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

==== 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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