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

== Atlas.11 Polar Regions ==

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The assessment in this section focuses on changes in average temperature, precipitation (rainfall and snow) and surface mass balance over the polar regions, Antarctica and the Arctic, including the most recent years of observations, updates to observed datasets, the consideration of recent studies using CMIP5 simulations and those using CMIP6 and CORDEX simulations. Findings are presented for West Antarctica (WAN) and East Antarctica (EAN), and three Arctic regions: Arctic Ocean (ARO), Greenland/Iceland (GIC) and Russian Arctic (RAR; Figure Atlas.29) with some reference also to North-Eastern North America (NEN), North-Western North America (NWN) and Northern Europe (NEU), which are covered more extensively in [[#Atlas.9|Atlas.9]] and [[#Atlas.8|Atlas.8]] respectively. Sub-regional changes are discussed when relevant, for example the Antarctic Peninsula (AP) as a sub-region of WAN. The Southern Ocean (SOO) region is assessed in [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] with changes in climatic impact-drivers assessed in [[IPCC:Wg1:Chapter:Chapter-12|Chapter 12]] ( [[IPCC:Wg1:Chapter:Chapter-12#12.4.9|Section 12.4.9]] and Table 12.11) and some extremes in [[IPCC:Wg1:Chapter:Chapter-11|Chapter 11]] (Tables 11.7–9 for RAR). [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] provides an overall assessment of the ice-sheet processes and changes, as part of the cryosphere, ocean and sea level change assessment.

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[[File:bf9019e059322ee5aaf6d66b87647adc IPCC_AR6_WGI_Atlas_Figure_29.png]]

'''Figure Atlas.29''' '''|''' '''Regional changes over land (except for ARO) in annual mean surface air temperature and precipitation relative to the 1995–2014 baseline for the reference regions in Arctic and Antarctica (warming since the 1850–1900 pre-industrial baseline is also provided as an offset).''' Bar plots in the left panel of each region triplet show the median (dots) and 10th–90th percentile range (bars) across each model ensemble for annual mean temperature changes for four datasets (CMIP5 in intermediate colours; a subset of CMIP5 used to drive CORDEX in light colours; CORDEX overlying the CMIP5 subset with dashed bars; and CMIP6 in solid colours); the first six groups of bars represent the regional warming over two time periods (near-term 2021–2040 and long-term 2081–2100) for three scenarios (SSP1-2.6/RCP2.6, SSP2-4.5/RCP4.5 and SSP5-8.5/RCP8.5), and the remaining bars correspond to four global warming levels (GWLs: 1.5°C, 2°C, 3°C and 4°C). The scatter diagrams of temperature against precipitation changes display the median (dots) and 10th–90th percentile ranges for the above four warming levels for December–January–February (DJF; middle panel) and June–July–August (JJA; right panel), respectively; for the CMIP5 subset only the percentile range of temperature is shown, and only for 3°C and 4°C GWLs. Changes are absolute for temperature (in °C) and relative (as %) for precipitation. See [[#Atlas.1.3|Atlas.1.3]] for more details on reference regions ( [[#Iturbide--2020|Iturbide et al., 2020]] ) and [[#Atlas.1.4|Atlas.1.4]] for details on model data selection and processing. The script used to generate this figure is available online ( [[#Iturbide--2021|Iturbide et al., 2021]] ) and similar results can be generated in the Interactive Atlas for flexibly defined seasonal periods. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

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=== Atlas.11.1 Antarctica ===

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==== Atlas.11.1.1 Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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===== Atlas.11.1.1.1 Key Features of the Regional Climate =====

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The Antarctic region, covered by an ice sheet and surrounded by the Southern Ocean, is characterized by polar climate. It is the coldest, windiest and driest continent on Earth and plays a pivotal role in regulating the global climate and hydrological cycle. Antarctica has a mean temperature of –35°C ( [[#Lenaerts--2016|Lenaerts et al., 2016]] ) and receives 171 mm yr <sup>–1</sup> water equivalent of snowfall (north of 82°S, estimate based on satellite measurements during 2006–2011; [[#Palerme--2014|Palerme et al., 2014]] ). Precipitation in Antarctica occurs mostly in the form of snowfall and diamond dust, with sporadic coastal rainfall during the summer over the Antarctic Peninsula and sub-Antarctic islands. Drizzle events sometimes occur during warm air intrusions ( [[#Nicolas--2017|Nicolas et al., 2017]] ) at relatively low temperatures ( [[#Silber--2019|Silber et al., 2019]] ). Precipitation constitutes the largest component of the surface mass balance (SMB) '','' which also includes sublimation (from the surface or drifting snow), meltwater runoff and redistribution by wind ( [[#Lenaerts--2019|Lenaerts et al., 2019]] ). SMB can be considered as a proxy of precipitation if averaged over an annual cycle ( [[#Gorodetskaya--2015|Gorodetskaya et al., 2015]] ; [[#Bracegirdle--2019|Bracegirdle et al., 2019]] ). Precipitation and SMB exhibit spatial and temporal variability controlled by atmospheric large-scale low-pressure systems and moisture advection from lower latitudes. SMB is an important component of the total ice-sheet mass balance ( [[IPCC:Wg1:Chapter:Chapter-9#9.4.2.1|Section 9.4.2.1]] ). The Antarctic contribution to sea level results from the imbalance between net snow accumulation and ice discharge into the ocean (Box 9.1). Ice shelves buttress the ice sheet and are influenced by oceanic and atmospheric drivers (Box 9.1).

Antarctic climate variability is influenced by the Southern Annular Mode (SAM) and regionally by other modes, including ENSO, Pacific–South American pattern, Pacific Decadal Variability (PDV), Indian Ocean Dipole and Zonal Wave 3 (Annex IV). Climate change in Antarctica and the Southern Ocean is influenced by interactions between the ice sheet, ocean, sea ice and atmosphere (Sections 9.2.3.2, 9.3.2 and 9.4.2; [[#Meredith--2019|Meredith et al., 2019]] ). In addition to Chapter 9, Antarctica is discussed across the report: global climate links (Chapters 2 and 10), attribution (Chapter 3), global water cycle (Chapter 8), extremes (Chapter 11), and climatic impact-drivers (Chapter 12).

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===== Atlas.11.1.1.2 Findings From previous IPCC Assessments =====

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The AR5 ( [[#Vaughan--2013|Vaughan et al., 2013]] ) reported warming over Antarctica since the 1950s, mostly over the AP and WAN, attributed to the positive trend in the SAM. These trends in the Antarctic temperature were given ''low confidence'' due to substantial multi-annual to multi-decadal variability, as well as uncertainties in magnitude and spatial trend structure. The AR5 reported ''low confidence'' that anthropogenic forcing has contributed to the temperature change in Antarctica. The AR5 highlighted a large interannual variability in snow accumulation with no significant trend since 1979 around Antarctica, and ''high confidence'' in the overall mass loss from Antarctica, accelerated since the 1990s.

In this and the following paragraphs, findings are from SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) unless otherwise stated. Warming trends were reported over parts of WAN with record surface warmth over WAN during the 1990s compared to the past 200 years, and AP surface melting intensifying since the mid-20th century. No significant temperature trends were reported over EAN and there was ''low confidence'' in both WAN and EAN trend estimates due to sparse in situ records and large interannual to inter-decadal variability. In the AP, concomitant increase in temperature and foehn winds due to positive SAM caused increased surface melting over the Larsen ice shelves ( ''medium confidence'' ). Strong warming between the mid-1950s and the late 1990s led to the collapse of the Larsen B ice shelf in 2002, which had been intact for 11,000 years ( ''medium confidence'' ).

Snowfall increased over the Antarctic Ice Sheet over AP and WAN, offsetting some of the 20th-century sea level rise ( ''medium confidence'' ). Longer records suggest either a decrease in snowfall over the Antarctic Ice Sheet over the last 1000 years or a statistically negligible change over the last 800 years ( ''low confidence'' ).

Recent warming in the AP and consequent ice-shelf collapse are ''likely'' linked to anthropogenic ozone and greenhouse gas forcing via the SAM and anthropogenically driven Atlantic sea surface. Also, there is ''high confidence'' in the influence of tropical sea surface temperature on the Antarctic temperature and Southern Hemisphere mid-latitude circulation, as well as the SAM. There is ''medium agreement'' but ''limited evidence'' of an anthropogenic forcing effect on Antarctic ice-sheet mass balance ( ''low confidence'' ) and partitioning between natural and human drivers of atmospheric and ocean circulation changes remains very uncertain.

