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

=== 4.4.1 Atmosphere ===

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==== 4.4.1.1 Average Global Surface Air Temperature ====

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The AR5 assessed that it is ''likely'' that GSAT will increase in the range 0.3°C–0.7°C over the period 2016–2035 relative to 1986–2005 ( ''medium confidence'' ), and that there were not large differences in the GSAT change among different RCPs in this period ( [[#Kirtman--2013|Kirtman et al., 2013]] ). The AR5 further assessed that it is ''more likely than not'' that the mean GSAT for the period 2016–2035 will be more than 1°C above the mean for 1850–1900, and it is ''very unlikely'' that it will be more than 1.5°C above the 1850–1900 mean ( ''medium confidence'' ). It was shown that in the period 2016–2035, differences in GSAT across RCP scenarios for a single climate model are typically smaller than differences between climate models under a single RCP scenario, indicating that model structural uncertainty is larger than scenario uncertainty over that period ( [[#Hawkins--2009|Hawkins and Sutton, 2009]] ).

Near-term (2021–2040) GSAT changes relative to 1995–2014 exhibit only minor dependence on SSP scenario, consistent with AR5 (Table 4.5). Averaged over the twenty years of the near term and across all scenarios, GSAT is ''very likely'' to be higher than over 1995–2014 by 0.4°C–1.0°C (Table 4.5), with most of the uncertainty arising from that in ECS and TCR ( ''high confidence'' ) ( [[#4.3.4|Section 4.3.4]] ; e.g., [[#Lehner--2020|Lehner et al., 2020]] ). The assessed near-term warming is thus larger than in AR5 by 0.1°C to 0.2°C. This upward revision has the same magnitude as the ad-hoc downward adjustment to near-term projected GSAT change that was performed in AR5 ( [[#box-4.1|Box 4.1]] ; [[#Kirtman--2013|Kirtman et al., 2013]] ).

Averaged near-term GSAT is ''as likely as not'' at least 1.5°C higher than during 1850–1900, across the five SSP scenarios used here (Table 4.5 and [[#4.3.4|Section 4.3.4]] ). This much higher likelihood of near-term warming reaching 1.5°C than in AR5 arises both because surface warming has continued since AR5 (the period 1995–2014 was warmer by 0.16°C than 1986–2005; Cross-Chapter Box 2.3, Table 1), and because of methodological and dataset updates (the AR6 assessment of 1986–2005 GSAT change relative to 1850–1900 is 0.08°C higher than in the AR5; Cross-Chapter Box 2.3, Table 1).

For annual mean GSAT, uncertainty in near-term projections arises in roughly equal measure from internal variability and model uncertainty ( ''high confidence'' ) ( [[#box-4.1|Box 4.1]] ). Forecasts initialized from recent observations simulate GSAT changes for the period 2019–2028 relative to the recent past that are consistent with the assessed ''very likely'' range in annual mean GSAT ( ''high confidence'' ) (Box 4.1, Figure 1, and Table 4.5). Because annual mean GSAT shows a higher level of internal variability than the 20-year mean, individual years are expected to cross the 1.5°C earlier than the assessed GSAT does. For example, [[#Smith--2018|Smith et al. (2018)]] apply a multi-model decadal-forecast ensemble to assess the likelihood that global warming of 1.5°C higher than over 1850–1900 will be temporarily exceeded in the near future.

When we repeat the uncertainty quantification for GSAT as in [[#4.3.4|Section 4.3.4]] but with the corresponding higher level of internal variability for annual instead of 20-year averages added in quadrature, we can estimate the likelihood that an individual year would cross the GSAT 1.5°C threshold. By 2030, GSAT in any individual year could exceed 1.5°C relative to 1850–1900 with a likelihood between 40 and 60 percent, across the scenarios considered here ( ''medium confidence'' ).

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==== 4.4.1.2 Spatial Patterns of Surface Warming ====

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Consistent with AR5 and earlier assessments, Figure 4.12 shows for SSP1-2.6 and SSP3-7.0 that the largest warming occurs at high latitudes, particularly in boreal winter in the Arctic ( [[#4.5.1.1|Section 4.5.1.1]] ), and larger warming over land than over the ocean ( [[#4.5.1.1|Section 4.5.1.1]] ). In both scenarios, the increase in seasonal mean surface temperatures over many NH land regions exceeds 1°C relative to 1995–2014. In the near term, the two scenarios show surface temperature changes that are similar in magnitude. The trajectories for well-mixed GHGs, and as a consequence the effective radiative forcing, in the scenarios have not yet diverged that much ( [[#O’Neill--2016|O’Neill et al., 2016]] ; [[#Riahi--2017|Riahi et al., 2017]] ). Based on the currently available CMIP6 models, regions that do not show robust warming in the near-term include the northern North Atlantic, parts of India, parts of North America and Eurasia in winter, and the subtropical eastern Pacific in the Southern Hemisphere.

