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

== 4.4 Near-term Global Climate Changes ==

<div id="h1-5-siblings" class="h1-siblings"></div>

This section assesses changes in large-scale climate over the period 2021–2040 and includes information from both projections and initialized decadal predictions. The structure is similar to ( [[#4.3|Section 4.3]] . Unless noted otherwise, the assessment assumes that there will be no major volcanic eruption in the near term. The climatic effects of volcanic eruptions are assessed in [[#4.4.4|Section 4.4.4]] and [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] ; [[#4.4.4|Section 4.4.4]] also assesses the climate effects of short-lived climate forcers.

<div id="4.4.1" class="h2-container"></div>

<span id="atmosphere-1"></span>
=== 4.4.1 Atmosphere ===

<div id="h2-17-siblings" class="h2-siblings"></div>

<div id="4.4.1.1" class="h3-container"></div>

<span id="average-global-surface-air-temperature"></span>
==== 4.4.1.1 Average Global Surface Air Temperature ====

<div id="h3-10-siblings" class="h3-siblings"></div>

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'' ).

<div id="4.4.1.2" class="h3-container"></div>

<span id="spatial-patterns-of-surface-warming"></span>
==== 4.4.1.2 Spatial Patterns of Surface Warming ====

<div id="h3-11-siblings" class="h3-siblings"></div>

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.

<div id="_idContainer039" class="Basic-Text-Frame _idGenObjectStyleOverride-1"></div>

[[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).

<div id="4.4.1.3" class="h3-container"></div>

<span id="precipitation-2"></span>
==== 4.4.1.3 Precipitation ====

<div id="h3-12-siblings" class="h3-siblings"></div>

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]] ).

<div id="_idContainer041" class="Basic-Text-Frame"></div>

[[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'' ).

<div id="4.4.1.4" class="h3-container"></div>

<span id="global-monsoon-precipitation-and-circulation"></span>
==== 4.4.1.4 Global Monsoon Precipitation and Circulation ====

<div id="h3-13-siblings" class="h3-siblings"></div>

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.

<div id="_idContainer043" class="_idGenObjectStyleOverride-1"></div>

[[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'' ).

<div id="4.4.2" class="h2-container"></div>

<span id="cryosphere-ocean-and-biosphere"></span>
=== 4.4.2 Cryosphere, Ocean and Biosphere ===

<div id="h2-18-siblings" class="h2-siblings"></div>

<div id="4.4.2.1" class="h3-container"></div>

<span id="arctic-sea-ice-1"></span>
==== 4.4.2.1 Arctic Sea Ice ====

<div id="h3-14-siblings" class="h3-siblings"></div>

The AR5 assessed that for RCP8.5, Arctic sea ice coverage in September will drop below 1 million km <sup>2</sup> , or become practically ice-free, at some point between 2040 and 2060 ( [[#Collins--2013|Collins et al., 2013]] ). Since AR5, there has been substantial progress in understanding the response of Arctic sea ice to near-term changes in external forcing. In particular, it is ''very likely'' that different trajectories of the near-term evolution of anthropogenic forcing cause distinctly different likelihood ranges for very low sea ice coverage to occur over the next two decades ( [[#Notz--2018|Notz and Stroeve, 2018]] ). For example, there is an ''unlikely'' drop of September Arctic sea ice coverage to below 1 million km <sup>2</sup> before 2040 for RCP 2.6, and a ''likely'' drop of September Arctic sea ice coverage to below 1 million km <sup>2</sup> before 2040 for RCP 8.5 ( ''medium confidence'' given the single study). The much higher likelihood of a practically sea ice free Arctic Ocean during summer before 2040 in RCP8.5 compared to RCP2.6 is consistent with related studies assessed in SROCC that find a substantially increased likelihood of an ice-free Arctic Ocean for 2.0°C compared to 1.5°C mean global warming relative to pre-industrial levels ( [[#Screen--2017|Screen and Williamson, 2017]] ; [[#Jahn--2018|Jahn, 2018]] ; [[#Niederdrenk--2018|Niederdrenk and Notz, 2018]] ; [[#Notz--2018|Notz and Stroeve, 2018]] ; [[#Sigmond--2018|Sigmond et al., 2018]] ; [[#Olson--2019|Olson et al., 2019]] ).

Based on results from CMIP6 models, we conclude that Arctic SIA will decrease in September in the near term (Figure 4.15, ''high confidence'' ). In the case of 10-year trends ending in the near term, 79% of the simulations considered across all the core SSPs project decreasing Arctic sea ice area in September. Due to less of an influence from internal variability, this number rises to 98% in the case of 30-year trends. A more detailed assessment of near-term Arctic sea ice changes can be found in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.3.1). A detailed assessment of Antarctic sea ice changes is in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Section 9.3.2).

<div id="_idContainer045" class="Basic-Text-Frame"></div>

[[File:cc363822ef266bdf1b51f3f8cad32393 IPCC_AR6_WGI_Figure_4_15.png]]

'''Figure 4.15 |''' '''CMIP6 linear trends in September Arctic sea-ice area for 10-year, 20-year, and 30-year periods ending in 2021–2040 following five SSPs.''' Plotted are the 5–95% ranges across the ensembles of simulations. The numbers at the top of the plot are the number of model simulations in each SSP ensemble. The numbers near the bottom of the plot indicate the percentage of simulations across all the SSPs with decreasing sea-ice area. Results are from concentration-driven simulations. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

<div id="4.4.2.2" class="h3-container"></div>

<span id="ocean-and-land-carbon-flux"></span>
==== 4.4.2.2 Ocean and Land Carbon Flux ====

<div id="h3-15-siblings" class="h3-siblings"></div>

Ocean carbon flux is both a key feature of the physical ocean in mitigating the rise of atmospheric CO <sub>2</sub> and a driver of changes in the ocean biosphere, including changes in ocean acidity. Based on results from CMIP6 models, we conclude that SSP2-4.5, SSP3-7.0, and SSP5-8.5 all clearly lead to increasing 10-, 20-, and 30-year trends in ocean carbon flux over the near term ( ''high confidence'' ) (Figure 4.16,). Increasing trends in ocean carbon flux are less obvious in the lower-emissions scenarios. Ensemble-mean trends in land carbon flux over the near term are generally increasing, but these are ''unlikely'' to be detected given a large component of terrestrial variability combined with model uncertainty. A more detailed assessment is in [[IPCC:Wg1:Chapter:Chapter-5|Chapter 5]] (Section 5.2.1).

<div id="_idContainer047" class="Basic-Text-Frame"></div>

[[File:c66d3bddc357e3e3fa62bed9693f8209 IPCC_AR6_WGI_Figure_4_16.png]]

'''Figure''' '''4.16 |''' '''CMIP6 trends in ocean and land carbon flux for 10-year, 20-year, and 30-year periods ending in 2021–2040. (a)''' Ocean carbon flux. '''(b)''' Land carbon flux. Plotted are the 5–95% ranges across the ensembles of simulations, for five SSPs. The numbers at the top of the plots are the number of model simulations in each SSP ensemble. Units are PgC yr <sup>–1</sup> per decade. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

In summary, it is ''likely'' that ocean carbon flux will increase in the near term under the higher emissions scenarios, while a large component of terrestrial variability makes it is ''unlikely'' that an increase in land carbon flux will be detected over this period.