In AR5, [[#Church--2013|Church et al. (2013)]] gave ''medium confidence'' in model projections of a future Antarctic SMB increase, implying a negative contribution to global mean sea level rise, consistent with a projection of significant Antarctic warming. [[#Church--2013|Church et al. (2013)]] also gave ''high confidence'' to the relationship between future temperature and precipitation increases in Antarctica on physical grounds and from ice-core evidence. In [[#Meredith--2019|Meredith et al. (2019)]] , the total mass balance projections derived from ice-sheet models were reported without separating the SMB, though projections were reported of increased precipitation and continued strengthening of the westerly winds in the Southern Ocean.

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==== Atlas.11.1.2 Assessment and Synthesis of Observations, Trends and Attribution ====

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Figure Atlas.30 (Antarctic map inset) shows near-surface air temperature trends for 1957–2016 and 1979–2016 at the stations where observations are available for at least 50 years and the detected trends have statistical significance of at least 90% according to the most recent (after SROCC) studies ( [[#Jones--2019|Jones et al., 2019]] ; [[#Turner--2020|Turner et al., 2020]] ). It is ''very likely'' that the western and northern AP has been warming significantly since the 1950s (0.49°C ± 0.28°C per decade during 1957–2016 and 0.46°C ± 0.15°C during 1951–2018 at Faraday-Vernadsky station; 0.29°C ± 0.16°C per decade during 1957–2016 at Esperanza station), with no significant trends reported in the eastern AP during the same period ( [[#Gonzalez--2018|Gonzalez and Fortuny, 2018]] ; [[#Jones--2019|Jones et al., 2019]] ; [[#Turner--2020|Turner et al., 2020]] ). Short-term cooling trends, strongest during austral summer, have been reported at AP stations during 1999–2016, but the absence of warming and cooling at some stations during 1999–2016 is consistent with natural variability, and there is no evidence of a shift in the overall warming trend observed since the 1950s ( [[#Turner--2016|Turner et al., 2016]] , 2020; [[#Gonzalez--2018|Gonzalez and Fortuny, 2018]] ; [[#Jones--2019|Jones et al., 2019]] ; [[#Bozkurt--2020|Bozkurt et al., 2020]] ).

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[[File:a9ef0d8eab44f07a928142d2fca84de7 IPCC_AR6_WGI_Atlas_Figure_30.png]]

'''Figure Atlas.30''' '''|''' '''(Upper panels) Time series of annual surface mass balance (SMB) rates (in Gt a''' –1 ''') for the Greenland Ice Sheet and its regions (shown in the inset map) for the periods 1972–2018 ( [[#Mouginot--2019|Mouginot et al., 2019]] ) and 1980–2012 ( [[#Fettweis--2020|Fettweis et al., 2020]] ) using 13 different models.''' '''(Lower panels)''' Time series of annual SMB rates (in Gt a <sup>–1</sup> ) for the grounded Antarctic Ice Sheet (excluding ice shelves) and its regions (shown in the inset map) for the periods 1979–2019 ( [[#Rignot--2019|Rignot et al., 2019]] ) and 1980–2016 ( [[#Mottram--2021|Mottram et al., 2021]] ) using five Polar-CORDEX regional climate models. The Antarctic inset map also shows the location of the stations discussed in [[#Atlas.11.1.2|Atlas.11.1.2]] where observations are available for at least 50 years. Colours indicate near-surface air temperature trends for 1957–2016 (circles) and 1979–2016 (diamonds) statistically significant at 90% (Jones et al. 2019; Turner et al. 2020). Stations with an asterisk (*) are where significance estimates disagree between the two publications. Further details on data sources and processing are available in the chapter data table (Table Atlas.SM.15).

Significant warming at the Byrd station (0.29°C ± 0.19°C per decade during 1957–2016) confirms and extends earlier trend estimates (0.42°C ± 0.24°C per decade during 1958–2010) and is representative of the entire WAN warming (0.22°C ± 0.12°C per decade from 1958 to 2012 averaged over WAN excluding AP, ''medium confidence'' due to lack of observations) ( [[#Bromwich--2013|Bromwich et al., 2013]] , 2014; [[#Jones--2019|Jones et al., 2019]] ). WAN and AP show statistically significant warming in the HadCRUTv5 observational dataset (Figure 2.11b). There is ''high confidence'' in the long-term warming trend at the AP and WAN, and also at the century scale based on reconstructions ( [[#Zagorodnov--2012|Zagorodnov et al., 2012]] ; [[#Stenni--2017|Stenni et al., 2017]] ; [[#Lyu--2020|Lyu et al., 2020]] ), confirming the trends estimated by earlier studies assessed in the SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ). The century-scale warming trend in the AP is ''very likely'' an emerging signal compared to natural variability, while the WAN warming trend falls in the high end of century-scale trends over the last 2000 years ( ''medium confidence'' ) ( [[#Stenni--2017|Stenni et al., 2017]] ).

In EAN, during 1957–2016, three stations showed significant warming (Scott 0.22°C ± 0.15°C, Novolazarevskaya 0.13°C ± 0.09°C, and Vostok 0.15°C ± 0.13°C per decade), while other stations with long-term observations indicated no statistically significant trends (Figure Atlas.3 0). During 1979–2016, three coastal stations showed cooling, while at the South Pole a warming trend was detected, increasing to 0.61°C ± 0.34°C per decade during 1989–2018 (Figure Atlas.3 0; [[#Jones--2019|Jones et al., 2019]] ; [[#Clem--2020|Clem et al., 2020]] ; [[#Turner--2020|Turner et al., 2020]] ). The century-scale warming in Queen Maud Land coast based on ice-core reconstructions is within the range of centennial internal variability ( [[#Stenni--2017|Stenni et al., 2017]] ).

While a trend towards a positive phase of the SAM since the 1970s ''likely'' explains a significant part of the warming at the northern AP, it had a cooling effect on continental WAN and EAN (particularly strong in DJF; Table Atlas.1). Warming in western AP and over WAN during 1957–2016 (Figure Atlas.3 0) and through to 2020 (Figure 2.11) is ''likely'' due to significant contribution of other factors, such as tropical Pacific forcing through PDV, ENSO, Amundsen Sea Low position/strength and also anthropogenic climate change ( [[#Jones--2019|Jones et al., 2019]] ; [[#Scott--2019|Scott et al., 2019]] ; [[#Wille--2019|Wille et al., 2019]] ; [[#Donat-Magnin--2020|Donat-Magnin et al., 2020]] ; [[#Turner--2020|Turner et al., 2020]] ). Since SROCC, new studies confirmed the influence of foehn wind and cloud radiative forcing on Larsen C surface melt ( [[#Elvidge--2020|Elvidge et al., 2020]] ; [[#Gilbert--2020|Gilbert et al., 2020]] ; [[#Turton--2020|Turton et al., 2020]] ). In WAN, summer surface-melt occurrence over ice shelves may have increased since the late 2000s ( [[#Scott--2019|Scott et al., 2019]] ) ''.'' It is ''likely'' that increased meltwater ponding and resulting hydrofracturing have been important mechanisms of the rapid disintegration of the Larsen B ice shelf ( [[#Banwell--2013|Banwell et al., 2013]] ; [[#MacAyeal--2013|MacAyeal and Sergienko, 2013]] ; [[#Robel--2019|Robel and Banwell, 2019]] ). Ice-shelf disintegration and relevant processes are discussed in Sections 9.4.2.1 and 9.4.2.3.

Direct observations of snowfall in Antarctica using traditional gauges are highly uncertain and records from precipitation radars ( [[#Gorodetskaya--2015|Gorodetskaya et al., 2015]] ; [[#Grazioli--2017|Grazioli et al., 2017]] ; [[#Scarchilli--2020|Scarchilli et al., 2020]] ) are not long enough to assess trends. Estimates of precipitation and SMB are largely model-based due to the paucity of in situ observations in Antarctica ( [[#Lenaerts--2019|Lenaerts et al., 2019]] ; [[#Hanna--2020|Hanna et al., 2020]] ). Antarctic SMB is dominated by precipitation and removal by sublimation with very small amounts of melt mostly important only on the ice shelves. Climate models and satellite records (IMBIE team et al., 2018; [[#Rignot--2019|Rignot et al., 2019]] ; [[#Mottram--2021|Mottram et al., 2021]] ) suggest that strong interannual variability of Antarctic-wide SMB over the satellite period currently masks any existing trend (Figure Atlas.3 0) in spite of a possible ozone depletion-related precipitation increase over the 1991–2005 period ( [[#Lenaerts--2018|Lenaerts et al., 2018]] ). No significant Antarctic-wide SMB trend is inferred since 1979 (IMBIE team et al., 2018; [[#Medley--2019|Medley and Thomas, 2019]] ). While ice-core reconstructions show a significant increase in the western AP SMB since the 1950s ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Medley--2019|Medley and Thomas, 2019]] ; [[#Wang--2019|Wang et al., 2019]] ), this trend is not reproduced by regional climate models or the reanalyses used to drive them (Figure Atlas.3 0; [[#van%20Wessem--2016|van Wessem et al., 2016]] ; [[#Wang--2019|Wang et al., 2019]] ).

