Editing IPCC:AR6/WGI/Chapter-9 (section)

== 9.4 Ice Sheets ==

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=== 9.4.1 Greenland Ice Sheet ===

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==== 9.4.1.1 Recent Observed Changes ====

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In this section we present regional mass change time series for the Greenland Ice Sheet and assess the different processes that are causing the increase in mass loss. The vast increase in observational products from various platforms (e.g, GRACE, PROMICE, ESA-CCI, NASA MEaSUREs) provide a consistent and clear picture of a shrinking Greenland Ice Sheet ( [[#Colgan--2019|Colgan et al., 2019]] ; Mottram et al.,2019; [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#King--2020|King et al., 2020]] ; [[#Mankoff--2020|Mankoff et al., 2020]] ; [[#Moon--2020|Moon et al., 2020]] ; [[#Sasgen--2020|Sasgen et al., 2020]] ; [[#Velicogna--2020|Velicogna et al., 2020]] ; [[#The%20IMBIE%20Team--2020|The IMBIE Team, 2020]] ). [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.4.1|Section 2.3.2.4.1]] provides an updated estimate of the total Greenland Ice Sheet mass change in a global context (Figure 2.24). The estimated ice-sheet extent at different times is shown in Figure 9.17, and the paleo perspective on Greenland Ice Sheet evolution is presented in [[#9.6.2|Section 9.6.2]] .

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[[File:1d111adb36b628cfd8a0d493ecd135b1 IPCC_AR6_WGI_Figure_9_16.png]]

'''Figure 9.16''' '''|''' '''Mass changes and mass change rates for Greenland and Antarctic ice sheet regions. (a)''' Time series of mass changes in Greenland for each of the major drainage basins shown in the inset figure ( [[#Bamber--2018b|Bamber et al., 2018b]] ; [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#The%20IMBIE%20Team--2021|The IMBIE Team, 2021]] ) for the periods 1972–2016, 1992–2018, and 1992–2020. '''(b)''' Time series of mass changes for three portions of Antarctica ( [[#Bamber--2018b|Bamber et al., 2018b]] ; [[#The%20IMBIE%20Team--2021|The IMBIE Team, 2021]] ) for the period 1992–2016 and 1992–2020. Estimates of mass change rates of surface mass balance, discharge and mass balance in '''(g)''' all of Greenland and '''(c–f, h–j)''' in seven Greenland regions ( [[#Bamber--2018b|Bamber et al., 2018b]] ; [[#Mankoff--2019|Mankoff et al., 2019]] ; [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#King--2020|King et al., 2020]] ). Estimates of mass change rates of surface mass balance, discharge and mass balance for '''(k)''' all of Antarctica and '''(l–n)''' for three regions of Antarctica ( [[#Bamber--2018b|Bamber et al., 2018b]] ; [[#The%20IMBIE%20Team--2018|The IMBIE Team, 2018]] ; [[#Rignot--2019|Rignot et al., 2019]] ). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

For the 20th century, SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) presented one reconstruction for 1900–1983 and estimated mass change for the Greenland Ice Sheet and its peripheral glaciers for the period 1901–1990. Since SROCC, a comprehensive new study has extended the satellite record back to 1972 (Figure 9.16; [[#Mouginot--2019|Mouginot et al., 2019]] ). The rate of ice-sheet mass change was positive (i.e., it gained mass) in 1972–1980 (47 ± 21 Gt yr <sup>–1</sup> ) and then negative (i.e., it lost mass; –51 ± 17 Gt yr <sup>–1</sup> and –41 ± 17 Gt yr <sup>–1</sup> ) in 1980–1990 and 1990–2000, respectively. Other ice discharge time series starting in 1985 ( [[#King--2018|King et al., 2018]] , 2020; [[#Mankoff--2019|Mankoff et al., 2019]] , 2020) agree with [[#Mouginot--2019|Mouginot et al. (2019)]] (see also Figure 9.16). There is ''limited evidence'' of temporally and spatially heterogeneous Greenland outlet glacier evolution during the 20th century ( [[#Lea--2014|Lea et al., 2014]] ; [[#Lüthi--2016|Lüthi et al., 2016]] ; [[#Andresen--2017|Andresen et al., 2017]] ; [[#Khan--2020|Khan et al., 2020]] ; [[#Vermassen--2020|Vermassen et al., 2020]] ). Historical photographs ( [[#Khan--2020|Khan et al., 2020]] ) show large mass losses of Jakobshavn and Kangerlussuaq Glaciers in West Greenland from 1880 until the 1940s, exceeding their 21st-century mass loss, whereas the Helheim Glacier in East Greenland remained stable, gained mass in the 1990s, then rapidly lost mass after 2000. Together, these three large outlet glaciers, draining about 12% of the ice sheet surface area, have lost 22 ± 3 Gt yr <sup>–1</sup> in the period 1880–2012 ( [[#Khan--2020|Khan et al., 2020]] ). Overall, these studies provide a variable picture of the Greenland Ice Sheet mass change in the 20th century. The updated mass loss of Greenland Ice Sheet, including peripheral glaciers for the period 1901–1990, is 120 [70–170] Gt yr <sup>–1</sup> (see Table 9.5 and Figures 9.16 and 9.17).

Post-1992, SROCC stated that it is ''extremely likely'' that the rate of mass change of Greenland Ice Sheet was more negative during 2012–2016 than during 1992–2001, with ''very high confidence'' that summer melting has increased since the 1990s to a level unprecedented over at least the last 350 years. Since SROCC, the updated synthesis of satellite observations by the Ice Sheet Mass Balance Intercomparison Exercise ( [[#The%20IMBIE%20Team--2020|The IMBIE Team, 2020]] ) and the GRACE Follow-On (GRACE-FO) Mission ( [[#Abich--2019|Abich et al., 2019]] ; [[#Kornfeld--2019|Kornfeld et al., 2019]] ), have confirmed the mass change record, and the record has been extended to 2020 ( [[#The%20IMBIE%20Team--2021|The IMBIE Team, 2021]] ) as presented in 2.3.2.4. The Greenland Ice Sheet lost 4890 [4140–5640] Gt of ice between 1992 and 2020, causing sea level to rise by 13.5 [11.4 to 15.6] mm ( [[#The%20IMBIE%20Team--2021|The IMBIE Team, 2021]] ; see also [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.4.1|Section 2.3.2.4.1]] , Figure 9.16 and Table 9.5). The IMBIE Team’s (2020) estimates are consistent with other post-AR5 reviews (Figure 9.17, Table 9.SM.1; [[#Bamber--2018a|Bamber et al., 2018a]] ; [[#Cazenave--2018|Cazenave et al., 2018]] ; [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#Slater--2021|Slater et al., 2021]] ). Recent GRACE-FO data ( [[#Sasgen--2020|Sasgen et al., 2020]] ; [[#Velicogna--2020|Velicogna et al., 2020]] ) show that, after two cold summers in 2017 and 2018, with relatively moderate mass change of about –100 Gt yr <sup>–1</sup> , the 2019 mass change (–532 ± 58 Gt yr <sup>–1</sup> ) was the largest annual mass loss in the record. The ''high agreement'' across a variety of methods confirms SROCC and [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] assessments. The mass-loss rate was, on average, 39 [–3 to 80] Gt yr <sup>–1</sup> over the period 1992–1999, 175 [131 to 220] Gt yr <sup>–1</sup> over the period 2000–2009 and 243 [197 to 290] Gt yr <sup>–1</sup> over the period 2010–2019 (see Table 9.SM.1).

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[[File:931014c67858a8fbad78d3bb5731ffd4 IPCC_AR6_WGI_Figure_9_17.png]]

'''Figure 9.17 |''' '''Greenland Ice Sheet cumulative mass change and equivalent sea level contribution. (a)''' A p-box ( [[#9.6.3.2|Section 9.6.3.2]] ) based estimate of the range of values of paleo Greenland Ice Sheet mass and sea level equivalents relative to present day and the median over all central estimates ( [[#Simpson--2009|Simpson et al., 2009]] ; [[#Argus--2010|Argus and Peltier, 2010]] ; [[#Colville--2011|Colville et al., 2011]] ; [[#Dolan--2011|Dolan et al., 2011]] ; [[#Fyke--2011|Fyke et al., 2011]] ; [[#Robinson--2011|Robinson et al., 2011]] ; [[#Born--2012|Born and Nisancioglu, 2012]] ; K.G. [[#Miller--2012|]] [[#Miller--2012|Miller et al., 2012]] ; [[#Dahl-Jensen--2013|Dahl-Jensen et al., 2013]] ; [[#Helsen--2013|Helsen et al., 2013]] ; [[#Nick--2013|Nick et al., 2013]] ; [[#Quiquet--2013|Quiquet et al., 2013]] ; [[#Stone--2013|Stone et al., 2013]] ; [[#Colleoni--2014|Colleoni et al., 2014]] ; [[#Lecavalier--2014|Lecavalier et al., 2014]] ; [[#Robinson--2014|Robinson and Goelzer, 2014]] ; [[#Calov--2015|Calov et al., 2015]] , 2018; [[#Dutton--2015|Dutton et al., 2015]] ; [[#Koenig--2015|Koenig et al., 2015]] ; [[#Peltier--2015|Peltier et al., 2015]] ; [[#Stuhne--2015|Stuhne and Peltier, 2015]] ; [[#Vizcaino--2015|Vizcaino et al., 2015]] ; [[#Goelzer--2016|Goelzer et al., 2016]] ; [[#Khan--2016|Khan et al., 2016]] ; [[#Yau--2016|Yau et al., 2016]] ; [[#de%20Boer--2017|de Boer et al., 2017]] ; [[#Simms--2019|Simms et al., 2019]] ); '''(b, left)''' cumulative mass loss (and sea level equivalent) since 2015 from 1972 ( [[#Mouginot--2019|Mouginot et al., 2019]] ) and 1992 ( [[#Bamber--2018b|Bamber et al., 2018b]] ; [[#The%20IMBIE%20Team--2020|The IMBIE Team, 2020]] ), the estimated mass loss from 1840 ( [[#Box--2013|Box and Colgan, 2013]] ; [[#Kjeldsen--2015|Kjeldsen et al., 2015]] ) indicated with a shaded box, and projections from Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) to 2100 under RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6 scenarios (thin lines from [[#Goelzer--2020|Goelzer et al. (2020)]] ; [[#Edwards--2021|Edwards et al. (2021)]] ; [[#Payne--2021|Payne et al. (2021)]] ) and ISMIP6 emulator under SSP5-8.5 and SSP1-2.6 to 2100 (shades and bold line; [[#Edwards--2021|Edwards et al., 2021]] ); (b, right) 17th – 83rd and 5th – 95th percentile ranges for ISMIP6 and ISMIP6 emulator at 2100. Schematic interpretations of individual reconstructions ( [[#Lecavalier--2014|Lecavalier et al., 2014]] ; [[#Goelzer--2016|Goelzer et al., 2016]] ; [[#Berends--2019|Berends et al., 2019]] ) of the spatial extent of the Greenland Ice Sheet are shown for the: '''(c)''' mid-Pliocene Warm Period; '''(d)''' the Last Interglacial; and '''(e)''' the Last Glacial Maximum: grey shading shows extent of grounded ice. Maps of mean elevation changes '''(f)''' 2010–2017 derived from CryoSat 2 radar altimetry ( [[#Bamber--2018b|Bamber et al., 2018b]] ) and '''(g)''' ISMIP6 model mean (2093–2100) projected changes for the MIROC5 climate model under the RCP8.5 scenario ( [[#Goelzer--2020|Goelzer et al., 2020]] ). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

The SROCC assessed with ''high confidence'' that surface mass balance (SMB),rather than discharge, has started to dominate the mass loss of the Greenland Ice Sheet (due to increased surface melting and runoff), increasing from 42% of the total mass loss for 2000–2005 to 68% for 2009–2012. While these estimates have been confirmed since SROCC ( [[#Mouginot--2019|Mouginot et al., 2019]] ), the new longer record, as well as further comprehensive studies ( [[#Khan--2015|Khan et al., 2015]] ; [[#Colgan--2019|Colgan et al., 2019]] ; [[#Mottram--2019|Mottram et al., 2019]] ; [[#The%20IMBIE%20Team--2020|The IMBIE Team, 2020]] ) and detailed discharge records ( [[#King--2020|King et al., 2020]] ; [[#Mankoff--2020|Mankoff et al., 2020]] ) reveal a more complex picture than the continuous trajectory this statement may have implied. Discharge was relatively constant from 1972–1999, varying by around 6% for the whole ice sheet, while SMB varied by a factor of over two interannually, leading to either mass gain or loss in a given year (Figure 9.16). During 2000–2005, the rate of discharge increased by 18%, then remained fairly constant again (increasing by 6% from 2006–2018). After 2000, SMB decreased more rapidly than discharge increased. In summary, the consistent temporal pattern in these longer datasets leads to ''high confidence'' that the Greenland Ice Sheet mass losses are increasingly dominated by SMB, but there is ''high confidence'' that mass loss varies strongly, due to large interannual variability in SMB.

On a regional scale, the surface elevation is lowering in all regions, and widespread terminus and calving front retreats have been observed (with no glaciers advancing; [[#Mottram--2019|Mottram et al., 2019]] ; [[#Moon--2020|Moon et al., 2020]] ). The largest mass losses have occurred along the west coast and in south-east Greenland (Figure 9.16), concentrated at a few major outlet glaciers ( [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#Khan--2020|Khan et al., 2020]] ). This regional pattern is consistent with independent Global Navigation Satellite System (GNSS) observations from the Greenland Global Positioning System (GPS) network which show elastic bedrock uplift of tens of centimetres between 2007–2019 as a result of ongoing ice mass loss ( [[#Bevis--2019|Bevis et al., 2019]] ). The regional time series (Figures 9.16; Atlas.30) show that SMB has been gradually decreasing in all regions, while the increase in discharge in the south-east, central east, north-west and central west has been linked to retreating tidewater glaciers (Figure 9.16). In summary, the detailed regional records show an increase in mass loss in all regions after the 1980s, caused by both increases in discharge and decreases in SMB ( ''high confidence'' ), although the timing and patterns vary between regions. The largest mass loss occurred in the north-west and the south-east of Greenland ( ''high confidence'' ).

The SROCC stated with ''high confidence'' that variability in large-scale atmospheric circulation is an important driver of short-term SMB changes for the Greenland Ice Sheet. This effect of atmospheric circulation variability on both precipitation and melt rates (and SROCC assessment) is confirmed by more recent publications ( [[#Välisuo--2018|Välisuo et al., 2018]] ; [[#Zhang--2019|]] [[#Zhang--2019|]] [[#Zhang--2019|B. Zhang et al., 2019]] ; [[#Velicogna--2020|Velicogna et al., 2020]] ). The strong mass loss in 2019 ( [[#Cullather--2020|Cullather et al., 2020]] ; [[#Hanna--2020|Hanna et al., 2020]] ; [[#Tedesco--2020|Tedesco and Fettweis, 2020]] ) was driven by highly anomalous atmospheric circulation patterns, both on daily ( [[#Cullather--2020|Cullather et al., 2020]] ) and seasonal time scales ( [[#Tedesco--2020|Tedesco and Fettweis, 2020]] ). Although surface melt is anticorrelated with the summer North Atlantic Oscillation Index ( [[#Välisuo--2018|Välisuo et al., 2018]] ; [[#Ruan--2019|Ruan et al., 2019]] ; [[#Sherman--2020|Sherman et al., 2020]] ), especially in West Greenland ( [[#Bevis--2019|Bevis et al., 2019]] ), Greenland Ice Sheet melt is more strongly correlated with the Greenland Blocking Index ( [[#Hanna--2016|Hanna et al., 2016]] , 2018) than with the summer North Atlantic Oscillation index ( [[#Huai--2020|Huai et al., 2020]] ).

The SROCC did not assess the role of cloud changes in detail. Studies since AR5 have shown that higher incident shortwave radiation in conjunction with reduced cloud cover leads to increased melt rates, particularly over the low-albedo ablation zone in the southern part of the Greenland Ice Sheet ( [[#Hofer--2017|Hofer et al., 2017]] ; [[#Niwano--2019|Niwano et al., 2019]] ; [[#Ruan--2019|Ruan et al., 2019]] ). Conversely, an increase in cloud cover over the high-albedo central parts of the ice sheet, leading to higher downwelling longwave radiation, was shown to lead either to increased melt ( [[#Bennartz--2013|Bennartz et al., 2013]] ) or reduced refreezing of meltwater ( [[#van%20Tricht--2016|van Tricht et al., 2016]] ). The elevation dependence of the cloud radiative effect and its control on surface meltwater generation and refreezing (W. [[#Wang--2019|]] [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ; [[#Hahn--2020|Hahn et al., 2020]] ) can induce a spatially consistent response of the integrated Greenland Ice Sheet melt to dominant patterns of cloud and atmospheric variability. The shortwave and longwave radiation effects on surface melt by clouds have been shown to compensate for each other during strong atmospheric river events, and the increase in melt is caused by increased sensible heat fluxes during such events ( [[#Mattingly--2020|Mattingly et al., 2020]] ). In summary, there is ''medium confidence'' that cloud cover changes are an important driver of the increasing melt rates in the southern and western part of the Greenland Ice Sheet.

The SROCC stated with ''high confidence'' that positive albedo feedbacks contributed substantially to the post-1990s Greenland Ice Sheet melt increase. Several (mostly positive) feedbacks involving surface albedo operate on ice sheets (e.g., [[#Fyke--2018|Fyke et al., 2018]] ). Melt amplification by the observed increase of bare ice exposure through snowline migration to higher parts of the ice sheet since 2000 ( [[#Shimada--2016|Shimada et al., 2016]] ; [[#Ryan--2019|Ryan et al., 2019]] ) was five times stronger than the effect of hydrological and biological processes that lead to reduced bare ice albedo ( [[#Ryan--2019|Ryan et al., 2019]] ). Impurities, in part biologically active ( [[#Ryan--2018|Ryan et al., 2018]] ), have been observed to lead to albedo reduction ( [[#Stibal--2017|Stibal et al., 2017]] ) and are estimated to have increased runoff from bare ice in the southwestern sector of the Greenland Ice Sheet by about 10% ( [[#Cook--2020|Cook et al., 2020]] ). In summary, new studies confirm that there is ''high confidence'' that the Greenland Ice Sheet melt increase since about 2000 has been amplified by positive albedo feedbacks, with the expansion of bare ice extent being the dominant factor, and albedo in the bare ice zone being primarily controlled by distributed biologically active impurities (see also [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.3|Section 7.3.4.3]] ).

