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

=== Atlas.5.5 South West Asia ===

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==== [[#Atlas.5.5.1|Atlas.5.5.1]] Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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===== Atlas.5.5.1.1 Key Features of the Regional Climate =====

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South West Asia includes the Arabian Peninsula (ARP) and West Central Asia (WCA) reference regions (Figure Atlas.1 7). ARP has a semi-arid or arid desert climate with very low annual mean precipitation and very high temperature. Its temperature is influenced by SST variations over the tropical ocean (e.g., ENSO) and the NAO and AO (see [[IPCC:Wg1:Chapter:Annex-iv|Annex IV]] for these and subsequent modes of variability; [[#Attada--2019|Attada et al., 2019]] ). Rainfall is influenced by the IOD and ENSO, with more rainfall during El Niño ( [[#Kang--2015|Kang et al., 2015]] ; [[#Kumar--2015|Kumar et al., 2015]] ; [[#Abid--2018|Abid et al., 2018]] ; [[#Kamil--2019|Kamil et al., 2019]] ) and less during La Niña ( [[#Atif--2020|Atif et al., 2020]] ). The wet season in ARP is mainly from November to April and the dry season is from June to August. Rainfall is confined mostly to the south-western part of the peninsula and contribution of extreme events to the total rainfall varies within 20–70% from region to region and season to season ( [[#Almazroui--2020b|Almazroui, 2020b]] ; [[#Almazroui--2020|Almazroui and Saeed, 2020]] ). WCA is separated from Eastern Europe by the Caucasus Mountains, is adjacent to ARP, with South Asia (SAS) to the south and West Siberia (WSB) to the north, and lies between the Mediterranean (MED), Tibetan Plateau (TIB) and East Central Asia (ECA) regions. WCA is heterogeneous in terrain with the Zagros Mountains and Iranian Plateau in the west and south-west, the Caspian Sea and lowland with deserts in the north and north-east. The regional climate of WCA is influenced by the NAO and ENSO and it is typically semi-arid or arid with a strong gradient in both precipitation and temperature from the mountains to the plains and from north to south.

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===== Atlas.5.5.1.2 Findings From Previous IPCC Assessments =====

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The IPCC AR5 established it is ''very likely'' that temperatures will continue to increase over WCA in all seasons whilst projections of decreased annual mean precipitation had ''medium confidence'' due to ''medium agreement'' resulting from model-dependent sub-regional and seasonal changes ( [[#Christensen--2013|Christensen et al., 2013]] ). The AR5 also concluded that for a better understanding of the climate of the region, results of high-resolution regional climate models also need to be assessed and CMIP5 models generally had difficulties simulating the mean temperature and precipitation climatology for South West Asia. This is partly related to the poor spatial resolution of the models not resolving the complex mountainous terrain and the influence of different drivers of the European, Asian and African climates. However, observational data scarcity and issues related to the comparison of observations with coarse-resolution models added to the uncertainty and remained poorly analysed in peer-reviewed literature on climate model performance ( [[#Christensen--2013|Christensen et al., 2013]] ).

The SR1.5 stated that even for 1.5°C and 2°C of global warming, South West Asia is among the regions with the strongest projected increase in hot extremes with more urban populations exposed to severe droughts in West Asia, while an increase of heavy precipitation events is projected in mountainous regions of Central Asia ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ; [[#IPCC--2018c|IPCC, 2018c]] ). Higher temperatures with less precipitation will ''likely'' result in higher risks of desertification, wildfires and dust storms exacerbated by land-use and land-cover changes in the region with consequent effects on human health. Further drying of the Aral Sea in Central Asia will ''likely'' have negative effects on the regional microclimate, adding to the growing wind erosion in adjacent deltaic areas and deserts that is already resulting in a reduction of the vegetation productivity including croplands. There is also a projected increase of precipitation intensity in the Arabian Peninsula which is ''likely'' to lead to higher soil erosion particularly in winter and spring due to floods ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ). WCA includes high mountains with enhanced warming above 500 m where, regardless of the emissions scenario, decreases in snow cover are projected due to increased winter snowmelt and more precipitation falling as rain ( ''high confidence'' ). A very strong interannual and decadal variability, as well as scarce in situ records for mountain snow cover, have prevented a quantification of recent trends in High Mountain Asia (Hock et al., 2019b).