According to the ice-core reconstructions, SMB over WAN (including AP) has ''likely'' increased during the 20th century with trends of 5.4 ± 2.9 Gt yr <sup>–1</sup> per decade (1900–2010; [[#Wang--2019|Wang et al., 2019]] ) mitigating global mean sea level rise by, respectively, 0.28 ± 0.17 mm per decade (WAN excluding AP, during 1901–2000) and 0.62 ± 0.17 mm per decade (AP, during 1979–2000; [[#Medley--2019|Medley and Thomas, 2019]] ). Significant spatial heterogeneity in SMB trends has been observed over AP and WAN:

* Western AP has ''likely'' experienced a significant increase in SMB beginning around 1930 and accelerating during 1970–2010, which is outside of the natural variability range of the past 300 years ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Medley--2019|Medley and Thomas, 2019]] ; [[#Wang--2019|Wang et al., 2019]] );
* eastern AP has no significant SMB trends during the same period ( ''low confidence'' , observations limited to one ice core and large interannual variability) ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Engel--2018|Engel et al., 2018]] );
* overall WAN SMB (excluding AP) was stable during 1980–2009 but exhibited high regional variability ( [[#Medley--2013|Medley et al., 2013]] ): significant increases (5–15 mm per decade during 1957–2000) to the east of the West Antarctic Ice Sheet divide and a significant decrease (–1 to –5 mm per decade during 1901–1956, and –5 to –15 mm per decade during 1957–2000) to the west ( [[#Medley--2019|Medley and Thomas, 2019]] ; [[#Wang--2019|Wang et al., 2019]] ).

The SMB of EAN increased during the 20th century which mitigated global mean sea level rise by 0.77 ± 0.40 mm per decade during 1901–2000 ( ''medium confidence'' ) ( [[#Medley--2019|Medley and Thomas, 2019]] ). EAN SMB has been increasing at a much lower rate since 1979 as shown by observations, while regional climate models show strong interannual variability masking any trend ( ''low confidence'' due to limited observations) (Figure Atlas.3 0; [[#Medley--2019|Medley and Thomas, 2019]] ; [[#Rignot--2019|Rignot et al., 2019]] ). EAN SMB changes during the 20th century and recent decades showed large spatial heterogeneity:

* With significant increases ''likely'' in Queen Maud Land (QML): 5.2 ± 3.7% per decade during 1920–2011 measured in ice cores near the Kohnen station ( [[#Medley--2018|Medley et al., 2018]] ), an increase on the plateau ( [[#Altnau--2015|Altnau et al., 2015]] ), and stable conditions during 1993–2010 along the annual stake line from Syowa (coast) to Dome F (plateau) (Y. [[#Wang--2015|]] [[#Wang--2015|Wang et al., 2015]] ); increases during 1911–2010 ( [[#Thomas--2017|Thomas et al., 2017]] ) with anomalously high SMB observed in 2009 and 2011 ( [[#Boening--2012|Boening et al., 2012]] ; [[#Lenaerts--2013|Lenaerts et al., 2013]] ; [[#Gorodetskaya--2014|Gorodetskaya et al., 2014]] );
* increases in Wilkes Land and Queen Mary Land during 1957–2000 ( ''low confidence'' due to limited observations and strong spatial variability) ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Medley--2019|Medley and Thomas, 2019]] );
* a ''likely'' stable SMB in the interior of the east Antarctic plateau during the 1901–2000 period and the last decades ( [[#Thomas--2017|Thomas et al., 2017]] ; [[#Medley--2019|Medley and Thomas, 2019]] );
* stable in Adelie Land (annual stake line during 1971–2008) ( ''low confidence'' due to ''limited evidence'' ) ( [[#Agosta--2012|Agosta et al., 2012]] ).

Regional trends during recent 50 year (1961–2010) and 100 year (1911–2010) periods are within the centennial variability of the past 1000 years, except for coastal QML (unusual 100-year increase in accumulation) and for coastal Victoria Land (unusual 100-year decrease in accumulation) ( [[#Thomas--2017|Thomas et al., 2017]] ). Nevertheless, the current EAN SMB is not unusual compared to the past 800 years ( [[#Frezzotti--2013|Frezzotti et al., 2013]] ).

The geographic pattern of accumulation changes since the 1950s bears a strong imprint of a trend towards a more positive phase of the SAM (e.g., [[#Medley--2019|Medley and Thomas, 2019]] ), which could be linked to ozone depletion ( [[#Lenaerts--2018|Lenaerts et al., 2018]] ) or large-scale atmospheric warming ( [[#Frieler--2015|Frieler et al., 2015]] ; [[#Medley--2019|Medley and Thomas, 2019]] ). More evidence has emerged showing the importance of the Pacific–South American pattern, ENSO and Pacific Ocean convection, and large-scale blocking causing warm-air intrusions and both extreme precipitation and melt events, responsible for large interannual SMB variability ( ''high confidence'' ) ( [[#Gorodetskaya--2014|Gorodetskaya et al., 2014]] ; [[#Bodart--2019|Bodart and Bingham, 2019]] ; [[#Scott--2019|Scott et al., 2019]] ; [[#Turner--2019|Turner et al., 2019]] ; [[#Wille--2019|Wille et al., 2019]] ; [[#Adusumilli--2021|Adusumilli et al., 2021]] ). This strengthens evidence for an important connection between Antarctic climate and tropical sea surface temperature stated by SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ). [[IPCC:Wg1:Chapter:Chapter-3#3.4.3|Section 3.4.3]] and SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) provide a discussion of attribution of Antarctic ice-sheet changes.

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==== Atlas.11.1.3 Assessment of Model Performance ====

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This section provides evaluation of atmospheric global and regional climate models, including reanalyses. Evaluation of the ice-sheet models and relevant processes, including selection of the atmospheric models used to drive ice-sheet models, is given in [[IPCC:Wg1:Chapter:Chapter-9#9.4.2.2|Section 9.4.2.2]] .

One of the major systematic biases in CMIP5 and earlier GCMs was an equatorward bias in the latitude of the Southern Hemisphere mid‐latitude westerly jet, which is significantly reduced in the CMIP6 ensemble ( [[#Bracegirdle--2020a|Bracegirdle et al., 2020a]] ). GCM Southern Ocean sea ice biases are also of importance as they influence 21st-century temperature projections in Antarctica and simulations of present-day temperatures are highly sensitive to these biases ( [[#Agosta--2015|Agosta et al., 2015]] ; [[#Bracegirdle--2015|Bracegirdle et al., 2015]] ). A positive bias in near-surface temperature over the Antarctic plateau is seen in CMIP5 models ( [[#Lenaerts--2016|Lenaerts et al., 2016]] ).

CMIP6 GCMs showed an improved representation of the Antarctic near-surface temperature compared to CMIP5 but little improvement (maintaining positive bias) in Antarctic precipitation estimates ( [[#Palerme--2017|Palerme et al., 2017]] ; [[#Roussel--2020|Roussel et al., 2020]] ). An analysis of the 1850–2000 SMB mean, trends, and interannual and spatial variability suggests slightly worse agreement with ice-core-based reanalyses in CMIP6 than CMIP5 ( [[#Gorte--2020|Gorte et al., 2020]] ). Comparison of CMIP5 models with CloudSat satellite products and an ice-core-based SMB reconstruction showed almost all the models overestimate current Antarctic precipitation, some by more than 100% ( [[#Palerme--2017|Palerme et al., 2017]] ; [[#Gorte--2020|Gorte et al., 2020]] ). GCM simulations of surface snow-melt processes are either of variable quality, with extremely simple representatons, or non-existent ( [[#Agosta--2015|Agosta et al., 2015]] ; [[#Trusel--2015|Trusel et al., 2015]] ). Though most meltwater refreezes in the snowpack in current climate simulations, this may be an issue in the future climate simulations under global warming as runoff is projected to increase ( [[#Kittel--2021|Kittel et al., 2021]] ). Since CMIP5, representation of snow ( [[#Lenaerts--2016|Lenaerts et al., 2016]] ) and stable surface boundary layers (Vignon et al., 2018) has improved in some atmospheric GCMs. In one example, the CMIP6 model CESM2 simulation of cloud and precipitation showed substantial improvements ( [[#Schneider--2020|Schneider et al., 2020]] ), though surface melting is still considerably overestimated compared to RCMs and satellite products ( [[#Trusel--2015|Trusel et al., 2015]] ; [[#Lenaerts--2016|Lenaerts et al., 2016]] ).