The SROCC reported with ''medium confidence'' that around half of the 1960–2014 Greenland Ice Sheet surface meltwater ran off, while most of the remainder infiltrated firn and snow, where it either refroze or accumulated in firn aquifers. Studies since SROCC show a decrease of firn air content between 1998–2008 and 2010–2017 ( [[#Vandecrux--2019|Vandecrux et al., 2019]] ) in the low-accumulation percolation area of western Greenland, reducing meltwater retention capacity. Moreover, meltwater infiltration into firn can be strongly limited by low-permeability ice slabs created by refreezing of infiltrated meltwater ( [[#Machguth--2016|Machguth et al., 2016]] ). Recent observations and modelling efforts indicate that rapidly expanding low-permeability layers have led to an increase in runoff area since 2001 ( [[#MacFerrin--2019|MacFerrin et al., 2019]] ). In summary, there is ''medium confidence'' that meltwater storage and refreezing can temporarily buffer a large-scale melt increase, but limiting factors have been identified.

The SROCC reported that there was ''medium confidence'' that ocean temperatures near the grounding zone of tidewater glaciers are critically important to their calving rate, but there was ''low confidence'' in understanding their response to ocean forcing. The increase in ice discharge in the late 1990s and early 2000s ( [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#King--2020|King et al., 2020]] ; [[#Mankoff--2020|Mankoff et al., 2020]] ) has been associated with a period of widespread tidewater glacier retreat ( [[#Murray--2015|Murray et al., 2015]] ; [[#Wood--2021|Wood et al., 2021]] ) and speed up ( [[#Moon--2020|Moon et al., 2020]] ). Since SROCC, new studies provide strong evidence for rapid submarine melting at tidewater glaciers ( [[#Sutherland--2019|Sutherland et al., 2019]] ; [[#Wagner--2019|Wagner et al., 2019]] ; [[#Bunce--2020|Bunce et al., 2020]] ; R.H. [[#Jackson--2020|]] [[#Jackson--2020|Jackson et al., 2020]] ). Changes in submarine melting and subglacial meltwater discharge can trigger increased ice discharge by reducing the buttressing to ice flow and promoting calving ( [[#Benn--2017|Benn et al., 2017]] ; [[#Todd--2018|Todd et al., 2018]] ; [[#Ma--2019|Ma and Bassis, 2019]] ; [[#Mercenier--2020|Mercenier et al., 2020]] ); through undercutting ( [[#Rignot--2015|Rignot et al., 2015]] ; [[#Slater--2017|]] [[#Slater--2017|D.A. Slater et al., 2017]] ; [[#Wood--2018|Wood et al., 2018]] ; [[#Fried--2019|Fried et al., 2019]] ) and frontal incision ( [[#Cowton--2019|Cowton et al., 2019]] ). Warming ocean waters have been implicated in the recent thinning and breakup of floating ice tongues in north-eastern and north-western Greenland ( [[#Mouginot--2015|Mouginot et al., 2015]] ; [[#Wilson--2017|Wilson et al., 2017]] ; [[#Mayer--2018|Mayer et al., 2018]] ; [[#Washam--2018|Washam et al., 2018]] ; [[#An--2021|An et al., 2021]] ; [[#Wood--2021|Wood et al., 2021]] ). On decadal time scales, tidewater glacier terminus position correlates with submarine melting ( [[#Slater--2019|Slater et al., 2019]] ). Over shorter time scales, individual glaciers or clusters of glaciers can behave differently and asynchronously ( [[#Bunce--2018|Bunce et al., 2018]] ; [[#Vijay--2019|Vijay et al., 2019]] ; [[#An--2021|An et al., 2021]] ), and there are not always clear associations between water temperature and glacier calving rates ( [[#Motyka--2017|Motyka et al., 2017]] ), retreat or speed-up ( [[#Joughin--2020|Joughin et al., 2020]] ; [[#Solgaard--2020|Solgaard et al., 2020]] ). Variations in ice mélange at the front of a glacier, associated with changes in ocean and air temperature, have also emerged as a plausible control on calving ( [[#Burton--2018|Burton et al., 2018]] ; [[#Xie--2019|Xie et al., 2019]] ; [[#Joughin--2020|Joughin et al., 2020]] ). In summary, there is ''high confidence'' that warmer ocean waters and increased subglacial discharge of surface melt at the margins of marine-terminating glaciers increase submarine melt, which leads to increased ice discharge. There is ''medium confidence'' that this contributed to the increased rate of mass loss from Greenland, particularly in the period 2000–2010 when increased discharge was observed in the south-east and north-west.

The SROCC reported that accurate bedrock topography is required for understanding and projecting the glacier response to ocean forcing. Accurate bathymetry is essential for establishing which water masses enter glacial fjords, and for reliable estimates of the submarine melt rates experienced by tidewater glaciers ( [[#Schaffer--2020|Schaffer et al., 2020]] ; T. [[#Slater--2020|]] [[#Slater--2020|Slater et al., 2020]] ; [[#Wood--2021|Wood et al., 2021]] ). Subglacial and lateral topography is known to strongly modulate tidewater glacier dynamics and the sensitivity of tidewater glaciers to climatic forcing ( [[#Enderlin--2013|Enderlin et al., 2013]] ; [[#Catania--2018|Catania et al., 2018]] ). Bathymetric mapping around the ice sheet has greatly improved with direct and gravimetric surveys ( [[#Millan--2018|Millan et al., 2018]] ; [[#An--2019a|An et al., 2019a]] , b; [[#Jakobsson--2020|Jakobsson et al., 2020]] ) leading to the improvement of Greenland-wide bathymetric and topographic mapping (e.g., [[#Morlighem--2017|Morlighem et al., 2017]] ). However, large uncertainties in ice thickness remain for around half of the outlet glaciers ( [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#Wood--2021|Wood et al., 2021]] ) and sea ice covered and iceberg-packed regions remain poorly sampled near glacier termini ( [[#Morlighem--2017|Morlighem et al., 2017]] ). There is ''high confidence'' that bathymetry (governing the water masses that flow into fjord cavities) and fjord geometry and bedrock topography (controlling ice dynamics) modulate the response of individual glaciers to climate forcing.

The AR5 assessed that it is ''likely'' that anthropogenic forcing has contributed to the surface melting of Greenland since 1993 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). [[IPCC:Wg1:Chapter:Chapter-3#3.4.3.2|Section 3.4.3.2]] assesses that it is ''very likely'' that human influence has contributed to the observed surface melting of the Greenland Ice Sheet over the past two decades. There is ''medium confidence'' of an anthropogenic contribution to recent mass loss from Greenland.

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==== 9.4.1.2 Model Evaluation ====

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The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) stated that substantial challenges remained for modelling of the Greenland SMB and the dynamical ice sheet. Since SROCC, further insights into modelling of the Greenland ice sheet has come from model intercomparison studies of the SMB ( [[#Fettweis--2020|Fettweis et al., 2020]] ) and dynamical ice sheets ( [[#Goelzer--2020|Goelzer et al., 2020]] ; [[#Payne--2021|Payne et al., 2021]] ). Further aspects relevant to the forcing of the ice sheet from large scale global climate models and regional climate models are discussed in Box 9.3 and Section Atlas.11.2.

The SROCC stated that climate model simulations of Greenland SMB had improved since AR5, giving ''medium confidence'' in the ability of climate models to simulate changes in Greenland SMB. Since SROCC, a multi-model intercomparison study ( [[#Fettweis--2020|Fettweis et al., 2020]] ) of regional and global climate models has shown that the greatest inter-model spread occurs in the ablation zone, due to deficiencies in an accurate model representation of the ablation zone extent and processes related to surface melt and runoff, confirming SROCC statement that there is large uncertainty in the bare ice model ( [[#Ryan--2019|Ryan et al., 2019]] ). This intercomparison showed that simple, well-tuned SMB models using positive degree day melt schemes can perform as well as more complex physically based models (Figure ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 30). Furthermore, the ensemble mean of the models produced the best estimate of the present-day SMB relative to observations (particularly in the ablation zone). Further assessment of Greenland Ice Sheet regional SMB can be found in Section Atlas.11.2.3. Recent progress confirms SROCC assessment that there is ''medium confidence'' in the ability of climate models to simulate changes in Greenland SMB.

The SROCC noted increased use of coupled climate–ice sheet models for simulating the Greenland ice sheet, but it also noted that remaining deficiencies in coupling between models of climate and ice sheets (e.g., low spatial resolution) limited the adequate representation of the feedbacks between them. Some Earth system models (ESMs) now incorporate multi-layer snow models and full energy balance models ( [[#Punge--2012|Punge et al., 2012]] ; [[#Cullather--2014|Cullather et al., 2014]] ; [[#van%20Kampenhout--2017|van Kampenhout et al., 2017]] , 2020; [[#Alexander--2019|Alexander et al., 2019]] ) or use elevation classes to compensate for their coarser resolution ( [[#Lipscomb--2013|Lipscomb et al., 2013]] ; [[#Sellevold--2019|Sellevold et al., 2019]] ; [[#Gregory--2020|Gregory et al., 2020]] ; [[#Muntjewerf--2020a|Muntjewerf et al., 2020a]] , b). Resulting SMB simulations compare better with regional climate models and observations ( [[#Alexander--2019|Alexander et al., 2019]] ; [[#van%20Kampenhout--2020|van Kampenhout et al., 2020]] ), but the remaining shortcomings lead to problems reproducing a present-day ice-sheet state close to observations. In summary, there is ''medium confidence'' in quantitative simulations of the present-day state of the Greenland Ice Sheet in ESMs.

The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) stated that there is ''low confidence'' in understanding coastal glacier response to ocean forcing because submarine melt rates, calving rates, bed and fjord geometry and the roles of ice mélange and subglacial discharge are poorly understood. Ice–ocean interactions remain poorly understood and difficult to model, with parametrizations often used for calving of marine-terminating glaciers ( [[#Mercenier--2018|Mercenier et al., 2018]] ) and submarine and plume-driven melt ( [[#Beckmann--2019|Beckmann et al., 2019]] ). Due to the difficulties of modelling the large number of marine-terminating glaciers and limited availability of high-resolution bedrock data, the majority of recent modelling work on Greenland outlet glaciers is focused on individual or a limited number of glaciers ( [[#Krug--2014|Krug et al., 2014]] ; [[#Bondzio--2016|Bondzio et al., 2016]] , 2017; [[#Morlighem--2016b|Morlighem et al., 2016b]] ; [[#Muresan--2016|Muresan et al., 2016]] ; [[#Choi--2017|Choi et al., 2017]] ; [[#Beckmann--2019|Beckmann et al., 2019]] ), or a specific region ( [[#Morlighem--2019|Morlighem et al., 2019]] ). Since SROCC, using a flowline model that includes calving and submarine melting, [[#Beckmann--2019|Beckmann et al. (2019)]] concluded that the AR5 upscaling of contributions from four of the largest glaciers ( [[#Nick--2013|Nick et al., 2013]] ) overestimated the total glacier contribution from the Greenland Ice Sheet, due to differences in response between large and small glaciers. The regional study of [[#Morlighem--2019|Morlighem et al. (2019)]] confirms that ice–ocean interactions have the potential to trigger extensive glacier retreat over decadal time scales, as indicated by observations ( [[#9.4.1.1|Section 9.4.1.1]] ). One focus of continental ice-sheet models has been the improved treatment of marine-terminating glaciers via the inclusion of calving processes and freely moving calving fronts ( [[#Aschwanden--2019|Aschwanden et al., 2019]] ; [[#Choi--2021|Choi et al., 2021]] ). An improved bedrock topographic dataset ( [[#Morlighem--2017|Morlighem et al., 2017]] ) allows for ice discharge to be better captured for outlet glaciers in continental ice-sheet models, and simulations indicate that bedrock topography controls the magnitude and rate of retreat ( [[#Aschwanden--2019|Aschwanden et al., 2019]] ; [[#Rückamp--2020|Rückamp et al., 2020]] ). Overall, although there is ''high confidence'' that the dynamic response of Greenland outlet glaciers is controlled by bedrock topography, there is ''low confidence'' in quantification of future mass loss from Greenland triggered by warming ocean conditions, due to limitations in the current understanding of ice–ocean interactions, its implementation in ice-sheet models, and knowledge of bedrock topography.

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) noted the progress made in Greenland Ice Sheet models since AR5. New since SROCC is a focus on improved representation of the present-day state of the ice sheet (Box 9.3; [[#Goelzer--2018|Goelzer et al., 2018]] , 2020). Improvements are closely linked to the growing number and quality of observations ( [[#9.4.1.1|Section 9.4.1.1]] ), new techniques to generate internally consistent input datasets ( [[#Morlighem--2014|Morlighem et al., 2014]] , 2016a), wider use of data assimilation techniques ( [[#Larour--2014|Larour et al., 2014]] , 2016; [[#Perego--2014|Perego et al., 2014]] ; [[#Goldberg--2015|Goldberg et al., 2015]] ; [[#Lee--2015|Lee et al., 2015]] ; [[#Schlegel--2015|Schlegel et al., 2015]] ; [[#Mosbeux--2016|Mosbeux et al., 2016]] ), increased model resolution ( [[#Aschwanden--2016|Aschwanden et al., 2016]] ) and tuning of key processes such as calving ( [[#Choi--2021|Choi et al., 2021]] ). A remaining challenge is ''low confidence'' in reproducing historical mass changes of the Greenland Ice Sheet (Box 9.3). However, there is ''medium confidence'' in ice-sheet models reproducing the present state of the Greenland Ice Sheet, leading to ''medium confidence'' in the current ability to accurately project its future evolution.

<div id="9.4.1.3" class="h3-container"></div>

<span id="projections-to-2100"></span>
==== 9.4.1.3 Projections to 2100 ====

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The AR5 and SROCC projected that changes in Greenland SMB will contribute to sea level in 2100 by 0.03 (0.01 to 0.07) m sea level equivalent (SLE) under RCP2.6, and 0.07 (0.03 to 0.16) m SLE under RCP8.5. New since SROCC are the projections of SMB obtained by an ESM, two regional climate models, and reconstructions based on temperature from the CMIP5 and CMIP6 ensembles ( [[#Hofer--2020|Hofer et al., 2020]] ; [[#Noël--2021|Noël et al., 2021]] ). The range of sea level contribution from Greenland SMB in [[#Noël--2021|Noël et al. (2021)]] is comparable to the AR5 assessment when either CMIP5 or CMIP6 models are used, while [[#Hofer--2020|Hofer et al. (2020)]] find a greater mass loss across all CMIP6 emissions scenarios when compared to CMIP5 scenarios. Using SSP5-8.5 instead of RCP8.5 increases the mean projected sea level from 2005–2100 by up to 0.06 m in the regional climate model simulations of [[#Hofer--2020|Hofer et al. (2020)]] who attribute the difference mainly to a greater Arctic amplification and associated cloud and sea ice feedbacks in the CMIP6 SSP5-8.5 simulations. In summary, these new projections with fixed ice-sheet topography do not provide sufficient evidence to change the AR5 and SROCC assessments.

Reviewing modelling studies since AR5 ( [[#Church--2013b|Church et al., 2013b]] ), SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) assessed Greenland’s contribution to future sea level to be relatively similar to AR5 (Table 9.2). The baseline for projections has shifted from 1986–2005 in SROCC, to 1995–2014 in this Report. Adjusted to the new 1995–2014 baseline by subtracting 0.01 m, SROCC projected a ''likely'' contribution of 0.07 (0.0–0.11) m SLE under RCP2.6, and 0.14 (0.08–0.27) m SLE under RCP8.5 by 2100. Since SROCC, new projections for the 21st century have included dynamic ice sheets coupled to ESMs ( [[#Muntjewerf--2020a|Muntjewerf et al., 2020a]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ) or regional atmospheric models (Table 9.2; [[#Le%20clec’h--2019|Le clec’h et al., 2019]] ). The coupled ESM–ice-sheet model CESM2–CISM2 (Community Earth System Model Version 2 and Community Ice Sheet Model 2) projects a sea level rise of 0.109 m in 2100 relative to 2015 under SSP5-8.5 ( [[#Muntjewerf--2020a|Muntjewerf et al., 2020a]] ) and a similar contribution under the idealized 1% yr <sup>–1</sup> increase in CO <sub>2</sub> scenario ( [[#Muntjewerf--2020b|Muntjewerf et al., 2020b]] ). The CESM2–CISM2 simulations include ice-sheet–atmosphere interactions and ice-sheet surface meltwater routed to the ocean. The coupled regional atmospheric model and ice-sheet model MAR-GRISLI (Modèle Atmosphérique Régional and Grenoble ice sheet and land ice model) projects a sea level rise of 0.079 m in 2100 relative to 2000 under RCP8.5 (Le Clec’h et al., 2019). An ESM of lower complexity coupled to an ice-sheet model gives a sea level contribution of 0.025 to 0.064 m under RCP2.6 and 0.056 to 0.12 m under RCP8.5 (the range is due to four simulations with different parameter sets for the atmosphere model) ( [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ). [[#Van%20Breedam--2020|Van Breedam et al. (2020)]] identify a simulation with a preferred parameter set that projects 0.034 m for RCP2.6 and 0.073 m for RCP8.5. Although the ocean does not directly force the ice-sheet models in these simulations, the new coupled models allow for interactions between ice-sheet dynamics, SMB and local climate. The coupled projections fall within the lower bounds of AR5 and SROCC and, as these studies do not prescribe ocean forcing directly, it is possible that the dynamic response is underestimated.

