Editing IPCC:AR6/WGII/Chapter-16 (section)

=== 16.6.3 Global Reasons for Concern ===

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In this section, we present the results of the expert elicitation in the form of the burning embers diagram, alongside a description of the recent literature and scientific evidence for each of the RFCs in turn. The consensus transition values are illustrated in Figure 16. 15 , an updated version of the burning embers diagram that describes the additional risk due to climate change for each RFC when a temperature level is reached and then sustained or exceeded (Table SM16.20 presents the consensus values of the transition range and median estimate in terms of global warming level by risk level for each of the five RFC embers). The shading of each ember provides a qualitative indication of the increase in risk with temperature, and we retain the colour scheme employed in the most recent versions of this figure, where white, yellow, red and purple indicate undetectable, moderate, high and very high additional risk, respectively. These transitions were assessed under conditions of low to no adaptation compared with today, in accordance with definitions provided in 16.3 (i.e., adaptation consists of fragmented, localised, incremental adjustments to existing practices), though the effect of adaptation on risk for individual RFCs and related literature is discussed further below.

The following subsections present the expert assessment and judgements made during the elicitation process to identify consensus transition values for each RFC. The description of these transitions is further extended with additional references to findings from underlying chapters in this report, and reviewed by [https://www.ipcc.ch/report/ar6/wg2/chapter/chapter-16 Chapter 16] authors as part of independent appraisal. No changes were made to the transition values assessed through the expert elicitation.

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==== 16.6.3.1 Unique and Threatened Systems (RFC1) ====

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This RFC addresses the potential for increased damage to or irreversible loss of a wide range of physical, biological and human systems that are unique (i.e., restricted to relatively narrow geographical ranges and have high endemism or other distinctive properties) and are threatened by future changes in climate ( [[#Smith--2001|Smith et al., 2001]] ; [[#Smith--2009|Smith et al., 2009]] ; [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ). The specific examples of such systems given in previous IPCC assessment reports has remained broadly consistent, with AR4 including ‘coral reefs, tropical glaciers, endangered species, unique ecosystems, biodiversity hotspots, small island states, and indigenous communities’ (Smith 2009), AR5 including ‘a wide range of physical, biological, and human systems that are restricted to relatively narrow geographical ranges’ and ‘are threatened by future changes in climate’ ( [[#Smith--2001|Smith et al., 2001]] ), and SR15 [[IPCC:Wg2:Chapter:Chapter-3|Chapter 3]] including ‘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’. In this cycle, we retain the definition used in SR15 as most explicit and inclusive of the previous definitions.

AR5 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ) assessed the transition from undetectable to moderate risk for RFC1 to lie below recent global temperatures (1986–2005, which at the time was considered to correspond to a global warming level of 0.6°C above pre-industrial levels; AR6 WGI now considers this time period of 1986–2005 to correspond to a global warming or approximately 0.7°C). At that time, there was at least ''medium confidence'' in attribution of a major role for climate change for impacts on at least one each of ecosystems, physical systems and human systems within this RFC. SR15 [[IPCC:Wg2:Chapter:Chapter-3#3.5.2|Section 3.5.2.1]] ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ), concurred with ''high confidence'' that the transition to moderate risk had already occurred before the time of writing.

The transitions here are informed by these assessments, along with the assessment in [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] on species high extinction risk and on ecosystem transitions. It also draws substantially from information in [https://www.ipcc.ch/chapter/cross-chapter-paper-1 Cross-Chapter Paper 1] and Table SM16.22 on risks to unique and threatened biological systems. Some unique and threatened systems, such as coral reefs and sea-ice-dependent ecosystems, were already showing attributable impacts with ''high confidence'' (see Table SM16.22 , [https://www.ipcc.ch/chapter/cross-chapter-paper-1 Cross-Chapter Paper 1] and Chapter 2) based on data collected in the mid to latter 20th century, when global warming of 0.5°C above pre-industrial levels had taken place, as noted already in AR3. In this AR6 assessment, the temperature range for the transition from undetectable to moderate risk is still located at a median value of 0.5°C above pre-industrial levels, with ''very high confidence.'' Since impacts were first detected in coral reef systems in the 1980s when warming of ~0.4°C of global warming had occurred (SR15 Chapter 3), this provides the temperature at which the transition begins. The September Arctic sea ice volume has declined by 55–65% between 1979 and 2010 (AR6 WGI, [[#Schweiger--2019|Schweiger et al., 2019]] ) as global warming increased from around 0.36°C in 1979 to around 0.9°C in 2010. These provide evidence of a start to the transition from undetectable to moderate risk at 0.4°C above pre-industrial levels. Recent evidence of observed impacts on mountaintop ecosystems and sea-ice-dependent species, and of range shifts in multiple ecosystems during 1990–2000, which AR6 WGI now assesses as corresponding to a global warming of 0.69°C (see WGI AR6 Cross-Chapter Box 2.3, Figure 1, [[#Gulev--2021|Gulev et al., 2021]] ), provides evidence for an upper limit to this transition of 0.7°C with ''very high confidence'' . Overall, the transition is located at a median of 0.5°C with lower and upper limits of 0.4°C and 0.7°C, respectively, with ''very high confidence.''

AR5 assessed the transition from moderate to high risk to lie around 1°C above 1986–2005 levels (which corresponded at that time to 1.6°C above pre-industrial levels but has been reassessed by AR6 WGI to correspond to 1.7°C) to reflect projected ‘increasing risk to unique and threatened systems, including Arctic sea ice and coral reefs, as well as threatened species as temperature increases over this range.’ SR15 relocated the transition slightly from 1.6°C to 1.5°C, owing to increased literature projecting the effects of climate change upon Arctic sea ice and new literature assessing projected impacts of climate change on biodiversity at 1.5°C warming.

In this AR6 assessment, the transition from moderate to high is based on the high level of observed impacts, and the areas projected to begin undergoing major transformations by 1.5°C (see Cross-Chapter Paper 1, [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] and SR15 ( [[#IPCC--2018a|IPCC, 2018a]] )). A substantial number of unique and threatened systems are assessed to be in a high risk state owing to the influence of anthropogenic climate change by the 2000–2010 period, when global warming had reached approximately 0.85°C (range 0.7–1°C) (see WGI AR6 Cross-Chapter Box 2.3, [[#Gulev--2021|Gulev et al., 2021]] ) using the 1995–2014 figure as a proxy for 2000–2010).

The most prominent example of a system assessed to be already in a high risk state is that of coral reefs, which are already degrading rapidly. Observed impacts on coral reefs increased significantly during 2014–2017 (Table SM16.22 , corresponding to a global warming of about 0.9°C). This includes mass bleaching in the Indian Ocean in 1998, 2010, 2015 and 2016 when bleaching intensity exceeded 20% in surveyed locations in the western Indian Ocean, eastern Indian Ocean and western Indonesia. In the tropical Pacific Ocean, climate-driven mass bleaching was reported in all countries in the region, with most bleaching reports coinciding with 2014–2017 marine heatwaves. Fifty percent of coral within shallow-water reefs of the northern and central two-thirds of the Great Barrier Reef were killed in 2015/2016. Subsequent coral recruitment in 2018 was reduced to only 11% of the long-term average, representing an unprecedented shift in the ecology of the northern and middle sections of the reef system to a highly degraded state. A second key example are sea-ice-dependent systems in the Arctic. During August to October of 2010–2019, corresponding to a global warming of about 0.9°C, average Arctic sea ice area has declined in area by 25% relative to 1979–1988 ( ''high confidence'' , AR6 WGI, Figure 9.13). September Arctic sea ice volume has declined by about 72% between 1979 and 2016, with the latter deemed a conservative estimate (AR6 WGI, [[#Schweiger--2019|Schweiger et al., 2019]] ).

Other important examples of observed impacts on unique ecosystems that indicate that risks are already at a high level (Table SM16.22) include mass tree mortalities, now well recorded in multiple unique forest and woodland ecosystems around the world. Sections 2.4.3.3 and 2.4.5 report that, between 1945 and 2007, drought-induced tree mortality (sometimes associated with insect damage and wildfire) has caused the mortality of up to 20% of trees in western North America, the African Sahel, and North Africa, linked to a warming of 0.3–0.9°C above pre-industrial levels, and is implicated in more than 100 other cases of drought-induced tree mortality in Africa, Asia, Australia, Europe, and North and South America ( ''high confidence'' ). Species in biodiversity hotspots already show changes in response to climate change (CCP1, ''high confidence'' ). [[#Román-Palacios--2020|Román-Palacios and Wiens (2020)]] attribute local extinctions of several taxonomic groups between the latter 20th century and 2003–2012, (corresponding to warming of less than 0.85°C) to climate-change-related temperature extremes for up to 44% (0–75%) of species. Widespread declines of up to 35% in the species richness of the unique pollinator group, bumble bees, between 1901–1974 and 2000–2014 are also attributed to climate change, via increasing exceedance of their thermal tolerance limits across Europe and North America ( [[#Soroye--2020|Soroye et al., 2020]] ). The first extinctions attributed to climate change have been now detected with the present 1.2°C warming, including that of the Bramble Cay melomys ( ''Melomys rubicola'' ), a sub-species of the lemuroid ringtail possum ( ''Hemibelideus lemuroides'' ), and golden toad ( ''Incilius periglenes'' ) (Chapter 2). An increasing frequency or unprecedented occurrence of mass animal mortality due to climate-change-enhanced heatwaves has also been observed in recent years on more than one continent, including temperature-vulnerable terrestrial birds and mammals in South Africa and Australia ( [[#Ratnayake--2019|Ratnayake et al., 2019]] ; [[#McKechnie--2021|McKechnie et al., 2021]] ). There have also been 90% declines in sea-ice-dependent species such as sea lions and penguins in the Antarctic (Table SM16.22 ). A strong effect of climate change on the observed contraction of ranges of polar fish species and strong expansion of ranges of arcto-boreal or boreal fish was observed between 2004 and 2012 (Frainer et al.., 2017). Even if current human-driven habitat loss is excluded, many hotspots are projected to cease to be refugia (i.e., to remain climatically suitable for &gt;75% of the species they contain which have been modelled), at 1.0–1.5°C (Cross-Chapter Paper 1).

Based on observed and modelled impacts to unique and threatened systems, including in particular coral reefs, sea-ice-dependent systems and biodiversity hotspots, AR6 assesses that the transition to high risks for RFC1 have already occurred at a median level of 0.9°C, with a lower bound at 0.7°C and an upper bound at the present-day level of global warming of 1.2°C ( [[#WMO--2020|WMO, 2020]] ) ( ''very high confidence'' ).

Identification of the transition to very high risk is associated by definition with the reaching of limits to natural and/or societal adaptation. Adaptation which occurs naturally is already included in the risk assessment, but experts also discussed the effect of additional human-planned adaptation in reducing risk levels in RFC1. This additional adaptation could help species to survive ''in situ'' despite a changing climate (for example, by reducing current anthropogenic stresses such as over-harvesting), or facilitate the ability of species to shift geographic range in response to changes in climate, and the potential benefits of nature-based solutions and restoration (see Cross-Chapter Box NATURAL, [[IPCC:Wg2:Chapter:Chapter-2#2.6.5.1|Section 2.6.5.1]] ).

When considering planned adaptation, the main option often considered in terrestrial ecosystems is the expansion of the protected area network, which is broadly beneficial in increasing the resilience of ecosystems to climate change (e.g., [[#Hannah--2020|Hannah et al., 2020]] ). However, this action is not effective if the unique and threatened systems in question reach a hard limit to adaptation (as in the case of the loss of Arctic summer sea ice, the submergence of a small island, the contraction and elimination of a species’ climatic niche from a mountaintop, or the degradation of a coral reef) ( [[#16.4|Section 16.4]] ). Furthermore, adaptation benefits deriving from restoration rapidly diminish with increasing temperature (Cross-Chapter Paper 1). One study quantifies how land management (in terms of protecting existing ecosystems or restoring lost ones) might reduce extinctions in biodiversity hotspots or globally significant terrestrial biodiversity areas more generally ( [[#Warren--2018b|Warren et al., 2018b]] ). While the latter suggests that substantial benefits can result globally in terrestrial systems, allowing less unique systems to persist at higher levels of warming but only under a high adaptation scenario in which globally applied terrestrial ecosystem restoration and protected area expansion takes place, this is less likely for many of the unique and threatened terrestrial systems which are more vulnerable than the globally significant biodiversity areas treated in that study (which excludes coral reefs and Arctic sea-ice-dependent systems). Such high levels of adaptation globally are likely infeasible owing to competition for land use with food production ( [[#Pörtner--2021|Pörtner et al., 2021]] ). Novel targeted adaptation interventions for coral reefs such as artificial upwelling and local radiation management show some promise for reducing the adverse effects of thermal stress and resulting coral bleaching ( [[#Condie--2021|Condie et al., 2021]] ), but are far from implementation ( [[#Sawall--2020|Sawall et al., 2020]] ; [[#Kleypas--2021|Kleypas et al., 2021]] ). Larger benefits in this RFC could theoretically accrue only if adaptation action became ubiquitous and extensive, which experts considered infeasible at the scales required. Small island communities are confronted by socio-ecological limits to adaptation well before 2100, especially those reliant on coral reef systems for their livelihoods, even for a low-emissions pathway (Chapter 3) ( ''high confidence'' ). At warming levels beyond 1.5°C, the potential to reach biophysical limits to adaptation due to limited water resources are reported for small islands ( ''medium confidence'' ) and unique systems dependent on glaciers and snowmelt (Chapter 4) ( ''medium confidence'' ).

