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

=== Atlas.5.2 North Asia ===

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==== [[#Atlas.5.2.1|Atlas.5.2.1]] Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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===== Atlas.5.2.1.1 Key Features of the Regional Climate =====

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North Asia extends from the Ural Mountains in the west to the Pacific Ocean in the east and from the Russian Arctic in the north to West and East Central Asia and East Asia in the south. Its most recognizable features are boreal forests and permafrost. In AR6 North Asia is divided into three reference regions (Figure Atlas.1 7): West Siberia (WSB) with a continental climate, warm summers and cold winters, many waterlogged areas and several natural zones due to a large extent from south to north and heterogeneity in regional climates; East Siberia (ESB) which is mainly highland with extensive permafrost and a more severe continental climate characterized by harsh, long winters and short, hot summers, and by less precipitation and snow cover than in neighbouring regions; and the Russian Far East (RFE) with a monsoon-influenced climate, cold winters and wet summers in the south, and cold winters and cool summers almost without precipitation in the north. WSB and ESB are mainly influenced by NAO and NAM (Annex IV.2.1) and the Arctic Oscillation (AO) with associated atmospheric blocking by the Siberian High (SH) that exhibits a pronounced decadal-to-multi-decadal variability (see also Table Atlas.1). RFE is under the influence of the ENSO (Annex IV.2.3) and the PDV (Annex IV.2.6) that mostly affect rainfall variability.

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===== Atlas.5.2.1.2 Findings From Previous IPCC Assessments =====

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In the previous IPCC assessment cycles, the three sub-regions comprising North Asia in this section, along with Eastern Europe and the Asian Arctic, were considered as either Northern Eurasia or Russia in AR4 and AR5. The AR5 WGI stated that for North and Central Asia CMIP5 models had difficulty in representing climatological means of both temperature and precipitation, which is partly related to the scarceness of observational data in northern parts of the region and to issues related to the estimation of biases with coarse-resolution models ( [[#Christensen--2013|Christensen et al., 2013]] ). In CMIP5 projections under different RCP scenarios, North Asian temperatures increase more in winter (DJF) than summer (JJA; [[#Seneviratne--2012|Seneviratne et al., 2012]] ). With most models projecting increased precipitation significantly above the 20-year natural variability, it was concluded that precipitation in North Asia will ''very likely'' increase ( [[#Christensen--2013|Christensen et al., 2013]] ).

The SRCCL identified aridification of the climate in southern East Siberia between 1976 and 2016 as causing an extension of the steppes polewards whilst climate change also extended the vegetation season, increasing forest productivity in most of boreal Siberia, but increasing risk of wildfire and tree mortality ( [[#Mirzabaev--2019|Mirzabaev et al., 2019]] ). The SROCC noted the warming climate has caused permafrost thaw and loss of ground ice, and thus land subsidence and collapse, disturbing ecosystems and human infrastructure. Permafrost stability, hydrology and vegetation were also impacted by recent extensive fires burning into the organic soil layer ( [[#Meredith--2019|Meredith et al., 2019]] ). The SR1.5 noted that future, higher levels of warming lead to greater impacts in key systems such as the Siberian ecosystems, identified as one of the threatened systems (‘Reason for Concern 1 – RFC1’; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) with impacts at 2°C expected to be greater than those at 1.5°C ( ''medium confidence'' ).

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==== [[#Atlas.5.2.2|Atlas.5.2.2]] Assessment and Synthesis of Observations, Trends and Attribution ====

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Increases in surface air temperature (SAT) have been observed since the mid-1970s over the whole of North Asia ( [[#Frolov--2014|Frolov et al., 2014]] ), and particularly over the north-eastern part (Figure Atlas.11; [[#Gruza--2015|Gruza et al., 2015]] ). Trends of annual SAT in the northern part of the region during the last decades were ''very likely'' twice as strong as the global average (Figure Atlas.11; [[#Frolov--2014|Frolov et al., 2014]] ; [[#Mokhov--2015|Mokhov, 2015]] ; [[#Sherstyukov--2016|Sherstyukov, 2016]] ) with trends in RFE of 0.8°C–1.2°C per decade for the 1976–2014 period and more intense warming strengthening from south to north observed in spring in ESB ( [[#Frolov--2014|Frolov et al., 2014]] ; [[#Ippolitov--2014|Ippolitov et al., 2014]] ; [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ).

