Editing IPCC:AR6/WGI/Chapter-11 (section)

=== 11.2.4 Projecting Changes in Extremes as a Function of Global Warming Levels ===

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The most important quantity used to characterize past and future climate change is global warming relative to its pre-industrial level. Changes in global warming are linked quasi-linearly to global cumulative carbon dioxide (CO <sub>2</sub> ) emissions (IPCC, 2013), and for their part, changes in regional climate, including many types of extremes, scale quasi-linearly with changes in global warming, often independently of the underlying emissions scenarios (SR1.5 Chapter 3; [[#Seneviratne--2016|Seneviratne et al., 2016]] ; [[#Matthews--2017|Matthews et al., 2017]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Kharin--2018|Kharin et al., 2018]] ; Y. [[#Sun--2018a|]] [[#Sun--2018|Sun et al., 2018]] a ; [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ; [[#Beusch--2020|Beusch et al., 2020]] ; [[#Li--2021|Li et al., 2021]] ). In addition, the use of global warming levels in the context of global policy documents – in particular the 2015 Paris Agreement ( [[#UNFCCC--2016|UNFCCC, 2016]] ) implies that information on changes in the climate system, and specifically extremes, as a function of global warming are of particular policy relevance. Cross-Chapter Box 11.1 provides an overview on the translation between information at global warming levels (GWLs) and scenarios.

The assessment of projections of future changes in extremes as function of GWL has an advantage in separating uncertainty associated with the global warming response (see Chapter 4) from the uncertainty resulting from the regional climate response as a function of GWLs ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). If the interest is in the projection of regional changes at certain GWLs, such as those defined by the Paris Agreement, projections based on time periods and emissions scenarios have unnecessarily larger uncertainty due to differences in model global transient climate responses. To take advantage of this feature and to provide easy comparison with SR1.5, assessments of projected changes in this chapter are largely provided in relation to future GWLs, with a focus on changes at +1.5°C, +2°C, and +4°C of global warming above pre-industrial levels (e.g., Tables 11.1 and 11.2 and regional tables in [[#11.9|Section 11.9]] ). These encompass a scenario compatible with the lowest limit of the Paris Agreement (+1.5°C), a scenario slightly overshooting the aims of the Paris Agreement (+2°C), and a ‘worst-case’ scenario with no mitigation (+4°C). Cross-Chapter Box 11.1 provides a background on the GWL sampling approach used in AR6, for the computation of GWL projections from climate models contributing to Phase 6 of the Coupled Model Intercomparison Project (CMIP6) as well as for the mapping of existing scenario-based literature for CMIP6 and the CMIP Phase 5 (CMIP5) to assessments as function of GWLs (see also [[#11.9|Section 11.9]] . and Table 11.3 for an example).

While regional changes in many types of extremes do scale robustly with global surface temperature, generally irrespective of emissions scenarios ( [[#11.1.4|Section 11.1.4]] , Figures 11.3, 11.6 and 11.7 and Cross-Chapter Box 11.1), effects of local forcing can distort this relation. For example, emissions scenarios with the same radiative forcing can have different regional extreme precipitation responses resulting from different aerosol forcing (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] b). Another example is related to forcing from land-use and land cover changes ( [[#11.1.6|Section 11.1.6]] ). Climate models often either overestimate or underestimate observed changes in annual maximum daily maximum temperature, depending on the region and considered models ( [[#Donat--2017|Donat et al., 2017]] ; [[#Vautard--2020|Vautard et al., 2020]] ). Part of the discrepancies may be due to the lack of representation of some land forcings, in particular crop intensification and irrigation (N.D. [[#Mueller--2016|]] [[#Mueller--2016|Mueller et al., 2016]] ; [[#Findell--2017|Findell et al., 2017]] ; [[#Thiery--2017|Thiery et al., 2017]] , 2020). Since these local forcings are not represented, and their future changes are difficult to project, these can be important caveats when using GWL scaling to project future changes for these regions. However, these caveats also apply to the use of scenario-based projections.

The SR1.5 (Chapter 3) assessed different climate responses at +1.5°C of global warming, including transient climate responses, short-term stabilization responses, and long-term equilibrium stabilization responses, and their implications for future projections of different extremes. Indeed, the temporal dimension – that is, when the given GWL occurs – also matters for projections, in particular beyond the 21st century, and for some climate variables related to components of the climate system associated with large inertia (e.g., sea level rise and associated extremes). Nonetheless, for assessments focused on conditions within the next decades, and for the main extremes considered in this chapter, derived projections are relatively insensitive to details of climate scenarios and can be well-estimated based on transient simulations (Cross-Chapter Box 11.1; see also SR1.5).

