e5712, doi:10.1371/journal.pone.0005712.
van Hooidonk, R. and M. Huber, 2012: Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions. Coral Reefs, 31(1), 121–131, doi:10.1007/s00338-011-0825-4.
Frieler, K. et al., 2013: Limiting global warming to 2° C is unlikely to save most coral reefs. Nature Climate Change, 3(2), 165–170, doi:10.1038/nclimate1674.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
van Hooidonk, R. et al., 2016: Local-scale projections of coral reef futures and implications of the Paris Agreement. Scientific Reports, 6(1), 39666, doi:10.1038/srep39666. ↩ - Frieler, K. et al., 2013: Limiting global warming to 2° C is unlikely to save most coral reefs. Nature Climate Change, 3(2), 165–170, doi:10.1038/nclimate1674.
Hoegh-Guldberg, O., 2014b: Coral reefs in the Anthropocene: persistence or the end of the line? Geological Society, London, Special Publications, 395(1), 167–183, doi:10.1144/sp395.17.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Hughes, T.P. et al., 2017a: Coral reefs in the Anthropocene. Nature, 546(7656), 82–90, doi:10.1038/nature22901. ↩ - Donner, S.D., W.J. Skirving, C.M. Little, M. Oppenheimer, and O. Hoegh-Guldberg, 2005: Global assessment of coral bleaching and required rates of adaptation under climate change. Global Change Biology, 11(12), 2251–2265, doi:10.1111/j.1365-2486.2005.01073.x. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Baker, A.C., P.W. Glynn, and B. Riegl, 2008: Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery trends and future outlook. Estuarine, Coastal and Shelf Science, 80(4), 435–471, doi:10.1016/j.ecss.2008.09.003. ↩
- King, A.D., D.J. Karoly, and B.J. Henley, 2017: Australian climate extremes at 1.5°C and 2°C of global warming. Nature Climate Change, 7(6), 412–416, doi:10.1038/nclimate3296. ↩
- Caldeira, K., 2013: Coral Bleaching: Coral 'refugia' amid heating seas. Nature Climate Change, 3(5), 444–445, doi:10.1038/nclimate1888.
van Hooidonk, R., J.A. Maynard, and S. Planes, 2013: Temporary refugia for coral reefs in a warming world. Nature Climate Change, 3(5), 508–511, doi:10.1038/nclimate1829.
Cacciapaglia, C. and R. van Woesik, 2015: Reef-coral refugia in a rapidly changing ocean. Global Change Biology, 21(6), 2272–2282, doi:10.1111/gcb.12851.
Keppel, G. and J. Kavousi, 2015: Effective climate change refugia for coral reefs. Global Change Biology, 21(8), 2829–2830, doi:10.1111/gcb.12936. ↩ - Kersting, D.K., E. Cebrian, J. Verdura, and E. Ballesteros, 2017: A new cladocora caespitosa population with unique ecological traits. Mediterranean Marine Science, 18(1), 38–42, doi:10.12681/mms.1955. ↩
- Bongaerts, P., T. Ridgway, E.M. Sampayo, and O. Hoegh-Guldberg, 2010: Assessing the 'deep reef refugia' hypothesis: focus on Caribbean reefs. Coral Reefs, 29(2), 309–327, doi:10.1007/s00338-009-0581-x.
Holstein, D.M., C.B. Paris, A.C. Vaz, and T.B. Smith, 2016: Modeling vertical coral connectivity and mesophotic refugia. Coral Reefs, 35(1), 23–37, doi:10.1007/s00338-015-1339-2. ↩ - Bongaerts, P., C. Riginos, R. Brunner, N. Englebert, and S.R. Smith, 2017: Deep reefs are not universal refuges: reseeding potential varies among coral species. Science Advances, 3(2), e1602373, doi:10.1126/sciadv.1602373. ↩
- Spalding, M.D. et al., 2014: The role of ecosystems in coastal protection: Adapting to climate change and coastal hazards. Ocean and Coastal Management, 90, 50–57, doi:10.1016/j.ocecoaman.2013.09.007.
Halpern, B.S. et al., 2015: Spatial and temporal changes in cumulative human impacts on the world's ocean. Nature Communications, 6(1), 7615, doi:10.1038/ncomms8615. ↩ - Cinner, J.E. et al., 2012: Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Global Environmental Change, 22(1), 12–20, doi:10.1016/j.gloenvcha.2011.09.018.
Cinner, J.E. et al., 2016: A framework for understanding climate change impacts on coral reef social-ecological systems. Regional Environmental Change, 16(4), 1133–1146, doi:10.1007/s10113-015-0832-z.
Pendleton, L. et al., 2016: Coral reefs and people in a high-CO2 world: Where can science make a difference to people? PLOS ONE, 11(11), 1–21, doi:10.1371/journal.pone.0164699. ↩ - Kennedy, E. et al., 2013: Avoiding Coral Reef Functional Collapse Requires Local and Global Action. Current Biology, 23(10), 912–918, doi:10.1016/j.cub.2013.04.020.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Sanderson, B.M. et al., 2017: Community climate simulations to assess avoided impacts in 1.5°C and 2°C futures. Earth System Dynamics, 8(3), 827–847, doi:10.5194/esd-8-827-2017.
Goodwin, P., S. Brown, I.D. Haigh, R.J. Nicholls, and J.M. Matter, 2018: Adjusting Mitigation Pathways to Stabilize Climate at 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 601–615, doi:10.1002/2017ef000732.
Mengel, M., A. Nauels, J. Rogelj, and C.F. Schleussner, 2018: Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action. Nature Communications, 9(1), 1–10, doi:10.1038/s41467-018-02985-8.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448.
Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738.
Brown, S. et al., 2018b: What are the implications of sea-level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Regional Environmental Change, 1–14, doi:10.1007/s10113-018-1311-0.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448.
Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩ - Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Goodwin, P., S. Brown, I.D. Haigh, R.J. Nicholls, and J.M. Matter, 2018: Adjusting Mitigation Pathways to Stabilize Climate at 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 601–615, doi:10.1002/2017ef000732. ↩
- Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Brown, S., R.J. Nicholls, J.A. Lowe, and J. Hinkel, 2016: Spatial variations of sea-level rise and impacts: An application of DIVA. Climatic Change, 134(3), 403–416, doi:10.1007/s10584-013-0925-y.
Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩ - Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Nicholls, R.J. et al., 2007: Coastal systems and low-lying areas. In: Climate Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. Linden, and C.E. Hanson (eds.)]. Cambridge University Press, Cambridge, UK, pp. 315–356.
Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩ - Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
- Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
- Arnell, N.W. et al., 2016: Global-scale climate impact functions: the relationship between climate forcing and impact. Climatic Change, 134(3), 475–487, doi:10.1007/s10584-013-1034-7. ↩
- Hinkel, J. et al., 2014: Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111(9), 3292–7, doi:10.1073/pnas.1222469111. ↩
- Arnell, N.W. et al., 2016: Global-scale climate impact functions: the relationship between climate forcing and impact. Climatic Change, 134(3), 475–487, doi:10.1007/s10584-013-1034-7.
Brown, S., R.J. Nicholls, J.A. Lowe, and J. Hinkel, 2016: Spatial variations of sea-level rise and impacts: An application of DIVA. Climatic Change, 134(3), 403–416, doi:10.1007/s10584-013-0925-y. ↩ - Clark, P.U. et al., 2016: Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nature Climate Change, 6(4), 360–369, doi:10.1038/nclimate2923. ↩
- Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Araos, M. et al., 2016: Climate change adaptation planning in large cities: A systematic global assessment. Environmental Science & Policy, 66, 375–382, doi:10.1016/j.envsci.2016.06.009.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩ - Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Hanson, S. et al., 2011: A global ranking of port cities with high exposure to climate extremes. Climatic Change, 104(1), 89–111, doi:10.1007/s10584-010-9977-4.
Hallegatte, S., C. Green, R.J. Nicholls, and J. Corfee-Morlot, 2013: Future flood losses in major coastal cities. Nature Climate Change, 3(9), 802–806, doi:10.1038/nclimate1979. ↩ - Hallegatte, S., C. Green, R.J. Nicholls, and J. Corfee-Morlot, 2013: Future flood losses in major coastal cities. Nature Climate Change, 3(9), 802–806, doi:10.1038/nclimate1979.
Cazenave, A. and G. Cozannet, 2014: Sea level rise and its coastal impacts. Earth's Future, 2(2), 15–34, doi:10.1002/2013ef000188.
Clark, P.U. et al., 2016: Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nature Climate Change, 6(4), 360–369, doi:10.1038/nclimate2923.
Jevrejeva, S., L.P. Jackson, R.E.M. Riva, A. Grinsted, and J.C. Moore, 2016: Coastal sea level rise with warming above 2°C. Proceedings of the National Academy of Sciences, 113(47), 13342–13347, doi:10.1073/pnas.1605312113. ↩ - Jevrejeva, S., L.P. Jackson, R.E.M. Riva, A. Grinsted, and J.C. Moore, 2016: Coastal sea level rise with warming above 2°C. Proceedings of the National Academy of Sciences, 113(47), 13342–13347, doi:10.1073/pnas.1605312113. ↩
- Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
- Wairiu, M., 2017: Land degradation and sustainable land management practices in Pacific Island Countries. Regional Environmental Change, 17(4), 1053–1064, doi:10.1007/s10113-016-1041-0. ↩
- Yates, M., G. Le Cozannet, M. Garcin, E. Salai, and P. Walker, 2013: Multidecadal Atoll Shoreline Change on Manihi and Manuae, French Polynesia. Journal of Coastal Research, 29, 870–882, doi:10.2112/jcoastres-d-12-00129.1. ↩
- Kench, P., D. Thompson, M. Ford, H. Ogawa, and R. Mclean, 2015: Coral islands defy sea-level rise over the past century: Records from a central Pacific atoll. Geology, 43(6), 515–518, doi:10.1130/g36555.1.
Kench, P.S., M.R. Ford, and S.D. Owen, 2018: Patterns of island change and persistence offer alternate adaptation pathways for atoll nations. Nature Communications, 9(1), 605, doi:10.1038/s41467-018-02954-1. ↩ - Romine, B.M., C.H. Fletcher, M.M. Barbee, T.R. Anderson, and L.N. Frazer, 2013: Are beach erosion rates and sea-level rise related in Hawaii? Global and Planetary Change, 108, 149–157, doi:10.1016/j.gloplacha.2013.06.009. ↩
- Kench, P., D. Thompson, M. Ford, H. Ogawa, and R. Mclean, 2015: Coral islands defy sea-level rise over the past century: Records from a central Pacific atoll. Geology, 43(6), 515–518, doi:10.1130/g36555.1.
McLean, R. and P. Kench, 2015: Destruction or persistence of coral atoll islands in the face of 20th and 21st century sea-level rise? Wiley Interdisciplinary Reviews: Climate Change, 6(5), 445–463, doi:10.1002/wcc.350.
Beetham, E., P.S. Kench, and S. Popinet, 2017: Future Reef Growth Can Mitigate Physical Impacts of Sea-Level Rise on Atoll Islands. Earth's Future, 5(10), 1002–1014, doi:10.1002/2017ef000589.
Kench, P.S., M.R. Ford, and S.D. Owen, 2018: Patterns of island change and persistence offer alternate adaptation pathways for atoll nations. Nature Communications, 9(1), 605, doi:10.1038/s41467-018-02954-1. ↩ - Farbotko, C. and H. Lazrus, 2012: The first climate refugees? Contesting global narratives of climate change in Tuvalu. Global Environmental Change, 22(2), 382–390, doi:10.1016/j.gloenvcha.2011.11.014.
Weir, T., L. Dovey, and D. Orcherton, 2017: Social and cultural issues raised by climate change in Pacific Island countries: an overview. Regional Environmental Change, 17(4), 1017–1028, doi:10.1007/s10113-016-1012-5. ↩ - Campbell, J. and O. Warrick, 2014: Climate Change and Migration Issues in the Pacific. United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) Pacific Office, Suva, Fiji, 34 pp. ↩
- McNamara, K.E. and H.J. Des Combes, 2015: Planning for Community Relocations Due to Climate Change in Fiji. International Journal of Disaster Risk Science, 6(3), 315–319, doi:10.1007/s13753-015-0065-2.
Gharbaoui, D. and J. Blocher, 2016: The Reason Land Matters: Relocation as Adaptation to Climate Change in Fiji Islands. In: Migration, Risk Management and Climate Change: Evidence and Policy Responses [Milan, A., B. Schraven, K. Warner, and N. Cascone (eds.)]. Springer, Cham, Switzerland, pp. 149–173, doi:10.1007/978-3-319-42922-9_8. ↩ - Albert, S. et al., 2017: Heading for the hills: climate-driven community relocations in the Solomon Islands and Alaska provide insight for a 1.5°C future. Regional Environmental Change, 1–12, doi:10.1007/s10113-017-1256-8. ↩
- Jamero, M.L. et al., 2017: Small-island communities in the Philippines prefer local measures to relocation in response to sea-level rise. Nature Climate Change, 7, 581–586, doi:10.1038/nclimate3344. ↩
- Jamero, M.L., M. Onuki, M. Esteban, and N. Tan, 2018: Community-based adaptation in low-lying islands in the Philippines: challenges and lessons learned. Regional Environmental Change, 1–12, doi:10.1007/s10113-018-1332-8. ↩
- Nunn, P.D., J. Runman, M. Falanruw, and R. Kumar, 2017: Culturally grounded responses to coastal change on islands in the Federated States of Micronesia, northwest Pacific Ocean. Regional Environmental Change, 17(4), 959–971, doi:10.1007/s10113-016-0950-2. ↩
- Sovacool, B.K., 2012: Perceptions of climate change risks and resilient island planning in the Maldives. Mitigation and Adaptation Strategies for Global Change, 17(7), 731–752, doi:10.1007/s11027-011-9341-7.
Mycoo, M., 2014: Sustainable tourism, climate change and sea level rise adaptation policies in Barbados. Natural Resources Forum, 38(1), 47–57, doi:10.1111/1477-8947.12033.
Nurse, L.A. et al., 2014: Small islands. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1613–1654.
Mycoo, M.A., 2017: Beyond 1.5°C: vulnerabilities and adaptation strategies for Caribbean Small Island Developing States. Regional Environmental Change, 1–13, doi:10.1007/s10113-017-1248-8. ↩ - Nurse, L.A. et al., 2014: Small islands. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1613–1654.
Benjamin, L. and A. Thomas, 2016: 1.5°C To Stay Alive?: AOSIS and the Long Term Temperature Goal in the Paris Agreement. IUCNAEL eJournal, 7, 122–129, http://www.iucnael.org/en/documents/1324-iucn-ejournal-issue-7.
Ourbak, T. and A.K. Magnan, 2017: The Paris Agreement and climate change negotiations: Small Islands, big players. Regional Environmental Change, 1–7, doi:10.1007/s10113-017-1247-9.
Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448.
Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩ - Sovacool, B.K., 2012: Perceptions of climate change risks and resilient island planning in the Maldives. Mitigation and Adaptation Strategies for Global Change, 17(7), 731–752, doi:10.1007/s11027-011-9341-7.
Mycoo, M., 2014: Sustainable tourism, climate change and sea level rise adaptation policies in Barbados. Natural Resources Forum, 38(1), 47–57, doi:10.1111/1477-8947.12033.
Nurse, L.A. et al., 2014: Small islands. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1613–1654.
Mycoo, M.A., 2017: Beyond 1.5°C: vulnerabilities and adaptation strategies for Caribbean Small Island Developing States. Regional Environmental Change, 1–13, doi:10.1007/s10113-017-1248-8. ↩ - Storlazzi, C.D. et al., 2018: Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding. Science Advances, 4(4), eaap9741, doi:10.1126/sciadv.aap9741. ↩
- Terry, J.P. and T.F.M. Chui, 2012: Evaluating the fate of freshwater lenses on atoll islands after eustatic sea-level rise and cyclone-driven inundation: A modelling approach. Global and Planetary Change, 88–89, 76–84, doi:10.1016/j.gloplacha.2012.03.008. ↩
- Kumar, L. and S. Taylor, 2015: Exposure of coastal built assets in the South Pacific to climate risks. Nature Climate Change, 5(11), 992–996, doi:10.1038/nclimate2702.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩ - Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Giardino, A., K. Nederhoff, and M. Vousdoukas, 2018: Coastal hazard risk assessment for small islands: assessing the impact of climate change and disaster reduction measures on Ebeye (Marshall Islands). Regional Environmental Change, 1–12, doi:10.1007/s10113-018-1353-3. ↩
- Monioudi, I. et al., 2018: Climate change impacts on critical international transportation assets of Caribbean small island developing states: The case of Jamaica and Saint Lucia. Regional Environmental Change, 1–15, doi:10.1007/s10113-018-1360-4. ↩
- Thomas, A. and L. Benjamin, 2017: Management of loss and damage in small island developing states: implications for a 1.5°C or warmer world. Regional Environmental Change, 1–10, doi:10.1007/s10113-017-1184-7. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Ross, A.C. et al., 2015: Sea-level rise and other influences on decadal-scale salinity variability in a coastal plain estuary. Estuarine, Coastal and Shelf Science, 157, 79–92, doi:10.1016/j.ecss.2015.01.022. ↩
- Yang, Z., T. Wang, N. Voisin, and A. Copping, 2015: Estuarine response to river flow and sea-level rise under future climate change and human development. Estuarine, Coastal and Shelf Science, 156, 19–30, doi:10.1016/j.ecss.2014.08.015. ↩
- Zaman, A.M. et al., 2017: Impacts on river systems under 2°C warming: Bangladesh Case Study. Climate Services, 7, 96–114, doi:10.1016/j.cliser.2016.10.002.
Brown, S. et al., 2018b: What are the implications of sea-level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Regional Environmental Change, 1–14, doi:10.1007/s10113-018-1311-0. ↩ - Brown, S. et al., 2018b: What are the implications of sea-level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Regional Environmental Change, 1–14, doi:10.1007/s10113-018-1311-0. ↩
- Zaman, A.M. et al., 2017: Impacts on river systems under 2°C warming: Bangladesh Case Study. Climate Services, 7, 96–114, doi:10.1016/j.cliser.2016.10.002. ↩
- Brown, S. et al., 2018b: What are the implications of sea-level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Regional Environmental Change, 1–14, doi:10.1007/s10113-018-1311-0. ↩
- Tessler, Z.D., C.J. Vörösmarty, I. Overeem, and J.P.M. Syvitski, 2018: A model of water and sediment balance as determinants of relative sea level rise in contemporary and future deltas. Geomorphology, 305, 209–220, doi:10.1016/j.geomorph.2017.09.040. ↩
- Gupta, H., S.-J. Kao, and M. Dai, 2012: The role of mega dams in reducing sediment fluxes: A case study of large Asian rivers. Journal of Hydrology, 464–465, 447–458, doi:10.1016/j.jhydrol.2012.07.038. ↩
- Auerbach, L.W. et al., 2015: Flood risk of natural and embanked landscapes on the Ganges-Brahmaputra tidal delta plain. Nature Climate Change, 5, 153–157, doi:10.1038/nclimate2472.
Takagi, H., N. Thao, and L. Anh, 2016: Sea-Level Rise and Land Subsidence: Impacts on Flood Projections for the Mekong Delta's Largest City. Sustainability, 8(9), 959, doi:10.3390/su8090959. ↩ - Brown, S. et al., 2018b: What are the implications of sea-level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Regional Environmental Change, 1–14, doi:10.1007/s10113-018-1311-0. ↩
- Brown, S. et al., 2018b: What are the implications of sea-level rise for a 1.5, 2 and 3°C rise in global mean temperatures in the Ganges-Brahmaputra-Meghna and other vulnerable deltas? Regional Environmental Change, 1–14, doi:10.1007/s10113-018-1311-0. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409.
Lovelock, C.E. et al., 2015: The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature, 526(7574), 559–563, doi:10.1038/nature15538. ↩ - Hill, T.D. and S.C. Anisfeld, 2015: Coastal wetland response to sea level rise in Connecticut and New York. Estuarine, Coastal and Shelf Science, 163, 185–193, doi:10.1016/j.ecss.2015.06.004. ↩
- Herbert, E.R. et al., 2015: A global perspective on wetland salinization: ecological consequences of a growing threat to freshwater wetlands. Ecosphere, 6(10), 1–43, doi:10.1890/es14-00534.1. ↩
- Blankespoor, B., S. Dasgupta, and B. Laplante, 2014: Sea-Level Rise and Coastal Wetlands. Ambio, 43(8), 996–1005, doi:10.1007/s13280-014-0500-4.
Cui, L., Z. Ge, L. Yuan, and L. Zhang, 2015: Vulnerability assessment of the coastal wetlands in the Yangtze Estuary, China to sea-level rise. Estuarine, Coastal and Shelf Science, 156, 42–51, doi:10.1016/j.ecss.2014.06.015.
Arnell, N.W. et al., 2016: Global-scale climate impact functions: the relationship between climate forcing and impact. Climatic Change, 134(3), 475–487, doi:10.1007/s10584-013-1034-7.
Crosby, S.C. et al., 2016: Salt marsh persistence is threatened by predicted sea-level rise. Estuarine, Coastal and Shelf Science, 181, 93–99, doi:10.1016/j.ecss.2016.08.018. ↩ - Raabe, E.A. and R.P. Stumpf, 2016: Expansion of Tidal Marsh in Response to Sea-Level Rise: Gulf Coast of Florida, USA. Estuaries and Coasts, 39(1), 145–157, doi:10.1007/s12237-015-9974-y. ↩
- Kirwan, M. and P. Megonigal, 2013: Tidal wetland stability in the face of human impacts and sea-level rise. Nature, 504, 53–60, doi:10.1038/nature12856.
Ellison, J.C., 2014: Climate Change Adaptation: Management Options for Mangrove Areas. In: Mangrove Ecosystems of Asia: Status, Challenges and Management Strategies [Faridah-Hanum, I., A. Latiff, K.R. Hakeem, and M. Ozturk (eds.)]. Springer New York, New York, NY, USA, pp. 391–413, doi:10.1007/978-1-4614-8582-7_18.
Martínez, M.L., G. Mendoza-González, R. Silva-Casarín, and E. Mendoza-Baldwin, 2014: Land use changes and sea level rise may induce a "coastal squeeze" on the coasts of Veracruz, Mexico. Global Environmental Change, 29, 180–188, doi:10.1016/j.gloenvcha.2014.09.009.
Spencer, T. et al., 2016: Global coastal wetland change under sea-level rise and related stresses: The DIVA Wetland Change Model. Global and Planetary Change, 139, 15–30, doi:10.1016/j.gloplacha.2015.12.018. ↩ - Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Le Cozannet, G., M. Garcin, M. Yates, D. Idier, and B. Meyssignac, 2014: Approaches to evaluate the recent impacts of sea-level rise on shoreline changes. Earth-Science Reviews, 138, 47–60, doi:10.1016/j.earscirev.2014.08.005. ↩
- Romine, B.M., C.H. Fletcher, M.M. Barbee, T.R. Anderson, and L.N. Frazer, 2013: Are beach erosion rates and sea-level rise related in Hawaii? Global and Planetary Change, 108, 149–157, doi:10.1016/j.gloplacha.2013.06.009. ↩
- Udo, K. and Y. Takeda, 2017: Projections of Future Beach Loss in Japan Due to Sea-Level Rise and Uncertainties in Projected Beach Loss. Coastal Engineering Journal, 59(2), 1740006, doi:10.1142/s057856341740006x. ↩
- Wahl, T., S. Jain, J. Bender, S.D. Meyers, and M.E. Luther, 2015: Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nature Climate Change, 5(12), 1093–1097, doi:10.1038/nclimate2736. ↩
- Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Hinkel, J. et al., 2014: Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111(9), 3292–7, doi:10.1073/pnas.1222469111.
Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩ - Saunders, M.I. et al., 2014: Interdependency of tropical marine ecosystems in response to climate change. Nature Climate Change, 4(8), 724–729, doi:10.1038/nclimate2274. ↩
- Arkema, K.K. et al., 2013: Coastal habitats shield people and property from sea-level rise and storms. Nature Climate Change, 3(10), 913–918, doi:10.1038/nclimate1944.
Temmerman, S. et al., 2013: Ecosystem-based coastal defence in the face of global change. Nature, 504(7478), 79–83, doi:10.1038/nature12859.
Ferrario, F. et al., 2014: The effectiveness of coral reefs for coastal hazard risk reduction and adaptation. Nature Communications, 5, 3794, doi:10.1038/ncomms4794.
Hinkel, J. et al., 2014: Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111(9), 3292–7, doi:10.1073/pnas.1222469111.
Spalding, M.D. et al., 2014: The role of ecosystems in coastal protection: Adapting to climate change and coastal hazards. Ocean and Coastal Management, 90, 50–57, doi:10.1016/j.ocecoaman.2013.09.007.
Elliff, C.I. and I.R. Silva, 2017: Coral reefs as the first line of defense: Shoreline protection in face of climate change. Marine Environmental Research, 127, 148–154, doi:10.1016/j.marenvres.2017.03.007. ↩ - Araos, M. et al., 2016: Climate change adaptation planning in large cities: A systematic global assessment. Environmental Science & Policy, 66, 375–382, doi:10.1016/j.envsci.2016.06.009.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩ - Araos, M. et al., 2016: Climate change adaptation planning in large cities: A systematic global assessment. Environmental Science & Policy, 66, 375–382, doi:10.1016/j.envsci.2016.06.009. ↩
- Ford, J.D., G. McDowell, and T. Pearce, 2015: The adaptation challenge in the Arctic. Nature Climate Change, 5(12), 1046–1053, doi:10.1038/nclimate2723.
Araos, M. et al., 2016: Climate change adaptation planning in large cities: A systematic global assessment. Environmental Science & Policy, 66, 375–382, doi:10.1016/j.envsci.2016.06.009.
Lesnikowski, A., J. Ford, R. Biesbroek, L. Berrang-Ford, and S.J. Heymann, 2016: National-level progress on adaptation. Nature Climate Change, 6(3), 261–264, doi:10.1038/nclimate2863. ↩ - Ranger, N., T. Reeder, and J. Lowe, 2013: Addressing 'deep' uncertainty over long-term climate in major infrastructure projects: four innovations of the Thames Estuary 2100 Project. EURO Journal on Decision Processes, 1(3–4), 233–262, doi:10.1007/s40070-013-0014-5.