Recent strong warming in polar regions (Section [[#Atlas.11.2|Atlas.11.2]] ) was accompanied by cooling in winter in mid-latitude regions particularly in the southern part of WSB and ESB ( [[#Cohen--2014|Cohen et al., 2014]] ; [[#Ippolitov--2014|Ippolitov et al., 2014]] ; [[#Gruza--2015|Gruza et al., 2015]] ; [[#Kharyutkina--2016|Kharyutkina et al., 2016]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Perevedentsev--2017|Perevedentsev et al., 2017]] ; [[#Wegmann--2018|Wegmann et al., 2018]] ). These temperature decreases were strongly correlated with significant warming over the Barents-Kara Sea (greater than 2.5°C per decade during 2003–2017) and sea ice loss, suggesting a causal link ( [[#Outten--2012|Outten and Esau, 2012]] ; [[#Semenov--2012|Semenov et al., 2012]] ; [[#Overland--2016|Overland et al., 2016]] ; [[#Semenov--2016|Semenov, 2016]] ; [[#Wegmann--2018|Wegmann et al., 2018]] ; [[#Meleshko--2019|Meleshko et al., 2019]] ; [[#Susskind--2019|Susskind et al., 2019]] ), though recent studies ( [[#Blackport--2019|Blackport et al., 2019]] ; [[#Clark--2019|Clark and Lee, 2019]] ) have shown that both phenomena result from mid-latitude circulation variability (see also Cross-Chapter Box 10.1). In addition, significant warming in the last decade has halved the cooling trend in southern WSB from –0.6°C per decade during 1976–2012 to –0.3°C per decade during 1976–2018 ( ''high confidence'' ) ( [[#Frolov--2014|Frolov et al., 2014]] ; [[#Roshydromet--2019|Roshydromet, 2019]] ).

Annual precipitation totals ''very likely'' increased over North Asia in the last half century along with more heavy and less light precipitation, more freezing rain and less freezing drizzle (Figure Atlas.11 and the Interactive Atlas; [[#Wen--2014|Wen et al., 2014]] ; [[#Groisman--2016|Groisman et al., 2016]] ; [[#Ye--2017|Ye et al., 2017]] ; [[#Chernokulsky--2019|Chernokulsky et al., 2019]] ). The highest increase was observed over regions of Siberia and RFE with estimated trends of 10–25 mm per decade for the 1976–2014 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ) or 5% per decade for the 1976–2018 period ( [[#Roshydromet--2019|Roshydromet, 2019]] ). Increases over southern RFE are the largest (over 50 mm per decade) and are mostly due to positive changes in convective precipitation intensity in the region in the summer season (JJA) during 1966–2016 ( ''medium confidence'' ) ( [[#Chernokulsky--2019|Chernokulsky et al., 2019]] ). A decreasing trend was observed in central WSB, northern ESB, the Baikal and Transbaikal regions, the Amur River region, and Primorie territories of RFE (the Kamchatka and Chukchi peninsulas) with up to –20 mm per decade for the 1976–2014 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ) or 15–20% per decade for the 1976–2018 period ( [[#Roshydromet--2019|Roshydromet, 2019]] ). Overall, solid precipitation predominantly decreased in North Asia and ''very likely'' caused both less snow cover extent (SCE) and snow water equivalent (SWE), attributable to the anthropogenic influence with ''high confidence'' (Sections 2.3.2.2 and 3.4.2).

