Editing IPCC:AR6/WGI/Chapter-4 (section)

=== 4.6.1 Patterns of Climate Change for Specific Levels of Global Warming ===

<div id="h2-24-siblings" class="h2-siblings"></div>

This subsection provides an assessment of changes in climate at 1.5°C, 2°C, 3°C, and 4°C of globalwarming relative to the period 1850–1900 ( [[IPCC:Wg1:Chapter:Chapter-1#1.6.2|Section 1.6.2]] ), in particular a discussion of the regional patterns of change in temperature ( [[#4.6.1.1|Section 4.6.1.1]] ), precipitation ( [[#4.6.1.2|Section 4.6.1.2]] ), and aspects of atmospheric circulation ( [[#4.6.1.3|Section 4.6.1.3]] ). An assessment of changes in extreme weather events as a function of different levels of global warming is provided in Chapter 11, while corresponding analyses of regional climate change are provided in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] and in the Atlas. This section builds upon assessments from AR5 ( [[#Bindoff--2013|Bindoff et al., 2013]] ; [[#Christensen--2013|Christensen et al., 2013]] ; [[#Collins--2013|Collins et al., 2013]] ; [[#Hartmann--2013|Hartmann et al., 2013]] ) and SR1.5 (SR1.5; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), as well as new literature related to projections of climate at 1.5°C, 2°C, and higher levels of global warming above pre-industrial levels.

Several methodologies have been applied to estimate the spatial patterns of climate change associated with a given level of global warming. These include performing model simulations under stabilisation scenarios designed to achieve a specific level of global warming, the analysis of epochs identified within transient simulations that systematically exceed different thresholds of global warming (e.g., [[#Mitchell--2017|Mitchell et al., 2017]] ; [[#Dosio--2018|Dosio et al., 2018]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#Kjellström--2018|Kjellström et al., 2018]] ), and analysis based on statistical methodologies that include empirical scaling relationships (ESR; [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Dosio--2018|Dosio and Fischer, 2018]] ; [[#Seneviratne--2018|Seneviratne et al., 2018]] ) and statistical pattern scaling (e.g., [[#Kharin--2018|Kharin et al., 2018]] ). These different methodologies are assessed in some detail in [[#4.2.4|Section 4.2.4]] ( [[#James--2017|James et al., 2017]] ) and generally provide qualitatively consistent results regarding changes in the spatial patterns of temperature and rainfall means and extremes (see Chapter 11) at different levels of global warming.

In this subsection, we present the projected patterns of climate change obtained following the epoch approach (also called the time-shift method, see [[#4.2.4|Section 4.2.4]] ) under the Tier 1 SSPs (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). For each model simulation considered under each of these SSPs, 20-year moving averages of the global average atmospheric surface temperature are first constructed, then this time series is used to detect the first year during when GSAT exceeds the 1.5°C, 2°C, 3°C and 4°C thresholds with respect to the 1850–1900 (Cross-Chapter Box 11.1). The temperature thresholds are not exceeded in all the model simulations across the Tier 1 SSPs. That is, decreasing numbers of simulations are available for the analysis of patterns of change as the temperature threshold increases. For each simulation within which a given temperature threshold is exceeded, a 20-year global climatology is subsequently constructed to represent that level of global warming, centred on the year for which the threshold was first exceeded. The composite of all such climatologies across the Tier 1 SSPs and model simulations constitute the spatial patterns of change for a given temperature threshold. Some of the complexities of scaling patterns of climate change with different levels of global warming are also discussed in the following sections. These include overshoot versus stabilization scenarios and limitations of pattern scaling for strong mitigation and stabilization scenarios ( [[#Tebaldi--2014|Tebaldi and Arblaster, 2014]] ). At least for the case of annual mean temperature and precipitation, strong evidence exists that even for strong mitigation and stabilization scenarios, patterns of change at lower levels of warming scale similarly to those reconstructed from transient simulations using either standard pattern-correlation or time-shift methodologies ( [[#Tebaldi--2018|Tebaldi and Knutti, 2018]] ).