Barnett, J. et al., 2014: A local coastal adaptation pathway. Nature Climate Change, 4(12), 1103–1108, doi:10.1038/nclimate2383.
Rosenzweig, C. and W. Solecki, 2014: Hurricane Sandy and adaptation pathways in New York: Lessons from a first-responder city. Global Environmental Change, 28, 395–408, doi:10.1016/j.gloenvcha.2014.05.003.
Buurman, J. and V. Babovic, 2016: Adaptation Pathways and Real Options Analysis: An approach to deep uncertainty in climate change adaptation policies. Policy and Society, 35(2), 137–150, doi:10.1016/j.polsoc.2016.05.002. ↩ - Hauer, M.E., J.M. Evans, and D.R. Mishra, 2016: Millions projected to be at risk from sea-level rise in the continental United States. Nature Climate Change, 6(7), 691–695, doi:10.1038/nclimate2961.
Geisler, C. and B. Currens, 2017: Impediments to inland resettlement under conditions of accelerated sea level rise. Land Use Policy, 66, 322–330, doi:10.1016/j.landusepol.2017.03.029. ↩ - Wong, P.P. et al., 2014: Coastal Systems and Low-Lying Areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 361–409. ↩
- Nicholls, R.J., T. Reeder, S. Brown, and I.D. Haigh, 2015: The risks of sea-level rise in coastal cities. In: Climate Change: A risk assessment [King, D., D. Schrag, Z. Dadi, Q. Ye, and A. Ghosh (eds.)]. Centre for Science and Policy (CSaP), University of Cambridge, Cambridge, UK, pp. 94–98. ↩
- Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Goodwin, P., S. Brown, I.D. Haigh, R.J. Nicholls, and J.M. Matter, 2018: Adjusting Mitigation Pathways to Stabilize Climate at 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 601–615, doi:10.1002/2017ef000732. ↩
- Taylor, M.A. et al., 2018: Future Caribbean Climates in a World of Rising Temperatures: The 1.5 vs 2.0 Dilemma. Journal of Climate, 31(7), 2907–2926, doi:10.1175/jcli-d-17-0074.1. ↩
- Reyer, C.P.O. et al., 2017a: Climate change impacts in Latin America and the Caribbean and their implications for development. Regional Environmental Change, 17(6), 1601–1621, doi:10.1007/s10113-015-0854-6. ↩
- Taylor, M.A. et al., 2018: Future Caribbean Climates in a World of Rising Temperatures: The 1.5 vs 2.0 Dilemma. Journal of Climate, 31(7), 2907–2926, doi:10.1175/jcli-d-17-0074.1. ↩
- Holding, S. et al., 2016: Groundwater vulnerability on small islands. Nature Climate Change, 6, 1100–1103, doi:10.1038/nclimate3128.
Karnauskas, K.B., J.P. Donnelly, and K.J. Anchukaitis, 2016: Future freshwater stress for island populations. Nature Climate Change, 6(7), 720–725, doi:10.1038/nclimate2987. ↩ - Karnauskas, K.B. et al., 2018: Freshwater Stress on Small Island Developing States: Population Projections and Aridity Changes at 1.5°C and 2°C. Regional Environmental Change, 1–10, doi:10.1007/s10113-018-1331-9. ↩
- Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117. ↩
- Taylor, M.A. et al., 2018: Future Caribbean Climates in a World of Rising Temperatures: The 1.5 vs 2.0 Dilemma. Journal of Climate, 31(7), 2907–2926, doi:10.1175/jcli-d-17-0074.1. ↩
- Kopp, R.E. et al., 2014: Probabilistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth's Future, 2(8), 383–406, doi:10.1002/2014ef000239.
Jevrejeva, S., L.P. Jackson, R.E.M. Riva, A. Grinsted, and J.C. Moore, 2016: Coastal sea level rise with warming above 2°C. Proceedings of the National Academy of Sciences, 113(47), 13342–13347, doi:10.1073/pnas.1605312113. ↩ - Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
- Widlansky, M.J., A. Timmermann, and W. Cai, 2015: Future extreme sea level seesaws in the tropical Pacific. Science Advances, 1(8), e1500560, doi:10.1126/sciadv.1500560. ↩
- Cai, W. et al., 2012: More extreme swings of the South Pacific convergence zone due to greenhouse warming. Nature, 488(7411), 365–369, doi:10.1038/nature11358. ↩
- Khouakhi, A., G. Villarini, and G.A. Vecchi, 2017: Contribution of Tropical Cyclones to Rainfall at the Global Scale. Journal of Climate, 30(1), 359–372, doi:10.1175/jcli-d-16-0298.1. ↩
- Khouakhi, A. and G. Villarini, 2017: Attribution of annual maximum sea levels to tropical cyclones at the global scale. International Journal of Climatology, 37(1), 540–547, doi:10.1002/joc.4704. ↩
- Wehner, M.F., K.A. Reed, B. Loring, D. Stone, and H. Krishnan, 2018a: Changes in tropical cyclones under stabilized 1.5 and 2.0°C global warming scenarios as simulated by the Community Atmospheric Model under the HAPPI protocols. Earth System Dynamics, 9(1), 187–195, doi:10.5194/esd-9-187-2018. ↩
- Storlazzi, C.D., E.P.L. Elias, and P. Berkowitz, 2015: Many Atolls May be Uninhabitable Within Decades Due to Climate Change. Scientific Reports, 5(1), 14546, doi:10.1038/srep14546.
Gosling, S.N. and N.W. Arnell, 2016: A global assessment of the impact of climate change on water scarcity. Climatic Change, 134(3), 371–385, doi:10.1007/s10584-013-0853-x. ↩ - White, I. and T. Falkland, 2010: Management of freshwater lenses on small Pacific islands. Hydrogeology Journal, 18(1), 227–246, doi:10.1007/s10040-009-0525-0.
Terry, J.P. and T.F.M. Chui, 2012: Evaluating the fate of freshwater lenses on atoll islands after eustatic sea-level rise and cyclone-driven inundation: A modelling approach. Global and Planetary Change, 88–89, 76–84, doi:10.1016/j.gloplacha.2012.03.008.
Holding, S. and D.M. Allen, 2015: Wave overwash impact on small islands: Generalised observations of freshwater lens response and recovery for multiple hydrogeological settings. Journal of Hydrology, 529(Part 3), 1324–1335, doi:10.1016/j.jhydrol.2015.08.052.
Donk, P., E. Van Uytven, P. Willems, and M.A. Taylor, 2018: Assessment of the potential implications of a 1.5°C versus higher global temperature rise for the Afobaka hydropower scheme in Suriname. Regional Environmental Change, 1–13, doi:10.1007/s10113-018-1339-1. ↩ - Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩
- Frieler, K. et al., 2013: Limiting global warming to 2° C is unlikely to save most coral reefs. Nature Climate Change, 3(2), 165–170, doi:10.1038/nclimate1674.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩ - Maynard, J. et al., 2015: Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nature Climate Change, 5(7), 688–694, doi:10.1038/nclimate2625. ↩
- Cheung, W.W.L., G. Reygondeau, and T.L. Frölicher, 2016a: Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science, 354(6319), 1591–1594, doi:10.1126/science.aag2331. ↩
- Vitousek, S. et al., 2017: Doubling of coastal flooding frequency within decades due to sea-level rise. Scientific Reports, 7(1), 1399, doi:10.1038/s41598-017-01362-7. ↩
- Quataert, E., C. Storlazzi, A. van Rooijen, O. Cheriton, and A. van Dongeren, 2015: The influence of coral reefs and climate change on wave-driven flooding of tropical coastlines. Geophysical Research Letters, 42(15), 6407–6415, doi:10.1002/2015gl064861. ↩
- Scott, D., M.C. Simpson, and R. Sim, 2012: The vulnerability of Caribbean coastal tourism to scenarios of climate change related sea level rise. Journal of Sustainable Tourism, 20(6), 883–898, doi:10.1080/09669582.2012.699063.
Kumar, L. and S. Taylor, 2015: Exposure of coastal built assets in the South Pacific to climate risks. Nature Climate Change, 5(11), 992–996, doi:10.1038/nclimate2702.
Rhiney, K., 2015: Geographies of Caribbean Vulnerability in a Changing Climate: Issues and Trends. Geography Compass, 9(3), 97–114, doi:10.1111/gec3.12199.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩ - Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Albert, S. et al., 2017: Heading for the hills: climate-driven community relocations in the Solomon Islands and Alaska provide insight for a 1.5°C future. Regional Environmental Change, 1–12, doi:10.1007/s10113-017-1256-8. ↩
- Thomas, A. and L. Benjamin, 2017: Management of loss and damage in small island developing states: implications for a 1.5°C or warmer world. Regional Environmental Change, 1–10, doi:10.1007/s10113-017-1184-7. ↩
- Monioudi, I. et al., 2018: Climate change impacts on critical international transportation assets of Caribbean small island developing states: The case of Jamaica and Saint Lucia. Regional Environmental Change, 1–15, doi:10.1007/s10113-018-1360-4. ↩
- Lallo, C.H.O. et al., 2018: Characterizing heat stress on livestock using the temperature humidity index (THI) – prospects for a warmer Caribbean. Regional Environmental Change, 1–12, doi:10.1007/s10113-018-1359-x. ↩
- Mycoo, M.A., 2017: Beyond 1.5°C: vulnerabilities and adaptation strategies for Caribbean Small Island Developing States. Regional Environmental Change, 1–13, doi:10.1007/s10113-017-1248-8.
Thomas, A. and L. Benjamin, 2017: Management of loss and damage in small island developing states: implications for a 1.5°C or warmer world. Regional Environmental Change, 1–10, doi:10.1007/s10113-017-1184-7. ↩ - Wei, T., S. Glomsrød, and T. Zhang, 2017: Extreme weather, food security and the capacity to adapt – the case of crops in China. Food Security, 9(3), 523–535, doi:10.1007/s12571-015-0420-6. ↩
- Rose, G., T. Osborne, H. Greatrex, and T. Wheeler, 2016: Impact of progressive global warming on the global-scale yield of maize and soybean. Climatic Change, 134(3), 417–428, doi:10.1007/s10584-016-1601-9. ↩
- Chen, C., G.S. Zhou, and L. Zhou, 2014: Impacts of climate change on rice yield in china from 1961 to 2010 based on provincial data. Journal of Integrative Agriculture, 13(7), 1555–1564, doi:10.1016/s2095-3119(14)60816-9.
Sun, S., X.-G. Yang, J. Zhao, and F. Chen, 2015: The possible effects of global warming on cropping systems in China XI The variation of potential light-temperature suitable cultivation zone of winter wheat in China under climate change. Scientia Agricultura Sinica, 48(10), 1926–1941.
He, Q. and G. Zhou, 2016: Climate-associated distribution of summer maize in China from 1961 to 2010. Agriculture, Ecosystems & Environment, 232, 326–335, doi:10.1016/j.agee.2016.08.020. ↩ - Cho, S.J. and B.A. McCarl, 2017: Climate change influences on crop mix shifts in the United States. Scientific Reports, 7, 40845, doi:10.1038/srep40845. ↩
- Ramirez-Cabral, N.Y.Z., L. Kumar, and S. Taylor, 2016: Crop niche modeling projects major shifts in common bean growing areas. Agricultural and Forest Meteorology, 218–219, 102–113, doi:10.1016/j.agrformet.2015.12.002. ↩
- Moriondo, M. et al., 2013a: Projected shifts of wine regions in response to climate change. Climatic Change, 119(3–4), 825–839, doi:10.1007/s10584-013-0739-y.
Moriondo, M. et al., 2013b: Olive trees as bio-indicators of climate evolution in the Mediterranean Basin. Global Ecology and Biogeography, 22(7), 818–833, doi:10.1111/geb.12061. ↩ - Lobell, D.B., W. Schlenker, and J. Costa-Roberts, 2011: Climate Trends and Global Crop Production Since 1980. Science, 333(6042), 616–620, doi:10.1126/science.1204531. ↩
- Kim, H.-Y., J. Ko, S. Kang, and J. Tenhunen, 2013: Impacts of climate change on paddy rice yield in a temperate climate. Global Change Biology, 19(2), 548–562, doi:10.1111/gcb.12047.
van Oort, P.A.J. and S.J. Zwart, 2018: Impacts of climate change on rice production in Africa and causes of simulated yield changes. Global Change Biology, 24(3), 1029–1045, doi:10.1111/gcb.13967. ↩ - Jaggard, K.W., A. Qi, and M.A. Semenov, 2007: The impact of climate change on sugarbeet yield in the UK: 1976–2004. The Journal of Agricultural Science, 145(4), 367–375, doi:10.1017/s0021859607006922.
Supit, I. et al., 2010: Recent changes in the climatic yield potential of various crops in Europe. Agricultural Systems, 103(9), 683–694, doi:10.1016/j.agsy.2010.08.009.
Gregory, P.J. and B. Marshall, 2012: Attribution of climate change: a methodology to estimate the potential contribution to increases in potato yield in Scotland since 1960. Global Change Biology, 18(4), 1372–1388, doi:10.1111/j.1365-2486.2011.02601.x.
Chen, C., G.S. Zhou, and L. Zhou, 2014: Impacts of climate change on rice yield in china from 1961 to 2010 based on provincial data. Journal of Integrative Agriculture, 13(7), 1555–1564, doi:10.1016/s2095-3119(14)60816-9.
Sun, S., X.-G. Yang, J. Zhao, and F. Chen, 2015: The possible effects of global warming on cropping systems in China XI The variation of potential light-temperature suitable cultivation zone of winter wheat in China under climate change. Scientia Agricultura Sinica, 48(10), 1926–1941.
He, Q. and G. Zhou, 2016: Climate-associated distribution of summer maize in China from 1961 to 2010. Agriculture, Ecosystems & Environment, 232, 326–335, doi:10.1016/j.agee.2016.08.020.
Daliakopoulos, I.N. et al., 2017: Yield Response of Mediterranean Rangelands under a Changing Climate. Land Degradation & Development, 28(7), 1962–1972, doi:10.1002/ldr.2717. ↩ - Chen, B. et al., 2014: The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 189, 11–18, doi:10.1016/j.agrformet.2014.01.002.
Sun, S., X.-G. Yang, J. Zhao, and F. Chen, 2015: The possible effects of global warming on cropping systems in China XI The variation of potential light-temperature suitable cultivation zone of winter wheat in China under climate change. Scientia Agricultura Sinica, 48(10), 1926–1941. ↩ - Ray, D.K., J.S. Gerber, G.K. MacDonald, and P.C. West, 2015: Climate variation explains a third of global crop yield variability. Nature Communications, 6(1), 5989, doi:10.1038/ncomms6989. ↩
- Moore, F.C. and D.B. Lobell, 2015: The fingerprint of climate trends on European crop yields. Proceedings of the National Academy of Sciences, 112(9), 2670–2675, doi:10.1073/pnas.1409606112.
Kent, C. et al., 2017: Using climate model simulations to assess the current climate risk to maize production. Environmental Research Letters, 12(5), 054012, doi:10.1088/1748-9326/aa6cb9. ↩ - Qian, B., X. Zhang, K. Chen, Y. Feng, and T. O'Brien, 2010: Observed Long-Term Trends for Agroclimatic Conditions in Canada. Journal of Applied Meteorology and Climatology, 49(4), 604–618, doi:10.1175/2009jamc2275.1.
Mueller, B. et al., 2015: Lengthening of the growing season in wheat and maize producing regions. Weather and Climate Extremes, 9, 47–56, doi:10.1016/j.wace.2015.04.001. ↩ - Van Dingenen, R. et al., 2009: The global impact of ozone on agricultural crop yields under current and future air quality legislation. Atmospheric Environment, 43(3), 604–618, doi:10.1016/j.atmosenv.2008.10.033. ↩
- Elliott, J. et al., 2014: Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proceedings of the National Academy of Sciences, 111(9), 3239–3244, doi:10.1073/pnas.1222474110.
Durand, J.-L. et al., 2018: How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? European Journal of Agronomy, 100, 67–75, doi:10.1016/j.eja.2017.01.002. ↩ - Singh, B.P., V.K. Dua, P.M. Govindakrishnan, and S. Sharma, 2013: Impact of Climate Change on Potato. In: Climate-Resilient Horticulture: Adaptation and Mitigation Strategies [Singh, H.C.P., N.K.S. Rao, and K.S. Shivashankar (eds.)]. Springer India, India, pp. 125–135, doi:10.1007/978-81-322-0974-4_12.
Abebe, A. et al., 2016: Growth, yield and quality of maize with elevated atmospheric carbon dioxide and temperature in north-west India. Agriculture, Ecosystems & Environment, 218, 66–72, doi:10.1016/j.agee.2015.11.014. ↩ - Porter, J.R. et al., 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros,, D.J. Dokken, K.J. March, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White Field (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533. ↩
- McGrath, J.M. and D.B. Lobell, 2013: Regional disparities in the CO2 fertilization effect and implications for crop yields. Environmental Research Letters, 8(1), 014054, doi:10.1088/1748-9326/8/1/014054. ↩
- Myers, S.S. et al., 2014: Increasing CO2 threatens human nutrition. Nature, 510(7503), 139–142, doi:10.1038/nature13179. ↩
- De Souza, A.P., J.-C. Cocuron, A.C. Garcia, A.P. Alonso, and M.S. Buckeridge, 2015: Changes in Whole-Plant Metabolism during the Grain-Filling Stage in Sorghum Grown under Elevated CO2 and Drought. Plant Physiology, 169(3), 1755–1765, doi:10.1104/pp.15.01054. ↩
- van Vuuren, D.P. et al., 2011a: The representative concentration pathways: an overview. Climatic Change, 109(1), 5, doi:10.1007/s10584-011-0148-z. ↩
- Zhu, C. et al., 2018: Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Science Advances, 4(5), eaaq1012, doi:10.1126/sciadv.aaq1012. ↩
- Porter, J.R. et al., 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros,, D.J. Dokken, K.J. March, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White Field (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533.
Rosenzweig, C. et al., 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268–73, doi:10.1073/pnas.1222463110. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Ricke, K.L., J.B. Moreno-cruz, J. Schewe, A. Levermann, and K. Caldeira, 2016: Policy thresholds in mitigation. Nature Geoscience, 9(1), 5–6, doi:10.1038/ngeo2607. ↩
- Bassu, S. et al., 2014: How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 20(7), 2301–2320, doi:10.1111/gcb.12520. ↩
- Challinor, A.J. et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nature Climate Change, 4(4), 287–291, doi:10.1038/nclimate2153. ↩
- Niang, I. et al., 2014: Africa. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199–1265. ↩
- Lana, M.A. et al., 2017: Yield stability and lower susceptibility to abiotic stresses of improved open-pollinated and hybrid maize cultivars. Agronomy for Sustainable Development, 37(4), 30, doi:10.1007/s13593-017-0442-x. ↩
- Huang, J., H. Yu, A. Dai, Y. Wei, and L. Kang, 2017: Drylands face potential threat under 2°C global warming target. Nature Climate Change, 7(6), 417–422, doi:10.1038/nclimate3275. ↩
- Rosenzweig, C. et al., 2017: Assessing inter-sectoral climate change risks: the role of ISIMIP. Environmental Research Letters, 12(1), 10301, doi:10.1088/1748-9326/12/1/010301.
Rosenzweig, C. et al., 2018: Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2°C assessments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160455, doi:10.1098/rsta.2016.0455. ↩ - Iizumi, T. et al., 2017: Responses of crop yield growth to global temperature and socioeconomic changes. Scientific Reports, 7(1), 7800, doi:10.1038/s41598-017-08214-4. ↩
- Faye, B. et al., 2018: Impacts of 1.5 versus 2.0°C on cereal yields in the West African Sudan Savanna. Environmental Research Letters, 13(3), 034014, doi:10.1088/1748-9326/aaab40.
Ruane, A.C. et al., 2018: Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments. Climate Research, 76(1), 17–39, doi:10.3354/cr01520. ↩ - Läderach, P., A. Martinez-Valle, G. Schroth, and N. Castro, 2013: Predicting the future climatic suitability for cocoa farming of the world's leading producer countries, Ghana and Côte d'Ivoire. Climatic Change, 119(3), 841–854, doi:10.1007/s10584-013-0774-8.
World Bank, 2013: Turn Down The Heat: Climate Extremes, Regional Impacts and the Case for Resilience. World Bank, Washington DC, USA, 255 pp., doi:10.1017/cbo9781107415324.004.
Sultan, B. and M. Gaetani, 2016: Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation. Frontiers in Plant Science, 7, 1262, doi:10.3389/fpls.2016.01262. ↩ - Asseng, S. et al., 2015: Rising temperatures reduce global wheat production. Nature Climate Change, 5(2), 143–147, doi:10.1038/nclimate2470.
Zhao, C. et al., 2017: Temperature increase reduces global yields of major crops in four independent estimates. Proceedings of the National Academy of Sciences, 114(35), 9326–9331, doi:10.1073/pnas.1701762114. ↩ - Li, S., Q. Wang, and J.A. Chun, 2017: Impact assessment of climate change on rice productivity in the Indochinese Peninsula using a regional-scale crop model. International Journal of Climatology, 37, 1147–1160, doi:10.1002/joc.5072. ↩
- Wei, T., S. Glomsrød, and T. Zhang, 2017: Extreme weather, food security and the capacity to adapt – the case of crops in China. Food Security, 9(3), 523–535, doi:10.1007/s12571-015-0420-6.
Zhang, Z., Y. Chen, C. Wang, P. Wang, and F. Tao, 2017: Future extreme temperature and its impact on rice yield in China. International Journal of Climatology, 37(14), 4814–4827, doi:10.1002/joc.5125. ↩ - Rosenzweig, C. et al., 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268–73, doi:10.1073/pnas.1222463110. ↩
- Welch, J.R. et al., 2010: Rice yields in tropical/subtropical Asia exhibit large but opposing sensitivities to minimum and maximum temperatures. Proceedings of the National Academy of Sciences, 107(33), 14562–7, doi:10.1073/pnas.1001222107.
Okada, M., T. Iizumi, Y. Hayashi, and M. Yokozawa, 2011: Modeling the multiple effects of temperature and radiation on rice quality. Environmental Research Letters, 6(3), 034031, doi:10.1088/1748-9326/6/3/034031. ↩ - Schlenker, W. and M.J. Roberts, 2009: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598, doi:10.1073/pnas.0906865106.
Jiao, M., G. Zhou, and Z. Zhang (eds.), 2016: Blue book of agriculture for addressing climate change: Assessment report of agro-meteorological disasters and yield losses in China (No.2) (in Chinese). Social Sciences Academic Press, Beijing, China.
Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84–87, doi:10.1038/nature16467. ↩ - Jiao, M., G. Zhou, and Z. Zhang (eds.), 2016: Blue book of agriculture for addressing climate change: Assessment report of agro-meteorological disasters and yield losses in China (No.2) (in Chinese). Social Sciences Academic Press, Beijing, China.
Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84–87, doi:10.1038/nature16467. ↩ - Deryng, D., D. Conway, N. Ramankutty, J. Price, and R. Warren, 2014: Global crop yield response to extreme heat stress under multiple climate change futures. Environmental Research Letters, 9(3), 034011, doi:10.1088/1748-9326/9/3/034011. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩ - Jiao, M., G. Zhou, and Z. Zhang (eds.), 2016: Blue book of agriculture for addressing climate change: Assessment report of agro-meteorological disasters and yield losses in China (No.2) (in Chinese). Social Sciences Academic Press, Beijing, China. ↩
- Jiao, M., G. Zhou, and Z. Chen (eds.), 2014: Blue book of agriculture for addressing climate change: Assessment report of climatic change impacts on agriculture in China (No.1) (in Chinese). Social Sciences Academic Press, Beijing, China.
van Bruggen, A.H.C., J.W. Jones, J.M.C. Fernandes, K. Garrett, and K.J. Boote, 2015: Crop Diseases and Climate Change in the AgMIP Framework. In: Handbook of Climate Change and Agroecosystems [Rosenzweig, C. and D. Hillel (eds.)]. ICP Series on Climate Change Impacts, Adaptation, and Mitigation Volume 3, Imperial College Press, London, UK, pp. 297–330, doi:10.1142/9781783265640_0012. ↩ - Notenbaert, A.M.O., J.A. Cardoso, N. Chirinda, M. Peters, and A. Mottet, 2017: Climate change impacts on livestock and implications for adaptation. International Center for Tropical Agriculture (CIAT), Rome, Italy, 30 pp. ↩
- Kipling, R.P. et al., 2016: Modeling European ruminant production systems: Facing the challenges of climate change. Agricultural Systems, 147, 24–37, doi:10.1016/j.agsy.2016.05.007. ↩
- Mortola, J.P. and P.B. Frappell, 2000: Ventilatory Responses to Changes in Temperature in Mammals and Other Vertebrates. Annual Review of Physiology, 62(1), 847–874, doi:10.1146/annurev.physiol.62.1.847. ↩
- André, G., B. Engel, P.B.M. Berentsen, T.V. Vellinga, and A.G.J.M. Oude Lansink, 2011: Quantifying the effect of heat stress on daily milk yield and monitoring dynamic changes using an adaptive dynamic model. Journal of Dairy Science, 94(9), 4502–4513, doi:10.3168/jds.2010-4139.
Renaudeau, D., J.L. Gourdine, and N.R. St-Pierre, 2011: A meta-analysis of the effects of high ambient temperature on growth performance of growing-finishing pigs. Journal of Animal Science, 89(7), 2220–2230, doi:10.2527/jas.2010-3329.
