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

=== 4.5.1 Atmosphere ===

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This section assesses how the global atmospheric indicators assessed in [[#4.3|Section 4.3]] manifest themselves in large-scale spatial patterns of atmospheric change in the mid-term (2041–2060) and long term (2081–2100). The patterns of change in any given future period represent a combination of unforced internal variability and a forced response including their interaction ( [[#Deser--2016|Deser et al., 2016]] ). The role of internal variability is much larger at the local to regional scale than in the global mean projections. We here assess multi-model mean patterns based on CMIP6 models without any weighting or emergent constraints. The mean represents an estimate of the forced response and is a more homogeneous pattern than the 20-year mean change patterns in any individual model realization ( [[#Knutti--2010|Knutti et al., 2010]] ).

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<span id="near-surface-air-temperature"></span>
==== 4.5.1.1 Near-surface Air Temperature ====

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Patterns of near-surface air temperature changes show widespread warming by 2041–2060 and 2081–2100 (Figure 4.19) for all SSPs relative to 1995–2014. The area fraction experiencing warming increases with the level of global mean warming. As GSAT continues to increase, it is ''very likely'' that by the middle and the end of the 21st century most of the global land and ocean areas will be warmer than in 1995–2014 ( ''high confidence'' , [[#4.3.1.1|Section 4.3.1.1]] ).

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[[File:d43b7f71006864008c202a2838895a53 IPCC_AR6_WGI_Figure_4_19.png]]

'''Figure 4.19 |''' '''Mid-and long-term change of annual mean surface temperature.''' Displayed are projected spatial patterns of multi-model mean change in annual mean near-surface air temperature (°C) in 2041–2060 and 2081–2100 relative to 1995–2014 for '''(top)''' SSP1-2.6 and '''(bottom)''' SSP3-7.0. 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 (see [[#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. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

The multi-model mean temperature change pattern (Figure 4.19) shows some robust key characteristics that are independent of the time horizon and scenario, such as a land–ocean warming contrast, amplified warming over the Arctic region, assessed below, or the comparatively small warming or even cooling in the North Atlantic subpolar gyre (Section 9.2.1.1). Furthermore, changes in aerosol concentrations and land use and land management can have a direct imprint on the regional warming pattern ( [[#Bright--2017|Bright et al., 2017]] ; [[#Kasoar--2018|Kasoar et al., 2018]] ). Note that the global average of the pattern shown in Figure 4.19 corresponds to CMIP6 multi-model mean GSAT warming ( [[#4.3.1|Section 4.3.1]] ) and is thus somewhat warmer than the warming pattern consistent with the central estimate of the GSAT range assessed in [[#4.3.4|Section 4.3.4]] . Since the regional mean warming scales well with global warming levels independent of the emissions scenario ( [[#4.2.4|Section 4.2.4]] ), the key characteristics of the spatial pattern assessed here are largely independent of the difference between CMIP6 multi-model global mean and assessed global GSAT change.

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===== 4.5.1.1.1 Land–ocean warming contrast =====

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It is ''virtually certain'' that future average warming will be higher over land than over the ocean. [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] of SRCCL (G. [[#Jia--2019|]] [[#Jia--2019|Jia et al., 2019]] ) assessed that it is certain that land temperatures have increased more than global mean temperatures since the pre-industrial period. This so-called land–ocean warming contrast is a striking feature of observed trends ( [[#Lambert--2007|Lambert and Chiang, 2007]] ; [[#Byrne--2018|Byrne and O’Gorman, 2018]] ) and projected changes in surface-air temperature ( [[#Sutton--2007|Sutton et al., 2007]] ; [[#Joshi--2008|Joshi and Gregory, 2008]] ; [[#Dong--2009|Dong et al., 2009]] ; [[#Lambert--2011|Lambert et al., 2011]] ; [[#Drost--2012|Drost et al., 2012]] ; [[#Bayr--2013|Bayr and Dommenget, 2013]] ; [[#Byrne--2013b|Byrne and O’Gorman, 2013b]] ; [[#Izumi--2013|Izumi et al., 2013]] ; [[#Joshi--2013|Joshi et al., 2013]] ). Between 1979 and 2016, average temperature over land increased by 42% more than over the ocean ( [[#Byrne--2018|Byrne and O’Gorman, 2018]] ). A similar warming contrast is found in CMIP5 projections though with large differences across models and latitudes ( [[#Sutton--2007|Sutton et al., 2007]] ; [[#Drost--2012|Drost et al., 2012]] ; [[#Byrne--2013b|Byrne and O’Gorman, 2013b]] ; [[#Joshi--2013|Joshi et al., 2013]] ), which is also consistent with paleoclimate evidence ( [[#Izumi--2013|Izumi et al., 2013]] ; [[#Schmidt--2014|Schmidt et al., 2014]] ). The ratio of land-to-ocean warming is greater than one for almost all regions ( ''high confidence'' ) and is larger for dry subtropical continents (about 1.5) than for moist regions in the tropics and mid-latitudes (about 1.2; [[#Byrne--2013a|Byrne and O’Gorman, 2013a]] ). Projected warming over land and ocean only is shown in Table 4.2 for different scenarios, and the global average ratio of land-to-ocean warming in CMIP6 is 1.5 with a ''likely'' range of 1.4 to 1.7, which is consistent with estimates based on CMIP5.

Since AR5, a robust physical understanding of the warming contrast been developed. A simple theory based on atmospheric dynamics and moisture transport shows that surface-air temperature and relative humidity over land are strongly coupled, and demonstrates that the warming contrast occurs because air over land is drier than over the ocean ( [[#Joshi--2008|Joshi et al., 2008]] ; [[#Byrne--2013a|Byrne and O’Gorman, 2013a]] , b, 2018). The warming contrast causes land relative humidity to decrease ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] , 2018; [[#Chadwick--2016|Chadwick et al., 2016]] ) and this feeds back on and strengthens the warming contrast. Differences in land-relative humidity responses across models are the primary cause of uncertainty in the land–ocean warming contrast ( [[#Byrne--2013b|Byrne and O’Gorman, 2013b]] ). These land-relative humidity changes are ultimately controlled by moisture transport between the land and ocean boundary layers ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] ; [[#Chadwick--2016|Chadwick et al., 2016]] ) and are also sensitive to characteristics of land surfaces that are challenging to model, including stomatal conductance and soil moisture ( [[#Berg--2016|Berg et al., 2016]] ; [[#Zarakas--2020|Zarakas et al., 2020]] ).

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<span id="polar-amplification"></span>
===== 4.5.1.1.2 Polar amplification =====

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It is ''very likely'' that under all SSPs the warming in the Arctic will be more pronounced than in the global average over the 21st century. Since AR5 the understanding of the physical mechanisms driving polar amplification has improved.

The Arctic surface is projected to warm more than the global average over the 21st century, with annual-average Arctic warming of about 3°C (SSP1-2.6), 10°C (SSP3-7.0) and 12°C in (SSP5-8.5) by 2081–2100 relative to 1995–2014 (Figure 4.19). This phenomenon, known as polar or Arctic amplification, is a ubiquitous feature of the response to GHG forcing simulated by climate models ( [[#Manabe--1975|Manabe and Wetherald, 1975]] , 1980; [[#Manabe--1980|Manabe and Stouffer, 1980]] ; [[#Robock--1983|Robock, 1983]] ; [[#Hansen--1984|Hansen et al., 1984]] ; [[#Manabe--1991|Manabe et al., 1991]] ; [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Winton--2006|Winton, 2006]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ) and has been observed over recent decades concurrent with Arctic sea ice loss ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1|Section 2.3.2.1]] ; [[#Serreze--2011|Serreze and Barry, 2011]] ). Based on robust scientific understanding and agreement across multiple lines of evidence (Section 7.4.4.1), there is ''high confidence'' that the rate of Arctic surface warming will continue to exceed the global average over the 21st century.