An important question is the identification of the GWL at which a given change in a climate extreme can begin to emerge from climate noise. Figure 11.8 displays analyses of the GWLs at which emergence in hot extremes – annual maximum daily temperature represented by TXx and heavy precipitation represented by Rx1day is identified in AR6 regions for the whole CMIP5 and CMIP6 ensembles. Overall, signals for extremes emerge very early for TXx, already below 0.2°C in many regions (Figure 11.8a,b), and at around 0.5°C in most regions. This is consistent with conclusions from the SR1.5 ( [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] for less-rare temperature extremes (TXx on the yearly time scale), which shows that a difference as small as 0.5°C of global warming – for example, between +1.5°C and +2°C of global warming – leads to detectable differences in temperature extremes in TXx in most WGI AR6 regions in CMIP5 projections (e.g., [[#Wartenburger--2017|Wartenburger et al., 2017]] ; [[#Seneviratne--2018b|Seneviratne et al., 2018b]] ). The GWL emergence for Rx1day is also largely consistent with analyses for less-extreme heavy precipitation events (Rx5day on the yearly time scale) in SR1.5 (see Chapter 3).

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[[File:cbeef96f6cb682637c8719e08f470d31 IPCC_AR6_WGI_Figure_11_8.png]]

'''Figure 11.8 |''' '''Global and regional-scale emergence of changes in temperature (a) and precipitation (b) extremes for the globe (glob.), global oceans (oc.), global lands (land), and the AR6 regions.''' Colours indicate the multi-model mean global warming level at which the difference in 20-year means of the annual maximum daily maximum temperature (TXx) and the annual maximum daily precipitation (Rx1day) become significantly different from their respective mean values during the 1850–1900 base period. Results are based on simulations from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6) multi-model ensembles. See Atlas.1.3.2 for the definition of regions. Adapted from [[#Seneviratne--2020|Seneviratne and Hauser (2020)]] under the terms of the Creative Commons Attribution licence.

To some extent, analyses as functions of GWLs replace the time axis with a global surface temperature axis. Nonetheless, information on the timing of given changes in extremes is obviously also relevant. (For information on the time frame at which given GWLs are reached, see Cross-Chapter Box 11.1 and [[IPCC:Wg1:Chapter:Chapter-4#4.6|Section 4.6]] ). Figure 11.5 provides a synthesis of attributed and projected changes in extremes as function of GWLs (see also Figures. 11.3, 11.6 and 11.7 for regional analyses).

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'''Cross-Chapter Box 11.1 | Translating Between Regional Information at Global Warming Levels''' '''Versus Scenario''' '''s for End Users'''

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'''Contributors:''' Erich Fischer (Switzerland), Mathias Hauser (Switzerland), Sonia I. Seneviratne (Switzerland), Richard Betts (United Kingdom), José M. Gutiérrez (Spain), Richard G. Jones (United Kingdom), June-Yi Lee (Republic of Korea), Malte Meinshausen (Australia/Germany), Friederike Otto (United Kingdom/Germany), Izidine Pinto (Mozambique), Roshanka Ranasinghe (The Netherlands/Sri Lanka/Australia), Joeri Rogelj (Germany/Belgium), Bjørn Samset (Norway), Claudia Tebaldi (United States of America), Laurent Terray (France)

'''Background'''

Traditionally, projections of climate variables are summarized and communicated as function of time and emissions scenarios. Recently, quantifying global and regional climate at specific global warming levels (GWLs) has become widespread, motivated by the inclusion of explicit GWLs in the long-term temperature goal of the Paris Agreement ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.2|Section 1.6.2]] ). GWLs, expressed as changes in global surface temperature relative to the 1850–1900 period (see Cross-Chapter Box 2.3), are used in SR1.5 and in the assessment of Reasons for Concerns in the WGII reports (see also Cross-Chapter Box 12.1). Cross-Chapter Box 11.1, Figure 1 illustrates how the assessment of the climate response at GWLs relates to the uncertainty in scenarios regarding the timing of the respective GWLs, as well as to the uncertainty in the associated regional climate responses, including extremes and other climatic impact-drivers (CIDs). For many (but not all) climate variables and CIDs, the response pattern for a given GWL is consistent across different scenarios (Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-term means (e.g., 20-year averages) compared to the pre-industrial period, are commonly used in the literature, and were also underlying main assessments of SR1.5 (Chapter 3).