Collier, R.J. and K.G. Gebremedhin, 2015: Thermal Biology of Domestic Animals. Annual Review of Animal Biosciences, 3(1), 513–532, doi:10.1146/annurev-animal-022114-110659. ↩ - De Rensis, F., I. Garcia-Ispierto, and F. López-Gatius, 2015: Seasonal heat stress: Clinical implications and hormone treatments for the fertility of dairy cows. Theriogenology, 84(5), 659–666, doi:10.1016/j.theriogenology.2015.04.021. ↩
- Wall, E., A. Wreford, K. Topp, and D. Moran, 2010: Biological and economic consequences heat stress due to a changing climate on UK livestock. Advances in Animal Biosciences, 1(01), 53, doi:10.1017/s2040470010001962. ↩
- Masike, S. and P. Urich, 2008: Vulnerability of traditional beef sector to drought and the challenges of climate change: The case of Kgatleng District, Botswana. Journal of Geography and Regional Planning, 1(1), 12–18, https://academicjournals.org/journal/jgrp/article-abstract/93cd326741. ↩
- Vitali, A. et al., 2009: Seasonal pattern of mortality and relationships between mortality and temperature-humidity index in dairy cows. Journal of Dairy Science, 92(8), 3781–3790, doi:10.3168/jds.2009-2127. ↩
- Barati, F. et al., 2008: Meiotic competence and DNA damage of porcine oocytes exposed to an elevated temperature. Theriogenology, 69(6), 767–772, doi:10.1016/j.theriogenology.2007.08.038. ↩
- Mortensen, C.J. et al., 2009: Embryo recovery from exercised mares. Animal Reproduction Science, 110(3), 237–244, doi:10.1016/j.anireprosci.2008.01.015. ↩
- Fox, N.J. et al., 2011: Predicting Impacts of Climate Change on Fasciola hepatica Risk. PLOS ONE, 6(1), e16126, doi:10.1371/journal.pone.0016126. ↩
- Guis, H. et al., 2012: Modelling the effects of past and future climate on the risk of bluetongue emergence in Europe. Journal of The Royal Society Interface, 9(67), 339–350, doi:10.1098/rsif.2011.0255. ↩
- Brito, B.P., L.L. Rodriguez, J.M. Hammond, J. Pinto, and A.M. Perez, 2017: Review of the Global Distribution of Foot-and-Mouth Disease Virus from 2007 to 2014. Transboundary and Emerging Diseases, 64(2), 316–332, doi:10.1111/tbed.12373. ↩
- Njeru, J., K. Henning, M.W. Pletz, R. Heller, and H. Neubauer, 2016: Q fever is an old and neglected zoonotic disease in Kenya: a systematic review. BMC Public Health, 16, 297, doi:10.1186/s12889-016-2929-9.
Simulundu, E. et al., 2017: Genetic characterization of orf virus associated with an outbreak of severe orf in goats at a farm in Lusaka, Zambia (2015). Archives of Virology, 162(8), 2363–2367, doi:10.1007/s00705-017-3352-y. ↩ - Craine, J.M., A.J. Elmore, K.C. Olson, and D. Tolleson, 2010: Climate change and cattle nutritional stress. Global Change Biology, 16(10), 2901–2911, doi:10.1111/j.1365-2486.2009.02060.x.
Hatfield, J.L. et al., 2011: Climate Impacts on Agriculture: Implications for Crop Production. Agronomy Journal, 103(2), 351–370, doi:10.2134/agronj2010.0303.
Izaurralde, R.C. et al., 2011: Climate Impacts on Agriculture: Implications for Forage and Rangeland Production. Agronomy Journal, 103(2), 371–381, doi:10.2134/agronj2010.0304. ↩ - Graux, A.-I., G. Bellocchi, R. Lardy, and J.-F. Soussana, 2013: Ensemble modelling of climate change risks and opportunities for managed grasslands in France. Agricultural and Forest Meteorology, 170, 114–131, doi:10.1016/j.agrformet.2012.06.010. ↩
- Perring, M.P., B.R. Cullen, I.R. Johnson, and M.J. Hovenden, 2010: Modelled effects of rising CO2 concentration and climate change on native perennial grass and sown grass-legume pastures. Climate Research, 42(1), 65–78, doi:10.3354/cr00863. ↩
- Palmer, M.A. et al., 2008: Climate change and the world's river basins: Anticipating management options. Frontiers in Ecology and the Environment, 6(2), 81–89, doi:10.1890/060148. ↩
- Knapp, J.R., G.L. Laur, P.A. Vadas, W.P. Weiss, and J.M. Tricarico, 2014: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science, 97(6), 3231–3261, doi:10.3168/jds.2013-7234.
Lee, M.A., A.P. Davis, M.G.G. Chagunda, and P. Manning, 2017: Forage quality declines with rising temperatures, with implications for livestock production and methane emissions. Biogeosciences, 14(6), 1403–1417, doi:10.5194/bg-14-1403-2017. ↩ - Boone, R.B., R.T. Conant, J. Sircely, P.K. Thornton, and M. Herrero, 2018: Climate change impacts on selected global rangeland ecosystem services. Global Change Biology, 24(3), 1382–1393, doi:10.1111/gcb.13995. ↩
- FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 200 pp. ↩
- McClanahan, T.R., E.H. Allison, and J.E. Cinner, 2015: Managing fisheries for human and food security. Fish and Fisheries, 16(1), 78–103, doi:10.1111/faf.12045.
Pauly, D. and A. Charles, 2015: Counting on small-scale fisheries. Science, 347(6219), 242–243, doi:10.1126/science.347.6219.242-b. ↩ - Cinner, J.E. et al., 2012: Vulnerability of coastal communities to key impacts of climate change on coral reef fisheries. Global Environmental Change, 22(1), 12–20, doi:10.1016/j.gloenvcha.2011.09.018.
Cinner, J.E. et al., 2016: A framework for understanding climate change impacts on coral reef social-ecological systems. Regional Environmental Change, 16(4), 1133–1146, doi:10.1007/s10113-015-0832-z.
FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 200 pp.
Pendleton, L. et al., 2016: Coral reefs and people in a high-CO2 world: Where can science make a difference to people? PLOS ONE, 11(11), 1–21, doi:10.1371/journal.pone.0164699. ↩ - Lacoue-Labarthe, T. et al., 2016: Impacts of ocean acidification in a warming Mediterranean Sea: An overview. Regional Studies in Marine Science, 5, 1–11, doi:10.1016/j.rsma.2015.12.005.
Clements, J.C. and T. Chopin, 2017: Ocean acidification and marine aquaculture in North America: Potential impacts and mitigation strategies. Reviews in Aquaculture, 9(4), 326341, doi:10.1111/raq.12140.
Clements, J.C., D. Bourque, J. McLaughlin, M. Stephenson, and L.A. Comeau, 2017: Extreme ocean acidification reduces the susceptibility of eastern oyster shells to a polydorid parasite. Journal of Fish Diseases, 40(11), 1573–1585, doi:10.1111/jfd.12626.
Parker, L.M. et al., 2017: Ocean acidification narrows the acute thermal and salinity tolerance of the Sydney rock oyster Saccostrea glomerata. Marine Pollution Bulletin, 122(1–2), 263–271, doi:10.1016/j.marpolbul.2017.06.052. ↩ - Callaway, R. et al., 2012: Review of climate change impacts on marine aquaculture in the UK and Ireland. Aquatic Conservation: Marine and Freshwater Ecosystems, 22(3), 389–421, doi:10.1002/aqc.2247.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Asplund, M.E. et al., 2014: Ocean acidification and host-pathogen interactions: Blue mussels, Mytilus edulis, encountering Vibrio tubiashii. Environmental Microbiology, 16(4), 1029–1039, doi:10.1111/1462-2920.12307.
Castillo, N., L.M. Saavedra, C.A. Vargas, C. Gallardo-Escárate, and C. Détrée, 2017: Ocean acidification and pathogen exposure modulate the immune response of the edible mussel Mytilus chilensis. Fish and Shellfish Immunology, 70, 149–155, doi:10.1016/j.fsi.2017.08.047. ↩ - Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Kittinger, J.N., 2013: Human Dimensions of Small-Scale and Traditional Fisheries in the Asia-Pacific Region. Pacific Science, 67(3), 315–325, doi:10.2984/67.3.1.
Pauly, D. and A. Charles, 2015: Counting on small-scale fisheries. Science, 347(6219), 242–243, doi:10.1126/science.347.6219.242-b.
Bell, J.D. et al., 2018: Adaptations to maintain the contributions of small-scale fisheries to food security in the Pacific Islands. Marine Policy, 88, 303–314, doi:10.1016/j.marpol.2017.05.019. ↩ - Poloczanska, E.S. et al., 2013: Global imprint of climate change on marine life. Nature Climate Change, 3(10), 919–925, doi:10.1038/nclimate1958.
Burrows, M.T. et al., 2014: Geographical limits to species-range shifts are suggested by climate velocity. Nature, 507(7493), 492–495, doi:10.1038/nature12976.
García Molinos, J. et al., 2015: Climate velocity and the future global redistribution of marine biodiversity. Nature Climate Change, 6(1), 83–88, doi:10.1038/nclimate2769.
Poloczanska, E.S. et al., 2016: Responses of Marine Organisms to Climate Change across Oceans. Frontiers in Marine Science, 3, 62, doi:10.3389/fmars.2016.00062. ↩ - McClanahan, T.R., J.C. Castilla, A.T. White, and O. Defeo, 2009: Healing small-scale fisheries by facilitating complex socio-ecological systems. Reviews in Fish Biology and Fisheries, 19(1), 33–47, doi:10.1007/s11160-008-9088-8.
Cheung, W.W.L. et al., 2010: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16(1), 24–35, doi:10.1111/j.1365-2486.2009.01995.x.
McClanahan, T.R., E.H. Allison, and J.E. Cinner, 2015: Managing fisheries for human and food security. Fish and Fisheries, 16(1), 78–103, doi:10.1111/faf.12045.
Pendleton, L. et al., 2016: Coral reefs and people in a high-CO2 world: Where can science make a difference to people? PLOS ONE, 11(11), 1–21, doi:10.1371/journal.pone.0164699. ↩ - Bell, J.D. et al., 2013: Mixed responses of tropical Pacific fisheries and aquaculture to climate change. Nature Climate Change, 3(6), 591–599, doi:10.1038/nclimate1838.
Bell, J.D. et al., 2018: Adaptations to maintain the contributions of small-scale fisheries to food security in the Pacific Islands. Marine Policy, 88, 303–314, doi:10.1016/j.marpol.2017.05.019. ↩ - McClanahan, T.R., E.H. Allison, and J.E. Cinner, 2015: Managing fisheries for human and food security. Fish and Fisheries, 16(1), 78–103, doi:10.1111/faf.12045. ↩
- Kittinger, J.N., 2013: Human Dimensions of Small-Scale and Traditional Fisheries in the Asia-Pacific Region. Pacific Science, 67(3), 315–325, doi:10.2984/67.3.1.
McClanahan, T.R., E.H. Allison, and J.E. Cinner, 2015: Managing fisheries for human and food security. Fish and Fisheries, 16(1), 78–103, doi:10.1111/faf.12045.
Song, A.M. and R. Chuenpagdee, 2015: Interactive Governance for Fisheries. Interactive Governance for Small-Scale Fisheries, 5, 435–456, doi:10.1007/978-3-319-17034-3.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Kittinger, J.N. et al., 2013: Emerging frontiers in social-ecological systems research for sustainability of small-scale fisheries. Current Opinion in Environmental Sustainability, 5(3–4), 352–357, doi:10.1016/j.cosust.2013.06.008.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - Cheung, W.W.L., R. Watson, and D. Pauly, 2013: Signature of ocean warming in global fisheries catch. Nature, 497(7449), 365–368, doi:10.1038/nature12156.
Hollowed, A.B. et al., 2013: Projected impacts of climate change on marine fish and fisheries. ICES Journal of Marine Science, 70(510), 1023–1037, doi:10.1093/icesjms/fst081.
Lam, V.W.Y., W.W.L. Cheung, and U.R. Sumaila, 2014: Marine capture fisheries in the Arctic: Winners or losers under climate change and ocean acidification? Fish and Fisheries, 17, 335–357, doi:10.1111/faf.12106.
FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 200 pp. ↩ - Fossheim, M. et al., 2015: Recent warming leads to a rapid borealization of fish communities in the Arctic. Nature Climate Change, 5(7), 673–677, doi:10.1038/nclimate2647. ↩
- Cheung, W.W.L. et al., 2009: Projecting global marine biodiversity impacts under climate change scenarios. Fish and Fisheries, 10(3), 235–251, doi:10.1111/j.1467-2979.2008.00315.x. ↩
- Hollowed, A.B. and S. Sundby, 2014: Change is coming to the northern oceans. Science, 344(6188), 1084–1085, doi:10.1126/science.1251166. ↩
- Cheung, W.W.L., G. Reygondeau, and T.L. Frölicher, 2016a: Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science, 354(6319), 1591–1594, doi:10.1126/science.aag2331. ↩
- Ekstrom, J.A. et al., 2015: Vulnerability and adaptation of US shellfisheries to ocean acidification. Nature Climate Change, 5(3), 207–214, doi:10.1038/nclimate2508.
Rodrigues, L.C. et al., 2015: Sensitivity of Mediterranean Bivalve Mollusc Aquaculture to Climate Change, Ocean Acidification, and Other Environmental Pressures: Findings from a Producer Survey. Journal of Shellfish Research, 34(3), 1161–1176, doi:10.2983/035.034.0341.
Handisyde, N., T.C. Telfer, and L.G. Ross, 2016: Vulnerability of aquaculture-related livelihoods to changing climate at the global scale. Fish and Fisheries, 18(3), 466–488, doi:10.1111/faf.12186.
Lee, J., 2016: Valuation of Ocean Acidification Effects on Shellfish Fisheries and Aquaculture. DP 132, Centre for Financial and Management Studies (CeFiMS), School of Oriental and African Studies (SOAS), University of London, London, UK, 14 pp.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048.
Clements, J.C. and T. Chopin, 2017: Ocean acidification and marine aquaculture in North America: Potential impacts and mitigation strategies. Reviews in Aquaculture, 9(4), 326341, doi:10.1111/raq.12140. ↩ - Lehodey, P., I. Senina, S. Nicol, and J. Hampton, 2015: Modelling the impact of climate change on South Pacific albacore tuna. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 246–259, doi:10.1016/j.dsr2.2014.10.028.
Matear, R.J., M.A. Chamberlain, C. Sun, and M. Feng, 2015: Climate change projection for the western tropical Pacific Ocean using a high-resolution ocean model: Implications for tuna fisheries. Deep Sea Research Part II: Topical Studies in Oceanography, 113, 22–46, doi:10.1016/j.dsr2.2014.07.003. ↩ - Bell, J.D. et al., 2013: Mixed responses of tropical Pacific fisheries and aquaculture to climate change. Nature Climate Change, 3(6), 591–599, doi:10.1038/nclimate1838.
Bell, J.D. et al., 2018: Adaptations to maintain the contributions of small-scale fisheries to food security in the Pacific Islands. Marine Policy, 88, 303–314, doi:10.1016/j.marpol.2017.05.019. ↩ - Paterson, R.R.M. and N. Lima, 2010: How will climate change affect mycotoxins in food? Food Research International, 43(7), 1902–1914, doi:10.1016/j.foodres.2009.07.010.
Thornton, P.K., P.J. Ericksen, M. Herrero, and A.J. Challinor, 2014: Climate variability and vulnerability to climate change: a review. Global Change Biology, 20(11), 3313–3328, doi:10.1111/gcb.12581.
FAO, 2016: The State of World Fisheries and Aquaculture 2016. Contributing to food security and nutrition for all. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy, 200 pp. ↩ - FAO, IFAD, UNICEF, WFP, and WHO, 2017: The State of Food Security and Nutrition in the World 2017. Building resilience for peace and food security. Food and Agricultural Organization of the United Nations (FAO), Rome, Italy, 117 pp. ↩
- UN, 2015: Transforming our world: The 2030 agenda for sustainable development. A/RES/70/1, United Nations General Assembly (UNGA), 35 pp., doi:10.1007/s13398-014-0173-7.2.
Pérez-Escamilla, R., 2017: Food Security and the 2015–2030 Sustainable Development Goals: From Human to Planetary Health. Current Developments in Nutrition, 1(7), e000513, doi:10.3945/cdn.117.000513.
UN, 2017: The Sustainable Development Goals Report 2017. United Nations (UN), New York, NY, USA, 64 pp. ↩ - Cheung, W.W.L. et al., 2010: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16(1), 24–35, doi:10.1111/j.1365-2486.2009.01995.x.
Rosenzweig, C. et al., 2013: The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies. Agricultural and Forest Meteorology, 170, 166–182, doi:10.1016/j.agrformet.2012.09.011.
Porter, J.R. et al., 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros,, D.J. Dokken, K.J. March, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White Field (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 485–533.
Rosenzweig, C. and D. Hillel (eds.), 2015: Handbook of Climate Change and Agroecosystems. Imperial College Press, London, UK, 1160 pp., doi:10.1142/p970.
Lam, V.W.Y., W.W.L. Cheung, G. Reygondeau, and U.R. Sumaila, 2016: Projected change in global fisheries revenues under climate change. Scientific Reports, 6(1), 32607, doi:10.1038/srep32607. ↩ - Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037.
Zhu, C. et al., 2018: Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Science Advances, 4(5), eaaq1012, doi:10.1126/sciadv.aaq1012. ↩ - Cheung, W.W.L., G. Reygondeau, and T.L. Frölicher, 2016a: Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science, 354(6319), 1591–1594, doi:10.1126/science.aag2331.
Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩ - Sultan, B. and M. Gaetani, 2016: Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation. Frontiers in Plant Science, 7, 1262, doi:10.3389/fpls.2016.01262.
Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117.
Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45.
Rosenzweig, C. et al., 2018: Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2°C assessments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160455, doi:10.1098/rsta.2016.0455. ↩ - Rosenzweig, C. et al., 2018: Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2°C assessments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160455, doi:10.1098/rsta.2016.0455. ↩
- Ruane, A.C. et al., 2018: Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments. Climate Research, 76(1), 17–39, doi:10.3354/cr01520. ↩
- Myers, S.S. et al., 2014: Increasing CO2 threatens human nutrition. Nature, 510(7503), 139–142, doi:10.1038/nature13179. ↩
- Medek, D.E., J. Schwartz, and S.S. Myers, 2017: Estimated Effects of Future Atmospheric CO2 Concentrations on Protein Intake and the Risk of Protein Deficiency by Country and Region. Environmental Health Perspectives, 125(8), 087002, doi:10.1289/ehp41. ↩
- Cheung, W.W.L. et al., 2010: Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change. Global Change Biology, 16(1), 24–35, doi:10.1111/j.1365-2486.2009.01995.x.
Hollowed, A.B. and S. Sundby, 2014: Change is coming to the northern oceans. Science, 344(6188), 1084–1085, doi:10.1126/science.1251166.
Lam, V.W.Y., W.W.L. Cheung, G. Reygondeau, and U.R. Sumaila, 2016: Projected change in global fisheries revenues under climate change. Scientific Reports, 6(1), 32607, doi:10.1038/srep32607.
Sundby, S., K.F. Drinkwater, and O.S. Kjesbu, 2016: The North Atlantic Spring-Bloom System-Where the Changing Climate Meets the Winter Dark. Frontiers in Marine Science, 3, 28, doi:10.3389/fmars.2016.00028.
Weatherdon, L., A.K. Magnan, A.D. Rogers, U.R. Sumaila, and W.W.L. Cheung, 2016: Observed and Projected Impacts of Climate Change on Marine Fisheries, Aquaculture, Coastal Tourism, and Human Health: An Update. Frontiers in Marine Science, 3, 48, doi:10.3389/fmars.2016.00048. ↩ - McGrath, J.M. and D.B. Lobell, 2013: Regional disparities in the CO2 fertilization effect and implications for crop yields. Environmental Research Letters, 8(1), 014054, doi:10.1088/1748-9326/8/1/014054.
Elliott, J. et al., 2014: Constraints and potentials of future irrigation water availability on agricultural production under climate change. Proceedings of the National Academy of Sciences, 111(9), 3239–3244, doi:10.1073/pnas.1222474110.
Pörtner, H.O. et al., 2014: Ocean Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 411–484.
Durand, J.-L. et al., 2018: How accurately do maize crop models simulate the interactions of atmospheric CO2 concentration levels with limited water supply on water use and yield? European Journal of Agronomy, 100, 67–75, doi:10.1016/j.eja.2017.01.002. ↩ - Rosenzweig, C. et al., 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268–73, doi:10.1073/pnas.1222463110.
Wei, T., S. Glomsrød, and T. Zhang, 2017: Extreme weather, food security and the capacity to adapt – the case of crops in China. Food Security, 9(3), 523–535, doi:10.1007/s12571-015-0420-6. ↩ - Kannan, K.P., S.M. Dev, and A.N. Sharma, 2000: Concerns on Food Security. Economic And Political Weekly, 35(45), 3919–3922, http://www.jstor.org/stable/4409916.
Ghosh, J., 2010: The unnatural coupling: Food and global finance. Journal of Agrarian Change, 10(1), 72–86, doi:10.1111/j.1471-0366.2009.00249.x.
Naylor, R.L. and W.P. Falcon, 2010: Food security in an era of economic volatility. Population and Development Review, 36(4), 693–723, doi:10.1111/j.1728-4457.2010.00354.x.
HLPE, 2011: Price volatility and food security. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. The High Level Panel of Experts on Food Security and Nutrition (HLPE), 79 pp. ↩ - Jiao, M., G. Zhou, and Z. Chen (eds.), 2014: Blue book of agriculture for addressing climate change: Assessment report of climatic change impacts on agriculture in China (No.1) (in Chinese). Social Sciences Academic Press, Beijing, China.
van Bruggen, A.H.C., J.W. Jones, J.M.C. Fernandes, K. Garrett, and K.J. Boote, 2015: Crop Diseases and Climate Change in the AgMIP Framework. In: Handbook of Climate Change and Agroecosystems [Rosenzweig, C. and D. Hillel (eds.)]. ICP Series on Climate Change Impacts, Adaptation, and Mitigation Volume 3, Imperial College Press, London, UK, pp. 297–330, doi:10.1142/9781783265640_0012. ↩ - IPCC, 2013: Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp. ↩
- Nelson, G.C. et al., 2014b: Agriculture and climate change in global scenarios: why don't the models agree. Agricultural Economics, 45(1), 85–101, doi:10.1111/agec.12091. ↩
- Mueller, S.A., J.E. Anderson, and T.J. Wallington, 2011: Impact of biofuel production and other supply and demand factors on food price increases in 2008. Biomass and Bioenergy, 35(5), 1623–1632, doi:10.1016/j.biombioe.2011.01.030.
Wright, B.D., 2011: The Economics of Grain Price Volatility. Applied Economic Perspectives and Policy, 33(1), 32–58, doi:10.1093/aepp/ppq033.
Roberts, M.J. and W. Schlenker, 2013: Identifying Supply and Demand Elasticities of Agricultural Commodities: Implications for the US Ethanol Mandate. American Economic Review, 103(6), 2265–2295, doi:10.1257/aer.103.6.2265. ↩ - Asiedu, B., J.-O. Adetola, and I. Odame Kissi, 2017a: Aquaculture in troubled climate: Farmers' perception of climate change and their adaptation. Cogent Food & Agriculture, 3(1), 1296400, doi:10.1080/23311932.2017.1296400.
Asiedu, B., F.K.E. Nunoo, and S. Iddrisu, 2017b: Prospects and sustainability of aquaculture development in Ghana, West Africa. Cogent Food & Agriculture, 3(1), 1349531, doi:10.1080/23311932.2017.1349531.
Utete, B., C. Phiri, S.S. Mlambo, N. Muboko, and B.T. Fregene, 2018: Vulnerability of fisherfolks and their perceptions towards climate change and its impacts on their livelihoods in a peri-urban lake system in Zimbabwe. Environment, Development and Sustainability, 1–18, doi:10.1007/s10668-017-0067-x. ↩ - Lobell, D.B., W. Schlenker, and J. Costa-Roberts, 2011: Climate Trends and Global Crop Production Since 1980. Science, 333(6042), 616–620, doi:10.1126/science.1204531.
von Lampe, M. et al., 2014: Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agricultural Economics, 45(1), 3–20, doi:10.1111/agec.12086.
d'Amour, C.B., L. Wenz, M. Kalkuhl, J.C. Steckel, and F. Creutzig, 2016: Teleconnected food supply shocks. Environmental Research Letters, 11(3), 035007, doi:10.1088/1748-9326/11/3/035007. ↩ - Wei, T., S. Glomsrød, and T. Zhang, 2017: Extreme weather, food security and the capacity to adapt – the case of crops in China. Food Security, 9(3), 523–535, doi:10.1007/s12571-015-0420-6. ↩
- Hasegawa, T. et al., 2014: Climate Change Impact and Adaptation Assessment on Food Consumption Utilizing a New Scenario Framework. Environmental Science & Technology, 48(1), 438–445, doi:10.1021/es4034149. ↩
- Neumann, K., P.H. Verburg, E. Stehfest, and C. Müller, 2010: The yield gap of global grain production: A spatial analysis. Agricultural Systems, 103(5), 316–326, doi:10.1016/j.agsy.2010.02.004.
Muller, C., 2011: Agriculture: Harvesting from uncertainties. Nature Climate Change, 1(5), 253–254, doi:10.1038/nclimate1179.
Roudier, P., B. Sultan, P. Quirion, and A. Berg, 2011: The impact of future climate change on West African crop yields: What does the recent literature say? Global Environmental Change, 21(3), 1073–1083, doi:10.1016/j.gloenvcha.2011.04.007. ↩ - Lipper, L. et al., 2014: Climate-smart agriculture for food security. Nature Climate Change, 4(12), 1068–1072, doi:10.1038/nclimate2437.
Martinez-Baron, D., G. Orjuela, G. Renzoni, A.M. Loboguerrero Rodríguez, and S.D. Prager, 2018: Small-scale farmers in a 1.5°C future: The importance of local social dynamics as an enabling factor for implementation and scaling of climate-smart agriculture. Current Opinion in Environmental Sustainability, 31, 112–119, doi:10.1016/j.cosust.2018.02.013.
Whitfield, S., A.J. Challinor, and R.M. Rees, 2018: Frontiers in Climate Smart Food Systems: Outlining the Research Space. Frontiers in Sustainable Food Systems, 2, 2, doi:10.3389/fsufs.2018.00002. ↩ - Harvey, C.A. et al., 2014: Climate-Smart Landscapes: Opportunities and Challenges for Integrating Adaptation and Mitigation in Tropical Agriculture. Conservation Letters, 7(2), 77–90, doi:10.1111/conl.12066. ↩
- Smith, K.R. et al., 2014: Human Health: Impacts, Adaptation, and Co-Benefits. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 709–754. ↩
- Ishida, H. et al., 2014: Global-scale projection and its sensitivity analysis of the health burden attributable to childhood undernutrition under the latest scenario framework for climate change research. Environmental Research Letters, 9(6), 064014, doi:10.1088/1748-9326/9/6/064014.