A variety of mechanisms contribute to Arctic amplification (Section 7.4.4.1.1). While surface-albedo feedbacks associated with the loss of sea ice and snow have long been known to play important roles ( [[#Arrhenius--1896|Arrhenius, 1896]] ; [[#Manabe--1980|Manabe and Stouffer, 1980]] ; [[#Robock--1983|Robock, 1983]] ; [[#Hall--2004|Hall, 2004]] ), it is now recognized that temperature (lapse-rate and Planck) feedbacks also contribute to Arctic amplification through a less efficient longwave radiative damping to space with warming at high latitudes ( [[#Winton--2006|Winton, 2006]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Goosse--2018|Goosse et al., 2018]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ). Increases in poleward atmospheric latent heat transport and oceanic heat transport also contribute to Arctic warming ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Bitz--2006|Bitz et al., 2006]] ; [[#Lee--2011|Lee et al., 2011]] , [[#Lee--2017|Lee et al., 2017]] ; [[#Alexeev--2013|Alexeev and Jackson, 2013]] ; [[#Marshall--2014|Marshall et al., 2014]] , 2015; [[#Woods--2016|Woods and Caballero, 2016]] ; [[#Nummelin--2017|Nummelin et al., 2017]] ; [[#Singh--2017|Singh et al., 2017]] ; [[#Merlis--2018|Merlis and Henry, 2018]] ; [[#Oldenburg--2018|Oldenburg et al., 2018]] ; [[#Armour--2019|Armour et al., 2019]] ; [[#Beer--2020|Beer et al., 2020]] ). Projected reduction in the strength of the AMOC over the 21st century is expected to reduce Arctic warming, but even a strong AMOC reduction would not eliminate Arctic amplification entirely ( ''medium confidence'' ) ( [[#Liu--2017|Liu et al., 2017]] ; [[#Liu--2018|Liu et al., 2018]] ; [[#Wen--2018|Wen et al., 2018]] ).

There remains substantial uncertainty in the magnitude of projected Arctic amplification ( [[#Smith--2020|Smith et al., 2020]] ), with the Arctic warming ranging from two to four times the global average in models ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Nummelin--2017|Nummelin et al., 2017]] ). This uncertainty primarily stems from different representations of polar surface-albedo, lapse-rate, and cloud feedbacks, and from different projected poleward energy transport changes ( [[#Holland--2003|Holland and Bitz, 2003]] ; [[#Crook--2011|Crook et al., 2011]] ; [[#Mahlstein--2011|Mahlstein and Knutti, 2011]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Bonan--2018|Bonan et al., 2018]] ). The magnitude of Arctic amplification may also depend on the mix of radiative forcing agents ( [[#Najafi--2015|Najafi et al., 2015]] ; [[#Sand--2016|Sand et al., 2016]] ; [[#Stjern--2019|Stjern et al., 2019]] ) such as the contribution of ozone depleting substances ( [[#Polvani--2020|Polvani et al., 2020]] ). Tropospheric aerosol emissions tend to reduce simulated Arctic warming over the middle of the 20th century ( [[#Gagné--2017b|Gagné et al., 2017b]] ) and consequently aerosol emission reductions in observations and SSP scenarios enhance simulated Arctic warming over recent and future decades (Section 6.4.3; [[#Gagné--2015|Gagné et al., 2015]] ; [[#Acosta%20Navarro--2016|Acosta Navarro et al., 2016]] ; [[#Wobus--2016|Wobus et al., 2016]] ; [[#Wang--2018|Wang et al., 2018]] ).

Climate models project a weaker polar amplification in the SH than in the NH under transient warming (Figure 4.19). Model simulations ( [[#Hall--2004|Hall, 2004]] ; [[#Danabasoglu--2009|Danabasoglu and Gent, 2009]] ; [[#Li--2013|Li et al., 2013]] ) and paleoclimate proxies indicate polar amplification in both hemispheres near equilibrium, but generally with less warming in the Antarctic than the Arctic (Section 7.4.4.1.2). The primary driver of delayed warming of the southern high latitudes is the upwelling in the Southern Ocean and associated ocean heat uptake that is then transported away from Antarctica by northward flowing surface waters ( [[#Frölicher--2015|Frölicher et al., 2015]] ; [[#Marshall--2015|Marshall et al., 2015]] ; [[#Armour--2016|Armour et al., 2016]] ; [[#Liu--2018|Liu et al., 2018]] ), although asymmetries in feedbacks between the poles also play a role (Section 7.4.4.1.1). Changes in westerly surface winds over the Southern Ocean have the potential to affect the rate of sea-surface warming, but there is currently ''low confidence'' in even the sign of the effect based on a diverse range of climate model responses to wind changes ( [[#Marshall--2014|Marshall et al., 2014]] ; [[#Ferreira--2015|Ferreira et al., 2015]] ; [[#Kostov--2017|Kostov et al., 2017]] ; [[#Seviour--2019|Seviour et al., 2019]] ). A substantial increase in freshwater input to the ocean from the Antarctic ice sheet could further slow the emergence of SH polar amplification by cooling the Southern Ocean surface ( [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ; [[#Schloesser--2019|Schloesser et al., 2019]] ), but this process is not represented in current climate models which lack dynamic ice sheets. Thus, while there is ''high confidence'' that the SH high latitudes will warm by more than the tropics on centennial time scales, there is ''low confidence'' that such a feature will emerge this century (Section 7.4.4.1).

<span id="seasonal-warming-patterns"></span>
====== Seasonal warming patterns ======

The warming pattern shows distinct seasonal characteristics. The majority of models show a stronger hemispheric winter than summer warming over land poleward of about 55°N and 55°S (Figure 4.20) and thereby a reduced amplitude of the temperature cycle ( [[#Dwyer--2012|Dwyer et al., 2012]] ; [[#Donohoe--2013|Donohoe and Battisti, 2013]] ). On the other hand, over most of the subtropics and mid-latitudinal land regions except for parts of Asia, models project stronger warming in hemispheric summer than winter ( [[#Donohoe--2013|Donohoe and Battisti, 2013]] ; [[#Santer--2018|Santer et al., 2018]] ), leading to an amplification of the seasonal cycle. This phenomenon has been studied particularly in the case of the amplified summer warming over the Mediterranean region ( [[#Seager--2014a|Seager et al., 2014a]] ; [[#Kröner--2017|Kröner et al., 2017]] ; [[#Brogli--2019|Brogli et al., 2019]] ).

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[[File:ed083936f8e40287a47ab4d11c8445d8 IPCC_AR6_WGI_Figure_4_20.png]]

'''Figure 4.20''' '''|''' '''Difference of surface temperature change between June–July–August (JJA) and December–January–February (DJF).''' Displayed are spatial patterns of multi-model mean difference in projected warming in JJA minus warming in DJF in 2081–2100 relative to 1995–2014 for '''(left)''' SSP1-2.6 and '''(right)''' SSP3-7.0. Diagonal lines mark areas where fewer than 80% of the models agree on the sign of change, and no overlay where at least 80% of the models agree. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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<span id="changes-in-temperature-variability"></span>
===== 4.5.1.1.3 Changes in temperature variability =====

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It has long been recognized that along with mean temperatures also variance and skewness of the temperature distribution may be changing ( [[#Gregory--1995|Gregory and Mitchell, 1995]] ; [[#Mearns--1997|Mearns et al., 1997]] ). By amplifying or dampening changes in the tail of temperature distribution such changes are potentially highly relevant to extremes (Section 11.3.1) and pose a serious challenge to adaptation measures. Changes in temperature variability can occur from diurnal to multi-decadal time scales and from the local to the global scale with potentially even opposing signals in different seasons and at the different spatial scales

Changes in GSAT variability are poorly understood. Based on model experiments it has been suggested that unforced variability of GSAT tends to decrease in a warmer world as a result of reduced albedo variability in high latitudes resulting from melting snow and sea ice ( [[#Huntingford--2013|Huntingford et al., 2013]] ; [[#Brown--2017|Brown et al., 2017]] ), but ''confidence'' remains ''low'' and an observed change has not been detected. An assessment of changes in global temperature variability is inherently challenging due to the interplay of unforced internal variability and forced changes.