<div id="4.4.3" class="h2-container"></div>

<span id="modes-of-variability-1"></span>
=== 4.4.3 Modes of Variability ===

<div id="h2-19-siblings" class="h2-siblings"></div>

This subsection assesses the near-term evolution of the large-scale modes of climate variability. Assessment of the physical mechanisms and the individual feedbacks involved in the future change of each mode and their role on future regional climate variability are provided in Sections [[#8.4.2|8.4.2]] , [[#9.2.3|9.2.3]] and [[#10.1.3|10.1.3]] , and [[IPCC:Wg1:Chapter:Chapter-10#cross-chapter-box-10.1|Cross-Chapter Box 10.1]] .

<div id="4.4.3.1" class="h3-container"></div>

<span id="northern-and-southern-annular-modes-1"></span>
==== 4.4.3.1 Northern and Southern Annular Modes ====

<div id="h3-16-siblings" class="h3-siblings"></div>

<div id="4.4.3.1.1" class="h4-container"></div>

<span id="the-northernannular-mode"></span>
===== 4.4.3.1.1 The NorthernAnnular Mode =====

<div id="h4-3-siblings" class="h4-siblings"></div>

The AR5 assessed from CMIP5 simulations that there is only ''medium confidence'' in near-term projections of a northward shift of NH storm track and westerlies, and an associated increase in the NAM index, because of the large response uncertainty and the potentially large influence of internal variability. A tendency in the near term towards a slightly more positive NAM in the three highest emissions scenarios during boreal fall, winter, and spring is apparent in Figure 4.17a. However, in general the projected near-term multi-model mean change in the NAM is small in magnitude compared to the inter-model and/or multi-realization variability within the ensemble (Figure 4.17a; [[#Deser--2012b|Deser et al., 2012b]] , 2017; [[#Barnes--2015|Barnes and Polvani, 2015]] ).

<div id="_idContainer049" class="Basic-Text-Frame"></div>

[[File:8869dd2f6128b93d068ceef75f2e735c IPCC_AR6_WGI_Figure_4_17.png]]

'''Figure 4.17''' '''|''' '''CMIP6 Annular Mode index change (hPa) from 1995–2014 to 2021–2040. (a)''' Northern Annular Mode (NAM); '''(b)''' Southern Annular Mode (SAM). The NAM is defined as the difference in zonal mean sea level pressure (SLP) at 35°N and 65°N ( [[#Li--2003|Li and Wang, 2003]] ) and the SAM as the difference in zonal mean SLP at 40°S and 65°S ( [[#Gong--1999|Gong and Wang, 1999]] ). The shadings are the 5–95% ranges across the simulations. The numbers near the top of each panel are the numbers of model simulations in each SSP ensemble. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

On seasonal to interannual time scales, there is new evidence since AR5 that initialized predictions show lower potential predictability for the boreal winter NAO than the correlation skill with respect to observations ( [[#Eade--2014|Eade et al., 2014]] ; [[#Baker--2018|Baker et al., 2018]] ; [[#Scaife--2018|Scaife and Smith, 2018]] ; [[#Athanasiadis--2020|Athanasiadis et al., 2020]] ). This has been referred to in the literature as a ‘signal-to-noise paradox’ and means that very large ensembles of predictions are needed to isolate the predictable component of the NAO. While the processes that contribute to the predictability of the winter NAO on seasonal time scales may be distinct from the processes that drive multi-decadal trends, there is emerging evidence that initialized predictions also underrepresent the predictability of the winter NAO on decadal time scales (D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ). Post-processing and aggregation of initialized predictions may therefore reveal significant skill for predicting the winter NAO on decadal time scales ( [[#Smith--2020|Smith et al., 2020]] ). Considering these new results since AR5, in the near-term it is ''likely'' that any anthropogenic forced signal in the NAM will be of comparable magnitude or smaller than natural internal variability in the NAM ( ''medium confidence'' ).

<div id="4.4.3.1.2" class="h4-container"></div>

<span id="the-southern-annular-mode"></span>
===== 4.4.3.1.2 The Southern Annular Mode =====

<div id="h4-4-siblings" class="h4-siblings"></div>

The AR5 assessed that it is ''likely'' that increases in GHGs and the projected recovery of the Antarctic ozone hole will be the principal drivers of future SAM trends. Additionally, the positive trend in austral summer/autumn SAM observed over the past several decades ( [[IPCC:Wg1:Chapter:Chapter-2#2.4.1.2|Section 2.4.1.2]] ; [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] in AR5, [[#Hartmann--2013|Hartmann et al., 2013]] ), is ''likely'' to weaken considerably as stratospheric ozone recovers through to the mid-21st century. The effects of ozone depletion and recovery on the SH circulation primarily occur in austral summer, while GHGs influence the SH circulation year round ( [[#Gillett--2013|Gillett and Fyfe, 2013]] ; [[#Grise--2014b|Grise and Polvani, 2014b]] ). Therefore, they are ''likely'' to be the dominant driver of projected circulation changes outside of austral summer ( [[#Gillett--2013|Gillett and Fyfe, 2013]] ; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Solomon--2016|Solomon and Polvani, 2016]] ). Based on current scenarios specifying future atmospheric decline of ozone depleting substances ( [[#WMO--2011|WMO, 2011]] ), chemistry-climate models project the Antarctic ozone hole in October to recover by around 2060 ( [[#WMO--2014|WMO, 2014]] , 2018; [[#Dhomse--2018|Dhomse et al., 2018]] ). Observational evidence since AR5 shows the onset of Antarctic ozone hole recovery ( [[#Solomon--2016|Solomon et al., 2016]] ; [[#WMO--2018|WMO, 2018]] ) that has been attributed to a pause in the summer SAM trend over the past couple of decades ( [[#Saggioro--2019|Saggioro and]] [[#Shepherd--2019|Shepherd, 2019]] ; [[#Banerjee--2020|Banerjee et al., 2020]] ). In austral summer, ozone recovery and increasing GHGs will have opposing effects on the SAM over the next several decades ( [[#Barnes--2014|Barnes et al., 2014]] ).

Since AR5, there have been advances in understanding the role of internal climate variability for projected near-term SH circulation trends ( [[#Solomon--2016|Solomon and Polvani, 2016]] ). A large initial-condition ensemble following the RCP4.5 emissions scenario shows a monotonic positive SAM trend in austral winter. In austral summer, the SAM trend over the first half of the 21st century is weaker compared to the strongly positive trend observed and simulated over the late 20th century. In that model, the number of realizations required to identify a detectable change in decadal mean austral winter SAM index from a year 2000 reference state decreased to below five by around 2025–2030 ( [[#Solomon--2016|Solomon and Polvani, 2016]] ). However, in December–January–February (DJF) the same criterion is not met until the second half of the 21st century, owing to the near-term opposing effects of ozone recovery and GHGs on the austral-summer SAM. In austral summer, forced changes in the SAM index in the near-term are therefore ''likely'' to be smaller than changes due to internal variability (Figure 4.17b; [[#Barnes--2014|Barnes et al., 2014]] ; [[#Solomon--2016|Solomon and Polvani, 2016]] ).