Assimilation of observations in reanalysis products yields realistic temperature patterns and seasonal variations, with the recent ERA5 reanalysis showing improved performance compared to others for mean and extreme temperature, wind and humidity, though a warm bias in near-surface air temperatures remains ( [[#Retamales-Muñoz--2019|Retamales-Muñoz et al., 2019]] ; [[#Tetzner--2019|Tetzner et al., 2019]] ; [[#Dong--2020|Dong et al., 2020]] ; [[#Gorodetskaya--2020|Gorodetskaya et al., 2020]] ). The ability of the reanalyses to simulate precipitation and SMB is more variable; they generally overestimate the latter ( [[#Gossart--2019|Gossart et al., 2019]] ; [[#Roussel--2020|Roussel et al., 2020]] ), but are well suited to provide atmospheric and sea surface boundary conditions to drive RCMs.

Recent higher-resolution simulations covering the entire Antarctic Ice Sheet with a grid spacing of 12 to 50 km include five Polar-CORDEX RCMs – RACMO2 ( [[#van%20Wessem--2018|van Wessem et al., 2018]] ), MAR ( [[#Agosta--2019|Agosta et al., 2019]] ; [[#Kittel--2021|Kittel et al., 2021]] ), COSMO-CLM2 ( [[#Souverijns--2019|Souverijns et al., 2019]] ), HIRHAM5 ( [[#Lucas-Picher--2012|Lucas-Picher et al., 2012]] ) and MetUM ( [[#Walters--2017|Walters et al., 2017]] ; [[#Mottram--2021|Mottram et al., 2021]] ) – and one stretched-grid GCM – ARPEGE ( [[#Beaumet--2019|Beaumet et al., 2019]] ). RCM simulations forced by ERA-Interim agree well with automatic weather station temperatures, with high correlation (R <sup>2</sup> &gt; 0.9) and low bias (&lt;1.5°C) except for high-resolution HIRHAM5 (–2.1°C) and MetUM (–3.4°C), which are not internally nudged models ( [[#Mottram--2021|Mottram et al., 2021]] ). RCMs generally underestimate the observed SMB but with biases lower than 20%, except for COSMO-CLM2 at lower elevations (&lt;1200 m) and HIRHAM5 and MetUM at higher elevations (&gt;2200 m) ( [[#Mottram--2021|Mottram et al., 2021]] ). These RCM simulations lead to estimates of the grounded Antarctic Ice Sheet SMB ranging from 2133 Gt yr <sup>–1</sup> to 2328 Gt yr <sup>–1</sup> when considering the four simulations compatible with the IMBIE2 Antarctic total mass budget (IMBIE team et al., 2018; [[#Mottram--2021|Mottram et al., 2021]] ). However, the simulated spatial pattern of SMB differs widely between models, suggesting the importance of missing or under-represented processes in the models, such as drifting-snow transport and sublimation ( [[#Agosta--2019|Agosta et al., 2019]] ), cloud-precipitation microphysical processes ( [[#van%20Wessem--2018|van Wessem et al., 2018]] ) and snowpack modelling ( [[#Mottram--2021|Mottram et al., 2021]] ). Comparisons of integrated SMB estimates between models are also complicated by different resolutions and continental ice masks, with models showing large differences in the absolute SMB ( [[#Mottram--2021|Mottram et al., 2021]] ) but better agreement for SMB annual rates (Figure Atlas.3 0).

Finer-resolution RCM studies demonstrate improved representation of precipitation and temperature gradients ( [[#van%20Wessem--2018|van Wessem et al., 2018]] ; [[#Bozkurt--2020|Bozkurt et al., 2020]] ; [[#Donat-Magnin--2020|Donat-Magnin et al., 2020]] ; [[#Elvidge--2020|Elvidge et al., 2020]] ), and strength of katabatic winds ( [[#Bintanja--2014|Bintanja et al., 2014]] ; [[#Souverijns--2019|Souverijns et al., 2019]] ) in coastal and mountainous regions. Adequate representation of some processes is still lacking, including drifting snow, sublimation of falling snow or the spectral dependency of snow albedo ( [[#Lenaerts--2019|Lenaerts et al., 2019]] ). Non-hydrostatic regional models, for example Polar-WRF, MetUM or HARMONIE-AROME at spatial resolutions up to 2 km further improve regional RCM simulations, but are still often unable to resolve relevant feedbacks and foehn processes ( [[#Grosvenor--2014|Grosvenor et al., 2014]] ; [[#Elvidge--2015|Elvidge et al., 2015]] , 2020; [[#Elvidge--2016|Elvidge and Renfrew, 2016]] ; [[#King--2017|King et al., 2017]] ; [[#Turton--2017|Turton et al., 2017]] ; [[#Bozkurt--2018b|Bozkurt et al., 2018b]] ; [[#Hines--2019|Hines et al., 2019]] ; Vignon et al., 2019; [[#Gilbert--2020|Gilbert et al., 2020]] ).

Existing uncertainties in the Antarctic climate representation by both GCMs and RCMs cause significant spread in the future Antarctic climate and SMB projections ( [[#Gorte--2020|Gorte et al., 2020]] ; [[#Kittel--2021|Kittel et al., 2021]] ). Run-time bias adjustment in atmospheric GCMs (Cross-Chapter Box 10.2; [[#Krinner--2019|Krinner et al., 2019]] , 2020) has been proposed to provide low-bias present and consistently corrected future RCM forcing (reducing the need for coupled model selection), which could be used directly for Antarctic climate projections ( [[#Krinner--2019|Krinner et al., 2019]] ).

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==== Atlas.11.1.4 Assessment and Synthesis of Projections ====

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This section provides an assessment of projections in temperature, precipitation and SMB. See [[IPCC:Wg1:Chapter:Chapter-9#9.4.2|Section 9.4.2]] for projected changes in the ice-sheet total mass balance and relevant processes, and see [[IPCC:Wg1:Chapter:Chapter-4#4.3.1|Section 4.3.1]] (Table 4.2) and [[IPCC:Wg1:Chapter:Chapter-4#4.5|Section 4.5.1]] for Antarctic temperature projections relative to other regions.

The Antarctic region is ''very likely'' to experience a significant increase in annual mean temperature and precipitation by the end of this century under all emissions scenarios used in CMIP5 and CMIP6 (Figure Atlas.29; [[#Bracegirdle--2015|Bracegirdle et al., 2015]] , 2020b; [[#Frieler--2015|Frieler et al., 2015]] ; [[#Lenaerts--2016|Lenaerts et al., 2016]] ; [[#Previdi--2016|Previdi and Polvani, 2016]] ; [[#Palerme--2017|Palerme et al., 2017]] ). Ensemble means (and 10th–90th percentile ranges) of end-of-century (2081–2100) projected Antarctic surface air temperature change from 35 CMIP6 models and relative to 1995–2014 are 1.2°C (0.5°C–2.0°C) for the SSP1-2.6 emissions scenarios, 2.3°C (1.3°C–3.4°C) for SSP2-4.5, 3.5°C (2°C–5°C) for SSP3-7.0, and 4.4°C (2.8°C–6.4°C) for SSP5-8.5 (Interactive Atlas). Both temperature and precipitation projections are characterized by a relatively large multi-model range (Figure Atlas.29 and the Interactive Atlas). A strong regional variability is present with the projected changes over coastal Antarctica not scaling linearly with global forcing. While continental mean temperatures are linearly related to global mean temperatures in CMIP6 models, the relative increase in coastal temperatures are higher for low-emissions scenarios due to stronger relative Southern Ocean warming and relatively stronger effects of ozone recovery ( [[#Bracegirdle--2020b|Bracegirdle et al., 2020b]] ). A higher multi-model average increase in temperature is projected by CMIP6 models compared to CMIP5, with a 1.3°C higher mean Antarctic near-surface temperature at the end of the 21st century ( [[#Kittel--2021|Kittel et al., 2021]] ). While similar median temperature changes are projected for WAN and EAN, the former shows larger spread and higher projected temperature range in both CMIP5 and CMIP6 models and for all scenarios (Figure Atlas.29). CORDEX-Antarctica simulations show a mean and range in the future temperature changes similar to the subset of CMIP5 models used to drive them for the RCP8.5 scenario and 1.5°C, 2°C and 3°C GWLs (Figure Atlas.29).