<div id="_idContainer044" class="Basic-Text-Frame"></div>

'''Table''' '''9.2 |''' '''Projected sea level contributions in metres from the Greenland Ice Sheet by 2100 relative to 199''' '''5–2''' '''014, unless otherwise stated, for selected Representative Concentration Pathway (RCP) and Shared Socio-economic Pathways (SSP) scenarios.''' Italics denote partial contributions. Historical dynamic response omitted from ISMIP6 simulations is estimated to be 0.19 ± 0.10 mm yr <sup>–1</sup> (0.02 m ± 0.01 m in 2100 relative to 2015). The climate forcing is described in Appendix 7.SM.2.

{| class="wikitable"
|-
| colspan="5"| '''Representative Concentration Pathways (RCPs)'''

|-
| '''Study'''

| '''RCP2.6'''

| '''RCP4.5'''

| '''RCP8.5'''

| '''Notes'''

|-
| IPCC AR5 and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] )

| 0.07 (0.03 to 0.11)

| 0.08 (0.04 to 0.15)

| 0.14 (0.08 to 0.27)

| Median and ''likely'' (66% range) contributions in 2100 relative to 1995–2014. Median of multiple studies

|-
| ''ISMIP6 CMIP5-forced'' ( [[#Goelzer--2020|Goelzer et al., 2020]] ); ''excludes historical dynamic response''

| ''0.01 to 0.05''

| n/a

| ''0.04 to 0.14''

| ''Range of multi-model contributions in 2100 relative to 2015 from 1 ESM for RCP2.6 and 6 ESMs for RCP8.5 (see caption)''

|-
| Coupled regional atmosphere–ice sheet model ( [[#Le%20clec’h--2019|Le clec’h et al., 2019]] )

| n/a

| n/a

| 0.079

| Contribution in 2100 relative to 2000 from AR-GRISLI model

|-
| Coupled Earth system model (ESM) of lower complexity-ice-sheet model ( [[#Van%20Breedam--2020|Van Breedam et al., 2020]] )

| 0.034 (0.025 to 0.064)

| n/a

| 0.073 (0.056 to 0.12)

| Contribution in 2100 relative to 2000 from LOVECLIM-AGISM model; preferred parameter set and range from four simulations with different parameters for atmosphereodel

|-
| colspan="5"|
|-
| colspan="5"| '''Shared Socio-economic Pathways (SSPs)'''

|-
| '''Study'''

| '''SSP1-2.6'''

| '''SSP2-4.5'''

| '''SSP5-8.5'''

| '''Notes'''

|-
| Coupled ESM–ice sheet model ( [[#Muntjewerf--2020a|Muntjewerf et al., 2020a]] )

| n/a

| n/a

| 0.109

| Contribution in 2100 relative to 2015 from coupled CESM2–CISM2

|-
| ''ISMIP6 CMIP6-forced'' ( [[#Payne--2021|Payne et al., 2021]] ) ''; excludes historical dynamic response''

| ''0.02 to 0.06''

| n/a

| ''0.08 to 0.25''

| ''Range of multi-model contributions in 2100 relative to 2015 from one ESM for SSP1-2.6 and four ESMs for SSP5-8.5''

|-
| ISMIP6 CMIP5 and CMIP6 forced ensemble including historical dynamic response

| 0.06 (0.05 to 0.07)

[0.04 to 0.08]

| n/a

| 0.11 (0.09 to 0.14)

[0.07 to 0.17]

| Median (66% range) [90% range] contribution from ISMIP6 CMIP5- and CMIP6-forced multi-model ensembles

|-
| ISMIP6 with AR5 parametric fit: used to estimate rates (Supplementary Material 9.SM.4.4) including historical dynamic response

| 0.08 (0.06 to 0.10)

[0.05 to 0.12]

| 0.10 (0.08 to 0.13) [0.07 to 0.15]

| 0.14 (0.11 to 0.18)

[0.10 to 0.22]

| Median (66% range) [90% range] contribution from AR5 parametric fit to ISMIP6 ensemble, relative to 1995–2014

|-
| ''Emulated ISMIP6; excludes historical dynamic response'' ( [[#Edwards--2021|Edwards et al., 2021]] )

| ''0.03 (–0.01 to 0.08)''

''[–0.04 to 0.12]''

| ''0.06 (0.01 to 0.10)''

''[–0.02 to 0.15]''

| ''0.11 (0.06 to 0.16)''

''[0.03 to 0.21]''

| ''Median (66% range) [90% range] contribution in 2100 relative to 2015 from emulator of ISMIP6 used with Chapter 7: Climate Forcing''

|-
| '''This assessment: emulated ISMIP6 total'''

| '''0.06 (0.01 to 0.10)'''

'''[–0.02 to 0.15]'''

| '''0.08 (0.04 to 0.13)'''

'''[0.01 to 0.18]'''

| '''0.13 (0.09 to 0.18)'''

'''[0.05 to 0.23]'''

| '''As above, but relative to 1995–2014 and including historical dynamic response'''

|}

Since SROCC, projections of the Greenland Ice Sheet are also available from The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) (Box 9.3; Annex II; Figure 9.17; [[#Nowicki--2016|Nowicki et al., 2016]] , 2020a). ISMIP6 multi-model projections are corrected with an assessment of the historical dynamical response to pre-2015 climate forcing (Box 9.3). For the period 2015–2100, the ISMIP6 uncorrected multi-model ensemble projects sea level contributions ranging from 0.01 to 0.05 m under RCP2.6, 0.04 to 0.14 m under RCP8.5 ( [[#Goelzer--2020|Goelzer et al., 2020]] ), 0.02 to 0.06 m under SSP1-2.6, and 0.08 to 0.25 m under SSP5-8.5 (Table 9.2; [[#Payne--2021|Payne et al., 2021]] ). The higher mass loss in the SSPs is attributed to a larger decrease in SMB due to the high climate sensitivity of the models used ( [[#Payne--2021|Payne et al., 2021]] ). This finding is confirmed by [[#Choi--2021|Choi et al. (2021)]] , where CMIP6 SSP5-8.5 SMB leads to larger ice loss than CMIP5 RCP8.5, while ice discharge is similar. As the ISMIP6 framework considers a subset of the RCPs/SSPs and CMIP models, SSP-based projections have been inferred from multiple approaches. First, the ISMIP6 CMIP5-forced ( [[#Goelzer--2020|Goelzer et al., 2020]] ) and CMIP6-forced ( [[#Payne--2021|Payne et al., 2021]] ) combined ensemble projections were corrected with the historical trend (Box 9.3) using bootstrapping. Second, an emulator of the ISMIP6 projections (Box 9.3; [[#Edwards--2021|Edwards et al., 2021]] ) is forced by distributions of global surface air temperature for each SSP from a two-layer energy budget emulator (Supplementary Material 7.SM.2) and then corrected with the historical trend in the same way. These two approaches result in projections that are similar in their median values to AR5 and SROCC projections (Table 9.2), but differ in their range. Similar results are obtained when the AR5 parametric fit is applied to the ISMIP6 models (Table 9.2, Supplementary Material 9.SM.4.4), which is used to estimate rates of change and post-2100 projections (Sections 9.4.1.4 and 9.6.3.2).

The SROCC noted that the study by [[#Aschwanden--2019|Aschwanden et al. (2019)]] projects a significantly higher Greenland contribution to sea level than the assessed ''likely'' range in AR5 and SROCC. Under RCP8.5, [[#Aschwanden--2019|Aschwanden et al. (2019)]] found that Greenland could contribute up to 0.33 m to sea level by 2100 relative to 2000 (the ensemble member that best reproduces the 2000–2015 mean SMB from a regional climate model projects Greenland mass losses of 0.08 m SLE under RCP2.6 and 0.18 m SLE under RCP8.5). The SROCC noted that the potentially high sea level contribution in this study could be due to the assumption of spatially uniform warming, which can overestimate surface melt rates. However, it also reflects the ''deep uncertainty'' surrounding atmospheric forcing, surface processes, submarine melt, calving and ice dynamics. [[#Goelzer--2020|Goelzer et al. (2020)]] ascribe 40% of the ISMIP6 multi-model ensemble spread to ice-sheet model uncertainty, 40% to climate model uncertainty and 20% to ocean forcing uncertainty. We note that this finding reflects the current challenges associated with the representation of ice–ocean interactions in models, and the uncertainty in basal conditions ( [[#9.4.1.2|Section 9.4.1.2]] ). However, this finding is consistent with the work of [[#Aschwanden--2019|Aschwanden et al. (2019)]] and thus, there is ''medium confidence'' that uncertainty in mass loss from the Greenland Ice Sheet is dominated by uncertainty in climate scenario and surface processes, whereas uncertainty in calving and frontal melt play a minor role.

The SROCC stated that surface processes, rather than ice discharged into the ocean, will dominate Greenland ice loss over the 21st century, regardless of the emissions scenario ( ''high confidence'' ). This is confirmed by the ISMIP6 projections ( [[#Goelzer--2020|Goelzer et al., 2020]] ; [[#Payne--2021|Payne et al., 2021]] ). The projected mass loss of Greenland is predominantly due to increased surface meltwater and loss in refreezing capacity resulting in decreasing SMB ( ''high confidence'' ), concurrent with rising temperatures and darkening of the ice-sheet surface ( [[#Fettweis--2013|Fettweis et al., 2013]] ; [[#Vizcaino--2015|Vizcaino et al., 2015]] ; Le Clec’h et al., 2019; [[#Muntjewerf--2020a|Muntjewerf et al., 2020a]] , b; [[#Sellevold--2020|Sellevold and Vizcaíno, 2020]] ). Mass changes due to SMB and outlet glacier dynamics are linked ( [[#Goelzer--2013|Goelzer et al., 2013]] ; [[#Fürst--2015|Fürst et al., 2015]] ; [[#Rückamp--2020|Rückamp et al., 2020]] ), as mass loss by one process decreases mass loss by the other – for example, SMB removes ice before it can reach the marine glacier terminus. There is ''medium confidence'' that the mass loss through ice discharge will decrease in the future ( [[#Fürst--2015|Fürst et al., 2015]] ; [[#Aschwanden--2019|Aschwanden et al., 2019]] ; [[#Golledge--2019|Golledge et al., 2019]] ), because an increase in mass loss (via increased discharge or surface runoff) leads, in most areas, to a retreat of the glacier margin onto land above sea level, isolating the ice sheet from marine influence.

In summary, it is ''virtually certain'' that the Greenland Ice Sheet will continue to lose mass this century under all emissions scenarios, and ''high confidence'' that total mass loss by 2100 will increase with cumulative emissions. The sea level assessment ( [[#9.6.3.3|Section 9.6.3.3]] ) is based on the emulated ISMIP6 projections, allowing a more consistent approach to a wider range of climate and ocean forcings. The Greenland Ice Sheet is ''likely'' to contribute 0.06 (0.01 to 0.10) m under SSP1-2.6 and 0.13 (0.09 to 0.18) m under SSP5-8.5 by 2100 relative to 1995–2014. These projections (as well as those of AR5 and SROCC) are lower than the study of [[#Aschwanden--2019|Aschwanden et al. (2019)]] or the range of possible sea level changes resulting from Structured Expert Judgement (SEJ; [[#9.6.3.2|Section 9.6.3.2]] ; [[#Bamber--2019|Bamber et al., 2019]] ), contributing to the ''deep uncertainty'' in projected sea level (Box 9.4). There is, however, ''high confidence'' that the loss from Greenland will become increasingly dominated by SMB and surface melt, as the ocean-forced dynamic response of glaciers will diminish as marine margins retreat to higher grounds.

<div id="9.4.1.4" class="h3-container"></div>

<span id="projections-beyond-2100"></span>
==== 9.4.1.4 Projections Beyond 2100 ====

<div id="h3-22-siblings" class="h3-siblings"></div>

The AR5 ( [[#Church--2013b|Church et al., 2013b]] ) assessed the contribution from Greenland to sea level projections in 2300 as 0.15 m SLE in low-emissions scenarios (about RCP2.6) and 0.31–1.19 m in high scenarios (approximately RCP6.0/RCP8.5). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) did not update AR5 estimates, given ''limited evidence'' and ''low agreement'' from three new studies ( [[#Vizcaino--2015|Vizcaino et al., 2015]] ; [[#Calov--2018|Calov et al., 2018]] ; [[#Aschwanden--2019|Aschwanden et al., 2019]] ). Since SROCC, a new study gives a sea level contribution of 0.11 to 0.20 m in low-emissions scenarios and 0.61 to 1.29 m in high-emissions scenarios ( [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ). The low-emissions projections by [[#Van%20Breedam--2020|Van Breedam et al. (2020)]] encompass AR5’s assessed contribution, while the high emissions projections are higher than that from AR5. The ‘optimal’ ensemble member of [[#Aschwanden--2019|Aschwanden et al. (2019)]] (see also [[#9.4.1.3|Section 9.4.1.3]] ) indicates that Greenland could contribute 0.25 m under RCP2.6 and 1.74 m under RCP8.5. Structured expert judgement ( [[#Bamber--2019|Bamber et al., 2019]] ) projects Greenland losses of 0.54 (0.28–1.28) m under 2°C warming and 0.97 (0.4–2.23) m under 5°C warming. These studies therefore agree that the AR5 and SROCC assessments are at the low end of the range of projections. In addition, observations suggest that Greenland Ice Sheet losses are tracking the upper range of AR5 projections (T. [[#Slater--2020|]] [[#Slater--2020|Slater et al., 2020]] ). Therefore, we update the ''likely'' range for the contribution of the Greenland Ice Sheet to global mean sea level (GMSL) by 2300 to 0.11–0.25 m under RCP2.6/SSP1-2.6 and 0.31–1.74 m under RCP8.5/SSP5-8.5. However, given the uncertainty in climatic drivers used to project ice-sheet change over the 21st century ( [[#Goelzer--2020|Goelzer et al., 2020]] ; [[#Hofer--2020|Hofer et al., 2020]] ; [[#Noël--2021|Noël et al., 2021]] ) and the large range in simulations since AR5 extending beyond 2100, we only have ''low confidence'' in the contribution to GMSL by 2300 and beyond.

The role of the elevation–mass feedback for future projections of Greenland can be assessed from paleo simulations. Ice-sheet model simulations of the Laurentide ( [[#Gomez--2015|Gomez et al., 2015]] ; [[#Gregoire--2016|Gregoire et al., 2016]] ) and Eurasian ( [[#Alvarez-Solas--2019|Alvarez-Solas et al., 2019]] ) ice sheets invoke at least some contribution to last glacial termination mass loss from SMB reduction, as a consequence of an elevation–mass balance feedback ( [[#Levermann--2016|Levermann and Winkelmann, 2016]] ). In a model spanning Meltwater Pulse 1A, this mechanism increased mass loss by approximately 66% ( [[#Gregoire--2016|Gregoire et al., 2016]] ) but in Last Interglacial simulations, the effect of this feedback is shown to depend on the surface scheme of the climate model employed ( [[#Plach--2019|Plach et al., 2019]] ). Given the agreement between theoretical analyses and paleo-ice-sheet model experiments, there is ''high confidence'' that the elevation–mass balance feedback is most relevant at multi-centennial and millennial time scales, consistent with future-focused studies (Aschwanden et al. 2019, Le Clec’h et al., 2019, [[#Gregory--2020|Gregory et al., 2020]] ).

The SROCC adopted the AR5 assessment that complete loss of Greenland ice, contributing about 7 m to sea level, over a millennium or more would occur for a sustained global mean surface temperature (GMST) between 1°C ( ''low confidence'' ) and 4°C ( ''medium confidence'' ) above pre-industrial levels. New studies since SROCC ( [[#Gregory--2020|Gregory et al., 2020]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ) confirm this assessment (see also Figure 9.30). [[#Clark--2016|Clark et al. (2016)]] estimate a complete loss to take about 8000 years at 5.5°C and about 3000 years at 8.6°C. Based on the agreement between new and previous studies, there is therefore ''high confidence'' that the rate at which Greenland Ice Sheet commitment is realized depends on the amount of warming.

Accounting for more detailed feedbacks between the atmosphere and the ice sheet ( [[#Gregory--2020|Gregory et al., 2020]] ) found a gradual relationship between sustained global mean warming and the corresponding near-equilibrium ice-sheet volume, in contrast to a sharp threshold as found by [[#Robinson--2012|Robinson et al. (2012)]] . Rather than a climatically controlled tipping point for irreversible loss of the Greenland Ice Sheet, [[#Gregory--2020|Gregory et al. (2020)]] found a threshold of irreversibility linked to ice-sheet size, similar to previous work ( [[#Ridley--2010|Ridley et al., 2010]] ). The results of [[#Gregory--2020|Gregory et al. (2020)]] show that, if the ice sheet loses mass equivalent to about 3–3.5 m of sea level rise, it would not regrow to its present state, and 2 m of the sea level rise would be irreversible. The point in time at which the current ice sheet might reach this critical volume depends on oceanic and atmospheric conditions, ice dynamics, and climate–ice sheet feedbacks ( [[#Gregory--2020|Gregory et al., 2020]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ). Therefore, projections differ in the magnitude and rate of temperature change to cross the threshold for irreversible loss. Projections from a large ensemble indicate that the mass threshold may be reached in as early as 400 years under extended RCP8.5 if warming reaches 10°C or more above present levels ( [[#Aschwanden--2019|Aschwanden et al., 2019]] ). In summary, there is ''high confidence'' in the existence of threshold behaviour of the Greenland Ice Sheet in a warmer climate; however, there is ''low agreement'' on the nature of the thresholds and the associated tipping points.

