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

==== 10.6.3.5 Model Simulation and Attribution of Drying Over the Historical Period ====

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The robust decline of Indian summer monsoon rainfall averaged over India in the second half of the 20th century ( [[#10.6.3.3|Section 10.6.3.3]] ) is not in line with expectations arising from thermodynamic constraints on the water cycle in a warming world ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.2|Section 8.2.2]] ) and has been regarded as a puzzle ( [[#Goswami--2006|Goswami et al., 2006]] ). Assessing the attribution of 20th-century changes to Indian rainfall is the subject of coordinated modelling under the Global Monsoon MIP (GMMIP; [[#Zhou--2016|Zhou et al., 2016]] ), but is complicated by long-standing dry biases in coupled CMIP3, CMIP5 ( [[#Sperber--2013|Sperber et al., 2013]] ) and CMIP6 (Figure 10.19b) global models. These dry biases are connected to a lower tropospheric circulation that is too weak ( [[#Sperber--2013|Sperber et al., 2013]] ) and wet biases in the equatorial Indian Ocean ( [[#Bollasina--2013|Bollasina and Ming, 2013]] ). [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.1|Section 8.3.2.4.1]] finds ''high confidence'' that anthropogenic aerosol emissions have dominated the observed declining trends of countrywide Indian summer monsoon rainfall, consistent with findings at the global-monsoon scale ( [[IPCC:Wg1:Chapter:Chapter-3#3.3.3.2|Section 3.3.3.2]] ).

Stronger Northern Hemisphere aerosol emissions cool it relative to the Southern Hemisphere, increasing northward energy transport at the expense of moisture transport towards India ( [[#Bollasina--2011|Bollasina et al., 2011]] ). The attribution to anthropogenic aerosols is supported in CMIP5 single-forcing experiments, including some testing the sensitivity to local and remote emissions ( [[#Guo--2015|Guo et al., 2015]] , 2016; [[#Shawki--2018|Shawki et al., 2018]] ), comparing CMIP5 GCMs forced by both aerosol and GHG to GHG only ( [[#Salzmann--2014|Salzmann et al., 2014]] ) and reducing emissions to pre-industrial levels ( [[#Takahashi--2018|Takahashi et al., 2018]] ). The large spread between individual model realisations of comparable magnitude to the aerosol-induced signal suggested to [[#Salzmann--2014|Salzmann et al. (2014)]] that internal variability may also play a role over regions such as northern-central India. Further uncertainty surrounds the level of radiative forcing. [[#Dittus--2020|Dittus et al. (2020)]] forced a GCM with historical aerosol emissions scaled between 0.2 and 1.5 times their observed values, representing the spread in CMIP5 effective radiative forcing. The strongest forcing led to around 0.5 mm day <sup>–1</sup> less late-20th century Indian monsoon rainfall than the weakest ( [[#Shonk--2020|Shonk et al., 2020]] ). Meanwhile, the uncertainty surrounding aerosol–cloud interactions could change the sign of long-term precipitation trends ( [[#Takahashi--2018|Takahashi et al., 2018]] ).

There is some evidence that declining Indian monsoon rainfall is due to regional SST warming patterns, themselves arising due to radiative forcing from GHG (e.g., in the Indian Ocean, [[#Guemas--2013|Guemas et al., 2013]] ). [[#Roxy--2015|Roxy et al. (2015)]] artificially raised SST in a GCM in the equatorial Indian Ocean (the region of strongest observed SST warming), leading to a weakened monsoon. [[#Annamalai--2013|Annamalai et al. (2013)]] used a GCM to suggest instead that preferential warming of the western North Pacific may force a Rossby-wave response to its west that weakens the monsoon through dry advection and subsidence. These hypotheses are not borne out in GHG-forced future projections ( [[#10.6.3.6|Section 10.6.3.6]] ).

A small anthropogenic contribution may be expected from local land-use/land-cover changes and land management. India is the world’s most irrigated region with around 0.5 mm/day in places, although peaking higher in summer ( [[#Cook--2015b|Cook et al., 2015b]] ; [[#McDermid--2017|McDermid et al., 2017]] ). Including irrigation in GCMs and RCMs slows the monsoon circulation and diminishes rainfall ( [[#Lucas-Picher--2011|Lucas-Picher et al., 2011]] ; [[#Guimberteau--2012|Guimberteau et al., 2012]] ; [[#Shukla--2014|Shukla et al., 2014]] ; [[#Tuinenburg--2014|Tuinenburg et al., 2014]] ; [[#Cook--2015b|Cook et al., 2015b]] ) due to reduced surface temperature ( [[#Thiery--2017|Thiery et al., 2017]] ), reducing the monsoon wind and moisture fluxes towards India ( [[#Mathur--2020|Mathur and AchutaRao, 2020]] ). However, implementation methodologies for irrigation in climate models are simplified and often do not account for spatial heterogeneity while overestimating demand and supply ( [[#10.3.3.6|Section 10.3.3.6]] ; [[#Nazemi--2015|Nazemi and Wheater, 2015]] ; [[#Pokhrel--2016|Pokhrel et al., 2016]] ). Changing forest cover to agricultural land in an RCM ( [[#Paul--2016|Paul et al., 2016]] ) finds weakened summer monsoon rainfall especially in central and eastern India, due to decreased local evapotranspiration. Decreased evapotranspiration from a warmer surface since the 1950s in the CMIP5 ensemble may also feedback on the supply of moisture ( [[#Ramarao--2015|Ramarao et al., 2015]] ). Based on an AGCM study and literature review, [[#Krishnan--2016|Krishnan et al. (2016)]] support the role of land-use/land-cover change in adding to the effects of aerosol in weakening the monsoon, in addition to dynamic effects on the circulation caused by rapid warming of the Indian Ocean.

In addition to anthropogenic forcing, there is evidence that internal variability in the Pacific is a significant driver. [[#Huang--2020b|Huang et al. (2020b)]] compared a large perturbed-physics ensemble (HadCM3C) with a SMILE for the historical period. Ensemble members replicating the negative Indian rainfall trend were accompanied by a strong phase change in the PDV from negative to positive, consistent with SST observations. [[#Jin--2017|Jin and Wang (2017)]] have demonstrated increasing Indian monsoon rainfall since 2002 in a variety of observed datasets, suggesting the increase is due either to a change in dominance of a particular forcing (for example from aerosol to GHG) or to a change in phase of internal variability such as the PDV. [[#Huang--2020b|Huang et al. (2020b)]] partially attribute the rainfall recovery to a phase change in the PDV, supported by a SMILE study combined with reanalyses ( [[#Ha--2020|Ha et al., 2020]] ).

The drying trend of Indian summer monsoon rainfall since the mid-20th century can be attributed with ''high confidence'' to aerosol as the dominant anthropogenic forcing with a further contribution from internal variability, supported by the review of [[#Wang--2021|]] [[#Wang--2021|B. Wang et al. (2021)]] including CMIP6 results. Understanding the interplay between anthropogenic and internal drivers will be important for understanding future change.

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