Pattern scaling performance based on scenario experiments is generally better for near-surface temperature than for precipitation ( [[#Ishizaki--2013|Ishizaki et al., 2013]] ). For precipitation, rapid adjustments due to different forcing agents must be accounted for ( [[#Richardson--2016|Richardson et al., 2016]] ). Possible non-linear responses to different forcing levels are also important ( [[#Good--2015|Good et al., 2015]] , 2016). Pattern scaling does not work as well at high forcing levels ( [[#Osborn--2018|Osborn et al., 2018]] ). It is also important to distinguish the forced response from internal variability when comparing similar warming levels ( [[#Suárez-Gutiérrez--2018|Suárez-Gutiérrez et al., 2018]] ). The purpose of this section is not to repeat the analysis for all the variables considered in Sections 4.4 and 4.5, but rather to show a selected number of key variables that are important from the perspective of understanding the response of the physical climate system to different levels of warming.

<div id="4.6.1.1" class="h3-container"></div>

<span id="temperature-1"></span>
==== 4.6.1.1 Temperature ====

<div id="h3-36-siblings" class="h3-siblings"></div>

Global warming of 1.5°C implies higher mean temperatures compared to 1850–1900, with generally higher warming over land compared to ocean areas ( ''virtuallycertain'' ) and larger warming in high latitudes compared to low latitudes (Figure 4.31). In addition, global warming of 2°C versus 1.5°C results in robust increases in the mean temperatures in almost all locations, both on land and in the ocean ( ''virtually certain'' ), with subsequent further warming at almost all locations at higher levels of global warming ( ''virtually certain'' ) ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). For each particular level of global warming, relatively larger mean warming is projected for land regions ( ''virtually certain'' ) (see Figure 4.31; [[#Christensen--2013|Christensen et al., 2013]] ; [[#Collins--2013|Collins et al., 2013]] ; [[#Seneviratne--2016|Seneviratne et al., 2016]] ). The projected changes at 1.5°C and 2°C global warming are consistent with observed historical global trends in temperature and their attribution to anthropogenic forcing (Chapter 3), as well as with observed changes under the recent global warming of 0.5°C ( [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). That is, spatial patterns of temperature changes associated with the 0.5°C difference in GMST warming between 1991–2010 and 1960–1979 ( [[#Schleussner--2017|Schleussner et al., 2017]] ; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) are consistent with projected changes under 1.5°C and 2°C of global warming.

<div id="_idContainer080" class="Basic-Text-Frame"></div>

[[File:68da52712f4289321d6b5d7412f0fcaf IPCC_AR6_WGI_Figure_4_31.png]]

'''Figure 4.31''' '''|''' '''Projected spatial patterns of change in annual average near-surface temperature (°C) at different levels of global warming.''' Displayed are '''(a–d)''' spatial patterns of change in annual average near-surface temperature at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900 and '''(e–g)''' spatial patterns of differences in temperature change at 2°C, 3°C, and 4°C of global warming compared to 1.5°C of global warming. The number of models used is indicated in the top right of the maps. No overlay indicates regions where the change is robust and ''likely'' emerges from internal variability. That is, where at least 66% of the models show a change greater than the internal-variability threshold ( [[#4.2.6|Section 4.2.6]] ) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The largest increase in annual mean temperature is found in the high latitudes of the Northern Hemisphere across all levels of global warming ( ''virtually certain'' ) (Figure 4.31). This phenomenon peaks in the Arctic and is known as Arctic amplification, with the underlying physical mechanisms assessed in detail in [[#4.5.1|Section 4.5.1]] and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.4.4.1). For the CMIP6 ensemble average considered here, Arctic annual mean temperatures warm by a factor of 2.3, 2.5, 2.4 and 2.4 for 1.5°C, 2°C, 3°C and 4°C of global warming, respectively. That is, Arctic warming scales approximately linearly with GSAT. Generally, when Arctic amplification is considered across individual models, warming occurs at a factor of two to four times the global level of warming. It is ''unlikely'' that warming in the Southern Hemisphere high latitudes in the 21st century will exceed the change in GSAT, or that it will substantially exceed warming in the tropics, for GSAT change ranging between 1.5°C and 4°C (Figure 4.31 and Table 4.2). Correspondingly, there is ''low confidence'' of Antarctic amplification occurring under transient, 21st century low mitigation scenarios (Table 4.2 and Section 7.4.4.1). The Antarctic continent is projected to warm at a higher rate than the mid-latitude Southern Ocean, however, at all levels of global warming (Figure 4.31). The relevant physical mechanisms that reduce the amplitude of polar amplification over Antarctica compared to the Arctic are assessed in detail in [[#4.5.1|Section 4.5.1]] and [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.4.4.1). In the Southern Hemisphere the strongest warming over land is to occur, at any given level of global warming, over the subtropical areas of South America, southern Africa and Australia ( ''high confidence'' ). The relatively strong warming in subtropical southern Africa may be attributed to strong soil-moisture–temperature coupling and projected increased dryness under enhanced subsidence ( [[#Engelbrecht--2015|Engelbrecht et al., 2015]] ; [[#Vogel--2017|Vogel et al., 2017]] ). Across the globe, in the tropics, subtropics, and mid- to high latitudes, temperatures tend to scale linearly with the level of increase in GSAT and patterns of change are largely scenario independent ( ''high confidence'' ).

