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

=== Atlas.5.3 South Asia ===

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==== [[#Atlas.5.3.1|Atlas.5.3.1]] Key Features of the Regional Climate and Findings from IPCC Previous Assessments ====

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===== Atlas.5.3.1.1 Key Features of the Regional Climate =====

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The countries in this region are mostly semi-arid to arid and therefore depend heavily on the summer monsoon (June–September, JJAS) which is when most of the precipitation falls over the South Asia region (SAS; Figure Atlas.1 7). The topographic mechanical effect of the Tibetan Plateau (TIB) promotes moisture convergence downstream which triggers the early summer monsoon onset particularly over the Bay of Bengal and south China. In winter, westerly disturbances (WD) originating over the Atlantic Ocean bring moisture. The interaction between the WD and the Himalayas causes precipitation over northern and western parts of South Asia that is crucial to maintain the glacier mass balance. The observed teleconnection patterns over SAS for temperature show cooling effects during NAM and warming effects when in positive phase with ENSO, IOB, AMM and AMV (Annex IV). IOD also influences South Asian precipitation (Annex IV).

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===== Atlas.5.3.1.2 Findings From Previous IPCC Assessments =====

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Recent IPCC reports assessed that it is ''very likely'' that the mean annual temperature over South Asia has increased during the past century (Figure 2.21 in [[#Hartmann--2013|Hartmann et al., 2013]] , Figure 24-2 in [[#Hijioka--2014|Hijioka et al., 2014]] ), and the frequency of cold (warm) days and nights have decreased (increased) across most of Asia since about 1950 (Figure 2.32 in [[#Hartmann--2013|Hartmann et al., 2013]] ). The AR5 assessed that there is ''high confidence'' that the large-scale patterns of surface temperature are generally well simulated by the CMIP5 models though with problems in some regions, particularly at higher elevations over the Himalayas ( [[#Flato--2013|Flato et al., 2013]] ). CMIP5 models projected for the 21st century a significant increase in temperature over South Asia ( ''high confidence'' from ''robust evidence'' ) and in projections of increased summer monsoon precipitation ( ''medium confidence'' ) ( [[#Collins--2013|Collins et al., 2013]] ). The AR5 assessed there is ''high confidence'' that high-resolution regional downscaling, which generate results complementary to those from global climate models, adds value to the simulation of spatial variations in climate in regions with highly variable topography (e.g., distinct orography, coastlines), and for mesoscale phenomena and extremes ( [[#Flato--2013|Flato et al., 2013]] ).

Inconsistent evidence was found on the declining trends in mean precipitation and increasing droughts from 1950 onwards considering 1960–1990 as the baseline period. Similarly, SREX (Table 3-3 in [[#Seneviratne--2012|Seneviratne et al., 2012]] ) reported ''low confidence'' (due to lack of literature) in trends in climate indices related to extreme precipitation events. The Indian summer monsoon circulation was found to have weakened, but this was compensated by increased local atmospheric moisture content leading to more rainfall ( ''medium confidence'' ). It is ''likely'' that the occurrence of snowfall events is decreasing in South Asia along with other regions due to an increase in winter temperatures ( [[#Hock--2019b|Hock et al., 2019b]] ). Based on satellite- and surface-based remote sensing it is ''very likely'' that aerosol optical depth has increased over southern Asia since 2000.

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==== [[#Atlas.5.3.2|Atlas.5.3.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Recent studies show that annual mean land temperatures over Indiawarmed at a rate of around 0.6°C per century during 1901–2018, which was primarily contributed by a significant increase in annual maximum temperature of 1.0°C per century, while the annual minimum temperature showed a lesser increasing trend of 0.18°C per century during this period, with a significant rise only in the recent few decades (1981–2010) at a rate of 0.17°C per decade ( [[#Srivastava--2017|Srivastava et al., 2017]] , 2019). The annual average of daily maximum and minimum temperatures has increased over almost all Pakistan with a faster increasing trend in the south ( ''high confidence'' ). Minimum temperatures have increased faster (0.17°C–0.37°C per decade) than maximum temperatures (0.17°C–0.29°C per decade) with the diurnal temperature range reduced (–0.15°C to –0.08°C per decade) in some regions ( [[#Khan--2019|Khan et al., 2019]] ).