There is ''high confidence'' that projected future surface air temperature increase over Antarctica will be accompanied by precipitation increase (Figure Atlas.29). CMIP6 models show a similar or larger but more constrained increase in precipitation (more models agreeing with larger precipitation increase) for the same GWLs compared to CMIP5. For example, over WAN during JJA for 3°C GWL, CMIP6 and CMIP5 models project a median 15% increase in precipitation with a 10th–90th percentile range of 7–25% in CMIP6 models and of 3–24% in CMIP5. Average precipitation changes relative to 1995–2014 over WAN and EAN are largely similar; they show projected increases for SSP2-4.5 (SSP5-8.5) of around 5% (5%) for 2021–2040, 7% (10%) for 2041–2060, and 12% (25%) for 2081–2100 with smaller increases projected for SSP1-2.6 emissions, reaching around 5% in 2081–2100. Regionally, the largest relative precipitation increase is projected (under all scenarios) for the eastern part of WAN, the western AP, large parts of the EAN plateau and over coastal EAN within 0°E–90°E longitudinal sector (Interactive Atlas). The largest increase in absolute precipitation amount is projected along the coastal regions, with the largest increase over coastal WAN and the western AP, and is projected to be largely driven by the increase in maximum five-day precipitation (Interactive Atlas), which is in line with the dominant contribution of extreme snowfall events to the total annual precipitation in the present Antarctic climate ( [[#Boening--2012|Boening et al., 2012]] ; [[#Gorodetskaya--2014|Gorodetskaya et al., 2014]] ; [[#Turner--2020|Turner et al., 2020]] ). Under all emissions scenarios, the coastal precipitation increase corresponds to the snowfall increase, except for the northern and central part of the western AP, where snowfall is projected to decrease and rainfall to increase (similarly to the tendency towards increased precipitation, decreased snowfall and increase in rainfall over the Southern Ocean; Interactive Atlas).

From 2000 to 2100, the grounded Antarctic SMB is projected to mitigate sea level rise for RCP4.5 (RCP8.5) by the following sea level equivalents (SLEs), 0.03 ± 0.02 m (0.08 ± 0.04 m SLE) from 30 CMIP5 models and for SSP2-4.5 (SSP5-8.5) by 0.03 ± 0.03 m SLE (0.07 ± 0.04 m SLE) from 24 CMIP6 models ( [[#Gorte--2020|Gorte et al., 2020]] ). Subsets or downscaling of CMIP AOGCMs lead to 21st-century cumulative projections in the range of 0.05 ± 0.03 m SLE for CMIP5 RCP8.5 and 0.08 ± 0.04 m SLE for CMIP6 SSP5-8.5 ( [[#Gorte--2020|Gorte et al., 2020]] ; [[#Nowicki--2020|Nowicki et al., 2020]] ; [[#Seroussi--2020|Seroussi et al., 2020]] ; [[#Kittel--2021|Kittel et al., 2021]] ). Use of model subsets reduces spread leading to either lower or higher climate sensitivity in the Antarctic depending on the selection method. For example, models selected by [[#Gorte--2020|Gorte et al. (2020)]] based on SMB ice-core reconstruction from [[#Medley--2019|Medley and Thomas (2019)]] tend to underestimate strongly winter sea ice area ( [[#Agosta--2015|Agosta et al., 2015]] ; [[#Roach--2020|Roach et al., 2020]] ) and show reduced 21st-century increase in Antarctic SMB compared to the full ensembles ( [[#Agosta--2015|Agosta et al., 2015]] ; [[#Bracegirdle--2015|Bracegirdle et al., 2015]] ). A different subset of models is used for ISMIP6 ( [[IPCC:Wg1:Chapter:Chapter-9#9.4.2.3|Section 9.4.2.3]] ) which gives a lower increase in Antarctic SMB than the full ensemble for CMIP5 but a larger increase for CMIP6.

Polar-CORDEX RCMs show higher variability in precipitation projections compared to CMIP5 models with a similar spatial pattern of the areas with precipitation increase over continental Antarctica but with higher local magnitude, and also showing a larger increase over the Weddell Sea ice shelves (Interactive Atlas). CMIP5 and CMIP6 models, bias adjusted based on regional climate model simulations, showed that the projected warming is expected to result in increased surface melting over the Antarctic ice shelves, with meltwater runoff under RCP8.5 and SSP5-8.5 becoming larger than precipitation over ice shelves over the period 2045–2050, surpassing intensities that were linked with the collapse of Larsen A and B ice shelves ( [[#Trusel--2015|Trusel et al., 2015]] ; [[#Kittel--2021|Kittel et al., 2021]] ). Given the existing uncertainty in the present precipitation and SMB simulations and the significant range in the projected precipitation increase under various emissions scenarios in CMIP5, CMIP6 and CORDEX models, there is ''medium confidence'' that the future Antarctic SMB will have a negative contribution to sea level during the 21st century under all emissions scenarios (see [[IPCC:Wg1:Chapter:Chapter-9#9.4.2.3|Section 9.4.2.3]] for assessment of the drivers of future Antarctic ice-sheet change and [[IPCC:Wg1:Chapter:Chapter-9#9.4.2.6|Section 9.4.2.6]] for longer time scales).

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<span id="atlas.11.1.5-summary"></span>
==== Atlas.11.1.5 Summary ====

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Observations show a ''very likely'' widespread, strong warming trend starting in the 1950s in the Antarctic Peninsula. Significant warming trends are observed in other West Antarctic regions and at selected stations in East Antarctica ( ''medium confidence'' ). Antarctic precipitation and SMB showed a significant positive trend over the 20th century according to the ice cores, while large interannual variability masks any existing trend over the satellite period since the end of the 1970s ( ''medium confidence'' ).

An assessment of model performance for the present day shows that high-resolution regional climate models with polar-optimized physics are important for estimating SMB and generating climate information, and show improved realizations compared to reanalyses and GCMs when evaluated against observations. At the same time, CMIP6 GCMs showed an improved representation of the Antarctic near-surface temperature compared to CMIP5, though still struggle with the representation of precipitation. There is therefore ''medium confidence'' in the capacity of climate models to simulate Antarctic climate and SMB changes.

Under all assessed emissions scenarios, both West and East Antarctica are ''very'' ''likely'' to have higher annual mean surface air temperatures and more precipitation, which will have a dominant influence on determining future changes in the SMB ( ''high confidence'' ). However, due to the challenges of model evaluation over the region and the possibility of increased meltwater runoff described above, there is only ''medium confidence'' that the future contribution of the Antarctic SMB to sea level this century will be negative under all greenhouse gas emissions scenarios.

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=== Atlas.11.2 Arctic ===

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==== Atlas.11.2.1 Key Features of the Regional Climate and Findings From Previous IPCC Assessments ====

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===== Atlas.11.2.1.1 Key Features of the Regional Climate =====

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The Arctic region comprises the Arctic Ocean (ARO), Russian Arctic (RAR), Greenland and Iceland (GIC), and other surrounding land areas in Europe (NEU) and North America (NEN, NWN) (Figure Atlas.29). The region is one of the coldest and driest regions on Earth and plays a key role influencing global and regional climates and the hydrological cycle. A number of physical processes contribute to amplified Arctic temperature variations as compared to the global temperature, in particular thermodynamic changes that include the increase in surface absorption of solar radiation due to surface albedo feedbacks related with sea ice, ice, and snow cover retreat as well as poleward energy transports, water-vapour-radiation and cloud-radiation feedbacks ( [[#Screen--2010|Screen and Simmonds, 2010]] ; [[#Serreze--2011|Serreze and Barry, 2011]] ; [[#Pithan--2014|Pithan and Mauritsen, 2014]] ; [[#Bintanja--2016|Bintanja and Krikken, 2016]] ; [[#Graversen--2016|Graversen and Burtu, 2016]] ; [[#Franzke--2017|Franzke et al., 2017]] ; [[#Stuecker--2018|Stuecker et al., 2018]] ). Precipitation in the Arctic is dominated by snowfall, with rainfall present mostly during the summer period. Arctic climate is influenced by the North Atlantic Oscillation (NAO), the leading mode of atmospheric variability in the North Atlantic basin with a northward extension into the Arctic affecting temperature, precipitation and sea ice over the region, with ENSO and Atlantic Multi-decadal Variability (AMV) also affecting parts of the region (Annex IV). Further, the Greenland Ice Sheet contribution to sea level results from the imbalance between mass gain by net snow accumulation and mass loss by meltwater runoff and ice discharge into the ocean ( [[#IMBIE%20team--2020|IMBIE team, 2020]] ), highlighting that the ice sheet is a major contributor to sea level changes.