AR5 assessed with ''high confidence'' that the transition from high to very high risks for RFC1 lies around 2°C above 1986–2005 levels (then considered to correspond to 2.6°C above pre-industrial levels) to reflect the very high risk to species and ecosystems projected to occur beyond that level as well as limited ability to adapt to impacts on coral reef systems and in Arctic sea-ice-dependent systems. Using the additional literature which became available on projected risks to Arctic sea ice, biodiversity and ecosystems at 1.5°C versus 2°C warming above pre-industrial levels, SR15 assessed that the transition from high to very high risks in RFC1 lay between 1.5°C and 2°C above pre-industrial levels.

In AR6, risks are considered to start to transition from high to very high risks above 1.2°C warming (present day, [[#WMO--2020|WMO, 2020]] ), with a median value of 1.5°C, owing in particular to the observation of a present-day onset of ecosystem degradation in coral reefs, which are projected in the SR15 report ‘to decline by a further 70–90% at 1.5°C ( ''very high confidence'' )’ . The literature for projected increases in risk to other unique and threatened systems and their limited ability to adapt above 2°C warming is substantial and robust, and the confidence level in very high risk remains high. At 2°C, 18% of 34,000 insects are projected to lose &gt;50% climatically determined geographic range, as compared with 6% at 1.5°C ( [[#Warren--2018a|Warren et al., 2018a]] ). The risk of species extinction increases with warming in all climate change projections, for all native species studied in biodiversity hotspots (Cross-Chapter Paper 1, ''high confidence'' ), being roughly threefold greater for endemic than more widespread species for global warming of 3°C above pre-industrial levels than 1.5°C) ( [[#Manes--2021|Manes et al., 2021]] , Cross-Chapter Paper 1) ( ''medium confidence'' ). The Arctic is projected to be practically ice free in September in some years for global warming of between 1.5°C and 2°C (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-9#9.3.1|Section 9.3.1.1]] , [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ), undermining the persistence of ice-dependent species such as polar bears, ringed seals and walrus ( [[#Meredith--2019|Meredith et al., 2019]] ), and adversely affecting Indigenous communities. Warming of 1.5°C is also assessed (Chapter 3) to reduce the habitability of small islands, due to the combined impacts of several key risks ( ''high confidence'' ). Hence, the transition from high to very high risk in these systems is assessed to occur with ''high confidence'' beginning at 1.2°C, passing through a median value of 1.5°C, and completing (i.e., reaching its upper bound) at 2°C warming.

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==== 16.6.3.2 Extreme Weather Events (RFC2) ====

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This RFC addresses the risks to human health, livelihoods, assets and ecosystems from extreme weather events such as heatwaves, heavy rain, drought and associated wildfires, and coastal flooding ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ). Previous assessments of this RFC have focused mainly on changes to the hazard component of the risk, using the projected increase in hazard as an indicator of higher risk. However, in AR6 an expanding (although still smaller) body of evidence now allows also incorporation of the exposure and/or vulnerability components of risk and, to a limited extent, their trends.

AR5 identified a transition from undetectable to moderate risk below ‘recent’ temperatures (i.e., during 1986–2005, which then corresponded to a global warming of 0.6°C above pre-industrial levels). SR15 [[IPCC:Wg2:Chapter:Chapter-3#3.5.2|Section 3.5.2.2]] ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ) concluded that differences of 0.5°C in global warming led to detectable changes in extreme weather and climate events on the global scale and for large regions. IPCC WGI AR6 [[IPCC:Wg2:Chapter:Chapter-11|Chapter 11]] confirms this assessment and concludes that ‘new evidence strengthens the conclusion from SR15 that even relatively small incremental increases in global warming (+0.5°C) cause statistically significant changes in extremes on the global scale and for large regions’. Substantial literature is available for comparisons at +1.5°C versus +2°C of global warming, but the conclusions are assessed to also apply at lower global warming levels and smaller increments of global warming given the identified linearity of regional responses of several extremes in relation to global warming ( [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ) and the identification of emergence of global signals in climate extremes for global warming levels as small as 0.1°C ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] , WGI AR6, Chapter 11, Figure 11.8; WGI Cross-Chapter Box 12.1). Further analyses are consistent with this assessment, based on model simulations ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Kirchmeier-Young--2019a|Kirchmeier-Young et al., 2019a]] ; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ) and observational evidence ( [[#Zwiers--2011|Zwiers et al., 2011]] ; [[#Dunn--2020|Dunn et al., 2020]] ). A global warming of +0.5°C above pre-industrial conditions corresponds approximately to climate conditions in the 1980s (Chapter 2, Figure 2.11), a time frame at which detectable changes in some extremes were established at the global scale based on observations ( [[#Dunn--2020|Dunn et al., 2020]] ). Heat-related mortality has also been assessed to have increased considerably because of climate change ( [[#Ebi--2021|Ebi et al., 2021]] ; [[#Vicedo-Cabrera--2021|Vicedo-Cabrera et al., 2021]] ). The onset, and also median location of the transitions of risk (Figure 16.15) from undetectable to moderate, is therefore considered to be 0.5°C. Further strong new evidence shows that changes in extremes emerged during the 1990s and 2000s ( [[#Dunn--2020|Dunn et al., 2020]] ) by which time +0.7°C of global warming had taken place (IPCC SR15, Chapter 1; WGI AR6, Chapter 2). In AR5 Section 19.6.3.3 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ), a transition to moderate risk was assessed to have taken place at the then ‘recent’ global warming level of 0.6°C, with ''high confidence'' . Owing to the increase in evidence, there is now ''very high confidence'' that the median value of the transition from undetectable to moderate risk is at 0.5°C and led by heat extremes, with the lower estimate set at 0.5°C as well, and upper estimate at 0.7°C.

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'''Figure 16.15 |''' '''The dependence of risk associated with the Reasons for Concern (RFCs) on the level of climate change, updated by expert elicitation and reflecting new literature and scientific evidence since AR5 and SR15.'''

'''(a)''' Global surface temperature (GST), relative to pre-industrial, 1850–1900 (WGI AR6 Figure SPM.8d). ( [[#IPCC--2021|IPCC, 2021]] a).

'''(b)''' Embers are shown for each RFC, assuming low to no adaptation (i.e., adaptation is fragmented, localised, incremental adjustments to existing practices). The dashed horizontal line denotes the present global warming of 1.09°C (IPCC WGI Figure SPM.8a ) which is used to separate the observed, past impacts below the line from the future projected risks above it. '''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 heatwaves, heavy rain, drought and associated wildfires, and coastal flooding. '''RFC3 Distribution of impacts''' : risks/impacts that disproportionately affect particular groups owing to uneven distribution of physical climate change hazards, exposure or vulnerability. '''RFC4 Global aggregate impacts''' : impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, lives affected, species lost or ecosystem degradation at a global scale. '''RFC5 Large-scale singular events''' : relatively large, abrupt and sometimes irreversible changes in systems caused by global warming, such as ice sheet disintegration or thermohaline circulation slowing. Comparison of the increase of risk across RFCs indicates the relative sensitivity of RFCs to increases in GSAT. The levels of risk illustrated reflect the judgements of IPCC author experts from WGI and WGII.

Further evidence of more recent observed changes in extreme weather and climate events, and their potential for associated adverse consequences across many aspects of society and ecosystems, has continued to accrue (WGI AR6 Chapter 11; WGI AR6 Chapter 12). Since a necessary condition for ‘moderate’ levels of risk is the detection and attribution of observed impacts, the following text provides an overview of some salient examples of this evidence. In particular, WGI AR6 [[IPCC:Wg2:Chapter:Chapter-11|Chapter 11]] ( [[#Seneviratne--2021|Seneviratne et al., 2021]] ) concludes that some recent hot extreme events that happened in the past decade (2010s) would have been ''extremely unlikely'' to occur without human influence on the climate system. Global warming in that decade reached approximately 1.09°C on average (IPCC WGI AR6 Chapter 2).

Assessment of a high level of risk requires a higher level of magnitude, severity and spatial extent of the risks. Events prior to that already had substantial impacts, such as the 2003 European heatwave (IPCC SREX Chapter 9). Examples of impactful events in the early 2010s (at ca. 0.95°C of global warming; WGI AR6 Chapter 2, [[#Gulev--2021|Gulev et al., 2021]] ) include the 2010 Russian heatwave ( [[#Barriopedro--2011|Barriopedro et al., 2011]] ) and the 2010 Amazon drought ( [[#Lewis--2011|Lewis et al., 2011]] ). Later impactful events include, among others, the 2013 heatwave in eastern China ( [[#Sun--2014|Sun et al., 2014]] ), the 2017 tropical cyclone Harvey ( [[#Risser--2017|Risser and Wehner, 2017]] ; [[#Van%20Oldenborgh--2017|Van Oldenborgh et al., 2017]] ) and the 2018 concurrent North Hemisphere heatwaves in Europe, North America and Asia ( [[#Vogel--2019|Vogel et al., 2019]] ). Very recent events with severe and unprecedented impacts attributed to anthropogenic climate change indicate that thresholds to high risks may already have been crossed at recent levels of global warming (ca. 1.1–1.2°C), including the Siberian fires and the 2019 Australian bushfires that were linked to extreme heat and drought conditions ( [[#Van%20Oldenborgh--2017|Van Oldenborgh et al., 2017]] ) and extreme precipitation linked to increased storm activity in the USA ( [[#Van%20Oldenborgh--2017|Van Oldenborgh et al., 2017]] ). Severe and unprecedented impacts occurred with current low levels of adaptation ( [[#16.2.3.4|Section 16.2.3.4]] ). The global-scale risk of wildfire considerably degrading ecosystems and increasing illnesses and death of people has been assessed to transition from undetectable to moderate over the range 0.6–0.9°C with ''high confidence'' (Chapter 2, Table SM2.5, Figure 2.11).

In addition, long-term trends in various types of extremes are now detectable (WGI AR6 Chapter 11, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). This includes increases in hot extremes over most land regions ( ''virtually certain'' ), increases in heavy precipitation at the global scale and over most regions with sufficient observations ( ''high confidence'' ), and increases in agricultural and ecological droughts in some regions ( ''medium confidence'' ) (WGI AR6 Chapter 11). There has also been overall a ''likely'' increase in the probability of compound events, such as an increase in concurrent heatwaves and droughts ( ''high confidence'' ) (WGI AR6 Chapter 11). There is ''medium confidence'' that weather conditions that promote wildfires (fire weather) have become more probable in southern Europe, northern Eurasia, the USA and Australia over the last century (WGI AR6 Chapter 11; SRCCL Chapter 2, [[#Jolly--2015|Jolly et al., 2015]] ; [[#Abatzoglou--2016|Abatzoglou and Williams, 2016]] ). Furthermore, food security and livelihoods are being affected by short-term food shortages caused by climate extremes ( [[IPCC:Wg2:Chapter:Chapter-5#5.12.1|Section 5.12.1]] ; Chapter 16, Food Security RKR) which have affected the productivity of all agricultural and fishery sectors ( ''high confidence'' ). The frequency of sudden food production losses has increased since at least mid-20th century on land and sea ( ''medium evidence'' , ''high agreement'' ). Droughts, floods and marine heatwaves contribute to reduced food availability and increased food prices, threatening food security, nutrition and livelihoods of millions ( ''high confidence'' ). Changes in sea surface temperatures drive simultaneous variation in climate extremes, increasing the risk of multi-breadbasket failures ( [[#Cai--2014|Cai et al., 2014]] ; [[#Perry--2017|Perry et al., 2017]] ). Droughts induced by the 2015–2016 El Niño, partially attributable to human influences ( ''medium confidence'' ), caused acute food insecurity in various regions, including eastern and southern Africa and the dry corridor of Central America ( ''high confidence'' ). Human-induced climate change warming also worsened the 2007 drought in southern Africa, causing food shortages, price spikes and acute food insecurity in Lesotho ( [[#Verschuur--2021|Verschuur et al., 2021]] ). In the fisheries and aquaculture sector, marine heatwaves are estimated to have doubled in frequency between 1982 and 2016, as well as increasing in intensity and length, with consequences for fish mortality (Chapter 5; [[#Smale--2019|Smale et al., 2019]] ; [[#Laufkötter--2020|Laufkötter et al., 2020]] ). In the northeast Pacific, a recent 5-year warm period impacted the migration, distribution and abundance of key fish resources ( ''high confidence'' ). At 1°C warming, the number of people affected by six categories of extreme events was found to have already increased by a factor of 2.3 relative to pre-industrial ( [[#Lange--2020|Lange et al., 2020]] ).