<div id="box-9.3" class="h2-container box-container"></div>

'''Box 9.3 | Insights into Land Ice Evolution From Model Intercomparison Projects'''

<div id="h2-16-siblings" class="h2-siblings"></div>

Projections of ice sheets and glaciers in AR5 (Church et al., 2013b) and SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) were assessed by collecting single model studies – with the exception of glaciers in SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ). Community benchmark experiments (ISMIP-HOM; [[#Pattyn--2008|Pattyn et al., 2008]] ) or Marine Ice Sheet Model Intercomparison Projects (MISMIP; [[#Pattyn--2012|Pattyn et al., 2012]] ); MISMIP3d, ( [[#Pattyn--2013|Pattyn and Durand, 2013]] ); MISMIP+ ( [[#Asay-Davis--2016|Asay-Davis et al., 2016]] ; [[#Cornford--2020|Cornford et al., 2020]] ) have substantially advanced ice-sheet modelling since AR5. Model Intercomparison Projects (MIPs) now inform projections of both ice sheets and glaciers: the Ice Sheet MIP for CMIP6 (ISMIP6; Sections 9.4.1.3 and 9.4.2.5), the Linear Antarctic Response MIP (LARMIP-2; [[#9.4.2.5|Section 9.4.2.5]] ) and GlacierMIP ( [[#9.5.1.3|Section 9.5.1.3]] ).

'''Regional forcing for land ice intercomparison projects'''

Simulations of ice sheets and glaciers are dependent on forcing provided by atmosphere and ocean models. Despite progress in representing processes, reducing biases and increasing resolution, regional and global models still have difficulties reproducing observed regional air temperature, surface mass balance (SMB) and ocean changes (Sections 9.4.1.2 and 9.4.2.2, and Atlas.11). An assessment of CMIP5 and CMIP6 climate models, as forcing for land ice models, has been undertaken ( [[#Walsh--2018|Walsh et al., 2018]] ; [[#Barthel--2020|Barthel et al., 2020]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ; [[#Nowicki--2020b|Nowicki et al., 2020b]] ) with the aim of selecting the best available historical forcings and sampling potential regional future climate changes. Despite improvement in simulation of atmospheric forcing, persistent biases remain in CMIP5 and CMIP6, which reduces the fidelity of historical and future simulations of land ice.

Box 9.3

'''ISMIP6 initial state intercomparison projects'''

The ISMIP6 initial state intercomparison projects (initMIP) for the Greenland ( [[#Goelzer--2018|Goelzer et al., 2018]] ) and Antarctic ( [[#Seroussi--2019|Seroussi et al., 2019]] ) ice sheets were designed to understand the uncertainty in sea level projections resulting from the choice of initialization procedures used for projections of sea level ( [[#Nowicki--2016|Nowicki et al., 2016]] ). Participating modelling groups (Annex II) were free to decide on the initialization method used to bring ice-sheet models to a present-day state, with the effect of these choices captured in a control simulation (starting from the present-day state, with no further climate forcing applied), which measures intrinsic model drift. Compared to the earlier SeaRISE intercomparison project ( [[#Bindschadler--2013|Bindschadler et al., 2013]] ; [[#Nowicki--2013|Nowicki et al., 2013]] ), the modelled present-day ice sheets are in closer agreement with observations, and the model drift has been reduced ( [[#Goelzer--2018|Goelzer et al., 2018]] ; [[#Seroussi--2019|Seroussi et al., 2019]] ). Nonetheless, historical simulations remain challenging for ice-sheet models, due to limited ice-sheet observations prior to the satellite era and biases in the historical atmospheric and oceanic forcings from climate models ( [[#Nowicki--2018|Nowicki and Seroussi, 2018]] ). ISMIP6 and LARMIP-2 therefore did not provide a protocol for the historical runs used to bring the ice sheets to present day, nor criteria for sub-selecting models from the multi-model ensemble based on the ability to reproduce historical changes ( [[#Levermann--2020|Levermann et al., 2020]] ; [[#Nowicki--2020a|Nowicki et al., 2020a]] ).

'''ISMIP6 projections for the Greenland and Antarctic ice sheets'''

The ISMIP6 projection protocol ( [[#Nowicki--2016|Nowicki et al., 2016]] , 2020a) was designed to sample the uncertainty in future sea level due to climate scenarios (via the use of high- and low-emissions scenarios and multiple climate models), ice–ocean interactions and inland response to ice-shelf collapse, and ice-sheet model diversity. The participating ice-sheet models are listed in Annex II. For each ice sheet, forcing was selected ( [[#Barthel--2020|Barthel et al., 2020]] ) from the CMIP5 ( [[#Taylor--2012|Taylor et al., 2012]] ) and CMIP6 ( [[#Eyring--2016|Eyring et al., 2016]] ) models. Atmospheric forcing fields consisted of anomalies in SMB and surface air temperatures; these were generated directly from the CMIP models for the Antarctic Ice Sheet and downscaled using the regional climate model (MAR) for the Greenland Ice Sheet ( [[#Hofer--2020|Hofer et al., 2020]] ). To sample the uncertainty due to ocean forcings, models used either a model-specific scheme with the ISMIP6-provided oceanic dataset or a standard ISMIP6 approach. For the Greenland Ice Sheet, the oceanic dataset consists of thermal forcing (temperature minus freezing temperature) extrapolated into fjords and subglacial runoff. The standard approach uses timelines of tidewater glacier retreat ( [[#Slater--2019|D.A. Slater et al., 2019]] , 2020). For the Antarctic Ice Sheet, the oceanic dataset consists of salinity, thermal forcing and temperature added to an observationally derived climatology and extrapolated under ice shelves. The standard approach is a basal melt rate that depends quadratically on thermal forcing, adapted from [[#Favier--2019|Favier et al. (2019)]] , with two different calibrations (Figure 9.19, [[#Jourdain--2020|Jourdain et al., 2020]] ) that reproduce observed basal melt rates across Antarctica or Pine Island Glacier, respectively (Sections 9.4.2.2, 9.4.2.3). Antarctic ice-shelf disintegration datasets ( [[#Nowicki--2020a|Nowicki et al., 2020a]] ) assume that ice shelves disintegrate when annual surface melt reaches a threshold ( [[#Trusel--2015|Trusel et al., 2015]] ).

The ISMIP6 projections (Goelzer et al.,2020; [[#Seroussi--2020|Seroussi et al., 2020]] ; [[#Payne--2021|Payne et al., 2021]] ) are reported as experiment minus control and represent the sea level resulting from future climate change only. The control simulation, which has constant climate conditions starting in 2015 from the historical run, captures drift associated with the choices made for the initialization method and historical run. Subtraction of this control removes any long-term dynamic response of the ice sheet to pre-2015 climate change. This response has been assessed using dynamic discharge derived from observations over the last 40 years ( [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#Rignot--2019|Rignot et al., 2019]] ), under an assumption that it persists at the past rate until 2100, rather than diminishing. The dynamic response to historical forcing is estimated as 0.19 ± 0.10 mm yr <sup>–1</sup> for the Greenland Ice Sheet ( [[#9.4.1.3|Section 9.4.1.3]] ) and 0.33 ± 0.16 mm yr <sup>–1</sup> for the Antarctic Ice Sheet ( [[#9.4.2.5|Section 9.4.2.5]] ). Over the period 2015–2100, this leads to an additional sea level contribution of 1.7 cm for Greenland and 2.8 cm for Antarctica.

'''LARMIP-2 projections for the Antarctic Ice Sheet'''

LARMIP-2 is focused on the uncertainty in the ocean forcing and associated ice-shelf melting ( [[#Levermann--2014|Levermann et al., 2014]] , 2020) with the majority of the models also participating in ISMIP6 (Annex II). The experiments start from present day and impose an additional basal ice-shelf melting of 8 m yr <sup>–1</sup> at the beginning of the 100-year simulation. A control run is used to remove drift resulting from initialization. The time derivative of the ice-sheet response yields a linear response function, which is then convoluted with a forcing of basal shelf melt time series for five Antarctic regions. The forcing time series for RCP2.6, 4.5, 6.0 and 8.5 were obtained from a random combination of global mean temperature for each Representative Concentration Pathway (RCP) from MAGICC-6.0 ( [[#Meinshausen--2011|Meinshausen et al., 2011]] ), a scaling factor and time delay for the relationship between global surface air temperature and subsurface ocean warming in a given sector of the Southern Ocean from one of 19 CMIP5 models ( [[#Taylor--2012|Taylor et al., 2012]] ) and a basal melting sensitivity from the interval [7–16] m yr <sup>–1</sup> °C <sup>–1</sup> to convert the regional subsurface warming into basal ice-shelf melting. This process is repeated 20,000 times to obtain a probability distribution of the sea level contribution for five Antarctic sectors. The linear response framework captures complex temporal responses of the ice sheets resulting from an increase in basal ice-shelf melting, but neglects the response to SMB and any self-dampening or self-amplifying processes, such as marine ice shelf instability (MISI). The LARMIP-2 method is applied to temperature projections for the Shared Socio-economic Pathways (SSPs; Supplementary Material 7.SM.2) and an estimate of SMB change from the AR5 parametric Antarctic Ice Sheet SMB model ( [[#Church--2013b|Church et al., 2013b]] ) is added to the results (Sections 9.4.2.4, 9.4.2.5 and 9.6.3.2). It is not necessary to add a long-term dynamic response to the LARMIP-2 projections, as this is incorporated in the basal melt time series.

'''GlacierMIP projections'''

GlacierMIP ( [[#Marzeion--2020|Marzeion et al., 2020]] ) was designed to estimate the glacier contribution to sea level rise, including from peripheral glaciers in Greenland and Antarctica that can be considered to be dynamically decoupled, or entirely separate, from the ice sheets. Glacier models are described in Annex II. Initial conditions were based on Randolph Glacier Inventory Version 6 ( [[#RGI%20Consortium--2017|RGI Consortium, 2017]] ) and initial ice thickness and volume were provided from an update of [[#Huss--2012|Huss and Farinotti (2012)]] , although some glacier models used their own estimates. Forcings were taken from 10 different CMIP5 general circulation models, selected based on availability of multiple RCPs, the choice in a previous model intercomparison ( [[#Hock--2019a|Hock et al., 2019a]] ), and performance in glacier-covered regions according to [[#Walsh--2018|Walsh et al. (2018)]] . In addition, two global glacier models performed the same experiment with 13 CMIP6 models ( [[#9.5.1.3|Section 9.5.1.3]] ).

'''Use of an emulator with ISMIP6 and GlacierMIP projections'''

The ISMIP6 and GlacierMIP projections are primarily based on a limited number of CMIP5 RCPs and CMIP6 SSPs, and a limited sampling of ice–ocean interaction parameters and ice-shelf collapse simulations. Emulators provide a method for expanding these projections to a range of SSPs with more comprehensive sampling of climate, ice-sheet and glacier modelling uncertainties. Sections 9.4.1.3, 9.4.2.5 and 9.5.1.3 show estimates from the emulator of [[#Edwards--2021|Edwards et al. (2021)]] . This is a Gaussian Process, rather than a physically based (Cross-Chapter Box 7.1) model derived from the ISMIP6 and GlacierMIP simulations; projections use distributions of global surface air temperature (GSAT) from the two-layer emulator (Supplementary Material 7.SM.2) and ice-sheet parameters as inputs, and include estimates of the emulator uncertainty. Therefore, probability intervals are not inflated by a further factor, as is often the case for multi-model ensemble projections, to account for missing uncertainties ( [[#9.6.3.2|Section 9.6.3.2]] ). The emulator is used in [[#9.6.3|Section 9.6.3]] to provide projections of the land ice contribution to sea level that are fully consistent with each other, ocean heat content, and the assessed equilibrium climate sensitivity and projections of GSAT across the entire report.

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=== 9.4.2 Antarctic Ice Sheet ===

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==== 9.4.2.1 Recent Observed Changes ====

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As stated in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.4|Section 2.3.2.4]] , satellite observations by Ice Sheet Mass Balance Intercomparison Exercise (IMBIE) combining multi-team estimates based on altimetry, gravity anomalies (GRACE) and the input-output method, already presented in SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ), are updated and extended to 2020 ( [[#The%20IMBIE%20Team--2021|The IMBIE Team, 2021]] ). The Antarctic Ice Sheet (AIS) lost 2670 [1800 to 3540] Gt mass over the period 1992–2020, equivalent to 7.4 [5.0 to 9.8] mm GMSL rise (for contribution to sea level budget, see Figures 9.16 and 9.18, and Table 9.5). Within uncertainties, this estimate agrees with a review of post-AR5 studies up to 2016 ( [[#Bamber--2018b|Bamber et al., 2018b]] ) and is consistent with recent single studies based on satellite laser altimetry ( [[#Smith--2020|Smith et al., 2020]] ), the input-output method ( [[#Rignot--2019|Rignot et al., 2019]] ) and gravimetry ( [[#Velicogna--2020|Velicogna et al., 2020]] ). The mass-loss rate was on average 49 [–2 to 100] Gt yr <sup>–1</sup> over the period 1992–1999, 70 [22 to 119] Gt yr <sup>–1</sup> over the period 2000–2009, and 148 [94 to 202] Gt yr <sup>–1</sup> over the period 2010–2016 (see Figures 9.16 and 9.18, and Table 9.SM.1). However, recent work suggests that the mass loss has not further increased since 2016 because of regional mass gains in Dronning Maud Land ( [[#Velicogna--2020|Velicogna et al., 2020]] ). Mass loss of the West Antarctic and Antarctic Peninsula ice sheets has increased since about 2000 ( ''very high confidence'' ), essentially due to increased ice discharge ( [[#Harig--2015|Harig and Simons, 2015]] ; [[#Paolo--2015|Paolo et al., 2015]] ; [[#Forsberg--2017|Forsberg et al., 2017]] ; [[#Bamber--2018b|Bamber et al., 2018b]] ; [[#Gardner--2018|Gardner et al., 2018]] ; [[#The%20IMBIE%20Team--2018|The IMBIE Team, 2018]] ; [[#Rignot--2019|Rignot et al., 2019]] ).

The SROCC reported with ''very high confidence'' that the acceleration, retreat and thinning of the principal West Antarctic outlet glaciers has dominated the observed Antarctic mass loss over the last decades, and stated with ''high confidence'' that these losses were driven by melting of ice shelves by warm ocean waters. The average West Antarctic Ice Sheet (WAIS) mass loss of 82 ± 9 Gt yr <sup>–1</sup> between 1992 and 2017 ( [[#The%20IMBIE%20Team--2021|The IMBIE Team, 2021]] ) leads to substantial observed surface lowering (e.g., [[#Schröder--2019|Schröder et al., 2019]] ; [[#Shepherd--2019|Shepherd et al., 2019]] ), particularly in coastal regions (Figure 9.18). Recent studies using satellite altimetry ( [[#Schröder--2019|Schröder et al., 2019]] ) and the input-output method ( [[#Rignot--2019|Rignot et al., 2019]] ) consistently show mass loss in these coastal regions since the late 1970s (Figure 9.16). Because of consistent multiple lines of evidence, there is ''high confidence'' in mass loss of the Totten Glacier in East Antarctica ( [[#Miles--2013|Miles et al., 2013]] ; X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ; [[#Mohajerani--2018|Mohajerani et al., 2018]] ; [[#Rignot--2019|Rignot et al., 2019]] ; [[#Schröder--2019|Schröder et al., 2019]] ; [[#Shepherd--2019|Shepherd et al., 2019]] ) since about 2000, dominated by changes in coastal ice dynamics (X. [[#Li--2016|]] [[#Li--2016|Li et al., 2016]] ). It is currently unclear whether mass loss of the EAIS over the last three decades has been significant ( [[#Rignot--2019|Rignot et al., 2019]] ) or, at 5 ± 46 Gt yr <sup>–1</sup> between 1992 and 2017, essentially zero within uncertainties ( [[#The%20IMBIE%20Team--2018|The IMBIE Team, 2018]] ). In summary, WAIS losses, through acceleration, retreat and thinning of the principal outlet glaciers, dominated the AIS mass losses over the last decades ( ''very high confidence'' ) and there is ''high confidence'' that this is the case since the late 1970s. Furthermore, parts of the EAIS have lost mass in the last two decades ( ''high confidence'' ).

