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

==== 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>