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

=== 10.6.3 Indian Summer Monsoon ===

<div id="h2-30-siblings" class="h2-siblings"></div>

<div id="10.6.3.1" class="h3-container"></div>

<span id="motivation-and-regional-context-1"></span>
==== 10.6.3.1 Motivation and Regional Context ====

<div id="h3-64-siblings" class="h3-siblings"></div>

The Indian summer monsoon provides 80% of the country’s annual rainfall from June to September, supplying the majority of water for agriculture, industry, drinking and sanitation to over a billion people. Any variations in the monsoon on time scales from days to decades can have large impacts ( [[#Challinor--2006|Challinor et al., 2006]] ; [[#Gadgil--2006|Gadgil and Gadgil, 2006]] ). Evidence from paleoclimate records (Sections 8.3.2.4.1) shows ''high confidence'' in a weakened Indian monsoon during cold epochs of the past such as the Younger Dryas (12,800–11,600 years ago) as measured by speleothem oxygen isotopes ( [[#Kathayat--2016|Kathayat et al., 2016]] ). There is a pressing need to understand if the monsoon will change in the future under anthropogenic forcing and to quantify such changes. Multiple datasets have shown robust negative trends since the 1950s until the turn of the century ( [[#Bollasina--2011|Bollasina et al., 2011]] ) followed by a recovery ( [[#Jin--2017|Jin and Wang, 2017]] ), yet repeated assessments project the monsoon to increase in strength under enhanced GHG forcing ( [[#Christensen--2007|Christensen et al., 2007]] , 2013; Sections 8.3.2.4.1 and 8.4.2.4.1). The apparent contradiction between future projections and observed historical trends makes the region an ideal choice for an in-depth assessment. The reader is also referred to the South Asia (SAS) regional assessment of precipitation extremes ( [[IPCC:Wg1:Chapter:Chapter-11#11.9|Section 11.9]] ), which is not discussed here for brevity.

<div id="10.6.3.2" class="h3-container"></div>

<span id="the-regional-climate-of-india"></span>
==== 10.6.3.2 The Regional Climate of India ====

<div id="h3-65-siblings" class="h3-siblings"></div>

Local geography gives rise to distinct differences in societal experience of the summer monsoon. The south-westerly monsoon winds are incident upon the Western Ghats mountains on the west coast, leading to orographic enhancement and heavy rains ( [[#Shige--2017|Shige et al., 2017]] ), which supply rivers with water for much of the southern peninsula, often the subject of inter-regional water disputes. The northern plains contain the Ganges river and also India’s most intensive agriculture, both rainfed and irrigated. Synoptic systems known as monsoon depressions cross the northern east coast, supplying much of the rain in central India ( [[#Hunt--2019|Hunt and Fletcher, 2019]] ). Further north, the eastern Himalayas are dominated by the summer monsoon, while the western Himalayas receive most rainfall from western disturbances during winter ( [[#Palazzi--2013|Palazzi et al., 2013]] ). Meanwhile, south-eastern India sits under a rain shadow (the only region to receive more rainfall during the winter monsoon).

<div id="10.6.3.3" class="h3-container"></div>

<span id="observational-issues-for-india"></span>
==== 10.6.3.3 Observational Issues for India ====

<div id="h3-66-siblings" class="h3-siblings"></div>

India has one of the oldest rain-gauge networks in the world, leading to the production of numerous observational products (reviewed in [[#Khouider--2020|Khouider et al., 2020]] ). Gridded gauge-based products dating back to the 19th century reveal pronounced decadal variability ( [[#Sontakke--2008|Sontakke et al., 2008]] ). Trends for India over the whole 20th century are inconclusive ( [[#Knutson--2018|Knutson and Zeng, 2018]] ), although declining over central and northern areas ( [[#Roxy--2015|Roxy et al., 2015]] ). Assessment of multiple observational datasets covering the Indian summer monsoon reveals significant declining rainfall over the second half of the 20th century ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4.1|Section 8.3.2.4.1]] and Figure 10.19c,d). A subsequent recovery has been noted since the early 2000s ( [[#Jin--2017|Jin and Wang, 2017]] ).