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[[File:f9142cf45da1a832c41b4ffe2541a9e1 IPCC_AR6_WGI_Figure_9_18.png]]

'''Figure 9.18''' '''|''' '''Antarctic Ice Sheet cumulative mass change and equivalent sea level contribution. (a)''' A p-box ( [[#9.6.3.2|Section 9.6.3.2]] ) based estimate of the range of values of paleo Antarctic ice sheet mass and sea level equivalents relative to present day and the median over all central estimates ( [[#Bamber--2009|Bamber et al., 2009]] ; [[#Argus--2010|Argus and Peltier, 2010]] ; [[#Dolan--2011|Dolan et al., 2011]] ; [[#Mackintosh--2011|Mackintosh et al., 2011]] ; [[#Golledge--2012|Golledge et al., 2012]] , 2013, 2014, 2015, 2017b; K.G. [[#Miller--2012|]] [[#Miller--2012|Miller et al., 2012]] ; [[#Whitehouse--2012|Whitehouse et al., 2012]] ; [[#Ivins--2013|Ivins et al., 2013]] ; [[#Argus--2014|Argus et al., 2014]] ; [[#Briggs--2014|Briggs et al., 2014]] ; [[#Maris--2014|Maris et al., 2014]] ; [[#de%20Boer--2015|de Boer et al., 2015]] , 2017; [[#Dutton--2015|Dutton et al., 2015]] ; [[#Pollard--2015|Pollard et al., 2015]] ; [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#Gasson--2016|Gasson et al., 2016]] ; [[#Goelzer--2016|Goelzer et al., 2016]] ; [[#Yan--2016|Yan et al., 2016]] ; [[#Kopp--2017|Kopp et al., 2017]] ; [[#Simms--2019|Simms et al., 2019]] ); '''(b left)''' cumulative mass loss (and sea level equivalent) since 2015, with satellite observations shown from 1993 ( [[#Bamber--2018a|Bamber et al., 2018a]] ; [[#The%20IMBIE%20Team--2018|The IMBIE Team, 2018]] ; [[#WCRP%20Global%20Sea%20Level%20Budget%20Group--2018|WCRP Global Sea Level Budget Group, 2018]] ) and observations from 1979 ( [[#Rignot--2019|Rignot et al., 2019]] ), and projections from Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) to 2100 under RCP8.5/SSP5-8.5 and RCP2.6/SSP1-2.6 scenarios (thin lines from [[#Seroussi--2020|Seroussi et al., 2020]] ; [[#Edwards--2021|Edwards et al., 2021]] ; [[#Payne--2021|Payne et al., 2021]] ) and ISMIP6 emulator under SSP5-8.5 and SSP1-2.6 to 2100 (shades and bold line; [[#Edwards--2021|Edwards et al., 2021]] ) ; (b, right) 17th–83rd, 5th–95th percentile ranges for ISMIP6, ISMIP6 emulator, and LARMIP-2 including surface mass balance (SMB) at 2100. (c – e) Schematic interpretations of individual reconstructions ( [[#Anderson--2002|Anderson et al., 2002]] ; [[#Bentley--2014|Bentley et al., 2014]] ; [[#de%20Boer--2015|de Boer et al., 2015]] ; [[#Goelzer--2016|Goelzer et al., 2016]] ) of the spatial extent of the Antarctic Ice Sheet are shown for the: '''(c)''' mid-Pliocene Warm Period, '''(d)''' Last Interglacial; and '''(e)''' Last Glacial Maximum ( [[#Fretwell--2013|Fretwell et al., 2013]] ): grey shading shows extent of grounded ice. (f – g) Maps of mean elevation changes '''(f)''' 1978–2017 derived from multi-mission satellite altimetry ( [[#Schröder--2019|Schröder et al., 2019]] ) and '''(g)''' ISMIP6: 2061–2100 projected changes for an ensemble using the Norwegian Climate Center’s Earth System Model (NorESM1-M) climate model under the RCP8.5 scenario ( [[#Seroussi--2020|Seroussi et al., 2020]] ). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

As stated in SROCC, snowfall and glacier flow are the largest components determining AIS mass changes, with glacier flow acceleration (dynamic thinning) on the WAIS and the Antarctic Peninsula driving total loss trends in recent decades ( ''very high confidence'' ), and a partial offset of the dominating dynamic-thinning losses by increased snowfall ( ''high confidence'' ). The SROCC attributed ''medium confidence'' to estimates of 20th-century snowfall increases equivalent to a sea level change of –7.7 ± 4.0 mm on the EAIS, and –2.8 ± 1.7 mm on the WAIS, respectively ( [[#Medley--2019|Medley and Thomas, 2019]] ). Loss of buttressing, which can be caused by ice-shelf thinning, gradual ice-shelf front retreat or ice-shelf disintegration, has been linked to instantaneous ice velocity increases, and thus dynamic thinning, since the early 1990s. This link is clearly evident in the Amundsen and, to a lesser degree, Bellingshausen sectors ( [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ), where passive shelf ice (ice that can be removed without major effects on the ice-shelf dynamics) is very limited or absent ( [[#Fürst--2016|Fürst et al., 2016]] ). Surface mass balance (SMB) changes, dominated by snowfall, exhibit strong regional and temporal variability, for example with multi-decadal increases in the Antarctic Peninsula inferred since the 1930s ( [[#Medley--2019|Medley and Thomas, 2019]] ), and dominate the interannual to decadal variability of the AIS mass balance ( [[#Rignot--2019|Rignot et al., 2019]] ). However, no significant continent-wide SMB trend is inferredsince 1979 ( [[#The%20IMBIE%20Team--2018|The IMBIE Team, 2018]] ; [[#Medley--2019|Medley and Thomas, 2019]] ; regional changes of Antarctic SMB are assessed further in [[IPCC:Wg1:Chapter:Atlas|Atlas]] [[IPCC:Wg1:Chapter:Chapter-11#11.1|Section 11.1]] ). In summary, there is ''very high confidence'' that the observed AIS mass loss since the early 1990s is primarily linked to ice-shelf changes.

The SROCC stated with ''high confidence'' that melting of ice shelves by warm ocean waters, leading to reduction of ice-shelf buttressing, has driven the observed ongoing thinning of major WAIS outlet glaciers. Since SROCC, digitized radar measurements have shown that the eastern ice shelf of Thwaites Glacier in the Amundsen Sea Embayment thinned between 10 and 33% during the three decades after 1978 ( [[#Schroeder--2019|Schroeder et al., 2019]] ), and the role of basal ice-shelf melting has been emphasized ( [[#Smith--2020|Smith et al., 2020]] ). Strong surface meltwater production has been noted as a precursor of ice-shelf disintegration in and since SROCC ( [[#Bell--2018|Bell et al., 2018]] ), and recent work placed strong meltwater production events ( [[#Lenaerts--2017|Lenaerts et al., 2017]] ; [[#Nicolas--2017|Nicolas et al., 2017]] ; [[#Wille--2019|Wille et al., 2019]] ) and seasons ( [[#Robel--2019|Robel and Banwell, 2019]] ) in this context. Antarctic ice-shelf basal meltwater flux varied between about 1100 ± 150 Gt yr <sup>–1</sup> in the mid-1990s and about 1570 ± 140 Gt yr <sup>–1</sup> in the late 2000s before decreasing to 1160 ± 150 Gt yr <sup>–1</sup> in 2018, and basal melt rates strongly vary with geographical position and depth, as a function of the surrounding water temperature ( [[#Adusumilli--2020|Adusumilli et al., 2020]] ). [[#9.2.2.3|Section 9.2.2.3]] assesses that the intrusion of warm Circumpolar Deep Water (CDW), which has warmed and shoaled since the 1980s, has been at least partially controlled by forcing with significant decadal variability ''. Limited evidence'' suggests that, beyond strong internal decadal wind variability, increased greenhouse gas forcing has slightly modified the mean local winds between 1920 and 2018, facilitating the intrusion of CDW heat on the Amundsen-Bellingshausen continental shelf, and increased ice shelf melt ( [[#9.2.2.3|Section 9.2.2.3]] ). However, theoretical understanding is still incomplete and in situ measurements within the ice–ocean boundary layer are sparse ( [[#Wåhlin--2020|Wåhlin et al., 2020]] ). Modelling, and therefore attribution of ice shelf basal melt, remains challenging because of insufficient process understanding, required spatial resolution, the paucity of in situ observations ( [[#Dinniman--2016|Dinniman et al., 2016]] ; [[#Asay-Davis--2017|Asay-Davis et al., 2017]] ; [[#Turner--2017|Turner et al., 2017]] ), and uncertainties of bathymetric datasets under ice-shelf cavities ( [[#Goldberg--2019|Goldberg et al., 2019]] , 2020; [[#Morlighem--2020|Morlighem et al., 2020]] ). In summary, ice-shelf thinning, mainly driven by basal melt, is widespread around the Antarctic coast and particularly strong around the WAIS ( ''high confidence'' ), although basal melt rates show substantial spatio-temporal variability.

Satellite observations suggest that changes in sea ice coverage and thickness can modulate iceberg calving, ice shelf flow and glacier terminus position around Antarctica ( [[#Miles--2013|Miles et al., 2013]] , 2016, 2017; [[#Massom--2015|Massom et al., 2015]] ; [[#Greene--2018|Greene et al., 2018]] ; [[#Bevan--2019|Bevan et al., 2019]] ), either through mechanical coupling or via changes to ocean stratification, influencing basal melting. A combined observational and modelling study ( [[#Massom--2018|Massom et al., 2018]] ) showed that regional loss of a protective sea ice buffer played a role in the rapid disintegration events of the Larsen A and B and Wilkins ice shelves in the Antarctic Peninsula between 1995 and 2009, by exposing damaged (rifted) outer ice shelf margins to enhanced flexure by storm-generated ocean swells. One observational study ( [[#Sun--2019|Sun et al., 2019]] ) suggests that the absence of sea ice in front of ice shelves, which leads to strengthened topographic waves, favours higher ice-shelf basal melt rates by increasing the baroclinic (depth varying) ocean heat flux which can enter the cavity ( [[#Wåhlin--2020|Wåhlin et al., 2020]] ). Paleo evidence for sea ice control on ice sheets is lacking, but geologic evidence shows a concordance between periods of ice-sheet growth and the expansion of sea ice ( [[#Patterson--2014|Patterson et al., 2014]] ; [[#Levy--2019|Levy et al., 2019]] ), both being favoured by reduced sea surface temperatures. Modelling confirms that sea ice controls the strength of ice mélange ( [[#Robel--2017|Robel, 2017]] ; [[#Schlemm--2021|Schlemm and Levermann, 2021]] ) and thus influences ice-shelf flexure and calving rates and stability of floating ice margins, but one model shows this had negligible effect on AIS retreat rates during past warm periods ( [[#Pollard--2018|Pollard et al., 2018]] ). Loss of ice-shelf-proximal sea ice is also associated with increased solar heating of surface waters and increased sub-shelf melting ( [[#Bendtsen--2017|Bendtsen et al., 2017]] ; [[#Stewart--2019|Stewart et al., 2019]] ). In summary, although in some cases sea ice decrease and glacier and ice-shelf flow and terminus position changes can have the same common cause, there is ''medium confidence'' that sea ice decrease ultimately favours the mass loss of nearby ice shelves through a variety of processes.

The SROCC stated with ''high confidence'' that ice-shelf disintegration has driven dynamic thinning in the northern Antarctic Peninsula over recent decades, and expressed ''high confidence'' in current ongoing mass loss from glaciers that fed now-disintegrated ice shelves. However, the mass loss rate has decreased in the 20 years since the immediate speed-up following ice-shelf disintegration in 1995 and 2002. Observed flow speed of these tributary glaciers is still 26% higher than before the ice shelf disintegration ( [[#Seehaus--2018|Seehaus et al., 2018]] ). Conversely, one study interpreted the increased flow speed of the Scar Inlet Ice Shelf’s tributary glaciers as a sign of evolving instability of the currently intact ice shelf ( [[#Qiao--2020|Qiao et al., 2020]] ).

Ongoing grounding line retreat, indicating dynamic thinning, is observed with ''high confidence'' in many areas of Antarctica, and particularly on the WAIS, with the highest rates being in the Amundsen and Bellingshausen Sea areas, and around Totten Glacier in East Antarctica, as stated in SROCC. Research published since SROCC has evidenced grounding line retreat of the West Antarctic Berry Glacier on the Getz Coast ( [[#Millan--2020|Millan et al., 2020]] ) and on the East Antarctic Denman Glacier ( [[#Brancato--2020|Brancato et al., 2020]] ), both since 1996. Furthermore observed grounding line retreat in excess of 1.5 km between 2003 and 2015 has been reported for parts of Marie Byrd Land ( [[#Christie--2018|Christie et al., 2018]] ). In summary, there is ''high confidence'' that grounding lines of marine-terminating glaciers are currently retreating in many areas around Antarctica, particularly around the WAIS, and additional areas of grounding line retreat have been evidenced since SROCC.

The SROCC stated with ''medium confidence'' that sustained mass losses of several major glaciers in the Amundsen Sea Embayment (ASE) are compatible with the onset of marine ice sheet instability (MISI). However, whether unstable WAIS retreat had begun, or was imminent, remained a critical uncertainty. New publications since SROCC have not substantially clarified this question. One study that combined satellite measurements with a numerical model and prescribed ice-shelf thinning ( [[#Gudmundsson--2019|Gudmundsson et al., 2019]] ) suggests that MISI is not required to explain the observed current mass loss rates of the WAIS, because they are consistent with external climate drivers. Furthermore, the fast grounding line retreat of the Pine Island Glacier in the ASE, which was triggered in the 1940s ( [[#Smith--2017|Smith et al., 2017]] ), observed after 1992 ( [[#Rignot--2014|Rignot et al., 2014]] ) and previously interpreted as a sign of MISI ( [[#Favier--2014|Favier et al., 2014]] ), seems to have stabilized recently ( [[#Milillo--2017|Milillo et al., 2017]] ; [[#Konrad--2018|Konrad et al., 2018]] ), and its current flow patterns do not suggest ongoing or imminent MISI ( [[#Bamber--2020|Bamber and Dawson, 2020]] ). However, sustained fast grounding line retreat has been observed for the Smith Glacier in the ASE ( [[#Scheuchl--2016|Scheuchl et al., 2016]] ), and an analysis of flow patterns and grounding line retreat of the ASE Thwaites Glacier between 1992 and 2017 ( [[#Milillo--2019|Milillo et al., 2019]] ) showed sustained, albeit spatially heterogeneous, grounding line retreat, highlighting ice–ocean interactions that lead to increased basal melt. In addition, Denman Glacier in East Antarctica was shown to hold potential for unstable retreat ( [[#Brancato--2020|Brancato et al., 2020]] ). In summary, the observed evolution of the ASE glaciers is compatible with, but not unequivocally indicating an ongoing MISI ( ''medium confidence'' ).

The SROCC reported ''limited evidence'' and ''medium agreement'' for anthropogenic forcing of the observed AIS mass balance changes. As stated in [[IPCC:Wg1:Chapter:Chapter-3#3.4.3.2|Section 3.4.3.2]] , there remains ''low confidence'' in attributing the causes of the observed mass of loss from the AIS since 1993, in spite of some additional process-based evidence to support attribution to anthropogenic forcing.

<div id="9.4.2.2" class="h3-container"></div>

<span id="model-evaluation-1"></span>
==== 9.4.2.2 Model Evaluation ====

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The AR5 ( [[#Church--2013b|Church et al., 2013b]] ; [[#Flato--2013|Flato et al., 2013]] ) stated that regional climate models and global models with bias-corrected SST and sea ice concentration tended to produce more accurate simulations of Antarctic SMB than coupled climate models. It also noted strong climate model temperature biases over the Antarctic, though the latter may reflect known biases in the reanalysis used ( [[#Fréville--2014|Fréville et al., 2014]] ). Section Atlas.11.1 assesses that there is ''medium confidence'' in the capacity of climate models to simulate Antarctic climatology and SMB changes.

( [[#9.2.3.2|Section 9.2.3.2]] assesses that there is ''low confidence'' in simulations of Southern Ocean temperature. Few ocean models resolve ice-shelf cavities, and biases in present-day melt rates can be substantial in some sectors, including the key region of the Amundsen Sea (e.g., an exception is the FESOM simulation in Figure 9.19 includes ice-shelf cavities and simulates ice-shelf basal melting and refreezing) ( [[#Naughten--2018|Naughten et al., 2018]] ). An increasing number of observational studies from which basal melt rates are calculated ( [[#Huhn--2018|Huhn et al., 2018]] ; [[#Adusumilli--2020|Adusumilli et al., 2020]] ; [[#Das--2020|Das et al., 2020]] ; [[#Hirano--2020|Hirano et al., 2020]] ; [[#Stevens--2020|Stevens et al., 2020]] ), combined with improved understanding of influences specific to water-masses and modes of melting or dissolving ( [[#Silvano--2018|Silvano et al., 2018]] ; [[#Adusumilli--2020|Adusumilli et al., 2020]] ; [[#Malyarenko--2020|Malyarenko et al., 2020]] ; [[#Wåhlin--2020|Wåhlin et al., 2020]] ), may help to refine these models in the future. However, given the limited number of available models and their biases, there is currently ''low confidence'' in the sub-shelf melt rates simulated by ocean models.

Improvements in the representation of grounding line evolution in ice-sheet models since AR5 (such as sub-grid schemes for basal friction and ice-shelf melt, and local grid refinement) means that most of the model simulations presented in SROCC were dominated by physical processes. Since then, these advances have been applied in several model intercomparison projects – such as ISMIP6 and LARMIP-2 (see Box 9.3); MISMIP+ (Cornford et al. 2020); and ABUMIP (Sun et al. 2020). All models participating in ISMIP6 and LARMIP-2 simulate ice-shelf and grounding-line evolution, and include sub-shelf melt parametrization, which was not the case in the Sea-level Response to Ice Sheet Evolution (SeaRISE) project intercomparison ( [[#Bindschadler--2013|Bindschadler et al., 2013]] ; [[#Nowicki--2013|Nowicki et al., 2013]] ). Simulations of grounding line evolution ( [[#Seroussi--2017|Seroussi et al., 2017]] , 2020) have benefitted from improved bedrock topography ( [[#Morlighem--2020|Morlighem et al., 2020]] ). Treatment of sub-shelf melting, however, remains one of the causes of large differences in AIS models, particularly for partially floating grid cells in models with coarse resolution ( [[#Levermann--2020|Levermann et al., 2020]] ; [[#Edwards--2021|Edwards et al., 2021]] ). Due to the limitations in resolving cavities in ocean models, as described above, basal melt rates are generally parameterized at the ice shelf base, based on ocean model simulations of temperatures and salinity instead ( [[#Nowicki--2020b|Nowicki et al., 2020b]] ; [[#Seroussi--2020|Seroussi et al., 2020]] ). While this has the advantage of connecting melt rates to emissions scenarios, a large variety of melt parametrizations exist ( [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#Lazeroms--2018|Lazeroms et al., 2018]] ; [[#Reese--2018|Reese et al., 2018]] ; [[#Hoffman--2019|Hoffman et al., 2019]] ; [[#Pelle--2019|Pelle et al., 2019]] ; [[#Jourdain--2020|Jourdain et al., 2020]] ), and there is ''low agreement'' due to limited observational constraints (ocean temperature, salinity, velocity, and ice shelf draft)( [[#Jourdain--2020|Jourdain et al., 2020]] ), uncertainty in the physics of parametrized processes, missing processes (e.g., tides), and uncertainty in the treatment of ice-sheet–climate feedbacks ( [[#Donat-Magnin--2017|Donat-Magnin et al., 2017]] ; [[#Bronselaer--2018|Bronselaer et al., 2018]] ; [[#Golledge--2019|Golledge et al., 2019]] ). Parametrizations are usually calibrated to present-day melt rates, but can respond differently to projected ocean warming ( [[#Favier--2019|Favier et al., 2019]] ; [[#Jourdain--2020|Jourdain et al., 2020]] ). Two different calibrations were used in ISMIP6 (Box 9.3; [[#Jourdain--2020|Jourdain et al., 2020]] ; [[#Nowicki--2020b|Nowicki et al., 2020b]] ): one reproducing melt rates averaged around the whole continent (MeanAnt: Figure 9.19), and the other reproducing melt rates near the grounding line of Pine Island Glacier (PIGL; see Figure 9.19), leading to large differences in melt rates. Evaluation with observations and two cavity-resolving models suggests that the MeanAnt parametrization better reproduces observed melt rates and projected increases in both the warm Amundsen Sea Embayment and cold Ronne-Filchner shelf cavity, as well as total Antarctic melting ( [[#Jourdain--2020|Jourdain et al., 2020]] ). The PIGL calibration represents the upper end for increased basal melt sensitivity that would be caused by continent-wide changes to ocean water properties and circulation under strong future forcing ( [[#Jourdain--2020|Jourdain et al., 2020]] ). The basal sliding law also has a strong influence on grounding line retreat and glacier acceleration in response to perturbations, and varies spatially ( [[#Sun--2020|Sun et al., 2020]] ). Sliding laws ( [[#Joughin--2019|Joughin et al., 2019]] ) can only be constrained with observations in regions experiencing significant change, and with sufficiently long observational records.