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'''Cross-Chapter Box 11.1, Figure 1 |'''

'''Schematic representation of relationship between emissions scenarios, global warming levels (GWLs), regional climate responses, and impacts.''' The illustration shows the implied uncertainty problem associated with differentiating between 1.5°C, 2°C, and other GWLs. Focusing on GWLs raises questions associated with emissions pathways to get to these temperatures (scenarios), as well as regional climate responses and the associated impacts at the corresponding GWL (the impacts question). Adapted from [[#James--2017|James et al. (2017)]] and [[#Rogelj--2013|Rogelj (2013)]] under the terms of the Creative Commons Attribution licence.

Numerous studies have compared the regional response to anthropogenic forcing at GWLs in annual and seasonal mean values and extremes of different climate and impact variables across different multi-model ensembles and/or different scenarios (e.g., Frieler et al. , 2012; Schewe et al. , 2014; Herger et al. , 2015; Schleussner et al. , 2016; Seneviratne et al. , 2016; Wartenburger et al. , 2017; Betts et al. , 2018; [[#Dosio--2018|Dosio and Fischer, 2018]] ; Samset et al. , 2019; Tebaldi et al. , 2020 ; see Sections 4.6.1, 8.5.3, 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4 for further details). The regional response patterns at given GWLs have been found to be consistent across different scenarios for many climate variables (Cross-Chapter Box 11.1 Figure 2; Pendergrass et al. , 2015; Seneviratne et al. , 2016; Wartenburger et al. , 2017; [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ) . The consistency tends to be higher for temperature-related variables than for variables in the hydrological cycle or variables characterizing atmospheric dynamics, and for intermediate to high-emissions scenarios than for low-emissions scenarios (e.g., for mean precipitation in the Representative Concentration Pathway (RCP) 2.6 scenario: [[#Pendergrass--2015|Pendergrass et al., 2015]] ; [[#Wartenburger--2017|Wartenburger et al., 2017]] ). Nonetheless, Cross-Chapter Box 11.1 Figure 2 illustrates that, even for mean precipitation, which is known to be forcing dependent (Sections 4.6.1 and 8.5.3), scenario differences in the response pattern at a given GWL are smaller than model uncertainty and internal variability in many regions ( [[#Herger--2015|Herger et al., 2015]] ). The response pattern is further found to be broadly consistent between models that reach a GWL relatively early, and those that reach it later under a given Shared Socio-economic Pathway (SSP; see Cross-Chapter Box 11.1, Figure 2g,h).

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[[File:ae46a5e11d09f48a6a3483bfefac89b4 IPCC_AR6_WGI_CCBox_11_1_Figure_2.png]]

'''Cross-Chapter Box 11.1, Figure 2 |'''

'''(a–c) Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model mean precipitation change at 2°C global warming level (GWL) (20-year mean) in three different Shared Socio-economic Pathway (SSP) scenarios relative to 1850–1900.''' All models reaching the corresponding GWL in the corresponding scenario are averaged. The number of models averaged across is shown at the top right of the panel. The maps for the other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown) are consistent. '''(d–f)''' Same as (a–c) but for annual mean temperature. '''(g)''' Annual mean temperature change at 2°C in CMIP6 models with high warming rate reaching the GWL in the corresponding scenario before the earliest year of the assessed very likely range ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ). '''(h)''' Climate response at 2°C GWL across all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not shown in (g). The close agreement of (g) and (h) demonstrates that the mean temperature response at 2°C is not sensitive to the rate of warming, and thereby the global mean surface air temperature (GSAT) warming of the respective models in 2081–2100. Uncertainty is represented using the advanced approach: No overlay indicates regions with robust signal, where ≥66% of models show change greater than the variability threshold and ≥80% of all models agree on the sign of change; diagonal lines indicate regions with no change or no robust signal, where &lt;66% of models show a change greater than the variability threshold; crossed lines indicate regions with conflicting signal, where ≥66% of models show change greater than the variability threshold and &lt;80% of all models agree on the sign of change. For more information on the advanced approach, please refer to the Cross-Chapter Box Atlas.1.