Snow characteristics depend on both temperature and precipitation, and observed trends over North Asia show large spatial heterogeneity and interannual variability (Figure Atlas.1 8) leading to ''medium confidence'' that maximum snow depth has increased over Siberia, the Okhotsk Sea coast and in southern RFE since the 1960s ( [[#Callaghan--2011|Callaghan et al., 2011]] ; [[#Loginov--2014|Loginov et al., 2014]] ), with trends during 1976–2016 of 1.8 cm (in WBS), 1.1 cm (in ESB), and 4.6 cm (in RFE) per decade ( [[#Bulygina--2017|Bulygina et al., 2017]] ). Snow cover duration increased in Yakutia, Sakhalin Island and some other coastal areas of the Pacific Ocean in RFE during 1980–2009 ( [[#Callaghan--2011|Callaghan et al., 2011]] ), and decreased in WSB and ESB ( [[#Bulygina--2017|Bulygina et al., 2017]] ; [[#Roshydromet--2019|Roshydromet, 2019]] ). However, [[#Gorbatenko--2019|Gorbatenko et al. (2019)]] reported that in south-eastern WSB maximal snow depth has increased by 5–20 cm and duration of steady snow cover by between 4 and 10 days during 1989–2016 (Figure Atlas.1 8).

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[[File:73294f618704ec90a160056e6f9d1cde IPCC_AR6_WGI_Atlas_Figure_18.png]]

'''Figure Atlas.18''' '''|''' '''Linear trends for the 1980–2015 period based on station data from the World Data Centre of the Russian Institute for Hydrometeorological Information''' (RIHMI-WDC; [[#Bulygina--2014|Bulygina et al., 2014]] ). '''(a)''' Snow-season duration from 1 July to 31 December (days per decade); '''(b)''' snow-season duration from 1 January to 30 June (days per decade); '''(c)''' maximum annual height of snow cover (mm per decade). Trends have been calculated using ordinary least squares regression and the crosses indicate non-significant trend values (at the 0.1 level) following the method of [[#Santer--2008|Santer et al. (2008)]] to account for serial correlation. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

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==== [[#Atlas.5.2.3|Atlas.5.2.3]] Assessment of Model Performance ====

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Temperature trends and means derived from reanalysis datasets (JRA-25 and MERRA) correctly represented temperature variability shown in observational data over the Asian territory of Russia for the 1976–2010 period ( [[#Loginov--2014|Loginov et al., 2014]] ). Assessment of CRU TS 3.22, CRUTEMP4, ERA-Interim and NCEP2 datasets against station data over North Asia for annual and seasonal air temperature has shown that the ERA-Interim reanalysis outperforms others for the 1981–2005 period ( [[#Kokorev--2015|Kokorev and Sherstiukov, 2015]] ). The latter reanalysis also underestimates summer precipitation and shows large wet biases over north-east Asia during spring and underestimates mean seasonal temperature over north-east Asia in spring (MAM), autumn (SON), and winter (DJF), but overestimates it in summer (JJA) compared with the CRU dataset ( ''medium confidence'' ) ( [[#Ozturk--2017|Ozturk et al., 2017]] ; [[#Top--2021|Top et al., 2021]] ).

GCMs capture the main synoptic processes affecting North Asia and the CMIP5 ensemble simulates the temporal evolution of the magnitude and position of the Siberian High (SH) over the period 1872–2005 ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). CMIP5 models simulate a weakened intensity of the winter SH and a strengthened interannual variability compared to observations ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). The characteristics of blocking events over the region (number, duration, intensity and frequency) were reasonably well reproduced by GCMs ( [[#Mokhov--2014|Mokhov et al., 2014]] ), and most overestimate the annual mean temperature over northern Eurasia (Interactive Atlas). Biases in simulated annual surface air temperature simulation primarily come from the winter (DJF) season and are relatively smaller in other seasons ( [[#Miao--2014|Miao et al., 2014]] ; [[#Peng--2019|Peng et al., 2019]] ). Most GCMs capture the main decadal SAT trend ( [[#Miao--2014|Miao et al., 2014]] ), though CMIP5 GCMs fail to capture the decreasing temperature trend over East Siberia ( [[#Fei--2015|Fei and Yong-Qi, 2015]] ). Possible causes of GCMs’ inability to represent the recent slowdown of warming is further discussed in Cross-Chapter Box 3.1. For CMIP5, models with higher resolution do not always perform better than those with lower resolutions ( ''medium confidence'' ) ( [[#Miao--2014|Miao et al., 2014]] ).