Changes in tropical temperature variability may arise from changes in the amplitude of ENSO ( [[#4.5.3.2|Section 4.5.3.2]] ). Over the extratropics, several studies have identified robust large-scale patterns of changes in variability of annual and particularly seasonal mean temperature, including (i) a reduction in mid- to high-latitude winter temperature variability and (ii) an increase in summer temperature variability over land in the tropics and subtropics ( [[#Huntingford--2013|Huntingford et al., 2013]] ; [[#Holmes--2016|Holmes et al., 2016]] ; see Figure 4.21). The multi-ensemble average across seven single-model initial-condition large ensembles projects a consistent reduction in year-to-year December–January–February (DJF) variability around about 50°N–70°N and June–July–August (JJA) variability around 55°S–70°S along the edge of the sea ice- and snow-covered region (Figure 4.21). There is growing evidence that year-to-year and day-to-day temperature variability decreases in winter over northern mid- to high-latitudes ( [[#Fischer--2011|Fischer et al., 2011]] ; [[#De%20Vries--2012|De Vries et al., 2012]] ; [[#Screen--2014|Screen, 2014]] ; [[#Schneider--2015|Schneider et al., 2015]] ; [[#Holmes--2016|Holmes et al., 2016]] ; [[#Borodina--2017|Borodina et al., 2017]] ; [[#Tamarin-Brodsky--2020|Tamarin-Brodsky et al., 2020]] ) which implies that the lowest temperatures rise more than the respective climatological mean temperatures ( ''medium confidence'' ). Over the NH, reduced high-latitude temperature variability is associated with disproportionally large warming in source region of cold-air advection due to Arctic amplification and land–sea contrast ( [[#De%20Vries--2012|De Vries et al., 2012]] ; [[#Screen--2014|Screen, 2014]] ; [[#Holmes--2016|Holmes et al., 2016]] ). It has further been argued that a reduction in snow and sea ice coverage from partly to completely snow- and ice-free ocean and land surface would substantially reduce cold-season temperature variability ( [[#Gregory--1995|Gregory and Mitchell, 1995]] ; [[#Fischer--2011|Fischer et al., 2011]] ; [[#Borodina--2017|Borodina et al., 2017]] ) and lead to a shortening of the cold season and earlier onset of the warm season ( [[#Cassou--2016|Cassou and Cattiaux, 2016]] ). Mid-latitudinal winter temperature variability is further affected by a complex interplay of a multitude of processes including potential changes in atmospheric circulation, but there is ''low confiden'' ce in the dominant contribution of Arctic warming compared to other drivers ( [[IPCC:Wg1:Chapter:Chapter-10#cross-chapter-box-10.1|Cross-Chapter Box 10.1]] ).

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[[File:fcdb5520d6e52e8460074f9c7ca3f057 IPCC_AR6_WGI_Figure_4_21.png]]

'''Figure''' '''4.21 |''' '''Percentage change in interannual variability of (left) December–January–February (DJF) and (right) June–July–August (JJA) mean temperature averaged across seven large initial condition ensembles.''' Average changes across seven single-model, initial-condition large ensembles are shown for RCP8.5 in 2081–2100 (and where not available for 2080–2099) relative to 1995–2014. Standard deviations are calculated across all members of the large ensembles for every given year to avoid inflation due to the underlying trend and then averaged across the period. Changes are averaged across the ensembles MPI-GE (100 members, [[#Maher--2019|Maher et al., 2019]] ), CanESM2 (50 members, [[#Kirchmeier-Young--2017|Kirchmeier-Young et al., 2017]] ), NCAR-CESM (30 members, [[#Kay--2015|Kay et al., 2015]] ), GFDL-CM3 (20 members, [[#Sun--2018|Sun et al., 2018]] ), GFDL-ESM2M (30 members, [[#Rodgers--2015|Rodgers et al., 2015]] ), CSIRO-Mk3-6-0 (30 members, [[#Jeffrey--2013|Jeffrey et al., 2013]] ) and EC-EARTH (16 members, [[#Hazeleger--2010|Hazeleger et al., 2010]] ). Also see [[#Deser--2020|Deser et al. (2020)]] for further information on those ensembles. Diagonal lines indicate areas with low model agreement where fewer than 80% of the models agree on the sign of the change, and no overlay areas with high model agreement where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In JJA, the multi-model average projects an increase in year-to-year JJA variability over Central Europe and North America (Figure 4.21). In particular an increase in daily to interannual summer temperature variability has been projected over central Europe as a result of larger year-to-year variability in soil moisture conditions varying between a wet and dry regime and leading to enhanced land–atmosphere interaction ( [[#Seneviratne--2006|Seneviratne et al., 2006]] ; [[#Fischer--2012|Fischer et al., 2012]] ; [[#Holmes--2016|Holmes et al., 2016]] ). Furthermore, the amplified warming in the source regions of warm-air advection due to land–ocean warming contrast and amplified Mediterranean warming ( [[#Seager--2014a|Seager et al., 2014a]] ; [[#Brogli--2019|Brogli et al., 2019]] ), may lead to disproportionally strong warming of the hottest days and summers and thereby increased variability. Enhanced temperature variability is further projected over some land regions in the subtropics and tropics ( [[#Bathiany--2018|Bathiany et al., 2018]] ).

In summary, there is ''medium confidence'' that continued warming will regionally lead to increased and decreased year-to-year temperature variability in the extratropics and there is ''medium confidence'' that year-to-year temperature variability will decrease over parts of the mid- to high- latitudes of the winter hemisphere.

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<span id="annual-mean-atmospheric-temperature"></span>
==== 4.5.1.2 Annual Mean Atmospheric Temperature ====

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Section 12.4.3.2 of AR5 assessed that there is ''high confidence'' in the overall pattern of projected end of 21st century tropospheric temperature change and that it is ''very likely'' that some of the largest warming will occur in the northern high latitudes. They further assessed that proportionately larger warming is ''likely'' to occur in the tropical upper troposphere than at the tropical surface, but with ''medium confidence'' owing to the relatively large observational uncertainties and contradictory analyses regarding model accuracy in simulating tropical upper tropospheric temperature trends.

CMIP6 projections show warming throughout the troposphere by the end of this century and a mix of warming and cooling in the stratosphere depending on the emissions scenario (Figure 4.22). The patterns of tropospheric temperature change are highly consistent with those derived from earlier generations of climate models as assessed in AR5, AR4 and TAR. In SSP1-2.6, the multi-model mean warming remains below 3°C everywhere in the troposphere except near the surface in the Arctic; this is similar to the findings in AR5 based on CMIP5 models for RCP2.6. In SSP3-7.0, the zonal mean tropospheric warming is also largest in the tropical upper troposphere, reaching more than 5°C, and near the surface in the Arctic where warming exceeds 8°C (Figure 4.22). It is ''likely'' that the warmer projected GSAT in the unconstrained CMIP6 model ensemble contributes to larger warming in the tropical upper troposphere and in the Arctic lower troposphere. This assessment is based on the understanding of polar amplification assessed in [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] (Section 7.4.4.1), and at low latitudes is based on the understanding of moist convective processes as well as the relationship between CMIP5- and CMIP6-simulated surface temperatures and tropical upper tropospheric warming over the historical period ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.2|Section 3.3.1.2]] ).

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[[File:278b2fb7f99af11bea733043c47c0ce7 IPCC_AR6_WGI_Figure_4_22.png]]

'''Figure 4.22''' '''|''' '''Long-term change of annual and zonal mean atmospheric temperature.''' Displayed are multi-model mean change in annual and zonal mean atmospheric temperature (°C) in 2081–2100 relative to 1995–2014 for '''(left)''' SSP1-2.6 and '''(right)''' SSP3-7.0. The number of models used is indicated in the top right of the maps. Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where 80% or more of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Projected stratospheric temperature trends are determined by a balance between the major radiative drivers from ozone recovery, rising CO <sub>2</sub> and other greenhouse gases (including stratospheric water vapour) ( [[#Maycock--2016|Maycock, 2016]] ), as well as future changes in the Brewer –Dobson circulation, which can alter the latitudinal pattern of stratospheric temperature trends ( [[#Fu--2015|Fu et al., 2015]] , 2019). In the lower stratosphere, the CMIP6 models project a weak cooling in the inner tropics in SSP1-2.6 and a warming at other latitudes (Figure 4.22). There is enhanced lower stratospheric warming over the Antarctic pole owing to the effects of ozone hole recovery on polar temperatures ( [[#Maycock--2016|Maycock, 2016]] ; [[#Solomon--2017|Solomon et al., 2017]] ). The projected strengthening of the Brewer –Dobson circulation in the future ( [[#Hardiman--2014|Hardiman et al., 2014]] ) also affects stratospheric temperature trends, with adiabatic cooling at low latitudes and warming in middle and high latitudes ( [[#Fu--2015|Fu et al., 2015]] , 2019). In SSP3-7.0, there is widespread cooling across much of the stratosphere, as expected from the higher GHG emissions, with a smaller warming in the Antarctic lower stratosphere. Owing to the importance of ozone recovery for the radiative balance of the stratosphere, future global and local stratospheric temperature trends do not scale with projected GSAT change.