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<span id="atlas.11.2.1.2-findings-from-previous-ipcc-assessments"></span>
===== Atlas.11.2.1.2 Findings From Previous IPCC Assessments =====

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The following summary from previous IPCC reports is derived from the SROCC ( [[#IPCC--2019a|IPCC, 2019a]] ) unless otherwise stated. Arctic surface air temperatures have increased from the mid-1950s, with feedbacks from loss of sea ice and snow cover contributing to the amplified warming ( ''high confidence'' ) ( [[#IPCC--2018c|IPCC, 2018c]] ), and have ''likely'' increased by more than double the global average over the last two decades ( ''high confidence'' ). Arctic snow cover in June has declined from 1967 to 2018 ( ''high confidence'' ). Arctic glaciers are losing mass ( ''very high confidence'' ) and this along with changes in high-mountain snowmelt have caused changes in hydrology, including river runoff, that are projected to continue in the near term ( ''high confidence'' ). The rate of ice loss from the Greenland Ice Sheet has increased; during 2006–2015 the loss was 278 ± 11 Gt yr <sup>–1</sup> with the rate for 2012–2016 higher than for 2002–2011 and several times higher than during 1992–2001 ( ''high confidence'' ).

The Arctic sea ice area is declining in all months of the year ( ''very high confidence'' ) with the September sea ice minimum ''very likely'' having reduced by 12.8 ± 2.3% per decade during the satellite era (1979–2018) to levels unprecedented for at least 1000 years ( ''medium confidence'' ).

The high latitudes are ''likely'' to experience an increase in annual mean precipitation under RCP8.5 ( [[#IPCC--2013c|IPCC, 2013c]] ). Further, changes in precipitation will not be uniform. Autumn and spring snow cover duration are projected to decrease by a further 5–10% from current conditions in the near term (2031–2050). No further losses are projected under RCP2.6 whereas a further 15–25% reduction in snow cover duration is projected by the end of century under RCP8.5 ( ''high confidence'' ).

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==== Atlas.11.2.2 Assessment and Synthesis of Observations, Trends and Attribution ====

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The Arctic has warmedat more than twice the global rate over the past 50 years with the greatest warming during the cold season ( ''high confidence'' ) ( [[#Davy--2018|Davy et al., 2018]] ; [[#Box--2019|Box et al., 2019]] ; [[#Przybylak--2020|Przybylak and Wyszyński, 2020]] ; [[#Xiao--2020|Xiao et al., 2020]] ). This is based on various Arctic amplification processes, in particular the combined effect of several related feedback processes, including between various radiation components and (a) the albedo of sea ice and snow, (b) water vapour, and (c) clouds, as well as poleward energy transports. The annual average Arctic surface air temperature increased by 2.7°C from 1971 to 2017, with a 3.1°C increase in the cold season (October–May) and a 1.8°C increase in the warm season (June–September) ( [[#AMAP--2019|AMAP, 2019]] ). Satellite-based data estimate the rate of annual warming for 1981–2012 over sea ice covered regions to be 0.47°C per decade, whereas the trend was significantly higher at 0.77°C per decade over Greenland and amplified in the northern Barents and Kara seas ( [[#Comiso--2014|Comiso and Hall, 2014]] ). The largest Arctic warming in 2003–2017 was reported over the Barents and Kara seas with trends larger than 2.5°C per decade ( [[#Susskind--2019|Susskind et al., 2019]] ), and Arctic temperatures from 2014 to 2018 have exceeded all previous records since 1900 ( [[#Blunden--2019|Blunden and Arndt, 2019]] ).

Over the ARO, long-term temperature records are available from Spitsbergen (Svalbard Airport). For the period 1898–2018, the annual mean warming was 0.32°C per decade, about 3.5 times the global mean temperature for the same period and since 1991, it was 1.7°C per decade or about seven times the global average for the same period ( [[#Nordli--2020|Nordli et al., 2020]] ). There is a positive trend in the annual temperature for all stations across Svalbard ( [[#Gjelten--2016|Gjelten et al., 2016]] ; [[#Hanssen-Bauer--2019|Hanssen-Bauer et al., 2019]] ; [[#Dahlke--2020|Dahlke et al., 2020]] ) of 0.64°C–1.01°C per decade for 1971–2017 ( [[#Hanssen-Bauer--2019|Hanssen-Bauer et al., 2019]] ), co-varying with regional changes in sea ice conditions ( [[#Dahlke--2020|Dahlke et al., 2020]] ). The largest temperature trends ''very likely'' occur in winter, with Svalbard Airport warming at 0.43°C per decade during 1898–2018 and 3.19°C per decade during 1991–2018 ( [[#Nordli--2020|Nordli et al., 2020]] ), and [[#Isaksen--2016|Isaksen et al. (2016)]] reporting on substantial warming in western Spitsbergen, particularly in winter, while the summer warming is moderate.

A multi-dataset analysis for NEN shows a consistent warming ( [[#Rapaić--2015|Rapaić et al., 2015]] ), with the largest annual temperature trend greater than 0.3°C per decade during 1981–2010 over eastern NEN and also significant warming over northern Quebec and most of the Canadian Arctic north of the treeline. For the longer 1950–2010 period, a consistent warming is found over central and western NEN, but no trend or no consensus is found over the Labrador coast. The latter is related with cooling of the North Atlantic region during the 1970s. For western Greenland, however, summer temperatures increased (2.2°C in June, 1.1°C in July) from 1994 to 2015 ( [[#Saros--2019|Saros et al., 2019]] ). For neighbouring Arctic regions of NEU, WSE and ESB, datasets show a consistent warming of annual mean temperature since the mid-1970s and 1980 ( [[#Atlas.8|Atlas.8]] and [[#Atlas.5.2|Atlas.5.2]] ).

Along with the amplified warming, the Arctic has become moister ( [[#Rinke--2019|Rinke et al., 2019]] ; [[#Nygård--2020|Nygård et al., 2020]] ). AMAP reported Arctic precipitation increases of 1.5–2.0% per decade, with the strongest increase in the cold season (October–May) ( ''medium confidence'' ) ( [[#AMAP--2019|AMAP, 2019]] ). Also, for neighbouring Arctic regions for example NEU, EEU and North Asia, mean annual precipitation has increased since the early 20th century ( [[#Atlas.8|Atlas.8]] and [[#Atlas.5.2|Atlas.5.2]] ). Estimated trends for precipitation and snowfall fraction are mixed for the Arctic, with increases and decreases for different regions and seasons ( [[#Vihma--2016|Vihma et al., 2016]] ). However, annual precipitation trends derived from different reanalyses do not agree, differ in sign and have low significance ( [[#Lindsay--2014|Lindsay et al., 2014]] ; [[#Boisvert--2018|Boisvert et al., 2018]] ). Direct precipitation measurements are difficult and include uncertainties (among others measuring frozen precipitation), therefore precipitation estimates in the Arctic rely on climate models and reanalyses.

An average of five reanalyses for 2000–2010 suggests around 40% of Arctic Ocean precipitation falls as snow, though there is large uncertainty in this estimate ( [[#Boisvert--2018|Boisvert et al., 2018]] ). Rainfall frequency is estimated to have increased over the Arctic by 2.7–5.4% over 2000–2016 ( [[#Boisvert--2018|Boisvert et al., 2018]] ) with more frequent rainfall events reported for NEU and ARO (Svalbard; [[#Maturilli--2015|Maturilli et al., 2015]] ; [[#AMAP--2019|AMAP, 2019]] ), and winter rain totals and frequency have increased in Svalbard since 2000 ( ''medium confidence'' ) ( [[#Łupikasza--2019|Łupikasza et al., 2019]] ). Rain-free winters have rarely occurred since 1998 ( [[#Peeters--2019|Peeters et al., 2019]] ).

Observational records (1966–2010) for the RAR region show changing precipitation characteristics ( [[#Ye--2016|Ye et al., 2016]] ), with higher precipitation intensity but lower frequency and little change in annual precipitation total. Precipitation intensity is reported to have increased in all seasons, strongest in winter and spring, weakest in summer, and at a rate of about 1–3% per degree Celsius of air temperature increase.

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==== Atlas.11.2.3 Assessment of Model Performance ====

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Evaluating simulated temperature and precipitation is problematic in the Arctic due to sparse weather station observations. The lack of reliable observed precipitation datasets for the Arctic thus makes it ''very unlikely'' to be able to evaluate objectively the skill of models to reproduce precipitation patterns ( [[#Takhsha--2018|Takhsha et al., 2018]] ).