There has been a noticeable declining trend in rainfall with monsoon deficits occurring with higher frequency in different regions in South Asia (see also [[IPCC:Wg1:Chapter:Chapter-8#8.3.2.4|Section 8.3.2.4]] on the South Asian monsoon). Concurrently, the frequency of heavy precipitation events has increased over India, while the frequency of moderate rain events has decreased since 1950 ( ''high confidence'' ) ( [[#Goswami--2006|Goswami et al., 2006]] ; [[#Dash--2009|Dash et al., 2009]] ; [[#Christensen--2013|Christensen et al., 2013]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Kulkarni--2017|Kulkarni et al., 2017]] ; [[#Roxy--2017|Roxy et al., 2017]] ). There is a considerable spread in the seasonal and annual mean precipitation climatology and interannual variability among the different observed precipitation datasets over India ( [[#Collins--2013|Collins et al., 2013]] ; [[#Prakash--2014|Prakash et al., 2014]] ; [[#Kim--2018|Kim et al., 2018]] ; [[#Ramarao--2019|Ramarao et al., 2019]] ). Yet, the regions of agreement among datasets lend ''high confidence'' that there has been a decrease in mean rainfall over most parts of the eastern and central north regions of India ( [[#Singh--2014|Singh et al., 2014]] ; [[#Roxy--2015|Roxy et al., 2015]] ; [[#Juneng--2016|Juneng et al., 2016]] ; [[#Krishnan--2016|Krishnan et al., 2016]] ; [[#Guhathakurta--2017|Guhathakurta and Revadekar, 2017]] ; [[#Jin--2017|Jin and Wang, 2017]] ; [[#Latif--2017|Latif et al., 2017]] ). A global modelling study with high resolution over South Asia ( [[#Sabin--2013|Sabin et al., 2013]] ) indicated that a juxtaposition of regional land-use changes, anthropogenic-aerosol forcing and the rapid warming signal of the Equatorial Indian Ocean was crucial to simulate the observed Indian summer monsoon weakening in recent decades ( ''medium confidence'' ).

A dipole-like structure in summer monsoon rainfall trends is observed over the northern Indo-Pakistan area with significant increases over Pakistan and decreases over central north India resulting from strengthening (weakening) of vertically integrated meridional moisture transport over the Arabian Sea (Bay of Bengal) ( ''low confidence'' ) ( [[#Latif--2017|Latif et al., 2017]] ). Positive annual precipitation trends are observed in global and regional datasets (Figure Atlas.11 and the Interactive Atlas) during 1961–2015 and over arid provinces of Pakistan (for rabi and kharif cropping seasons) during 1951–2015 of 2.8–34.8 mm per decade ( [[#Khan--2020|Khan et al., 2020]] ) imply ''high confidence'' for increased precipitation in Pakistan. Observations located in the monsoon-dominated strip in Pakistan indicate that the mean monsoon onset became earlier during 1971–2010 ( [[#Ali--2020|Ali et al., 2020]] ).

Snow and glaciers are major water resources of all countries in South Asia. Glacier melting is mainly controlled by natural phenomena but anthropogenic emissions of black carbon (BC) are now making a significant contributing to total glacial melting in the Hindu Kush Himalaya (HKH) region ( [[#Menon--2002|Menon, 2002]] ; [[#Ramanathan--2007|Ramanathan et al., 2007]] ; [[#Ramanathan--2008|Ramanathan and Carmichael, 2008]] ). BC concentration is seven to 10 times higher in mid-altitudes (1000–4000 metres above sea level) than at high altitudes (&gt;4000 metres above sea level). The concentration of BC sampled from the surface of snow/ice samples as well as ice-core records shows decreasing ice albedo and an acceleration in glacier melting (Cross-Chapter Box 10.4; [[#Wester--2019|Wester et al., 2019]] ). Karakoram and western HKH snow cover is increasing, a phenomena known as the ‘Karakoram anomaly’, and partially attributed to an increase in the strength of westerly disturbances ( [[#Wester--2019|Wester et al., 2019]] ).

Significant glacier retreat has been observed since 1960 in TIB with lower rates in the interior of the region ( [[#Yao--2007|Yao et al., 2007]] ). A large inter-decadal variation in snow cover is also observed from 1960 to 2010. Observations and model simulations showed that the increasing temperature of frozen grounds is leading to thawing and reduced depth of permafrost, with further significant reductions projected under future global warming scenarios ( ''medium confidence'' ) ( [[#Yang--2019|Yang et al., 2019]] ).

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==== [[#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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==== [[#Atlas.5.3.4|Atlas.5.3.4]] Assessment and Synthesis of Projections ====

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CMIP5 and CMIP6 surface temperature projections for South Asia are consistent across the range of GWLs withincreases greater than the global average, more so over TIB (Figure Atlas.1 7). CMIP6 models show higher sensitivity to greenhouse gas emissions, projecting higher warming for a given emissions scenario. The north-western parts of South Asia, mainly covering the Karakorum and Himalayan mountain ranges, are projected to warm more (over 6°C under SSP5-8.5, with higher warming in winters than in summer; Interactive Atlas) and this will accelerate glacier melting in the region. The warming pattern of maximum and minimum temperatures are projected to intensify in higher latitudes compared with mid-latitudes of South Asia in CMIP5 simulations for all RCP scenarios ( [[#Ullah--2020|Ullah et al., 2020]] ).

Seasonal precipitation projections show increased winter precipitation over the western Himalayas and decreased precipitation over the eastern Himalayas. On the other hand, summer precipitation projections show a robust increase over most of South Asia, with the largest over the arid region of southern Pakistan and in adjacent areas of India, under SSP5-8.5 ( [[#Almazroui--2020b|Almazroui et al., 2020b]] ). Daily bias-adjusted projections from 13 CMIP6 GCMs using all emissions scenarios project a warmer (3°C–5°C) and wetter (13–30%) climate in South Asia in the 21st century ( [[#Mishra--2020|Mishra et al., 2020]] ).

