Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.6.3.6 Future Climate Projections from Global Simulations ====

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The AR5 ( [[#Christensen--2013|Christensen et al., 2013]] ) concluded that Indian summer monsoon rainfall will strengthen under all RCP future climate scenarios, while the circulation will weaken ( ''medium confidence'' ). SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) found only ''low confidence'' in projections of monsoon change at 1.5°C and 2°C, or any difference between them. The AR6 assessment of ( [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] ( [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.4.1|Section 8.4.2.4.1]] ) finds more precipitation in future projections (also depicted in Figure 10.19c,d,e), supported by reviews of CMIP3, CMIP5 and CMIP6 models ( [[#Turner--2012|Turner and Annamalai, 2012]] ; [[#Kitoh--2017|Kitoh, 2017]] ; Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ; [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ).

Given the assessment for a future wetter monsoon dominated by GHG emissions and attribution of the late-20th century decline to aerosol (Sections 8.3.2.4.1 and 10.6.3.5), the change between dominant forcings will lead, at some point, to a positive trend. For example, RCP4.5 experiments in an AGCM forced by coupled model-derived future SSTs showed continuation of 20th-century drying, before a rainfall recovery ( [[#Krishnan--2016|Krishnan et al., 2016]] ). By holding aerosol emissions at 2005 levels, lower monsoon rainfall is found throughout the 21st century than in a standard RCP8.5 scenario ( [[#Zhao--2019|Zhao et al., 2019]] ), suggesting that the timing of the recovery will be partially controlled by the rate at which aerosol emissions decline. The spread in spatial distribution of aerosol emissions in SSPs may also play a role in near-term projections ( [[#Samset--2019|Samset et al., 2019]] ). Under divergent air-quality policies, SSP3 features a dipole of declining sulphate emissions for China but increases over India, leading to suppression of GHG-related precipitation increases there ( [[#Wilcox--2020|Wilcox et al., 2020]] ). For the near-term future around the mid-21st century, the interplay between internal variability and external forcing must be considered ( [[#Singh--2019|Singh and AchutaRao, 2019]] ). [[#Huang--2020a|Huang et al. (2020a)]] used two SMILEs to show that internal variability related to PDV could potentially overcome the GHG-forced upward trend in Indian monsoon rainfall, consistent with assessments of the global monsoon for the near term ( [[IPCC:Wg1:Chapter:Chapter-4#4.4.1.4|Section 4.4.1.4]] ). Emergence of the anthropogenic signal for South Asian precipitation is shown from the 2050s onwards in CMIP6 (Figure 10.15b).

In long-term projections, robust signals consist of a weakened upper-tropospheric meridional temperature gradient, either due to upper-level heating over the tropical Pacific ( [[#Sooraj--2015|Sooraj et al., 2015]] ) or Indian oceans ( [[#Sabeerali--2018|Sabeerali and Ajayamohan, 2018]] ) in CMIP5, and increased seasonal mean rainfall, including in CMIP6 ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ; [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ). The weakened temperature gradient combines with increased atmospheric stability to weaken the monsoon overturning circulation, with some findings showing northward movement of the lower-tropospheric monsoon winds in response to a stronger land–sea temperature contrast in CMIP3 and CMIP5 ( [[#Sandeep--2015|Sandeep and Ajayamohan, 2015]] ; [[#Endo--2018|Endo et al., 2018]] ). The northward shift was also found in the genesis of synoptic systems (monsoon depressions) in a single high-resolution AGCM forced by an ensemble of SSTs derived from four GCMs under the RCP8.5 scenario ( [[#Sandeep--2018|Sandeep et al., 2018]] ).

Projections can also be expressed in terms of global-mean warming levels (GWLs) rather than time horizons (Cross-Chapter Box 11.1). Advancing on SR1.5, amplification of mean and extreme monsoon rainfall at 2.0°C compared to 1.5°C has been found both by an AGCM forced by future SST patterns ( [[#Chevuturi--2018|Chevuturi et al., 2018]] ) and by using time slices in CMIP5 GCMs ( [[#Yaduvanshi--2019|Yaduvanshi et al., 2019]] ; J. [[#Zhang--2020|]] [[#Zhang--2020|Zhang et al., 2020]] ). These findings are consistent with the general scaling of Indian monsoon precipitation per degree of warming in CMIP5 ( [[#Zhang--2019|Zhang et al., 2019]] ) and CMIP6 ( [[#Wang--2021|]] [[#Wang--2021|B. Wang et al., 2021]] ). Increasing GWLs also lead to emergence of the anthropogenic signal over larger proportions of the South Asian region (Figure 10.15a).

Decomposition of the increased rainfall signal showed that while the dynamic component led to a drying tendency, this was overcome by the thermodynamic contribution ( [[#Sooraj--2015|Sooraj et al., 2015]] ; Z. [[#Chen--2020|]] [[#Chen--2020|Chen et al., 2020]] ). Alternative decomposition experiments using AGCMs and their coupled counterparts found increases in the lower-tropospheric temperature gradient and monsoon rainfall to be dominated by the fast radiative response to GHG increase rather than SST changes ( [[#Li--2017|Li and Ting, 2017]] ; [[#Endo--2018|Endo et al., 2018]] ). The response to SST forcing featured a large model spread, particularly arising from the dynamic component ( [[#Li--2017|Li and Ting, 2017]] ). [[#Chen--2015|Chen and Zhou (2015)]] found that the Indo-Pacific SST warming pattern dominated the uncertainty in Indian monsoon rainfall change. Finally, in assessing the relative impact of CO <sub>2</sub> radiative forcing and plant physiological changes in quadrupled CO <sub>2</sub> experiments in four Earth system models, [[#Cui--2020|Cui et al. (2020)]] showed little impact of plant physiology on annual rainfall for the Indian region.

While several of the above studies selected model subsets to constrain future projections based on standard performance metrics of the historical period, such as pattern correlation and root-mean-square error, [[#Latif--2018|Latif et al. (2018)]] included a performance measure based on agreement with historical rainfall trends. This is an unproven constraint for regional projections ( [[#10.3.3.9|Section 10.3.3.9]] ), since the 20th-century rainfall trend over India is assessed to have been driven chiefly by aerosol and other factors such as PDV (Sections 8.3.2.4.1 and 10.6.3.5), while the dominant late-21st century forcing is GHG emissions. Modern emergent-constraint techniques ( [[#10.3.4.2|Section 10.3.4.2]] ) are being applied to the Indian monsoon such as G. [[#Li--2017|]] [[#Li--2017|Li et al. (2017)]] , who found that models with excessive tropical western Pacific rainfall tend to project a greater Indian monsoon rainfall change in future, due to an exaggerated cloud-radiation feedback. Correcting for this bias reduces the future change.

In summary, long-term future scenarios dominated by GHG increases (such as the RCPs) suggest increases in Indian summer monsoon rainfall ( ''high confidence'' ), dominated by thermodynamic mechanisms leading to increases in the available moisture. In the near-term, there is ''high confidence'' ( ''medium agreement'' , ''robust evidence'' ) that increased rainfall trends due to GHGs could be overcome by aerosol forcing or internal variability.

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