Sixteen CMIP5 model simulations of SAT variability over Eurasia were evaluated against CRU observations for permafrost sub-regions ( [[#Peng--2019|Peng et al., 2019]] ), showing a warm bias in north-west Eurasia, capturing the climate warming over the 20th century and its acceleration during the late 20th century. CMIP5 GCMs generally underestimate daily temperature range compared with observations over north-eastern Russia ( [[#Sillmann--2013|Sillmann et al., 2013]] ; [[#Lindvall--2015|Lindvall and Svensson, 2015]] ). Currently there is no literature on the CMIP6 ensemble over the region though a few single-model studies are available ( [[#Voldoire--2019|Voldoire et al., 2019]] ; T. [[#Wu--2019|]] [[#Wu--2019|Wu et al., 2019]] ).

There is very limited use of RCMs for North Asia. CORDEX-CAS covers North Asia, except parts of RFE, and ARCTIC-CORDEX covers the northern regions (Figure Atlas.6). For CORDEX-CAS three RCMs (REMO, ALARO-0 and CLMcom) have been used and have warm biases for maximum temperatures, cold biases for minimum temperatures and a wet bias in the north during the winter ( [[#Top--2021|Top et al., 2021]] ). Rain gauges, however, are known to have problems in terms of measuring properly solid precipitation (e.g., due to drifting snow) which can greatly affect the accuracy of precipitation observations over North Asia ( [[#Harris--2014|Harris et al., 2014]] ).

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==== [[#Atlas.5.2.4|Atlas.5.2.4]] Assessment and Synthesis of Projections ====

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CMIP5 and CMIP6 projections are consistent in the direction and ranges of surface temperature change which are higher than the global average and with ensemble-mean warming of around 6°C for the 4°C GWL. Projected precipitation changes are also consistent with significant increases in winter, of up to 40% in the ensemble mean for the highest warming levels, and lower increases in summer except for WSB where changes are small and suggest drying at the 4°C GWL (Figure Atlas.1 7 and the Interactive Atlas).

The CMIP5 ensemble projects a warming of the annual mean SAT over northern Eurasia in the 21st century, ''likely'' in the range of 0.8°C–1.0°C (RCP2.6), 2.3°C–3.1°C (RCP4.5) and up to 7.2°C (RCP8.5) ( [[#Miao--2014|Miao et al., 2014]] ; [[#Peng--2019|Peng et al., 2019]] ). Mid-latitude permafrost sub-regions in Eurasia are projected to warm more than the global mean and non-permafrost territories, with ensemble area-averaged changes of 1.7°C (RCP2.6), 3.2°C (RCP4.5) or 6.4°C (RCP8.5) in 2081–2100 relative to 1986–2005 ( [[#Peng--2019|Peng et al., 2019]] ).

