Editing IPCC:AR6/WGI/Chapter-3 (section)

==== 3.3.3.2 Global Monsoon ====

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Monsoons are seasonal transitions of regimes in atmospheric circulation and precipitation with the annual cycle of solar insolation, in association with redistribution of moist static energy ( [[#Wang--2008|Wang and Ding, 2008]] ; P.X. [[#Wang--2014|]] [[#Wang--2014|Wang et al., 2014]] ; [[#Biasutti--2018|Biasutti et al., 2018]] ). The global monsoon can be defined to encompasses all monsoon systems based on precipitation contrast in the solstice seasons ( [[#Wang--2008|Wang and Ding, 2008]] ; Figure 3.17). All regional monsoons are intimately connected to the global tropical atmospheric overturning by mass ( [[#Trenberth--2000|Trenberth et al., 2000]] ), momentum and energy budgets ( [[#Biasutti--2018|Biasutti et al., 2018]] ; [[#Geen--2020|Geen et al., 2020]] ). Assessments of regional monsoon changes are made in Sections 8.3.2.4, 10.4.2.1 and 10.6.3.

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[[File:fdafa2a0d46675b23309ca966f9b3f3a IPCC_AR6_WGI_Figure_3_17.png]]

Figure 3.17 | '''Model evaluation of global monsoon domain, intensity, and circulation. (a, b)''' Climatological summer-winter range of precipitation rate, scaled by annual mean precipitation rate (shading) and 850 hPa wind velocity (arrows) based on (a) GPCP and ERA5 and (b) a multi-model ensemble mean of CMIP6 historical simulations for 1979–2014. The region enclosed by red lines is the monsoon domain based on the definition by [[#Wang--2008|Wang and Ding (2008)]] . '''(c, d)''' Five-year running mean anomalies of (c) global land monsoon precipitation index defined as the percentage anomaly of the summertime precipitation rate averaged over the monsoon regions over land, relative to its average for 1979–2014 (the period indicated by light grey shading) and (d) the tropical monsoon circulation index defined as the vertical shear of zonal winds between 850 and 200 hPa levels averaged over 0°–20°N, from 120°W eastward to 120°E in Northern Hemisphere summer ( [[#Wang--2013|Wang et al., 2013]] ; m s <sup>–1</sup> ) in CMIP5 historical and RCP4.5 simulations, and CMIP6 historical and AMIP simulations. Summer and winter are defined for individual hemispheres: May to September is defined as Northern Hemisphere summer and Southern Hemisphere winter, and November to March is defined as Northern Hemisphere winter and Summer Hemisphere summer. The numbers of models and simulations are given in the legend. The multi-model ensemble mean and percentiles are calculated after weighting individual ensemble members with the inverse of the ensemble size of the same model, so that individual models are equally weighted irrespective of ensemble size. Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

AR5 assessed that CMIP5 models simulated monsoons better than CMIP3 models but that biases remained in domains and intensity ( ''high confidence'' ) ( [[#Flato--2013|Flato et al., 2013]] ). There were no detection and attribution assessment statements on the decreasing trend of global monsoon precipitation over land from the 1950s to the 1980s or the increasing trend of global monsoon precipitation afterwards. In the paleoclimate context, it was determined with ''high confidence'' that orbital forcing produces strong interhemispheric rainfall variability evident in multiple types of proxies ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ).

Paleoclimate proxy evidence shows that the global monsoon has varied with orbital forcing and greenhouse gases ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ; [[#Seth--2019|Seth et al., 2019]] ). These large-magnitude intensifications and weakenings in the global monsoon involved in some cases orders-of-magnitude changes in precipitation locally ( [[#Harrison--2014|Harrison et al., 2014]] ; [[#Tierney--2017|Tierney et al., 2017]] ). Paleoclimate modelling and limited data from past climate states with high CO <sub>2</sub> suggest that precipitation intensifies in the monsoon domain under elevated greenhouse gases, providing context for present and future trends ( [[#Passey--2009|Passey et al., 2009]] ; [[#Haywood--2013|Haywood et al., 2013]] ; [[#Zhang--2013b|Zhang et al., 2013b]] ). In model simulations of the mid-Pliocene, when globally averaged temperature was higher than present day, precipitation was larger in West African, South Asian and East Asian monsoons than under pre-industrial conditions, consistent with proxy evidence ( [[#Zhang--2015|Zhang et al., 2015]] ; [[#Sun--2016|Sun et al., 2016]] , 2018; [[#Corvec--2017|Corvec and Fletcher, 2017]] ; X. [[#Li--2018|]] [[#Li--2018|Li et al., 2018]] ). [[#Prescott--2019|Prescott et al. (2019)]] and R. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al. (2019)]] find an important role for orbital forcing and CO <sub>2</sub> in the mid-Pliocene monsoon expansion and intensification. Models are also able to capture interhemispherically contrasting monsoon changes in the Last Interglacial in response to orbital forcing and greenhouse gases, with wetter West African and Asian monsoons and a drier South American monsoon as seen in proxies ( [[#Govin--2014|Govin et al., 2014]] ; [[#Gierz--2017|Gierz et al., 2017]] ; [[#Pedersen--2017|Pedersen et al., 2017]] ). In overall agreement with proxy evidence, a model with transient forcing simulates wetting and drying respectively of the Southern and Northern Hemisphere monsoons during the last deglaciation, with an important contribution from Atlantic Meridional Overturning Circulation (AMOC) slowdown ( [[#Otto-Bliesner--2014|Otto-Bliesner et al., 2014]] ; [[#Mohtadi--2016|Mohtadi et al., 2016]] ).