<div id="_idContainer054" class="Basic-Text-Frame"></div>

[[File:6a7505023aeaa709cd7d8681f24af85e IPCC_AR6_WGI_Figure_10_19.png]]

'''Figure 10.''' '''19 |''' '''Changes in the Indian summer monsoon in the historical and future periods.''' Observational uncertainty demonstrated by a snapshot of rain-gauge density (% of 0.05° subgrid boxes containing at least one gauge) in the APHRO-MA 0.5° daily precipitation dataset for June to September 1956. '''(b)''' Multi-model ensemble (MME) mean bias of 34 CMIP6 models for June to September precipitation (mm day <sup>–1</sup> ) compared to CRU TS observations for the 1985–2010 period. '''(c)''' Maps of rainfall trends (mm day <sup>–1</sup> per decade) in CRU TS observations (1950–2000), the CMIP6 MME-mean of SSP5-8.5 future projections for 2015–2100 (34 models), the CMIP6 hist-GHG and hist-aer runs, both measured over 1950 to 2000. '''(d)''' Low-pass filtered time series of June to September precipitation anomalies (%, relative to 1995–2014 baseline) averaged over the central India box shown in panel (b). The averaging region (20°N–28°N, 76°E–87°E) follows other works ( [[#Bollasina--2011|Bollasina et al., 2011]] ; [[#Jin--2017|Jin and Wang, 2017]] ; [[#Huang--2020b|Huang et al., 2020b]] ). Time series are shown for CRU TS (brown), GPCC (dark blue), REGEN (green), APHRO-MA (light brown) observational estimates and the IITM all-India rainfall product (light blue) in comparison with the CMIP6 mean of 13 models for the all-forcings historical (pink) the aerosol-only (hist-aer, grey) and greenhouse gas-only (hist-GHG, blue). Dark red and blue lines show low-pass filtered MME-mean change in the CMIP6 historical/SSP5-8.5 (34 models) and CMIP5 historical/RCP8.5 (41 models) experiments for future projections to 2100. The filter is the same as that used in Figure 10.11 (d). To the right, box-and-whisker plots show the 2081–2100 change averaged over the CMIP5 (blue) and CMIP6 (dark red) ensembles. Note that some models exceed the plotting range (CMIP5: GISS-E2-R-CC, GISS-E2-R, IPSL-CM5B-LRl and CMIP6: CanESM5-CanOE, CanESM5 and GISS-E2-1-G). '''(e)''' Precipitation linear trend (% per decade) over Central India for historical 1950–2000 (left) and future 2015–2100 (right) periods in Indian Monsoon rainfall in observed estimates (black crosses), the CMIP5 historical-RCP8.5 simulations (blue), the CMIP6 ensemble (dark red) for historical all-forcings experiment and SSP5-8.5 future projection, the CMIP6 hist-GHG (light blue triangles), hist-aer (grey triangles) and historical all-forcings (same sample as for hist-aer and hist-GHG, pink circles). Ensemble means are also shown. Box-and-whisker plots show the trend distribution of the three coupled and the d4PDF atmosphere-only (for past period only) SMILEs used throughout (Chapter 10 and follow the methodology used in Figure 10.6. '''(f)''' Example spread of trends (mm day <sup>–1</sup> per decade) for the period 2016–2045 in RCP8.5 SMILE experiments of the MPI-ESM model, showing the difference between the three driest and three wettest trends among ensemble members over central India. All trends are estimated using ordinary least-squares regression. Further details on data sources and processing are available in the chapter data table (Table 10.SM.11).

Observational products containing critical inhomogeneities in gauge distribution and reporting over time are acknowledged as suitable for mesoscale analysis ( [[#Rajeevan--2009|Rajeevan and Bhate, 2009]] ), while use for climate trends requires consistent reporting over time from quality-controlled gauges (e.g., about 2000 gauges since the 1950s in [[#Rajeevan--2006|Rajeevan et al., 2006]] ). A newer 0.25°-gridded product covering 1901 onwards ( [[#Pai--2014|Pai et al., 2014]] , 2015), based on Shepard’s interpolation method for irregularly-spaced stations ( [[#Shepard--1968|Shepard, 1968]] ), shows increased intensity of daily rainfall and extremes over some regions, especially in the late-20th century. However, changes to the inputted gauges may have introduced an artificial jump in extreme rainfall since 1975 over central India ( [[#10.2.2.3|Section 10.2.2.3]] ; [[#Lin--2019|Lin and Huybers, 2019]] ). They suggest that this method may have masked declines in mean rainfall and highlight the need for availability of raw gauge data to allow transparent assessments. [[#Khouider--2020|Khouider et al. (2020)]] have successfully tested a probabilistic interpolation method for India to overcome problems inherent in algorithms based on inverse-distance weighting when applied to data-sparse regions. An example snapshot of the uneven distribution of rain gauges in a common observational product is shown in Figure 10.19a.