In summary, new results since AR5 do not generally alter the understanding of projected zonal mean atmospheric temperature changes. There is ''high confidence'' in the overall pattern of projected tropospheric temperature changes given its robustness across many generations of climate models. It is further ''very likely'' that projected long-term tropospheric warming will be larger than the global mean in the Arctic lower troposphere. It is ''likely'' that tropical upper tropospheric warming will be larger than at the tropical surface, however with an uncertain magnitude owing to the potentially large role of natural internal variability and differences across models in the simulated free tropospheric temperature response to a given forcing scenario ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.2|Section 3.3.1.2]] ). It is ''very likely'' that global mean stratospheric cooling will be larger by the end of the 21st century in a pathway with higher atmospheric CO <sub>2</sub> concentrations.

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<span id="near-surface-relative-humidity"></span>
==== 4.5.1.3 Near-surface Relative Humidity ====

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The AR5 contrasted future changes in near-surface relative humidity (RH) over land and ocean, concluding with ''medium confidence'' that reductions in near-surface RH over many land areas are ''likely'' . The decrease in near-surface RH over most land areas is associated with the larger warming rates over land than over the ocean and is termed the last-saturation-temperature constraint, as explained in AR5.

Since AR5, significant effort has been devoted to understanding the mechanisms for the decrease in near-surface land RH under global warming, and the relevance of RH changes for the land–sea warming contrast and the water cycle. For the near-surface RH decrease over land, both the moisture transport from the ocean and land–atmosphere feedback processes contribute. For changes in specific humidity over land, the moisture transport from the ocean is dominant while the role of evapotranspiration is secondary ( [[#Byrne--2016|Byrne and O’Gorman, 2016]] ; [[#Chadwick--2016|Chadwick et al., 2016]] ). Nevertheless, the changes in near-surface land RH are also strongly influenced by evapotranspiration, which is suppressed by the drying of soils and plant responses to increasing CO <sub>2</sub> related to stomatal closure under climate change ( [[#Byrne--2015|Byrne and O’Gorman, 2015]] ; [[#Berg--2016|Berg et al., 2016]] ; [[#Chadwick--2016|Chadwick et al., 2016]] ; [[#Swann--2016|Swann et al., 2016]] ; [[#Lemordant--2018|Lemordant et al., 2018]] ). The combination of oceanic and continental influences can explain the spatially diverse trends in the near-surface RH over land in the observations for the recent decades, with a generally dominant negative trend at the global scale ( [[#Vicente-Serrano--2018|Vicente-Serrano et al., 2018]] ). There is a strong feedback between the near-surface land RH decrease and land–ocean warming contrast under future warming projections ( [[#4.5.1.1|Section 4.5.1.1]] ).

Changes in land RH can modulate the response of the water cycle to global warming ( [[#Chadwick--2013|Chadwick et al., 2013]] ; [[#Byrne--2015|Byrne and O’Gorman, 2015]] ). Most CMIP5 models project higher precipitation associated with higher near-surface RH and temperature under climate change ( [[#Lambert--2017|Lambert et al., 2017]] ). Over land, the spatial gradients of fractional changes in near-surface RH contribute to a drying tendency in precipitation minus evapotranspiration with warming, which partly explains why the ‘wet gets wetter, dry gets drier’ paradigm does not hold over land ( [[#Byrne--2015|Byrne and O’Gorman, 2015]] ). Terrestrial aridity is projected to increase over land, as manifested by a decrease in the ratio of precipitation to potential evapotranspiration, in which the decrease in near-surface land RH has a contribution of about 35% in CMIP5 models under doubled CO <sub>2</sub> forcing ( [[#Fu--2014|Fu and Feng, 2014]] ). The aridity can be further amplified by the feedbacks of projected drier soils on land surface temperature, RH, and precipitation ( [[#Berg--2016|Berg et al., 2016]] ).

The CMIP6 multi-model ensemble projects general decreases in near-surface relative humidity over a large fraction of land areas, but moderate increases over the ocean (Figure 4.23). The projected changes depend on emissions scenario and season. Changes in near-surface RH under SSP1-2.6 are insignificant compared to natural variability. Under SSP3-7.0, during boreal summer, significant decreases relative to natural variability are projected in continental Europe and the Middle East, North America, South America and South Africa.

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[[File:3cb603d098b10e17f8f8965d1788fdd4 IPCC_AR6_WGI_Figure_4_23.png]]

'''Figure''' '''4.23 |''' '''Long-term changes in seasonal mean relative humidity.''' Displayed are projected spatial patterns of multi-model mean change (%) in seasonal '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) mean near-surface relative humidity in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. 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. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, there is ''medium confidence'' that continued warming will lead to decreased near-surface relative humidity over a large fraction of land areas, but moderate increases over the ocean. There is ''high confidence'' that near-surface relative humidity will decrease over parts of the tropical and subtropical latitudes over land.

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<span id="precipitation-3"></span>
==== 4.5.1.4 Precipitation ====

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The AR5 assessed that changes in mean precipitation in a warmer world will exhibit substantial spatial variation. Also, the contrast of mean precipitation between dry and wet regions and between dry and wet seasons will increase over most of globe as temperatures increase. The general pattern of change indicates that high latitude land masses are ''likely'' to experience greater amounts of precipitation due to the increased specific humidity of the warmer troposphere as well as increased transport of water vapour from the tropics by the end of this century under the RCP8.5 scenario. Many mid-latitude and subtropical arid and semi-arid regions will ''likely'' experience less precipitation, while many moist mid-latitude regions will ''likely'' experience more precipitation by the end of this century under the RCP8.5 scenario.

Since AR5, progress has been achieved in understanding changes in patterns and rates of precipitation with GSAT rise. The projected precipitation changes can be decomposed into a part that is related to atmospheric circulation referred to as dynamical component and a part related to water vapour changes, the thermodynamic component. Based on process understanding and modelling ( [[#Fläschner--2016|Fläschner et al., 2016]] ; [[#Samset--2016|Samset et al., 2016]] ), global mean precipitation will ''very likely'' increase by 1–3% per °C of GSAT warming (Section 8.2.1). The increase in atmospheric water vapour is a robust change under global warming, the sensitivity of global precipitation change to warming is smaller (2% per °C) as compared to water vapour change (7% per °C; [[#Held--2006|Held and Soden, 2006]] ). Global energy balance places a strong constraint on the global mean precipitation ( [[#Allen--2002|Allen and Ingram, 2002]] ; [[#Pendergrass--2014|Pendergrass and Hartmann, 2014]] ; [[#Myhre--2018|Myhre et al., 2018]] ; [[#Siler--2019|Siler et al., 2019]] ). Tropospheric radiative cooling constrains global precipitation ( [[#Pendergrass--2014|Pendergrass and Hartmann, 2014]] ), leading to a slow SST-dependent response and a forcing-dependent rapid adjustment. Rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers ( [[#Samset--2016|Samset et al., 2016]] ; [[#Myhre--2017|Myhre et al., 2017]] ). The rapid regional precipitation response to increased CO <sub>2</sub> is robust across models, implying that the uncertainty in long-term changes is mainly associated with the response to SST-mediated feedbacks ( [[#Richardson--2016|Richardson et al., 2016]] ). Precipitation response to fast adjustments and slow temperature-driven responses are assessed in detail in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.1|Section 8.2.1]] ).