With continued global warming and anticipated reductions in anthropogenic aerosol emissions in the future, CMIP5 models project an increase in the mean and variability of summer monsoon precipitation over India by the end of the 21st century, together with substantial increases in daily precipitation extremes ( ''medium confidence'' ) ( [[#Krishnan--2020|Krishnan et al., 2020]] ), see also [[IPCC:Wg1:Chapter:Chapter-8#8.4.2.4|Section 8.4.2.4]] on changes in the South Asian monsoon. The CMIP5 GCMs consistently project an increase in moisture transport over the Arabian Sea and Bay of Bengal towards the end of the 21st century, an increase in moisture convergence and consequent increases in monsoon rainfall over the Indo-Pakistan region which are higher under RCP8.5 than RCP4.5 ( [[#Srivastava--2014|Srivastava and Delsole, 2014]] ; [[#Mei--2015|Mei et al., 2015]] ; [[#Latif--2018|Latif et al., 2018]] ). Out of 20 CMIP5 GCMs, four showed an increase in magnitude and lengthening of the summer monsoon across India under RCP8.5. The intensity of both strong and weak monsoons is projected to increase during the period 2051–2099 ( [[#Srivastava--2014|Srivastava and Delsole, 2014]] ).

Summer precipitation changes in South Asia are consistent between CMIP3 and CMIP5 projections, but the model spread is large for winter precipitation changes. Changes in summer monsoon rainfall will dominate annual changes over South Asia ( [[#Woo--2019|Woo et al., 2019]] ). CMIP3 GCMs project a gradual increase in annual precipitation over monsoon-dominated areas of Pakistan throughout the 21st Century and increases in humid and semi-arid climate areas ( [[#Saeed--2018|Saeed and Athar, 2018]] ).

Warming of 2.5°C–5°C is projected over northern Pakistan and India ( [[#Syed--2014|Syed et al., 2014]] ). CORDEX-South Asia projections over north-east India under RCP4.5 for the period 2011–2060, show increasing trends for both seasonal maximum and minimum temperature over north-east India (Interactive Atlas). The future projections of South Asian monsoon from the CORDEX-CORE exhibit a spatially robust delay in the monsoon onset, an increase in seasonality, and a reduction in the rainy season length over parts of South Asia at higher levels of radiative forcing ( [[#Ashfaq--2021|Ashfaq et al., 2021]] ).

With TIB continuing to warm, snow cover and snow water equivalent are projected to decrease but with regional differences due to synoptic influences (Cross-Chapter Box 10.4; [[#Wester--2019|Wester et al., 2019]] ). There is ''limited evidence'' on whether the ‘Karakoram Anomaly’ will persist in coming decades, but its long-term persistence is ''unlikely'' with continued projected warming ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-9#9.5.1.1|Section 9.5.1.1]] ). It is projected that peak river flow at higher altitudes will commence earlier, due to warming influences on snow cover area and snow/glacier melt rates and with more precipitation falling as rain rather than snow, and the magnitude and seasonality of flow will change over South Asia ( [[#Charles--2016|Charles et al., 2016]] ).

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==== [[#Atlas.5.3.5|Atlas.5.3.5]] Summary ====

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Mean, minimum and maximum daily temperatures in South Asia are increasing and winters are getting warmer faster than summers ( ''high confidence'' ). The South Asian monsoon has shown contrasting behaviour over India and Pakistan. There is ''high confidence'' that there has been a decrease in mean rainfall over most parts of the eastern and central north regions of India and an increase in precipitation in Pakistan.

Global model performance over the region has improved from CMIP3 to CMIP5 to CMIP6 in the multi-model ensemble-mean simulation of the amplitude and phase of the seasonal cycles of temperature and precipitation. However, there was no appreciable improvement in regions with steep orography, and there has remained substantial inter-model spread in seasonal and annual mean temperatures over South Asia with generally cold biases which are largest in the complex Karakorum and Himalayan mountain ranges. CMIP6 GCMs also show a dry bias (15–20%) in mean annual precipitation in the majority of the South Asia region with a wet bias in Nepal, Pakistan and northern India.

It is ''likely'' that surface temperatures over South Asia will increase more than the global average and more so over TIB, with projected increases of 4.6°C (3.4°C–6.0°C) during 2081–2100 compared with 1995–2014 under SSP5-8.5 and 1.3°C (0.7°C– 2.0°C) under SSP1-2.6 (Interactive Atlas). Summer monsoon precipitation in South Asia is ''likely'' to increase by the end of the 21st century while winter monsoons are projected to be drier. Over the same time periods CMIP6 models project an increase in annual precipitation in the range 14–36% under SSP5-8.5 and 0.4–16% under SSP1-2.6 ( ''medium confidence'' ).

With continued warming, TIB snow cover and snow water equivalent are ''likely'' to decrease and with more precipitation falling as rain rather than snow in SAS. It is projected that the peak river flow at higher altitudes will commence earlier due to the effect of warming on snow cover and snow/glacier melt rates, causing changes in magnitude and seasonality of flow.

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