<div id="4.6.1.2" class="h3-container"></div>

<span id="precipitation-4"></span>
==== 4.6.1.2 Precipitation ====

<div id="h3-37-siblings" class="h3-siblings"></div>

While global mean precipitation increases as GSAT rises with the ''very likely'' range of 1–3% per 1°C ( ''high confidence'' ) (Sections 8.2.1 and 8.4.1), patterns of precipitation change do not scale as linearly with GSAT increase. Nevertheless, common features of precipitation change in the multi-model mean across scenarios still exist for different levels of global warming (Figure 4.32). Precipitation will ''very likely'' increase in the high latitudes and over tropical regions, and will ''likely'' increase in large parts of the monsoon region, but are ''likely'' to decrease over the subtropical regions, including the Mediterranean, southern Africa, parts of Australia and South America at all four levels of global warming. The increases and decreases in precipitation will amplify at higher levels of global warming ( ''high confidence'' ) (Figure 4.32). Changes in extreme precipitation events under different levels of global warming are assessed in Chapter 11.

<div id="_idContainer082" class="Basic-Text-Frame"></div>

[[File:03b9f70ce7cce3b59a103943cd279d67 IPCC_AR6_WGI_Figure_4_32.png]]

'''Figure 4.32 |''' '''Projected spatial patterns of change in annual average precipitation (expressed as a percentage change) at different levels of global warming.''' Displayed are '''(a–d)''' spatialpatterns of change in annual precipitation at 1.5°C, 2°C, 3°C, and 4°C of global warming relative to the period 1850–1900. No map overlay indicates regions where the change is robust and ''likely'' emerges from internal variability, that is, where at least 66% of the models show a change greater than the internal-variability threshold ( [[#4.2.6|Section 4.2.6]] ) and at least 80% of the models agree on the sign of change. Diagonal lines indicate regions with no change or no robust significant change, where fewer than 66% of the models show change greater than the internal-variability threshold. Crossed lines indicate areas of conflicting signals where at least 66% of the models show change greater than the internal-variability threshold but fewer than 80% of all models agree on the sign of change. Values were assessed from a 20-year period at a given warming level, based on model simulations under the Tier-1 SSPs of CMIP6. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The SR1.5 stated ''low confidence'' regarding changes in global monsoons at 1.5°C versus 2°C of global warming, as well as differences in monsoon responses at 1.5°C versus 2°C. Generally, statistically significant changes in regional annual average precipitation are expected at a global mean warming of 2.5°C–3°C or more ( [[#Tebaldi--2015|Tebaldi et al., 2015]] ). Over the austral-winter rainfall regions of south-western South America, South Africa and Australia, projected decreases in mean annual rainfall show '''high agreement''' across models and a strong climate change signal even under 1.5°C of global warming, with further amplification of the signal at higher levels of global warming ( ''high confidence'' ) ( [[#Mindlin--2020|Mindlin et al., 2020]] ). This is a signal evident in observed rainfall trends over these regions (Sections 2.3.1.3 and 8.3.1.6 ). Also, over the Asian monsoon regions, increases in rainfall will occur at 1.5°C and 2°C of global warming ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ). At warming levels of 1.5°C and 2°C, the changes in global monsoons are strongly dependent on the modelling strategies used, such as fully coupled transient, fully coupled quasi-equilibrium, and atmosphere-only quasi-equilibrium simulations. In particular, the differences of regional monsoon changes among model setups are dominated by strategy choices such as transient versus quasi-equilibrium set-up, prescription of SST, and treatment of aerosols ( [[#Zhang--2021|Zhang and Zhou, 2021]] ).

The global and land area fractions with significant precipitation changes with global warming are shown in Figure 4.33. It is ''virtually certain'' that average warming will be higher over land. As warming increases, a larger global and land area will experience statistically significant increases or decreases in precipitation ( ''medium confidence'' ). The increase of the area fraction with significant precipitation increase is larger over land than over the ocean, but the increase of the area fraction with significant precipitation decrease is larger over the ocean than over land (Figure 4.33). Precipitation variability in most climate models increases over the global land area in response to warming ( [[#Pendergrass--2017|Pendergrass et al., 2017]] ).