CMIP6 models show a tendency in the near-term towards a more positive SAM index especially in the austral winter (June–July–August, JJA; Figure 4.17b). In all seasons, the differences between the central estimates of the change in the SAM index for each SSP are much smaller than the inter-model ensemble spread. The number of CMIP6 realizations in Figure 4.17b is larger than the suggested threshold of five realizations needed to detect a significant near-term change in decadal-mean austral winter SAM index for a single CMIP5 model ( [[#Solomon--2016|Solomon and Polvani, 2016]] ), and yet the 5–95% intervals on the CMIP6 ensemble spread encompass zero for all core SSPs. This suggests both internal variability and model uncertainty contribute to the CMIP6 ensemble spread in near-term SAM index changes. Based on these results, it is ''more likely than not'' that in the near-term under all assessed SSP scenarios the SAM index would become more positive than in present-day in austral autumn, winter and spring.

An influence of forcing agents other than stratospheric ozone and GHGs, such as anthropogenic aerosols, on SAM changes over the historical period has been reported in some climate models ( [[#Rotstayn--2013|Rotstayn, 2013]] ), but the response across a larger set of CMIP5 models is not robust ( [[#Steptoe--2016|Steptoe et al., 2016]] ) and depends on how tropospheric temperature responds to aerosols ( [[#Choi--2019|Choi et al., 2019]] ). This gives ''low confidence'' in the potential influence of anthropogenic aerosols on the SAM in the future.

<div id="4.4.3.2" class="h3-container"></div>

<span id="el-niñosouthern-oscillation-1"></span>
==== 4.4.3.2 El Niño–Southern Oscillation ====

<div id="h3-17-siblings" class="h3-siblings"></div>

The AR5 assessed that it is ''very likely'' that the ENSO will remain the dominant mode of interannual variability in the future but did not specify its change in near term. A subset of CMIP5 models that simulate the ENSO Bjerknes index most realistically show an increase of ENSO SST amplitude in the near-term future and decline thereafter ( [[#Kim--2014|Kim et al., 2014]] ). However, detection of robust near-term changes of ENSO SST variability in response to anthropogenic forcing is difficult to achieve due to pronounced unforced low-frequency modulations of ENSO ( [[#Wittenberg--2009|Wittenberg, 2009]] ; [[#Maher--2018|Maher et al., 2018]] ; [[#Wengel--2018|Wengel et al., 2018]] ). Figure 4.10 in [[#4.3.3.2|Section 4.3.3.2]] , using CMIP6 models, also shows no robust change in ENSO SST variability in the near term.

While there is no strong model consensus on the change in amplitude of ENSO SST variability, the amplitude of ENSO-associated rainfall variability ''likely'' increases ( [[#Power--2013|Power et al., 2013]] ; [[#Cai--2015|Cai et al., 2015]] ). Analysis of CMIP6 models shows a slight increasing trend in amplitude of rainfall variability over Niño 3.4 region in the near term attributable to mean moisture increase, regardless of changes in ENSO SST variability (Figure 4.10). However, there are no distinguishable changes in the rainfall variability among five SSPs with significant model spread in the near term. Hence, no robust change in amplitude of ENSO SST and rainfall variability is expected in the near term although the rainfall variability slightly increases ( ''medium confidence'' ).

<div id="4.4.3.3" class="h3-container"></div>

<span id="indian-ocean-basin-and-dipole-modes"></span>
==== 4.4.3.3 Indian Ocean Basin and Dipole Modes ====

<div id="h3-18-siblings" class="h3-siblings"></div>

Important modes of interannual climate variability with pronounced climate impacts in the Africa–Indo-Pacific areas of the globe are the Indian Ocean Dipole (IOD), which is closely related to, and often coincides with, ENSO phases ( [[#Stuecker--2017|Stuecker et al., 2017]] ), and the Indian Ocean basin (IOB) mode. This is often described as a capacitor effect in response to ENSO ( [[#Xie--2009|Xie et al., 2009]] ; [[#Du--2013|Du et al., 2013]] ) and can feed back onto ENSO evolution ( [[#Cai--2019|Cai et al., 2019]] ). IOD and IOB are extensively described in [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] (Section AIV2.4).

The projected climate mean state changes in the tropical Indian Ocean resemble a positive IOD state, with faster warming in the west compared to the east. This mean state change will potentially lead to a reduction in the amplitude of IOD events, albeit with no robust change in IOD frequency ( [[#Cai--2014b|Cai et al., 2014b]] ). There is no robust evidence yet suggesting a cessation of IOD variability or a significant change in the IOB mode in the near-term.

<div id="4.4.3.4" class="h3-container"></div>

<span id="tropical-atlantic-modes"></span>
==== 4.4.3.4 Tropical Atlantic Modes ====

<div id="h3-19-siblings" class="h3-siblings"></div>

Interannual variability of the tropical Atlantic can be described in terms of two main climate modes: the Atlantic equatorial mode and the Atlantic meridional mode (AMM; Annex IV, Section AIV2.5). The Atlantic equatorial mode, also commonly referred to as the Atlantic Niño or Atlantic Zonal Mode, is associated with SST anomalies near the equator, peaking in the eastern basin, while the AMM is characterized by an inter-hemispheric gradient of SST and wind anomalies. Both modes are associated with changes in the ITCZ and related winds and exert a strong influence on the climate in adjacent and remote regions.

Despite considerable improvements in CMIP5 with respect to CMIP3, most CMIP5 models have difficulties in simulating the mean climate of the tropical Atlantic ( [[#Mohino--2019|Mohino et al., 2019]] ) and are not able to correctly simulate the main aspects of Tropical Atlantic Variability (TAV) and associated impacts. This is presumably the main reason why there is a lack of specific studies dealing with near-term changes in tropical Atlantic modes. Nevertheless, AR5 reported that the ocean is more predictable than continental areas at the decadal time scale ( [[#Kirtman--2013|Kirtman et al., 2013]] ). In particular, the predictability in the tropical oceans is mainly associated with decadal variations of the external forcing component. Since the AMV affects the tropical Atlantic, near-term variations of the AMV can modulate the equatorial mode and the AMM as well as associated impacts.

There are no specific studies focusing on near-term changes in tropical Atlantic modes; nevertheless, decadal predictions show that although the North Atlantic stands out in most CMIP5 models as the primary region where skill might be improved because of initialization, encouraging results have also been found in the tropical Atlantic ( [[#Meehl--2014|Meehl et al., 2014]] ). The effect of initialization in the tropical Atlantic is not only visible in surface temperature but also in the subsurface ocean ( [[#Corti--2015|Corti et al., 2015]] ). In particular, initialization improves the skill via remote ocean conditions in the North Atlantic subpolar gyre and tropical Pacific, which influence the tropical Atlantic through atmospheric teleconnections ( [[#Dunstone--2011|Dunstone et al., 2011]] ; [[#Vecchi--2014|Vecchi et al., 2014]] ; [[#García-Serrano--2015|García-Serrano et al., 2015]] ). Improvements of some aspects of climate prediction systems (initialization techniques, large ensembles, increasing model resolution) have also led to skill improvements over the tropical Atlantic ( [[#Pohlmann--2013|Pohlmann et al., 2013]] ; [[#Monerie--2017|Monerie et al., 2017]] ; [[#Yeager--2017|Yeager and Robson, 2017]] ).