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[[File:01a88c300b9a720e313ba0e3579d1219 IPCC_AR6_WGI_Figure_4_12.png]]

'''Figure 4.12''' '''|''' '''Near-term change of seasonal mean surface temperature.''' Displayed are projected spatial patterns of CMIP6 multi-model mean change (°C) in '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) near-surface air temperature for 2021–2040 from SSP1-2.6 and SSP3-7.0 relative to 1995–2014. 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).

The ERF patterns from aerosols and well-mixed GHGs are distinct (Chapter 7), and warming patterns therefore depend on the precise mix of forcing agents in the scenarios. The spatial efficacies – the change in surface temperature per unit ERF – for CO <sub>2</sub> , sulphate and black carbon aerosols and solar forcing have been recently evaluated in climate models ( [[#Modak--2016|Modak et al., 2016]] , 2018; [[#Duan--2018|Duan et al., 2018]] ; [[#Modak--2019|Modak and Bala, 2019]] ; [[#Richardson--2019|Richardson et al., 2019]] ). On average, the spatial patterns of near-surface warming are largely similar for different external drivers ( [[#Xie--2013|Xie et al., 2013]] ; [[#Richardson--2019|Richardson et al., 2019]] ; [[#Samset--2020|Samset et al., 2020]] ), despite the patterns of forcing being different and despite the large spread across different models ( [[#Richardson--2019|Richardson et al., 2019]] ).

Internal variability in near-surface temperature change is large in many regions, particularly in mid-latitudes and polar regions ( [[#Hawkins--2012|Hawkins and Sutton, 2012]] ). Projections from individual realizations can therefore exhibit divergent regional responses in the near-term in areas where the amplitude of a forced signal is relatively small compared to internal variability ( [[#Deser--2012b|Deser et al., 2012b]] , 2014, 2016).

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==== 4.4.1.3 Precipitation ====

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The AR5 assessed that zonal mean precipitation will ''very likely'' increase in high and some of the mid latitudes and will ''more likely than not'' decrease in the subtropics. The AR5 further assessed that the near-term changes in precipitation are largely uncertain at regional scales, and much of the non-robustness in near-term projections is attributable to internal variability and model uncertainty.

The mean patterns of seasonal precipitation change in CMIP6 models are consistent with AR5, increasing at high latitudes, over oceanic regions, and in wet regions over the tropics; and decreasing in dry regions including large parts of the subtropics (Figure 4.13). The magnitude of projected changes in precipitation in the near term, especially on regional scales is small compared to the magnitude of internal variability (Section 10.4.3; [[#Hawkins--2011|Hawkins and Sutton, 2011]] , 2016; [[#Hoerling--2011|Hoerling et al., 2011]] ; [[#Deser--2012b|Deser et al., 2012b]] ; [[#Power--2012|Power et al., 2012]] ). Analyses of CMIP5, CMIP6, and single-model large-ensemble simulations show that for the uncertainty in near-term precipitation projections, model uncertainty and internal variability dominate while the scenario uncertainty is very small (Section 8.5; [[#Lehner--2020|Lehner et al., 2020]] ). Based on large ensembles of climate change experiments, it was shown that internal variability decreases over time for both temperature and precipitation on decadal scales ( [[#Zhang--2018|Zhang and Delworth, 2018]] ; [[#Tebaldi--2021|Tebaldi et al., 2021]] ). The precipitation projections from CMIP6 models shows larger model uncertainty associated with the higher average transient climate response ( [[#Lehner--2020|Lehner et al., 2020]] ).

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[[File:7e1103fc80f568cb364133c0178eef16 IPCC_AR6_WGI_Figure_4_13.png]]

'''Figure 4.13 |''' '''Near-term change ofseasonal mean precipitation.''' Displayed are projected spatial patterns of CMIP6 multi-model mean change (%) in '''(top)''' December–January–February (DJF) and '''(bottom)''' June–July–August (JJA) precipitation from SSP1-2.6 and SSP3-7.0 in 2021–2040 relative to 1995–2014. 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).

The ‘wet get wetter, dry get drier’ paradigm, which has been used to explain the global precipitation pattern responding to global warming ( [[#Held--2006|Held and Soden, 2006]] ), might 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]] ). Over the tropical oceans, precipitation changes are largely driven by the pattern of SST changes ( [[#He--2018|He et al., 2018]] ), and in the subtropics, precipitation response is driven primarily by the fast adjustment to CO <sub>2</sub> forcing ( [[#He--2017|He and Soden, 2017]] ). In addition to the response to GHG forcing, forcing from natural and anthropogenic aerosols exert impacts on regional patterns of precipitation (Section 10.3.1; [[#Ramanathan--2005|Ramanathan et al., 2005]] ; [[#Bollasina--2011|Bollasina et al., 2011]] ; [[#Polson--2014|Polson et al., 2014]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; L. [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Shawki--2018|Shawki et al., 2018]] ). The large uncertainties in near-term regional precipitation projections arise due to the interplay between internal variability and anthropogenic external forcing ( [[#Endo--2018|Endo et al., 2018]] ; Wang et al.,2021). Uncertainties in future aerosol emissions scenarios contribute to uncertainties in regional precipitation projections ( [[#Wilcox--2020|Wilcox et al., 2020]] ). Aerosol changes induce a drying in the SH tropical band compensated by wetter conditions in the NH counterpart ( [[#Acosta%20Navarro--2017|Acosta Navarro et al., 2017]] ). The spatially uneven distribution of the aerosol forcing may also induce changes in tropical precipitation caused by shifts in the mean location of the intertropical convergence zone (ITCZ) ( [[#Hwang--2013|Hwang et al., 2013]] ; [[#Ridley--2015|Ridley et al., 2015]] ; [[#Voigt--2017|Voigt et al., 2017]] ). Because of the large uncertainty in the aerosol radiative forcing and the dynamical response to the aerosol forcing there is ''medium confidence'' in the impacts of aerosols on near-term projected changes in precipitation. Precipitation changes in the near term show seasonal amplification, precipitation increase in the rainy season and decrease in the dry season ( [[#Fujita--2019|Fujita et al., 2019]] ).