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==== [[#Atlas.5.5.2|Atlas.5.5.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Since AR5, there has been an increasing number of studies on past climate change in South West Asia though meteorological stations are sparsely scattered in the region. They are mainly located in the plains below 2 km of altitude, very scarce in mountainous areas and have declined in number in WCA since the end of the Soviet Union in 1991. This increases the uncertainty in both temperature and precipitation trends, particularly for elevated areas ( ''high confidence'' ) ( [[#Christensen--2013|Christensen et al., 2013]] ; [[#Huang--2014|Huang et al., 2014]] ). So researchers use other sources of climate data in the region, particularly freely available gridded data (Annex I).

Globally, drylands showed an enhanced warming over the past century of 1.2°C–1.3°C, significantly higher than the warming over humid lands (0.8°C–1.0°C) (J. [[#Huang--2017|]] [[#Huang--2017|Huang et al., 2017]] ). A strong increase in annual surface air temperature of 0.27°C–0.47°C per decade has been found over WCA between 1960 and 2013 ( ''very high confidence'' ) ( [[#Han--2013|Han and Yang, 2013]] ; [[#Li--2013|Li et al., 2013]] ; [[#Hu--2014|Hu et al., 2014]] , 2017; [[#Huang--2014|Huang et al., 2014]] ; [[#Deng--2017|Deng and Chen, 2017]] ; [[#Zhang--2017|Zhang et al., 2017]] , 2019a; H. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ; [[#Haag--2019|Haag et al., 2019]] ; [[#Yu--2019|Yu et al., 2019]] ) ''.'' Warming is most prominent in the spring based on the CRU dataset with rates ''likely'' ranging from 0.64°C–0.82°C per decade ( [[#Hu--2014|Hu et al., 2014]] ). Analysis of seasonal temperature trends based on high-resolution 1 km × 1 km downscaled dataset CHELSA and 20 stations in Uzbekistan has confirmed the maximum significant trend in temperature from 0.6°C up to 1°C per decade in spring from 1979 to 2013 and no significant trend in winter ( [[#Khaydarov--2019|Khaydarov and Gerlitz, 2019]] ). There is ''very high confidence'' ( ''robust evidence'' , ''high agreement'' ) that the shrinking of the Aral Sea has induced an increase in surface air temperature around the Aral Sea region in the range of 2°C–6°C ( [[#Baidya%20Roy--2014|Baidya Roy et al., 2014]] ; [[#McDermid--2017|McDermid and Winter, 2017]] ; [[#Sharma--2018|Sharma et al., 2018]] ). The plateau of Iran has experienced significant increases in the average monthly values of daily maximum and minimum temperatures with spatially varying rates of 0.1°C–0.3°C up to 0.3°C–0.4°C per decade and greater spatial variation in minimum temperatures ( ''high confidence'' ) ( [[#Mahmoudi--2019|Mahmoudi et al., 2019]] ; [[#Fathian--2020|Fathian et al., 2020]] ; [[#Sharafi--2020|Sharafi and Mir Karim, 2020]] ).

Observed warming over northern ARP is higher than over the south, where minimum temperatures are increasing faster than maximum temperatures ( [[#Almazroui--2020a|Almazroui, 2020a]] ). The rate of mean temperature increase is estimated at 0.10°C per decade over 1901–2010 ( [[#Attada--2019|Attada et al., 2019]] ), while it has reached 0.63°C ( ''likely'' in the range of 0.24°C–0.81°C) per decade for the more recent period of 1978–2019 ( [[#Almazroui--2020a|Almazroui, 2020a]] ).