The general picture is one of annual or more frequent occurrences of severe extremes with widespread impacts (as also reflected in [[#16.2|Section 16.2]] ), and of multiple extremes, meeting the criteria for the ‘severe and widespread’ nature of risks that is required for classification at a ‘high’ level of risk. This is consistent with AR5 Chapter 19 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ), and gives ''high confidence'' that the lower threshold for entering high risks associated with extreme weather events is +1°C, and that the best estimate is that this transition already occurred now that global warming has reached its present-day level of ca. 1.2°C ( [[#WMO--2020|WMO, 2020]] ), slightly above the 1.09°C average conditions in the 2010s, that is, 2011–2020 (IPCC WGI AR6 Chapter 2, [[#Gulev--2021|Gulev et al., 2021]] ).

A range of literature projects further substantial increases in several extreme event types with a global warming of +1.5°C, notably hot extremes in most regions, heavy precipitation in several regions, and drought in some regions (IPCC SR15; WGI AR6 , Chapter 11). In particular, heavy precipitation and associated flooding are projected to intensify and be more frequent in most regions in Africa and Asia ( ''high confidence'' ), North America ( ''medium'' to ''high confidence'' depending on the region) and Europe ( ''medium confidence'' ). Also, more frequent and/or severe agricultural and ecological droughts are projected in a few regions in all continents except Asia, compared with 1850–1900 ( ''medium confidence'' ); increases in meteorological droughts are also projected in a few regions ( ''medium confidence'' ). Increases at 1.5°C of global warming are projected in marine heatwaves ( [[#Laufkötter--2020|Laufkötter et al., 2020]] ) and the occurrence of fire weather ( [[#IPCC--2019a|IPCC, 2019a]] ). Heat-related mortality is assessed to increase from moderate to high levels of risk under about 1.5°C warming under SSP3, a socioeconomic scenario with large challenges to adaptation ( [[#Ebi--2021|Ebi et al., 2021]] ) especially in urban centres (Chapter 6). An additional 350 million people living in urban areas are estimated to be exposed to water scarcity from severe droughts at 1.5°C warming (Sections 6.1, 6.2.2; CCP2 Coastal Cities). In summary, there is ''high confidence'' that the best estimate for the transition from moderate to high risk is 1.2°C of global warming, with 1°C as lower estimate and 1.5°C as upper estimate. The latter would be set to 1.3°C for an assessment at ''medium confidence'' .

As in RFC1, one of the criteria for identification of very high risks is limits to adaptation. Though the literature explicitly considering societal adaptation to extreme weather events is limited, there is evidence that investments in hydro-meteorological information, early-warning systems and anticipatory forecast-based finance are a cost-effective way to prevent some of the most adverse effects of extreme events ( [[#Coughlan%20de%20Perez--2016|Coughlan de Perez et al., 2016]] ; [[#Fakhruddin--2019|Fakhruddin and Schick, 2019]] ; [[#Merz--2020|Merz et al., 2020]] ). Despite a lack of systematic methods for assessing general adaptation effectiveness, there is some evidence of risk reduction for particular places and hazards, especially flood and heat vulnerability ( [[#16.3.2.4|Section 16.3.2.4]] ), including investment in flood protection, building design and monitoring and forecasting, air conditioning, reduced social vulnerability, and improved population health. One study finds declining global mortality and economic loss due to extreme weather events over the past four decades ( [[#Formetta--2019|Formetta and Feyen, 2019]] ) especially in low-income countries. Using SSP2 as a proxy for expanded adaptation, [[#Ebi--2021|Ebi et al. (2021)]] assess that the transition to high risk for heat-related mortality increases to 1.8°C (compared with 1.5°C with less adaptation under SSP3). There is evidence of adaptation avoiding heat-related mortality at low levels of global warming, using early-warning and response systems and sustainable alterations of the thermal environment at the individual, building, urban and landscape levels ( [[#Jay--2021|Jay et al., 2021]] ). Despite the evidence that adaptation can reduce risks of heat stress, the impact of projected climate change on temperature-related mortality is expected to be a net increase under a wide range of climate change scenarios, even with adaptation (Chapter 7, ''high confidence'' ) ''.'' Much of the adaptation literature focuses on coping with long-term gradual climate change and largely does not take into account the increased difficulty of adapting to climate extremes and general higher variability in climate that is projected to occur in the future. However, expanding and more coordinated adaptation, including wider implementation and multi-level coordination, has the potential to reduce the risks to crops from heatwaves at intermediate (but not high) levels of warming.(IPCC AR5 Ch7, [[#Ahmed--2018|Ahmed et al., 2018]] ; [[#Ahmed--2019|Ahmed et al., 2019]] , [[#16.3.2.2|Section 16.3.2.2]] ; [[#EEA--2019|EEA, 2019]] ; [[#Raza--2019|Raza et al., 2019]] ; [[#Tripathi--2020|Tripathi and Sindhi, 2020]] ).

The transition from high to very high risk for the RFC2 was not assessed in the AR5 or in SR15. Some new evidence suggests, however, that very high risks associated with weather and climate extremes would be reached at higher levels of global warming. In particular, changes in several hazards would be more widespread and pronounced at 2°C compared with 1.5°C global warming, including increases in multiple and concurrent extremes (IPCC WGI AR6 SPM; IPCC WGI AR6 Chapter 11, IPCC WGI AR6 Chapter 12). On average over land, high temperature events that would have occurred once in 50 years in the absence of anthropogenic climate change are projected to become 13.9 times more likely with 2°C warming, and 39.2 times more likely with 4°C warming (IPCC AR6 WGI SPM Figure SPM.6, [[#IPCC--2021|IPCC, 2021]] ), indicating a nonlinear increase with warming. [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] assessed that risk of wildfire transitions from moderate to high over the range 1.5°C to 2.5°C warming ( ''medium confidence'' , Table SM2.5 , Figure 2.11). The intensity of heavy precipitation events increases overall by about 7% for each additional degree of global warming (IPCC AR6 WGI SPM), while their frequency increases nonlinearly. Events that would have occurred once every 10 years in a climate without human influence are projected to become 1.7 times more likely with 2°C warming, and 2.7 times more likely with 4°C warming (IPCC AR6 WGI SPM Figure SPM.6). Several AR6 regions are projected to be affected by increases in agricultural and ecological droughts at 2°C of global warming, including western North America, central North America, northern Central America, southern Central America, the Caribbean, northern South America, northeastern South America, South American Monsoon, southwestern South America, southern South America, West and Central Europe, the Mediterranean, western Southern Africa, eastern Southern Africa, Madagascar, eastern Australia and southern Australia (IPCC WGI AR6, Chapter 11, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). This is a substantially larger number compared with projections at 1.5°C (IPCC WGI AR6, Chapter 11, [[#Seneviratne--2021|Seneviratne et al., 2021]] ). In these drying regions, events that would have occurred once every 10 years in a climate without human influence are projected to happen 2.4 times more frequently at 2°C of global warming (IPCC WGI AR6 SPM Figure SPM.6). Urban land exposed to floods and droughts is ''very likely'' to have more than doubled between 2000 and 2030, and the risk of flooding accelerates after 2050 (Chapter 4). At 2°C of global warming, there are also significant projected increases in fluvial flood frequency and resultant risks associated with higher populations exposed to these flood risks ( [[#Alfieri--2017|Alfieri et al., 2017]] ; [[#Dottori--2018|Dottori et al., 2018]] ).

Heat-related mortality is assessed to increase from high to very high by 3°C under SSP3, a socioeconomic scenario with large challenges to adaptation ( [[#Ebi--2021|Ebi et al., 2021]] ). SRCCL assessed that very high risks would be reached in association with wildfire above 3°C of global warming ( [[#IPCC--2019a|IPCC, 2019a]] ). [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] has assessed that risk of fire weather itself transitions from high to very high over the range 3°C to 4.5°C warming ( ''medium confidence'' , Table SM2.5, Figure 2.11). Matthews et al. (2017) show that, at 1.5°C of global warming, about 40% of all megacities would be affected at least 1 d yr −1 with a heat index above 40.6°C (i.e., with 40.6°C ‘feels-like’ temperatures, accounting for moisture effects). This number would reach about 65% of megacities at 2.7°C and close to 80% at 4°C. In addition, there is evidence for a higher risk of concurrent heat extremes at different locations with increasing global warming ( [[#Vogel--2019|Vogel et al., 2019]] ), meaning that several cities could be affected by deadly heatwaves simultaneously. Laufkötter et al. (2020) found that marine heatwave events would become annual to decadal events under 3°C of global warming, with consequences for aquaculture (Chapter 5). [[#Gaupp--2019|Gaupp et al. (2019)]] conclude that risks of simultaneous crop failure across worldwide breadbasket regions, due to changes in maximum temperatures in the crop-growth-relevant season or cumulative precipitation in relevant time frames, increase disproportionately between 1.5°C and 2°C of global warming. Populations exposed to extreme weather and climate events may consume inadequate or insufficient food, leading to malnutrition and increasing the risk of disease (Chapter 5, ''high confidence'' ). Hence, there is the potential for very high risks associated with changes in climate extremes for food security in the low adaptation case, already above 2°C of global warming. Finally, studies suggest that regional thresholds for climate extremes could be reached at 2°C of global warming, for instance in the Mediterranean ( [[#Guiot--2016|Guiot and Cramer, 2016]] ). [[#Samaniego--2018|Samaniego et al. (2018)]] conclude that soil moisture droughts in that region would become two to three times longer than at the end of the 20th century at 2°C, and three to four times longer (125 d long yr –1 ) at 3°C of global warming. There is clear evidence of very high risk at 3°C global warming for wildfires, marine heatwaves and heatwaves in megacities (the latter being set at 2.7°C).

Based on the available evidence, we assess that there is ''medium confidence'' that the transition to very high risk would happen at a median value 2°C of global warming, considering the increased risk for breadbasket failure and irreversible impacts associated with changes in extremes at this warming level (e.g., damages to ecosystems, health impacts, severe coastal storms), but that due to the disproportionate increases in risk between 1.5°C and 2°C this transition begins already at 1.8°C. The higher range for this transition is set with ''medium confidence'' at 2.5°C in this low/no adaptation scenario, owing to the further projected nonlinear increases in risks associated with high temperature events above 2°C (WGI AR6 Figure SPM.6, [[#IPCC--2021|IPCC, 2021]] ; Cross-Chapter Box 12.1, [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ), and also the limits to adaptation associated with dealing with a rapid escalation of extreme weather events globally during this century; extreme events are particularly difficult to adapt to and thus more often exceed hard limits to adaptation, particularly in natural ecosystem settings ( [[#16.4|Section 16.4]] ).

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==== 16.6.3.3 Distribution of Impacts (RFC3) ====

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RFC3 reflects how key risks are distributed unevenly across regions and different population groups, due to the non-uniform spatial distributions of physical climate change hazards, exposure and vulnerability across regions. It addresses how risks disproportionately affect particularly vulnerable societies and socio-ecological systems, including disadvantaged people and communities in countries at all levels of development. AR5 concluded that low-latitude and less developed areas generally face greater risk than higher-latitude and more developed countries, including for food- and health-related risks. This conclusion remains valid and is now supported by greater evidence across a range of sectors and geographic regions.

Note that the assessment here is largely based on the national and regional distribution of impacts, rather than sub-national distribution or explicit consideration of vulnerable elements of society. Climate risks are also strongly related to inequalities, often but not always intersecting with poverty ( [[#16.1|Section 16.1]] ), geographic location, and political and socio-cultural aspects. Thus, countries with high inequality tend to be more vulnerable, and more exposed, to climate hazards ( [[#16.1|Section 16.1]] ). While the literature assessed here tends to be insufficiently granular to resolve local inequalities, it does confirm the AR5 finding that low-latitude and less developed areas generally face greater risk.