Hasegawa, T., S. Fujimori, K. Takahashi, T. Yokohata, and T. Masui, 2016: Economic implications of climate change impacts on human health through undernourishment. Climatic Change, 136(2), 189–202, doi:10.1007/s10584-016-1606-4.
Springmann, M. et al., 2016: Global and regional health effects of future food production under climate change: a modelling study. The Lancet, 387(10031), 1937–1946, doi:10.1016/s0140-6736(15)01156-3. ↩ - Hales, S., S. Kovats, S. Lloyd, and D. Campbell-Lendrum (eds.), 2014: Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s. World Health Organization (WHO), Geneva, Switzerland, 115 pp.
Ishida, H. et al., 2014: Global-scale projection and its sensitivity analysis of the health burden attributable to childhood undernutrition under the latest scenario framework for climate change research. Environmental Research Letters, 9(6), 064014, doi:10.1088/1748-9326/9/6/064014.
Hasegawa, T., S. Fujimori, K. Takahashi, T. Yokohata, and T. Masui, 2016: Economic implications of climate change impacts on human health through undernourishment. Climatic Change, 136(2), 189–202, doi:10.1007/s10584-016-1606-4. ↩ - Springmann, M. et al., 2016: Global and regional health effects of future food production under climate change: a modelling study. The Lancet, 387(10031), 1937–1946, doi:10.1016/s0140-6736(15)01156-3. ↩
- Myers, S.S. et al., 2017: Climate Change and Global Food Systems: Potential Impacts on Food Security and Undernutrition. Annual Review of Public Health, 38(1), 259–277, doi:10.1146/annurev-publhealth-031816-044356.
Zhu, C. et al., 2018: Carbon dioxide (CO2) levels this century will alter the protein, micronutrients, and vitamin content of rice grains with potential health consequences for the poorest rice-dependent countries. Science Advances, 4(5), eaaq1012, doi:10.1126/sciadv.aaq1012. ↩ - Smajgl, A. et al., 2015: Responding to rising sea levels in the Mekong Delta. Nature Climate Change, 5(2), 167–174, doi:10.1038/nclimate2469. ↩
- de Sherbinin, A., 2014: Climate change hotspots mapping: what have we learned? Climatic Change, 123(1), 23–37, doi:10.1007/s10584-013-0900-7.
Lebel, L., C.T. Hoanh, C. Krittasudthacheewa, and R. Daniel (eds.), 2014: Climate risks, regional integration and sustainability in the Mekong region. Strategic Information and Research Development Centre (SIRD), Selangor, Malaysia, 417 pp. ↩ - Zhang, Z., Y. Chen, C. Wang, P. Wang, and F. Tao, 2017: Future extreme temperature and its impact on rice yield in China. International Journal of Climatology, 37(14), 4814–4827, doi:10.1002/joc.5125. ↩
- Smith, T.F., D.C. Thomsen, S. Gould, K. Schmitt, and B. Schlegel, 2013: Cumulative Pressures on Sustainable Livelihoods: Coastal Adaptation in the Mekong Delta. Sustainability, 5(1), 228–241, doi:10.3390/su5010228.
Ling, F.H., M. Tamura, K. Yasuhara, K. Ajima, and C. Van Trinh, 2015: Reducing flood risks in rural households: survey of perception and adaptation in the Mekong delta. Climatic change, 132(2), 209–222, doi:10.1007/s10584-015-1416-0.
Zhang, F., J. Tong, B. Su, J. Huang, and X. Zhu, 2016: Simulation and projection of climate change in the south Asian River basin by CMIP5 multi-model ensembles (in Chinese). Journal of Tropical Meteorology, 32(5), 734–742. ↩ - Renaud, F.G., T.T.H. Le, C. Lindener, V.T. Guong, and Z. Sebesvari, 2015: Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre Province, Mekong Delta. Climatic Change, 133(1), 69–84, doi:10.1007/s10584-014-1113-4. ↩
- Le Dang, H., E. Li, J. Bruwer, and I. Nuberg, 2013: Farmers' perceptions of climate variability and barriers to adaptation: lessons learned from an exploratory study in Vietnam. Mitigation and Adaptation Strategies for Global Change, 19(5), 531–548, doi:10.1007/s11027-012-9447-6.
Smajgl, A. et al., 2015: Responding to rising sea levels in the Mekong Delta. Nature Climate Change, 5(2), 167–174, doi:10.1038/nclimate2469. ↩ - Smajgl, A. et al., 2015: Responding to rising sea levels in the Mekong Delta. Nature Climate Change, 5(2), 167–174, doi:10.1038/nclimate2469. ↩
- Wu, D., Y. Zhao, Y.-S. Pei, and Y.-J. Bi, 2013: Climate Change and its Effects on Runoff in Upper and Middle Reaches of Lancang-Mekong river (in Chinese). Journal of Natural Resources, 28(9), 1569–1582, doi:10.11849/zrzyxb.2013.09.012. ↩
- Wu, D., Y. Zhao, Y.-S. Pei, and Y.-J. Bi, 2013: Climate Change and its Effects on Runoff in Upper and Middle Reaches of Lancang-Mekong river (in Chinese). Journal of Natural Resources, 28(9), 1569–1582, doi:10.11849/zrzyxb.2013.09.012.
Hoang, L.P. et al., 2016: Mekong River flow and hydrological extremes under climate change. Hydrology and Earth System Sciences, 20(7), 3027–3041, doi:10.5194/hess-20-3027-2016. ↩ - Renaud, F.G., T.T.H. Le, C. Lindener, V.T. Guong, and Z. Sebesvari, 2015: Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre Province, Mekong Delta. Climatic Change, 133(1), 69–84, doi:10.1007/s10584-014-1113-4. ↩
- ICEM, 2013: USAID Mekong ARCC Climate Change Impact and Adaptation: Summary. Prepared for the United States Agency for International Development by ICEM – International Centre for Environmental Management, 61 pp. ↩
- Sebesvari, Z., S. Rodrigues, and F. Renaud, 2017: Mainstreaming ecosystem-based climate change adaptation into integrated water resources management in the Mekong region. Regional Environmental Change, 1–14, doi:10.1007/s10113-017-1161-1. ↩
- Chapman, A.D., S.E. Darby, H.M. Hông, E.L. Tompkins, and T.P.D. Van, 2016: Adaptation and development trade-offs: fluvial sediment deposition and the sustainability of rice-cropping in An Giang Province, Mekong Delta. Climatic Change, 137(3–4), 1–16, doi:10.1007/s10584-016-1684-3. ↩
- Chapman, A. and S. Darby, 2016: Evaluating sustainable adaptation strategies for vulnerable mega-deltas using system dynamics modelling: Rice agriculture in the Mekong Delta's An Giang Province, Vietnam. Science of The Total Environment, 559, 326–338, doi:10.1016/j.scitotenv.2016.02.162. ↩
- Schipper, L., W. Liu, D. Krawanchid, and S. Chanthy, 2010: Review of climate change adaptation methods and tools. MRC Technical Paper No. 34, Mekong River Commission (MRC), Vientiane, Lao PDR, 76 pp. ↩
- Gass, P., H. Hove, and P. Jo-Ellen, 2011: Review of Current and Planned Adaptation Action: East and Southeast Asia. Adaptation Partnership, 217 pp. ↩
- Renaud, F.G., T.T.H. Le, C. Lindener, V.T. Guong, and Z. Sebesvari, 2015: Resilience and shifts in agro-ecosystems facing increasing sea-level rise and salinity intrusion in Ben Tre Province, Mekong Delta. Climatic Change, 133(1), 69–84, doi:10.1007/s10584-014-1113-4.
Hoang, L.P. et al., 2016: Mekong River flow and hydrological extremes under climate change. Hydrology and Earth System Sciences, 20(7), 3027–3041, doi:10.5194/hess-20-3027-2016.
Tran, T.A., J. Pittock, and L.A. Tuan, 2018: Adaptive co-management in the Vietnamese Mekong Delta: examining the interface between flood management and adaptation. International Journal of Water Resources Development, 1–17, doi:10.1080/07900627.2018.1437713. ↩ - Smajgl, A. et al., 2015: Responding to rising sea levels in the Mekong Delta. Nature Climate Change, 5(2), 167–174, doi:10.1038/nclimate2469.
Hoang, L.P. et al., 2018: Managing flood risks in the Mekong Delta: How to address emerging challenges under climate change and socioeconomic developments. Ambio, 47(6), 635–649, doi:10.1007/s13280-017-1009-4. ↩ - Ling, F.H., M. Tamura, K. Yasuhara, K. Ajima, and C. Van Trinh, 2015: Reducing flood risks in rural households: survey of perception and adaptation in the Mekong delta. Climatic change, 132(2), 209–222, doi:10.1007/s10584-015-1416-0. ↩
- Gustafson, S., A. Joehl Cadena, and P. Hartman, 2016: Adaptation planning in the Lower Mekong Basin: merging scientific data with local perspective to improve community resilience to climate change. Climate and Development, 1–15, doi:10.1080/17565529.2016.1223593.
Gustafson, S. et al., 2018: Merging science into community adaptation planning processes: a cross-site comparison of four distinct areas of the Lower Mekong Basin. Climatic Change, 149(1), 91–106, doi:10.1007/s10584-016-1887-7.
Tran, T.A., J. Pittock, and L.A. Tuan, 2018: Adaptive co-management in the Vietnamese Mekong Delta: examining the interface between flood management and adaptation. International Journal of Water Resources Development, 1–17, doi:10.1080/07900627.2018.1437713. ↩ - Sok, S. and X. Yu, 2015: Adaptation, resilience and sustainable livelihoods in the communities of the Lower Mekong Basin, Cambodia. International Journal of Water Resources Development, 31(4), 575–588, doi:10.1080/07900627.2015.1012659.
Kim, Y. et al., 2017: A perspective on climate-resilient development and national adaptation planning based on USAID's experience. Climate and Development, 9(2), 141–151, doi:10.1080/17565529.2015.1124037. ↩ - Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037.
Hales, S., S. Kovats, S. Lloyd, and D. Campbell-Lendrum (eds.), 2014: Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s. World Health Organization (WHO), Geneva, Switzerland, 115 pp. ↩ - Semenza, J.C. and B. Menne, 2009: Climate change and infectious diseases in Europe. The Lancet Infectious Diseases, 9(6), 365–375, doi:10.1016/s1473-3099(09)70104-5. ↩
- Smith, K.R. et al., 2014: Human Health: Impacts, Adaptation, and Co-Benefits. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 709–754. ↩
- Mitchell, D., 2016: Human influences on heat-related health indicators during the 2015 Egyptian heat wave. Bulletin of the American Meteorological Society, 97(12), S70–S74, doi:10.1175/bams-d-16-0132.1.
Mitchell, D. et al., 2016: Attributing human mortality during extreme heat waves to anthropogenic climate change. Environmental Research Letters, 11(7), 074006, doi:10.1088/1748-9326/11/7/074006.
Ebi, K.L., N.H. Ogden, J.C. Semenza, and A. Woodward, 2017: Detecting and Attributing Health Burdens to Climate Change. Environmental Health Perspectives, 125(8), 085004, doi:10.1289/ehp1509. ↩ - Smith, K.R. et al., 2014: Human Health: Impacts, Adaptation, and Co-Benefits. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 709–754. ↩
- Ebi, K.L. et al., 2018: Health risks of warming of 1.5°C, 2°C, and higher, above pre-industrial temperatures. Environmental Research Letters, 13(6), 063007, doi:10.1088/1748-9326/aac4bd. ↩
- Ebi, K.L. et al., 2018: Health risks of warming of 1.5°C, 2°C, and higher, above pre-industrial temperatures. Environmental Research Letters, 13(6), 063007, doi:10.1088/1748-9326/aac4bd. ↩
- Doyon, B., D. Belanger, and P. Gosselin, 2008: The potential impact of climate change on annual and seasonal mortality for three cities in Québec, Canada. International Journal of Health Geographics, 7, 23, doi:10.1186/1476-072x-7-23.
Jackson, J.E. et al., 2010: Public health impacts of climate change in Washington State: projected mortality risks due to heat events and air pollution. Climatic Change, 102(1–2), 159–186, doi:10.1007/s10584-010-9852-3.
Hanna, E.G., T. Kjellstrom, C. Bennett, and K. Dear, 2011: Climate Change and Rising Heat: Population Health Implications for Working People in Australia. Asia-Pacific Journal of Public Health, 23(2), 14s–26s, doi:10.1177/1010539510391457.
Huang, C.R., A.G. Barnett, X.M. Wang, and S.L. Tong, 2012: The impact of temperature on years of life lost in Brisbane, Australia. Nature Climate Change, 2(4), 265–270, doi:10.1038/nclimate1369.
Petkova, E.P., R.M. Horton, D.A. Bader, and P.L. Kinney, 2013: Projected Heat-Related Mortality in the U.S. Urban Northeast. International Journal of Environmental Research and Public Health, 10(12), 6734–6747, doi:10.3390/ijerph10126734.
Hajat, S., S. Vardoulakis, C. Heaviside, and B. Eggen, 2014: Climate change effects on human health: Projections of temperature-related mortality for the UK during the 2020s, 2050s and 2080s. Journal of Epidemiology and Community Health, 68(7), 641–648, doi:10.1136/jech-2013-202449.
Hales, S., S. Kovats, S. Lloyd, and D. Campbell-Lendrum (eds.), 2014: Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s. World Health Organization (WHO), Geneva, Switzerland, 115 pp.
Honda, Y. et al., 2014: Heat-related mortality risk model for climate change impact projection. Environmental Health and Preventive Medicine, 19(1), 56–63, doi:10.1007/s12199-013-0354-6.
Vardoulakis, S. et al., 2014: Comparative Assessment of the Effects of Climate Change on Heat-and Cold-Related Mortality in the United Kingdom and Australia. Environmental Health Perspectives, 122(12), 1285–1292, doi:10.1289/ehp.1307524.
Garland, R.M. et al., 2015: Regional Projections of Extreme Apparent Temperature Days in Africa and the Related Potential Risk to Human Health. International Journal of Environmental Research and Public Health, 12(10), 12577–12604, doi:10.3390/ijerph121012577.
Huynen, M.M.T.E. and P. Martens, 2015: Climate Change Effects on Heat- and Cold-Related Mortality in the Netherlands: A Scenario-Based Integrated Environmental Health Impact Assessment. International Journal of Environmental Research and Public Health, 12(10), 13295–13320, doi:10.3390/ijerph121013295.
Li, T. et al., 2015: Heat-related mortality projections for cardiovascular and respiratory disease under the changing climate in Beijing, China. Scientific Reports, 5(1), 11441, doi:10.1038/srep11441.
Schwartz, J.D. et al., 2015: Projections of temperature-attributable premature deaths in 209 US cities using a cluster-based Poisson approach. Environmental Health, 14(1), 85, doi:10.1186/s12940-015-0071-2.
Wang, L., J.B. Huang, Y. Luo, Y. Yao, and Z.C. Zhao, 2015: Changes in Extremely Hot Summers over the Global Land Area under Various Warming Targets. PLOS ONE, 10(6), e0130660, doi:10.1371/journal.pone.0130660.
Guo, Y. et al., 2016: Projecting future temperature-related mortality in three largest Australian cities. Environmental Pollution, 208, 66–73, doi:10.1016/j.envpol.2015.09.041.
Li, T. et al., 2016: Aging Will Amplify the Heat-related Mortality Risk under a Changing Climate: Projection for the Elderly in Beijing, China. Scientific Reports, 6(1), 28161, doi:10.1038/srep28161.
Chung, E.S., H.-K. Cheong, J.-H. Park, J.-H. Kim, and H. Han, 2017: Current and Projected Burden of Disease From High Ambient Temperature in Korea. Epidemiology, 28, S98–S105, doi:10.1097/ede.0000000000000731.
Kendrovski, V. et al., 2017: Quantifying Projected Heat Mortality Impacts under 21st-Century Warming Conditions for Selected European Countries. International Journal of Environmental Research and Public Health, 14(7), 729, doi:10.3390/ijerph14070729.
Mishra, V., M. Sourav, R. Kumar, and D.A. Stone, 2017: Heat wave exposure in India in current, 1.5°C, and 2.0°C worlds. Environmental Research Letters, 12(12), 124012, doi:10.1088/1748-9326/aa9388.
Arnell, N.W., J.A. Lowe, B. Lloyd-Hughes, and T.J. Osborn, 2018: The impacts avoided with a 1.5°C climate target: a global and regional assessment. Climatic Change, 147(1–2), 61–76, doi:10.1007/s10584-017-2115-9.
Mitchell, D. et al., 2018b: Extreme heat-related mortality avoided under Paris Agreement goals. Nature Climate Change, 8(7), 551–553, doi:10.1038/s41558-018-0210-1. ↩ - Russo, S., A.F. Marchese, J. Sillmann, and G. Immé, 2016: When will unusual heat waves become normal in a warming Africa? Environmental Research Letters, 11(5), 1–22, doi:10.1088/1748-9326/11/5/054016.
Mora, C. et al., 2017: Global risk of deadly heat. Nature Climate Change, 7(7), 501–506, doi:10.1038/nclimate3322.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45.
Harrington, L.J. and F.E.L. Otto, 2018: Changing population dynamics and uneven temperature emergence combine to exacerbate regional exposure to heat extremes under 1.5°C and 2°C of warming. Environmental Research Letters, 13(3), 034011, doi:10.1088/1748-9326/aaaa99.
King, A.D. et al., 2018: Reduced heat exposure by limiting global warming to 1.5°C. Nature Climate Change, 8(7), 549–551, doi:10.1038/s41558-018-0191-0. ↩ - Russo, S., A.F. Marchese, J. Sillmann, and G. Immé, 2016: When will unusual heat waves become normal in a warming Africa? Environmental Research Letters, 11(5), 1–22, doi:10.1088/1748-9326/11/5/054016.
Mora, C. et al., 2017: Global risk of deadly heat. Nature Climate Change, 7(7), 501–506, doi:10.1038/nclimate3322.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45.
Harrington, L.J. and F.E.L. Otto, 2018: Changing population dynamics and uneven temperature emergence combine to exacerbate regional exposure to heat extremes under 1.5°C and 2°C of warming. Environmental Research Letters, 13(3), 034011, doi:10.1088/1748-9326/aaaa99.
King, A.D. et al., 2018: Reduced heat exposure by limiting global warming to 1.5°C. Nature Climate Change, 8(7), 549–551, doi:10.1038/s41558-018-0191-0. ↩ - Hales, S., S. Kovats, S. Lloyd, and D. Campbell-Lendrum (eds.), 2014: Quantitative risk assessment of the effects of climate change on selected causes of death, 2030s and 2050s. World Health Organization (WHO), Geneva, Switzerland, 115 pp.
Huynen, M.M.T.E. and P. Martens, 2015: Climate Change Effects on Heat- and Cold-Related Mortality in the Netherlands: A Scenario-Based Integrated Environmental Health Impact Assessment. International Journal of Environmental Research and Public Health, 12(10), 13295–13320, doi:10.3390/ijerph121013295.
Li, T. et al., 2016: Aging Will Amplify the Heat-related Mortality Risk under a Changing Climate: Projection for the Elderly in Beijing, China. Scientific Reports, 6(1), 28161, doi:10.1038/srep28161. ↩ - Huang, C.R., A.G. Barnett, X.M. Wang, and S.L. Tong, 2012: The impact of temperature on years of life lost in Brisbane, Australia. Nature Climate Change, 2(4), 265–270, doi:10.1038/nclimate1369.
Hajat, S., S. Vardoulakis, C. Heaviside, and B. Eggen, 2014: Climate change effects on human health: Projections of temperature-related mortality for the UK during the 2020s, 2050s and 2080s. Journal of Epidemiology and Community Health, 68(7), 641–648, doi:10.1136/jech-2013-202449.
Vardoulakis, S. et al., 2014: Comparative Assessment of the Effects of Climate Change on Heat-and Cold-Related Mortality in the United Kingdom and Australia. Environmental Health Perspectives, 122(12), 1285–1292, doi:10.1289/ehp.1307524.
Gasparrini, A. et al., 2015: Mortality risk attributable to high and low ambient temperature: A multicountry observational study. The Lancet, 386(9991), 369–375, doi:10.1016/s0140-6736(14)62114-0.
Huynen, M.M.T.E. and P. Martens, 2015: Climate Change Effects on Heat- and Cold-Related Mortality in the Netherlands: A Scenario-Based Integrated Environmental Health Impact Assessment. International Journal of Environmental Research and Public Health, 12(10), 13295–13320, doi:10.3390/ijerph121013295.
Schwartz, J.D. et al., 2015: Projections of temperature-attributable premature deaths in 209 US cities using a cluster-based Poisson approach. Environmental Health, 14(1), 85, doi:10.1186/s12940-015-0071-2. ↩ - Dunne, J.P., R.J. Stouffer, and J.G. John, 2013: Reductions in labour capacity from heat stress under climate warming. Nature Climate Change, 3(4), 1–4, doi:10.1038/nclimate1827.
Kjellstrom, T., B. Lemke, and M. Otto, 2013: Mapping Occupational Heat Exposure and Effects in South-East Asia: Ongoing Time Trends 1980–2011 and Future Estimates to 2050. Industrial Health, 51(1), 56–67, doi:10.2486/indhealth.2012-0174.
Sheffield, P.E. et al., 2013: Current and future heat stress in Nicaraguan work places under a changing climate. Industrial Health, 51, 123–127, doi:10.2486/indhealth.2012-0156.
Habibi Mohraz, M., A. Ghahri, M. Karimi, and F. Golbabaei, 2016: The Past and Future Trends of Heat Stress Based On Wet Bulb Globe Temperature Index in Outdoor Environment of Tehran City, Iran. Iranian Journal of Public Health, 45(6), 787–794, http://ijph.tums.ac.ir/index.php/ijph/article/view/7085.
Kjellstrom, T., C. Freyberg, B. Lemke, M. Otto, and D. Briggs, 2018: Estimating population heat exposure and impacts on working people in conjunction with climate change. International Journal of Biometeorology, 62(3), 291–306, doi:10.1007/s00484-017-1407-0. ↩ - Lee, S.-M. and S.-K. Min, 2018: Heat Stress Changes over East Asia under 1.5° and 2.0°C Global Warming Targets. Journal of Climate, 31(7), 2819–2831, doi:10.1175/jcli-d-17-0449.1. ↩
- Takakura, J. et al., 2017: Cost of preventing workplace heat-related illness through worker breaks and the benefit of climate-change mitigation. Environmental Research Letters, 12(6), 064010, doi:10.1088/1748-9326/aa72cc. ↩
- Zhao, Y. et al., 2016: Potential escalation of heat-related working costs with climate and socioeconomic changes in China. Proceedings of the National Academy of Sciences, 113(17), 4640–4645, doi:10.1073/pnas.1521828113. ↩
- Heal, M.R. et al., 2013: Health burdens of surface ozone in the UK for a range of future scenarios. Environment International, 61, 36–44, doi:10.1016/j.envint.2013.09.010.
Tainio, M. et al., 2013: Future climate and adverse health effects caused by fine particulate matter air pollution: case study for Poland. Regional Environmental Change, 13(3), 705–715, doi:10.1007/s10113-012-0366-6.
Likhvar, V. et al., 2015: A multi-scale health impact assessment of air pollution over the 21st century. Science of The Total Environment, 514, 439–449, doi:10.1016/j.scitotenv.2015.02.002.
Silva, R.A. et al., 2016: The effect of future ambient air pollution on human premature mortality to 2100 using output from the ACCMIP model ensemble. Atmospheric Chemistry and Physics, 16(15), 9847–9862, doi:10.5194/acp-16-9847-2016.
Dionisio, K.L. et al., 2017: Characterizing the impact of projected changes in climate and air quality on human exposures to ozone. Journal of Exposure Science and Environmental Epidemiology, 27, 260–270, doi:10.1038/jes.2016.81.
Lee, J.Y., S. Hyun Lee, S.-C. Hong, and H. Kim, 2017: Projecting future summer mortality due to ambient ozone concentration and temperature changes. Atmospheric Environment, 156, 88–94, doi:10.1016/j.atmosenv.2017.02.034. ↩ - Tainio, M. et al., 2013: Future climate and adverse health effects caused by fine particulate matter air pollution: case study for Poland. Regional Environmental Change, 13(3), 705–715, doi:10.1007/s10113-012-0366-6.
Likhvar, V. et al., 2015: A multi-scale health impact assessment of air pollution over the 21st century. Science of The Total Environment, 514, 439–449, doi:10.1016/j.scitotenv.2015.02.002.
Silva, R.A. et al., 2016: The effect of future ambient air pollution on human premature mortality to 2100 using output from the ACCMIP model ensemble. Atmospheric Chemistry and Physics, 16(15), 9847–9862, doi:10.5194/acp-16-9847-2016. ↩ - Ren, Z. et al., 2016: Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination. Scientific Reports, 6, 20604, doi:10.1038/srep20604.
Song, Y. et al., 2016: Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050. Malaria journal, 15(1), 345, doi:10.1186/s12936-016-1395-2.
Semakula, H.M. et al., 2017: Prediction of future malaria hotspots under climate change in sub-Saharan Africa. Climatic Change, 143(3), 415–428, doi:10.1007/s10584-017-1996-y. ↩ - Ren, Z. et al., 2016: Predicting malaria vector distribution under climate change scenarios in China: Challenges for malaria elimination. Scientific Reports, 6, 20604, doi:10.1038/srep20604. ↩
- Fischer, D., S.M. Thomas, F. Niemitz, B. Reineking, and C. Beierkuhnlein, 2011: Projection of climatic suitability for Aedes albopictus Skuse (Culicidae) in Europe under climate change conditions. Global and Planetary Change, 78(1–2), 54–64, doi:10.1016/j.gloplacha.2011.05.008.
Colón-González, F.J., C. Fezzi, I.R. Lake, P.R. Hunter, and Y. Sukthana, 2013: The Effects of Weather and Climate Change on Dengue. PLOS Neglected Tropical Diseases, 7(11), e2503, doi:10.1371/journal.pntd.0002503.
Fischer, D. et al., 2013: Climate change effects on Chikungunya transmission in Europe: geospatial analysis of vector's climatic suitability and virus' temperature requirements. International Journal of Health Geographics, 12(1), 51, doi:10.1186/1476-072x-12-51.