Recent studies have shown that the AMV can modulate not only the characteristics of the Atlantic Niños, but also their inter-basin teleconnections (Indian and Pacific). In particular, the Atlantic Niño–ENSO relationship is strongest during negative AMV phases ( [[#Martín-Rey--2014|Martín-Rey et al., 2014]] ; [[#Losada--2016|Losada and Rodríguez-Fonseca, 2016]] ) when equatorial Atlantic SST variability is enhanced ( [[#Martín-Rey--2017|Martín-Rey et al., 2017]] ; [[#Lübbecke--2018|Lübbecke et al., 2018]] ).

Based on CMIP5 and available CMIP6 results, we conclude that there is a lack of studies on the near-term evolution of TAV and associated teleconnections for a comprehensive assessment. However, some studies show that despite severe model biases there are skilful predictions in the mean state of tropical Atlantic surface temperature several years ahead ( ''medium confidence'' ), though skill in simulated variability has not been assessed yet. Decadal changes in the Atlantic Niño spatial configuration and associated teleconnections might be modulated by the AMV, but there is ''limited evidence'' and therefore ''low confidence'' in these results.

<div id="4.4.3.5" class="h3-container"></div>

<span id="pacific-decadal-variability"></span>
==== 4.4.3.5 Pacific Decadal Variability ====

<div id="h3-20-siblings" class="h3-siblings"></div>

Climate variability of the Pacific Ocean on decadal and inter-decadal time scales is described in terms of a number of quasi-oscillatory SST patterns such as the Pacific Decadal Oscillation (PDO; [[#Mantua--1997|Mantua et al., 1997]] ) and the Inter-decadal Pacific Oscillation (IPO; [[#Folland--2002|Folland, 2002]] ), which are referred to as the Pacific Decadal Variability (PDV; [[#Newman--2016|Newman et al., 2016]] ). PDV comprises an inter-hemispheric pattern that varies at decadal to inter-decadal time scales (Figure 3.35). However, although the spatial domains to derive the IPO and PDO indices differ, and despite uncertainty related to trend removal and time-filtering ( [[#Newman--2016|Newman et al., 2016]] ; [[#Tung--2019|Tung et al., 2019]] ), the IPO and PDO are highly correlated in time and they will be assessed together as the PDV (Annex IV, Section AIV.2.6).

The AR5 assessed that near-term predictions of PDV (then referred to as PDO or IPO) were largely model dependent ( [[#Mochizuki--2012|Mochizuki et al., 2012]] ; [[#van%20Oldenborgh--2012|van Oldenborgh et al., 2012]] ), not robust to sampling of initialization start-dates, overall not statistically significant, and worse than persistence ( [[#Doblas-Reyes--2013|Doblas-Reyes et al., 2013]] ), although some studies showed positive skill for PDV ( [[#Mochizuki--2010|Mochizuki et al., 2010]] ; [[#Chikamoto--2013|Chikamoto et al., 2013]] ). The CMIP5 decadal-prediction ensemble yielded no prediction skill of SST over the key PDV centres of action in the Pacific Ocean, both at two-to-five-year and six-to-nine-year forecast averages ( [[#Doblas-Reyes--2013|Doblas-Reyes et al., 2013]] ; [[#Guemas--2013|Guemas et al., 2013]] ; [[#Boer--2019|Boer and Sospedra-Alfonso, 2019]] ).

Since AR5, the processes causing the multi-decadal variability in the Pacific Ocean have become better understood ( [[#Newman--2016|Newman et al., 2016]] ; [[#Henley--2017|Henley, 2017]] ). However, the relative importance oftropical and extratropical processes underlying PDV remains unclear; although it seems to be stochastically driven rather than self-excited ( [[#Liu--2012|Liu, 2012]] ; [[#Liu--2018|Liu and Di Lorenzo, 2018]] ), the South Pacific being a key region for the tropical branch of PDV ( [[#Chung--2019|Chung et al., 2019]] ; [[#Liguori--2019|Liguori and Di Lorenzo, 2019]] ).

Because PDV represents not one, but many dynamical processes, it represents a challenge as a target for near-term climate predictions and projections. The new generation of decadal forecast systems keeps showing poor ( [[#Shaffrey--2017|Shaffrey et al., 2017]] ) to moderate (D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ) multi-year prediction skill for PDV, although the potential for forecasting capabilities is demonstrated in case studies ( [[#Meehl--2012|Meehl and Teng, 2012]] ; [[#Meehl--2014|Meehl et al., 2014]] ). For the near-term, a transition of PDV from the negative phase (1999–2012) towards a positive phase is predicted in the coming years (2013–2022; [[#Meehl--2016|Meehl et al., 2016]] ).

The PDV has been shown to influence the pace of global warming ( [[IPCC:Wg1:Chapter:Chapter-3#cross-chapter-box-3.1|Cross-Chapter Box 3.1]] ), but the extent to which PDV is externally forced or internally generated ( [[#Mann--2020|Mann et al., 2020]] ) remains an open question, and there is still no robust evidence. Thus, there is ''low confidence'' on how the PDV will evolve in the near-term ( [[#Bordbar--2019|Bordbar et al., 2019]] ).

<div id="4.4.3.6" class="h3-container"></div>

<span id="atlantic-multi-decadal-variability"></span>
==== 4.4.3.6 Atlantic Multi-decadal Variability ====

<div id="h3-21-siblings" class="h3-siblings"></div>

The Atlantic Multi-decadal Variability (AMV) is a large-scale climate mode accounting for the main fluctuations in North Atlantic SST on multi-decadal time scales (Section AIV.2.7). The AMV influences air temperatures and precipitation over adjacent and remote continents, and its undulations can partially explain the observed variations in the NH mean temperatures ( [[#Steinman--2015|Steinman et al., 2015]] ). The origin of this variability is still uncertain. Ocean dynamics (e.g., changes in the AMOC), external forcing, and local atmospheric forcing all seem to play a role ( [[#Menary--2015|Menary et al., 2015]] ; [[#Ruprich-Robert--2015|Ruprich-Robert and Cassou, 2015]] ; [[#Brown--2016|Brown et al., 2016]] ; [[#Cassou--2018|Cassou et al., 2018]] ; [[#Wills--2019|Wills et al., 2019]] ). Recent studies have discussed that the ocean dynamics play an active role in generating AMV ( [[#Oelsmann--2020|Oelsmann et al., 2020]] ) and its interplay with the NAO ( [[#Vecchi--2017|Vecchi et al., 2017]] ; R. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ; [[#Kim--2020|Kim et al., 2020]] ), although natural and anthropogenic external forcing might be crucial in modulating its amplitude and timing ( [[#Bellucci--2017|Bellucci et al., 2017]] ; [[#Bellomo--2018|Bellomo et al., 2018]] ; [[#Andrews--2020|Andrews et al., 2020]] ; Borchertet al., 2021; [[#Mann--2021|Mann et al., 2021]] ; see Sections 3.7.7 and AIV.2.7).

The AR5 assessed with high confidence that initialized predictions can improve the skill for temperature over the North Atlantic, in particular in the sub-polar branch of AMV, compared to the projections, for the first five years (see AR5 WGI Figures 11.3 and 11.4). However, non-initialized predictions showed positive correlation over the same time-range as well, consistent with the notion that part of this variability is caused by external forcing ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ).