AR6 continues to highlight the uneven regional distribution of projected climate change risks. Biodiversity loss is projected to affect a greater number of regions with increasing warming, and to be highest in northern South America, southern Africa, most of Australia, and northern high latitudes ( [[IPCC:Wg2:Chapter:Chapter-2#2.5.1.3|Section 2.5.1.3]] , ''medium confidence'' ). Climate change is projected to increase the number of people at risk of hunger in mid-century, concentrated in Sub-Saharan Africa, South Asia and Central America (Chapter 5, ''high confidence'' ), increasing undernutrition, stunting and related childhood mortality particularly in Africa and Asia and disproportionately affecting children and pregnant women (Chapter 7, ''high confidence'' ), strongly mediated by socioeconomic factors (Sections 7.2.4.4, 7.3.1, ''very high confidence'' ). Strong geographical differences in heat-related mortality are projected to emerge later this century, mainly driven by growth in regions with tropical and subtropical climates ( [[IPCC:Wg2:Chapter:Chapter-7#7.3.1|Section 7.3.1]] , ''very high confidence'' ).

In AR5 and SR15, the transition from undetectable to moderate risk was located below what were at the time ‘recent’ temperatures of between 0.5°C and 0.8°C above pre-industrial levels, with ''medium'' to ''high confidence'' , based on evidence of distributional impacts on crop production and water resources. New literature has continued to confirm this transition has already taken place, including more recent observed impacts for regions and groups within the food and water sectors, strongly linked to Representative Key Risks for health, water and food security (Sections 16.2, 16.5, 5.4.1, 5.5.1, 5.8.1, 5.12; Chapter 7).

In AR6, moderate risks have already been assessed to have occurred in Africa for economic growth and reduced inequality, biodiversity and ecosystems, mortality and morbidity due to heat extremes and infectious disease, and food production in fisheries and crop production (Figure 9.6). In Europe, moderate risks to heat stress, mortality and morbidity have already been reached, as well as for water scarcity in some regions (Figure 13.30, Figure 13.3 1). In Australasia, moderate risks are assessed as present already for heat-related mortality risk as well as cascading effects on cities and settlements, and also very high risks already present in coral reef systems, and high risks to kelp forests and alpine biodiversity (Figure 17.6). In North America, moderate risks have already been reached for freshwater scarcity, water quality (Figure 14.4), agriculture, forestry, tourism, transport, energy and mining, and construction (Figure 14.10).

For this assessment, the transition to moderate risk was assessed to have occurred between 0.7°C and 1.0°C of warming with ''high confidence'' , demonstrating that a moderate level of risk exists at present. The 0.2°C increase in this temperature range as compared with AR5 reflects the fact that AR6 WGI has assessed that the level of global warming reached by 1986–2005 was 0.52–0.82°C (as opposed to 0.55–0.67°C in previous assessments), and also reflects the opportunity for observations to be have made of the observed consequences of the additional rise in temperature that has taken place since the literature underpinning the AR5 assessment was published.

In AR5, the transition from moderate to high risk was assessed to occur between 1.6°C and 2.6°C above the pre-industrial levels with ''medium confidence'' . In SR15, new literature on projected risks allowed this range to be narrowed to 1.5–2°C. There is now substantial literature providing ''robust evidence'' of larger regional risks at 2°C warming than 1.5°C and in a range of systems, including crop production (with risks of simultaneous crop failure) (Thiault et al.; [[#Gaupp--2019|Gaupp et al., 2019]] ), aquaculture and fisheries ( [[#Cheung--2018b|Cheung et al., 2018b]] ; [[#Froehlich--2018|Froehlich et al., 2018]] ; [[#Stewart-Sinclair--2020|Stewart-Sinclair et al., 2020]] ), nutrition-related health ( [[#Springmann--2016|Springmann et al., 2016]] ; [[#Lloyd--2018|Lloyd et al., 2018]] ; [[#Sulser--2021|Sulser et al., 2021]] ) and exposure to stressors such as drought, floods ( [[#Alfieri--2017|Alfieri et al., 2017]] ; [[#Hirabayashi--2021|Hirabayashi et al., 2021]] ) and extreme heat ( [[#Dosio--2018|Dosio et al., 2018]] ; [[#Harrington--2018|Harrington et al., 2018]] ; [[#Sun--2019|Sun et al., 2019]] ). One study ( [[#Gaupp--2019|Gaupp et al., 2019]] ) found that the risk of simultaneous crop failure in maize is estimated to increase from 6% to 40% at 1.5°C relative to the historical baseline climate. In particular, further research on projected regional yield declines of wheat and maize between 1.5°C and 2°C, especially in Africa, has accrued [[#Asseng--2015|Asseng et al. (2015)]] , including in Ethiopia ( [[#Abera--2018|Abera et al., 2018]] ) with associated economic effects ( [[#Wang--2019|Wang et al., 2019]] ). Optimum maize production areas in East Asia are projected to reduce in area by 38% for global warming of 1.5–2.0°C ( [[#He--2019|He et al., 2019]] ). A study of Jamaica also estimated that warming of less than 1.5°C will have an overall negative impact on crop suitability and a general reduction in the range of crops, but above 1.5°C, irreversible changes to Jamaica’s agriculture sector were projected ( [[#Rhiney--2018|Rhiney et al., 2018]] ).

Projections of increasing flood risk associated with global warming of 1.5°C and 2°C continue to highlight regional disparities, with larger-than-average increases projected in Asia and Africa ( [[#Hirabayashi--2021|Hirabayashi et al., 2021]] ), including in China, India and Bangladesh ( [[#Alfieri--2017|Alfieri et al., 2017]] ). Similarly, nearly 80% of the 8–80 million additional people projected to be at risk of hunger owing to climate change are located in Africa and Asia ( [[#Springmann--2016|Springmann et al., 2016]] ; [[#Lloyd--2018|Lloyd and Oreskes, 2018]] ; [[#Nelson--2018|Nelson et al., 2018]] ). [[#Schleussner--2016b|Schleussner et al. (2016b)]] analysed hotspots of multi-sectoral risks with 1.5°C and especially 2°C warming, and highlighted projected crop yield reductions in West Africa, Southeast Asia, and Central and northern South America; a reduction in water availability in the Mediterranean; and widespread bleaching of tropical coral reefs.

High risks to crop production are assessed to occur in Africa with ~1.5–2°C warming (Figure 9.6), to agriculture in North America with ~1.5°C warming (Figure 14.10), and with ~2.8°C in Europe (Figure 13.30). High risks of mortality and morbidity due to heat extremes and infectious disease are assessed to be reached in Africa with ~1.5°C warming (Figure 9.6); heat stress, mortality and morbidity in Europe are assessed to reach a high level of risk at ~2°C (Figure 13.30). Heat-related mortality risk transitions to a high level by ~1.5–2°C warming in Australasia, while cascading effects on cities reach high risk with ~1.2°C warming (Figure 17.6). Risks to water scarcity, forestry, tourism and transportation in North America are projected to reach high levels with ~2°C warming (Figure 14.4, Figure 14.10).

Two complementary multi-sectoral analyses indicate that South Asia and Africa become hotspots of multi-sectoral climate change risk, largely due to changes in water-related indicators which also affect crop production ( [[#Arnell--2018|Arnell et al., 2018]] ; [[#Byers--2018|Byers et al., 2018]] ). For instance, [[#Byers--2018|Byers et al. (2018)]] found that the doubling in global exposure to multi-sector risks that accrues as warming increases from 1.5°C to 2°C is concentrated in Asian and African regions (especially East Africa), which together account for 85–95% of the global exposure.

Considering this evidence, for this assessment, the temperature range for the transition from moderate to high risk is located between 1.5°C and 2°C above pre-industrial levels, with ''high confidence'' in the lower bound of 1.5°C, but ''medium confidence'' in the upper bound of 2°C, because simulation studies do not account for climate variability and therefore risks could be higher.

Very high risk implies limited ability to adapt. Adaptation potential not only differs across sectors and regions, but also occurs on different time scales depending on the nature and implementation level of the adaptation option under consideration and the system in which it is to be deployed. The costs of adaptation actions that would be needed to offset projected climate change impacts for major crop production are projected to rise once global warming reaches 1.5°C ( [[#Iizumi--2020|Iizumi et al., 2020]] ). It has been estimated that the number of additional people at risk of hunger with 2.0°C global warming could be reduced from 40 million to 30 million by raising the level of adaptation action ( [[#Baldos--2014|Baldos and Hertel, 2014]] ), but beyond this level of warming residual impacts are projected to escalate ( [[#Iizumi--2020|Iizumi et al., 2020]] ). [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] assessed the potential of existing farm management practices to reduce yield losses, finding an average 8% loss reduction in mid-century and 11% by end-century ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.1|Section 5.4.4.1]] ), which is insufficient to offset the negative impacts from climate change, particularly in currently warmer regions ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.3.2|Section 5.4.3.2]] ). The literature indicates that, globally, crop production may be sustained below 2.0°C warming with adaptation, but negative impacts will prevail at 2.0°C warming and above in currently warm regions ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.4.1|Section 5.4.4.1]] ). Importantly, residual damage (that which cannot be avoided despite adaptation) is projected to rise around 2.0°C global warming ( [[#Iizumi--2020|Iizumi et al., 2020]] ). Evidence of constraints and limits for food, fibre and other ecosystem products for the different regions is evident for the various regions ( [[#16.4.3.1|Section 16.4.3.1]] ) indicating limited ability to adapt. Adaptation costs are also higher relative to GDP in low-income countries, for example for the building of sea dikes ( [[#Brown--2021|Brown et al., 2021]] ).

In previous reports, the transition from high to very high risk for the distribution of impacts was not assessed due to limited available literature, but there is now sufficient evidence to do so. A range of literature quantifies the increasing regional probability of drought as compared with the present day, with projected increases in the area exposed to drought ( [[#Carrão--2018|Carrão et al., 2018]] ; [[#Pokhrel--2021|Pokhrel et al., 2021]] ), as well as in the duration ( [[#Naumann--2018|Naumann et al., 2018]] ) and frequency of droughts, with higher warming levels. [[#Naumann--2018|Naumann et al. (2018)]] showed that, for drying areas, drought durations are projected to rise from 2 months per °C below 1.5°C to 4.2 months per °C near 3°C warming. Most of Africa, Australia, southern Europe, southern and central USA, Central America, the Caribbean, northwest China, and parts of Southern America are projected to experience more frequent droughts. Adverse effects of climate change on food production are projected to become much more severe ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.3.2|Section 5.4.3.2]] ) when global temperatures rise more than 2°C globally, but there are predicted to be much more negative impacts experienced sooner on food security in low to mid-latitudes ( [[#Richardson--2018a|Richardson et al., 2018a]] ) ( [[IPCC:Wg2:Chapter:Chapter-5#5.4.1|Section 5.4.1]] ). For instance, climate change by 2050 is projected to increase the number of people at risk of hunger by between 8 and 80 million with 2–3°C warming compared with no-climate-change conditions ( [[#Baldos--2014|Baldos and Hertel, 2014]] ; [[#Hasegawa--2018|Hasegawa et al., 2018]] ; [[#Nelson--2018|Nelson et al., 2018]] ; [[#Janssens--2020|Janssens et al., 2020]] ). In addition to effects upon crop yield, agricultural labour productivity, food access and food-related health are projected to be negatively impacted by 2–3°C warming ( [[#Springmann--2016|Springmann et al., 2016]] ; [[#de%20Lima--2021|de Lima et al., 2021]] ). Regionally, substantial regional disparity in risks to food production is projected to persist at these higher levels of warming. Risks for heat-related morbidity and mortality, ozone-related mortality, malaria, dengue, Lyme disease and West Nile fever are projected to increase regionally and globally (Chapter 7) with potential infestation areas for disease-carrying vectors in multiple geographic regions that could be five times higher at 4°C than at 2°C ( [[#Liu-Helmersson--2019|Liu-Helmersson et al., 2019]] ).

Very high risks to crop production are assessed to occur in Africa above ~2.5°C warming (Figure 9.6) and below 4°C in Europe (Figure 13.29 ). Very high risks of mortality and morbidity due to heat extremes and infectious disease are assessed to occur in Africa with 2.5°C warming (Figure 9.6); heat stress, mortality and morbidity in Europe are assessed to reach a very high level of risk at ~3.2°C (Figure 13.30). Heat-related mortality risk and cascading effects on cities both transition to a very high level by ~2.5°C warming in Australasia (Figure 11.7). Risks to water scarcity in North America are projected to reach very high levels with 3.5°C warming (Figure 14.4). Hence, this assessment concludes with ''medium confidence'' that a transition from high to very high risks, in terms of distribution of impacts, begins at 2°C global warming, with a full transition to very high risks completed by 3.5°C. However, it should be noted that many studies upon which this assessment has been based have not taken into account the impacts of extreme weather events and oscillations in sea surface temperatures; hence, risks at a given level of global warming might be underestimated in the literature.