Bouzid, M., F.J. Colón-González, T. Lung, I.R. Lake, and P.R. Hunter, 2014: Climate change and the emergence of vector-borne diseases in Europe: case study of dengue fever. BMC Public Health, 14(1), 781, doi:10.1186/1471-2458-14-781.
Ogden, N.H., R. Milka, C. Caminade, and P. Gachon, 2014a: Recent and projected future climatic suitability of North America for the Asian tiger mosquito Aedes albopictus . Parasites & Vectors, 7(1), 532, doi:10.1186/s13071-014-0532-4.
Mweya, C.N. et al., 2016: Climate Change Influences Potential Distribution of Infected Aedes aegypti Co-Occurrence with Dengue Epidemics Risk Areas in Tanzania. PLOS ONE, 11(9), e0162649, doi:10.1371/journal.pone.0162649.
Colón-González, F.J. et al., 2018: Limiting global-mean temperature increase to 1.5–2°C could reduce the incidence and spatial spread of dengue fever in Latin America. Proceedings of the National Academy of Sciences, 115(24), 6243–6248, doi:10.1073/pnas.1718945115. ↩ - Tjaden, N.B. et al., 2017: Modelling the effects of global climate change on Chikungunya transmission in the 21st century. Scientific Reports, 7(1), 3813, doi:10.1038/s41598-017-03566-3. ↩
- Semenza, J.C. et al., 2016: Climate change projections of West Nile virus infections in Europe: implications for blood safety practices. Environmental Health, 15(S1), S28, doi:10.1186/s12940-016-0105-4. ↩
- Ogden, N.H. et al., 2014b: Estimated effects of projected climate change on the basic reproductive number of the Lyme disease vector Ixodes scapularis. Environmental Health Perspectives, 122(6), 631–638, doi:10.1289/ehp.1307799.
Levi, T., F. Keesing, K. Oggenfuss, and R.S. Ostfeld, 2015: Accelerated phenology of blacklegged ticks under climate warming. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1665), 20130556, doi:10.1098/rstb.2013.0556. ↩ - González, C., A. Paz, and C. Ferro, 2014: Predicted altitudinal shifts and reduced spatial distribution of Leishmania infantum vector species under climate change scenarios in Colombia. Acta Tropica, 129, 83–90, doi:10.1016/j.actatropica.2013.08.014.
Ceccarelli, S. and J.E. Rabinovich, 2015: Global Climate Change Effects on Venezuela's Vulnerability to Chagas Disease is Linked to the Geographic Distribution of Five Triatomine Species. Journal of Medical Entomology, 52(6), 1333–1343, doi:10.1093/jme/tjv119. ↩ - Rosenzweig, C., W. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Urban Climate Change Research Network (UCCRN). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp., doi:10.1017/9781316563878. ↩
- Matthews, T.K.R., R.L. Wilby, and C. Murphy, 2017: Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences, 114(15), 3861–3866, doi:10.1073/pnas.1617526114. ↩
- Yu, R., P. Zhai, and Y. Lu, 2018: Implications of differential effects between 1.5 and 2°C global warming on temperature and precipitation extremes in China's urban agglomerations. International Journal of Climatology, 38(5), 2374–2385, doi:10.1002/joc.5340. ↩
- Bader, D.A. et al., 2018: Urban Climate Science. In: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network [Rosenzweig, C., W. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S.A. Ibrahim (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 27–60.
Dawson, R.J. et al., 2018: Urban areas in coastal zones. In: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network [Rosenzweig, C., W.D. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S.A. Ibrahim (eds.)]. Cambridge Univeristy Press, Cambridge, United Kingdom and New York, NY, USA, pp. 319–362. ↩ - Argüeso, D., J.P. Evans, A.J. Pitman, A. Di Luca, and A. Luca, 2015: Effects of city expansion on heat stress under climate change conditions. PLOS ONE, 10(2), e0117066, doi:10.1371/journal.pone.0117066.
Suzuki-Parker, A., H. Kusaka, and Y. Yamagata, 2015: Assessment of the Impact of Metropolitan-Scale Urban Planning Scenarios on the Moist Thermal Environment under Global Warming: A Study of the Tokyo Metropolitan Area Using Regional Climate Modeling. Advances in Meteorology, 2015, 1–11, doi:10.1155/2015/693754. ↩ - Rosenzweig, C., W. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Urban Climate Change Research Network (UCCRN). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp., doi:10.1017/9781316563878. ↩
- Akbari, H., S. Menon, and A. Rosenfeld, 2009: Global cooling: Increasing world-wide urban albedos to offset CO2. Climatic Change, 94(3–4), 275–286, doi:10.1007/s10584-008-9515-9.
Oleson, K.W., G.B. Bonan, and J. Feddema, 2010: Effects of white roofs on urban temperature in a global climate model. Geophysical Research Letters, 37(3), L03701, doi:10.1029/2009gl042194.
Matthews, T.K.R., R.L. Wilby, and C. Murphy, 2017: Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences, 114(15), 3861–3866, doi:10.1073/pnas.1617526114. ↩ - Yu, R., P. Zhai, and Y. Lu, 2018: Implications of differential effects between 1.5 and 2°C global warming on temperature and precipitation extremes in China's urban agglomerations. International Journal of Climatology, 38(5), 2374–2385, doi:10.1002/joc.5340. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710.
Mitchell, D. et al., 2018a: The myriad challenges of the Paris Agreement. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20180066, doi:10.1098/rsta.2018.0066. ↩ - Matthews, T.K.R., R.L. Wilby, and C. Murphy, 2017: Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences, 114(15), 3861–3866, doi:10.1073/pnas.1617526114.
Yu, R., P. Zhai, and Y. Lu, 2018: Implications of differential effects between 1.5 and 2°C global warming on temperature and precipitation extremes in China's urban agglomerations. International Journal of Climatology, 38(5), 2374–2385, doi:10.1002/joc.5340. ↩ - McCarthy, M.P., M.J. Best, and R.A. Betts, 2010: Climate change in cities due to global warming and urban effects. Geophysical Research Letters, 37(9), L09705, doi:10.1029/2010gl042845. ↩
- Georgescu, M., M. Moustaoui, A. Mahalov, and J. Dudhia, 2012: Summer-time climate impacts of projected megapolitan expansion in Arizona. Nature Climate Change, 3(1), 37–41, doi:10.1038/nclimate1656.
Argüeso, D., J.P. Evans, L. Fita, and K.J. Bormann, 2014: Temperature response to future urbanization and climate change. Climate Dynamics, 42(7–8), 2183–2199, doi:10.1007/s00382-013-1789-6.
Conlon, K., A. Monaghan, M. Hayden, and O. Wilhelmi, 2016: Potential impacts of future warming and land use changes on intra-urban heat exposure in Houston, Texas. PLOS ONE, 11(2), e0148890, doi:10.1371/journal.pone.0148890.
Kusaka, H., A. Suzuki-Parker, T. Aoyagi, S.A. Adachi, and Y. Yamagata, 2016: Assessment of RCM and urban scenarios uncertainties in the climate projections for August in the 2050s in Tokyo. Climatic Change, 137(3–4), 427–438, doi:10.1007/s10584-016-1693-2.
Grossman-Clarke, S., S. Schubert, and D. Fenner, 2017: Urban effects on summertime air temperature in Germany under climate change. International Journal of Climatology, 37(2), 905–917, doi:10.1002/joc.4748. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Revi, A. et al., 2014: Urban areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.
Jean-Baptiste, N. et al., 2018: Housing and Informal Settlements. In: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network [Rosenzweig, C., W.D. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S.E. Ali Ibrahim (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, USA, pp. 399–431, doi:10.1017/9781316563878.018.
Rosenzweig, C., W. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Urban Climate Change Research Network (UCCRN). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp., doi:10.1017/9781316563878. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- McGranahan, G., D. Balk, and B. Anderson, 2007: The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environment and Urbanization, 19(1), 17–37, doi:10.1177/0956247807076960.
Hallegatte, S., C. Green, R.J. Nicholls, and J. Corfee-Morlot, 2013: Future flood losses in major coastal cities. Nature Climate Change, 3(9), 802–806, doi:10.1038/nclimate1979.
Revi, A. et al., 2014: Urban areas. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 535–612.
Rosenzweig, C., W. Solecki, P. Romero-Lankao, S. Mehrotra, S. Dhakal, and S. Ali Ibrahim (eds.), 2018: Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network. Urban Climate Change Research Network (UCCRN). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 811 pp., doi:10.1017/9781316563878. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. Wiley Interdisciplinary Reviews: Climate Change, 8(2), e457, doi:10.1002/wcc.457. ↩
- Krey, V. et al., 2012: Urban and rural energy use and carbon dioxide emissions in Asia. Energy Economics, 34, S272–S283, doi:10.1016/j.eneco.2012.04.013.
Kamei, M., K. Hanaki, and K. Kurisu, 2016: Tokyo's long-term socioeconomic pathways: Towards a sustainable future. Sustainable Cities and Society, 27, 73–82, doi:10.1016/j.scs.2016.07.002.
Yu, R., Z. Jiang, and P. Zhai, 2016: Impact of urban land-use change in eastern China on the East Asian subtropical monsoon: A numerical study. Journal of Meteorological Research, 30(2), 203–216, doi:10.1007/s13351-016-5157-4.
Jiang, L. and B.C.O. Neill, 2017: Global urbanization projections for the Shared Socioeconomic Pathways. Global Environmental Change, 42, 193–199, doi:10.1016/j.gloenvcha.2015.03.008. ↩ - Scott, D., C.M. Hall, and S. Gössling, 2016a: A review of the IPCC Fifth Assessment and implications for tourism sector climate resilience and decarbonization. Journal of Sustainable Tourism, 24(1), 8–30, doi:10.1080/09669582.2015.1062021.
Scott, D. and S. Gössling, 2018: Tourism and Climate Change Mitigation. Embracing the Paris Agreement: Pathways to Decarbonisation. European Travel Commission (ETC), Brussels, Belgium, 39 pp. ↩ - Lemelin, H., J. Dawson, and E.J. Stewart (eds.), 2012: Last Chance Tourism: Adapting Tourism Opportunities in a Changing World. Routledge, Abingdon, UK, 238 pp., doi:10.1080/14927713.2012.747359.
Stewart, E.J. et al., 2016: Implications of climate change for glacier tourism. Tourism Geographies, 18(4), 377–398, doi:10.1080/14616688.2016.1198416.
Piggott-McKellar, A.E. and K.E. McNamara, 2017: Last chance tourism and the Great Barrier Reef. Journal of Sustainable Tourism, 25(3), 397–415, doi:10.1080/09669582.2016.1213849. ↩ - Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩
- Grillakis, M.G., A.G. Koutroulis, K.D. Seiradakis, and I.K. Tsanis, 2016: Implications of 2°C global warming in European summer tourism. Climate Services, 1, 30–38, doi:10.1016/j.cliser.2016.01.002. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩
- Ciscar, J.C. (ed.), 2014: Climate impacts in Europe – The JRC PESETA II project. EUR – Scientific and Technical Research, Publications Office of the European Union, doi:10.2791/7409. ↩
- Markham, A., E. Osipova, K. Lafrenz Samuels, and A. Caldas, 2016: World Heritage and Tourism in a Changing Climate. United Nations Environment Programme (UNEP), United Nations Educational, Scientific and Cultural Organization (UNESCO) and Union of Concerned Scientists, 108 pp.
Scott, D., C.M. Hall, and S. Gössling, 2016a: A review of the IPCC Fifth Assessment and implications for tourism sector climate resilience and decarbonization. Journal of Sustainable Tourism, 24(1), 8–30, doi:10.1080/09669582.2015.1062021.
Jones, A. and M. Phillips (eds.), 2017: Global Climate Change and Coastal Tourism: Recognizing Problems, Managing Solutions and Future Expectations. Centre for Agriculture and Biosciences International (CABI), 360 pp.
Steiger, R., D. Scott, B. Abegg, M. Pons, and C. Aall, 2017: A critical review of climate change risk for ski tourism. Current Issues in Tourism, 1–37, doi:10.1080/13683500.2017.1410110. ↩ - Steiger, R., D. Scott, B. Abegg, M. Pons, and C. Aall, 2017: A critical review of climate change risk for ski tourism. Current Issues in Tourism, 1–37, doi:10.1080/13683500.2017.1410110. ↩
- Scott, D., R. Steiger, M. Rutty, and P. Johnson, 2015: The future of the Olympic Winter Games in an era of climate change. Current Issues in Tourism, 18(10), 913–930, doi:10.1080/13683500.2014.887664.
Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩ - Marzeion, B. and A. Levermann, 2014: Loss of cultural world heritage and currently inhabited places to sea-level rise. Environmental Research Letters, 9(3), 034001, doi:10.1088/1748-9326/9/3/034001. ↩
- Scott, D. and S. Verkoeyen, 2017: Assessing the Climate Change Risk of a Coastal-Island Destination. In: Global climate change and coastal tourism: recognizing problems, managing solutions and future expectations [Jones, A. and M. Phillips (eds.)]. Centre for Agriculture and Biosciences International (CABI), Wallingford, UK, pp. 62–73, doi:10.1079/9781780648439.0062. ↩
- Rosselló-Nadal, J., 2014: How to evaluate the effects of climate change on tourism. Tourism Management, 42, 334–340, doi:10.1016/j.tourman.2013.11.006.
Scott, D., M. Rutty, B. Amelung, and M. Tang, 2016b: An Inter-Comparison of the Holiday Climate Index (HCI) and the Tourism Climate Index (TCI) in Europe. Atmosphere, 7(6), 80, doi:10.3390/atmos7060080. ↩ - Scott, D. and S. Gössling, 2018: Tourism and Climate Change Mitigation. Embracing the Paris Agreement: Pathways to Decarbonisation. European Travel Commission (ETC), Brussels, Belgium, 39 pp. ↩
- Arent, D.J. et al., 2014: Key economic sectors and services. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–708.
Hong, J. and W.S. Kim, 2015: Weather impacts on electric power load: partial phase synchronization analysis. Meteorological Applications, 22(4), 811–816, doi:10.1002/met.1535. ↩ - Arent, D.J. et al., 2014: Key economic sectors and services. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–708. ↩
- Block, P. and K. Strzepek, 2012: Power Ahead: Meeting Ethiopia's Energy Needs Under a Changing Climate. Review of Development Economics, 16(3), 476–488, doi:10.1111/j.1467-9361.2012.00675.x. ↩
- Hsiang, S. et al., 2017: Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.aal4369. ↩
- Park, C. et al., 2018: Avoided economic impacts of energy demand changes by 1.5 and 2°C climate stabilization. Environmental Research Letters, 13(4), 045010, doi:10.1088/1748-9326/aab724. ↩
- Arnell, N.W., J.A. Lowe, B. Lloyd-Hughes, and T.J. Osborn, 2018: The impacts avoided with a 1.5°C climate target: a global and regional assessment. Climatic Change, 147(1–2), 61–76, doi:10.1007/s10584-017-2115-9. ↩
- van Vliet, M.T.H. et al., 2016: Multi-model assessment of global hydropower and cooling water discharge potential under climate change. Global Environmental Change, 40, 156–170, doi:10.1016/j.gloenvcha.2016.07.007. ↩
- Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩
- Donk, P., E. Van Uytven, P. Willems, and M.A. Taylor, 2018: Assessment of the potential implications of a 1.5°C versus higher global temperature rise for the Afobaka hydropower scheme in Suriname. Regional Environmental Change, 1–13, doi:10.1007/s10113-018-1339-1. ↩
- Chilkoti, V., T. Bolisetti, and R. Balachandar, 2017: Climate change impact assessment on hydropower generation using multi-model climate ensemble. Renewable Energy, 109, 510–517, doi:10.1016/j.renene.2017.02.041. ↩
- McFarland, J. et al., 2015: Impacts of rising air temperatures and emissions mitigation on electricity demand and supply in the United States: a multi-model comparison. Climatic Change, 131(1), 111–125, doi:10.1007/s10584-015-1380-8. ↩
- de Queiroz, A.R., L.M. Marangon Lima, J.W. Marangon Lima, B.C. da Silva, and L.A. Scianni, 2016: Climate change impacts in the energy supply of the Brazilian hydro-dominant power system. Renewable Energy, 99, 379–389, doi:10.1016/j.renene.2016.07.022. ↩
- Jacob, D. et al., 2018: Climate Impacts in Europe Under +1.5°C Global Warming. Earth's Future, 6(2), 264–285, doi:10.1002/2017ef000710. ↩
- Carvalho, D., A. Rocha, M. Gómez-Gesteira, and C. Silva Santos, 2017: Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections. Renewable Energy, 101, 29–40, doi:10.1016/j.renene.2016.08.036. ↩
- Tobin, I. et al., 2015: Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Climatic Change, 128(1), 99–112, doi:10.1007/s10584-014-1291-0.
Tobin, I. et al., 2016: Climate change impacts on the power generation potential of a European mid-century wind farms scenario. Environmental Research Letters, 11(3), 034013, doi:10.1088/1748-9326/11/3/034013. ↩ - Hosking, J.S. et al., 2018: Changes in European wind energy generation potential within a 1.5°C warmer world. Environmental Research Letters, 13(5), 054032, doi:10.1088/1748-9326/aabf78. ↩
- Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩
- Tobin, I. et al., 2018: Vulnerabilities and resilience of European power generation to 1.5°C, 2°C and 3°C warming. Environmental Research Letters, 13(4), 044024, doi:10.1088/1748-9326/aab211. ↩
- Arent, D.J. et al., 2014: Key economic sectors and services. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–708. ↩
- Arent, D.J. et al., 2014: Key economic sectors and services. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 659–708. ↩
- Yumashev, D., K. van Hussen, J. Gille, and G. Whiteman, 2017: Towards a balanced view of Arctic shipping: estimating economic impacts of emissions from increased traffic on the Northern Sea Route. Climatic Change, 143(1–2), 143–155, doi:10.1007/s10584-017-1980-6. ↩
- Melia, N., K. Haines, and E. Hawkins, 2016: Sea ice decline and 21st century trans-Arctic shipping routes. Geophysical Research Letters, 43(18), 9720–9728, doi:10.1002/2016gl069315. ↩
- Adger, W.N. et al., 2014: Human Security. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 755–791. ↩
- Olsson, L. et al., 2014: Livelihoods and Poverty. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 793–832. ↩
- Hallegatte, S. et al., 2016: Shock Waves: Managing the Impacts of Climate Change on Poverty. Climate Change and Development Series, World Bank, Washington DC, USA, 227 pp., doi:10.1596/978-1-4648-0673-5.
Hallegatte, S. and J. Rozenberg, 2017: Climate change through a poverty lens. Nature Climate Change, 7(4), 250–256, doi:10.1038/nclimate3253. ↩ - Hallegatte, S. et al., 2016: Shock Waves: Managing the Impacts of Climate Change on Poverty. Climate Change and Development Series, World Bank, Washington DC, USA, 227 pp., doi:10.1596/978-1-4648-0673-5.
Hallegatte, S. and J. Rozenberg, 2017: Climate change through a poverty lens. Nature Climate Change, 7(4), 250–256, doi:10.1038/nclimate3253. ↩ - Burke, M., S.M. Hsiang, and E. Miguel, 2015b: Global non-linear effect of temperature on economic production. Nature, 527(7577), 235–239, doi:10.1038/nature15725. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037. ↩
- Nicholson, C.T.M., 2014: Climate change and the politics of causal reasoning: the case of climate change and migration. The Geographical Journal, 180(2), 151–160, doi:10.1111/geoj.12062.
Baldwin, A. and E. Fornalé, 2017: Adaptive migration: pluralising the debate on climate change and migration. The Geographical Journal, 183(4), 322–328, doi:10.1111/geoj.12242.
Bettini, G., 2017: Where Next? Climate Change, Migration, and the (Bio)politics of Adaptation. Global Policy, 8(S1), 33–39, doi:10.1111/1758-5899.12404.
Constable, A.L., 2017: Climate change and migration in the Pacific: options for Tuvalu and the Marshall Islands. Regional Environmental Change, 17(4), 1029–1038, doi:10.1007/s10113-016-1004-5.
Islam, M.R. and M. Shamsuddoha, 2017: Socioeconomic consequences of climate induced human displacement and migration in Bangladesh. International Sociology, 32(3), 277–298, doi:10.1177/0268580917693173.
Suckall, N., E. Fraser, and P. Forster, 2017: Reduced migration under climate change: evidence from Malawi using an aspirations and capabilities framework. Climate and Development, 9(4), 298–312, doi:10.1080/17565529.2016.1149441. ↩ - Mueller, V., C. Gray, and K. Kosec, 2014: Heat stress increases long-term human migration in rural Pakistan. Nature Climate Change, 4(3), 182–185, doi:10.1038/nclimate2103.
Piguet, E. and F. Laczko (eds.), 2014: People on the Move in a Changing Climate: The Regional Impact of Environmental Change on Migration. Global Migration Issues, Springer Netherlands, Dordrecht, The Netherlands, 253 pp., doi:10.1007/978-94-007-6985-4.
Mastrorillo, M. et al., 2016: The influence of climate variability on internal migration flows in South Africa. Global Environmental Change, 39, 155–169, doi:10.1016/j.gloenvcha.2016.04.014.
Sudmeier-Rieux, K., M. Fernández, J.C. Gaillard, L. Guadagno, and M. Jaboyedoff, 2017: Introduction: Exploring Linkages Between Disaster Risk Reduction, Climate Change Adaptation, Migration and Sustainable Development. In: Identifying Emerging Issues in Disaster Risk Reduction, Migration, Climate Change and Sustainable Development [Sudmeier-Rieux, K., M. Fernández, I.M. Penna, M. Jaboyedoff, and J.C. Gaillard (eds.)]. Springer International Publishing, Cham, pp. 1–11, doi:10.1007/978-3-319-33880-4_1. ↩ - Cai, R., S. Feng, M. Oppenheimer, and M. Pytlikova, 2016: Climate variability and international migration: The importance of the agricultural linkage. Journal of Environmental Economics and Management, 79, 135–151, doi:10.1016/j.jeem.2016.06.005. ↩
- Backhaus, A., I. Martinez-Zarzoso, and C. Muris, 2015: Do climate variations explain bilateral migration? A gravity model analysis. IZA Journal of Migration, 4(1), 3, doi:10.1186/s40176-014-0026-3. ↩
- Coniglio, N.D. and G. Pesce, 2015: Climate variability and international migration: an empirical analysis. Environment and Development Economics, 20(04), 434–468, doi:10.1017/s1355770x14000722. ↩
- Farbotko, C. and H. Lazrus, 2012: The first climate refugees? Contesting global narratives of climate change in Tuvalu. Global Environmental Change, 22(2), 382–390, doi:10.1016/j.gloenvcha.2011.11.014.
Weir, T., L. Dovey, and D. Orcherton, 2017: Social and cultural issues raised by climate change in Pacific Island countries: an overview. Regional Environmental Change, 17(4), 1017–1028, doi:10.1007/s10113-016-1012-5. ↩ - Constable, A.L., 2017: Climate change and migration in the Pacific: options for Tuvalu and the Marshall Islands. Regional Environmental Change, 17(4), 1029–1038, doi:10.1007/s10113-016-1004-5. ↩
- Bedarff, H. and C. Jakobeit, 2017: Climate Change, Migration, and Displacement. The Underestimated Disaster. 38 pp.
Speelman, L.H., R.J. Nicholls, and J. Dyke, 2017: Contemporary migration intentions in the Maldives: the role of environmental and other factors. Sustainability Science, 12(3), 433–451, doi:10.1007/s11625-016-0410-4.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩ - Campbell, J. and O. Warrick, 2014: Climate Change and Migration Issues in the Pacific. United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP) Pacific Office, Suva, Fiji, 34 pp. ↩
- Hsiang, S.M. and A.H. Sobel, 2016: Potentially Extreme Population Displacement and Concentration in the Tropics Under Non-Extreme Warming. Scientific Reports, 6, 25697, doi:10.1038/srep25697. ↩
- Hsiang, S.M. and A.H. Sobel, 2016: Potentially Extreme Population Displacement and Concentration in the Tropics Under Non-Extreme Warming. Scientific Reports, 6, 25697, doi:10.1038/srep25697. ↩
- Adams, C., T. Ide, J. Barnett, and A. Detges, 2018: Sampling bias in climate-conflict research. Nature Climate Change, 8(3), 200–203, doi:10.1038/s41558-018-0068-2. ↩
- Hsiang, S.M., M. Burke, and E. Miguel, 2013: Quantifying the influence of climate on human conflict. Science, 341(6151), 1235367, doi:10.1126/science.1235367.
Hsiang, S.M. and M. Burke, 2014: Climate, conflict, and social stability: what does the evidence say? Climatic Change, 123(1), 39–55, doi:10.1007/s10584-013-0868-3.
Buhaug, H., 2015: Climate-conflict research: some reflections on the way forward. Wiley Interdisciplinary Reviews: Climate Change, 6(3), 269–275, doi:10.1002/wcc.336.
Buhaug, H., 2016: Climate Change and Conflict: Taking Stock. Peace Economics, Peace Science and Public Policy, 22(4), 331–338, doi:10.1515/peps-2016-0034.
Carleton, T.A. and S.M. Hsiang, 2016: Social and economic impacts of climate. Science, 353(6304), aad9837, doi:10.1126/science.aad9837.
Carleton, T.A., S.M. Hsiang, and M. Burke, 2016: Conflict in a changing climate. The European Physical Journal Special Topics, 225(3), 489–511, doi:10.1140/epjst/e2015-50100-5. ↩ - Theisen, O.M., N.P. Gleditsch, and H. Buhaug, 2013: Is climate change a driver of armed conflict? Climatic Change, 117(3), 613–625, doi:10.1007/s10584-012-0649-4.
Buhaug, H. et al., 2014: One effect to rule them all? A comment on climate and conflict. Climatic Change, 127(3–4), 391–397, doi:10.1007/s10584-014-1266-1.
Selby, J., 2014: Positivist Climate Conflict Research: A Critique. Geopolitics, 19(4), 829–856, doi:10.1080/14650045.2014.964865.
Brzoska, M. and C. Fröhlich, 2016: Climate change, migration and violent conflict: vulnerabilities, pathways and adaptation strategies. Migration and Development, 5(2), 190–210, doi:10.1080/21632324.2015.1022973.