Consistent with AR5, we conclude that projected changes of seasonal mean precipitation in the near term will increase at high latitudes. Near-term projected changes in precipitation are uncertain mainly because of natural internal variability, model uncertainty, and uncertainty in natural and anthropogenic aerosol forcing ( ''medium confidence'' ).

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==== 4.4.1.4 Global Monsoon Precipitation and Circulation ====

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The global monsoon is a forced response of the coupled atmosphere–land–ocean system to the annual cycle of solar insolation and characterized by a seasonal reversal of circulation and a seasonal alternation of dry and wet conditions (Section 8.3.2, Figure 8.11 and Annex V). The global monsoon concept helps to dissect the mechanisms and controlling factors of monsoon variability at various temporal-spatial scales ( [[#Wang--2008|Wang and Ding, 2008]] ; P.X. [[#Wang--2017|Wang et al., 2017]] ).

In AR5, there was no specific assessment on global monsoon changes in the near term, but information can be derived from CMIP5 projections of the spatial patterns of precipitation change. While the basic pattern of wet regions, including global monsoon regions, tending to get wetter and dry regions tending to get drier is apparent, large response uncertainty is evident in the substantial spread in the magnitude of projected change from different simulations. Over the global land monsoon regions, model uncertainty and internal variability together explain 99.7% of the fraction of total variance ( [[#Zhou--2020|Zhou et al., 2020]] ), near-term projected multi-model mean precipitation changes are almost everywhere smaller than the estimated standard deviation of internal variability (Figure 4.13).

The global land monsoon precipitation index, defined as the area-weighted precipitation rate in the global land monsoon domain, tends to increase in the near term under all five core SSPs (Figure 4.14a) ( [[#Chen--2020|Chen et al., 2020]] ), but changes are small compared to the intermodel spread in the historical period. The Northern Hemisphere summer monsoon circulation index, defined as the vertical shear of zonal winds between 850 and 200 hPa averaged in a zone stretching from Mexico eastward to the Philippines (0°–20°N, 120°W–120°E), tends to decrease under four of the five SSP scenarios (Figure 4.14b), potentially offsetting monsoon precipitation increase. Projected changes in the global monsoon circulation are also uncertain, because they are influenced by internal variability such as AMV and PDV (see [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.2|Section 3.3.3.2]] ) and structural differences across models. In the near-term, for CMIP6 projections (Figure 4.14a), the multi-model mean (5–95% range) of global land monsoon precipitation change is 1.9% (–0.4 to 4.9%), 1.6% (–1.0 to 5.2%), 1.3% (–1.7 to 3.7%), and 1.9% (–0.8 to 5.2%) under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively.

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[[File:9b4c9680c4a745f6a5bef98539f6ee60 IPCC_AR6_WGI_Figure_4_14.png]]

'''Figure 4.1''' '''4 |''' '''Time series of global land monsoon precipitation and Northern Hemisphere summer monsoon (NHSM) circulation index anomalies. (a)''' Global land monsoon precipitation index anomalies (unit: %) defined as the area-weighted mean precipitation rate in the global land monsoon domain (as defined by [[#Wang--2013a|Wang et al. (2013a)]] for the CMIP6 historical simulation (1950–2014) and five SSPs (2015–2100). '''(b)''' Anomalies in NHSM circulation index (unit: m s <sup>–1</sup> ), defined as the vertical shear of zonal winds between 850 and 200 hPa averaged in a zone stretching from Mexico eastward to the Philippines (0°–20°N, 120°W–120°E; [[#Wang--2013a|Wang et al., 2013a]] ) for the CMIP6 historical simulation and five SSPs. One realization is averaged from each model. Anomalies are shown relative to the present-day (1995–2014) mean. The curves show averages over the simulations, the shadings around the SSP1-2.6 and SSP3-7.0 curves show 5–95% ranges, and the numbers near the top show the number of model simulations used. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, we assess that near-term changes in global monsoon precipitation and circulation will be affected by the combined effects of model uncertainty and internal variability, such as AMV and PDV, which together are larger than the forced signal ( ''medium confidence'' ).

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