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==== 16.6.3.4 Global Aggregate Impacts (RFC4) ====

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This RFC considers impacts to socio-ecological systems that can be aggregated globally into a single metric, such as monetary damages, lives affected, species lost or ecosystem degradation at a global scale ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ; [[#O’Neill--2017|O’Neill et al., 2017]] ). RFC4 shares underlying key risk components with other RFCs (e.g., RFC1 and RFC2, see [[#O’Neill--2017|O’Neill et al., 2017]] ) and thus draws on a similar literature as those assessments; however, this RFC focuses on impacts that reach levels of concern at the global level and also weighs the composite effect of risk elements ranging from economic to biodiversity.

In AR5 Section 19.6.3.5 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ), the transition from undetectable to moderate risk was assessed between 1.6°C and 2.6°C above pre-industrial levels (i.e., 1°C and 2°C above the 1986–2005 level) based on impacts to both Earth’s biodiversity and the overall global economy with ''medium confidence'' . The risk transition between moderate and high risk was set around 3.6°C above pre-industrial levels (i.e., 3°C above the 1986–2005 level), based on literature finding extensive species vulnerability and biodiversity damage with associated loss of ecosystem goods and services at 3.5°C ( [[#Foden--2013|Foden et al., 2013]] ; [[#Warren--2013|Warren et al., 2013]] ). In SR15 [[IPCC:Wg2:Chapter:Chapter-3#3.5.2|Section 3.5.2.4]] ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ), economic literature on potential socioeconomic threshold events and empirical studies of global economic damages, combined with new evidence on biome shifts, extinction risk, species range loss (especially noting the integral role of insects in ecosystem function) and ecosystem degradation, were assessed, and the upper bound of the transition to moderate risk was lowered to 1.5°C warming above pre-industrial levels, and the transition from moderate and high risk was lowered to between 1.5°C and 2.5°C ( ''medium confidence'' ). The boundary between high risk and very high risk was not assessed in either of these reports because the temperature threshold was beyond the scope of the assessment in the case of SR15 and literature available for this highest transition in AR5 was limited.

Since AR5, many new global estimates of the aggregate, economy-wide risks of climate change have been produced, though, as was the case in AR5, these continue to exhibit a low level of agreement, including for today’s level of global warming, due primarily to differences in methods. Cross-Working Group Box ECONOMIC in this chapter includes a more thorough discussion of advancements and limitations of global economic impact estimates and methodologies, finding significant variation in estimates that increases with warming, indicating higher risk in terms of economic costs at higher temperatures ( ''high confidence'' ). Climate change has been found to exacerbate poverty through declines in agricultural productivity, changes in agricultural prices and extreme weather events ( [[#Hertel--2014|Hertel and Lobell, 2014]] ; [[#Hallegatte--2017|Hallegatte and Rozenberg, 2017]] ). In terms of biodiversity risks, the literature indicates that losses in terrestrial and marine ecosystems increase substantially between 1.5°C and 2°C of warming ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ). Since SR15, further evidence of degradation of biodiversity and ecosystem services and ocean acidification at the global aggregate level has continued to accrue due to climate change (see Chapter 2).

For this RFC, the transition from undetectable to moderate risk to global aggregate impacts is assessed with ''medium confidence'' to occur between 1.0°C (start of transition) and 1.5°C (completion of transition) with a median judgement of transition at 1.3°C, based on evidence of a combination of economic consequences, widespread impacts to climate-sensitive livelihoods, changes in biomes, and loss of terrestrial and marine biodiversity. The start of the transition from undetectable to moderate risk is located at recent temperatures based on observed impacts to biodiversity ( [[#16.2.3.1|Section 16.2.3.1]] ). Experts noted aggregate impacts on biodiversity are detectable, with damages that have had global significance (e.g., drought, pine bark beetles, coral reef ecosystems). Consistent with the start of this transition at 1°C, a similar elicitation conducted in [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] assessed that risks to biodiversity globally have already transitioned to a moderate level with 1°C warming, while risks of widespread tree mortality are already moderate with 0.9°C warming and moderate risks of ecosystem structure change began with warming of 0.5°C (Table SM2.5, Figure 2.11). Human-induced warming has slowed growth of agricultural productivity over the past 50 years in mid- and low latitudes (Chapter 5; [[#Hurlbert--2019|Hurlbert et al., 2019]] ). Although there is not yet strong evidence of attributable loss of life and livelihoods at the global level (Sections 16.5.2.3.4, 16.5.2.3.5), experts found that regional evidence of such observed impacts was still relevant to defining the beginning of the transition (e.g., Table SM16.22, Chapter 9). Informing the median value and upper bound of the transition to moderate risk, empirical studies and scenario analyses have found that regions with high dependence on climate-sensitive livelihoods like agriculture, fisheries and forestry would be severely impacted even at low levels of warming under conditions of low adaptation (RKR-D, [[#Lobell--2011|Lobell et al., 2011]] ; [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ).

The transition to high risk is assessed with ''medium confidence'' to occur between 1.5°C (start of transition) and 2.5°C (completion of transition) with a median judgement of transition at 2.0°C. Though economic estimates exhibit wide variation and ''low agreement'' at warming levels above 1.5°C, many estimates are nonlinear, with marginal economic impacts increasing with temperature (see Cross-Working Group Box ECONOMIC in this Chapter). At 1.5°C warming, most aggregate global impacts to GDP are negative across different estimation methods, including bottom-up estimation (e.g., Takakura et al., 2019), meta-analysis (e.g., [[#Howard--2017|Howard and Sterner, 2017]] ) and empirical estimations (e.g., [[#Pretis--2018|Pretis et al., 2018]] ; [[#Kalkuhl--2020|Kalkuhl and Wenz, 2020]] ). At 2°C [[#Watts--2021|Watts et al. (2021)]] estimate a relative decrease in effective labour by 10%, which would have profound economic consequences. [[#Byers--2018|Byers et al. (2018)]] found that global exposure to multi-sector risks approximately doubles between 1.5°C and 2°C, while the percentage of the global population exposed to flooding is projected to rise by 24% with 1.5°C warming and by 30% with 2.0°C warning ( [[#Hirabayashi--2021|Hirabayashi et al., 2021]] ).

[[#16.5.2.3|Section 16.5.2.3.4]] (RKR-D, underlying key risk on poverty) reports that, under medium warming pathways, climate change risks to poverty would become severe if vulnerability is high and adaptation is low ( ''limited evidence'' , ''high agreement'' ). At and beyond 1.5°C, approximately 200 million people with livelihoods derived from small-scale fisheries would face severe risk, given sensitivity to ocean warming, acidification and coral reef loss ( [[#Cheung--2018a|Cheung et al., 2018a]] ; [[#Froehlich--2018|Froehlich et al., 2018]] ; [[#Free--2019|Free et al., 2019]] ). Warming between 1.5°C and 2°C could expose 330–396 million people to lower agricultural yields and associated livelihood impacts ( [[#Byers--2018|Byers et al., 2018]] ; [[#Hoegh-Guldberg--2018a|Hoegh-Guldberg et al., 2018a]] ), due to a high dependency of climate-sensitive livelihoods to agriculture globally ( [[#World%20Bank--2020|World Bank, 2020]] ). Models project that climate change will increase the number of people at risk of hunger in 2050 by 8–80 million people globally, with the range depending on the level of warming (1.5–2.9°C) and SSPs ( [[#Nelson--2018|Nelson et al., 2018]] ; [[#Mbow--2019|Mbow et al., 2019]] ; [[#Janssens--2020|Janssens et al., 2020]] ). Higher atmospheric concentrations of carbon dioxide reduce the nutritional quality of wheat, rice and other major crops, potentially affecting millions of people at a doubling of carbon dioxide relative to pre-industrial ( ''very high confidence'' ) ( [[IPCC:Wg2:Chapter:Chapter-7#7.3.1|Section 7.3.1]] ). Global ocean animal biomass is projected to decrease on average by 5% per 1°C increase; hence, a 2.5°C level of warming is associated with ~13% decline in ocean animal biomass, which would considerably reduce marine food provisioning, fisheries distribution and revenue value, with further consequences for ecosystem functioning (Chapter 5, ''medium confidence'' ).

Losses in terrestrial and marine biodiversity increase substantially beyond 1.5°C of warming ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ). [[#16.5.2.3|Section 16.5.2.3.2]] (RKR-B, risks to terrestrial and marine ecosystems) finds that substantial biodiversity loss globally, abrupt local ecosystem mortality impacts, and ecological species disruption are all projected at global warming levels below 3°C, with insular systems and biodiversity hotspots at risk below 2°C ( ''medium confidence'' ). Insects play a critical role in providing vital ecosystem services that underpin human systems, with major losses of their climatically determined geographic range at 2°C warming implying adverse effects on ecosystem functioning. Consistent with the transitions presented here, a similar burning ember developed in [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] assessed a transition from moderate to high risks globally for marine and terrestrial biodiversity (e.g., widespread death of trees, damages to ecosystems, and reduced provision of ecosystem services, and structural change, including biome shifts) beginning between 1.0°C and 2.0°C warming ( Table SM2.5, Figure 2.11).

Though explicit treatment of adaptation is limited in the RFC4 impacts literature (i.e., studies that compare risks for specific adaptation scenarios in terms of globally aggregated impacts with quantified findings), there is evidence of the potential for investments in improved hydro-meteorological information and early-warning systems to avoid some of the most adverse social and economic impacts from extreme weather events in both developed and developing countries, with benefits at a globally significant level ( [[#Hallegatte--2012|Hallegatte, 2012]] ). Studies of adaptation in the agriculture sector (e.g., changing crop variety, timing of crop planting, new types of irrigation, etc.) and infrastructure (e.g., coastal protection, hardening of critical infrastructure, flood and climate-resistant building materials and water storage) show large potential benefits in terms of reduced impacts to lives and livelihoods ( [[#van%20Hooff--2015|van Hooff et al., 2015]] ; [[#Mees--2017|Mees, 2017]] ). At higher warming levels, however, potential adaptations to address biodiversity loss are expected to be limited due to the projected rate and magnitude of change as well as the resources required ( [[#Hannah--2020|Hannah et al., 2020]] ).

The transition to very high risks is assessed to occur within a range of 2.5–4.5°C with ''medium confidence'' over the range, and ''low confidence'' assessed over a narrowed ‘best estimate’ range of 2.7–3.7°C. The lower end of the range reflects the loss of an increasingly large fraction of biodiversity globally. [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] has assessed a transition from high to very high risks globally for biodiversity (marine and terrestrial) completing at ~2.5°C warming, noting widespread death of trees, damages to ecosystems, and reduced provision of ecosystem services over the temperature range 2.5–4.5°C ( Table SM2.5, Figure 2.11) and, similarly, a transition from high to very high risks of ecosystem structure change (including biome shifts) between 3°C and 5°C warming ( Table SM2.5, Figure 2.11). A global study of 115,000 common species projects climatically determined geographic range losses of over 50% in 49% of insects, 44% of plants and 26% of vertebrates with global warming of 3.2°C, implying an associated effect on provisional and regulating ecosystem services that support human well-being, including pollination and detritivory ( [[#Warren--2018a|Warren et al., 2018a]] ). The risk of abrupt impacts on ecosystems as multiple species approach tolerance limits simultaneously is projected to threaten up to 15% of ecological communities with 4°C of warming ( [[#Trisos--2020|Trisos et al., 2020]] ). Under a 4°C warming scenario, models project global annual damages associated with SLR of $31,000 billion yr –1 in 2100 ( [[#Brown--2021|Brown et al., 2021]] )

In terms of global economic impact, while an emerging economic literature is addressing many gaps and critiques of previous damage estimates for high warming (e.g., [[#Jensen--2014|Jensen and Traeger, 2014]] ; [[#Burke--2015|Burke et al., 2015]] ; [[#Lontzek--2015|Lontzek et al., 2015]] ; [[#Moore--2015|Moore and Diaz, 2015]] ; [[#Lemoine--2016|Lemoine and Traeger, 2016]] ; [[#Moore--2017a|Moore et al., 2017a]] ; Cai and Lontzek; Takakura et al., 2019, discussed further in Cross-Working Group Box ECONOMIC; [[#Carleton--2020|Carleton et al., 2020]] ; [[#Méjean--2020|Méjean et al., 2020]] ; [[#Rode--2021|Rode et al., 2021]] ), there remains wide variation across disparate methodologies, though the spread of estimates increases with warming in all methodologies, indicating higher risk in terms of economic costs at higher temperatures ( ''high confidence'' ). [[#16.5.2.3|Section 16.5.2.3.4]] (RKR-D) finds that risks to aggregate economic output would become severe at the global scale at high warming (~4.4°C) and minimal adaptation ( ''medium confidence'' ), defining severity as ‘the potential for persistent annual economic losses due to climate change to match or exceed losses during the world’s worst historical economic recessions’. Furthermore, climate change impacts on income inequality could compound risks to living standards ( ''high confidence'' , 16.5.2.3.4). [[IPCC:Wg2:Chapter:Chapter-4|Chapter 4]] finds that, at 4°C, 4 billion people are projected to be exposed to physical water scarcity ( ''medium confidence'' ).