Burrows, K. and P. Kinney, 2016: Exploring the Climate Change, Migration and Conflict Nexus. International Journal of Environmental Research and Public Health, 13(4), 443, doi:10.3390/ijerph13040443.
Christiansen, S.M., 2016: Introduction. In: Climate Conflicts – A Case of International Environmental and Humanitarian Law. Springer International Publishing, Cham, Switzerland, pp. 1–17, doi:10.1007/978-3-319-27945-9_1.
Reyer, C.P.O. et al., 2017c: Climate change impacts in Central Asia and their implications for development. Regional Environmental Change, 17(6), 1639–1650, doi:10.1007/s10113-015-0893-z.
Waha, K. et al., 2017: Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Regional Environmental Change, 17(6), 1623–1638, doi:10.1007/s10113-017-1144-2. ↩ - Buhaug, H., 2016: Climate Change and Conflict: Taking Stock. Peace Economics, Peace Science and Public Policy, 22(4), 331–338, doi:10.1515/peps-2016-0034.
von Uexkull, N., M. Croicu, H. Fjelde, and H. Buhaug, 2016: Civil conflict sensitivity to growing-season drought. Proceedings of the National Academy of Sciences, 113(44), 12391–12396, doi:10.1073/pnas.1607542113. ↩ - von Uexkull, N., M. Croicu, H. Fjelde, and H. Buhaug, 2016: Civil conflict sensitivity to growing-season drought. Proceedings of the National Academy of Sciences, 113(44), 12391–12396, doi:10.1073/pnas.1607542113. ↩
- Serdeczny, O. et al., 2016: Climate change impacts in Sub-Saharan Africa: from physical changes to their social repercussions. Regional Environmental Change, 1–16, doi:10.1007/s10113-015-0910-2.
Almer, C., J. Laurent-Lucchetti, and M. Oechslin, 2017: Water scarcity and rioting: Disaggregated evidence from Sub-Saharan Africa. Journal of Environmental Economics and Management, 86, 193–209, doi:10.1016/j.jeem.2017.06.002. ↩ - Waha, K. et al., 2017: Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Regional Environmental Change, 17(6), 1623–1638, doi:10.1007/s10113-017-1144-2. ↩
- Hsiang, S.M., M. Burke, and E. Miguel, 2013: Quantifying the influence of climate on human conflict. Science, 341(6151), 1235367, doi:10.1126/science.1235367. ↩
- Hsiang, S.M., M. Burke, and E. Miguel, 2013: Quantifying the influence of climate on human conflict. Science, 341(6151), 1235367, doi:10.1126/science.1235367. ↩
- Hsiang, S.M. and M. Burke, 2014: Climate, conflict, and social stability: what does the evidence say? Climatic Change, 123(1), 39–55, doi:10.1007/s10584-013-0868-3. ↩
- Burke, M., S.M. Hsiang, and E. Miguel, 2015a: Climate and Conflict. Annual Review of Economics, 7(1), 577–617, doi:10.1146/annurev-economics-080614-115430. ↩
- Schleussner, C.-F., J.F. Donges, R. Donner, and H.J. Schellnhuber, 2016a: Armed-conflict risks enhanced by climate-related disasters in ethnically fractionalized countries. Proceedings of the National Academy of Sciences, 113(33), 9216–21, doi:10.1073/pnas.1601611113. ↩
- Piontek, F. et al., 2014: Multisectoral climate impact hotspots in a warming world. Proceedings of the National Academy of Sciences, 111(9), 3233–3238, doi:10.1073/pnas.1222471110. ↩
- Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩
- Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩
- Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Cramer, W. et al., 2014: Detection and Attribution of Observed Impacts. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, and M.D. Mastrandrea (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 979–1037. ↩
- Hansen, G. and D. Stone, 2016: Assessing the observed impact of anthropogenic climate change. Nature Climate Change, 6(5), 532–537, doi:10.1038/nclimate2896. ↩
- Hansen, G. and D. Stone, 2016: Assessing the observed impact of anthropogenic climate change. Nature Climate Change, 6(5), 532–537, doi:10.1038/nclimate2896. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Hoegh-Guldberg, O., 1999: Climate change, coral bleaching and the future of the world's coral reefs. Marine and Freshwater Research, 50(8), 839–866, doi:10.1071/mf99078.
Hughes, T.P. et al., 2017b: Global warming and recurrent mass bleaching of corals. Nature, 543(7645), 373–377, doi:10.1038/nature21707.
Hughes, T.P., J.T. Kerry, and T. Simpson, 2018: Large-scale bleaching of corals on the Great Barrier Reef. Ecology, 99(2), 501, doi:10.1002/ecy.2092. ↩ - Alongi, D.M., 2008: Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change. Estuarine, Coastal and Shelf Science, 76(1), 1–13, doi:10.1016/j.ecss.2007.08.024.
Hoegh-Guldberg, O. et al., 2014: The Ocean. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1655–1731.
Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩ - Duke, N.C. et al., 2017: Large-scale dieback of mangroves in Australia's Gulf of Carpentaria: A severe ecosystem response, coincidental with an unusually extreme weather event. Marine and Freshwater Research, 68(10), 1816–1829, doi:10.1071/mf16322.
Lovelock, C.E., I.C. Feller, R. Reef, S. Hickey, and M.C. Ball, 2017: Mangrove dieback during fluctuating sea levels. Scientific Reports, 7(1), 1680, doi:10.1038/s41598-017-01927-6. ↩ - Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Slangen, A.B.A. et al., 2016: Anthropogenic forcing dominates global mean sea-level rise since 1970. Nature Climate Change, 6(7), 701–705, doi:10.1038/nclimate2991. ↩
- Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Ebi, K.L., N.H. Ogden, J.C. Semenza, and A. Woodward, 2017: Detecting and Attributing Health Burdens to Climate Change. Environmental Health Perspectives, 125(8), 085004, doi:10.1289/ehp1509. ↩
- Gattuso, J.-P. et al., 2015: Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science, 349(6243), aac4722, doi:10.1126/science.aac4722. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- O'Neill, B.C. et al., 2017: IPCC Reasons for Concern regarding climate change risks. Nature Climate Change, 7, 28–37, doi:10.1038/nclimate3179. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- O'Neill, B.C. et al., 2017: IPCC Reasons for Concern regarding climate change risks. Nature Climate Change, 7, 28–37, doi:10.1038/nclimate3179. ↩
- Frieler, K. et al., 2013: Limiting global warming to 2° C is unlikely to save most coral reefs. Nature Climate Change, 3(2), 165–170, doi:10.1038/nclimate1674. ↩
- Chadburn, S.E. et al., 2017: An observation-based constraint on permafrost loss as a function of global warming. Nature Climate Change, 1–6, doi:10.1038/nclimate3262. ↩
- Johnson, W.C., B. Werner, and G.R. Guntenspergen, 2016: Non-linear responses of glaciated prairie wetlands to climate warming. Climatic Change, 134(1–2), 209–223, doi:10.1007/s10584-015-1534-8. ↩
- Schleussner, C.-F., P. Pfleiderer, and E.M. Fischer, 2017: In the observational record half a degree matters. Nature Climate Change, 7, 460–462, doi:10.1038/nclimate3320. ↩
- Meehl, G.A. et al., 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 747–846.
Moritz, M.A. et al., 2012: Climate change and disruptions to global fire activity. Ecosphere, 3(6), art49, doi:10.1890/es11-00345.1.
Romero-Lankao, P. et al., 2014: North America. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1439–1498. ↩ - Hinkel, J. et al., 2014: Coastal flood damage and adaptation costs under 21st century sea-level rise. Proceedings of the National Academy of Sciences, 111(9), 3292–7, doi:10.1073/pnas.1222469111.
Hauer, M.E., J.M. Evans, and D.R. Mishra, 2016: Millions projected to be at risk from sea-level rise in the continental United States. Nature Climate Change, 6(7), 691–695, doi:10.1038/nclimate2961. ↩ - Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45. ↩
- Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Warren, R. et al., 2018c: Risks associated with global warming of 1.5°C or 2°C. Briefing Note, Tyndall Centre for Climate Change Research, UK, 4 pp. ↩
- Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩
- Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩
- Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩
- Warren, R. et al., 2018c: Risks associated with global warming of 1.5°C or 2°C. Briefing Note, Tyndall Centre for Climate Change Research, UK, 4 pp. ↩
- Shindell, D.T., G. Faluvegi, K. Seltzer, and C. Shindell, 2018: Quantified, localized health benefits of accelerated carbon dioxide emissions reductions. Nature Climate Change, 8, 1–5, doi:10.1038/s41558-018-0108-y. ↩
- Hsiang, S. et al., 2017: Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.aal4369. ↩
- Yohe, G.W., 2017: Characterizing transient temperature trajectories for assessing the value of achieving alternative temperature targets. Climatic Change, 145(3–4), 469–479, doi:10.1007/s10584-017-2100-3. ↩
- Oppenheimer, M. et al., 2014: Emergent risks and key vulnerabilities. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1039–1099. ↩
- Lemoine, D. and C.P. Traeger, 2016: Economics of tipping the climate dominoes. Nature Climate Change, 6(5), 514–519, doi:10.1038/nclimate2902. ↩
- Cai, Y., T.M. Lenton, and T.S. Lontzek, 2016: Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction. Nature Climate Change, 6(5), 520–525, doi:10.1038/nclimate2964. ↩
- Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environmental Research Letters, 8(4), 044018, doi:10.1088/1748-9326/8/4/044018. ↩
- Warren, R. et al., 2013: Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss. Nature Climate Change, 3(7), 678–682, doi:10.1038/nclimate1887.
Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩ - Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds of the Greenland ice sheet. Nature Climate Change, 2(6), 429–432, doi:10.1038/nclimate1449. ↩
- Schewe, J., A. Levermann, and M. Meinshausen, 2011: Climate change under a scenario near 1.5 degrees C of global warming: monsoon intensification, ocean warming and steric sea level rise. Earth System Dynamics, 2(1), 25–35, doi:10.5194/esd-2-25-2011.
Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216.
Levermann, A. et al., 2014: Projecting Antarctic ice discharge using response functions from SeaRISE ice-sheet models. Earth System Dynamics, 5(2), 271–293, doi:10.5194/esd-5-271-2014.
Marzeion, B. and A. Levermann, 2014: Loss of cultural world heritage and currently inhabited places to sea-level rise. Environmental Research Letters, 9(3), 034001, doi:10.1088/1748-9326/9/3/034001.
Fürst, J.J., H. Goelzer, and P. Huybrechts, 2015: Ice-dynamic projections of the Greenland ice sheet in response to atmospheric and oceanic warming. The Cryosphere, 9(3), 1039–1062, doi:10.5194/tc-9-1039-2015.
Golledge, N.R. et al., 2015: The multi-millennial Antarctic commitment to future sea-level rise. Nature, 526(7573), 421–425, doi:10.1038/nature15706. ↩ - Joughin, I., B.E. Smith, and B. Medley, 2014: Marine Ice Sheet Collapse Potentially Under Way for the Thwaites Glacier Basin, West Antarctica. Science, 344(6185), 735–738, doi:10.1126/science.1249055.
Golledge, N.R. et al., 2015: The multi-millennial Antarctic commitment to future sea-level rise. Nature, 526(7573), 421–425, doi:10.1038/nature15706.
DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596), 591–7, doi:10.1038/nature17145. ↩ - Rahmstorf, S. et al., 2015b: Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation. Nature Climate Change, 5(5), 475–480, doi:10.1038/nclimate2554.
Srokosz, M.A. and H.L. Bryden, 2015: Observing the Atlantic Meridional Overturning Circulation yields a decade of inevitable surprises. Science, 348(6241), 1255575, doi:10.1126/science.1255575.
Caesar, L., S. Rahmstorf, A. Robinson, G. Feulner, and V. Saba, 2018: Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191–196, doi:10.1038/s41586-018-0006-5. ↩ - Caesar, L., S. Rahmstorf, A. Robinson, G. Feulner, and V. Saba, 2018: Observed fingerprint of a weakening Atlantic Ocean overturning circulation. Nature, 556(7700), 191–196, doi:10.1038/s41586-018-0006-5. ↩
- Cai, W. et al., 2015: Increased frequency of extreme La Niña events under greenhouse warming. Nature Climate Change, 5, 132–137, doi:10.1038/nclimate2492. ↩
- Wang, G. et al., 2017: Continued increase of extreme El Niño frequency long after 1.5°C warming stabilization. Nature Climate Change, 7(8), 568–572, doi:10.1038/nclimate3351. ↩
- Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9.
Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩ - Rogelj, J., D.L. McCollum, A. Reisinger, M. Meinshausen, and K. Riahi, 2013: Probabilistic cost estimates for climate change mitigation. Nature, 493(7430), 79–83, doi:10.1038/nature11787.
Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩ - Hasegawa, T., S. Fujimori, K. Takahashi, T. Yokohata, and T. Masui, 2016: Economic implications of climate change impacts on human health through undernourishment. Climatic Change, 136(2), 189–202, doi:10.1007/s10584-016-1606-4. ↩
- Rogelj, J., D.L. McCollum, A. Reisinger, M. Meinshausen, and K. Riahi, 2013: Probabilistic cost estimates for climate change mitigation. Nature, 493(7430), 79–83, doi:10.1038/nature11787. ↩
- IFPRI, 2018: 2018 Global Food Policy Report. International Food Policy Research Institute (IFPRI), Washington DC, USA, 150 pp., doi:10.2499/9780896292970. ↩
- Gallup, J.L., J.D. Sachs, and A.D. Mellinger, 1999: Geography and Economic Development. International Regional Science Review, 22(2), 179–232, doi:10.1177/016001799761012334.
Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9.
Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩ - Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩
- Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9.
Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩ - Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩
- Burke, M., S.M. Hsiang, and E. Miguel, 2015b: Global non-linear effect of temperature on economic production. Nature, 527(7577), 235–239, doi:10.1038/nature15725.
Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩ - Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩
- Yohe, G.W., 2017: Characterizing transient temperature trajectories for assessing the value of achieving alternative temperature targets. Climatic Change, 145(3–4), 469–479, doi:10.1007/s10584-017-2100-3. ↩
- Hsiang, S. et al., 2017: Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.aal4369. ↩
- Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩
- Pretis, F., M. Schwarz, K. Tang, K. Haustein, and M.R. Allen, 2018: Uncertain impacts on economic growth when stabilizing global temperatures at 1.5°C or 2°C warming. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160460, doi:10.1098/rsta.2016.0460. ↩
- Burke, M., W.M. Davis, and N.S. Diffenbaugh, 2018: Large potential reduction in economic damages under UN mitigation targets. Nature, 557(7706), 549–553, doi:10.1038/s41586-018-0071-9. ↩
- Notz, D. and J. Stroeve, 2016: Observed Arctic sea-ice loss directly follows anthropogenic CO2 emission. Science, 354(6313), 747–750, doi:10.1126/science.aag2345.
Rosenblum, E. and I. Eisenman, 2016: Faster Arctic Sea Ice Retreat in CMIP5 than in CMIP3 due to Volcanoes. Journal of Climate, 29(24), 9179–9188, doi:10.1175/jcli-d-16-0391.1.
Screen, J.A. and D. Williamson, 2017: Ice-free Arctic at 1.5°C? Nature Climate Change, 7(4), 230–231, doi:10.1038/nclimate3248.
Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159. ↩ - Sanderson, B.M. et al., 2017: Community climate simulations to assess avoided impacts in 1.5°C and 2°C futures. Earth System Dynamics, 8(3), 827–847, doi:10.5194/esd-8-827-2017. ↩
- Screen, J.A. and D. Williamson, 2017: Ice-free Arctic at 1.5°C? Nature Climate Change, 7(4), 230–231, doi:10.1038/nclimate3248.
Jahn, A., 2018: Reduced probability of ice-free summers for 1.5°C compared to 2°C warming. Nature Climate Change, 8(5), 409–413, doi:10.1038/s41558-018-0127-8.
Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159. ↩ - Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159. ↩
- Niederdrenk, A.L. and D. Notz, 2018: Arctic Sea Ice in a 1.5°C Warmer World. Geophysical Research Letters, 45(4), 1963–1971, doi:10.1002/2017gl076159. ↩
- Larsen, J.N. et al., 2014: Polar regions. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1567–1612. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032.
Bring, A. et al., 2016: Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. Journal of Geophysical Research: Biogeosciences, 121(3), 621–649, doi:10.1002/2015jg003131. ↩ - Meier, W.N. et al., 2014: Arctic sea ice in transformation: A review of recent observed changes and impacts on biology and human activity. Reviews of Geophysics, 52(3), 185–217, doi:10.1002/2013rg000431. ↩
- Reasoner, M.A. and W. Tinner, 2009: Holocene Treeline Fluctuations. In: Encyclopedia of Paleoclimatology and Ancient Environments. Springer, Dordrecht, The Netherlands, pp. 442–446, doi:10.1007/978-1-4020-4411-3_107. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Chen, B. et al., 2014: The impact of climate change and anthropogenic activities on alpine grassland over the Qinghai-Tibet Plateau. Agricultural and Forest Meteorology, 189, 11–18, doi:10.1016/j.agrformet.2014.01.002. ↩
- Arnell, N.W. et al., 2016: Global-scale climate impact functions: the relationship between climate forcing and impact. Climatic Change, 134(3), 475–487, doi:10.1007/s10584-013-1034-7.
Brown, S., R.J. Nicholls, J.A. Lowe, and J. Hinkel, 2016: Spatial variations of sea-level rise and impacts: An application of DIVA. Climatic Change, 134(3), 403–416, doi:10.1007/s10584-013-0925-y.
Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩ - Arnell, N.W. et al., 2016: Global-scale climate impact functions: the relationship between climate forcing and impact. Climatic Change, 134(3), 475–487, doi:10.1007/s10584-013-1034-7. ↩
- Wartenburger, R. et al., 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609–3634, doi:10.5194/gmd-2017-33.
Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩ - Nelson, G.C. et al., 2010: Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options. IFPRI Research Monograph, International Food Policy Research Institute (IFPRI), Washington DC, USA, 140 pp., doi:10.2499/9780896291867. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Marx, A. et al., 2018: Climate change alters low flows in Europe under global warming of 1.5, 2, and 3°C. Hydrology and Earth System Sciences, 22(2), 1017–1032, doi:10.5194/hess-22-1017-2018. ↩
- Thober, T. et al., 2018: Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming. Environmental Research Letters, 13(1), 014003, doi:10.1088/1748-9326/aa9e35. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Döll, P. et al., 2018: Risks for the global freshwater system at 1.5°C and 2°C global warming. Environmental Research Letters, 13(4), 044038, doi:10.1088/1748-9326/aab792. ↩
- Guiot, J. and W. Cramer, 2016: Climate change: The 2015 Paris Agreement thresholds and Mediterranean basin ecosystems. Science, 354(6311), 4528–4532, doi:10.1126/science.aah5015.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Donnelly, C. et al., 2017: Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Climatic Change, 143(1–2), 13–26, doi:10.1007/s10584-017-1971-7. ↩ - Weber, T. et al., 2018: Analysing regional climate change in Africa in a 1.5°C, 2°C and 3°C global warming world. Earth's Future, 6, 1–13, doi:10.1002/2017ef000714. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Weber, T. et al., 2018: Analysing regional climate change in Africa in a 1.5°C, 2°C and 3°C global warming world. Earth's Future, 6, 1–13, doi:10.1002/2017ef000714. ↩ - Sylla, M.B. et al., 2015: Projected Changes in the Annual Cycle of High-Intensity Precipitation Events over West Africa for the Late Twenty-First Century. Journal of Climate, 28(16), 6475–6488, doi:10.1175/jcli-d-14-00854.1. ↩
- Liu, W. et al., 2018: Global drought and severe drought-affected populations in 1.5 and 2°C warmer worlds. Earth System Dynamics, 9(1), 267–283, doi:10.5194/esd-9-267-2018. ↩
- World Bank, 2013: Turn Down The Heat: Climate Extremes, Regional Impacts and the Case for Resilience. World Bank, Washington DC, USA, 255 pp., doi:10.1017/cbo9781107415324.004. ↩
- Läderach, P., A. Martinez-Valle, G. Schroth, and N. Castro, 2013: Predicting the future climatic suitability for cocoa farming of the world's leading producer countries, Ghana and Côte d'Ivoire. Climatic Change, 119(3), 841–854, doi:10.1007/s10584-013-0774-8.
Sultan, B. and M. Gaetani, 2016: Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation. Frontiers in Plant Science, 7, 1262, doi:10.3389/fpls.2016.01262. ↩ - Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Cheung, W.W.L., G. Reygondeau, and T.L. Frölicher, 2016a: Large benefits to marine fisheries of meeting the 1.5°C global warming target. Science, 354(6319), 1591–1594, doi:10.1126/science.aag2331.
Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩ - Sultan, B. and M. Gaetani, 2016: Agriculture in West Africa in the Twenty-First Century: Climate Change and Impacts Scenarios, and Potential for Adaptation. Frontiers in Plant Science, 7, 1262, doi:10.3389/fpls.2016.01262.
Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117.
Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45.
Rosenzweig, C. et al., 2018: Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2°C assessments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160455, doi:10.1098/rsta.2016.0455. ↩ - Ahmed, K.F., G. Wang, M. Yu, J. Koo, and L. You, 2015: Potential impact of climate change on cereal crop yield in West Africa. Climatic Change, 133(2), 321–334, doi:10.1007/s10584-015-1462-7.
Parkes, B., D. Defrance, B. Sultan, P. Ciais, and X. Wang, 2018: Projected changes in crop yield mean and variability over West Africa in a world 1.5 K warmer than the pre-industrial era. Earth System Dynamics, 9(1), 119–134, doi:10.5194/esd-9-119-2018. ↩ - Engelbrecht, F.A. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environmental Research Letters, 10(8), 085004, doi:10.1088/1748-9326/10/8/085004. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Weber, T. et al., 2018: Analysing regional climate change in Africa in a 1.5°C, 2°C and 3°C global warming world. Earth's Future, 6, 1–13, doi:10.1002/2017ef000714. ↩
- Niang, I. et al., 2014: Africa. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199–1265.
Engelbrecht, F.A. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environmental Research Letters, 10(8), 085004, doi:10.1088/1748-9326/10/8/085004.
Karl, T.R. et al., 2015: Possible artifacts of data biases in the recent global surface warming hiatus. Science, 348(6242), 1469–1472, doi:10.1126/science.aaa5632.
James, R., R. Washington, C.-F. Schleussner, J. Rogelj, and D. Conway, 2017: Characterizing half-a-degree difference: a review of methods for identifying regional climate responses to global warming targets. Wiley Interdisciplinary Reviews: Climate Change, 8(2), e457, doi:10.1002/wcc.457. ↩ - Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Boone, R.B., R.T. Conant, J. Sircely, P.K. Thornton, and M. Herrero, 2018: Climate change impacts on selected global rangeland ecosystem services. Global Change Biology, 24(3), 1382–1393, doi:10.1111/gcb.13995. ↩
- World Bank, 2013: Turn Down The Heat: Climate Extremes, Regional Impacts and the Case for Resilience. World Bank, Washington DC, USA, 255 pp., doi:10.1017/cbo9781107415324.004. ↩
- Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452. ↩
- Lehner, F. et al., 2017: Projected drought risk in 1.5°C and 2°C warmer climates. Geophysical Research Letters, 44(14), 7419–7428, doi:10.1002/2017gl074117.
Betts, R.A. et al., 2018: Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160452, doi:10.1098/rsta.2016.0452.
Byers, E. et al., 2018: Global exposure and vulnerability to multi-sector development and climate change hotspots. Environmental Research Letters, 13(5), 055012, doi:10.1088/1748-9326/aabf45.
Rosenzweig, C. et al., 2018: Coordinating AgMIP data and models across global and regional scales for 1.5°C and 2°C assessments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160455, doi:10.1098/rsta.2016.0455. ↩ - Mahlstein, I., R. Knutti, S. Solomon, and R.W. Portmann, 2011: Early onset of significant local warming in low latitude countries. Environmental Research Letters, 6(3), 034009, doi:10.1088/1748-9326/6/3/034009. ↩
- Weber, T. et al., 2018: Analysing regional climate change in Africa in a 1.5°C, 2°C and 3°C global warming world. Earth's Future, 6, 1–13, doi:10.1002/2017ef000714. ↩
- Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016. ↩
- Hsiang, S.M. and A.H. Sobel, 2016: Potentially Extreme Population Displacement and Concentration in the Tropics Under Non-Extreme Warming. Scientific Reports, 6, 25697, doi:10.1038/srep25697. ↩
- Nurse, L.A. et al., 2014: Small islands. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1613–1654.
Ourbak, T. and A.K. Magnan, 2017: The Paris Agreement and climate change negotiations: Small Islands, big players. Regional Environmental Change, 1–7, doi:10.1007/s10113-017-1247-9. ↩ - Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Karnauskas, K.B. et al., 2018: Freshwater Stress on Small Island Developing States: Population Projections and Aridity Changes at 1.5°C and 2°C. Regional Environmental Change, 1–10, doi:10.1007/s10113-018-1331-9. ↩
- Benjamin, L. and A. Thomas, 2016: 1.5°C To Stay Alive?: AOSIS and the Long Term Temperature Goal in the Paris Agreement. IUCNAEL eJournal, 7, 122–129, http://www.iucnael.org/en/documents/1324-iucn-ejournal-issue-7. ↩
- Taylor, M.A. et al., 2018: Future Caribbean Climates in a World of Rising Temperatures: The 1.5 vs 2.0 Dilemma. Journal of Climate, 31(7), 2907–2926, doi:10.1175/jcli-d-17-0074.1. ↩
- Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Lallo, C.H.O. et al., 2018: Characterizing heat stress on livestock using the temperature humidity index (THI) – prospects for a warmer Caribbean. Regional Environmental Change, 1–12, doi:10.1007/s10113-018-1359-x. ↩
- Engelbrecht, C.J. and F.A. Engelbrecht, 2016: Shifts in Köppen-Geiger climate zones over southern Africa in relation to key global temperature goals. Theoretical and Applied Climatology, 123(1–2), 247–261, doi:10.1007/s00704-014-1354-1. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Armour, K.C., I. Eisenman, E. Blanchard-Wrigglesworth, K.E. McCusker, and C.M. Bitz, 2011: The reversibility of sea ice loss in a state-of-the-art climate model. Geophysical Research Letters, 38(16), L16705, doi:10.1029/2011gl048739.