The uncertainty among local and international observational products for India can pose challenges when evaluating climate models (as in [[#10.2.2.6|Section 10.2.2.6]] ; [[#Prakash--2015|Prakash et al., 2015]] ). For the seasonal mean summer monsoon rainfall, [[#Collins--2013a|Collins et al. (2013a)]] found large biases separating many CMIP5 models from the available observational products. However, for seasonal mean variability, the spread across observational products was larger than across the CMIP5 ensemble.

<div id="10.6.3.4" class="h3-container"></div>

<span id="relevant-anthropogenic-and-natural-drivers-for-long-term-change"></span>
==== 10.6.3.4 Relevant Anthropogenic and Natural Drivers for Long-term Change ====

<div id="h3-67-siblings" class="h3-siblings"></div>

The relevant drivers for long-term change in the mean Indian summer monsoon are summarized briefly:

* Increased greenhouse gas (GHG) concentrations (chiefly CO <sub>2</sub> ) are a strong contributor to changes in the monsoon, with repercussions for the meridional temperature contrast driving the monsoon circulation ( [[#Ueda--2006|Ueda et al., 2006]] ; [[#Roxy--2015|Roxy et al., 2015]] ), for the monsoon winds in the lower troposphere ( [[#Cherchi--2011|Cherchi et al., 2011]] ; [[#Krishnan--2013|Krishnan et al., 2013]] ), or for the availability of moisture from the Indian Ocean ( [[#May--2011|May, 2011]] ).
* Industrial emissions of sulphate aerosol predominantly in the Northern Hemisphere could change inter-hemispheric energy transports and weaken the monsoon ( [[#Polson--2014|Polson et al., 2014]] ; [[#Undorf--2018|Undorf et al., 2018]] ). The effect of local anthropogenic emissions of black carbon (chiefly from cooking fires) is uncertain ( [[#Lau--2006|Lau and Kim, 2006]] ; [[#Nigam--2010|Nigam and Bollasina, 2010]] ).
* India’s green revolution over the late-20th century led to considerable land-use change, with massive expansion of agriculture at the expense of forest and shrublands. As a result, India’s northern plains feature widespread irrigation, suggested to be a cause of drying ( [[#Mathur--2020|Mathur and AchutaRao, 2020]] ).
* Decadal modes of variability such as the Pacific Decadal Variability (PDV, Annex IV) and Atlantic Multi-decadal Variability (AMV, Annex IV), which may be partly forced ( [[IPCC:Wg1:Chapter:Chapter-3#3.7.7|Section 3.7.7]] ), are known to cause decadal modulation of the monsoon ( [[#Krishnamurthy--2014|Krishnamurthy and Krishnamurthy, 2014]] ; [[#Naidu--2020|Naidu et al., 2020]] ).

The interplay of these external and internal drivers is key to understanding past and future monsoon change.

<div id="10.6.3.5" class="h3-container"></div>

<span id="model-simulation-and-attribution-of-drying-over-the-historical-period"></span>
==== 10.6.3.5 Model Simulation and Attribution of Drying Over the Historical Period ====

<div id="h3-68-siblings" class="h3-siblings"></div>

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.

<div id="10.6.3.6" class="h3-container"></div>

<span id="future-climate-projections-from-global-simulations"></span>
==== 10.6.3.6 Future Climate Projections from Global Simulations ====

<div id="h3-69-siblings" class="h3-siblings"></div>

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.

<div id="10.6.3.7" class="h3-container"></div>

<span id="future-climate-projections-from-regional-downscaling"></span>
==== 10.6.3.7 Future Climate Projections from Regional Downscaling ====