An overall increasing trend of annual precipitation (0.66 mm per decade) was found over Central Asia based on GPCC v7 data for the period 1901–2013 ( [[#Hu--2017|Hu et al., 2017]] ), but annual trends were found not significant over the shorter period 1960–2013 (Figure Atlas.11 and Interactive Atlas). Winter precipitation saw a significant increase of 1.1 mm per decade ( [[#Song--2016|Song and Bai, 2016]] ). These estimates have ''low'' to ''medium confidence'' since the satellite precipitation products have large systematic and random errors in mountainous regions. Moreover CMORPH and TRMM products fail to capture the precipitation events in the ice/snow covered regions in winter and show a substantial false-alarm percentage in summer, but the gauge-corrected GSMAP performs better than other products ( [[#Song--2016|Song and Bai, 2016]] ; [[#Guo--2017b|Guo et al., 2017b]] ; [[#Hu--2017|Hu et al., 2017]] ; S. [[#Chen--2019|]] [[#Chen--2019|Chen et al., 2019]] ). Over the elevated part of eastern WCA precipitation increases in the range of 1.3–4.8 mm per decade during 1960–2013 were observed ( ''very high confidence'' ) ( [[#Han--2013|Han and Yang, 2013]] ; [[#Li--2013|Li et al., 2013]] ; [[#Hu--2014|Hu et al., 2014]] , 2017; [[#Huang--2014|Huang et al., 2014]] ; [[#Deng--2017|Deng and Chen, 2017]] ; [[#Zhang--2017|Zhang et al., 2017]] , 2019a; H. [[#Guo--2018|]] [[#Guo--2018|Guo et al., 2018]] ; [[#Haag--2019|Haag et al., 2019]] ; [[#Yu--2019|Yu et al., 2019]] ). Reductions in spring precipitation and increases in winter have been reported for Uzbekistan over the period 1979–2013 based on station data but these are not significant ( [[#Khaydarov--2019|Khaydarov and Gerlitz, 2019]] ). There is ''very low confidence'' of the impact of the Aral Sea shrinking on precipitation ( [[#Chen--2011|Chen et al., 2011]] ; [[#Jin--2017|Jin et al., 2017]] ).

A decreasing trend of precipitation is reported for ARP with the mean value of –6.3 mm per decade (range of –30 mm–16 mm) for the period 1978–2019 ( ''low confidence'' ) with large interannual variability over Saudi Arabia, which covers 80% of the region ( [[#AlSarmi--2011|AlSarmi and Washington, 2011]] ; [[#Almazroui--2012|Almazroui et al., 2012]] ; [[#Donat--2014|Donat et al., 2014]] ). The same decreasing trend in precipitation totals and an increasing trend in the number of consecutive dry days are found for most of the Iranian Plateau ( ''medium confidence'' ) ( [[#Rahimi--2019|Rahimi and Fatemi, 2019]] ; [[#Fathian--2020|Fathian et al., 2020]] ; [[#Sharafi--2020|Sharafi and Mir Karim, 2020]] ). January-to-March mean snow cover and depth over mountainous areas decreased between 2000 and 2019 ( ''low'' to ''medium confidence'' due to ''limited evidence'' ) ( [[#Safarianzengir--2020|Safarianzengir et al., 2020]] ).

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==== [[#Atlas.5.5.3|Atlas.5.5.3]] Assessment of Model Performance ====

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There is ''limited evidence'' about the performance of GCMs and RCMs in representing the current climate of South West Asia due to very few studies evaluating models over this region, but literature is now emerging particularly on CMIP5/CMIP6 and CORDEX simulations.

Over ARP, surface temperature biases for 18 of 30 CMIP5 models are within one standard deviation of the observed variability ( [[#Almazroui--2017|Almazroui et al., 2017]] ). A warm bias in summer and a cold bias for other months along with an underestimation of wet-season precipitation and an overestimation in the dry season have been reported in 26 CMIP5 models ( [[#Lelieveld--2016|Lelieveld et al., 2016]] ). Thirty CMIP6 GCMs have limited skill in simulating annual precipitation patterns, annual cycle statistics and long-term precipitation trends over Central Asia partially due to considerable wet biases of up to 100% in the southern Xinjiang and Hexi Corridor regions ( [[#Guo--2021|Guo et al., 2021]] ). Also, CMIP6 models display a wide range of performance in reproducing ENSO teleconnections that influence the region ( [[#Barlow--2021|Barlow et al., 2021]] ).

RCM simulations using the CORDEX-MENA domain reproduce the main features of the mean surface climatology over ARP with moderate biases ( ''high confidence'' ). RegCM4 driven by five GCMs (HadGEM2, GFDL, CNRM, CanESM2 and ECHAM6) showed an ensemble-mean cold bias of about –0.7°C and a dry bias of –13% over ARP ( [[#Almazroui--2016|Almazroui, 2016]] ) with a cold (warm) bias over western (south-eastern) areas ( [[#Syed--2019|Syed et al., 2019]] ). Temperature biases in 30-year historical simulations with WRF using three different radiation parametrizations were within ±2°C and mostly caused by surface long-wave radiation errors which affected nighttime minimum temperatures over 70% of the domain ( [[#Zittis--2017|Zittis and Hadjinicolaou, 2017]] ). Mean absolute errors in COSMO-CLM driven by ERA-Interim were about 1.2°C for temperature, 15 mm per month for precipitation and 9% for total cloud cover, and with new parametrizations of albedo and aerosols optimized for the region the RCM simulated the main climate features of this very complex area ( [[#Bucchignani--2016|Bucchignani et al., 2016]] ). RegCM4.4 also simulated the main features of the observed climatology (especially for dry regions) with temperature biases within ±3.0°C. Annual precipitation was overestimated with winter and spring underestimated ( [[#Ozturk--2018|Ozturk et al., 2018]] ).