Over the Central Asia CORDEX domain, RegCM4.3.5 simulations driven by two different CMIP5 GCMs (HadGEM2-ES and MPI-ESM-MR) project SAT warming for 2071–2100 relative to 1971–2000 of about 3°C–4°C during the summer for RCP4.5 to over 7°C for all seasons for RCP8.5. Projected warming is most evident on the large continental Siberian Plateau with boreal and sub-boreal climates and biomes (i.e., taiga forests and tundra) during the winter season ( [[#Ozturk--2017|Ozturk et al., 2017]] ). The Voeikov Main Geophysical Observatory (MGO) RCM, driven by five CMIP5 GCMs for the RCP8.5 scenario, projects a faster increase in annual minimum temperature as compared with maximum temperature over the whole territory of Russia ( [[#Kattsov--2017|Kattsov et al., 2017]] ), and the smallest change in growing season lengths (i.e., periods with daily temperatures over 5°C, 10°C and 15°C) in the area of northern taiga in WSB and ESB comparable with other territories of Russia during the 21st century ( [[#Torzhkov--2019|Torzhkov et al., 2019]] ).

For precipitation, MGO RCM projects for the Arctic-CORDEX domain under the RCP8.5 scenario increases in annual totals for northern North Asia, a decrease in summer over ESB for 2006–2100 relative to 1951–2005 and significant increases in the upper limit of intense precipitation over most of the region in winter ( [[#Kattsov--2017|Kattsov et al., 2017]] ; [[#Khlebnikova--2018|Khlebnikova et al., 2018]] ). Other RCM projections show that in most seasons and for all future periods, precipitation in Siberia is not projected to change with respect to the 1971–2000 period, except under the RCP8.5 scenario for the winter and autumn ( [[#Ozturk--2017|Ozturk et al., 2017]] ). This very limited and controversial evidence leads to ''low confidence'' in RCM precipitation projections for North Asia and since the projections of GCMs and ESMs are more physically consistent, assessment of future precipitation changes is based on CMIP5/CMIP6 presented in Figure Atlas.1 7 and the Interactive Atlas.

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==== [[#Atlas.5.2.5|Atlas.5.2.5]] Summary ====

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Annual surface air temperature and precipitation have ''very likely'' increased and maximum snow depth has ''likely'' increased over most of North Asia since the mid-1970s. The highest warming has been found in spring in ESB and RFE, strengthening from south to north with linear trends of 0.8°C–1.2°C per decade over the 1976–2014 period ( ''high confidence'' ). A temperature decrease was identified just in winter in the southern part of WSB and ESB as a result of natural variability, but halved from –0.6°C per decade in 1976–2012 to –0.3°C per decade for the longer 1976–2018 period due to recent warmer winters ( ''high confidence'' ). Over North Asia annual precipitation increases with estimated trends of 5–15 mm per decade in the 1976–2014 period have been recorded with an exception over the Kamchatka and the Chukchi peninsulas, where decreases of up to –20 mm per decade in the same period have been found ( ''medium confidence'' ). Snow cover duration has ''very likely'' decreased over Siberia and increases in maximum snow depths of 1.8 cm, 1.1 cm and 4.6 cm per decade have been observed for WSB, ESB and RFE respectively from 1976 to 2016 ( ''limited evidence'' ).

Most of the CMIP5 and some CMIP6 GCMs overestimate the annual mean air temperature and precipitation over the North Asia region ( ''medium confidence'' ). GCMs generally represent the observed decadal temperature trend ( ''medium confidence'' ) and biases primarily come from the winter (DJF) season ( ''high confidence'' ). Results of a very limited number of RCMs applied over the whole region show that they have warmer biases for maximum and colder biases for minimum temperatures ( ''limited evidence'' , ''medium agreement'' ). Sparsity of observational data particularly in the northern part of ESB and the whole of the RFE results in ''low confidence'' in the assessments of model performance in North Asia.

Surface air temperature and precipitation in North Asia are projected to increase further ( ''high confidence'' ) with warming higher than the global average and around 6°C at the 4°C GWL. Temperature change in 2080–2099 relative to 1981–2000 is ''likely'' in the range of 3°C in summer to 4.9°C in winter under the RCP4.5 scenario, and 5.6°C in summer to 9.7°C in winter under the RCP8.5 scenario. Precipitation is projected to increase with ensemble-mean changes of 9% in summer under both RCP4.5 and RCP8.5, and of 22% and 56% in winter respectively.

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