The thermodynamic response to global warming is associated with a ‘wet get wetter’ mechanism, with enhanced moisture flux leading to subtropical dry regions getting drier and tropical and mid-latitude wet regions getting wetter ( [[#Held--2006|Held and Soden, 2006]] ; [[#Chou--2009|Chou et al., 2009]] ). Recent studies suggest that the dry-get-drier argument does not hold, especially over subtropical land regions ( [[#Greve--2014|Greve et al., 2014]] ; [[#Feng--2015|Feng and Zhang, 2015]] ; [[#Greve--2015|Greve and Seneviratne, 2015]] ). The discrepancy may be partly arising due to differences in model climatologies and by change in the location of wet and dry regions ( [[#Polson--2017|Polson and Hegerl, 2017]] ). Over the 21st century, significant rate of precipitation change is associated with a spatial stabilization and intensification of moistening and drying patterns ( [[#Chavaillaz--2016|Chavaillaz et al., 2016]] ). In the tropics, weakening of circulation leads to a ‘wet gets drier, dry gets wetter’ pattern ( [[#Chadwick--2013|Chadwick et al., 2013]] ). Climate model agreement for precipitation change in the tropics is lower than for other regions ( [[#Knutti--2013|Knutti and Sedláček, 2013]] ; [[#McSweeney--2013|McSweeney and Jones, 2013]] ). Sources of inter-model uncertainty in regional rainfall projections arise from circulation changes ( [[#Kent--2015|Kent et al., 2015]] ; [[#Chadwick--2016|Chadwick, 2016]] ) and spatial shifts in convection and convergence, associated with SST pattern change and land–sea thermal contrast change ( [[#Kent--2015|Kent et al., 2015]] ; [[#Chadwick--2017|Chadwick et al., 2017]] ) with a secondary contribution from the response to direct CO <sub>2</sub> forcing ( [[#Chadwick--2016|Chadwick, 2016]] ). Factors governing changes in large-scale precipitation patterns are assessed in detail in Sections [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|8.2.2]] and [[IPCC:Wg1:Chapter:Chapter-10#10.4.1|10.4.1]] .

Long-term multi-model mean change in seasonal precipitation (JJA and DJF) from CMIP6 models (Figure 4.24) shows substantial regional differences and seasonal contrast. Changes in seasonal precipitation under SSP1-2.6 are small compared to internal variability. Consistent with the AR5, patterns of precipitation change are ''very likely'' to increase in the high latitudes especially during local winter and over tropical oceans under SSP3-7.0 ( ''high confidence'' ). CMIP6 projections show an increase in precipitation over larger parts of the monsoon regions and decreases in many subtropical regions including the Mediterranean, southern Africa and south-west Australia ( ''medium confidence'' ). The large-scale patterns of precipitation shown in Figure 4.24 are consistent with the patterns presented in Section 8.4.1.3. Precipitation changes exhibit strong seasonal characteristics ( [[IPCC:Wg1:Chapter:Chapter-8#box-8.2|Box 8.2]] ), and, in many regions, the sign of the precipitation changes varies with season. Precipitation variability is projected to increase over a majority of global land area, as assessed in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (Section 8.4.1.3.3), over a wide range of time scales in response to warming ( [[#Pendergrass--2017|Pendergrass et al., 2017]] ).

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[[File:40ed6b163e8333e1955471a23a207836 IPCC_AR6_WGI_Figure_4_24.png]]

'''Figure 4.24 |''' '''Long-term change of seasonal mean precipitation.''' Displayed are projected spatial patterns of multi-model mean change (%) in '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) mean precipitation in 2081–2100 relative to 1995–2014, for (left) SSP1-2.6 and (right) SSP3-7.0. The number of models used is indicated in the top right of the maps. 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. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Most of the projected changes in precipitation exhibit a sharp contrast between land and ocean (Sections 8.2.1 and 8.4.1). Temperature-driven intensification of land-mean precipitation during the 20th century has been masked by fast precipitation responses to anthropogenic sulphate and volcanic forcing ( [[#Allen--2002|Allen and Ingram, 2002]] ; [[#Richardson--2018a|Richardson et al., 2018a]] ). Based on the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), land-mean precipitation is expected to increase more rapidly with the projected decrease in sulphate forcing and continued warming, contributing to increased global mean precipitation (Table 4.3) and will be clearly observable by the mid-21st century based on RCP4.5 and RCP8.5 scenarios ( [[#Richardson--2018a|Richardson et al., 2018a]] ).

Consistent with the findings of AR5, a gradual increase in global mean precipitation is projected over the 21st century with an increase of approximately 2.9% (1.0–5.2%) under SSP1-2.6 and 4.7% (2.3–8.2%) under SSP3-7.0 during 2081–2100 relative to 1995–2014. The corresponding increase in annual mean global land precipitation is 3.3% (0–6.6%), in the SSP1-2.6 and 5.8% (0.5–9.6%) in the SSP3-7.0 (Table 4.3). CMIP6 models show greater increases in precipitation over land than either globally or over the ocean ( ''high confidence'' ).

Based on the assessment of CMIP6 models, we conclude that it is ''very likely'' that, in the long term, global mean land and global mean ocean precipitation will increase with increasing GSAT. Annual mean and global mean precipitation will ''very likely'' increase by 1–3% per °C GSAT warming. The patterns of precipitation change will exhibit substantial regional differences and seasonal contrast as GSAT increases over the 21st century ( ''high confidence'' ). Precipitation will ''very likely'' increase over high latitudes and the tropical ocean and will ''likely'' increase in large parts of the monsoon regions. However, it is ''likely'' to decrease over the subtropics, including Mediterranean, southern Africa and south-west Australia, in response to GHG-induced warming.

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<span id="global-monsoon-precipitation-and-circulation-1"></span>
==== 4.5.1.5 Global Monsoon Precipitation and Circulation ====

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The AR5 assessed changes of the global monsoon in the context of long-term trends across the 21st century and the change by 2081–2100. The AR5 showed growing evidence of improved skill of climate models in reproducing the climatological features of the global monsoon. Taken together with identified model agreement on future changes, the global monsoon precipitation, aggregated over all regional monsoon regions, is ''likely'' to strengthen in the 21st century with increases in its area and intensity, while the monsoon circulation weakens. In all RCP scenarios, the global monsoon area is ''very likely'' to increase, and the global monsoon precipitation intensity is ''likely'' to increase, resulting in a ''very likely'' increase in the global monsoon total precipitation, by 2081–2100 ( [[#Kitoh--2013|Kitoh et al., 2013]] ).

Since AR5, there has been progress in understanding physical mechanisms for the projected changes in global monsoon and quantifying the sources of uncertainty in projections. The increase in global monsoon precipitation under warming is primarily attributed to the increase of moisture convergence, which comes mainly from the thermodynamic effect due to increasing atmospheric moisture but is partly offset by reduced convergence (W. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Chen--2020|Chen et al., 2020]] ). The dynamic effect, such as monsoon circulation changes, dominates regional differences in the projected monsoon precipitation changes ( [[#Chen--2020|Chen et al., 2020]] ). Specifically, NH monsoon precipitation will increase more strongly than its SH counterpart, due to an increase in hemispheric temperature difference between the NH and SH, enhancement of the Hadley circulation, and atmospheric moistening, countered by stabilization of the troposphere ( [[#Lee--2014|Lee and Wang, 2014]] ). The seasonality of global monsoon rainfall is projected to be enhanced in response to warming, featuring a greater wet–dry season contrast (Lee and Wang 2014; Zhang et al. 2019). In addition, the interannual variability of global monsoon rainfall is projected to intensify mainly over land, with a strengthened relationship between global monsoon and ENSO ( [[#Hsu--2013|Hsu et al., 2013]] ; [[#Wang--2020|Wang et al., 2020]] , 2021).