In contrast to linear pattern scaling ( [[#Mitchell--2003|Mitchell, 2003]] ; [[#Collins--2013|Collins et al., 2013]] ), the use of GWLs as a dimension of integration does not require linearity in the response of a climate variable. It is therefore useful even for metrics that do not show a linear response, such as the frequency of heat extremes over land and oceans ( [[#Fischer--2015|Fischer and Knutti, 2015]] ; [[#Frölicher--2018|Frölicher et al., 2018]] ; [[#Kharin--2018|Kharin et al., 2018]] ; [[#Perkins-Kirkpatrick--2017|Perkins-Kirkpatrick and Gibson, 2017]] ) if the relationship of the variable of interest to the GWL is scenario independent. The latter means that the response is independent of the pathway and relative contribution of various radiative forcings. For some more complex indices like warm-spell duration, or for regions with strong aerosol changes, discrepancies can be larger (Z. [[#Wang--2017|]] [[#Wang--2017|Wang et al., 2017]] b; [[#King--2018|King et al., 2018]] ; [[#Tebaldi--2020|Tebaldi et al., 2020]] ). (See also the subsection below on GWLs vs scenarios for further caveats.)

The limited scenario dependence of the GWL-based response for many variables implies that the regional response to emissions scenarios can be split in almost independent contributions of: (i) the transient global warming response to scenarios (see Chapter 4); and (ii) the regional response as function of a given GWL, which has also been referred to as ‘regional climate sensitivity’ ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ). This property has also been used to develop regionally resolved emulators for global climate models, using global surface temperature as input ( [[#Beusch--2020|Beusch et al., 2020]] ; [[#Tebaldi--2020|Tebaldi et al., 2020]] ). Analyses of the CMIP6 and CMIP5 multi-model ensembles shows that the GWL-based responses are very similar for temperature and precipitation extremes across the ensembles ( [[#Seneviratne--2020|Seneviratne and Hauser, 2020]] ; [[#Wehner--2020|Wehner, 2020]] ; Li et al. , 2021 ). This is despite their difference in global warming response (Chapter 4), confirming a substantial decoupling between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the GWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty induced by scenario, global mean model response and internal variability (Cross-Chapter Box, Figure 1).

'''Mapping between GWL- and scenario-based responses in model analyses'''
To map scenario-based climate projections into changes at specific GWLs, first, all individual Earth system model (ESM) simulations that reach a certain GWL are identified. Second, the climate response patterns at the respective GWL are calculated using an approach termed here ‘GWL-sampling’ – sometimes also referred to as epoch analysis, time shift, or time sampling approach – taking into account all models and scenarios (Cross-Chapter Box, Figure 3). Note that the range of years when a given GWL is reached in the CMIP6 ensemble is different from the AR6 assessed range of projected global surface temperature ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ; Table 4.5). The latter further takes into account different lines of evidence, including the assessed observed warming between pre-industrial and present day, information from observational constraints on CMIP6, and emulators using the assessed transient climate response (TCR) and equilibrium climate sensitivity (ECS) ranges ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ). Hence the [[IPCC:Wg1:Chapter:Chapter-4|Chapter 4]] assessed range (Table 4.5) is the reference to determine when a given GWL is ''likely'' reached under given scenarios, while the mapping between scenarios/time frames and GWLs is used to assess the respective regional responses happening at these time frames (which also allows accounting for the global surface temperature assessment, rather than using scenarios analyses directly from CMIP6 output).

In the model-based asssessment of Chapters 4, 8, 10, 11, 12 and the Atlas, the estimation of changes at GWLs are generally defined as the 20-year time period in which the mean global surface air temperature (GSAT; Cross-Chapter Box 2.3) first exceeds a certain anomaly relative to 1850–1900 – for simulations that start after 1850, relative to all years up to 1900 (Cross-Chapter Box Figure 3). The years when each individual model reaches a given GWL for CMIP6 and CMIP5 can be found in [[#Hauser--2021|Hauser et al. (2021)]] . The changes at given GWLs are identified for each ensemble member (for all scenarios) individually. Thereby, a given GWL is potentially reached a few years earlier or later in different realizations of the same model due to internal variability, but the temperature averaged across the 20-year period analysed in any simulation is consistent with the GWL. Instead of blending the information from the different scenarios, the Interactive [[IPCC:Wg1:Chapter:Atlas|Atlas]] can be used to compare the GWL spatial patterns and timings across the different scenarios (see Section ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 1.3.1).