Since AR5, near-term initialized predictions, both multi-model ( [[#Bellucci--2015a|Bellucci et al., 2015a]] ; [[#García-Serrano--2015|García-Serrano et al., 2015]] ; D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ) and single-model ensembles ( [[#Marotzke--2016|Marotzke et al., 2016]] ; [[#Simpson--2018|Simpson et al., 2018]] ; [[#Yeager--2018|Yeager et al., 2018]] ; Hermanson et al., 2020; [[#Bilbao--2021|Bilbao et al., 2021]] ), confirm substantial skill in hindcasting North Atlantic SST anomalies on a time range of eight to ten years. On the same time range, [[#Borchert--2021|Borchert et al. (2021)]] show a substantial improvement in the prediction of the subpolar gyre SST (the northern component of the AMV) in CMIP6 models compared to CMIP5, in both initialized and non-initialized simulations. The higher skill of CMIP6 models can be attributed to a more accurate response of SST variations in the subpolar gyre to natural forcing, possibly originating from the AMOC-related delayed response to volcanic eruptions ( [[#Hermanson--2020|Hermanson et al., 2020]] ).

Initialization contributes to the reduction of uncertainty and to predicting subpolar SST amplitude ( [[#Borchert--2021|Borchert et al., 2021]] ). Yet, skill in predicting the AMV is not always translated into equally successful predictions of temperature and precipitation over the nearby land and ocean regions ( [[#Langehaug--2017|Langehaug et al., 2017]] ). This might be related to systematic model errors in the simulation of the spatial and temporal structure of the AMV and too weak associated teleconnections ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ), and also to the larger noise in regional land variables compared to the AMV index. However, AMV predictions can be used as proxies to predict other variables such as precipitation over Western Europe and Eurasia and SAT over Mediterranean, Northern Europe and north-east Asia ( [[#Årthun--2018|Årthun et al., 2018]] ; [[#Borchert--2019|Borchert et al., 2019]] ; [[#Ruggieri--2021|Ruggieri et al., 2021]] ) whose relationship with North Atlantic SST is robust in observations, but not well captured in climate models.

Encouraging results about the prediction of land precipitation linked to the warm AMV phase ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] and Annex IV, Figure AIV.2.7) on a two-to-nine-year time scale are reported in the multi-model study by D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al. (2019)]] . Positive correlations with observations are found in the Sahel, South America, the Maritime Continent. Analyses from large-ensemble decadal prediction systems such as the community Earth system model decadal prediction large ensemble (CESM-DPLE; [[#Yeager--2018|Yeager et al., 2018]] ) show an improvement with respect to the CMIP5 decadal hindcasts ( [[#Martin--2014b|Martin and Thorncroft, 2014b]] ) in forecasting Sahel precipitation over three to seven years, which is consistent with the current understanding of AMV impact over Africa ( [[#Mohino--2016|Mohino et al., 2016]] ; D.M. [[#Smith--2019|]] [[#Smith--2019|Smith et al., 2019]] ). CESM-DPLE predicts drought conditions over the Sahel through 2020, which is compatible with a shift towards a negative phase of AMV as a result of a weakening of the AMOC, advocated by a number of studies ( [[#Hermanson--2014|Hermanson et al., 2014]] ; [[#Robson--2014|Robson et al., 2014]] ; [[#Yeager--2015|Yeager et al., 2015]] ).

In summary, the ''confidence'' in the predictions of AMV and its effects is ''medium'' . However, there is ''high'' ''confidence'' that the AMV skill over five-to-eight-year lead times is improved by using initialized predictions, compared to non-initialized simulations.

<div id="4.4.4" class="h2-container"></div>

<span id="response-to-short-lived-climate-forcers-and-volcanic-eruptions"></span>
=== 4.4.4 Response to Short-lived Climate Forcers and Volcanic Eruptions ===

<div id="h2-20-siblings" class="h2-siblings"></div>

Mitigation of SLCFs affects future climate projections and could alter the time of emergence of anthropogenic climate change signals. The AR5 assessed that emission reductions aimed at decreasing local air pollution could have a near-term warming impact on climate ( ''high confidence'' ) ( [[#Kirtman--2013|Kirtman et al., 2013]] ). Because of their shorter lifetimes, reductions in emissions of SLCF species mainly influence near-term GSAT trends ( [[#Chalmers--2012|Chalmers et al., 2012]] ; [[#Shindell--2017|Shindell et al., 2017]] ; [[#Shindell--2019|Shindell and Smith, 2019]] ), but on decadal time scales the near-term response to even very large reductions in SLCFs may be difficult to detect in the presence of large internal climate variability ( [[#Samset--2020|Samset et al., 2020]] ). The changes in SLCF emissions during the COVID-19 pandemic has resulted in a small net radiative forcing without a discernible impact on GSAT (Cross-Chapter Box 6.1). SLCF mitigation also leads to a higher GSAT in the mid- to long-term ( [[#Smith--2013|Smith and Mizrahi, 2013]] ; [[#Stohl--2015|Stohl et al., 2015]] ; [[#Hienola--2018|Hienola et al., 2018]] ) and can influence peak warming during the 21st century ( [[#Rogelj--2014|Rogelj et al., 2014]] ; [[#Hienola--2018|Hienola et al., 2018]] ). This section focuses on the total effect of SLCF changes on GSAT projections in the SSP scenarios. A more detailed breakdown of the separate climate effects of SLCF species and precursor species can be found in Sections 6.7.2 and 6.7.3.

A model experiment based on the SSP3-7.0 scenario with aerosols, their precursors, and non-methane tropospheric ozone precursors set to SSP1-1.9 abundances (SSP3-7.0-lowSLCF-highCH4; [[#Collins--2017|Collins et al., 2017]] ) shows a projected multi-model mean GSAT anomaly that is higher by 0.22°C at mid-century (2045-2054) compared to SSP3-7.0 (Figure 4.18; [[#Allen--2020|Allen et al., 2020]] ), but this difference is smaller than the inter-model spread of the SSP3-7.0 projections based on the CMIP6 models. Note the SSP3-7.0-lowSLCF-highCH4 experiment does not perturb methane from SSP3-7.0 concentrations. A modified SSP3-7.0-lowSLCF-lowCH4 scenario that also includes methane mitigation shows a lower GSAT by mid-century compared to SSP3-7.0 ( [[#Allen--2021|Allen et al., 2021]] ).

<div id="_idContainer051" class="Basic-Text-Frame"></div>

[[File:b69103b33e1f4d30dd6d8b7549058f46 IPCC_AR6_WGI_Figure_4_18.png]]

'''Figure 4.18 |''' '''Influence of SLCFs on projected GSAT change.''' Change is shown relative to the 1995–2014 average (left axis) and relative to the 1850–1900 average (right axis). The comparison is for CMIP6 models for the AerChemMIP ( [[#Collins--2017|Collins et al., 2017]] ) SSP3-7.0-lowSLCF-highCH4 experiment (red dashed; note in the original experiment protocol this is called SSP3-7.0-lowNTCF), where concentrations of short-lived species are reduced compared to reference SSP3-7.0 scenario (red solid). Black shows the historical simulation until 2014 for the same 9 models as the projections. The curves show averages over the r1 simulations contributed to the CMIP6 exercise, the shadings around the historical and SSP3-7.0 curves shows 5–95% ranges and the numbers near the top show the number of model simulations.