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==== 16.6.3.5 Large-scale Singular Events (RFC5) ====

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This RFC, large-scale singular events (sometimes called tipping points or critical thresholds), considers abrupt, drastic and sometimes irreversible changes in physical, ecological or social systems in response to smooth variations in driving forces (accompanied by natural variability) ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ; [[#O’Neill--2017|O’Neill et al., 2017]] ). SR15 [[IPCC:Wg2:Chapter:Chapter-3#3.5.2|Section 3.5.2.5]] presented four examples, including the cryosphere (West Antarctic ice sheet, Greenland ice sheet), thermohaline circulation (slowdown of the Atlantic Meridional Overturning Circulation), the El Niño-Southern Oscillation (ENSO) as a global mode of climate variability, and the role of the Southern Ocean in the global carbon cycle ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ). While most of the literature assessed here focuses on the resultant changes to climate-related hazards such as sea level rise, in this assessment, evidence about the implications of accelerated sea level rise for human and natural systems is also considered. If sea level rise is accelerated by ice sheet melt, the associated impacts are projected to occur decades earlier than otherwise, directly affecting coastal systems including cities and settlements by the sea (CCP2) and wetlands (Chapter 2). The associated disruption to ports is projected to severely compromise global supply chains and maritime trade with local–global geo-political and economic consequences. To compensate for this acceleration, adaptation would need to occur much faster and at a much greater scale than otherwise, or indeed than has previously been observed (CCP2). The costs of accommodating port growth and adapting to sea level rise amount to USD 22–768 billion before 2050 globally ( ''medium evidence'' , ''high agreement'' ) (see Sections 2.1, 2.2; Cross-Chapter Box SLR in Chapter 3).

In AR5 Section 19.6.3.6 ( [[#Oppenheimer--2014|Oppenheimer et al., 2014]] ), the boundary between undetectable and moderate risk is set at levels between 0.6°C and 1.6°C above pre-industrial levels (i.e., 0°C and 1°C above the 1986–2005 level) with ''high confidence'' , based on emerging early-warning signals of regime shifts in Arctic and warm water coral reef systems. The risk transition boundary between moderate and high risk was set between 1.6°C and 3.6°C above pre-industrial levels (i.e., 1°C and 3°C above the 1986–2005 level), with ''medium confidence'' based on projections of ice sheet loss, with faster increase between 1°C and 2°C than between 2°C and 3°C. The literature available at the time did not allow AR5 to assess the boundary between high and very high risk.

In SR15 [[IPCC:Wg2:Chapter:Chapter-3#3.5.2|Section 3.5.2.5]] ( [[#Hoegh-Guldberg--2018b|Hoegh-Guldberg et al., 2018b]] ), new assessments of the potential collapse of the West Antarctic ice sheet (WAIS) initiated by marine ice sheet instability (MISI) resulted in lowering the upper end of the transition from undetectable and moderate risk from 1.6°C to 1°C warming above pre-industrial levels, and lowering the upper end of the transition from moderate to high risk to 2.5°C. Although SR15 did not produce embers beyond 2.5°C, authors reported that the transition to very high risk was assessed as lying above 5°C in light of growing literature on ice sheet contributions to SLR.

AR6 provides new evidence that relates to the location of the transition from undetectable to moderate risk. At the time of SR15, observations were suggesting that MISI might already be taking place in some parts of the WAIS, while AR5 supported assessment of an additional MISI contribution to SLR of several additional tenths of a metre over the next two centuries. Since SR15, new observations (WGI AR6 [[IPCC:Wg2:Chapter:Chapter-9#9.4.2.1|Section 9.4.2.1]] , [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ) support the assessment of enhanced grounding line retreat and subsequent mass loss through basal melt in various parts of Antarctica, and year 2100 sea level projections for the RCP8.5 scenario have increased by 10–12 cm owing to ice dynamics. However, the onset of MISI is driven by ocean warming in specific locations (ice cavities beneath floating ice shelves), and the relation between these ocean temperatures and global mean temperature is indirect and ambiguous. In addition, MISI implies a self-sustaining instability in the absence of further forcing. Because forcing is still increasing, it cannot be unambiguously assessed whether MISI is driving the observed retreat of grounding lines in the WAIS, or whether this retreat is a purely forced response (and would stop if the warming stops) or is just a manifestation of natural variability in upwelling of warmer waters on the Antarctic continental shelves and, as a result, is just a temporary effect. Consistent with SROCC, AR6 states with ''medium confidence'' that sustained mass losses of several major glaciers in the Amundsen Sea Embayment (ASE) are compatible with the onset of MISI, but that whether unstable WAIS retreat has already begun or is imminent remains a critical uncertainty.

Whether associated with MISI or not, WGI AR6 ( [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ) now assesses with ''very high confidence'' that mass loss from both the Antarctic (whether associated with MISI or not) and Greenland Ice Sheets, is more than seven times higher over the period 2010–2016 than over the period 1992–1999 for Greenland and four times higher for the same time intervals for Antarctica. Given their multi-century commitments to global SLR, this reinforces the assessment of estimating the boundary between undetectable and moderate risks for ice sheets to lie between 0.7°C (the level of global warming in the 1990s when melting began to accelerate) and 1°C (as in SR15), with a median of 0.9°C.

In the Amazon Forest, increases in tree mortality and a decline in the carbon sink are already reported ( [[#Brienen--2015|Brienen et al., 2015]] ; [[#Hubau--2020|Hubau et al., 2020]] ), and old-growth Amazon Rainforest may have become a net carbon source for the period 2010–2019 ( [[#Qin--2021|Qin et al., 2021]] ). Estimates which include land use emissions indicate the region may have become a net carbon source ( [[#Gatti--2021|Gatti et al., 2021]] ). Fire activity is an important driver, and both bigger fires ( [[#Lizundia-Loiola--2020|Lizundia-Loiola et al., 2020]] ) and longer fire season ( [[#Jolly--2015|Jolly et al., 2015]] ) have been reported in South America, although this is strongly linked to land use and land use change as well as climate ( [[#Kelley--2021|Kelley et al., 2021]] ), and indeed land use change may be a stronger driver of potential loss of the Amazon Forest than climate change. The risk of climate-change-related loss of the Amazon Forest is assessed already above ‘undetectable’, but has only emerged over the last few years, when global warming had reached 1°C, and is linked to land use as well as GSAT levels. [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] has assessed ecosystem carbon loss from tipping points in tropical forest and loss of Arctic permafrost, and finds a transition to moderate risk over the range 0.6–0.9°C ( ''medium confidence'' ). Specifically, WGII AR6 Table SM2.5 finds that ‘Primary tropical forest comprised a net source of carbon to the atmosphere, 2001–2019 (emissions 0.6 Gt y −1 , net 0.1 Gt y −1 ) ( [[#Harris--2021|Harris et al., 2021]] ). Anthropogenic climate change has thawed Arctic permafrost ( [[#Guo--2020|Guo et al., 2020]] ), carbon emissions 1.7 ± 0.8 Gt y −1 , 2003–2017 ( [[#Natali--2019|Natali et al., 2019]] )’. This also supports the upper limit for this transition lying at 1°C.

The potential global loss of an entire ecosystem type, coral reefs, is also considered a large-scale singular event. In the 1990s when global warming was around 0.7°C large-scale coral reef bleaching also became apparent ( [[#16.2.3.1|Section 16.2.3.1]] ), also supporting the lower boundary for this transition in respect of coral reefs.

Overall, given the above evidence on ice sheets, Amazon Forest and coral reefs, the transition from undetectable to moderate risk is assessed to occur between 0.7°C and 1°C warming with a median of 0.9°C with ''high confidence'' .

The transition from moderate to high risk is informed by an assessment of risks at higher levels of warming than present. Nearly all climate models show warmer temperatures around Antarctica in conjunction with rising global mean temperature, and all ice sheet models show sustained mass loss from the WAIS after temperature increase halts (thus implying MISI takes place) at various levels between 1.5°C and 5°C. An increasing fraction of ice sheet models shows additional sustained mass loss from the East Antarctic Ice Sheet (EAIS) for peak warming between 2°C and 4°C, and all ice sheet models show mass loss for peak warming higher than 4°C. Therefore, we assess an increasing link between MISI, WAIS collapse and Antarctic mass loss, for increasing temperature levels ( ''high confidence'' ).

There is ''high confidence'' in the existence of threshold behaviour of the Greenland Ice Sheet in a warmer climate (WGI AR6 Ch 9, [[#Fox-Kemper--2021|Fox-Kemper et al., 2021]] ); however, there is ''low agreement'' on the nature of the thresholds and the associated tipping points. Similarly, the likelihood for accelerated and irreversible mass loss from Antarctica increases with increasing temperatures, but thresholds cannot yet be unambiguously identified. By the year 2100, sea level projections (AR6 WGI Figure SPM.8 ( [[#IPCC--2021|IPCC, 2021]] )) now range from 0.57 m (0.37–0.85 m) for the SSP1–1.9 scenario to 1.35 m (1.02–1.89 m) for the SS5–8.5 scenario and become 1.99 m for the latter scenario (1.02–4.83 m) in the case of low-likelihood, high-impact outcomes resulting from ice sheet instability, for which there is ''limited evidence'' . It should be noted that inclusion of such low-likelihood, high-impact outcomes dominated by not-well-understood processes affecting ice dynamics on the large ice caps of Greenland, and in particular Antarctica, would also enhance the sea level projections for other scenarios, but to a lesser extent for increasingly weaker forcing. No quantitative assessment of their effect in other scenarios than SSP5–8.5 yet exists as such simulations with ice sheet models have not been carried out, or only in a very limited amount.

It should be noted that ice sheets may take many centuries to respond, implying that risk levels increase over time for the same warming level. Therefore, we base judgements about risk transitions related to ice sheets primarily on their implications for 2000-year commitments to SLR from sustained mass loss from both ice sheets as projected by various ice sheet models, reaching 2.3–3.1 m at 1.5°C peak warming and 2–6 m at 2.0°C peak warming (WGI AR6 TS, Box TS.4 Figure 1; [[#Arias--2021|Arias et al., 2021]] ). This is an important feature of the approach to this RFC (i.e., it is not primarily focused on implications for the next 100–200 years). In addition, since the AR5, there is new evidence about the Last Interglacial (LIG), when global mean temperature was about 0.5–1.5°C above the pre-industrial era. AR6 assesses that it is ''virtually certain'' that sea level was higher than today at that time, ''likely'' by 5–10 m ( ''medium confidence'' ) (B.5.4 WGI AR6 SPM,( [[#IPCC--2021|IPCC, 2021]] )). Mid-Pliocene temperatures of 2.5°C (about 3 million years ago when global temperatures were 2.5–4°C higher) also provide evidence as an upper limit for the transition to high risk associated with long-term equilibrium SLR of 5–25 m (WGI AR6 SPM B.5.4). Projected SLR for 2300 in an RCP8.5 or SSP5–8.5 scenario (consistent with a peak warming range of 4–6°C, varies between 1.7–6.8 m and 2.2–5.9 m, respectively (WGI AR6 TS Box TS.4, [[#Arias--2021|Arias et al., 2021]] ), and when accounting for marine ice cliff instability taking place on Antarctica, these numbers may increase to a range of 9.5–16.2 m (WGI AR6 TS Box TS.4, [[#Arias--2021|Arias et al., 2021]] ).

CMIP6 climate models project drying in the Amazon—especially in June–July–August, irrespective of future forcing scenario, but which increases with GSAT/higher scenarios ( [[#Lee--2021|Lee et al., 2021]] ). For higher GSAT levels, [[#Burton--2021|Burton et al. (2021)]] explore different forcing scenarios and found, regardless of scenario, burned area increases markedly with GSAT. New understanding of the role of vegetation stomata will act to exacerbate this drying ( [[#Richardson--2018b|Richardson et al., 2018b]] ). A transition to high risk of savannisation for the Amazon alone was assessed to lie between 1.5°C and 3°C with a median value of 2.0°C. A mean temperature increase of 2°C could reduce Arctic permafrost area ~15% by 2100 (Comyn-Platt et al., 2018). [[IPCC:Wg2:Chapter:Chapter-2|Chapter 2]] has assessed ecosystem carbon loss from tipping points in tropical forest and loss of Arctic permafrost, and finds a transition from moderate to high risk over the range 1.5°C to 3°C with a median of 2°C ( ''medium confidence'' , Table SM2.5, Figure 2.11). Its assessment of the transition from high to very high risk is located over the range 3–5°C ( ''low confidence'' , Table SM2.5, Figure 2.11) based on the potential for Amazon Forest dieback between 4°C and 5°C temperature increase above the pre-industrial period ( [[#Salazar--2010|Salazar and Nobre, 2010]] ).