During the mid-Holocene, global monsoons were stronger especially in the Northern Hemisphere with an expansion of the West African monsoon domain in response to orbital forcing ( [[#Biasutti--2018|Biasutti et al., 2018]] ; [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ). Simulations of the mid-Holocene with CMIP5 and CMIP6 models qualitatively capture the stronger Northern Hemisphere monsoon ( [[#Jiang--2015|Jiang et al., 2015]] ; [[#Brierley--2020|Brierley et al., 2020]] ), mainly driven by atmospheric circulation changes ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ). However, the models underestimate the monsoon expansion found in proxy reconstructions ( [[#Perez-Sanz--2014|Perez-Sanz et al., 2014]] ; [[#Harrison--2015|Harrison et al., 2015]] ; [[#Tierney--2017|Tierney et al., 2017]] ), which may be linked to mean biases in the monsoon domain ( [[#Brierley--2020|Brierley et al., 2020]] ) and may be improved by imposing vegetation and dust changes ( [[#Pausata--2016|Pausata et al., 2016]] ). The models simulate the weaker Southern Hemisphere monsoon during the mid-Holocene ( [[#D’Agostino--2020|D’Agostino et al., 2020]] ), consistent with proxy evidence ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] ). These studies indicate that models can qualitatively reproduce past global monsoon changes seen in proxies, though issues remain in quantitatively reproducing proxy observations. Studies of last millennium simulations show that simulated global monsoon precipitation increases with global mean temperature, while changes in monsoon circulation and hemispheric monsoon precipitation depend on forcing sources ( [[#Liu--2012|Liu et al., 2012]] ; [[#Chai--2018|Chai et al., 2018]] ). Compared to greenhouse gas and solar variations, volcanic forcing is more effective in changing the global monsoon precipitation over the last millennium ( [[#Chai--2018|Chai et al., 2018]] ).

Reproducing monsoons in terms of domain, precipitation amount, and timings of onset and retreat over the historical period also remains difficult. While CMIP5 historical simulations broadly capture global monsoon domains and intensity based on summer and winter precipitation differences, they underestimate the extent and intensity of East Asian and North American monsoons while overestimating them over the tropical western North Pacific ( [[#Lee--2014|Lee and Wang, 2014]] ; M. [[#Yan--2016|]] [[#Yan--2016|]] [[#Yan--2016|Yan et al., 2016]] ). [[#Wang--2020|]] [[#Wang--2020|B. Wang et al. (2020)]] reported that CMIP6 models simulate the global monsoon domain and precipitation better (Figure 3.17a,b), albeit with biases in annual mean precipitation and the timings of onset and withdrawal of the Southern Hemisphere monsoon. Notable inter-model differences were identified in CMIP5, with the multi-model ensemble mean outperforming individual models ( [[#Lee--2014|Lee and Wang, 2014]] ). Common biases were identified across CMIP5 models in moist static energy and upper-tropospheric temperature associated with the South Asian summer monsoon, which may arise from overly smoothed model topography ( [[#Boos--2012|Boos and Hurley, 2012]] ). However, in atmospheric models with increasing resolution approaching 20 km, improvements in monsoon precipitation are not universal across regions and models, and overall improvements are unclear ( [[#Johnson--2016|Johnson et al., 2016]] ; [[#Ogata--2017|Ogata et al., 2017]] ; L. [[#Zhang--2018b|]] [[#Zhang--2018|Zhang et al., 2018]] b ) ''.''

