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

==== 10.6.3.7 Future Climate Projections from Regional Downscaling ====

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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.

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