For the uncertainty in mean monsoon precipitation projections, the model uncertainty is the dominant contributor throughout the century and explains more than 70% of the inter-model variance during near term, mid-term, and long term. The contribution of internal variability is only important at the beginning in early decades, while scenario uncertainty becomes important at the end of the 21st century. The sources of uncertainty for the mean and extreme monsoon precipitation mainly differ in the long-term projection, when the contribution of scenario uncertainty is comparable to the model uncertainty for extreme precipitation ( [[#Zhou--2020|Zhou et al., 2020]] ). Although the magnitude of internal variability differs between CMIP5 models and single-model, initial-condition large ensembles, the impact is only evident in the beginning decades. For the mid- and long term, the magnitude difference does not alter that model uncertainty is the dominant source of uncertainty in the projections of global land monsoon precipitation ( [[#Zhou--2020|Zhou et al., 2020]] ).

Based on the projections of changes in precipitation from CMIP6 under the four SSPs, the global monsoon precipitation is ''likely'' to strengthen in the 21st century with increases in its intensity, while NH summer monsoon circulation weakens (Figure 4.14). Global land monsoon precipitation will ''likely'' increase by 1.3–2.4% per °C GSAT warming among the four scenarios considered here. In the long term, the multi-model mean change (5–95% range of the available 41 projections) of global land monsoon precipitation index is 2.9% (–0.8 to +7.8%), 3.7% (–2.5 to +8.6%), 3.77% (–3.2 to +8.1%), and 5.7% (–2.8 to +12.3%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. This enhancement is caused by thermodynamic responses due to increased moisture, which is partly offset by dynamic responses due to a weakened circulation ( [[#Chen--2020|Chen et al., 2020]] ). The patterns of monsoon rainfall change in the mid- to long-term include a north–south asymmetry characterized by greater increase in the NH than the SH, and an East–West asymmetry characterized by enhanced Asian-African monsoons and weakened North American monsoon ( ''medium confidence'' ) ( [[#Lee--2014|Lee and Wang, 2014]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ; [[#Pascale--2017|Pascale et al., 2017]] ; [[#Wang--2021|Wang et al., 2021]] ).

Based on the assessment of CMIP6 models, we conclude that it is ''likely'' that, in the mid- to long term, the global land monsoon precipitation will increase with GSAT rise despite a weakened monsoon circulation. The global land monsoon precipitation will ''likely'' increase by 1.3–2.4% per °C GSAT warming among the four scenarios. Monsoon precipitation responses depend on region and emissions scenario ( ''high confidence'' ).

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<span id="sea-level-pressure-large-scale-atmospheric-circulation-storm-tracks-and-blocking"></span>
==== 4.5.1.6 Sea Level Pressure, Large-scale Atmospheric Circulation, Storm Tracks and Blocking ====

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This subsection provides a global overview of long-term changes in atmospheric dynamical features that is complementary to the regional assessment of links to the hydrological cycle in [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] (Section 8.4.2), and assessment of the connections to extreme events in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] (Section 11.7.2).

<div id="4.5.1.6.1" class="h4-container"></div>

<span id="sea-level-pressure"></span>
===== 4.5.1.6.1 Sea level pressure =====

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The AR5 assessed that mean sea level pressure is projected to decrease in high latitudes and to increase in mid-latitudes. Such a pattern is associated with a poleward shift in the storm track and an increase in the annular mode index. This broad pattern is also found in CMIP6 models (Figure 4.25). Under SSP1-2.6, the pattern in sea level pressure change resembles that for SSP3-7.0, but the amplitudes are small compared to internal variability in 20-year means (Figure 4.25). One exception is found in the SH mid-latitudes, where pressure robustly increases in SSP3-7.0 in both austral summer and winter, but shows no robust change in SSP1-2.6. This is ''likely'' attributable to the larger GHG forcing in SSP3-7.0 compared to SSP1-2.6, which contributes to a poleward shift of the SH mid-latitude circulation and becomes relatively more important than the effect of ozone recovery which drives an equatorward shift in the circulation (see [[#4.5.3.1|Section 4.5.3.1]] on the Southern Annular Mode; [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ). The poleward shift in SH mid-latitude circulation in SSP3-7.0 ''likely'' contributes to the wetting trend at high southern latitudes (Figure 4.25).

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[[File:a1782691bffb170fc1f2303f8eafb95f IPCC_AR6_WGI_Figure_4_25.png]]

'''Figure 4.25 |''' '''Long-term change of seasonal-mean sea level pressure.''' Displayed are projected spatial patterns of multi-model mean change in '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) mean sea level pressure (hPa) in 2081–2100 relative to 1995–2014, for '''(left)''' SSP1-2.6 and '''(right)''' SSP3-7.0. 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. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

As was found in AR5, several regional sea level pressure features stand out from the zonal-mean change. Sea level pressure markedly decreases in north-eastern North America and north-eastern Asia in boreal winter. In boreal summer, sea level pressure robustly decreases in the Mediterranean and the Middle-East, a decrease that has been linked to a large-scale heat low forced by the amplified warming of the region ( [[#Haarsma--2009|Haarsma et al., 2009]] ). It is ''likely'' that sea level pressure will increase across the south-western North America and Central America in boreal summer under SSP3-7.0 due to an intensification of the eastern North Pacific subtropical summer high ( [[#Li--2012|Li et al., 2012]] ) and a weakening of the North American monsoon ( [[#4.5.1.5|Section 4.5.1.5]] ; [[#Pascale--2017|Pascale et al., 2017]] ; [[#Wang--2020|Wang et al., 2020]] ). These changes in circulation are connected to drying across the eastern subtropical Pacific and Central America regions (Figure 4.24).

<div id="4.5.1.6.2" class="h4-container"></div>

<span id="zonal-wind-and-westerly-jets"></span>
===== 4.5.1.6.2 Zonal wind and westerly jets =====

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Storm tracks and mid-latitude westerly jets are dynamically related aspects of mid-latitude circulation. The AR5 assessed that a poleward shift of the SH westerlies and storm track is ''likely'' by the end of the 21st century under RCP8.5 ( ''medium confidence'' ). In contrast, ''low confidence'' was assessed for the storm-track response in the NH.

Under both SSP1-2.6 and SSP3-7.0 there is a strengthening and lifting of the subtropical jets in both hemispheres (Figure 4.26), consistent with the response to large-scale tropospheric warming found in earlier generations of climate models ( [[#Collins--2013|Collins et al., 2013]] ). In the SH, GHG emissions tend to force a poleward shift of the jet, but this is opposed, particularly in austral summer, by the stratospheric ozone hole recovery ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ). Consistent with sea level pressure changes, CMIP6 models project a strengthening and poleward shift of the SH jet in austral summer and winter under SSP3-7.0, but smaller and non-robust changes in SH mid-latitude zonal winds under SSP1-2.6 (Figure 4.26; see also [[#4.5.3.1|Section 4.5.3.1]] ). CMIP6 models show an improved simulation of the SH jet stream latitude ( [[#Bracegirdle--2020a|Bracegirdle et al., 2020a]] ; [[#Curtis--2020|Curtis et al., 2020]] ). This has been linked to a reduction in the projected poleward shift of the SH jet in austral summer compared to the CMIP5 models ( [[#Curtis--2020|Curtis et al., 2020]] ; [[#Goyal--2021|Goyal et al., 2021]] ), although differences in the pattern of SST response may also play a role ( [[#Wood--2020|Wood et al., 2020]] ). In the NH extratropics, the changes in lower-tropospheric zonal-mean zonal winds by the end of the century are generally smaller than in the SH. In boreal winter, there is a weak poleward shift of the NH zonal-mean westerly jet maximum in SSP3-7.0.