In instrumental records, global summer monsoon precipitation intensity (measured by summer precipitation averaged over the monsoon domain) decreased from the 1950s to 1980s, followed by an increase ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.1.4.2|Section 2.3.1.4.2]] and Figure 3.17c), arising mainly from variations in Northern Hemispheric land monsoons. A CMIP5 multi-model study by Y. [[#Zhang--2018|Zhang et al. (2018)]] found that observed 1951–2004 trends of the global and Northern Hemisphere summer land monsoon precipitation intensity are well captured by historical simulations, and CMIP6 models show similar results for global land summer monsoon precipitation (Figure 3.17c). However, the 1960s peak in the Northern Hemisphere summer monsoon circulation is outside the 5th–95th percentile range of CMIP5 and CMIP6 historical simulations for two out of three reanalyses (Figure 3.17d). Modelling studies show that greenhouse gas increases act to enhance Northern Hemisphere summer monsoon precipitation intensity ( [[#Liu--2012|Liu et al., 2012]] ; [[#Polson--2014|Polson et al., 2014]] ; [[#Chai--2018|Chai et al., 2018]] ; L. [[#Zhang--2018b|]] [[#Zhang--2018|Zhang et al., 2018]] b ). Since the mid-20th century, however, modelling studies show that this effect was overwhelmed by the influence of anthropogenic aerosols in CMIP5 ( [[#Polson--2014|Polson et al., 2014]] ; [[#Guo--2015|Guo et al., 2015]] ; Y. [[#Zhang--2018|Zhang et al., 2018]] ; [[#Giannini--2019|Giannini and Kaplan, 2019]] ) and in CMIP6 (T. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ). Weakening of the monsoon circulation and reduction of moisture availability are important in this aerosol influence (T. [[#Zhou--2020|]] [[#Zhou--2020|Zhou et al., 2020]] ). Besides these human influences, the global monsoon is sensitive to internal variability and natural forcing including ENSO and volcanic aerosols on interannual time scales and PDV and AMV on decadal to multi-decadal time scales ( [[#Wang--2013|Wang et al., 2013]] , 2018; F. [[#Liu--2016|]] [[#Liu--2016|Liu et al., 2016]] ; [[#Jiang--2019|Jiang and Zhou, 2019]] ; [[#Zuo--2019|Zuo et al., 2019]] ); though AMV in the 20th century may have been partly driven by aerosols, see [[#3.7.7|Section 3.7.7]] . Indeed, AMIP simulations better reproduce the observed multi-decadal variations of the global monsoon precipitation and circulation (Figure 3.17c,d). Y. [[#Zhang--2018|Zhang et al. (2018)]] find that the multi-model ensemble mean trend of global land monsoon precipitation in historical simulations, dominated by anthropogenic aerosol forcing contributions, emerges out of the 90% range of internally-driven trends in pre-industrial control simulations. However, it should be noted that CMIP5 models tend to under-represent the PDV magnitude ( [[#3.7.6|Section 3.7.6]] ), suggesting potential overconfidence in the detection of the forced signal. An observed enhancement in global summer monsoon precipitation since the 1980s is accompanied by an intensification of the Northern Hemisphere summer monsoon circulation (Figure 3.17c,d). These trends appear to be at the extreme of the range of the CMIP6 historical simulation ensemble but are well captured by AMIP simulations (Figure 3.17c,d). While the precipitation increase is consistent with greenhouse gas forcing, the circulation intensification is opposite to the simulated response to greenhouse gas forcing, and these enhancements have been attributed to PDV and AMV ( [[#Wang--2013|Wang et al., 2013]] ; [[#Kamae--2017|Kamae et al., 2017]] ).

In summary, while greenhouse gas increases acted to enhance the global land monsoon precipitation over the 20th century ( ''medium confidence'' ), consistent with projected future enhancement ( [[IPCC:Wg1:Chapter:Chapter-4#4.5.1.5|Section 4.5.1.5]] ), this tendency was overwhelmed by anthropogenic aerosols from the 1950s to the 1980s, which contributed to weakening of global land summer monsoon precipitation intensity for this period ( ''medium confidence'' ). There is ''medium confidence'' that the intensification of global monsoon precipitation and Northern Hemisphere summer monsoon circulation since the 1980s is dominated by internal variability. These assessments are supported respectively by multi-model detection and attribution studies which find an important role for anthropogenic aerosols in the weakening trend, and studies that identify a role for AMV and PDV in inducing the Northern Hemisphere summer monsoon circulation enhancement since the 1980s. Supported by multi-model simulations that are qualitatively consistent with proxy evidence, there is ''high confidence'' that orbital forcing contributed to higher Northern Hemisphere monsoon precipitation in the mid-Pliocene and mid-Holocene than pre-industrial. While CMIP5 models can capture the domain and precipitation intensity of the global monsoon, biases remain in their regional representations, and they are unsuccessful in quantitatively reproducing changes in paleo reconstructions ( ''high confidence'' ). CMIP6 models reproduce the domain and precipitation intensity of the global monsoon observed over the instrumental period better than CMIP5 models ( ''medium confidence'' ). However, CMIP5 and CMIP6 models fail to fully capture the variations of the Northern Hemisphere summer monsoon circulation (Figure 3.17d), but there is ''low confidence'' in this assessment due to a lack of evidence in the literature.

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