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[[File:d2fb69c7e007aee6fe14976159bbf4c8 IPCC_AR6_WGI_Figure_9_19.png]]

'''Figure 9.19''' '''|''' '''Ice-shelf basal melt rates for present-day (upper panels) and changes from present-day to the end of the 21st century under the RCP8.5 scenario (lower panels).''' Present-day melt rates were estimated through: the input-output method constrained by satellite observations and atmosphere/snow simulations ( [[#Rignot--2013|Rignot et al., 2013]] ) and representative of 2003–2008 (upper left); the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) non-local-PIGL parametrization constrained by observation-based ocean properties ( [[#Jourdain--2020|Jourdain et al., 2020]] ) and representative of 1995–2014 (upper centre); the Finite Element Sea ice/Ice Shelf Ocean Model (FESOM) simulation over 2006–2015, forced by atmospheric conditions from a Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model mean (MMM) under the RCP8.5 scenario ( [[#Naughten--2018|Naughten et al., 2018]] ) (upper right). Future anomalies are calculated as 2081–2100 minus present-day using the ISMIP6 non-local-MeanAnt and non-local-PIGL parametrizations ( [[#Jourdain--2020|Jourdain et al., 2020]] ) (lower left and centre, respectively) based on projections from the Norwegian Climate Center’s Earth System Model (NorESM1-M) CMIP5 model, and the FESOM-MMM projection (lower right). Note the symmetric-log colour bar (linear around zero, logarithmic for stronger negative and positive values). Inset highlights the Amundsen Sea Region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

The SROCC noted that AIS simulations are increasingly evaluated or formally calibrated with modern observations and/or paleodata – to obtain more realistic initial conditions (ice-sheet geometry, velocity and forcing) and to constrain uncertainty in probabilistic projections. This trend continues ( [[#Nias--2019|Nias et al., 2019]] ; [[#Gilford--2020|Gilford et al., 2020]] ; [[#Hamlington--2020b|Hamlington et al., 2020b]] ; [[#Wernecke--2020|Wernecke et al., 2020]] ). However, while the large-scale characteristics of the initial ice-sheet state have improved significantly (Box 9.3), capturing the smaller-scale rates of change, including mass trends, remains challenging for many models ( [[#Goldberg--2015|Goldberg et al., 2015]] ; [[#Reese--2020|Reese et al., 2020]] ; [[#Seroussi--2020|Seroussi et al., 2020]] ; [[#Siegert--2020|Siegert et al., 2020]] ). This increases uncertainty in projections, especially for the 21st century ( [[#9.4.2.5|Section 9.4.2.5]] ). However, uncertainties in ice-sheet model simulations have been much better quantified since AR5, through model intercomparison projects (in particular, ISMIP6 and LARMIP-2; see Box 9.3), perturbed parameter ensembles, and increasing use of statistical emulation ( [[#Gilford--2020|Gilford et al., 2020]] ; [[#Levermann--2020|Levermann et al., 2020]] ; [[#Wernecke--2020|Wernecke et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ; [[#Edwards--2021|Edwards et al., 2021]] ) to better sample the parameter space. By exploring uncertainties more fully, these methods have the potential to identify better simulations of the historical period.

An important difficulty is how to evaluate simulations of processes that are: not currently observed; or rare; or indirectly deduced – in particular, the ice-shelf disintegrations and cliff failures that would drive the proposed marine ice cliff instability (MICI; [[#9.4.2.4|Section 9.4.2.4]] and Box 9.4; [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#DeConto--2021|DeConto et al., 2021]] ). Models of ice-cliff failure can only be indirectly and partially evaluated, using existing (i.e., static) cliffs and laboratory experiments ( [[#Clerc--2019|Clerc et al., 2019]] ). The SROCC stated that there was ''low agreement'' on the exact MICI mechanism and ''limited evidence'' of its occurrence in the present or the past, and that the validity of MICI remains unproven. Only one ice-sheet model represents MICI ( [[#Pollard--2015|Pollard et al., 2015]] ; [[#DeConto--2016|DeConto and Pollard, 2016]] ; [[#DeConto--2021|DeConto et al., 2021]] ). The mechanism has not been found to be essential for reproducing Mid Pliocene Warm Period and Last Interglacial reconstructions or satellite observations, though Last Interglacial data slightly favours it in this model ( [[#Edwards--2019|Edwards et al., 2019]] ; [[#Gilford--2020|Gilford et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ).

In summary, there is now ''medium confidence'' in many ice-sheet processes in ice-sheet models, including grounding line evolution. However, there remains ''low confidence'' in the ocean forcing affecting the basal melt rates, and ''low confidence'' in simulating mechanisms that have the potential to cause widespread, sustained and very rapid ice loss from Antarctica through MICI.

<div id="9.4.2.3" class="h3-container"></div>

<span id="drivers-of-future-antarctic-ice-sheet-change"></span>
==== 9.4.2.3 Drivers of Future Antarctic Ice Sheet Change ====

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<div id="9.4.2.3.1" class="h4-container"></div>

<span id="surface-mass-balance"></span>
===== 9.4.2.3.1 Surface mass balance =====

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The AR5 projected a negative contribution from Antarctic surface mass balance (SMB) changes to sea level over the 21st century (i.e., mitigating sea level rise), due to increased snowfall associated with warmer air temperatures. Sensitivity of SMB to Antarctic surface air temperature change varied from 3.7 to 7% °C <sup>–1</sup> , and the sea level projections assumed a sensitivity of 5.1 ± 1.5% °C <sup>–1</sup> from CMIP3 era models ( [[#Gregory--2006|Gregory and Huybrechts, 2006]] ) to estimate SMB changes from Antarctic temperatures in the CMIP5 ensemble. Since the AR5, analyses of CMIP5 and CMIP6 models have found Antarctic temperature sensitivity for accumulation (precipitation minus sublimation) of 3.5 to 8.7% °C <sup>–1</sup> ( [[#Frieler--2015|Frieler et al., 2015]] ), for SMB of 6.0 to 9.9% °C <sup>–1</sup> ( [[#Previdi--2016|Previdi and Polvani, 2016]] ) and for precipitation of around 4 to 9% °C <sup>–1</sup> (±1 standard deviation ranges; [[#Bracegirdle--2020|Bracegirdle et al., 2020]] ). An accumulation sensitivity estimate derived from ice core data lies in the middle of the range, around 6% °C <sup>–1</sup> ( [[#Frieler--2015|Frieler et al., 2015]] ). These are consistent, within uncertainties, with each other and AR5, under the approximation that SMB is dominated by snowfall.

The AR5 found that the median and ''likely'' sea level contributions due to SMB from 1986–2005 to 2100 were –0.05 (–0.09 to –0.02) m under RCP8.5 and –0.02 (–0.05 to 0.00) m under RCP2.6. The SROCC did not present a separate SMB contribution, instead showing total Antarctic projections derived from ice-sheet models ( [[#9.4.2.5|Section 9.4.2.5]] ). Projections of the SMB contribution to sea level tend to be slightly more negative since AR5, due at least in part to the higher range in equilibrium climate sensitivity values in CMIP6 ( [[#Payne--2021|Payne et al., 2021]] ). Mean and ±1 standard deviation ranges for grounded Antarctic Ice Sheet SMB changes from 2000 to 2100 computed from CMIP5 models are –0.08 (–0.13 to –0.04) m sea level equivalent (SLE) for RCP8.5 and, similarly for CMIP6 models, are –0.07 (–0.11 to –0.03) m for SSP5-8.5 ( [[#Gorte--2020|Gorte et al., 2020]] ). The general circulation models (GCMs) used to drive ice-sheet models in ISMIP6 (Box 9.3) project mean grounded AIS SMB changes from 2005 to 2100 of –0.06 (range –0.08 to –0.03) m SLE under RCP8.5 for the six CMIP5 models ( [[#Seroussi--2020|Seroussi et al., 2020]] ) and –0.09 (range –0.10 to –0.07) m SLE under SSP5-8.5 for the four CMIP6 models, which have climate sensitivity values of 4.8°C –5.3°C ( [[#Payne--2021|Payne et al., 2021]] ). We apply the AR5 parametric AIS SMB model ( [[#9.6.3.2|Section 9.6.3.2]] ) to updated projections of global mean temperature from a two-layer energy budget emulator (Supplementary Material 7.SM.2), which gives a median –0.05 (5–95% range –0.07 to –0.02) m SLE for SSP5-8.5 ( [[#9.4.2.5|Section 9.4.2.5]] , Table 9.3), that is, similar to the AR5 assessment and slightly smaller than the CMIP6 estimate. This estimate is used to augment the LARMIP-2 dynamic projections (Box 9.3) in Sections 9.4.2.5 and 9.4.2.6. Overall, CMIP5 and CMIP6 GCM simulations of sea level fall by 2100 due to Antarctic SMB increases are around 2–4 cm greater than estimates derived with the statistical method used in AR5. Further details about projections of Antarctic temperature, precipitation and SMB are provided in Section Atlas.11.1.4, which assesses that, due to the challenges of model evaluation ( [[#9.4.2.2|Section 9.4.2.2]] ) and the possibility of increased meltwater runoff ( [[#Kittel--2021|Kittel et al., 2021]] ), there is only ''medium confidence'' that the future contribution of Antarctic SMB to sea level this century will be negative under all greenhouse gas emissions scenarios. Longer time scales are discussed in 9.4.2.6.

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<span id="sub-shelf-melting"></span>
===== 9.4.2.3.2 Sub-shelf melting =====

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The SROCC highlighted that an important ongoing deficiency in projections of Antarctic sub-shelf melting is the lack of ice–ocean coupling in most continental-scale studies. Increased basal melting is mainly caused by warmer CDW ( [[#9.2.2.3|Section 9.2.2.3]] ) on the continental shelves, and warming surface waters intruding under ice shelves ( [[#Naughten--2018|Naughten et al., 2018]] ). Predicting whether or not open ocean water masses will freely penetrate ice shelf cavities, or will be partially blocked by ocean density gradients, is complex ( [[#Wåhlin--2020|Wåhlin et al., 2020]] ); while melting related to CDW inflow is currently dominant in the Amundsen Sea Embayment, melt in other embayments is limited by deep inflows of high-salinity shelf water or seasonally warmed shallow incursions of Antarctic Surface Water ( [[#Stewart--2019|Stewart et al., 2019]] ; [[#Adusumilli--2020|Adusumilli et al., 2020]] ). There is little consensus regarding future change in CDW ( [[#9.2.2.3|Section 9.2.2.3]] ), and more generally ''low confidence'' in future change in the temperature of Antarctic ice-shelf cavities ( [[#9.2.3.2|Section 9.2.3.2]] ).

The response of sub-shelf melting to ocean warming is also poorly constrained. A key unknown is whether, and when, cold ice-shelf cavities might become more similar to the Amundsen Sea Embayment, not only in ocean temperature but also ice–ocean heat exchange, which depends on the cavity geometry and ocean circulation ( [[#Little--2009|Little et al., 2009]] ). Only two ocean models with ice-shelf cavities have been used to make sub-shelf basal melting projections for Special Report on Emissions Scenarios and Representative Concentration Pathway (RCP) scenarios ( [[#Hellmer--2012|Hellmer et al., 2012]] ; [[#Timmermann--2013|Timmermann and Hellmer, 2013]] ; [[#Timmermann--2017|Timmermann and Goeller, 2017]] ; [[#Naughten--2018|Naughten et al., 2018]] ). The FESOM simulation, forced by a CMIP5 multi-model mean under RCP8.5, projects a 90% increase in melting (Figure 9.19), although this could be overestimated due to an underestimation of present-day melt rates ( [[#9.4.2.2|Section 9.4.2.2]] ; [[#Naughten--2018|Naughten et al., 2018]] ). The temperature–melt relationship was parameterized by ISMIP6 in terms of heat exchange velocity in m a <sup>–1</sup> , and by LARMIP-2 as basal melt sensitivity in m a <sup>–1</sup> °C <sup>–1</sup> (Box 9.3; [[#Jourdain--2020|Jourdain et al., 2020]] ; [[#Levermann--2020|Levermann et al., 2020]] ; [[#Reese--2020|Reese et al., 2020]] ), and both vary widely around the continent, depending on cavity type. Median values of ISMIP6 heat exchange velocity vary by a factor of 5–10 when calibrating to either mean Antarctic or high Pine Island Glacier observed melt rates ( [[#9.4.2.2|Section 9.4.2.2]] ; Box 9.3; [[#Jourdain--2020|Jourdain et al., 2020]] ). Basal melt sensitivities near the grounding line estimated by [[#Reese--2020|Reese et al. (2020)]] with a box model of ocean overturning range from 3.9 m a <sup>–1</sup> °C <sup>–1</sup> for the Weddell Sea to 10.5 m a <sup>–1</sup> °C <sup>–1</sup> for the Amundsen Sea region, with a continental mean of 5.3 m a <sup>–1</sup> °C <sup>–1</sup> . Similarly high Amundsen Sea sensitivities are estimated in coupled ice–ocean simulations of Thwaites Glacier (mean 9.4 m a <sup>–1</sup> °C <sup>–1</sup> ; range 6–16 m a <sup>–1</sup> °C <sup>–1</sup> ) ( [[#Seroussi--2017|Seroussi et al., 2017]] ). These large variations lead to large differences in basal melt rates and projected sea level contributions when applied to the whole ice sheet in ISMIP6 and LARMIP-2 (Box 9.3). Projections of melt rates from the two ISMIP6 calibrations are higher than those from FESOM, driven by a CMIP5 multi-model mean (Figure 9.19; [[#Jourdain--2020|Jourdain et al., 2020]] ). The ISMIP6 ensemble mostly uses the mean Antarctic calibration, but includes some simulations with the Pine Island Glacier calibration, and the ISMIP6 emulator samples more of these higher values; LARMIP-2 uses basal melt sensitivities (7–16 m a <sup>–1</sup> °C <sup>–1</sup> ) consistent with estimates for the Amundsen Sea Embayment. Due to the limited availability of cavity-resolving ocean models, and the wide regional variation in estimates of basal melt sensitivity to ocean temperature, there is only ''low confidence'' in projected future sub-ice-shelf melt rates. The impact of this uncertainty on AIS model projections to 2100 is discussed in [[#9.4.2.5|Section 9.4.2.5]] .

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<span id="ice-shelf-disintegration"></span>
===== 9.4.2.3.3 Ice-shelf disintegration =====

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Antarctic ice shelves modulate grounded ice flow through buttressing, so their weakening or disintegration is crucial for the timing and magnitude of ice loss and onset of instabilities ( [[#9.4.2.4|Section 9.4.2.4]] ; Box 9.4). Projections of ice-shelf disintegration are uncertain in terms of atmospheric warming and the response of the shelf surface – that is, surface melting, and whether shelves then disintegrate due to hydrofracturing and flexing, or are resilient through refreezing or drainage ( [[#Bell--2018|Bell et al., 2018]] ). The SROCC stated it is not expected that widespread ice-shelf loss will occur before the end of the 21st century, but this was based on only one study, using a regional climate model forced by five GCMs ( [[#Trusel--2015|Trusel et al., 2015]] ), so there was ''low confidence'' in this assessment. The study of [[#DeConto--2016|DeConto and Pollard (2016)]] projected the appearance of extensive surface meltwater several decades earlier than [[#Trusel--2015|Trusel et al. (2015)]] and was therefore assessed to be too uncertain to include in SROCC projections of the AIS.

Since SROCC, further studies have highlighted the modelling uncertainties in this area. Coastal surface air temperature projections in CMIP6 models show large inter-model differences driven by sea ice retreat and exhibit more warming relative to global mean temperature under low emissions than high, due to delayed response of the Southern Ocean to stabilized emissions and stratospheric ozone recovery ( [[#Bracegirdle--2020|Bracegirdle et al., 2020]] ). The updated study of [[#DeConto--2021|DeConto et al. (2021)]] includes improvements to the climate simulations relative to those in [[#DeConto--2016|DeConto and Pollard (2016)]] , and the resulting surface meltwater projections are now consistent with [[#Trusel--2015|Trusel et al. (2015)]] . However, the net effect of meltwater feedbacks on ice shelves is uncertain. Ice discharge is expected to lead to surface ocean and atmosphere cooling: this increases ocean stratification and sub-shelf melting, but also reduces ice-shelf surface melting and delays hydrofracturing ( [[#Golledge--2019|Golledge et al., 2019]] ; [[#Sadai--2020|Sadai et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ). The new studies are insufficient to change SROCC’s ''low confidence'' assessment on ice-shelf loss. The consequence of this uncertainty on projections is discussed in [[#9.4.2.5|Section 9.4.2.5]] and Box 9.4.

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<span id="ice-sheet-instabilities"></span>
==== 9.4.2.4 Ice-sheet Instabilities ====

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A major uncertainty in future Antarctic mass losses is the possibility of rapid and/or irreversible ice losses through instability of marine parts of the ice sheet, via the proposed mechanisms of marine ice sheet instability (MISI) and marine ice cliff instability (MICI), and whether these processes will lead to a collapse of the West Antarctic Ice Sheet (WAIS).