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[[File:f54d6787f8e183a379169ed8be4933e4 IPCC_AR6_WGI_Figure_4_26.png]]

'''Figure 4.26 |''' '''Long-term change of zonal-mean, zonal wind.''' Displayed are multi-model mean changes in '''(left)''' boreal winter(December–January–February, DJF) and '''(right)''' austral winter (June–July–August, JJA) zonal mean, zonal wind (m s <sup>–1</sup> ) in 2081–2100 for (top) SSP1-2.6 and (bottom) SSP3-7.0 relative to 1995–2014. The 1995–2014 climatology is shown in contours with spacing 10 m s <sup>–1</sup> . Diagonal lines indicate regions where less than 80% of the models agree on the sign of the change and no overlay where at least 80% of the models agree on the sign of the change. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

CMIP5 and CMIP6 models show a strong seasonal and regional dependence in the response to climate change of NH westerlies ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Grise--2014b|Grise and Polvani, 2014b]] ; [[#Simpson--2014|Simpson et al., 2014]] ; [[#Zappa--2015|Zappa et al., 2015]] ; [[#Harvey--2020|Harvey et al., 2020]] ; [[#Oudar--2020|Oudar et al., 2020]] ). CMIP5 projections indicate a poleward shift of the westerlies in the North Atlantic in boreal summer, while the North Pacific jet weakens in this season ( [[#Simpson--2014|Simpson et al., 2014]] ; [[#Davini--2020|Davini and D’Andrea, 2020]] ; [[#Harvey--2020|Harvey et al., 2020]] ). There is a poleward shift in the westerlies in both the North Pacific and North Atlantic in Autumn ( [[#Barnes--2013|Barnes and Polvani, 2013]] ; [[#Simpson--2014|Simpson et al., 2014]] ). However, the shift of the westerlies is more uncertain in the other seasons, particularly in the North Atlantic in winter ( [[#Simpson--2014|Simpson et al., 2014]] ; [[#Zappa--2017|Zappa and Shepherd, 2017]] ). Here, the circulation response is not well described as a simple shift, since the North Atlantic jet tends to be squeezed on both its equatorward and poleward flanks, together with an eastward extension into Europe ( [[#Li--2018|Li et al., 2018]] ; [[#Peings--2018|Peings et al., 2018]] ; [[#Simpson--2019a|Simpson et al., 2019a]] ; [[#Harvey--2020|Harvey et al., 2020]] ; [[#Oudar--2020|Oudar et al., 2020]] ). Simulations indicate that most of the changes in winter storminess over the Euro-Atlantic region will occur only after exceeding the 1.5°C warming level ( [[#Barcikowska--2018|Barcikowska et al., 2018]] ).

Progress since AR5 has improved understanding of the climate change aspects that can drive these different, and potentially opposite, responses in the mid-latitude jets and storm tracks. A poleward shift of the jets and storm tracks is expected in response to an increase in the atmospheric stratification and in the upper-tropospheric equator-to-pole meridional temperature gradient, while it is opposed by the decrease in the meridional temperature gradient in the lower troposphere associated with the polar amplification of global warming ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Shaw--2016|Shaw et al., 2016]] ). Recent analyses have identified additional climate aspects that can drive mid-latitude jet changes, including patterns in sea surface warming ( [[#Mizuta--2014|Mizuta et al., 2014]] ; [[#Langenbrunner--2015|Langenbrunner et al., 2015]] ; [[#Ceppi--2018|Ceppi et al., 2018]] ; [[#Wood--2020|Wood et al., 2020]] ), land–sea warming contrast ( [[#Shaw--2015|Shaw and Voigt, 2015]] ), loss of sea ice ( [[#Deser--2015|Deser et al., 2015]] ; [[#Harvey--2015|Harvey et al., 2015]] ; [[#Screen--2018b|Screen et al., 2018b]] ; [[#Zappa--2018|Zappa et al., 2018]] ), and changes in the strength of the stratospheric polar vortex ( [[#Manzini--2014|Manzini et al., 2014]] ; [[#Grise--2017|Grise and Polvani, 2017]] ; [[#Simpson--2018|Simpson et al., 2018]] ; [[#Ceppi--2019|Ceppi and]] [[#Shepherd--2019|Shepherd, 2019]] ). From an energetics perspective,the uncertainty in the response of the jet streams depends on the response of clouds, their non-spatially uniform radiative feedbacks shaping the meridional profile of warming ( [[#Ceppi--2014|Ceppi et al., 2014]] ; [[#Voigt--2015|Voigt and Shaw, 2015]] , 2016; [[#Ceppi--2016|Ceppi and Hartmann, 2016]] ; [[#Ceppi--2017|Ceppi and Shepherd, 2017]] ; [[#Lipat--2018|Lipat et al., 2018]] ; [[#Albern--2019|Albern et al., 2019]] ; [[#Voigt--2019|Voigt et al., 2019]] ). Climate models seem to underestimate the forced component of the year-to-year variability in the atmospheric circulation, particularly in the North Atlantic sector ( [[#Scaife--2018|Scaife and Smith, 2018]] ), which suggests some relevant dynamical processes may not be well represented. Whether and how this may affect long-term projections is unknown. In conclusion, due to the influence from competing dynamical drivers and the absence of observational evidence, there is ''medium confidence'' in a projected poleward shift of the NH zonal-mean low-level westerlies in autumn and summer and ''low confidence'' in the other seasons. There is also overall ''low confidence'' in projected regional changes in the NH low-level westerlies, particularly for the North Atlantic basin in boreal winter.

The anthropogenic forced signal in extratropical atmospheric circulation may well be small compared to internal variability ( [[#Deser--2012b|Deser et al., 2012b]] , 2014) and, as assessed in AR5, there is generally '''low agreement''' across models in many aspects of regional atmospheric circulation change particularly in the NH ( [[#Shepherd--2014|Shepherd, 2014]] ). The latter means that, in some regions, a multi-model average perspective of atmospheric circulation change represents a small residual after averaging over large intermodel spread. This is in strong contrast to thermodynamic aspects of climate change, such as surface temperature change, for which model results are generally highly consistent (see, e.g., Figure 4.19). Furthermore, models share systematic biases in some aspects of extratropical atmospheric circulation such as mid-latitude jets, which can have complex implications for understanding forced changes ( [[#Simpson--2016|Simpson and Polvani, 2016]] ). Given these issues, an emerging field of research since AR5 has focused on the development of ‘storylines’ for regional atmospheric circulation change ( [[#Shepherd--2019|Shepherd, 2019]] ). The storyline approach is grounded in the identification of a set of physical predictors of atmospheric circulation change, such as those described above ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Manzini--2014|Manzini et al., 2014]] ; [[#Shepherd--2018|Shepherd et al., 2018]] ), which act together to determine a specific outcome in the projected atmospheric circulation change. The consequences of multi-model spread in the physical predictors of atmospheric circulation change can be investigated, conditioned on a specified level of global warming (see also [[IPCC:Wg1:Chapter:Chapter-1#1.4.4.2|Section 1.4.4.2]] and Box 10.2; [[#Zappa--2017|Zappa and Shepherd, 2017]] ; [[#Zappa--2019|Zappa, 2019]] ; [[#Mindlin--2020|Mindlin et al., 2020]] ).

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<span id="storm-tracks"></span>

===== 4.5.1.6.3 Storm tracks =====

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As stated in AR5, the number of extratropical cyclones (ETC) composing the storm tracks is projected to weakly decline in future projections, but by no more than a few percent change. The reduction is mostly located on the equatorward flank of the storm tracks, which is associated with the Hadley cell expansion and a poleward shift in the mean genesis latitude of ETCs ( [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ). Furthermore, the poleward propagation of individual ETCs is expected to increase with warming ( [[#Graff--2014|Graff and LaCasce, 2014]] ; [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ), thus contributing to a poleward shift in the mid-latitude transient-eddy kinetic energy. The increased poleward propagation results from the strengthening of the upper tropospheric jet and increased cyclone-associated precipitation ( [[#Tamarin-Brodsky--2017|Tamarin-Brodsky and Kaspi, 2017]] ), which are robust aspects of climate change.

In the NH boreal winter, CMIP6 models show a northward shift of the ETC density in the North Pacific, a tripolar pattern in the North Atlantic, and a weakening of the Mediterranean storm track (Figure 4.27a). CMIP6 models show overall ''low agreement'' on changes in ETC density in the North Atlantic in boreal winter (Figure 4.27a). A poleward shift of the storm track is evident in the SH (Figure 4.27b), particularly in the Indian and Pacific Ocean sectors. CMIP6 models still feature long-standing biases in the representation of storm tracks; for example, the winter storm track into Europe is too zonal, though different measures of storm track activity indicate some improvements compared to the previous generations of models ( [[#Harvey--2020|Harvey et al., 2020]] ; [[#Priestley--2020|Priestley et al., 2020]] ).