One of the criteria for locating a transition to very high risk is a limited ability to adapt. In natural systems, limiting warming to 1.5°C rather than 2°C would enhance the ability of coastal wetlands to adapt naturally to SLR, since natural sedimentation rates more likely keep up with SLR (SR15, Hoegh-Guldberg 2018). In human systems, there is ''medium confidence'' that technical limits will be reached for hard protection to SLR beyond 2100 under high-emissions scenarios, with limits associated with socioeconomic and governance issues reached before 2100 (CCP2).

We therefore estimate the boundary between moderate and high risk to lie between 1.5°C and 2.5°C, with a median at 2.0°C, with ''medium confidence'' based on projections for melting ice sheets and drying in the Amazon. We also estimate the boundary between high and very high risk to lie between 2.5°C and 4°C, but with ''low confidence'' due to uncertainties in the projections of SLR at higher levels of warming and differences between levels of warming at which very high risks were assessed in different systems.

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'''Cross-Working Group Box ECONOMIC | Estimating Global Economic Impacts from Climate Change'''
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Authors: Steven Rose (USA), Delavane Diaz (USA), Tamma Carleton (USA), Laurent Drouet (Italy), Celine Guivarch (France), Aurélie Méjean (France), Franziska Piontek (Germany)

This Cross-Working Group Box assesses literature estimating the potential global aggregate economic costs of climate change and the social cost of carbon (SCC), where the former are sometimes referred to as estimates of global ‘climate damages’ and the latter are estimates of the potential monetised impacts to society of an additional metric ton of carbon dioxide emitted to the atmosphere. These measures include the economic costs of climate change that could be felt in market sectors such as agriculture, energy services, labour productivity and coastal resources, as well as non-market impacts such as other types of human health risks (including mortality effects) and ecosystems. Global economic impacts estimates can inform decisions about global climate management strategy, while SCC estimates can inform globally incremental emissions decisions. In practice, economic damage estimates have been used to explore economically efficient (‘economically optimal’) global emissions pathways (e.g., [[#Nordhaus--2017|Nordhaus and Moffat, 2017]] ), while SCCs have been used to inform federal and state-level policy assessment in some countries ( [[#Greenstone--2013|Greenstone et al., 2013]] ; [[#Rose--2016|Rose and Bistline, 2016]] ), but the type of SCC and application matters ( [[#Rose--2017|Rose, 2017]] ). This literature has been assessed in previous WGII reports (e.g., [[#Arent--2014|Arent et al., 2014]] ), and this box serves this need for this report. The assessment in this box was performed jointly across WGII and WGIII, building on the foundation of WGII AR6 Chapter 16’s ‘Risk to living standards’ assessment ( [[#16.5.2.3|Section 16.5.2.3.4]] ), which includes consideration of severe risks to global aggregate economic output, and WGIII AR6 Chapter 3’s assessment of the benefits of mitigation. It also informs Chapter 16’s global aggregate impacts Reasons for Concern and supports Chapter 18’s assessment of global emissions transitions, risk management and climate resilient development. In keeping with the broad risk framing presented in [[IPCC:Wg2:Chapter:Chapter-1|Chapter 1]] of this report, other lines of evidence regarding climate risks, beyond monetary estimates, should be considered in decision making, including key risks and Reasons for Concern.

'''Methods for estimating global economic costs of climate impacts'''
There are several broad approaches to estimating climate damages, including biophysical process models, structural economic models, statistical methods (also called empirical or econometric) and hybrid approaches, with each methodology having strengths and weaknesses. Process models simulate physical, natural science and/or engineering processes and their response to climate variables, which are then monetised (e.g., [[#Anthoff--2014|Anthoff and Tol, 2014]] ; [[#Sieg--2019|Sieg et al., 2019]] ; [[#Narita--2020|Narita et al., 2020]] ). Process approaches have the advantage of being explicit and interpretable, though they can be computationally intensive; may omit relevant impact channels, interactions and market dynamics affecting valuation; and often lack a rigorous empirical basis for calibration (Fisher-Vanden et al.). Structural economic modelling represents climate impacts on inputs, production, household consumption, aggregate investment, and markets for economic sectors and regional economies (e.g., [[#Reilly--2007|Reilly et al., 2007]] ; [[#Roson--2012|Roson and Van der Mensbrugghe, 2012]] ; [[#Anthoff--2014|Anthoff and Tol, 2014]] ; [[#Dellink--2019|Dellink et al., 2019]] ; Takakura et al., 2019), often using computable general equilibrium (CGE) frameworks. Structural models can evaluate how market and non-market impacts might enter and transmit through economies, and adaptation responses within input and output markets, consumer and investment choices, and inter-regional trade (e.g., [[#Darwin--2001|Darwin and Tol, 2001]] ; [[#Dellink--2019|Dellink et al., 2019]] ; Takakura et al., 2019). Statistical methods estimate economic impacts in a given sector (e.g., [[#Auffhammer--2018|Auffhammer, 2018]] ) or in aggregate (e.g., [[#Dell--2014|Dell et al., 2014]] ; [[#Burke--2015|Burke et al., 2015]] ; [[#Hsiang--2017|Hsiang et al., 2017]] ; [[#Pretis--2018|Pretis et al., 2018]] ; [[#Kahn--2019|Kahn et al., 2019]] ), inferred from observed changes in economic factors, weather and climate, with responses and net results constrained by available data. Since AR5, hybrid approaches have taken different forms to integrate process, statistical and/or structural methods, and represent a potentially promising means of leveraging the strengths of different approaches (e.g., [[#Moore--2015|Moore and Diaz, 2015]] ; and [[#Hsiang--2017|Hsiang et al., 2017]] ; [[#Moore--2017a|Moore et al., 2017a]] ; [[#Ricke--2018|Ricke et al., 2018]] ; [[#Yumashev--2019|Yumashev et al., 2019]] ; [[#Chen--2020b|Chen et al., 2020b]] ). There is also a small literature that uses expert elicitation to gather subjective assessments of climate risks and potential economic impacts ( [[#Nordhaus--1994|Nordhaus, 1994]] ; [[#IPCC--2019a|IPCC, 2019a]] ; [[#Pindyck--2019|Pindyck, 2019]] ).

In addition to differences in methods, there are also differences in scope—geographic, sectoral and temporal. Global estimates are frequently based on an aggregation of independent sector and/or regional modelling and estimates; however, there are examples of estimates from global modelling that simulate multiple types of climate impacts and their potential interactions within a single, coherent framework (e.g., [[#Roson--2012|Roson and Van der Mensbrugghe, 2012]] ; [[#Dellink--2019|Dellink et al., 2019]] ; Takakura et al., 2019). Differences in scope also represent strengths and weaknesses between the methodologies, with narrower scope allowing for more detailed assessment, but missing potential interactions with the scope not covered (e.g., other geographic areas, sectors, markets or periods of time).

Comprehensive economic estimates are challenging to produce for many reasons, including complex interactions among physical, natural and social systems; pervasive climate, socioeconomic and system response uncertainties; and the heterogeneous nature of climate impacts that vary across space and time. Critiques and commentaries of global estimation methods ( [[#Pindyck--2013|Pindyck, 2013]] ; [[#Stern--2013|Stern, 2013]] ; [[#van%20den%20Bergh--2015|van den Bergh and Botzen, 2015]] ; [[#Cropper--2017|Cropper et al., 2017]] ; [[#Diaz--2017|Diaz and Moore, 2017]] ; [[#Pindyck--2017|Pindyck, 2017]] ; [[#Rose--2017|Rose et al., 2017]] ; [[#Stoerk--2018|Stoerk et al., 2018]] ; [[#DeFries--2019|DeFries et al., 2019]] ; [[#Pezzey--2019|Pezzey, 2019]] ; [[#Calel--2020|Calel et al., 2020]] ; [[#Warner--2020|Warner et al., 2020]] ; [[#EPRI--2021|EPRI, 2021]] ; [[#Grubb--2021|Grubb et al., 2021]] ; [[#Newell--2021|Newell et al., 2021]] ) include, among other things, concerns about statistical methods estimating weather but not climate relationships, making out-of-sample extrapolations, and model specification uncertainty, concerns about the observational grounding of structural modelling, and overall concerns about the lack of adaptation consideration, as well as representation and evaluation of potential large-scale singular events such as ice sheet destabilisation or biodiversity destruction, some questioning the ability to generate robust estimates (i.e., estimates insensitive to reasonable alternative inputs and specifications), and general concerns about methodological details, transparency and justification.

Additional methodological challenges to address (see, for instance, [[#EPRI--2021|EPRI, 2021]] ; [[#Piontek--2021|Piontek et al., 2021]] ) include how to capture and represent uncertainty and variability in potential damage responses for a given climate and societal condition, combine estimates from different methods and sources (including aggregating independent sectoral and regional results), assess sensitivity and evaluate robustness of estimates (including sensitivity to model specification), capture interactions and spillovers between regions and sectors, estimate societal welfare implications (versus gross domestic product [GDP] changes) of market and non-market impacts, consider distributional effects, represent micro- and macro-adaptation processes (and adaptation costs), specify non-gradual damages and nonlinearities, and improve understanding of potential long-run economic growth effects. Note that the treatment of time preference, risk aversion and equity considerations have important welfare implications for the aggregation of both potential economic impacts and climate change mitigation costs.

In addition to updated and new methods and estimates, newer literature has explored non-gradual damages, such as climatic and socioeconomic tipping points ( [[#Lontzek--2015|Lontzek et al., 2015]] ; [[#Méjean--2020|Méjean et al., 2020]] ), potential damage to economic growth (e.g., [[#Burke--2015|Burke et al., 2015]] ; [[#Moore--2015|Moore and Diaz, 2015]] ), valuing uncertainty in potential damages ( [[#Jensen--2014|Jensen and Traeger, 2014]] ; [[#Lemoine--2016|Lemoine and Traeger, 2016]] ; Cai and Lontzek) and representing adaptation (Takakura et al., 2019; [[#Carleton--2020|Carleton et al., 2020]] ; [[#Rode--2021|Rode et al., 2021]] ). Going forward, to help advance science and decisions, a key research priority is to understand and evaluate methodological strengths and weaknesses in damage estimation, and reconcile the differences affecting comparability in such a way that it informs use of the different lines of evidence. This will require greater transparency and assessment of details and assumptions in individual methods, communication and evaluation of alternatives for specifying or calibrating climate damage functional representations with respect to climate and non-climate drivers and potential nonlinearities, including evaluating data sufficiency for levels within and beyond observations and for characterising physical system dynamics, and evaluating the sensitivity of results to model specification and input parameter choices ( [[#Cropper--2017|Cropper et al., 2017]] ). Improving the robustness of economic impact estimates is an active area of research. Below we describe the latest estimates.

'''Global estimates of the economic costs of climate impacts'''
Since AR5, many new estimates of the global economic costs of climate change have been produced. Figure Cross-Working Group Box ECONOMIC.1 shows a wide spread of estimates, with growing variance at higher levels of warming, both within and across methodology types (i.e., statistical, structural or meta-analysis). Meta-analysis is used here to refer to studies that treat other studies’ estimates as data points in an attempt to derive a synthesised functional form.

Global aggregate economic impact estimates (Figure Cross-Working Group Box ECONOMIC.1) are generally found to increase with global average temperature change, as well as vary by other drivers, such as income and population and the composition of the economy. Most estimates are nonlinear with higher marginal economic impacts at higher temperature, although some recover declining marginal economic impacts, and functional forms cannot be determined for all studies. The drivers of nonlinearity found in economic impact estimates, and the differences in nonlinearity across estimates (e.g., convex versus concave, degree of curvature), are not well understood, with methodology construction, assumptions and data all being potential factors. Relative to AR5, there have been more estimates and greater variation in estimates, including some recent estimates significantly higher than the range reported in AR5. For most of the studies shown in Figure Cross-Working Group Box ECONOMIC.1, the visible variation within a study represents alternative socioeconomic projections and climate modelling, not economic impacts response uncertainty for a given socioeconomic and climate condition. Response uncertainty could be significant, as indicated by some of the results shown in the figure (e.g., [[#Burke--2015|Burke et al., 2015]] ; [[#Rose--2017|Rose et al., 2017]] ), but methodological differences in how uncertainty is characterised (model specification, errors and confidence intervals versus distributions of results) limit comparability and assessment. Note that modelling factors between global temperature change and the economic impact calculation, such as regional temperature pattern assumptions or assumed SLR dynamics, can also impact calculated estimates (e.g., Warren et al.., 2021 PAGE09 estimates versus those in Rose et al.., 2017, Chen et al.., 2020 PAGE-ICE estimates versus Burke et al.., 2015).

