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

=== 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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