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

=== 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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==== 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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