MISI is a proposed self-reinforcing mechanism within marine ice sheets that lie on a bed that slopes down towards the interior of the ice sheet, whereby, in the absence of ice-shelf buttressing, the position of the grounding line is inherently unstable until reaching an upward sloping bed. The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) noted advances in modelling MISI since AR5, but that ‘significant discrepancies’ remained in projections due to poor understanding of mechanisms, and lack of observational data to constrain the models. Since SROCC, modelling uncertainties have been more thoroughly explored, rather than constrained (compatibility of current observations in the Amundsen Sea Embayment with MISI is assessed in [[#9.4.2.1|Section 9.4.2.1]] ). Internal climate variability might either slow ( [[#Hoffman--2019|Hoffman et al., 2019]] ) or amplify ( [[#Robel--2019|Robel et al., 2019]] ) MISI, and stable grounding line positions can be reached on downward sloping beds if ice shelves provide buttressing ( [[#Sergienko--2019|Sergienko and Wingham, 2019]] ; [[#Cornford--2020|Cornford et al., 2020]] ). Ice-sheet model simulations that remove all Antarctic ice shelves (and prevent them from reforming) show 2–10 m SLE Antarctic mass loss after 500 years due to MISI, of which WAIS collapse contributes 2–5 m ( [[#Sun--2020|Sun et al., 2020]] ), with the majority of the mass loss in the first one to two centuries. Much of the multi-model variation is due to the sliding law ( [[#9.4.2.2|Section 9.4.2.2]] ). However, it is not expected that widespread ice-shelf loss will occur before the end of the 21st century ( [[#9.4.2.3|Section 9.4.2.3]] ; Box 9.4). A recent update of bed topography that unveiled large and overdeepened subglacial troughs in East Antarctica potentially vulnerable to MISI ( [[#Morlighem--2020|Morlighem et al., 2020]] ) has only been used by a few models ( [[#Seroussi--2020|Seroussi et al., 2020]] ; [[#Sun--2020|Sun et al., 2020]] ), so current projections could underestimate vulnerability in these regions. The sea level rise contribution of the AIS therefore crucially depends on the behaviour of individual ice shelves and outlet glacier systems and whether they enter MISI for a given level of warming (Box 9.4; [[#Pattyn--2020|Pattyn and Morlighem, 2020]] ). As for Antarctic simulations generally (Sections 9.4.2.2 and 9.4.2.3), there is ''medium confidence'' in simulating MISI but ''low confidence'' in projecting the sub-shelf melting and ice-shelf disintegration that drive it.

The SROCC noted ''limited evidence'' from geological records and ice-sheet modelling, suggesting that parts of the AIS experienced rapid (centennial) retreat ''likely'' due to MISI between 20,000 and 9,000 years ago, and also described more uncertain evidence for the Last Interglacial (LIG) and mid-Pliocene Warm Period (MPWP). Recent support for past MISI is provided by model simulations of the WAIS during the LIG ( [[#Clark--2020|Clark et al., 2020]] ), the British Ice Sheet during the last termination ( [[#Gandy--2018|Gandy et al., 2018]] ) and the Laurentide Ice Sheet during the Younger Dryas ( [[#Pico--2019|Pico et al., 2019]] ), which show progressive retreat despite declining temperatures, indicative of a true (ice dynamic) instability. Direct observational evidence of rapid paleo ice-sheet grounding line retreat is rare but, on the Larsen continental shelf, retreat rates of &gt;10 km yr <sup>–1</sup> during the deglaciation have been estimated ( [[#Dowdeswell--2020|Dowdeswell et al., 2020]] ). MISI has also been inferred from sedimentological evidence of ice loss from Wilkes Subglacial Basin, East Antarctica ( [[#Bertram--2018|Bertram et al., 2018]] ; [[#Wilson--2018|Wilson et al., 2018]] ; [[#Blackburn--2020|Blackburn et al., 2020]] ) but these reconstructions cannot unambiguously identify unstable from progressive retreat. Therefore, there is ''limited evidence'' to identify the operation of instability mechanisms such as MISI in paleo ice-sheet retreat.

The SROCC assessed that ice-sheet interactions with the solid Earth are not expected to substantially slow sea level rise from marine-based ice in Antarctica over the 21st century ( ''medium confidence'' ), but that these processes could become important on multi-century and longer time scales. More recent modelling of deglaciation of the Ross Embayment by [[#Lowry--2020|Lowry et al. (2020)]] is consistent with this assessment. However, new projections for Pine Island Glacier ( [[#Kachuck--2020|Kachuck et al., 2020]] ) support previous work ( [[#Barletta--2018|Barletta et al., 2018]] ) suggesting that lower mantle viscosity in this region leads to a negative feedback on decadal time scales. Grounding line stabilization by the solid Earth response may therefore occur over the 21st century in the Amundsen Sea Embayment, where most mass loss is occurring ( [[#9.4.2.1|Section 9.4.2.1]] ), but more generally occurs over multi-centennial to millennial time scales ( ''medium confidence'' ).

The MICI hypothesis describes rapid, unmitigated calving triggered by ice-shelf collapse ( [[#Pollard--2015|Pollard et al., 2015]] ). The SROCC noted that the MICI mechanism led one model ( [[#DeConto--2016|DeConto and Pollard, 2016]] ) to lose mass far more rapidly, but excluded the mechanism from its projections due to uncertainty in the timing of the ice-shelf disintegration ( [[#9.4.2.3|Section 9.4.2.3]] ). They stated that MICI could lead to sea level contributions beyond 2100 considerably higher than the ''likely'' range projected by other models. However, given the ''low agreement'' on the exact MICI mechanism and ''limited evidence'' of its occurrence in the present or the past ( [[#9.4.2.2|Section 9.4.2.2]] ), its potential to affect future sea level rise was very uncertain. Since SROCC, new simulations show later ice-shelf disintegration, in agreement with other models ( [[#9.4.2.3|Section 9.4.2.3]] ; [[#DeConto--2021|DeConto et al., 2021]] ), and therefore lower projections at 2100 ( [[#9.4.2.5|Section 9.4.2.5]] ). New theoretical evidence suggests that ice-cliff collapse may only occur after very rapid ice shelf disintegration caused by unusually high meltwater production ( [[#Clerc--2019|Clerc et al., 2019]] ; [[#Robel--2019|Robel and Banwell, 2019]] ), and that the subsequent rate of retreat depends on the terminus geometry ( [[#Bassis--2019|Bassis and Ultee, 2019]] ). As SROCC noted, only Crane Glacier on the Peninsula has shown retreat consistent with MICI, after the Larsen B ice shelf collapsed, and MICI-style behaviour at Jakobshavn and Helheim Glaciers in Greenland might not be representative of wider Antarctic glaciers. Observations from Greenland show that steep cliffs commonly evolve into short floating extensions, rather than collapsing catastrophically ( [[#Joughin--2020|Joughin et al., 2020]] ). As assessed in [[#9.4.2.2|Section 9.4.2.2]] and 9.4.2.3, there is therefore ''low confidence'' in simulating mechanisms that have the potential to cause widespread, sustained and very rapid ice loss from Antarctica this century through MICI, and ''low confidence'' in projecting the driver of ice-shelf disintegration.

In summary, poorly understood processes of instabilities, characterized by ''deep uncertainty'' , have the potential to strongly increase Antarctic mass loss under high greenhouse gas emissions on century-to-multicentury time scales (Box 9.4). These instabilities are therefore considered separately in assessments of the future contribution to global mean sea level (GMSL; Sections 9.4.2.5, 9.4.2.6, 9.6.3.2 and 9.6.3.5).

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<span id="projections-to-2100-1"></span>
==== 9.4.2.5 Projections to 2100 ====

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The AR5 assessed the median and ''likely'' (66–100% probability) sea level contributions of the AIS in 2100 relative to 1986–2005 to be 0.06 (–0.04 to +0.16) m SLE under RCP2.6 and 0.04 (–0.08 to +0.14) m SLE under RCP8.5 (Table 9.3; no change when using the AR6 baseline). The AR5 stated that only the collapse of the marine-based sectors of the AIS, if initiated, could cause GMSL to rise substantially above the ''likely'' range during the 21st century, with ''medium confidence'' that this would not exceed several tenths of a metre during this period. The assessment of the dynamical contribution had no dependence on emissions scenarios, due to the lack of literature, so the decrease in sea level contribution in the higher-emissions scenario was solely due to increased SMB ( [[#9.4.2.3|Section 9.4.2.3]] ). The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) assessed the total contribution based on five new ice-sheet modelling studies that incorporated marine ice-sheet dynamics, combining their estimates and interpreting the 5–95th percentile range of the resulting distribution as the ''likely'' range (17–83% probability interval, i.e., not open-ended as in the AR5). The median and ''likely'' range contributions by 2100 were 0.04 (0.01–0.11) m under RCP2.6 and 0.12 (0.03–0.28) m under RCP8.5 (Table 9.3). The positive scenario-dependence in SROCC – where increases in dynamic losses driven by ocean warming and ice-shelf disintegration under higher emissions ( [[#9.4.2.3|Section 9.4.2.3]] ) dominate over increases in SMB – arose from a combination of physical processes and model limitations. Modelling improvements in these studies included improved representations of grounding line response to drivers, more extensive exploration of uncertainties, and inclusion of a positive feedback of meltwater on climate ( [[#Golledge--2019|Golledge et al., 2019]] ). However, two of the projections did not include SMB changes that would offset dynamic losses ( [[#Levermann--2014|Levermann et al., 2014]] ; [[#Ritz--2015|Ritz et al., 2015]] ), and the scenario dependence may have been further amplified by highly sensitive sub-shelf melt parametrizations and use of simplified SMB schemes ( [[#Golledge--2015|Golledge et al., 2015]] , 2019; [[#Bulthuis--2019|Bulthuis et al., 2019]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ).

Since SROCC, new projections have arisen from multi-model intercomparison projects ISMIP6 and LARMIP-2 (Box 9.3) and one model that includes MICI ( [[#9.4.2.4|Section 9.4.2.4]] ; Table 9.3; [[#DeConto--2021|DeConto et al., 2021]] ). Corrections are added to allow comparison: all ISMIP6-derived projections have an estimate of the historical dynamical response to pre-2015 climate forcing added, which increases contributions (Box 9.3; Figure 9.18); the LARMIP-2 dynamic projections are combined with an estimate of SMB, which decreases contributions (Sections 9.4.2.3 and 9.6.3.2); and the ISMIP6 emulated and LARMIP-2 projections were re-estimate using the global surface air temperature distributions from the two-layer energy budget emulator described in Supplementary Material 7.SM.2. The majority of the new projections indicate that, under all emissions scenarios, the AIS will lose mass overall and contribute to sea level rise. Most thinning occurs in the Amundsen Sea sector in WAIS and Totten Glacier in EAIS (Figure 9.18). The most negative contribution is –0.02 m (5th percentile of ISMIP6 combined RCP8.5 and SSP5-8.5 projections after correction) and the largest contribution is 0.57 m SLE (95th percentile; [[#Levermann--2020|Levermann et al., 2020]] ), or 0.63 m SLE with MICI (95th percentile; [[#DeConto--2021|DeConto et al., 2021]] ). ISMIP6 ensemble ranges are wider for the high scenarios (RCP8.5/SSP5-8.5) than the low (RCP2.6/SSP1-2.6), in part because more simulations were available. The ISMIP6 simulations that apply an ice-shelf collapse scenario based on exceedance of a surface meltwater threshold ( [[#Trusel--2015|Trusel et al., 2015]] ), driven by CMIP5 models, show only a small increase in mass loss (around 0–0.04 m), mostly from the Peninsula, due in part to the small number of ice shelves predicted to collapse this century ( [[#Seroussi--2020|Seroussi et al., 2020]] ). Simulations driven by the CMIP5 model HadGEM2-ES, which has unusually extreme warming in the Ross Sea ( [[#Barthel--2020|Barthel et al., 2020]] ), show a larger mass loss (up to about 0.05 m) in East Antarctica under ice-shelf collapse ( [[#Edwards--2021|Edwards et al., 2021]] ). The ISMIP6 projections do not include the efficient meltwater drainage or atmospheric feedbacks that could reduce mass loss further ( [[#Seroussi--2020|Seroussi et al., 2020]] ).

The relationship between emissions scenario and AIS response varies across the studies, with emulated ISMIP6 projections showing a slight negative scenario dependence in the median (–0.01 m) from SSP1-2.6 to SSP5-8.5, and LARMIP-2-based projections showing a slight positive scenario-dependence in the median (0.02 m; Table 9.3). A lack of clear scenario dependence in the median masks large individual variations across climate and ice-sheet models, whereby the net AIS contribution response to emissions scenario depends on the relative magnitudes of the atmosphere, ocean and ice-sheet responses ( [[#Barthel--2020|Barthel et al., 2020]] ; [[#Seroussi--2020|Seroussi et al., 2020]] ; [[#Edwards--2021|Edwards et al., 2021]] ). Climate and ice-sheet models do not project that the AIS response will be the same under high or low greenhouse gas emissions in 2100; rather, there is no consensus on the sign of the change. In contrast, strong scenario dependence is seen from RCP4.5 to RCP8.5 in projections that allow MICI ( [[#9.4.2.4|Section 9.4.2.4]] ; [[#DeConto--2021|DeConto et al., 2021]] ), though less so than earlier projections ( [[#DeConto--2016|DeConto and Pollard, 2016]] ) due to later ice-shelf disintegrations. A negative or positive scenario dependence of the AIS response this century cannot be deduced from recent observations, because there is still ''low confidence'' in attributing the causes of observed mass loss ( [[#9.4.2.1|Section 9.4.2.1]] ), and neither regional mass increases by SMB nor regional mass losses by ice flow have a linear relationship with global mean temperature (Sections 9.4.2.1, 9.4.2.2, 9.4.2.3). There is therefore ''low agreement'' on the relationship between emissions scenario and AIS response. However, in the longer term, mass loss is expected to dominate ( [[#9.4.2.6|Section 9.4.2.6]] ).

The LARMIP-2 median projections are higher than those of the ISMIP6 emulator (by 0.04–0.07 m), and the 95th percentiles are two to three times higher. Two possible reasons for the differences between the emulated ISMIP6 and LARMIP-2 projections are assessed: the set of ice-sheet models (Annex II) and the parameter values determining sub-shelf melt sensitivity to ocean temperature ( [[#9.4.2.3|Section 9.4.2.3]] ; Box 9.3). Using only the 13 ice-sheet models common to ISMIP6 and LARMIP-2 reduces the LARMIP-2 median projections by 0.02–0.03 m SLE and the 95th percentiles by 0.04–0.08 m SLE (Table 9.3). This approximately halves the difference in medians, but has a relatively small effect on the upper end. Sub-shelf melt sensitivity has a larger effect, due to the wide variation of estimates from different regions and methods. Using only the Pine Island Glacier sub-shelf melt distribution (Sections 9.4.2.2 and 9.4.2.3) in the ISMIP6 emulator gives a median Antarctic projection of about 0.08 m in 2100 in all scenarios before historical correction, compared with around 0 m using only the mean Antarctic distribution; the published projections use a joint distribution ( [[#Edwards--2021|Edwards et al., 2021]] ). [[#Reese--2020|Reese et al. (2020)]] find that using the basal melt sensitivities of LARMIP-2 yields an order of magnitude greater mass loss under RCP8.5 than with the ISMIP6 mean Antarctic values. Halving the basal melt sensitivity parameter range (i.e., in line with a continental mean estimate: [[#9.4.2.3|Section 9.4.2.3]] ) would lead to a halving of the LARMIP-2 dynamic contribution. This would reconcile the LARMIP-2 and ISMIP6 emulator median and 95th percentile projections using the common subset of models within about 0.02–0.05 m. There is therefore ''limited evidence'' that the ISMIP6 and LARMIP-2 projections could be reconciled by using common ice-sheet models and basal melt sensitivity values.

It is not possible to distinguish which of ISMIP6 and LARMIP-2 is more realistic, due to limitations in historical simulations (Box 9.3) and understanding of basal melting ( [[#9.4.2.3.2|Section 9.4.2.3.2]] ), so the projections are combined using a ‘p-box’ approach ( [[#9.6.3.2|Section 9.6.3.2]] ). The mean of the ISMIP6 emulated and LARMIP-2 medians gives the assessed median projections, and the outer edges of the 17–83% ranges give the outer edges of the assessed ''likely'' (17–83%) ranges – that is, encompassing the structural and parametric uncertainties of both methods, giving ''medium confidence'' in their combined projections. The main difference between this assessment and SROCC is to increase the medians of the lower scenarios by 0.05–0.07 m, so that all SSPs are similar to SROCC assessment of RCP8.5, and to substantially increase the upper ends of the ''likely'' ranges: by 0.14–0.16 m for RCP2.6/SSP1-2.6 and RCP4.5/SSP2-4.5, and 0.06 m for RCP8.5/SSP5-8.5. The increase relative to SROCC is partly due to the increase in LARMIP-2 projections relative to the original LARMIP study ( [[#Levermann--2014|Levermann et al., 2014]] ), arising from the larger number of participating ice-sheet models ( [[#Levermann--2020|Levermann et al., 2020]] ). The historical dynamic response to pre-2015 climate forcing applied to the ISMIP6 emulator could be overestimated, due to the assumption of a constant future rate (Box 9.3). This assessment encompasses SROCC and all projections since, except the 83rd percentiles of projections that allow MICI under RCP8.5 ( [[#DeConto--2021|DeConto et al., 2021]] ) and the Structured Expert Judgement (SEJ) under 5°C shown in SROCC ( [[#Bamber--2019|Bamber et al., 2019]] ). Both are used in further p-box estimates to give the outer limits of ''low'' ''confidence'' assessments ( [[#9.6.3.2|Section 9.6.3.2]] ).