Four RCMs (REMO, RegCM4.3.5, ALARO-0, and COSMO-CLM5.0) driven by ERA-Interim, NCEP2 reanalyses and two different GCMs reproduced reasonably well the spatio-temporal patterns for temperature and precipitation though underestimated diurnal temperature range and had cold biases over mountainous and high plateau regions in all seasons. There is ''low confidence'' in this result because of low station density and a lack of high-elevation stations, and with biases dependent on the choice of the observational dataset. However, the performance of both GCMs and RCMs is better than reanalyses when compared to available observations ( [[#Mannig--2013|Mannig et al., 2013]] ; [[#Ozturk--2017|Ozturk et al., 2017]] ; [[#Russo--2019|Russo et al., 2019]] ; [[#Top--2021|Top et al., 2021]] ).

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==== [[#Atlas.5.5.4|Atlas.5.5.4]] Assessment and Synthesis of Projections ====

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Temperature and precipitation projections from CMIP5/CMIP6 and CORDEX for different GWLs, SSP and RCP scenarios, time periods and baselines are shown in Figure Atlas.1 7 and further details can be explored in the Interactive Atlas.

In WCA, projections for different GWLs are consistent not only in annual and seasonal warming but in the ranges of the projections. Under RCP8.5, annual mean temperature will ''likely'' exceed 2°C by mid-century (compared with 1995–2014) and reach up to 4.8°C–6°C by the end of the century ( [[#Yang--2017|Yang et al., 2017]] ), with faster warming projected by the CMIP6 ensemble under SSP5-8.5. In individual county-level studies on GCM future climate projections, temperatures increased by up to 7°C by the end of the century, depending on season and emissions scenario ( [[#Allaberdiyev--2010|Allaberdiyev, 2010]] ; [[#MENRPG--2015|MENRPG, 2015]] ; [[#MNP--2015|MNP, 2015]] ; [[#Gevorgyan--2016|Gevorgyan et al., 2016]] ; [[#Osborn--2016|Osborn et al., 2016]] ; [[#Aalto--2017|Aalto et al., 2017]] ; [[#IDOE--2017|IDOE, 2017]] ; [[#Salman--2017|Salman et al., 2017]] ). Statistical downscaling of 18 CMIP5 GCMs projected an annual temperature increase of 0.37°C per decade (under RCP4.5) with the maximum in northern WCA and warming most conspicuous in summer ( [[#Luo--2019|Luo et al., 2019]] ). RCM downscaling of GCMs over Central Asia projected a larger increase of temperature under RCP8.5 for the 2071–2100 period, ranging from 5°C to 8°C ( [[#Ozturk--2017|Ozturk et al., 2017]] ).

In ARP, the projected change in ensemble mean annual temperature from 30 CMIP6 models is from 1.6°C (SSP1-2.6) to 5.3°C (SSP5-8.5) by 2070–2099 compared to 1981–2010 ( [[#Almazroui--2020a|Almazroui et al., 2020a]] ). The projected warming is the highest in the north, reaching 5.9°C and lowest in the south (4.7°C). COSMO-CLM projections over the CORDEX-MENA domain show for ARP and WCA a strong warming with marked seasonality for the end of the 21st century, ranging from 2.5°C in winter under RCP4.5 to 8°C in summer under RCP8.5 and with large increases found over high-altitude areas in winter and spring ( [[#Bucchignani--2018|Bucchignani et al., 2018]] ; [[#Ozturk--2018|Ozturk et al., 2018]] ). The CMIP5 multi-model mean warming in boreal summer in 2070–2099, compared with 1951–1980, is projected to be about 2.5°C and 6.5°C at the 2°C and 4°C global warming levels respectively ( [[#Huang--2014|Huang et al., 2014]] ).