<div id="_idContainer084" class="Basic-Text-Frame"></div>

[[File:234ba024605e8d9502eda050a0989ea3 IPCC_AR6_WGI_Figure_4_33.png]]

'''Figure 4.33''' '''|''' '''Area fraction of significant precipitation change at 1.5°C, 2°C, 3°C, and 4°C of global warming.''' Range of land fraction '''(top)''' and global area fraction '''(bottom)''' with significant precipitation increase '''(left-hand side)''' and decrease '''(right-hand side)''' in the projected annual precipitation change (%) at levels of global warming compared to the period 1850–1900. Values were assessed from a 20-year period at a given warming level from SSP1-2.6, SSP3-7.0 and SSP5-8.5 in CMIP6. The solid line illustrates the CMIP6-multi model mean and the shaded band is the 5–95% range across models that reach a given level of warming. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, based on the assessment of CMIP6 models, there is ''high confidence'' that global mean precipitation will increase with increase in global mean surface temperature. Precipitation will ''very likely'' increase in the high latitudes and over tropical regions, ''likely'' increase in large parts of the monsoon region, but will ''likely'' decrease over the subtropical regions. There is ''high confidence'' that increases and decreases in precipitation will amplify over higher levels of global warming. As warming increases, there is ''medium confidence'' that a larger land area will experience statistically significant increases or decreases in precipitation.

<div id="4.6.1.3" class="h3-container"></div>

<span id="atmospheric-circulation"></span>
==== 4.6.1.3 Atmospheric Circulation ====

<div id="h3-38-siblings" class="h3-siblings"></div>

The AR5 reported that the application of pattern scaling to extract information on variables other than surface temperature and precipitation was relativelyunexplored. Since AR5, new studies have examined the relationship between projections of mid-latitude atmospheric circulation and GSAT both in terms of interpreting spread in responses across the CMIP5 multi-model ensemble ( [[#Grise--2014a|Grise and Polvani, 2014a]] , 2016) and to investigate variations in the circulation response as a function of GSAT change over time within a given forcing experiment ( [[#Grise--2017|Grise and Polvani, 2017]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ).

At a fixed time horizon, the CMIP5 multi-model spread in GSAT explains only a small fraction of the spread in the shift of the Northern Hemisphere mid-latitude circulation due to an abrupt quadrupling in CO <sub>2</sub> ( [[#Grise--2016|Grise and Polvani, 2016]] ). The fraction of model spread explained by GSAT in the shift of the Southern Hemisphere circulation is larger, but still fairly small ( [[#Grise--2014a|Grise and Polvani, 2014a]] , 2016). At a fixed time horizon and for a given emissions scenario, CMIP5 multi-model spread in storm track shifts, and the closely related mid-latitude jets, can be better explained by multi-model spread in lower and upper level meridional temperature gradients than by GSAT ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Grise--2016|Grise and Polvani, 2016]] ).

In the North Atlantic, North Pacific, and Southern Hemisphere, the transient response of the mid-latitude jets to forcing behaves non-linearly with GSAT ( [[#Grise--2017|Grise and Polvani, 2017]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ). This is a consequence of the time-dependence of the relationship between radiative forcing and GSAT and the temporal evolution of SST patterns ( [[#Ceppi--2018|Ceppi et al., 2018]] ), with a potential seasonal component in the SH associated with polar stratospheric temperature changes ( [[#Grise--2017|Grise and Polvani, 2017]] ). Consequently, the epoch approach applied to a transient simulation of the 21st century will overestimate the mid-latitude circulation response in a stabilized climate. Dedicated time slice experiments simulating stabilized climates are therefore required to assess differences in mid-latitude circulation at given levels of global warming ( [[#Li--2018|Li et al., 2018]] ). A further complication in the SH is the competing influences of ozone recovery and increasing GHG concentrations on the austral-summer mid-latitude circulation during the first half the 21st century ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ). Using transient 21st century experiments to diagnose changes in SH mid-latitude circulation at different levels of warming therefore confounds the effects of ozone recovery and GHG increases ( [[#Ceppi--2018|Ceppi et al., 2018]] ). Given these various limitations, we do not apply epoch analysis to assess mid-latitude atmospheric circulation changes and related annular modes of variability.

<div id="4.6.2" class="h2-container"></div>

<span id="climate-goals-overshoot-and-path-dependence"></span>