The CMIP5 models reproduce the observed Arctic warming over the past century ( ''medium confidence'' ) ( [[#Chylek--2016|Chylek et al., 2016]] ; [[#Hao--2018|Hao et al., 2018]] ; [[#Huang--2019|Huang et al., 2019]] ). The simulated mean Arctic warming for 1900–2014 averaged over 40 CMIP5 models is 2.7°C compared to the observed values of 2.2°C (NASA GISS data smoothed using a 1200-km radius) or 1.7°C (using a 250-km smoothing radius) ( [[#Chylek--2016|Chylek et al., 2016]] ). However, there are large inter-model differences in the simulated warming which ranges from 1.2°C to 5.0°C. Although the CMIP5 models reproduce the spatially averaged observed warming over the past 50 to 100 years, the pattern is different from that of observations and reanalysis ( [[#Xie--2016|Xie et al., 2016]] ; [[#Franzke--2017|Franzke et al., 2017]] ; [[#Hao--2018|Hao et al., 2018]] ). Zonal mean temperature trends in the CMIP5 models overestimate the warming in the cold season over high latitudes in the Northern Hemisphere ( [[#Xie--2016|Xie et al., 2016]] ). Overall, the amplified Arctic warming in recent decades is overestimated by CMIP5 models ( [[#Huang--2019|Huang et al., 2019]] ). Possible reasons are modelled sea surface temperature biases and an overestimated temperature response to the Arctic sea ice decline. Furthermore, some models, which have a warm or weak bias in their Arctic temperature simulations, closely relate the Arctic warming to changes in the large-scale atmospheric circulation. In other models, which show large cold biases, the albedo feedback effect plays a more important role for the temperature trend magnitude. This implies that the dominant simulated Arctic warming mechanism and trend may be dependent on the bias of the model mean state ( [[#Franzke--2017|Franzke et al., 2017]] ). Compared to CMIP5 models, [[#Davy--2020|Davy and Outten (2020)]] found lower biases in CMIP6 models’ representation of sea ice extent and volume with improved extents linked to a better seasonal cycle in the Barents Sea.

Rapid temperature changes, such as the pronounced increase of 2°C yr <sup>–1</sup> during 2003–2012 over the Kara and Barents seas in March is well captured in Arctic CORDEX simulations ( [[#Kohnemann--2017|Kohnemann et al., 2017]] ). The models show adequate skill in capturing the general temperature patterns ( [[#Koenigk--2015|Koenigk et al., 2015]] ; [[#Matthes--2015|Matthes et al., 2015]] ; [[#Hamman--2016|Hamman et al., 2016]] ; [[#Cassano--2017|Cassano et al., 2017]] ; [[#Brunke--2018|Brunke et al., 2018]] ; [[#Diaconescu--2018|Diaconescu et al., 2018]] ; [[#Takhsha--2018|Takhsha et al., 2018]] ), but tend to show a cold temperature bias which is largest in winter and depends on the reference dataset. [[#Cassano--2017|Cassano et al. (2017)]] showed a large sensitivity of the simulated surface climate to changes in atmospheric model physics. In particular, large changes in radiative flux biases, driven by changes in simulated clouds, lead to large differences in temperature and precipitation biases.

The CMIP5 models perform well in simulating 20th-century snowfall for the Northern Hemisphere, although there is a positive bias in the multi-model ensemble relative to the observed data in many regions ( [[#Krasting--2013|Krasting et al., 2013]] ). Lack of sufficient spatial resolution in the model topography has a serious impact on the simulation of snowfall. The patterns of relative maxima and minima of snowfall, however, are captured reasonably well by the models.

Arctic CORDEX RCMs reproduce the dominant features of regional precipitation patterns and extremes (e.g., [[#Glisan--2014|Glisan and Gutowski, 2014]] ; [[#Hamman--2016|Hamman et al., 2016]] ). Due to their higher spatial resolution, RCMs simulates larger amounts of orographic precipitation compared to reanalyses. Overall, the simulated precipitation is within the reanalysis and global model ensemble spread, but the Arctic river basin precipitation is closer to observations ( [[#Brunke--2018|Brunke et al., 2018]] ). However, [[#Takhsha--2018|Takhsha et al. (2018)]] show that the RCMs’ precipitation bias highly depends on the observational reference dataset used.

The annual mean precipitation pattern of ensemble global atmospheric simulations with a high horizontal resolution agrees well with the observations, with precipitation maxima over the Greenland and Norwegian seas ( [[#Kusunoki--2015|Kusunoki et al., 2015]] ). However, the simulated Arctic average annual precipitation shows a positive bias with excessive precipitation over Alaska and the western Arctic ( [[#Kattsov--2017|Kattsov et al., 2017]] ).

Regarding the Greenland Ice Sheet (region GIC), modelled surface mass balance (SMB) has decreased since the end of the 1990s ( [[#Fettweis--2020|Fettweis et al., 2020]] ). A multi-model intercomparison study ( [[#Fettweis--2020|Fettweis et al., 2020]] ) emphasized a simulated positive mean annual SMB of 338 ± 68 Gt yr <sup>–1</sup> between 1980 and 2012, with a decreasing average rate of 7.3 ± 2.0 Gt yr <sup>–2</sup> , mainly driven by an increase in meltwater runoff. [[#Mouginot--2019|Mouginot et al. (2019)]] stated that SMB played a strong role in the ice-sheet mass loss, where SMB dominated in the last two decades. [[#Mottram--2019|Mottram et al. (2019)]] found that SMB processes dominate the ice-sheet mass budget over most of the interior, highlighting that the ice sheet is a contributor to global mean sea level rise between 1991 and 2015. More specifically, SMB models have improved ( [[#Fettweis--2020|Fettweis et al., 2020]] ; [[#Hanna--2021|Hanna et al., 2021]] ) due to increased availability and quality of remotely sensed ( [[#Koenig--2016|Koenig et al., 2016]] ; [[#Overly--2016|Overly et al., 2016]] ) and in situ observations ( [[#Machguth--2016|Machguth et al., 2016]] ; [[#Fausto--2018|Fausto et al., 2018]] ; [[#Vandecrux--2019|Vandecrux et al., 2019]] , 2020). [[#Fettweis--2020|Fettweis et al. (2020)]] showed that the models’ ensemble mean provides the best estimate of the present-day SMB relative to observations. This is the case for the patterns in all seven regions (regional division after [[#Mouginot--2019|Mouginot et al., 2019]] ) apart from the SE accumulation zone where large discrepancies in modelled snowfall accumulation occurred where the spread can reach 2-m water equivalent per year. [[#Montgomery--2020|Montgomery et al. (2020)]] confirmed this, highlighting that RCMs (MAR and RACMO) are underestimating accumulation in south-east Greenland and that models misrepresent spatial heterogeneity due to an orographically forced bias in snowfall near the coast. Further, for north-east Greenland, [[#Karlsson--2020|Karlsson et al. (2020)]] found RCMs underestimate snow accumulation rates by up to 35%. The regional time series show that SMB has been gradually decreasing in all seven regions (1979–2017), although the trend is less strong in central-eastern and south-eastern regions. In the south-west, north-east and north-west, SMB turns negative or close to zero after 2000 and remains above zero in other regions ( ''medium confidence'' ) (Figure Atlas.3 0).

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==== Atlas.11.2.4 Assessment and Synthesis of Projections ====

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Mean temperature in the Arctic is projected to continue to rise throughthe 21st century significantly higher than the global average (Figure Atlas.29 and the Interactive Atlas). For the regions NWN and NEN, see [[#Atlas.9|Atlas.9]] . The Arctic is projected to reach a 2°C annual mean warming above the 1981–2005 baseline about 25 to 50 years before the globe as a whole under RCP8.5 and RCP4.5 ( [[#Post--2019|Post et al., 2019]] ). The Arctic warming may be as much as 4°C in the annual mean and 7°C in late autumn under 2°C global warming, regardless of which scenario is considered ( ''high confidence'' ) ( [[#Post--2019|Post et al., 2019]] ).

Projections from 40 CMIP5 models of the 2014–2100 Arctic annual warming under RCP4.5 vary from 0.9°C to 6.7°C, with a multi-model mean of 3.7°C ( [[#Chylek--2016|Chylek et al., 2016]] ). All models agree on a projected Arctic amplification (of at least 1.5 times), but they disagree on the magnitude and spatial patterns. Arctic warming trends projected by models that include a full direct and indirect aerosol effect (‘fully aerosol–cloud interactive’) are significantly higher than those projected by models without a full indirect aerosol effect ( [[#Chylek--2016|Chylek et al., 2016]] ).

Projected Arctic warming exhibits a very pronounced seasonal cycle, with exceptionally strong warming in the winter. In projections from 30 CMIP5 models, winter warming over ARO varies regionally from 3°C to 5°C by mid-century and 5°C to 9°C by late-century under RCP4.5 ( ''high confidence'' ) ( [[#AMAP--2017|AMAP, 2017]] ). Averaged over the Arctic and based on 36 CMIP5 models, winter warming is 5.8°C ± 1.5°C by mid-century and 7.1°C ± 2.3°C by 2100 under RCP4.5 ( [[#Overland--2019|Overland et al., 2019]] ), and an exceptionally strong warming of up to 14.1°C ± 2.9°C is projected in December under RCP8.5 ( [[#Bintanja--2016|Bintanja and Krikken, 2016]] ). [[#Bintanja--2013|Bintanja and Van Der Linden (2013)]] estimated the Arctic winter warming over the 21st century to exceed the summer warming by at least a factor of four, irrespective of the magnitude of the climate forcing.