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'''Cross-Chapter Box11.1, Figure 3'''

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'''Illustration of the AR6 global warming level (GWL) sampling approach to derive the timing and the response at a given GWL for the case of Coupled Model Intercomparison Project Phase 6 (CMIP6) data.''' For the mapping of scenarios/time slices into GWLs for CMIP6, please refer to Table 4.2. Respective numbers for the CMIP5 multi-model experiment are provided in [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM.1). Note that the time frames used to derive the GWL time slices can also include a different number of years (e.g., 30 years for some analyses).

'''Mapping between GWL- and scenario-based responses for literature'''
A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based information is required instead, an approximated mapping of the multi-model mean can be derived based on the known GWL in the given experiments for a particular time period. As a rough approximation, CMIP6 multi-model mean projections for the near-term (2021–2040) correspond to changes at about 1.5°C, and projections for the high-end scenario (SSP5-8.5) for the long-term (2081–2100) correspond to about 4°C–5°C of global warming (see Table 4.2 for changes in the CMIP6 ensemble and the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] Supplementary Material (11.SM.1) and [[#Hauser--2021|Hauser (2021)]] for details on other time periods and CMIP5). These approximated changes are used for some of the GWL-based assessments provided in the [https://www.ipcc.ch/report/ar6/wg1/chapter/chapter-11 Chapter 11] regional tables ( [[#11.9|Section 11.9]] and Table 11.3) when literature based on scenario projections is used to assess estimated changes at given GWLs.

'''GWLs versus scenarios'''
The use of scenarios remains a key element to inform mitigation decisions (Cross-Chapter Box 1.4), to assess which emissions pathways are consistent with a certain GWL (Cross-Chapter Box 1.4, Figure 1), to estimate when certain GWLs are reached ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.4|Section 4.3.4]] ), and to assess for which variables it is meaningful to use GWLs as a dimension of integration. The use of scenarios is also essential for variables whose climate response strongly depends on the contribution of radiative forcing (e.g., aerosols) or land-use and land management changes, are time and warming rate dependent (e.g., sea level rise), or differ between transient and quasi-equilibrium states. Furthermore, the use of concentration or emission-driven scenario simulations is required if regional climate assessments need to account for the uncertainty in GSAT changes or climate-carbon feedbacks.

Forcing dependence of the GWL response is found for global mean precipitation ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.3|Section 8.4.3]] ), but less for regional patterns of mean precipitation changes (Cross-Chapter Box 11.1, Figure 2). Limited dependence is found for extremes, as highlighted above. In the cryosphere, elements that are quick to respond to warming like sea ice area, permafrost and snow, show little scenario dependence (Sections 9.3.1.1, 9.5.2.3 and 9.5.3.3), whereas slow-responding variables such as ice volumes of glaciers and ice sheets respond with a substantial delay and, due to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent for sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway followed to reach a given GWL ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.4|Section 9.6.3.4]] ).

In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of overshoot, for example in the Atlantic Meridional Overturning Circulation (e.g., Palter et al. 2018). Finally, as assessed in IPCC SR1.5, there is a difference in the response even for temperature-related variables if a GWL is reached in a rapidly warming transient state or in an equilibrium state when the land–sea warming contrast is less pronounced (e.g., King et al. 2020). However, in this Report, GWLs are used in the context of projections for the 21st century when the climate response is mostly not in equilibrium and where projections for many variables are less dependent on the pathway than for projections beyond 2100 ( [[IPCC:Wg1:Chapter:Chapter-9#9.6.3.4|Section 9.6.3.4]] ).

'''Key conclusions on assessments based on GWLs'''

GWL-based projections can inform society and policymakers on how climate would change under GWLs consistent with the aims of the Paris Agreement (stabilization at 1.5°C/well below 2°C), as well as on the consequences of missing these aims and reaching GWLs of 3°C or 4°C by the end of the century. The AR6 assessment shows that every bit of global warming matters and that changes in global warming of 0.5°C lead to statistically significant changes in mean climate and climate extremes on global scale and for large regions (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6 and 11.9, Figures 11.8 and 11.9, [[IPCC:Wg1:Chapter:Atlas|Atlas]] and Interactive Atlas), as also assessed in IPCC SR1.5.

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