Boucher, O. et al., 2012: Reversibility in an Earth System model in response to CO2 concentration changes. Environmental Research Letters, 7(2), 024013, doi:10.1088/1748-9326/7/2/024013.
Ridley, J.K., J.A. Lowe, and H.T. Hewitt, 2012: How reversible is sea ice loss? The Cryosphere, 6(1), 193–198, doi:10.5194/tc-6-193-2012. ↩ - Schröder, D. and W.M. Connolley, 2007: Impact of instantaneous sea ice removal in a coupled general circulation model. Geophysical Research Letters, 34(14), L14502, doi:10.1029/2007gl030253.
Sedláček, J., O. Martius, and R. Knutti, 2011: Influence of subtropical and polar sea-surface temperature anomalies on temperatures in Eurasia. Geophysical Research Letters, 38(12), L12803, doi:10.1029/2011gl047764.
Tietsche, S., D. Notz, J.H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Arctic summer sea ice. Geophysical Research Letters, 38(2), L02707, doi:10.1029/2010gl045698. ↩ - Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Schröder, D. and W.M. Connolley, 2007: Impact of instantaneous sea ice removal in a coupled general circulation model. Geophysical Research Letters, 34(14), L14502, doi:10.1029/2007gl030253.
Sedláček, J., O. Martius, and R. Knutti, 2011: Influence of subtropical and polar sea-surface temperature anomalies on temperatures in Eurasia. Geophysical Research Letters, 38(12), L12803, doi:10.1029/2011gl047764.
Tietsche, S., D. Notz, J.H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Arctic summer sea ice. Geophysical Research Letters, 38(2), L02707, doi:10.1029/2010gl045698. ↩ - Settele, J. et al., 2014: Terrestrial and Inland Water Systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359. ↩
- Bring, A. et al., 2016: Arctic terrestrial hydrology: A synthesis of processes, regional effects, and research challenges. Journal of Geophysical Research: Biogeosciences, 121(3), 621–649, doi:10.1002/2015jg003131.
DeBeer, C.M., H.S. Wheater, S.K. Carey, and K.P. Chun, 2016: Recent climatic, cryospheric, and hydrological changes over the interior of western Canada: a review and synthesis. Hydrology and Earth System Sciences, 20(4), 1573–1598, doi:10.5194/hess-20-1573-2016.
Jiang, Y. et al., 2016: Importance of soil thermal regime in terrestrial ecosystem carbon dynamics in the circumpolar north. Global and Planetary Change, 142, 28–40, doi:10.1016/j.gloplacha.2016.04.011.
Yang, Z. et al., 2016: Warming increases methylmercury production in an Arctic soil. Environmental Pollution, 214, 504–509, doi:10.1016/j.envpol.2016.04.069. ↩ - Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105. ↩
- Drijfhout, S. et al., 2015: Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proceedings of the National Academy of Sciences, 112(43), E5777–E5786, doi:10.1073/pnas.1511451112. ↩
- Dolman, A.J. et al., 2010: A Carbon Cycle Science Update Since IPCC AR4. Ambio, 39(5–6), 402–412, doi:10.1007/s13280-010-0083-7. ↩
- Burke, E.J., S.E. Chadburn, C. Huntingford, and C.D. Jones, 2018: CO2 loss by permafrost thawing implies additional emissions reductions to limit warming to 1.5 or 2°C. Environmental Research Letters, 13(2), 024024, doi:10.1088/1748-9326/aaa138. ↩
- Drijfhout, S. et al., 2015: Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models. Proceedings of the National Academy of Sciences, 112(43), E5777–E5786, doi:10.1073/pnas.1511451112. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105. ↩
- Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105. ↩
- Jiang, D.B. and Z.P. Tian, 2013: East Asian monsoon change for the 21st century: Results of CMIP3 and CMIP5 models. Chinese Science Bulletin, 58(12), 1427–1435, doi:10.1007/s11434-012-5533-0. ↩
- Jiang, D.B. and Z.P. Tian, 2013: East Asian monsoon change for the 21st century: Results of CMIP3 and CMIP5 models. Chinese Science Bulletin, 58(12), 1427–1435, doi:10.1007/s11434-012-5533-0. ↩
- Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105. ↩
- Niang, I. et al., 2014: Africa. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199–1265. ↩
- Engelbrecht, F.A. et al., 2015: Projections of rapidly rising surface temperatures over Africa under low mitigation. Environmental Research Letters, 10(8), 085004, doi:10.1088/1748-9326/10/8/085004.
Sylla, M.B., N. Elguindi, F. Giorgi, and D. Wisser, 2016: Projected robust shift of climate zones over West Africa in response to anthropogenic climate change for the late 21st century. Climatic Change, 134(1), 241–253, doi:10.1007/s10584-015-1522-z.
Weber, T. et al., 2018: Analysing regional climate change in Africa in a 1.5°C, 2°C and 3°C global warming world. Earth's Future, 6, 1–13, doi:10.1002/2017ef000714. ↩ - Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105. ↩
- Nobre, C.A. et al., 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–68, doi:10.1073/pnas.1605516113. ↩
- Nobre, C.A. et al., 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–68, doi:10.1073/pnas.1605516113. ↩
- Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105.
Nobre, C.A. et al., 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–68, doi:10.1073/pnas.1605516113. ↩ - Lyra, A. et al., 2017: Projections of climate change impacts on central America tropical rainforest. Climatic Change, 141(1), 93–105, doi:10.1007/s10584-016-1790-2. ↩
- Huntingford, C. et al., 2013: Simulated resilience of tropical rainforests to CO2-induced climate change. Nature Geoscience, 6, 268–273, doi:10.1038/ngeo1741.
Nobre, C.A. et al., 2016: Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39), 10759–68, doi:10.1073/pnas.1605516113. ↩ - Cox, P.M. et al., 2013: Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature, 494, 341–344, doi:10.1038/nature11882. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Gauthier, S., P. Bernier, T. Kuuluvainen, A.Z. Shvidenko, and D.G. Schepaschenko, 2015: Boreal forest health and global change. Science, 349(6250), 819–822, doi:10.1126/science.aaa9092. ↩
- Lenton, T.M. et al., 2008: Tipping elements in the Earth's climate system. Proceedings of the National Academy of Sciences, 105(6), 1786–93, doi:10.1073/pnas.0705414105.
Lenton, T.M., 2012: Arctic Climate Tipping Points. Ambio, 41(1, SI), 10–22, doi:10.1007/s13280-011-0221-x. ↩ - Lucht, W., S. Schaphoff, T. Erbrecht, U. Heyder, and W. Cramer, 2006: Terrestrial vegetation redistribution and carbon balance under climate change. Carbon Balance and Management, 1(1), 6, doi:10.1186/1750-0680-1-6.
Kriegler, E., J.W. Hall, H. Held, R. Dawson, and H.J. Schellnhuber, 2009: Imprecise probability assessment of tipping points in the climate system. Proceedings of the National Academy of Sciences, 106(13), 5041–5046, doi:10.1073/pnas.0809117106. ↩ - Matthews, T.K.R., R.L. Wilby, and C. Murphy, 2017: Communicating the deadly consequences of global warming for human heat stress. Proceedings of the National Academy of Sciences, 114(15), 3861–3866, doi:10.1073/pnas.1617526114. ↩
- Akbari, H., S. Menon, and A. Rosenfeld, 2009: Global cooling: Increasing world-wide urban albedos to offset CO2. Climatic Change, 94(3–4), 275–286, doi:10.1007/s10584-008-9515-9.
Oleson, K.W., G.B. Bonan, and J. Feddema, 2010: Effects of white roofs on urban temperature in a global climate model. Geophysical Research Letters, 37(3), L03701, doi:10.1029/2009gl042194. ↩ - Niang, I. et al., 2014: Africa. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Barros, V.R., C.B. Field, D.J. Dokken, M.D. Mastrandrea, K.J. Mach, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199–1265.
Schleussner, C.-F. et al., 2016b: Differential climate impacts for policy-relevant limits to global warming: The case of 1.5°C and 2°C. Earth System Dynamics, 7(2), 327–351, doi:10.5194/esd-7-327-2016.
Huang, J., H. Yu, A. Dai, Y. Wei, and L. Kang, 2017: Drylands face potential threat under 2°C global warming target. Nature Climate Change, 7(6), 417–422, doi:10.1038/nclimate3275.
Iizumi, T. et al., 2017: Responses of crop yield growth to global temperature and socioeconomic changes. Scientific Reports, 7(1), 7800, doi:10.1038/s41598-017-08214-4. ↩ - Bassu, S. et al., 2014: How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 20(7), 2301–2320, doi:10.1111/gcb.12520. ↩
- Lana, M.A. et al., 2017: Yield stability and lower susceptibility to abiotic stresses of improved open-pollinated and hybrid maize cultivars. Agronomy for Sustainable Development, 37(4), 30, doi:10.1007/s13593-017-0442-x. ↩
- Thornton, P.K., P.G. Jones, P.J. Ericksen, and A.J. Challinor, 2011: Agriculture and food systems in sub-Saharan Africa in a 4°C+ world. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1934), 117–136, doi:10.1098/rsta.2010.0246. ↩
- Lallo, C.H.O. et al., 2018: Characterizing heat stress on livestock using the temperature humidity index (THI) – prospects for a warmer Caribbean. Regional Environmental Change, 1–12, doi:10.1007/s10113-018-1359-x. ↩
- Warren, R. et al., 2018c: Risks associated with global warming of 1.5°C or 2°C. Briefing Note, Tyndall Centre for Climate Change Research, UK, 4 pp. ↩
- Cai, Y., T.M. Lenton, and T.S. Lontzek, 2016: Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction. Nature Climate Change, 6(5), 520–525, doi:10.1038/nclimate2964.
Lemoine, D. and C.P. Traeger, 2016: Economics of tipping the climate dominoes. Nature Climate Change, 6(5), 514–519, doi:10.1038/nclimate2902. ↩ - Lemoine, D. and C.P. Traeger, 2016: Economics of tipping the climate dominoes. Nature Climate Change, 6(5), 514–519, doi:10.1038/nclimate2902. ↩
- Shindell, D.T., G. Faluvegi, K. Seltzer, and C. Shindell, 2018: Quantified, localized health benefits of accelerated carbon dioxide emissions reductions. Nature Climate Change, 8, 1–5, doi:10.1038/s41558-018-0108-y. ↩
- Hsiang, S. et al., 2017: Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.aal4369.
Yohe, G.W., 2017: Characterizing transient temperature trajectories for assessing the value of achieving alternative temperature targets. Climatic Change, 145(3–4), 469–479, doi:10.1007/s10584-017-2100-3. ↩ - Hsiang, S. et al., 2017: Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.aal4369. ↩
- Yohe, G.W., 2017: Characterizing transient temperature trajectories for assessing the value of achieving alternative temperature targets. Climatic Change, 145(3–4), 469–479, doi:10.1007/s10584-017-2100-3. ↩
- Fawcett, A.A. et al., 2015: Can Paris pledges avert severe climate change? Science, 350(6265), 1168–1169, doi:10.1126/science.aad5761. ↩
- Hsiang, S. et al., 2017: Estimating economic damage from climate change in the United States. Science, 356(6345), 1362–1369, doi:10.1126/science.aal4369. ↩
- Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345, doi:10.1016/j.gloenvcha.2016.10.002.
Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°C. Nature Climate Change, 8(4), 325–332, doi:10.1038/s41558-018-0091-3. ↩ - Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345, doi:10.1016/j.gloenvcha.2016.10.002.
Hirsch, A.L. et al., 2018: Biogeophysical Impacts of Land-Use Change on Climate Extremes in Low-Emission Scenarios: Results From HAPPI-Land. Earth's Future, 6(3), 396–409, doi:10.1002/2017ef000744.
Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°C. Nature Climate Change, 8(4), 325–332, doi:10.1038/s41558-018-0091-3.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩ - Wiltshire, A. and T. Davies-Barnard, 2015: Planetary limits to BECCS negative emissions. AVOID 2, UK, 24 pp. ↩
- Williamson, P., 2016: Emissions reduction: Scrutinize CO2 removal methods. Nature, 530(7589), 153–155, doi:10.1038/530153a. ↩
- Smith, P. et al., 2013: How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Global Change Biology, 19(8), 2285–2302, doi:10.1111/gcb.12160.
Smith, P. et al., 2015: Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6(1), 42–50, doi:10.1038/nclimate2870. ↩ - Yamagata, Y. et al., 2018: Estimating water-food-ecosystem trade-offs for the global negative emission scenario (IPCC-RCP2.6). Sustainability Science, 13(2), 301–313, doi:10.1007/s11625-017-0522-5. ↩
- Boysen, L. et al., 2017: The limits to global-warming mitigation by terrestrial carbon removal. Earth's Future, 5(5), 463–474, doi:10.1002/2016ef000469. ↩
- Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520(7545), 45–50, doi:10.1038/nature14324. ↩
- Smith, P. et al., 2013: How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Global Change Biology, 19(8), 2285–2302, doi:10.1111/gcb.12160.
Tavoni, M. and R. Socolow, 2013: Modeling meets science and technology: an introduction to a special issue on negative emissions. Climatic Change, 118(1), 1–14, doi:10.1007/s10584-013-0757-9. ↩ - Ruane, A.C. et al., 2018: Biophysical and economic implications for agriculture of +1.5° and +2.0°C global warming using AgMIP Coordinated Global and Regional Assessments. Climate Research, 76(1), 17–39, doi:10.3354/cr01520. ↩
- Fajardy, M. and N. Mac Dowell, 2017: Can BECCS deliver sustainable and resource efficient negative emissions? Energy & Environmental Science, 10(6), 1389–1426, doi:10.1039/c7ee00465f.
Booth, M.S., 2018: Not carbon neutral: Assessing the net emissions impact of residues burned for bioenergy. Environmental Research Letters, 13(3), 035001, doi:10.1088/1748-9326/aaac88.
Sterman, J.D., L. Siegel, and J.N. Rooney-Varga, 2018: Does replacing coal with wood lower CO2 emissions? Dynamic lifecycle analysis of wood bioenergy. Environmental Research Letters, 13(1), 015007, doi:10.1088/1748-9326/aaa512. ↩ - Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nature Climate Change, 4(10), 924–929, doi:10.1038/nclimate2353. ↩
- van Vuuren, D.P. et al., 2009: Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials. Energy Policy, 37(12), 5125–5139, doi:10.1016/j.enpol.2009.07.024.
Haberl, H., T. Beringer, S.C. Bhattacharya, K.-H. Erb, and M. Hoogwijk, 2010: The global technical potential of bio-energy in 2050 considering sustainability constraints. Current Opinion in Environmental Sustainability, 2(5), 394–403, doi:10.1016/j.cosust.2010.10.007.
Haberl, H. et al., 2013: Bioenergy: how much can we expect for 2050? Environmental Research Letters, 8(3), 031004, doi:10.1088/1748-9326/8/3/031004.
Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nature Climate Change, 4(10), 924–929, doi:10.1038/nclimate2353.
Daioglou, V. et al., 2016: Projections of the availability and cost of residues from agriculture and forestry. GCB Bioenergy, 8(2), 456–470, doi:10.1111/gcbb.12285.
Fajardy, M. and N. Mac Dowell, 2017: Can BECCS deliver sustainable and resource efficient negative emissions? Energy & Environmental Science, 10(6), 1389–1426, doi:10.1039/c7ee00465f. ↩ - van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nature Climate Change, 8(5), 391–397, doi:10.1038/s41558-018-0119-8. ↩
- Obersteiner, M. et al., 2016: Assessing the land resource-food price nexus of the Sustainable Development Goals. Science Advances, 2(9), e1501499, doi:10.1126/sciadv.1501499.
Bertram, C. et al., 2018: Targeted policies can compensate most of the increased sustainability risks in 1.5°C mitigation scenarios. Environmental Research Letters, 13(6), 064038, doi:10.1088/1748-9326/aac3ec.
Humpenöder, F. et al., 2018: Large-scale bioenergy production: how to resolve sustainability trade-offs? Environmental Research Letters, 13(2), 024011, doi:10.1088/1748-9326/aa9e3b. ↩ - Smith, P. et al., 2015: Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6(1), 42–50, doi:10.1038/nclimate2870. ↩
- Humpenöder, F. et al., 2014: Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environmental Research Letters, 9(6), 064029, doi:10.1088/1748-9326/9/6/064029. ↩
- Leadley, P. et al., 2016: Relationships between the Aichi Targets and land-based climate mitigation. Convention on Biological Diversity (CBD), 26 pp. ↩
- Stanturf, J.A., B.J. Palik, and R.K. Dumroese, 2014: Contemporary forest restoration: A review emphasizing function. Forest Ecology and Management, 331, 292–323, doi:10.1016/j.foreco.2014.07.029. ↩
- Pistorious, T. and L. Kiff, 2017: From a biodiversity perspective: risks, trade-offs, and international guidance for Forest Landscape Restoration. UNIQUE forestry and land use GmbH, Freiburg, Germany, 66 pp. ↩
- Griscom, B.W. et al., 2017: Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645–11650, doi:10.1073/pnas.1710465114. ↩
- Nelson, G.C. et al., 2014a: Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences, 111(9), 3274–3279, doi:10.1073/pnas.1222465110.
Dalin, C. and I. Rodríguez-Iturbe, 2016: Environmental impacts of food trade via resource use and greenhouse gas emissions. Environmental Research Letters, 11(3), 035012, doi:10.1088/1748-9326/11/3/035012. ↩ - Muratori, M., K. Calvin, M. Wise, P. Kyle, and J. Edmonds, 2016: Global economic consequences of deploying bioenergy with carbon capture and storage (BECCS). Environmental Research Letters, 11(9), 095004, doi:10.1088/1748-9326/11/9/095004. ↩
- Warren, R., J. Price, J. VanDerWal, S. Cornelius, and H. Sohl, 2018b: The implications of the United Nations Paris Agreement on Climate Change for Key Biodiversity Areas. Climatic change, 147(3–4), 395–409, doi:10.1007/s10584-018-2158-6. ↩
- Rippke, U. et al., 2016: Timescales of transformational climate change adaptation in sub-Saharan African agriculture. Nature Climate Change, 6(6), 605–609, doi:10.1038/nclimate2947. ↩
- Tilman, D., C. Balzer, J. Hill, and B.L. Befort, 2011: Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences, 108(50), 20260–20264, doi:10.1073/pnas.1116437108. ↩
- Mahowald, N.M., D.S. Ward, S.C. Doney, P.G. Hess, and J.T. Randerson, 2017a: Are the impacts of land use on warming underestimated in climate policy? Environmental Research Letters, 12(9), 94016. ↩
- Mueller, N.D. et al., 2016: Cooling of US Midwest summer temperature extremes from cropland intensification. Nature Climate Change, 6(3), 317–322, doi:10.1038/nclimate2825. ↩
- Feng, X. et al., 2016: Revegetation in China's Loess Plateau is approaching sustainable water resource limits. Nature Climate Change, 6(11), 1019–1022, doi:10.1038/nclimate3092.
Sonntag, S., J. Pongratz, C.H. Reick, and H. Schmidt, 2016: Reforestation in a high-CO2 world – Higher mitigation potential than expected, lower adaptation potential than hoped for. Geophysical Research Letters, 43(12), 6546–6553, doi:10.1002/2016gl068824.
Bright, R.M. et al., 2017: Local temperature response to land cover and management change driven by non-radiative processes. Nature Climate Change, 7(4), 296–302, doi:10.1038/nclimate3250. ↩ - Luyssaert, S. et al., 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nature Climate Change, 4(5), 1–5, doi:10.1038/nclimate2196.
Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres, 122(4), 2269–2288, doi:10.1002/2016jd026125. ↩ - Jeong, S.-J. et al., 2014: Effects of double cropping on summer climate of the North China Plain and neighbouring regions. Nature Climate Change, 4(7), 615–619, doi:10.1038/nclimate2266.
Mueller, B. et al., 2015: Lengthening of the growing season in wheat and maize producing regions. Weather and Climate Extremes, 9, 47–56, doi:10.1016/j.wace.2015.04.001.
Seifert, C.A. and D.B. Lobell, 2015: Response of double cropping suitability to climate change in the United States. Environmental Research Letters, 10(2), 024002, doi:10.1088/1748-9326/10/2/024002. ↩ - Lobell, D. et al., 2009: Regional differences in the influence of irrigation on climate. Journal of Climate, 22(8), 2248–2255, doi:10.1175/2008jcli2703.1.
Sacks, W.J., B.I. Cook, N. Buenning, S. Levis, and J.H. Helkowski, 2009: Effects of global irrigation on the near-surface climate. Climate Dynamics, 33(2–3), 159–175, doi:10.1007/s00382-008-0445-z.
Cook, B.I., M.J. Puma, and N.Y. Krakauer, 2011: Irrigation induced surface cooling in the context of modern and increased greenhouse gas forcing. Climate Dynamics, 37(7–8), 1587–1600, doi:10.1007/s00382-010-0932-x.
Qian, Y. et al., 2013: A Modeling Study of Irrigation Effects on Surface Fluxes and Land-Air-Cloud Interactions in the Southern Great Plains. Journal of Hydrometeorology, 14(3), 700–721, doi:10.1175/jhm-d-12-0134.1.
de Vrese, P., S. Hagemann, and M. Claussen, 2016: Asian irrigation, African rain: Remote impacts of irrigation. Geophysical Research Letters, 43(8), 3737–3745, doi:10.1002/2016gl068146.
Pryor, S.C., R.C. Sullivan, and T. Wright, 2016: Quantifying the Roles of Changing Albedo, Emissivity, and Energy Partitioning in the Impact of Irrigation on Atmospheric Heat Content. Journal of Applied Meteorology and Climatology, 55(8), 1699–1706, doi:10.1175/jamc-d-15-0291.1.
Thiery, W. et al., 2017: Present-day irrigation mitigates heat extremes. Journal of Geophysical Research: Atmospheres, 122(3), 1403–1422, doi:10.1002/2016jd025740. ↩ - Lobell, D.B., G. Bala, and P.B. Duffy, 2006: Biogeophysical impacts of cropland management changes on climate. Geophysical Research Letters, 33(6), L06708, doi:10.1029/2005gl025492.
Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proceedings of the National Academy of Sciences, 111(27), 9757–9761, doi:10.1073/pnas.1317323111. ↩ - Lawrence, P.J. et al., 2012: Simulating the biogeochemical and biogeophysical impacts of transient land cover change and wood harvest in the Community Climate System Model (CCSM4) from 1850 to 2100. Journal of Climate, 25(9), 3071–3095, doi:10.1175/jcli-d-11-00256.1. ↩
- van Vuuren, D.P. et al., 2011b: RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C. Climatic Change, 109(1), 95, doi:10.1007/s10584-011-0152-3. ↩
- Seneviratne, S.I. et al., 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophysical Research Letters, 40(19), 5212–5217, doi:10.1002/grl.50956.
Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proceedings of the National Academy of Sciences, 111(27), 9757–9761, doi:10.1073/pnas.1317323111.
Wilhelm, M., E.L. Davin, and S.I. Seneviratne, 2015: Climate engineering of vegetated land for hot extremes mitigation: An Earth system model sensitivity study. Journal of Geophysical Research: Atmospheres, 120(7), 2612–2623, doi:10.1002/2014jd022293.
Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres, 122(4), 2269–2288, doi:10.1002/2016jd026125.
Thiery, W. et al., 2017: Present-day irrigation mitigates heat extremes. Journal of Geophysical Research: Atmospheres, 122(3), 1403–1422, doi:10.1002/2016jd025740. ↩ - Mueller, B. and S.I. Seneviratne, 2012: Hot days induced by precipitation deficits at the global scale. Proceedings of the National Academy of Sciences, 109(31), 12398–12403, doi:10.1073/pnas.1204330109.
Seneviratne, S.I. et al., 2013: Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophysical Research Letters, 40(19), 5212–5217, doi:10.1002/grl.50956. ↩ - Davin, E.L., S.I. Seneviratne, P. Ciais, A. Olioso, and T. Wang, 2014: Preferential cooling of hot extremes from cropland albedo management. Proceedings of the National Academy of Sciences, 111(27), 9757–9761, doi:10.1073/pnas.1317323111. ↩
- Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres, 122(4), 2269–2288, doi:10.1002/2016jd026125.
Seneviratne, S.I. et al., 2018a: Land radiative management as contributor to regional-scale climate adaptation and mitigation. Nature Geoscience, 11, 88–96, doi:10.1038/s41561-017-0057-5.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩ - Schlenker, W. and M.J. Roberts, 2009: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594–15598, doi:10.1073/pnas.0906865106.
van der Velde, M., F.N. Tubiello, A. Vrieling, and F. Bouraoui, 2012: Impacts of extreme weather on wheat and maize in France: evaluating regional crop simulations against observed data. Climatic Change, 113(3–4), 751–765, doi:10.1007/s10584-011-0368-2.
Asseng, S. et al., 2013: Uncertainty in simulating wheat yields under climate change. Nature Climate Change, 3(9), 827–832, doi:10.1038/nclimate1916.
Butler, E.E. and P. Huybers, 2013: Adaptation of US maize to temperature variations. Nature Climate Change, 3(1), 68–72, doi:10.1038/nclimate1585.
Lobell, D.B. et al., 2014: Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest. Science, 344(6183), 516–519, doi:10.1126/science.1251423.
Asseng, S. et al., 2015: Rising temperatures reduce global wheat production. Nature Climate Change, 5(2), 143–147, doi:10.1038/nclimate2470. ↩ - Smith, P. et al., 2010: Competition for land. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1554), 2941–2957, doi:10.1098/rstb.2010.0127. ↩
- Pittelkow, C.M. et al., 2014: Productivity limits and potentials of the principles of conservation agriculture. Nature, 517(7534), 365–367, doi:10.1038/nature13809. ↩
- Hirsch, A.L., M. Wilhelm, E.L. Davin, W. Thiery, and S.I. Seneviratne, 2017: Can climate-effective land management reduce regional warming? Journal of Geophysical Research: Atmospheres, 122(4), 2269–2288, doi:10.1002/2016jd026125. ↩
- Bindoff, N.L. et al., 2013a: Detection and Attribution of Climate Change: from Global to Regional. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 867–952.
Boucher, O. et al., 2013b: Clouds and Aerosols. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 573–657.