<div id="h3-70-siblings" class="h3-siblings"></div>

Coordinated monsoon-relevant dynamical downscaling efforts such as CORDEX South Asia ( [[#Gutowski%20Jr.--2016|Gutowski Jr. et al., 2016]] ; [[#Choudhary--2018|Choudhary et al., 2018]] ) are relevant to the Indian summer monsoon, first with assessment of their added value ( [[#10.3.3.2|Section 10.3.3.2]] and Atlas.5.3.3). [[#Singh--2017|Singh et al. (2017)]] compared nine CORDEX-South Asia RCMs against their driving CMIP5 GCMs, for present-day rainfall patterns and processes related to intra-seasonal variability. They found no consistent improvement other than for spatial patterns (e.g., rainfall close to better-resolved orography); some characteristics were made worse. Both the rainfall pattern and its bias were worsened in CORDEX compared to CMIP5 in [[#Mishra--2018|Mishra et al. (2018)]] . In contrast, [[#Varikoden--2018|Varikoden et al. (2018)]] found improved representation of historical rainfall patterns, such as over the Western Ghats mountains (consistent with [[#Singh--2017|Singh et al., 2017]] ), reducing the dry bias; improvements were not found over the northern plains, which are dominated by synoptic variability known as monsoon depressions. Similarly, [[#Sabin--2013|Sabin et al. (2013)]] compared a uniform 1° resolution model ensemble with another zoomed to about 35 km over South Asia. Local zooming improved simulation of orographic precipitation and the monsoon trough. For the future, a surrogate approach (like pseudo-global warming, see [[#10.3.2.2|Section 10.3.2.2]] ) was used in an RCM to test the impacts of warming or moistening on monsoon depressions ( [[#Sørland--2016|Sørland and Sorteberg, 2016]] ; [[#Sørland--2016|Sørland et al., 2016]] ). The depressions are found to give more rainfall in future, dominated by strengthened synoptic circulation from the warming perturbation. By forcing an RCM with a perturbed parameter ensemble of a GCM, [[#Bal--2016|Bal et al. (2016)]] made projections under SRES A1B for the 2020s, 2050s and 2080s. They noted increases in rainfall of 15–24% for India. Finally, evidence from a single CORDEX South Asia RCM showed a mixed signal for changes in peak season rainfall under RCP2.6 and RCP8.5 ( [[#Ashfaq--2021|Ashfaq et al., 2021]] ).

Statistical downscaling and other post-processing require calibration in historical conditions (e.g., [[#Akhter--2019|Akhter et al., 2019]] ) and assessment of fitness-for-purpose ( [[#10.3.3.9|Section 10.3.3.9]] ) before use for future projections. Given the noted biases in GCM monsoon simulation ( [[#10.6.3.5|Section 10.6.3.5]] ), [[#Vigaud--2013|Vigaud et al. (2013)]] used a variant of quantile mapping to bias adjust ( [[#10.3.1.3.2|Section 10.3.1.3.2]] and Cross-Chapter Box 10.2) GCM outputs. For the historical period, the pattern, mean and seasonal cycle of rainfall versus the input GCMs were improved. Increased future monsoon rain, albeit in older SRES A2 projections, was found for southern India. [[#Salvi--2013|Salvi et al. (2013)]] used regression-based perfect prognosis ( [[#10.3.1.3.1|Section 10.3.1.3.1]] ) for the whole country at 0.5° resolution based on five ensemble members of a GCM in SRES scenarios. They noted increases over rainy regions of west coast and north-east India, but decreases in the north, west and south-east. [[#Madhusoodhanan--2018|Madhusoodhanan et al. (2018)]] statistically downscaled 20 CMIP5 models to 0.05° resolution. While the global models projected increased rainfall, the downscaled ensemble depicted both increasing and decreasing trends, indicating uncertainty. However, key physical processes operating at below-GCM scale cannot be resolved nor calibrated for, such as aspects of the flow around topography. This is notably an issue given the resolution disparity between the driving global models and output, and the regional challenges in observational data used for calibration ( [[#10.6.3.3|Section 10.6.3.3]] ).

There are mixed messages as to whether downscaling adds value to climate projections of the Indian summer monsoon; however, there is ''high confidence'' in projections of precipitation changes in orographic regions given the consistent improved representation in these regions among several dynamical downscaling studies.

<div id="10.6.3.8" class="h3-container"></div>

<span id="storyline-approaches-for-india"></span>
==== 10.6.3.8 Storyline Approaches for India ====

<div id="h3-71-siblings" class="h3-siblings"></div>

Formal storyline approaches (see Box 10.2) have been used infrequently for the Indian summer monsoon, representing a knowledge gap. In an expert-elicitation approach ( [[#Dessai--2018|Dessai et al., 2018]] ), physically plausible futures substantiated by climate processes were constructed, focusing on a river basin in southern India. Possible outcomes were framed based on changes in two drivers: availability of moisture from the Arabian Sea and strength of the low-level flow. The narratives identified were able to explain 70% of the variance in monsoon rainfall over 1979–2013, the implication being that climate uncertainties could be easily communicated to stakeholders in the context of present-day variability.