Building on CMIP6 results for the effects of reducing SLCF emissions from a baseline of SSP3-7.0, the overall contribution of SLCFs to GSAT changes in the marker SSPs are now quantified using a simple climate model emulator. For consistency with Section 6.7.2 and Figure 6.22, the basket of SLCF compounds considered includes aerosols, ozone, methane, black carbon on snow and hydrofluorocarbons (HFCs) with lifetimes of less than 50 years. In the five marker SSPs considered, the net effect of SLCFs contributes to a higher GSAT in the near, mid- and long term (Table 4.6 and Section 6.7.2). In the SSP1-1.9 and SSP1-2.6 scenarios, SLCFs contribute to a higher GSAT by a central estimate of around 0.3°C compared to 1995–2014 across the three-time horizons. In the long-term, the 0.3C warming due to SLCFs in SSP1-2.6 can be compared to the assessed ''very likely'' GSAT change for this period of 0.5°C–1.5°C ( [[#4.3.4|Section 4.3.4]] and Table 4.5). The SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios all show a larger SLCF effect on GSAT in the long term relative to the near term. In SSP3-7.0, the long-term warming due to SLCFs by 0.7°C can be compared with the assessed ''very likely'' GSAT anomaly for this period of 2.0°C –3.7°C ( [[#4.3.4|Section 4.3.4]] ). In summary, it is ''very likely'' that changes in SLCFs contribute to an overall warmer GSAT over the near, mid- and long term in the five SSP scenarios considered (Table 4.6, Section 6.7.2 and Figure 6.22).

In addition to effects on GSAT, SLCFs affect other aspects of the global climate system (Section 6.7.2). The additional warming at high northern latitudes associated with projected reductions in aerosol emissions over the 21st century leads to a more rapid reduction in Arctic sea ice extent in the RCP scenarios ( [[#Gagné--2015|Gagné et al., 2015]] ). Furthermore, mitigation of non-methane SLCFs in the SSP3-7.0-lowSLCF-highCH4 scenario causes an increase in global mean precipitation, with larger regional changes in southern and eastern Asia ( [[#Allen--2020|Allen et al., 2020]] ).

<div id="_idContainer052" class="Basic-Text-Frame"></div>

'''Table''' '''4.6 |''' '''The net effect of SLCFs on GSAT change.''' Changes in 20-year averaged GSAT relative to 1995–2014 for 2021–2040, 2041–2060, and 2081–2100 for the five marker SSP scenarios. Values give the median and, in parentheses, the 5–95% range calculated from a 2237-member ensemble of the two-layer emulator that is driven with the ERF projections, including uncertainties, described in [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] Supplementary Material 7.SM.1.4. The ensemble is constrained to assessed ranges of ECS, TCR, ocean heat content change, GSAT response, and carbon cycle metrics (Section 7.3.5; [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] Supplementary Material 7.SM.2.2). The GSAT contribution of individual forcer responses use the difference between parallel runs of the constrained two-layer model with all anthropogenic forcing and all anthropogenic forcing with the component of interest (e.g., methane) removed ( [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] Supplementary Material 7.SM.2.3). Values are given to one decimal place.

{| class="wikitable"
|-
| '''Time Period'''

| '''SSP1-1.9 (°C)'''

| '''SSP1-2.6 (°C)'''

| '''SSP2-4.5 (°C)'''

| '''SSP3-7.0 (°C)'''

| '''SSP5-8.5 (°C)'''

|-
| Near Term (2021–2040)

| 0.2 (0.1, 0.3)

| 0.2 (0.1, 0.3)

| 0.2 (0.1, 0.3)

| 0.2 (0.1, 0.3)

| 0.3 (0.2, 0.4)

|-
| Mid-Term (2041–2060)

| 0.2 (0.0, 0.4)

| 0.2 (0.0, 0.4)

| 0.3 (0.2, 0.4)

| 0.3 (0.2, 0.4)

| 0.5 (0.3, 0.7)

|-
| Long Term (2081–2100)

| 0.1 (-0.1, 0.4)

| 0.2 (0.0, 0.4)

| 0.3 (0.1, 0.6)

| 0.5 (0.4, 0.8)

| 0.7 (0.4, 1.0)

|}

The main uncertainties in climate effects of SLCFs in the future come from: (i) the uncertainty in anthropogenic aerosol ERF (Section 7.3.3); (ii) uncertainty in the relative emissions of different SLCFs that have warming and cooling effects in the current climate (Section 6.2); and (iii) physical uncertainty including the efficacy of the climate response to SLCFs compared to long-lived GHGs ( [[#Marvel--2016|Marvel et al., 2016]] ; [[#Richardson--2019|Richardson et al., 2019]] ). One example of physical uncertainty is that the shortwave radiative forcing from methane was neglected in previous calculations ( [[#Etminan--2016|Etminan et al., 2016]] ; [[#Collins--2018|Collins et al., 2018]] ), which affects understanding of present day and future methane ERF ( [[#Modak--2018|Modak et al., 2018]] ). Another example of physical uncertainty is projected changes in lightning-NO <sub>x</sub> production, which contribute to future ozone radiative forcing ( [[#Banerjee--2014|Banerjee et al., 2014]] , 2018; [[#Finney--2018|Finney et al., 2018]] ).

Another factor that could substantially alter projections in the near-term would be the occurrence of a large explosive volcanic eruption, or even a decadal to multi-decadal sequence of small-to-moderate volcanic eruptions as witnessed over the early 21st century ( [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] ; [[#Santer--2014|Santer et al., 2014]] ). An eruption similar to the last large tropical eruption, Mount Pinatubo in the Philippines in June 1991, is expected to cause substantial Northern Hemisphere (NH) cooling, peaking between 0.09°C and 0.38°C and lasting for three to five years, as indicated by climate model simulations over the past millennium (e.g., [[#Jungclaus--2010|Jungclaus et al., 2010]] ). Phase 3 of Paleoclimate Modelling Intercomparison Project (PMIP3) simulated a significant NH cooling in response to individual volcanic events (peaks between 0.1°C and 0.5°C, depending on model, during the first year after the eruption) that lasts for three to five years. On a regional scale, the double volcanic events that occurred in 536 and 540 CE resulted in a cooling of 2°C ( [[#Buntgen--2016|Büntgen et al., 2016]] ; [[#Toohey--2016|Toohey et al., 2016]] ).