[[#Overland--2014|Overland et al. (2014)]] highlighted the difference between the near-term ‘adaptation timescale’ and the long-term ‘mitigation timescale’ for the Arctic. Only in the latter half of the century do the projections under RCP4.5 and RCP8.5 noticeably separate. End-of-the-century warming is approximately twice as large under RCP8.5 demonstrating the impact of the lower emissions under RCP4.5 ( ''high confidence'' ) ( [[#AMAP--2017|AMAP, 2017]] ). More specifically under the strong forcing scenario, annual mean surface air temperature in the Arctic is projected to increase by 8.5°C ± 2.1°C over the course of the 21st century ( [[#Bintanja--2017|Bintanja and Andry, 2017]] ), and emerges as a ‘new Arctic’ climate being significantly different from that of the mid-20th century ( [[#Landrum--2020|Landrum and Holland, 2020]] ). The end-of-the-century warming (2080–2099 relative to 1980–1999, RCP8.5) can exceed 15°C in autumn and winter over the Arctic Ocean ( [[#Koenigk--2015|Koenigk et al., 2015]] ). Projections averaged over the four best-performing CMIP5 models show an Arctic annual warming of 4.1°C (RCP2.6), 5.7°C (RCP4.5) and 10.6°C (RCP8.5) by 2100 compared to 1951–1980 ( [[#Hao--2018|Hao et al., 2018]] ). Also, for neighbouring Arctic regions, for example NEU, WSB and ESB, temperature is projected to increase towards the end of the century under both RCP4.5 and RCP8.5 ( [[#Atlas.8|Atlas.8]] and [[#Atlas.5.2|Atlas.5.2]] ).

The ensemble of CMIP6 shows ''likely'' greater warming compared to CMIP5 (Figure Atlas.29). There is weak agreement among the models in projected temperature change over the Arctic North Atlantic under SSPs until the mid-century, but a robust warming signal clearly emerges even there by 2100 (Interactive Atlas). Generally, the largest annual warming is simulated over the Arctic Ocean, particularly over the Barents Sea and the Kara Sea. Future warming in CORDEX RCMs and the CMIP5 models are similar ( [[#Spinoni--2020|Spinoni et al., 2020]] ). The RCM warming over the AO is smaller, while the warming over land is larger in winter and spring but smaller in summer, compared with CMIP5 ( [[#Koenigk--2015|Koenigk et al., 2015]] ).

Mean precipitation in ARO, GIC and RAR is projected to rise in a warming climate (Figure Atlas.29), with different rates for the different seasons and scenarios. For NWN and NEN, see [[#Atlas.9|Atlas.9]] . The CMIP5 multi-model mean projected precipitation increase in the Arctic is ''likely'' of the order of 50% under RCP8.5 by the end of the 21st century, which is among the highest globally ( [[#Bintanja--2014|Bintanja and Selten, 2014]] ). Over 70°N–90°N, the precipitation increase is ''likely'' 62 ± 20% and 56 ± 13% for RCP4.5 and RCP8.5 respectively. For ARO (Svalbard), the increase in annual precipitation by 2100 is estimated to be about 45% for RCP4.5 and 65% for RCP8.5 (CMIP5 ensemble median; [[#Van%20der%20Bilt--2019|Van der Bilt et al., 2019]] ). However, importantly the simulated Arctic precipitation increase varies by a factor of three to four between models ( [[#Bintanja--2014|Bintanja and Selten, 2014]] ). The projected increase is strongest in late autumn and winter ( [[#Vihma--2016|Vihma et al., 2016]] ). The interannual variability of Arctic precipitation will likely increase markedly (up to 40% over the 21st century), especially in summer ( ''medium confidence'' ) ( [[#Bintanja--2020|Bintanja et al., 2020]] ).

The CMIP6 projections confirm precipitation will ''likely'' increase almost everywhere in the Arctic (Interactive Atlas). The largest increase is simulated over the Barents Sea, Kara Sea and East Siberian Sea regions, and over north-east Greenland. A pronounced uncertainty in the projection exists over the Arctic North Atlantic and south Greenland. There, the precipitation signal is not significant even by the end of the 21st century and under high-emissions scenarios (RCP8.5, SSP5-8.5). Consistent with the generally higher warming in CMIP6, compared to CMIP5, the projected precipitation increase is also higher ( ''high confidence'' ) (Figure Atlas.29).

The Arctic mean annual precipitation sensitivity has been estimated at a 4.5% increase per degree Celsius of temperature rise, compared to a global average of 1.6–1.9% per degree Celsius of temperature rise ( [[#Bintanja--2014|Bintanja and Selten, 2014]] ) based on a set of 37 CMIP5 GCMs. [[#Koenigk--2015|Koenigk et al. (2015)]] stress the different precipitation sensitivity in winter (0.8 mm per month per degree Celsius of temperature rise) and summer (2 mm per month per degree Celsius of temperature rise). The pattern and amplitude of precipitation changes agree in CORDEX simulations with their driving CMIP5 models ( ''high confidence'' ) ( [[#Koenigk--2015|Koenigk et al., 2015]] ; [[#Spinoni--2020|Spinoni et al., 2020]] ). However, more small-scale variations over land and coastlines, and significantly larger precipitation changes in summer are obvious in the downscaling.

Rain is projected to become the dominant form of precipitation in the Arctic region by the end of the 21st century ( [[#Bintanja--2018|Bintanja, 2018]] ). The CMIP5 models show a decrease in annual Arctic snowfall under both RCP4.5 and RCP8.5 ( ''high confidence'' ) ( [[#Krasting--2013|Krasting et al., 2013]] ; [[#Bintanja--2017|Bintanja and Andry, 2017]] ). In the central Arctic, the snowfall fraction barely remains larger than 50%, with only Greenland still having snowfall fractions larger than 80% ( [[#Bintanja--2017|Bintanja and Andry, 2017]] ). The most dramatic reductions in snowfall fraction are projected to occur over the North Atlantic and, especially, the Barents Sea.

With ongoing warming and polar amplification in the Arctic, the Greenland Ice Sheet SMB will inevitably continue to change ( ''high confidence'' ) ( [[#Lenaerts--2019|Lenaerts et al., 2019]] ). For the ice sheet, despite large differences between model scenarios, future projections and regions agree that increasing temperatures will increase runoff which will in turn dominate the future decrease of SMB ( [[#Rae--2012|Rae et al., 2012]] ; [[#van%20Angelen--2014|van Angelen et al., 2014]] ; [[#Mottram--2017|Mottram et al., 2017]] ; [[#Hofer--2020|Hofer et al., 2020]] ), confirming the high sensitivity of the SMB to atmospheric warming. Changes in SMB will continue to dominate future mass loss from the ice sheet, and likely even more when marine-terminating glaciers retreat onto land, and solid ice discharge is reduced ( [[#Vizcaino--2014|Vizcaino, 2014]] ; [[#Lenaerts--2019|Lenaerts et al., 2019]] ).

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==== Atlas.11.2.5 Summary ====

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It is ''very likely'' that the Arctic has warmed at more than twice the global rate over the past 50 years and ''likely'' that annual precipitation has increased with the highest increases during the cold season. This is based on various Arctic amplification processes, in particular, a combination of several feedback-related processes such as sea ice and snow-cover albedo, poleward energy transports, and water-vapour-cloud-radiation feedbacks. The frequency of rainfall increased over the Arctic by 2.7–5.4% over the 2000–2016 period with more frequent rainfall events being reported for northern Europe and Svalbard ( ''medium confidence'' ).

The CMIP5 models reproduce the observed Arctic warming over the past century but overestimate the amplified Arctic warming in the recent decades ( ''medium confidence'' ). Arctic CORDEX simulations show adequate skill in capturing regional temperature and precipitation patterns and precipitation extremes ( ''high confidence'' ). SMB models have improved due to increased availability and quality of remotely sensed and in situ observations, and an ensemble mean of SMB model simulations provides the best estimate of the present-day SMB ( ''medium confidence'' ).

It is ''very likely'' that the Arctic annual mean temperature and precipitation will continue to increase, reaching around 11.5°C ± 3.4°C and 49 ± 19% over the 2081–2100 period (with respect to a 1995–2014 baseline) under the SSP5-8.5 scenario or 4.0°C ± 2.5°C and 17 ± 11% under the SSP1-2.6 scenario. These CMIP6 results show ''likely'' higher Arctic annual mean temperatures compared to CMIP5 for a given time-period and emissions scenario, though the projections are consistent for global warming levels.

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