In summary, it is ''likely'' that the AIS will continue to lose mass throughout this century under all emissions scenarios – that is, dynamic losses driven by ocean warming and ice-shelf disintegration will ''likely'' continue to outpace increasing snowfall ( ''medium confidence'' ). The upper end of projections is not well constrained, due to different assumptions about the future sensitivity of sub-shelf basal melting to ocean warming and the proposed marine ice cliff instability triggered by ice-shelf disintegration (Sections 9.4.2.3 and 9.4.2.4; Box 9.4).

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'''Table 9.3''' '''|''' '''Projected sea level contributions in metres from the Antarctic Ice Sheet in 2100 relative to 199''' '''5–2''' '''014, unless otherwise stated, for selected Representative Concentration Pathway (RCP) and Shared Socio-economic Pathways (SSP) scenarios.''' Italics denote partial contributions. The historical dynamic response omitted from ISMIP6 simulations is estimated to be 0.33 ± 0.16 mm yr <sup>–1</sup> (0.03 m ± 0.01 m in 2100 relative to 2015; Box 9.3). The climate forcing is described in Supplementary Material 7.SM.2.

{| class="wikitable"
|-
| colspan="5"| '''Representative Concentration Pathways (RCPs)'''

|-
| '''Study'''

| '''RCP2.6'''

| '''RCP4.5'''

| '''RCP8.5'''

| '''Notes'''

|-
| IPCC AR5 ( [[#Church--2013b|Church et al., 2013b]] )

| 0.06 (–0.04 to +0.16)

| 0.05 (–0.05 to +0.15)

| 0.04 (–0.08 to +0.14)

| Median and ''likely'' (≥ 66% range) contribution

|-
| IPCC SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] )

| 0.04 (0.01 to 0.11)

| 0.06 (0.01 to 0.15)

| 0.12 (0.03 to 0.28)

| Median and ''likely'' (66% range) contribution. Combination of five studies

|-
| ''ISMIP6 CMIP5-forced'' ( [[#Seroussi--2020|Seroussi et al., 2020]] ) ''; excludes historical dynamic response''

| ''–0.01 to +0.16''

| ''–''

| ''–0.08 to +0.30''

| ''Range of ISMIP6 multi-model contributions in 2100 relative to 2015 from 2 ESMs for RCP2.6 and 6 ESMs for RCP8.5''

|-
| ''LARMIP-2; excludes surface mass balance (SMB)'' ( [[#Levermann--2020|Levermann et al., 2020]] )

| ''0.13 (0.07 to 0.24)''

''[0.04 to 0.37]''

| ''0.14 (0.07 to 0.28)''

''[0.05 to 0.44]''

| ''0.17 (0.09 to 0.36)''

''[0.06 to 0.58]''

| ''Median (67% range) [90% range] LARMIP-2 multi-model dynamic contribution in 2100 relative to 1900''

|-
| MICI ( [[#DeConto--2021|DeConto et al., 2021]] )

| 0.08 (0.06 to 0.12)

[0.06 to 0.15]

| 0.09 (0.07 to 0.11)

[0.07 to 0.15]

| 0.34 (0.19 to 0.53)

[0.11 to 0.63]

| Median (66% range) [90% range]

|-
| colspan="5"|
|-
| colspan="5"| '''Shared Socio-economic Pathways (SSPs)'''

|-
| '''Study'''

| '''SSP1-2.6'''

| '''SSP2-4.5'''

| '''SSP5-8.5'''

| '''Notes'''

|-
| colspan="5"| Multi-model ensemble projections

|-
| ''ISMIP6 CMIP6-forced'' ( [[#Payne--2021|Payne et al., 2021]] ) ''; excludes historical dynamic response''

| ''–0.05 to +0.01''

| ''–''

| ''–0.09 to +0.11''

| ''Range of ISMIP6 multi-model contributions in 2100 relative to 2015 from 1 ESM for SSP1-2.6 and 4 ESMs for SSP5-8.5''

|-
| ISMIP6 all (CMIP5 and CMIP6-forced) including historical dynamic response

| –0.05 (0.04 to 0.08)

[0.03 to 0.11]

| ''–''

| 0.04 (0.00 to 0.12) [–0.02 to +0.23]

| Median (66% range) [90% range] contribution from ISMIP6 CMIP5 and CMIP5-forced multi-model ensembles, (see caption)

|-
| ''Emulated ISMIP6; excludes historical dynamic response'' ( [[#Edwards--2021|Edwards et al., 2021]] )

| ''0.04 (–0.01 to +0.10)''

''[–0.05 to +0.14]''

| ''0.04 (–0.02 to +0.10)''

''[–0.06 to +0.14]''

| ''0.04 (–0.01 to +0.09)''

''[–0.05 to +0.14]''

| ''Median (66% range) [90% range] contribution in 2100 relative to 2015 from emulator of ISMIP6 used with [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] climate forcing''

|-
| '''Emulated ISMIP6 total'''

| '''0.09 (0.03 to 0.14)'''

'''[–0.01 to +0.19]'''

| '''0.09 (0.03 to 0.14)'''

'''[–0.01 to +0.18]'''

| '''0.08 (0.03 to 0.14)'''

'''[0.00 to 0.18]'''

| '''Emulated ISMIP6, but relative to 1995–2014 and including historical dynamic response (see caption)'''

|-
| ''SMB''

| ''–0.02 (–0.03 to –0.01)''

''[–0.04 to –0.01]''

| ''–0.03 (–0.04 to –0.02)''

''[–0.06 to –0.01]''

| ''–0.05 (–0.07 to –0.03)''

''[–0.09 to –0.02]''

| ''Median (66% range) [90% range] SMB estimated for the AR5, used to correct LARMIP-2 below''

|-
| ''LARMIP-2; excludes SMB''

| ''0.15 (0.08 to 0.29)''

''[0.05 to 0.44]''

| ''0.17 (0.09 to 0.33)''

''[0.06 to 0.49]''

| ''0.20 (0.10 to 0.39)''

''[0.07 to 0.61]''

| ''Median (66% range) [90% range] dynamic contribution from LARMIP-2 multi-model method used with [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] climate forcing''

|-
| ''LARMIP-2 subset of models; excludes SMB''

| ''0.14 (0.08 to 0.26) [0.05 to 0.39]''

| ''0.15 (0.08 to 0.29) [0.05 to 0.45]''

| ''0.17 (0.10 to 0.35) [0.06 to 0.54]''

| ''As above, but using only the 13 of 16 ice-sheet models common to both ISMIP6 and LARMIP-2''

|-
| ''LARMIP-2 subset of models; includes SMB''

| ''0.11 (0.05 to 0.24) [0.03 to 0.37]''

| ''0.12 (0.05 to 0.26) [0.02 to 0.42]''

| ''0.12 (0.05 to 0.30) [0.01 to 0.49]''

| ''As above, but including the SMB estimate''

|-
| '''LARMIP-2 total'''

| '''0.13 (0.06 to 0.27)'''

'''[0.03 to 0.41]'''

| '''0.14 (0.06 to 0.29)'''

'''[0.02 to 0.46]'''

| '''0.15 (0.05 to 0.34)'''

'''[0.01 to 0.57]'''

| ''Median (66% range) [90% range] dynamic contribution from LARMIP-2 multi-model method used with [[IPCC:Wg1:Chapter:Chapter-7|Chapter 7]] climate forcing, including the SMB estimate''

|-
| '''This assessment: combination of emulated ISMIP6 and LARMIP-2'''

| '''0.11 (0.03 to 0.27)'''

'''[–0.01 to +0.41]'''

| '''0.11 (0.03 to 0.29)'''

'''[–0.01 to +0.46]'''

| '''0.12 (0.03 to 0.34)'''

'''[0.00 to 0.57]'''

| '''Median (66% range) [90% range] assessment combining emulated ISMIP6 and LARMIP-2'''

|}

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<span id="projections-beyond-2100-1"></span>
==== 9.4.2.6 Projections Beyond 2100 ====

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The SROCC assessed the median and ''likely'' range of Antarctic SLE contributions at 2300 as 0.16 (0.07–0.37) m under RCP2.6 and 1.46 (0.60–2.89) m under RCP8.5, based on three studies. It was noted that ''deep uncertainty'' remained beyond 2100: while solid Earth feedbacks could reduce ice loss over multi-century time scales, MICI ( [[#9.4.2.4|Section 9.4.2.4]] ) might give contributions higher than the ''likely'' ranges. The SROCC also presented structured expert judgement (SEJ) projections for comparison ( [[#Bamber--2019|Bamber et al., 2019]] ), which give higher values. Since SROCC, three studies have made projections to 2300: (i) [[#Rodehacke--2020|Rodehacke et al. (2020)]] assessed two methods for implementing precipitation changes (based on repeating 2071–2100 forcings beyond 2100), which both gave negative projections at 2300 because the dynamic response was very small (–0.11 to –0.01 m SLE for RCP2.6; –0.25 to –0.07 m for RCP8.5 forcing); (ii) In contrast, simulations forced by 2081–2100 ocean-only projections under RCP8.5/SSP5-8.5 beyond 2100, using two implementations of the ISMIP6 ‘non-local’ basal melt parametrizations (Box 9.3 and [[#9.4.2.2|Section 9.4.2.2]] ) and two sliding laws, are all positive (0.08 m to 0.96 m SLE by 2300), though these do not include the negative contribution from SMB changes ( [[#Lipscomb--2021|Lipscomb et al., 2021]] ); (iii) Finally, [[#DeConto--2021|DeConto et al. (2021)]] update projections for the MICI hypothesis ( [[#9.4.2.4|Section 9.4.2.4]] ) using the extensions of the RCPs to 2300, and obtain far higher contributions: median (17–83%) ranges of 1.09 (0.71–1.35) m SLE under RCP2.6 and 9.60 (6.87–13.54) m SLE under RCP8.5. These are larger than previous estimates ( [[#DeConto--2016|DeConto and Pollard, 2016]] ), particularly at the upper end: 0.68 (0.29–1.13) m SLE for RCP2.6 and 8.40 (7.47–9.76) m for RCP8.5 ( [[#Edwards--2019|Edwards et al., 2019]] ), which can largely be explained by the higher maximum ice cliff calving rate. LARMIP-2 dynamic projections (Box 9.3) are also estimated under the extended SSPs and corrected with SMB (as in [[#9.4.2.5|Section 9.4.2.5]] ), giving median (17–83%) ranges of 0.40 (0.18–0.78) m SLE at 2300 under SSP1-2.6 and 1.57 (0.68–3.14) m under SSP5-8.5. The longer time scale may invalidate the linear response assumption of LARMIP-2, which neglects any self-dampening or self-amplifying processes. The ranges of projections for 2300 without MICI ( [[#Golledge--2015|Golledge et al., 2015]] ; [[#Bulthuis--2019|Bulthuis et al., 2019]] ; [[#Levermann--2020|Levermann et al., 2020]] ; [[#Rodehacke--2020|Rodehacke et al., 2020]] ; [[#Lipscomb--2021|Lipscomb et al., 2021]] ; ‘assessed ice-sheet contributions’ in [[#9.6.3.5|Section 9.6.3.5]] are –0.14 to +0.78 m SLE under RCP2.6/SSP1-2.6, and –0.27 to 3.14 m SLE under RCP8.5/SSP5-8.5). The lower bounds are the 5th percentile of [[#Bulthuis--2019|Bulthuis et al. (2019)]] and the lowest mean/median from [[#Rodehacke--2020|Rodehacke et al. (2020)]] , respectively; the upper bounds are the 83% percentiles of the LARMIP-2 estimates. These ranges are wider than SROCC ''likely'' ranges, and more consistent with the SEJ ( [[#Bamber--2019|Bamber et al., 2019]] ). However, projections in which Antarctica contributes much more than the assessed ranges under sustained very high greenhouse gas emissions – that is, around 7–14 m to GMSL by 2300 ( [[#DeConto--2021|DeConto et al., 2021]] ), cannot be ruled out, and are taken as a sensitivity case ( [[#9.6.3.5|Section 9.6.3.5]] ; Table 9.11). In summary, there is ''high confidence'' that Antarctic mass loss will be greater beyond 2100 under high greenhouse gas emissions, but the large range of projections mean we have only ''low confidence'' in the likely AIS contribution to GMSL by 2300 for a given scenario. ''Deep uncertainty'' remains in the role of AIS instabilities under very high emissions.

The West and East Antarctic ice sheets are considered to be tipping elements – that is, susceptible to critical thresholds. The SR1.5 ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) assessed that a threshold for WAIS instability may be close to 1.5°C–2°C ( ''medium confidence'' ), as only RCP2.6 led to long-term projections of less than 1 m ( [[#Golledge--2015|Golledge et al., 2015]] ; [[#DeConto--2016|DeConto and Pollard, 2016]] ). Based on the agreement of a further study ( [[#Bulthuis--2019|Bulthuis et al., 2019]] ), SROCC confirmed that low emissions would limit Antarctic ice loss over multi-century time scales ( ''high confidence'' ), but it was not possible to determine whether this was sufficient to prevent substantial ice loss ( ''medium confidence'' ). Since SROCC, new studies have revisited this topic ( [[#Garbe--2020|Garbe et al., 2020]] ; [[#Rodehacke--2020|Rodehacke et al., 2020]] ; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ; [[#Lipscomb--2021|Lipscomb et al., 2021]] ), allowing a more complete assessment along with other studies ( [[#Feldmann--2015|Feldmann and Levermann, 2015]] ; [[#Clark--2016|Clark et al., 2016]] ; [[#Golledge--2017a|Golledge et al., 2017a]] ; [[#Edwards--2019|Edwards et al., 2019]] ) and the extension to LARMIP-2 above. The majority project 0–1.3 m SLE on multi-century time scales under scenarios of 1°C–2°C warming. Projections can increase up to 2 m SLE under high basal melt sensitivity to ocean warming ( [[#9.4.2.3|Section 9.4.2.3]] ; [[#Lipscomb--2021|Lipscomb et al., 2021]] ) or MICI ( [[#9.4.2.4|Section 9.4.2.4]] ). On multi-millennial time scales (≥2,000 years), many projections remain below 1.6 m SLE under 1°C–2°C warming – that is, less than about half of the WAIS in SLE (see also [[#9.6.3.5|Section 9.6.3.5]] and Figure 9.30). Other studies project majority or total loss of WAIS under 1°C–2°C warming, exceeding 2 m SLE, under the higher end of the warming range (≥1.5°C), or high ocean warming (≥0.5°C) and/or high basal melting around WAIS, or MICI. All but two of these multi-millennial studies use variants of the same ice-sheet model, though different modelling choices mean they can be considered quasi-independent. Simulations of previous interglacial periods often show near or total WAIS disintegration, with mass loss exceeding 3 m SLE (e.g. Figure 9.18), although limitations of these studies or inferences that can be drawn under different forcings limit confidence in the robustness of these as quantitative analogues (Sections 9.4.2.4 and 9.6.2). Overall, increased evidence and agreement on the time scales and drivers of mass loss confirm the SR1.5 assessment that a threshold for WAIS instability may be close to 1.5°C–2°C ( ''medium confidence'' ), and that the probability of passing a threshold is larger for 2°C warming than for 1.5°C ( ''medium confidence'' ), particularly under strong ocean warming. New projections agree with previous studies that only part of WAIS would be lost on multi-century time scales if warming remains less than 2°C ( ''medium confidence'' ). There is ''limited agreement'' about whether complete disintegration would eventually occur at this level of warming, but ''medium confidence'' this would take millennia.

Under around 2°C–3°C peak warming, complete or near-complete loss of the WAIS is projected in most studies after multiple millennia ( ''low confidence'' ), with continent-wide mass losses of around 2–5 m SLE or more; this could occur on multi-century time scales under very high basal melting ( [[#Lipscomb--2021|Lipscomb et al., 2021]] ) or widespread ice-shelf loss and/or MICI ( ''low confidence'' ) ( [[#Sun--2020|Sun et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ). Mass losses under around 2°C–3°C warming could be less than 2 m SLE, particularly for multi-century time scales, low basal melting, or less responsive sliding laws. If warming exceeds around 3°C above pre-industrial, part of the EAIS (typically the Wilkes Subglacial Basin) is projected to be lost on multi-millennial time scales ( ''low confidence'' ), with total AIS mass loss equivalent to around 6–12 m or more sea level rise; mass loss could be much smaller if the dynamic response is small ( [[#Bulthuis--2019|Bulthuis et al., 2019]] ; [[#Rodehacke--2020|Rodehacke et al., 2020]] ), or much faster under widespread ice-shelf loss and/or MICI ( [[#Sun--2020|Sun et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ). A study by [[#Garbe--2020|Garbe et al. (2020)]] suggests that 6°C sustained warming and associated mass loss of about 12 m SLE may be a critical threshold beyond which the ice sheet reorganizes to a new state, leading to large losses from East Antarctica (including the Aurora Subglacial Basin) and leading to a further 10 m sea level contribution per degree of warming; other studies also show much higher mass loss per °C at higher levels of warming ( [[#9.6.3.5|Section 9.6.3.5]] and Figure 9.30; [[#Van%20Breedam--2020|Van Breedam et al., 2020]] ; [[#DeConto--2021|DeConto et al., 2021]] ).

The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) assessed that Antarctic mass losses could be irreversible over decades to millennia ( ''low confidence'' ). [[#Garbe--2020|Garbe et al. (2020)]] show that the AIS is always volumetrically smaller when regrowing under a given warming level than when it retreats under the same forcing. Even if retreat followed by regrowth results in a net zero change in volume, the spatial distribution of mass may be altered, especially in parts of West Antarctica vulnerable to MISI. Projections that start reducing CO <sub>2</sub> concentrations from 2030 onwards, reaching pre-industrial levels around 2300, show sea level contributions exceeding 1 m by 2500 when including MICI ( [[#DeConto--2021|DeConto et al., 2021]] ). New research therefore confirms SROCC assessment that mass loss from the AIS is irreversible on decadal to millennial time scales ( ''low confidence'' ) (FAQ 9.1), and suggests that reducing atmospheric CO <sub>2</sub> concentrations or temperatures to pre-industrial levels may not be sufficient to prevent or reverse substantial Antarctic mass losses ( ''low confidence'' ).

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