Since AR5, there has been growing progress in understanding the climate impacts of volcanic eruptions. Volcanic forcing is regarded as the dominant driver of forced variability in preindustrial surface air temperature ( [[#Schurer--2013|Schurer et al., 2013]] , 2014). Large eruptions in the tropics and high latitudes were primary drivers of interannual-to-decadal temperature variability in the Northern Hemisphere during the past 2,500 years, with cooling persisting for up to ten years after some of the largest eruptive episodes ( [[#Sigl--2015|Sigl et al., 2015]] ). Repeated clusters of volcanic eruptions can induce a net negative radiative forcing that results in a centennial- and global-scale cooling trend via a decline in mixed-layer oceanic heat content ( [[#McGregor--2015|McGregor et al., 2015]] ). The response to multi-decadal changes in volcanic forcing (representing clusters of eruptions) shows similar cooling in both simulations and reconstructions of NH temperature. Volcanic eruptions generally result in decreased global precipitation for up to a few years following the eruption ( [[#Iles--2014|Iles and Hegerl, 2014]] , 2015; [[#Man--2014|Man et al., 2014]] ), with climatologically wet regions drying and climatologically dry regions wetting ( ''medium confidence'' ), which is opposite to the response under global warming ( [[#Held--2006|Held and Soden, 2006]] ; [[#Iles--2013|Iles et al., 2013]] ; [[#Zuo--2019a|Zuo et al., 2019a]] , b). El Niño-like warming appears after large volcanic eruptions, as seen in both observations ( [[#Adams--2003|Adams et al., 2003]] ; [[#McGregor--2010|McGregor et al., 2010]] ; [[#Khodri--2017|Khodri et al., 2017]] ) and climate model simulations ( [[#Ohba--2013|Ohba et al., 2013]] ; [[#Pausata--2015|Pausata et al., 2015]] ; [[#Colose--2016|Colose et al., 2016]] ; [[#Stevenson--2016|Stevenson et al., 2016]] ; [[#Khodri--2017|Khodri et al., 2017]] ; [[#Predybaylo--2017|Predybaylo et al., 2017]] ; [[#Zuo--2018|Zuo et al., 2018]] ). The large tropical eruptions are coincident with positive Indian Ocean dipole events ( [[#Maher--2015|Maher et al., 2015]] ).

In AR5, uncertainty due to future volcanic activity was not considered in the assessment of the CMIP5 21st century climate projections ( [[#Taylor--2012|Taylor et al., 2012]] ; [[#O’Neill--2016|O’Neill et al., 2016]] ). Since AR5, there has been considerable progress in quantifying the impacts of volcanic eruptions on decadal climate prediction and longer-term climate projections ( [[#Meehl--2015|Meehl et al., 2015]] ; [[#Swingedouw--2015|Swingedouw et al., 2015]] , 2017; [[#Timmreck--2016|Timmreck et al., 2016]] ; [[#Bethke--2017|Bethke et al., 2017]] ; [[#Illing--2018|Illing et al., 2018]] ). By exploring 60 possible volcanic futures under RCP4.5, it has been demonstrated that the inclusion of time-varying volcanic forcing may enhance climate variability on annual-to-decadal time scales ( [[#Bethke--2017|Bethke et al., 2017]] ). Consistent with a tropospheric cooling response, the change in ensemble spread in the volcanic cases is skewed towards lower GSAT relative to the non-volcanic cases ( [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] , Figure 1). In these simulations with multiple volcanic forcing futures there is: (i) an increase in the frequency of extremely cold individual years; (ii) an increased likelihood of decades with negative GSAT trend (decades with negative GSAT trends become 50% more commonplace); (iii) later anthropogenic signal emergence (the mean time at which the signal of global warming emerges from the noise of natural climate variability is delayed almost everywhere) ( ''high confidence'' ); and (iv) a 10% overall reduction in global land monsoon precipitation and a 20% overall increase in the ensemble spread ( [[#Man--2021|Man et al., 2021]] ).

<div id="cross-chapter-box-4.1" class="h2-container box-container"></div>

'''Cross-Chapter Box 4.1 | The Climate Effects of Volcanic Eruption'''

<div id="h2-21-siblings" class="h2-siblings"></div>

'''Contributing Authors:''' Sarah L. Connors (France/United Kingdom), Amanda Maycock (United Kingdom), Peter W. Thorne (Ireland/United Kingdom), Nicolas Bellouin (United Kingdom/France), Ingo Bethke (Norway/Germany), Deliang Chen (Sweden), Annalisa Cherchi (Italy), Alejandro Di Luca (Australia/Canada/Argentina), Piers Forster (United Kingdom), Nathan P. Gillett (Canada), Darrell S. Kaufmann (The United States of America), June-Yi Lee (Republic of Korea), Elizaveta Malinina (Canada/Russian Federation), Seung-Ki Min (Republic of Korea), Johannes Quaas (Germany), Alex C. Ruane (The United States of America), Jean-Baptiste Sallée (France), Sonia I. Seneviratne (Switzerland), Chris Smith (United Kingdom), Matthew Toohey (Canada, Germany/Canada), Andrew Turner (United Kingdom), Cunde Xiao (China), Tianjun Zhou (China)

Before the industrial period, explosive volcanic eruptions were the largest source of forced climate variability globally on interannual to centennial time scales ( [[IPCC:Wg1:Chapter:Chapter-2#2.2|Section 2.2]] ). While usually omitted from scenarios used for future climate projections, as they are unpredictable, volcanic eruptions have the potential to influence future climate on multi-annual to decadal time scales and affect many climatic impact drivers (as defined in Sections 12.1 and 12.3). Since AR5, more comprehensive paleo evidence and observations, as well as improved modelling have advanced understanding of the climate response to past volcanic eruptions. Building on multiple chapter assessments, this box synthesizes how volcanic eruptions affect climate and considers implications of possible future events.

'''How frequent are volcanic eruptions?'''

Proxy records show that large volcanic eruptions with effective radiative forcing (ERF) more negative than –1 W m <sup>–2</sup> occurred on average twice a century throughout the last 2500 years, the most recent being Pinatubo in 1991 ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]] ). About eight larger eruptions (ERF stronger than –5 W m <sup>–2</sup> ) also occurred during this period (Figure 2.2), notably Tambora about 1815 and Samalas about 1257. A Samalas-type eruption may occur one to two times per millennium on average ( [[#Newhall--2018|Newhall et al., 2018]] ). Typically, three in every four centuries have experienced at least one eruption stronger than –1 W m <sup>–2</sup> (Pinatubo or larger). The volcanic aerosol burden was 14% lower during the 20th century compared to the average of the preceding 24 centuries ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.2|Section 2.2.2]] ), whereas the 13th century was among the most volcanically active, with four eruptions exceeding that of Pinatubo-1991 ( [[#Sigl--2015|Sigl et al., 2015]] ).

'''Past climate responses to volcanic activity'''

Major eruptions drive a range of climate system responses for several years depending upon whether the eruption occurs in the tropics (stratospheric aerosol dispersion into both hemispheres) or the extratropics (dispersion into the hemisphere of eruption) owing to the Brewer-Dobson circulation. The climatic response also depends on the effective injection height, sulphur mass injected, and time of year of the eruption ( [[#Marshall--2019|Marshall et al., 2019]] , 2020). These factors determine the total mass, lifetime and optical properties of volcanic aerosol in the stratosphere and influence the stratospheric aerosol optical depth (SAOD). The ERF from volcanic stratospheric aerosol is assessed to be –20 ± 5 W m <sup>–2</sup> per unit sAOD (Section 7.3.4.6).