Wu, P., N. Christidis, and P. Stott, 2013: Anthropogenic impact on Earth's hydrological cycle. Nature Climate Change, 3(9), 807–810, doi:10.1038/nclimate1932.
Sarojini, B.B., P.A. Stott, and E. Black, 2016: Detection and attribution of human influence on regional precipitation. Nature Climate Change, 6(7), 669–675, doi:10.1038/nclimate2976.
Wang, H., S.P. Xie, and Q. Liu, 2016: Comparison of climate response to anthropogenic aerosol versus greenhouse gas forcing: Distinct patterns. Journal of Climate, 29(14), 5175–5188, doi:10.1175/jcli-d-16-0106.1. ↩ - Kloster, S. et al., 2009: A GCM study of future climate response to aerosol pollution reductions. Climate Dynamics, 34(7–8), 1177–1194, doi:10.1007/s00382-009-0573-0.
Acosta Navarro, J. et al., 2017: Future Response of Temperature and Precipitation to Reduced Aerosol Emissions as Compared with Increased Greenhouse Gas Concentrations. Journal of Climate, 30, 939–954, doi:10.1175/jcli-d-16-0466.1. ↩ - Wang, H., S.P. Xie, and Q. Liu, 2016: Comparison of climate response to anthropogenic aerosol versus greenhouse gas forcing: Distinct patterns. Journal of Climate, 29(14), 5175–5188, doi:10.1175/jcli-d-16-0106.1. ↩
- Wang, Z. et al., 2017: Scenario dependence of future changes in climate extremes under 1.5°C and 2°C global warming.. Scientific Reports, 7, 46432, doi:10.1038/srep46432. ↩
- Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
Pierrehumbert, R.T., 2014: Short-Lived Climate Pollution. Annual Review of Earth and Planetary Sciences, 42(1), 341–379, doi:10.1146/annurev-earth-060313-054843. ↩ - Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740. ↩
- Shindell, D.T. et al., 2017: A climate policy pathway for near- and long-term benefits. Science, 356(6337), 493–494, doi:10.1126/science.aak9521. ↩
- Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570. ↩
- Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570. ↩
- Myhre, G. et al., 2013: Anthropogenic and natural radiative forcing. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 658–740.
Wang, B., H.H. Shugart, and M.T. Lerdau, 2017: Sensitivity of global greenhouse gas budgets to tropospheric ozone pollution mediated by the biosphere. Environmental Research Letters, 12(8), 084001, doi:10.1088/1748-9326/aa7885. ↩ - Wang, B., H.H. Shugart, and M.T. Lerdau, 2017: Sensitivity of global greenhouse gas budgets to tropospheric ozone pollution mediated by the biosphere. Environmental Research Letters, 12(8), 084001, doi:10.1088/1748-9326/aa7885. ↩
- Ciais, P. et al., 2013: Carbon and Other Biogeochemical Cycles. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 465–570.
Mahowald, N.M. et al., 2017b: Aerosol Deposition Impacts on Land and Ocean Carbon Cycles. Current Climate Change Reports, 3(1), 16–31, doi:10.1007/s40641-017-0056-z. ↩ - Forseth, I., 2010: Terrestrial Biomes. Nature Education Knowledge, 3(10), 11, http://www.nature.com/scitable/knowledge/library/terrestrial-biomes-13236757. ↩
- Smith, P. et al., 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 811–922. ↩
- Clarke, L.E. et al., 2014: Assessing Transformation Pathways. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 413–510. ↩
- Paustian, K. et al., 2006: Introduction. In: 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme: Vol. 4 [Eggleston, H.S., L. Buendia, K. Miwa, T. Ngara, and K. Tanabe (eds.)]. IGES, Japan, pp. 1–21.
Griscom, B.W. et al., 2017: Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645–11650, doi:10.1073/pnas.1710465114.
Houghton, R.A. and A.A. Nassikas, 2018: Negative emissions from stopping deforestation and forest degradation, globally. Global Change Biology, 24(1), 350–359, doi:10.1111/gcb.13876. ↩ - Smith, P. et al., 2015: Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6(1), 42–50, doi:10.1038/nclimate2870.
van Vuuren, D.P. et al., 2016: Carbon budgets and energy transition pathways. Environmental Research Letters, 11(7), 075002, doi:10.1088/1748-9326/11/7/075002. ↩ - Rogelj, J. et al., 2015: Energy system transformations for limiting end-of-century warming to below 1.5°C. Nature Climate Change, 5(6), 519–527, doi:10.1038/nclimate2572.
Holz, C., L. Siegel, E.B. Johnston, A.D. Jones, and J. Sterman, 2017: Ratcheting Ambition to Limit Warming to 1.5°C – Trade-offs Between Emission Reductions and Carbon Dioxide Removal. Environmental Research Letters, doi:10.1088/1748-9326/aac0c1.
Kriegler, E. et al., 2017: Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century. Global Environmental Change, 42, 297–315, doi:10.1016/j.gloenvcha.2016.05.015.
Fuss, S. et al., 2018: Negative emissions-Part 2: Costs, potentials and side effects. Environmental Research Letters, 13(6), 063002, doi:10.1088/1748-9326/aabf9f.
van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nature Climate Change, 8(5), 391–397, doi:10.1038/s41558-018-0119-8. ↩ - Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environmental Research Letters, 11(8), 085001, doi:10.1088/1748-9326/11/8/085001. ↩
- Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩
- Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison. Global Change Biology, 22(12), 3967–3983, doi:10.1111/gcb.13337.
Alexander, P. et al., 2017: Assessing uncertainties in land cover projections. Global Change Biology, 23(2), 767–781, doi:10.1111/gcb.13447.
Popp, A. et al., 2017: Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42, 331–345, doi:10.1016/j.gloenvcha.2016.10.002.
Seneviratne, S.I. et al., 2018c: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160450, doi:10.1098/rsta.2016.0450. ↩ - Bonsch, M. et al., 2016: Trade-offs between land and water requirements for large-scale bioenergy production. GCB Bioenergy, 8(1), 11–24, doi:10.1111/gcbb.12226.
Obersteiner, M. et al., 2016: Assessing the land resource-food price nexus of the Sustainable Development Goals. Science Advances, 2(9), e1501499, doi:10.1126/sciadv.1501499.
Bertram, C. et al., 2018: Targeted policies can compensate most of the increased sustainability risks in 1.5°C mitigation scenarios. Environmental Research Letters, 13(6), 064038, doi:10.1088/1748-9326/aac3ec.
Humpenöder, F. et al., 2018: Large-scale bioenergy production: how to resolve sustainability trade-offs? Environmental Research Letters, 13(2), 024011, doi:10.1088/1748-9326/aa9e3b. ↩ - Woolf, D., J.E. Amonette, F.A. Street-Perrott, J. Lehmann, and S. Joseph, 2010: Sustainable biochar to mitigate global climate change. Nature Communications, 1(5), 56, doi:10.1038/ncomms1053.
Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Global Change Biology, 22(3), 1315–1324, doi:10.1111/gcb.13178. ↩ - Boucher, O. et al., 2013a: Rethinking climate engineering categorization in the context of climate change mitigation and adaptation. Wiley Interdisciplinary Reviews: Climate Change, 5(1), 23–35, doi:10.1002/wcc.261.
Fuss, S. et al., 2014: Betting on negative emissions. Nature Climate Change, 4(10), 850–853, doi:10.1038/nclimate2392.
Anderson, K. and G. Peters, 2016: The trouble with negative emissions. Science, 354(6309), 182–183, doi:10.1126/science.aah4567.
Vaughan, N.E. and C. Gough, 2016: Expert assessment concludes negative emissions scenarios may not deliver. Environmental Research Letters, 11(9), 095003, doi:10.1088/1748-9326/11/9/095003.
Williamson, P., 2016: Emissions reduction: Scrutinize CO2 removal methods. Nature, 530(7589), 153–155, doi:10.1038/530153a.
Minx, J.C., W.F. Lamb, M.W. Callaghan, L. Bornmann, and S. Fuss, 2017: Fast growing research on negative emissions. Environmental Research Letters, 12(3), 035007, doi:10.1088/1748-9326/aa5ee5.
Fuss, S. et al., 2018: Negative emissions-Part 2: Costs, potentials and side effects. Environmental Research Letters, 13(6), 063002, doi:10.1088/1748-9326/aabf9f.
Minx, J.C. et al., 2018: Negative emissions–Part 1: Research landscape and synthesis. Environmental Research Letters, 13(6), 063001, doi:10.1088/1748-9326/aabf9b.
Nemet, G.F. et al., 2018: Negative emissions-Part 3: Innovation and upscaling. Environmental Research Letters, 13(6), 063003, doi:10.1088/1748-9326/aabff4.
Strefler, J. et al., 2018: Between Scylla and Charybdis: Delayed mitigation narrows the passage between large-scale CDR and high costs. Environmental Research Letters, 13(044015), 1–6, doi:10.1088/1748-9326/aab2ba.
Vaughan, N.E. et al., 2018: Evaluating the use of biomass energy with carbon capture and storage in low emission scenarios. Environmental Research Letters, 13(4), 044014, doi:10.1088/1748-9326/aaaa02. ↩ - Krey, V., G. Luderer, L. Clarke, and E. Kriegler, 2014: Getting from here to there – energy technology transformation pathways in the EMF27 scenarios. Climatic Change, 123(3), 369–382, doi:10.1007/s10584-013-0947-5.
Bauer, N. et al., 2018: Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison. Climatic Change, 1–16, doi:10.1007/s10584-018-2226-y.
Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°C. Nature Climate Change, 8(4), 325–332, doi:10.1038/s41558-018-0091-3. ↩ - Strefler, J. et al., 2018: Between Scylla and Charybdis: Delayed mitigation narrows the passage between large-scale CDR and high costs. Environmental Research Letters, 13(044015), 1–6, doi:10.1088/1748-9326/aab2ba. ↩
- Bauer, N. et al., 2018: Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison. Climatic Change, 1–16, doi:10.1007/s10584-018-2226-y.
Grubler, A. et al., 2018: A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative emission technologies. Nature Energy, 3(6), 515–527, doi:10.1038/s41560-018-0172-6.
van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nature Climate Change, 8(5), 391–397, doi:10.1038/s41558-018-0119-8. ↩ - Krey, V., G. Luderer, L. Clarke, and E. Kriegler, 2014: Getting from here to there – energy technology transformation pathways in the EMF27 scenarios. Climatic Change, 123(3), 369–382, doi:10.1007/s10584-013-0947-5.
Krause, A. et al., 2017: Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators. Biogeosciences, 14(21), 4829–4850, doi:10.5194/bg-14-4829-2017.
Bauer, N. et al., 2018: Global energy sector emission reductions and bioenergy use: overview of the bioenergy demand phase of the EMF-33 model comparison. Climatic Change, 1–16, doi:10.1007/s10584-018-2226-y.
Rogelj, J. et al., 2018: Scenarios towards limiting global mean temperature increase below 1.5°C. Nature Climate Change, 8(4), 325–332, doi:10.1038/s41558-018-0091-3. ↩ - Grubler, A. et al., 2018: A low energy demand scenario for meeting the 1.5°C target and sustainable development goals without negative emission technologies. Nature Energy, 3(6), 515–527, doi:10.1038/s41560-018-0172-6. ↩
- Robledo-Abad, C. et al., 2017: Bioenergy production and sustainable development: science base for policymaking remains limited. GCB Bioenergy, 9(3), 541–556, doi:10.1111/gcbb.12338. ↩
- Jaiswal, D. et al., 2017: Brazilian sugarcane ethanol as an expandable green alternative to crude oil use. Nature Climate Change, 7(11), 788–792, doi:10.1038/nclimate3410. ↩
- Dale, V.H. et al., 2017: Status and prospects for renewable energy using wood pellets from the southeastern United States. GCB Bioenergy, 9(8), 1296–1305, doi:10.1111/gcbb.12445. ↩
- Kline, K.L. et al., 2017: Reconciling food security and bioenergy: priorities for action. GCB Bioenergy, 9(3), 557–576, doi:10.1111/gcbb.12366. ↩
- Slade, R., A. Bauen, and R. Gross, 2014: Global bioenergy resources. Nature Climate Change, 4(2), 99–105, doi:10.1038/nclimate2097. ↩
- Heck, V., D. Gerten, W. Lucht, and A. Popp, 2018: Biomass-based negative emissions difficult to reconcile with planetary boundaries. Nature Climate Change, 8(2), 151–155, doi:10.1038/s41558-017-0064-y. ↩
- Schulze, E.-D., C. Körner, B.E. Law, H. Haberl, and S. Luyssaert, 2012: Large-scale bioenergy from additional harvest of forest biomass is neither sustainable nor greenhouse gas neutral. GCB Bioenergy, 4(6), 611–616, doi:10.1111/j.1757-1707.2012.01169.x.
Jonker, J.G.G., M. Junginger, and A. Faaij, 2014: Carbon payback period and carbon offset parity point of wood pellet production in the South-eastern United States. GCB Bioenergy, 6(4), 371–389, doi:10.1111/gcbb.12056.
Booth, M.S., 2018: Not carbon neutral: Assessing the net emissions impact of residues burned for bioenergy. Environmental Research Letters, 13(3), 035001, doi:10.1088/1748-9326/aaac88.
Sterman, J.D., L. Siegel, and J.N. Rooney-Varga, 2018: Does replacing coal with wood lower CO2 emissions? Dynamic lifecycle analysis of wood bioenergy. Environmental Research Letters, 13(1), 015007, doi:10.1088/1748-9326/aaa512. ↩ - Cunningham, S.C., 2015: Balancing the environmental benefits of reforestation in agricultural regions. Perspectives in Plant Ecology, Evolution and Systematics, 17(4), 301–317, doi:10.1016/j.ppees.2015.06.001.
Naudts, K. et al., 2016: Europe's forest management did not mitigate climate warming. Science, 351(6273), 597–600, doi:10.1126/science.aad7270.
Smith, P., J. Price, A. Molotoks, R. Warren, and Y. Malhi, 2018: Impacts on terrestrial biodiversity of moving from a 2°C to a 1.5°C target. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160456, doi:10.1098/rsta.2016.0456. ↩ - de Jong, J. et al., 2014: Consequences of an increased extraction of forest biofuel in Sweden – A synthesis from the biofuel research programme 2007–2011, supported by Swedish Energy Agency. Summary of the synthesis report. ER 2014:09, Swedish Energy Agency, Eskilstuna, Sweden, 37 pp. ↩
- Griscom, B.W. et al., 2017: Natural climate solutions. Proceedings of the National Academy of Sciences, 114(44), 11645–11650, doi:10.1073/pnas.1710465114. ↩
- Fuss, S. et al., 2018: Negative emissions-Part 2: Costs, potentials and side effects. Environmental Research Letters, 13(6), 063002, doi:10.1088/1748-9326/aabf9f. ↩
- Smith, P. et al., 2015: Biophysical and economic limits to negative CO2 emissions. Nature Climate Change, 6(1), 42–50, doi:10.1038/nclimate2870. ↩
- Lenton, T.M., 2012: Arctic Climate Tipping Points. Ambio, 41(1, SI), 10–22, doi:10.1007/s13280-011-0221-x. ↩
- Schröder, D. and W.M. Connolley, 2007: Impact of instantaneous sea ice removal in a coupled general circulation model. Geophysical Research Letters, 34(14), L14502, doi:10.1029/2007gl030253.
Sedláček, J., O. Martius, and R. Knutti, 2011: Influence of subtropical and polar sea-surface temperature anomalies on temperatures in Eurasia. Geophysical Research Letters, 38(12), L12803, doi:10.1029/2011gl047764.
Tietsche, S., D. Notz, J.H. Jungclaus, and J. Marotzke, 2011: Recovery mechanisms of Arctic summer sea ice. Geophysical Research Letters, 38(2), L02707, doi:10.1029/2010gl045698. ↩ - Armour, K.C., I. Eisenman, E. Blanchard-Wrigglesworth, K.E. McCusker, and C.M. Bitz, 2011: The reversibility of sea ice loss in a state-of-the-art climate model. Geophysical Research Letters, 38(16), L16705, doi:10.1029/2011gl048739.
Boucher, O. et al., 2012: Reversibility in an Earth System model in response to CO2 concentration changes. Environmental Research Letters, 7(2), 024013, doi:10.1088/1748-9326/7/2/024013.
Ridley, J.K., J.A. Lowe, and H.T. Hewitt, 2012: How reversible is sea ice loss? The Cryosphere, 6(1), 193–198, doi:10.5194/tc-6-193-2012. ↩ - Clark, P.U. et al., 2016: Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nature Climate Change, 6(4), 360–369, doi:10.1038/nclimate2923. ↩
- Clark, P.U. et al., 2016: Consequences of twenty-first-century policy for multi-millennial climate and sea-level change. Nature Climate Change, 6(4), 360–369, doi:10.1038/nclimate2923. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Bouttes, N., J.M. Gregory, and J.A. Lowe, 2013: The Reversibility of Sea Level Rise. Journal of Climate, 26(8), 2502–2513, doi:10.1175/jcli-d-12-00285.1.
Mengel, M., A. Nauels, J. Rogelj, and C.F. Schleussner, 2018: Committed sea-level rise under the Paris Agreement and the legacy of delayed mitigation action. Nature Communications, 9(1), 1–10, doi:10.1038/s41467-018-02985-8. ↩ - Marzeion, B., G. Kaser, F. Maussion, and N. Champollion, 2018: Limited influence of climate change mitigation on short-term glacier mass loss. Nature Climate Change, 8, 305–308, doi:10.1038/s41558-018-0093-1. ↩
- Bouttes, N., J.M. Gregory, and J.A. Lowe, 2013: The Reversibility of Sea Level Rise. Journal of Climate, 26(8), 2502–2513, doi:10.1175/jcli-d-12-00285.1.
Zickfeld, K. et al., 2013: Long-Term climate change commitment and reversibility: An EMIC intercomparison. Journal of Climate, 26(16), 5782–5809, doi:10.1175/jcli-d-12-00584.1. ↩ - Levermann, A. et al., 2013: The multimillennial sea-level commitment of global warming. Proceedings of the National Academy of Sciences, 110(34), 13745–13750, doi:10.1073/pnas.1219414110. ↩
- Gregory, J.M. and P. Huybrechts, 2006: Ice-sheet contributions to future sea-level change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1844), 1709–1731, doi:10.1098/rsta.2006.1796. ↩
- Robinson, A., R. Calov, and A. Ganopolski, 2012: Multistability and critical thresholds of the Greenland ice sheet. Nature Climate Change, 2(6), 429–432, doi:10.1038/nclimate1449. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- Schoof, C., 2007: Ice sheet grounding line dynamics: Steady states, stability, and hysteresis. Journal of Geophysical Research, 112(F3), F03S28, doi:10.1029/2006jf000664. ↩
- DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596), 591–7, doi:10.1038/nature17145. ↩
- Golledge, N.R. et al., 2015: The multi-millennial Antarctic commitment to future sea-level rise. Nature, 526(7573), 421–425, doi:10.1038/nature15706.
DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596), 591–7, doi:10.1038/nature17145. ↩ - Schoof, C., 2007: Ice sheet grounding line dynamics: Steady states, stability, and hysteresis. Journal of Geophysical Research, 112(F3), F03S28, doi:10.1029/2006jf000664. ↩
- Church, J. et al., 2013: Sea level change. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1137–1216. ↩
- DeConto, R.M. and D. Pollard, 2016: Contribution of Antarctica to past and future sea-level rise. Nature, 531(7596), 591–7, doi:10.1038/nature17145. ↩
- Golledge, N.R. et al., 2015: The multi-millennial Antarctic commitment to future sea-level rise. Nature, 526(7573), 421–425, doi:10.1038/nature15706. ↩
- Slater, A.G. and D.M. Lawrence, 2013: Diagnosing present and future permafrost from climate models. Journal of Climate, 26(15), 5608–5623, doi:10.1175/jcli-d-12-00341.1. ↩
- Chadburn, S.E. et al., 2017: An observation-based constraint on permafrost loss as a function of global warming. Nature Climate Change, 1–6, doi:10.1038/nclimate3262. ↩
- Collins, M. et al., 2013: Long-term Climate Change: Projections, Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1029–1136. ↩
- Seneviratne, S.I. et al., 2018b: The many possible climates from the Paris Agreement's aim of 1.5°C warming. Nature, 558(7708), 41–49, doi:10.1038/s41586-018-0181-4. ↩
- Seneviratne, S.I., M.G. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529(7587), 477–83, doi:10.1038/nature16542. ↩
- Loarie, S.R. et al., 2009: The velocity of climate change. Nature, 462(7276), 1052–1055, doi:10.1038/nature08649.
LoPresti, A. et al., 2015: Rate and velocity of climate change caused by cumulative carbon emissions. Environmental Research Letters, 10(9), 095001, doi:10.1088/1748-9326/10/9/095001. ↩ - Seneviratne, S.I. et al., 2018b: The many possible climates from the Paris Agreement's aim of 1.5°C warming. Nature, 558(7708), 41–49, doi:10.1038/s41586-018-0181-4. ↩
- Millar, R.J. et al., 2017: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nature Geoscience, 10(10), 741–747, doi:10.1038/ngeo3031. ↩
- Haustein, K. et al., 2017: A real-time Global Warming Index. Scientific Reports, 7(1), 15417, doi:10.1038/s41598-017-14828-5.
Seneviratne, S.I. et al., 2018b: The many possible climates from the Paris Agreement's aim of 1.5°C warming. Nature, 558(7708), 41–49, doi:10.1038/s41586-018-0181-4. ↩ - Baker, H.S. et al., 2018: Higher CO2 concentrations increase extreme event risk in a 1.5°C world. Nature Climate Change, 8(7), 604–608, doi:10.1038/s41558-018-0190-1. ↩
- Matthews, D. and K. Caldeira, 2008: Stabilizing climate requires near-zero emissions. Geophysical Research Letters, 35(4), L04705, doi:10.1029/2007gl032388.
Solomon, S., G.-K. Plattner, R. Knutti, and P. Friedlingstein, 2009: Irreversible climate change due to carbon dioxide emissions. Proceedings of the National Academy of Sciences, 106(6), 1704–1709, doi:10.1073/pnas.0812721106. ↩ - Obersteiner, M. et al., 2018: How to spend a dwindling greenhouse gas budget. Nature Climate Change, 8(1), 7–10, doi:10.1038/s41558-017-0045-1.
van Vuuren, D.P. et al., 2018: Alternative pathways to the 1.5°C target reduce the need for negative emission technologies. Nature Climate Change, 8(5), 391–397, doi:10.1038/s41558-018-0119-8. ↩ - Meinshausen, M., S.C.B. Raper, and T.M.L. Wigley, 2011: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration. Atmospheric Chemistry and Physics, 11(4), 1417–1456, doi:10.5194/acp-11-1417-2011. ↩
- Clarke, L.E. et al., 2014: Assessing Transformation Pathways. In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel, and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 413–510. ↩
- Seneviratne, S.I. et al., 2018b: The many possible climates from the Paris Agreement's aim of 1.5°C warming. Nature, 558(7708), 41–49, doi:10.1038/s41586-018-0181-4. ↩
- Meehl, G.A. et al., 2007: Global Climate Projections. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor, and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 747–846. ↩
- Seneviratne, S.I. et al., 2018b: The many possible climates from the Paris Agreement's aim of 1.5°C warming. Nature, 558(7708), 41–49, doi:10.1038/s41586-018-0181-4. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032. ↩
- Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485. ↩
- Liu, W. et al., 2018: Global drought and severe drought-affected populations in 1.5 and 2°C warmer worlds. Earth System Dynamics, 9(1), 267–283, doi:10.5194/esd-9-267-2018. ↩
- Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646. ↩
- Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environmental Research Letters, 8(4), 044018, doi:10.1088/1748-9326/8/4/044018. ↩
- Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738. ↩
- Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87. ↩
- Yokoki, H., M.Tamura, M.Yotsukuri, N.Kumano, and Y.Kuwahara, 2018: Global distribution of projected sea level changes using multiple climate models and economic assessment of sea level rise. CLIVAR Exchanges: A joint special edition on Sea Level Rise, 36–39, doi:10.5065/d6445k82. ↩
- Gerten, D. et al., 2013: Asynchronous exposure to global warming: freshwater resources and terrestrial ecosystems. Environmental Research Letters, 8(3), 034032, doi:10.1088/1748-9326/8/3/034032.
Alfieri, L. et al., 2017: Global projections of river flood risk in a warmer world. Earth's Future, 5(2), 171–182, doi:10.1002/2016ef000485.
Liu, W. et al., 2018: Global drought and severe drought-affected populations in 1.5 and 2°C warmer worlds. Earth System Dynamics, 9(1), 267–283, doi:10.5194/esd-9-267-2018.
Warren, R., J. Price, E. Graham, N. Forstenhaeusler, and J. VanDerWal, 2018a: The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5°C rather than 2°C. Science, 360(6390), 791–795, doi:10.1126/science.aar3646.
Warszawski, L. et al., 2013: A multi-model analysis of risk of ecosystem shifts under climate change. Environmental Research Letters, 8(4), 044018, doi:10.1088/1748-9326/8/4/044018.
Brown, S. et al., 2018a: Quantifying Land and People Exposed to Sea-Level Rise with No Mitigation and 1.5°C and 2.0°C Rise in Global Temperatures to Year 2300. Earth's Future, 6(3), 583–600, doi:10.1002/2017ef000738.
Rasmussen, D.J. et al., 2018: Extreme sea level implications of 1.5°C, 2.0°C, and 2.5°C temperature stabilization targets in the 21st and 22nd centuries. Environmental Research Letters, 13(3), 034040, doi:10.1088/1748-9326/aaac87.
Yokoki, H., M.Tamura, M.Yotsukuri, N.Kumano, and Y.Kuwahara, 2018: Global distribution of projected sea level changes using multiple climate models and economic assessment of sea level rise. CLIVAR Exchanges: A joint special edition on Sea Level Rise, 36–39, doi:10.5065/d6445k82.
Nicholls, R.J. et al., 2018: Stabilization of global temperature at 1.5°C and 2.0°C: implications for coastal areas. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2119), 20160448, doi:10.1098/rsta.2016.0448. ↩
Earth Science Guided Reading and Study Workbook Chapter 3
Source: https://www.ipcc.ch/sr15/chapter/chapter-3/
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