The storylines terminology could be used to loosely describe the interplay between internal variability and forced change (see [[#10.6.3.6|Section 10.6.3.6]] ), such as considering the difference between groups of wettest and driest ensemble members of a SMILE for the near-term future in Figure 10.19f. However, given the interest in low-likelihood high-impact scenarios ( [[#Sutton--2018|Sutton, 2018]] ), we can also consider possible storylines for the Indian monsoon constructed from evidence in paleoclimate records and modelling. For example, a future AMOC collapse could cause reduced monsoon rainfall ( [[IPCC:Wg1:Chapter:Chapter-8#8.6.1|Section 8.6.1]] ; [[#Liu--2017|Liu et al., 2017]] ), offsetting increases expected due to GHG. Large tropical volcanic eruptions are also known to weaken the Asian summer monsoon, in observations and model simulations over the last millennium ( [[IPCC:Wg1:Chapter:Chapter-8#8.5.2.3|Section 8.5.2.3]] ; [[#Zambri--2017|Zambri et al., 2017]] ), although a hemispheric dependence is found, with Southern Hemisphere eruptions even strengthening the monsoon around India ( [[#Zuo--2019|Zuo et al., 2019]] ). Typically, future climate projections do not consider plausible eruption scenarios and their mitigating effects on greenhouse warming (see also Cross-Chapter Box 4.1). A single-model ensemble ( [[#Bethke--2017|Bethke et al., 2017]] ) demonstrates a future drier Indian monsoon relative to conditions in which volcanic eruptions are not considered, although the effects of GHG warming dominate beyond the mid-term.

The few studies on low-likelihood high-impact scenarios, often in single models, together with findings in SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ), noting the small radiative forcing in 1.5°C or 2°C scenarios, or the absence of large aerosol emissions at the end of the 21st century in RCPs, give us ''low confidence'' in abrupt changes to the monsoon on this time scale.

<div id="10.6.3.9" class="h3-container"></div>

<span id="regional-climate-information-distilled-from-multiple-lines-of-evidence"></span>
==== 10.6.3.9 Regional Climate Information Distilled from Multiple Lines of Evidence ====

<div id="h3-72-siblings" class="h3-siblings"></div>

Above, we presented assessments from observational and model attribution studies of the historical period, followed by future climate projections in global and regional models, and storylines approaches including low-likelihood high impact events. Miscellaneous lines of evidence are considered here.

Our assessment could also be informed by attempting to constrain future projections of the Indian summer monsoon using paleoclimate evidence. In modelling work of the mid-Holocene ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ), the increased obliquity (axial tilt) and altered orbital precession lead to an enhanced monsoon with a stronger dynamic component (strengthening the mean monsoon overturning) controlling the increase in monsoon rainfall. In future climates however, the dynamic contribution decreases ( [[#10.6.3.6|Section 10.6.3.6]] ), yet the increased thermodynamic component (greater moisture availability) overcomes this to cause a wetter monsoon. Monsoon changes under different epochs may not be governed by the same mechanisms ( [[#D’Agostino--2019|D’Agostino et al., 2019]] ; [[#Hill--2019|Hill, 2019]] ), making the mid-Holocene, in particular, unsuitable as a period to compare with.

Finally, the recent national climate-change assessment for India ( [[#Krishnan--2020|Krishnan et al., 2020]] ) has distilled multiple lines of evidence to show declining summer monsoon rainfall over the second half of the 20th century, attributable to emissions of anthropogenic aerosols, while future projections informed by CMIP5 modelling and dominated by GHG forcing show increased mean rainfall by the end of the 21st century.

There is ''very high confidence'' ( ''robust evidence'' , ''high agreement'' ) of a negative trend of summer monsoon rainfall over the second half of the 20th century averaged over all of India. There is ''medium agreement'' over trends at the regional level owing to uncertainty among observational products, which hinders model evaluation, downscaling and assessment of changes to extremes. There is ''high confidence'' ( ''robust evidence'' , ''medium agreement'' ) that anthropogenic aerosol emissions over the Northern Hemisphere and internal variability have contributed to the negative trend, while there is ''high confidence'' ( ''robust evidence'' , ''medium agreement'' ) that Indian summer monsoon rainfall will increase at the end of the 21st century in response to increased GHG forcing, due to the dominance of thermodynamic mechanisms. No contradictory evidence is found from downscaling methods. The contrast between declining rainfall in the observational record and long-term future increases can be explained using multiple lines of evidence. They are not contradictory since they are attributable to different mechanisms (primarily aerosols and greenhouse gases, respectively). The long-term future changes are generally consistent across global (including at high resolution) and regional climate models, and supported by theoretical arguments. Furthermore, while there are subtle differences found in past periods with a climate similar to the future climate (the mid-Holocene), different physical mechanisms at play suggest that paleoclimate evidence does not reduce confidence in the future projections. In the near term, there is ''high confidence'' that internal variability will dominate.

<div id="10.6.4" class="h2-container"></div>

<span id="mediterranean-summer-warming"></span>