Due to the direct radiative effect of volcanic stratospheric aerosols, large volcanic eruptions lead to an overall decrease of GSAT, which can extend to multi-decadal or century time scales in the case of clustered volcanism ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.1|Section 3.3.1.1]] ; [[#Schurer--2013|Schurer et al., 2013]] ; [[#McGregor--2015|McGregor et al., 2015]] ; [[#Sigl--2015|Sigl et al., 2015]] ; [[#Kobashi--2017|Kobashi et al., 2017]] ; [[#Zambri--2017|Zambri et al., 2017]] ; [[#Brönnimann--2019|Brönnimann et al., 2019]] ; [[#Neukom--2019|Neukom et al., 2019]] ). Large eruptions also increase the frequency of extremely cold individual years and the likelihood of cooling trends occurring in individual decades ( [[IPCC:Wg1:Chapter:Chapter-3#cross-chapter-box-3.1|Cross-Chapter Box 3.1]] and [[#4.4.4|Section 4.4.4]] ; [[#Paik--2018|Paik and Min, 2018]] ). Re-dating of ice core chronologies now confirms that the coldest decades of the past approximately 2000 years are the outcome of volcanic eruptions ( [[#Sigl--2015|Sigl et al., 2015]] ; [[#Buntgen--2016|Büntgen et al., 2016]] ; [[#Toohey--2016|Toohey et al., 2016]] ; [[#Neukom--2019|Neukom et al., 2019]] ). CMIP5 and CMIP6 models reproduce the decreased GSAT that follows periods of intense volcanism. New reconciliations between simulations and proxy-based reconstructions of past eruptions have been achieved through better Earth System Model representation of volcanic plume chemical compositions ( [[#Legrande--2016|Legrande et al., 2016]] ; [[#Marshall--2020|Marshall et al., 2020]] ; F. [[#Zhu--2020|]] [[#Zhu--2020|Zhu et al., 2020]] ). Yet, remaining disagreements reflect differences in the volcanic forcing datasets used in the simulations ( ''medium confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.1.1|Section 3.3.1.1]] and Figure 3.2c).

Although incomplete, proxy records show large impacts upon contemporary society from eruptions such as 1257 Samalas and 1815 Tambora, the latter resulting in ‘the year without a summer’ with multiple harvest failures across the Northern Hemisphere (e.g., [[#Raible--2016|Raible et al., 2016]] ). Comparing CMIP5 multi-model simulations with observations has improved understanding of the hydrological responses to 20th century eruptions, particularly global land monsoon drying, and associated uncertainties ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.2.3|Section 3.3.2.3]] ). Global mean land precipitation decreases for up to a few years following the eruption, with climatologically wet regions drying and dry regions wetting (Sections 3.3.2.3 and 4.4.4). Changes in monsoon circulations occur with a general weakening of tropical precipitation (Section 8.5.2.3) and a decrease in extreme precipitation over global monsoon regions (Section 11.4.4). Monsoon precipitation in one hemisphere tends to be enhanced by eruptions occurring in the other hemisphere or reduced if they occur in the same hemisphere (Sections 3.3.2.3 and 8.5.2.3). Volcanic eruptions have been linked to the onset of El Niño followed by La Niña although this connection remains contentious ( [[#Adams--2003|Adams et al., 2003]] ; [[#Bradley--2003|Bradley et al., 2003]] ; [[#McGregor--2010|McGregor et al., 2010]] ; [[#Khodri--2017|Khodri et al., 2017]] ; F. [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|]] [[#Liu--2018|Liu et al., 2018]] ; [[#Sun--2019|Sun et al., 2019]] ; [[#Paik--2020|Paik et al., 2020]] ; [[#Predybaylo--2020|Predybaylo et al., 2020]] ). Volcanic activity could drive short-term (one-to-three-year) positive changes in the annual SAM index through modulations in the extratropical temperature gradient and wave driving of the polar stratosphere ( [[#Yang--2018|Yang and Xiao, 2018]] ). In the cryosphere, Arctic sea ice extent increases for years to decades ( [[#Gagné--2017a|Gagné et al., 2017a]] ), and modelling indicates that sea ice/ocean feedbacks can prolong cooling long after volcanic aerosols are removed ( [[#Miller--2012|Miller et al., 2012]] ). On annual time scales, the ocean buffers the atmospheric response to volcanic eruptions by storing the cooling in the ocean subsurface, then feeding it back to the atmosphere. Large eruptions affect ocean heat content and thermosteric sea level over decadal-to-centennial scales (Section 9.2.2.1).

'''Potential implications on 21st century projections'''

Given the unpredictability of individual eruptions, volcanic forcing is prescribed as a constant background loading in CMIP6 models ( [[#Eyring--2016|Eyring et al., 2016]] ). This means the effects of potential large volcanic eruptions are largely absent from model projections, and few studies have addressed the potential implications on 21st century warming. One study considered future scenarios with hypothetical volcanic eruptions consistent with levels of Common Era volcanic activity ( [[#Bethke--2017|Bethke et al., 2017]] ) under RCP4.5 and found that climate projections could be substantially altered ( [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] , Figure 1). Although temporary, close to pre-industrial level temperatures could be experienced globally for a few years after a 1257 Samalas-sized eruption. Several other key climate indicators are also changed substantially, consistent with evidence from past events. [[#Bethke--2017|Bethke et al. (2017)]] suggest that an eruption early in the 21st century could delay the timing of crossing 1.5°C global warming by several years. Clustered eruptions would have substantial impact upon GSAT evolution throughout the century ( [[#cross-chapter-box-4.1|Cross-Chapter Box 4.1]] , Figure 1), and could have far-reaching implications, as observed for past eruptions. For near-term response options, decadal prediction models can update 21st-century projections once a volcanic eruption occurs ( [[#Timmreck--2016|Timmreck et al., 2016]] ).

<div id="_idContainer054" class="Body-copy_Boxes_Blue-Boxes_•-Box-body"></div>

[[File:d5013b2ba1c5abd9898efdb7a90aece2 IPCC_AR6_WGI_CCBox_4_1_Figure_1.png]]

'''Cross-Chapter Box 4.1, Fig'''

'''ure 1 |'''

'''Potential impact of volcanic eruption on future global temperature change.''' CMIP5 projections of possible 21st-century futures under RCP4.5 after a 1257 Samalas magnitude volcanic eruption in 2044, from [[#Bethke--2017|Bethke et al. (2017)]] . '''(a)''' Volcanic ERF of the most volcanically active ensemble member, estimated from SAOD. '''(b)''' Annual mean global surface air temperature. Ensemble mean (solid) of future projections including volcanoes (blue) and excluding volcanoes (red) with 5–95% range (shading) and ensemble minima/maxima (dots); evolution of the most volcanically active member (black). Data created using a SMILE approach with NorESM1 in its CMIP5 configuration. See Sections 2.2.2 and 4.4.4 for more details. Further details on data sources and processing are available in the chapter data table (Table 4.SM.1).

'''Summary'''

It is ''likely'' that at least one large eruption will occur during the 21st century. Such an eruption would reduce GSAT for several years, decrease global mean land precipitation, alter monsoon circulation, modify extreme precipitation, and change the profile of many regional climatic impact-drivers. A low-likelihood, high-impact outcome would be several large eruptions that would greatly alter the 21st century climate trajectory compared to SSP-based ESM projections.

<div id="4.5" class="h1-container"></div>

<span id="mid--to-long-term-global-climate-change"></span>