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[[File:efca96cede6e9a3da0fcdf9a22d101a5 IPCC_AR6_WGI_Figure_4_27.png]]

'''Figure''' '''4.27 |''' '''Changes in extratropical storm track density.''' Displayed are projected spatial pattern of multi-model mean change of extratropical storm track density in winter (Northern Hemisphere December –January–Februrary, NH DJF, and Southern Hemisphere June–July–August, SH JJA) in 2080–2100 for SSP5-8.5 relative to 1979–2014 based on 13 CMIP6 models. Diagonal lines indicate regions where fewer than 80% of the models agree on the sign of the change and no overlay where at least 80% of the models agree on the sign of change. Units are number density per 5° spherical cap per month. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

Regarding the dynamical intensity of the storm tracks (Section 11.7.2), the number of ETCs associated with intense surface wind speeds and undergoing explosive pressure deepening are projected to strongly decrease in the NH winter ( [[#Seiler--2016|Seiler and Zwiers, 2016]] ; [[#Chang--2018|Chang, 2018]] ). The weakening of surface winds of ETCs in the NH is attributed to the reduced low-level baroclinicity from SST and sea ice changes ( [[#Harvey--2014|Harvey et al., 2014]] ; [[#Seiler--2016|Seiler and Zwiers, 2016]] ; J. [[#Wang--2017a|]] [[#Wang--2017|Wang et al., 2017]] a ). There are, however, regional exceptions such as in the northern North Pacific, where explosive and intense ETCs are projected to increase in association with the poleward shift of the jet and increased upper-level baroclinicity ( [[#Seiler--2016|Seiler and Zwiers, 2016]] ). Eddy kinetic energy and intense cyclone activity are also projected to decrease in the NH summer in association with a weakening of the jet ( [[#Lehmann--2014|Lehmann et al., 2014]] ; [[#Chang--2016|Chang et al., 2016]] ). However, explosive cyclones tend to be too weak in climate models ( [[#Seiler--2016|Seiler and Zwiers, 2016]] ; [[#Priestley--2020|Priestley et al., 2020]] ), though this bias seems to be reduced in high-resolution simulations ( [[#Jiaxiang--2020|Jiaxiang et al., 2020]] ). Furthermore, models may not fully capture the contribution of the future increase in mesoscale latent heating to cyclone intensification ( [[#Li--2014|Li et al., 2014]] ; [[#Pfahl--2015|Pfahl et al., 2015]] ; [[#Willison--2015|Willison et al., 2015]] ; [[#Michaelis--2017|Michaelis et al., 2017]] ). In conclusion, there is only ''medium confidence'' in the projected decrease in the frequency of intense NH ETCs.

In contrast to the Northern Hemisphere, the Southern Hemisphere shows an increase in the frequency of intense ETCs in CMIP5 models ( [[#Chang--2017|Chang, 2017]] ), and there is ''high confidence'' that wind speeds associated with ETCs are expected to intensify in the SH storm track for high emissions scenarios. These changes in intensity are accompanied by an overall southward shift of the SH winter storm track (Figure 4.27b) due to the poleward shift in the upper-level jet and the increase in the meridional SST gradient linked to the slower warming of the Southern Ocean ( [[#Grieger--2014|Grieger et al., 2014]] ).

Regardless of dynamical intensity changes, there is ''high confidence'' that the number of ETCs associated with extreme precipitation is projected to increase with warming, due to the increased moisture-loading capacity of the atmosphere (Section 8.4.2; [[#Yettella--2017|Yettella and Kay, 2017]] ; [[#Hawcroft--2018|Hawcroft et al., 2018]] ).

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<span id="atmospheric-blocking"></span>
===== 4.5.1.6.4 Atmospheric blocking =====

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Blocking is associated with a class of quasi-stationary, high-pressure weather systems in the middle and high latitudes that disrupt the prevailing westerly flow. These events can persist for extended periods, such as a week or longer, and can cause long-lived extreme weather conditions, from heat waves in summer to cold spells in winter (see Section 11.7.2 for a detailed discussion of these features and [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3%20|Section 3.3.3.3]] for the assessment of blocking biases in models simulations). The AR5 assessed with ''medium confidence'' that the frequency of blocking would not increase under enhanced GHG concentrations, while changes in blocking intensity and persistence remained uncertain.

The CMIP5 projections suggest that the response of blocking frequency to climate change might be quite complex ( [[#Dunn-Sigouin--2013|Dunn-Sigouin et al., 2013]] ; [[#Masato--2013|Masato et al., 2013]] ). An eastward shift of winter blocking activity in the NH is indicated ( [[#Masato--2013|Masato et al., 2013]] ; [[#Kitano--2016|Kitano and Yamada, 2016]] ; [[#Lee--2017|Lee and Ahn, 2017]] ; [[#Matsueda--2017|Matsueda and Endo, 2017]] ) while during boreal summer, blocking frequency tends to decrease in mid-latitudes ( [[#Matsueda--2017|Matsueda and Endo, 2017]] ), with the exception of the eastern Europe–western Russia region ( [[#Masato--2013|Masato et al., 2013]] ). The projected decrease of blocking in boreal summer partially contrasts with the observed increase in Greenland blocking ( [[#Hanna--2018|Hanna et al., 2018]] ; [[#Davini--2020|Davini and D’Andrea, 2020]] ). However, as shown in [[#Woollings--2018|Woollings et al. (2018)]] , the spatial distribution and the magnitude of the suggested changes are sensitive to the blocking detection methods ( [[#Schwierz--2004|Schwierz et al., 2004]] ; [[#Barriopedro--2010|Barriopedro et al., 2010]] ; [[#Davini--2012|Davini et al., 2012]] ). In the SH, blocking frequency is projected to decrease in the Pacific sector during austral spring and summer. However, seasonal and regional changes are not totally consistent across the models ( [[#Parsons--2016|Parsons et al., 2016]] ), and, as assessed in [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.3|Section 3.3.3.3]] , model biases might affect their response.

To better understand the uncertainty in future blocking activity, a process-oriented approach has been proposed that aims to link blocking responses to different features of the global warming pattern. Upper-level tropical warming might be the key factor leading to a reduced blocking, because of the strengthening of zonal winds ( [[#Kennedy--2016|Kennedy et al., 2016]] ). The more controversial influence of near-surface Arctic warming might lead to an increased blocking frequency ( [[#Mori--2014|Mori et al., 2014]] ; [[#Francis--2015|Francis and Vavrus, 2015]] ) (see Chapter 10, Box 10.1).

Figure 4.28 shows a clear decrease in blocking activity over Greenland and North Pacific for SSP7.0 and SSP8.5. Models with the largest decrease in blocking frequency in boreal winter are those showing the smallest frequency bias during the historical period ( [[#Davini--2020|Davini and D’Andrea, 2020]] ). In conclusion, there is ''medium confidence'' that the frequency of atmospheric blocking events over Greenland and the North Pacific will decrease in boreal winter in the SSP3-7.0 and SSP5-8.5 scenarios.

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[[File:bfba819d75ace59a766e614db907a4ec IPCC_AR6_WGI_Figure_4_28.png]]

'''Figure''' '''4.28 |''' '''Projected winter atmospheric blocking frequencies.''' Box plot showing December –March atmospheric blocking frequencies from historical simulations over 1995–2014 and projections over 2081–2100, over '''(a)''' the Central European region (20°W–20°E, 45°N–65°N); '''(b)''' the Greenland region (65°W–20°W, 62.5°N–72.5°N); '''(c)''' the North Pacific region (130°E–150°W, 60°N–75°N). Values show the percentage of blocked days per season following the ( [[#Davini--2012|Davini et al., 2012]] ) index. Median values are the thick black horizontal bar. The lower whiskers extend from the first quartile to the smallest value in the ensemble, and the upper whiskers extend from the third quartile to the largest value. The whiskers are limited to an upper bound that is 1.5 times the interquartile range (the distance between the third and first quartiles). Black dots show outliers from the whiskers. The numbers below each bar report the number of models included. Observationally-based values are obtained as the average of the ERA-Interim Reanalysis, the JRA-55 Reanalysis and the NCEP/NCAR Reanalysis. Adapted from [[#Davini--2020|Davini and D’Andrea (2020)]] . Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

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<span id="ocean"></span>