From Figure Cross-Working Group Box ECONOMIC.1, we find a large span of damage estimates, even without considering uncertainty/confidence in damage responses, including for today’s level of warming (about 1°C). There is also evidence that some regions benefit from low levels of warming, leading to net benefits globally at these temperatures. The size of the span of estimates grows with global warming level, with variation across statistical estimates larger than variation in structural estimates. The structural and meta-analyses estimates appear to be in closer agreement, but that outcome is contingent on the meta-analyses’ data considerations and approach. Meta analyses to date have not assessed the alternative methods and dealt with the lack of comparability between methods.

[[File:8d6b177d58ccc741c8088e43921be606 IPCC_AR6_WGII_Figure_16_Cross-Working_Group_Box_ECONOMIC-1.png]]

'''Figure Cross-Working Group Box ECONOMIC.1 |''' '''Global aggregate economic impact estimates by global warming level (annual % global GDP loss relative to GDP without additional climate change).''' Top row panels present estimates by methodology type: (a) statistical modelling, (b) structural modelling and (c) meta-analyses, with all estimates from a paper in the same colour and estimates from methodologies other than that highlighted by the panel in grey for reference. Second row left panel (d) presents AR5 estimates. Second row right panel (e) presents all estimates in one figure, with the same colours as panels (a–d) using outlined dots for the statistical modelling estimates, solid dots for structural modelling estimates, and triangles for meta-analysis estimates. In all panels, lines represent functions, with dashed and dotted lines 5th and 95th percentile functions from structural modelling. To avoid duplication, estimates from papers using the economic impacts estimates or model formulations already represented in the figure are not included (e.g., [[#Diaz--2017|Diaz and Moore, 2017]] ; [[#Chen--2020b|Chen et al., 2020b]] ; [[#Glanemann--2020|Glanemann et al., 2020]] ; [[#Warren--2021|Warren et al., 2021]] ). The exception is Burke et al. (2018), with the different estimates shown representing variation across climate scenarios for a given aggregate economic impacts specification from Burke et al. (2015)—the ‘pooled, short run’ statistical specification. Results shown for the latter are estimates with the author’s different statistical model specifications (and a fixed climate scenario, SSP5). From top to bottom, the Burke et al. (2015) estimates are for the ‘pooled, long run’, ‘differentiated, long run’, ‘pooled, short run’ (authors’ base case) and ‘differentiated, short run’ statistical specifications. For [[#Howard--2017|Howard and Sterner (2017)]] , the authors’ preferred function is shown. Overall, estimates shown in the figure can correspond to different future years, reflecting different socioeconomic conditions and climate pathways to a global warming level. Global average temperature change bars relative to the period 1850–1900 are shown below the economic cost estimates to provide context to potential future warming. Shown are the WGI AR6 assessed best estimates and 90% intervals for the illustrative emissions scenarios considered for the near term 2021–2040, mid-term 2041–2060 and long term 2081–2100.

Differences in methodology type and scope complicate comparison, assessment and synthesis ( [[#Cropper--2017|Cropper et al., 2017]] ; [[#Diaz--2017|Diaz and Moore, 2017]] ; [[#EPRI--2021|EPRI, 2021]] ; [[#Piontek--2021|Piontek et al., 2021]] ). In particular, structural economic modelling and empirical aggregate output modelling are fundamentally different, which has been identified as an issue affecting the comparability of results ( [[#Cropper--2017|Cropper et al., 2017]] ). The different methodologies affect outcomes, with global aggregate estimates based on statistical methodologies typically higher than those from structural modelling (Figure Cross-Working Group Box ECONOMIC.1). This is, in part, due to the relationships in observational data captured by statistical modelling, assumed persistence of impacts in statistical modelling, broader adaptation responses in structural modelling, and differences in the representation of future societies and how they might evolve, respond and interact. Within statistical modelling, results are also found to be very sensitive to the statistical model specification (e.g., [[#Burke--2015|Burke et al., 2015]] ; [[#Newell--2021|Newell et al., 2021]] ). Within structural modelling, differences in representations of biophysical changes and economic structural dynamics contribute to differences across structural estimates (e.g., [[#Rose--2017|Rose et al., 2017]] ).

The wide range of estimates, and the lack of comparability between methodologies, does not allow for identification of a robust range of estimates with confidence ( ''high confidence'' ). Evaluating and reconciling differences in methodologies is a research priority for facilitating use of the different lines of evidence ( ''high confidence'' ). However, the existence of higher estimates than AR5 indicate that global aggregate economic impacts could be higher than previously estimated ( ''low confidence'' due to the lack of comparability across methodologies and robustness of estimates).

While Figure Cross-Working Group Box ECONOMIC.1 summarises global aggregate estimates, the literature exhibits significant heterogeneity in regional economic impacts that are also sensitive to methodology, model specification and societal assumptions (with, for instance, larger estimates due to the assumed size of society, but offsetting adaptive capacity improvements and adaptation responses). Regional results illustrate the potential for overall net benefits in more temperate regions at lower levels of warming with potential lower energy demand and comparative advantages in agricultural markets; however, at higher levels of warming, net losses are estimated. In addition, economic impacts for poorer households and poorer countries represent a smaller share in aggregate quantifications expressed in GDP terms than their influence on well-being or welfare ( [[#Byers--2018|Byers et al., 2018]] ; [[#Hallegatte--2020|Hallegatte et al., 2020]] ).

'''Social cost of carbon methods and estimates'''
The global economic impact estimates discussed in the previous section serve as a key input into the calculation of the value of potential net damages caused by a marginal ton of carbon dioxide emissions, or the SCC. To compute an SCC, damage estimates are commonly combined in a multi-century modelling framework with socioeconomic and emissions projections, a physical model of the climate, including a SLR component, and assumptions about the discount rate, with current frameworks having highly stylised representations of these components. Though we do not present quantitative estimates here, due to the challenge of comparability, for economic impacts methodologies (as discussed above) as well as other SCC estimation elements, large variations in SCC estimates are found in the literature assessed due to, among other things, differences in modelling component representations, input and parameter assumptions, considerations of uncertainty, and discounting, inflation, and emissions year (e.g., [[#Tol--2009|Tol, 2009]] ; [[#Tol--2018|Tol, 2018]] ; [[#Pezzey--2019|Pezzey, 2019]] ; [[#Iese--2021|Iese et al., 2021]] ). There are also different ‘variants’ of SCC estimates that differ conceptually, and in magnitude, depending on the reference condition for evaluating the impact of a marginal metric ton—is it being evaluated relative to a no-climate-policy baseline, an economically efficient pathway that weighs the benefits and costs of emissions mitigation, or a pathway based on a particular climate policy or goal such as 2°C or a concentration target ( [[#Rose--2017|Rose et al., 2017]] )? The variant of SCC has implications for its applicability to different policy contexts ( [[#Rose--2016|Rose and Bistline, 2016]] ).

In addition to the economic impacts methodological challenges discussed above with respect to aggregate economic impact estimates, the additional components needed for SCC calculations give rise to a new set of technical issues and critiques, including incorporation of uncertainties in the components beyond climate damages, links between components, and discounting ( [[#van%20den%20Bergh--2015|van den Bergh and Botzen, 2015]] ; [[#Cropper--2017|Cropper et al., 2017]] ; [[#Diaz--2017|Diaz and Moore, 2017]] ; [[#Pindyck--2017|Pindyck, 2017]] ; [[#Rose--2017|Rose et al., 2017]] ; [[#EPRI--2021|EPRI, 2021]] ). For component-specific discussions and assessment, see [[#Cropper--2017|Cropper et al. (2017)]] , Rose et al. (2017) and [[#EPRI--2021|EPRI (2021)]] .

Substantial progress has been made in recent years to better reflect complexities in the global economy, the climate system, and their interaction. For example, recent studies have explored damages to natural capital ( [[#Bastien-Olvera--2021|Bastien-Olvera and Moore, 2021]] ), the influence of imperfect substitutability between environmental services and market goods ( [[#Sterner--2008|Sterner and Persson, 2008]] ; [[#Weitzman--2012|Weitzman, 2012]] ; [[#Drupp--2021|Drupp and Hänsel, 2021]] ), the implications of heterogeneous climate change impacts across income groups ( [[#Dennig--2015|Dennig et al., 2015]] ; [[#EPRI--2021|EPRI, 2021]] ; [[#Errickson--2021|Errickson et al., 2021]] ), the potential for persistent climate impacts to economic growth instead of effects on levels of economic output ( [[#Dietz--2015|Dietz and Stern, 2015]] ; [[#Moore--2015|Moore and Diaz, 2015]] ; [[#Ricke--2018|Ricke et al., 2018]] ; [[#Kikstra--2021|Kikstra et al., 2021]] ; [[#Newell--2021|Newell et al., 2021]] ), valuing the risks of climate tipping points ( [[#Cai--2019|Cai and Lontzek, 2019]] ; Rising et al., 2020), valuing uncertainty under risk aversion ( [[#Jensen--2014|Jensen and Traeger, 2014]] ; [[#Lemoine--2016|Lemoine and Traeger, 2016]] ), and modelling a distinction between intertemporal inequality aversion and risk aversion in the social welfare utility function ( [[#Crost--2013|Crost and Traeger, 2013]] ; [[#Jensen--2014|Jensen and Traeger, 2014]] ; [[#Daniel--2015|Daniel et al., 2015]] ). These new studies have, in general, raised estimates of the SCC ( [[#Crost--2013|Crost and Traeger, 2013]] ; [[#Jensen--2014|Jensen and Traeger, 2014]] ; [[#Gerlagh--2015|Gerlagh and Michielsen, 2015]] ; [[#Moore--2015|Moore and Diaz, 2015]] ; [[#Faulwasser--2018|Faulwasser et al., 2018]] ; [[#Guivarch--2018|Guivarch and Pottier, 2018]] ; [[#Budolfson--2019|Budolfson et al., 2019]] ; [[#Cai--2019|Cai and Lontzek, 2019]] ; [[#Dietz--2019|Dietz and Venmans, 2019]] ; [[#Kalkuhl--2020|Kalkuhl and Wenz, 2020]] ), in some cases by an order of magnitude ( [[#Ricke--2018|Ricke et al., 2018]] ). However, challenges persist in terms of moving from conceptual to practical application, such as pinning down parameter specifications, modelling specific mechanisms for impacts, and more fully representing adaptation.

Despite these scientific advances, SCC estimates vary widely in the literature. Technical issues with past and current modelling (e.g., [[#Pezzey--2019|Pezzey, 2019]] ; [[#Pindyck--2019|Pindyck, 2019]] ; [[#EPRI--2021|EPRI, 2021]] ) and the challenge of comparability across methodologies imply that many estimates are not robust ( ''high confidence'' ). Also, as a result, the issue of directional bias of past estimates remains unsettled. Better representation of uncertainty in methods can improve robustness, while detailed methodology assessment and comparison will help define the relative biases of methods ( ''high confidence'' ).

'''Application to decision making'''
The literature has also assessed the application of aggregate economic impact cost and SCC estimates ( [[#Rose--2016|Rose and Bistline, 2016]] ; [[#Rose--2017|Rose et al., 2017]] ; [[#Kaufman--2020|Kaufman et al., 2020]] ) and identified conceptual and technical issues that need to be considered when using results to inform policy decisions. These issues include: accounting for endogenous marginal benefits and socioeconomic conditions in evaluating policies with non-incremental global emissions implications; consistency in assumptions and treatment of uncertainty across benefit and cost calculations; fully accounting for the streams of both mitigation costs and benefits over time; avoiding inefficiently valuing or pricing emissions more than once across policies and jurisdictions; and accounting for emissions leakage to capture net climate implications. Furthermore, concerns about the robustness of estimates have led some to recommend considering alternatives, such as using marginal mitigation cost estimates based on modelling of policy goals instead of the SCC (e.g., [[#Rose--2012|Rose, 2012]] ; [[#Pezzey--2019|Pezzey, 2019]] ; [[#Kaufman--2020|Kaufman et al., 2020]] ), although this comes with its own set of assumptions and technical challenges.

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