Future projections of precipitation in South West Asia have large uncertainties and thus ''low confidence'' . There are few significant changes, little consensus on the sign and with a tendency for reduction in CMIP5 being reversed in CMIP6 across all warming levels ( [[#Ozturk--2018|Ozturk et al., 2018]] ). Statistical downscaling of 18 CMIP5 GCMs under RCP4.5 projected an increase in precipitation of 4.6 mm per decade in South West Asia during 2021–2060 relative to 1965–2004 ( [[#Luo--2019|Luo et al., 2019]] ). CMIP5 simulations project a general decrease in precipitation over lowlands in Turkey, Iran, Afghanistan and Pakistan ( [[#Ozturk--2017|Ozturk et al., 2017]] ), and an increase over high-mountain regions ( [[#Aalto--2017|Aalto et al., 2017]] ; [[#Salman--2018|Salman et al., 2018]] ). At a 4°C global warming level, the multi-model mean annual precipitation for Turkmenistan and parts of Tajikistan and Uzbekistan is projected to decrease by 20%, with somewhat stronger relative decreases in summer ( [[#Reyer--2017|Reyer et al., 2017]] ). Over northern WCA, the CMIP5 ensemble mean projects increases of over 3 mm per decade under RCP2.6 and over 6 mm per decade under RCP4.5 and RCP8.5 over the 21st century ( [[#Huang--2014|Huang et al., 2014]] ). Mean annual precipitation is projected to rise by 5.2% at the end of the 21st century (2070–2099) under RCP8.5, compared to 1976–2005, while mean annual snowfall is projected to decrease by 26.5% in Central Asia ( [[#Yang--2017|Yang et al., 2017]] ). However, regardless of the sign of the precipitation change in the high-mountain regions of Central Asia, the influence of the warming on the snowpack will ''very likely'' cause important changes in the timing and amount of the spring melt ( [[#Diffenbaugh--2013|Diffenbaugh et al., 2013]] ).

In ARP, the projected change in ensemble mean annual precipitation from 30 CMIP6 models ranges from 3.8% (–2.6 to 28.8%) to 31.8% (12.0–106.5%) under SSP1-2.6 and SSP5-8.5 emissions for the period 2080–2100 compared with 1995–2014 ( [[#Almazroui--2020a|Almazroui et al., 2020a]] ). North-west ARP precipitation is projected to decrease between –6 to –27% per decade and in the south precipitation to increase by up to 8.6% per decade. CMIP6 projections are in line with those from CMIP3 and CMIP5, however they are less variable in the central area in CMIP6. The uncertainty associated with precipitation over ARP is large because of very low annual amounts and high variability.

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==== [[#Atlas.5.5.5|Atlas.5.5.5]] Summary ====

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Increases in annual surface air temperature over South West Asia are ''very likely'' in the range of 0.24°C–0.81°C per decade over the last 50–60 years. Annual precipitation change over ARP since 1970 is estimated at –6.3 mm per decade (and in the range of –30 to 16 mm per decade) and over WCA is generally not significant except over the elevated part of eastern WCA where increases between 1.3 mm and 4.8 mm per decade during 1960–2013 have been observed ( ''very high confidence'' ). In mountainous areas, the scarcity and decline of the number of observation sites since the end of the former Soviet Union in 1991 increase the uncertainty of the long-term temperature and precipitation estimates ( ''high confidence'' ).

Mean temperature biases in RCMs are within ±3°C in South West Asia, and annual precipitation biases are positive in almost all parts of the region, except over the ARP where they are negative in the wet season (November to April) and over WCA in winter and spring (from December to May) ( ''medium confidence'' ). Since regional model evaluation literature has only recently emerged there is ''medium evidence'' about the performance of RCMs in South West Asia though with ''medium'' to ''high agreement'' on mean temperature and precipitation biases. RCMs simulate colder temperatures than observed over mountainous and high plateau regions ( ''limited evidence'' , ''high agreement'' ).

Further warming over South West Asia is projected in the 21st century to be greater than the global average, with rates varying from 0.25°C to 0.8°C per decade depending on the season and scenario, and the maximum rates found in the northern part of the region in summer ( ''high confidence'' ). The influence of the warming on the snowpack will ''very likely'' cause changes in the timing and amount of the spring melt. CMIP6 projected changes in annual precipitation totals are in the range of –3 to 29% (SSP1-2.6) and 12–107% (SSP5-8.5) in ARP ( ''medium confidence'' ). Strong spatio-temporal differences with overall precipitation decreases are projected in the central and northern parts of WCA in summer (JJA) with increases in winter (DJF) ( ''medium confidence'' ).

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