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

==== [[#Atlas.5.3.3|Atlas.5.3.3]] Assessment of Model Performance ====

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Whilst simulations of Indian summer monsoon rainfall (ISMR) have improved in CMIP5 compared to CMIP3 in terms of northward propagation, time for peak monsoon and withdrawal ( [[#Sperber--2013|Sperber et al., 2013]] ), they fail to simulate the trends in monsoon rainfall and the post-1950 weakening of monsoon circulation ( [[#Saha--2014|Saha et al., 2014]] ). This is partially attributed to the failure of coarse-resolution CMIP5 models to simulate fine-resolution processes such as orographic effects or land surface feedback, and problems in cloud parametrization result in an overestimation of convective precipitation fraction (M.S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ). In CMIP6, a significant improvement is found in capturing the monsoon spatio-temporal patterns over India, particularly in the Western Ghats and north-eastern Himalayan foothills ( [[#Gusain--2020|Gusain et al., 2020]] ). Over Pakistan the CMIP6 models simulate surface temperature better in JJA than DJF ( [[#Karim--2020|Karim et al., 2020]] ). The CMIP6 ensemble underestimates annual mean temperature over all of South Asian with mixed results for precipitation ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). The CMIP6 GCMs have a large cold bias in both mean annual maximum and minimum temperatures in the complex Karakorum and Himalayan mountain ranges but exhibit warm biases in mean annual minimum temperature in most of the rest of South Asia.

Regional climate model (RCM) downscaling of CMIP5 models as part of CORDEX South Asia uses higher resolution (50 km) and improved surface fields such as topography and coastlines to resolve better the complexities of the monsoon and other hydrological processes ( [[#Giorgi--2009|Giorgi et al., 2009]] ). The added value of their simulations, relative to the driving GCMs, presents a complex picture. CORDEX RCMs better represent spatial patterns of temperature ( [[#Sanjay--2017|Sanjay et al., 2017]] ), the spatial features of precipitation distribution associated with the Indian summer monsoon ( [[#Choudhary--2018|Choudhary and Dimri, 2018]] ), and the simulation of monsoon active- and break-phase composite precipitation ( [[#Karmacharya--2017b|Karmacharya et al., 2017b]] ). The RCMs follow the driving GCMs in underestimating seasonal mean surface air temperature and overestimating spatial variability in precipitation. They amplify CMIP5 cold biases over almost the entire region, including over the HKH region, Afghanistan and south-west Pakistan during winter ( [[#Iqbal--2017|Iqbal et al., 2017]] ), and substantial cold biases of 6°C–10°C are found over the Himalayan watersheds of the Indus basin ( [[#Nengker--2018|Nengker et al., 2018]] ; [[#Hasson--2019|Hasson et al., 2019]] ). Neither RCMs nor their driving CMIP5 GCMs reproduce well the region’s precipitation climatology ( [[#Mishra--2015|Mishra, 2015]] ). In addition, important characteristics of ISMR such as northward and eastward propagation, onset, seasonal rainfall patterns, intra-seasonal oscillations and patterns of extremes did not show consistent improvement (S. [[#Singh--2017|]] [[#Singh--2017|Singh et al., 2017]] ). Also, these RCM simulations have not demonstrated added value in capturing the observed changes in ISMR characteristics over recent decades, though RegCM4 simulations at 25 km showed high accuracy in capturing monsoon precipitation characteristics and atmospheric dynamics in historical simulations ( [[#Ashfaq--2021|Ashfaq et al., 2021]] ).

Evaluation of four global reanalysis products (ERA5 and ERA-Interim, JRA-55 and MERRA-2; [[#Atlas.1.4.2|Atlas.1.4.2]] ) for snow depth and snow cover over TIB was performed against 33 in situ station observations, Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow-depth dataset ( [[#Orsolini--2019|Orsolini et al., 2019]] ). Most of the reanalyses showed a systematic overestimation. Only ERA-Interim assimilated IMS snow cover at high altitudes, whereas ERA5 did not and the excessive snowfall, snow depth and snow cover in ERA5 was attributed to this difference. The analysis of annual maximum consecutive snow-covered days for the period 1980–2018 over TIB using JRA-55 and passive microwave satellite observations showed a decreasing trend in all time periods and in recent snow seasons for MERRA-2 ( [[#Bian--2020|Bian et al., 2020]] ). The uncertainty assessment of model physics in snow modelling over TIB using ground-based observations and high-resolution snow cover satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FengYun-3B suggests that errors can be overcome by optimizing parametrizations of the snow cover fraction rather than optimizing physics-scheme options (Y. [[#Jiang--2020|]] [[#Jiang--2020|Jiang et al., 2020]] ).

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