Editing IPCC:AR6/WGI/Chapter-3 (section)

=== 3.4.3 Glaciers and Ice Sheets ===

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While ( [[IPCC:Wg1:Chapter:Chapter-9|Chapter 9]] (Sections 9.4 and 9.5) discusses process understanding for glaciers and ice sheets, as well as evaluation of global and regional-scale glacier and ice-sheet models, our focus here is on the attribution of large-scale changes in glaciers and ice sheets. Land ice in the form of glaciers has been included in CMIP climate and Earth system models as components of the land surface models for many years. However, their representation is simplified and is omitted altogether in the less complex modelling systems. In CMIP3 ( [[#Meehl--2007|Meehl et al., 2007]] ) and CMIP5 ( [[#Taylor--2012|Taylor et al., 2012]] ) land ice area fraction, a component of land surface models, was defined as a time-independent quantity, and in most model configurations was preset at the simulation initialization as a permanent land feature. In CMIP6 considerable progress has been made in improving and evaluating the representation of modelled land ice. For glaciers, an example is the expansion of the Joint UK Land Environment Simulator (JULES) land surface model to enable elevated tiles, and hence more accurately simulate the altitudinal atmospheric effects on glaciers ( [[#Shannon--2019|Shannon et al., 2019]] ). Moreover, standalone glacier models have now been systematically compared in GlacierMIP ( [[#Hock--2019a|Hock et al., 2019a]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ). The Antarctic and Greenland Ice Sheets were absent in global climate models that pre-date CMIP6 ( [[#Eyring--2016a|Eyring et al., 2016a]] ), however some preliminary analyses that used results from CMIP5 to drive standalone ice-sheet models were included in AR5 ( [[#Church--2013a|Church et al., 2013a]] ). For the first time in CMIP, the latest CMIP6 phase includes a coordinated effort to simulate temporally evolving ice sheets within the Ice Sheet Model Intercomparison Project (ISMIP6; Box 9.3; [[#Nowicki--2016|Nowicki et al., 2016]] ). Our understanding of aspects of the global water storage contained in glaciers and ice sheets, and their contribution to sea-level rise, has improved since AR5 and SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ; [[#Meredith--2019|Meredith et al., 2019]] ) both in models and observations (see assessment of observations and model evaluation for the Greenland Ice Sheet in Sections 2.3.2.4.1 and 9.4.1; Antarctica in Sections 2.3.2.4.2 and 9.4.2; and glaciers in Sections 2.3.2.3 and 9.5.1).

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==== 3.4.3.1 Glaciers ====

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Glaciers are defined as perennial surface land ice masses independent of the Antarctic and Greenland Ice Sheets (Sections 9.5 and 2.3.2.3). The AR5 assessed that anthropogenic influence had ''likely'' contributed to the retreat of glaciers observed since the 1960s ( [[#Bindoff--2013|Bindoff et al., 2013]] ), based on a high level of scientific understanding and robust estimates of observed mass loss, internal variability, and glacier response to climatic drivers. The SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) concluded that atmospheric warming was ''very likely'' the primary driver of glacier recession.

Simulations of glacier mass changes under climate change rely on glacier models driven by climate model output, often in collaborative research efforts such as GlacierMIP ( [[#Hock--2019a|Hock et al., 2019a]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ). The GlacierMIP project is a systematic coordinated modelling effort designed to further understanding of glacier loss using global models. While the low resolution and remaining biases of climate model-derived boundary forcing data is a limitation, the release of the Randolph Glacier Inventory ( [[#Pfeffer--2014|Pfeffer et al., 2014]] ; [[#RGI%20Consortium--2017|RGI Consortium, 2017]] ) has supported more sophisticated, systematic and comprehensive modelling of glaciers worldwide ( [[#Hock--2019a|Hock et al., 2019a]] ).

A regional study considering 85 Northern Hemisphere glacier systems concluded that there is a discernible human influence on glacier mass balance, with glacier model simulations driven by CMIP5 historical and greenhouse gas-only simulations showing a glacier mass loss, whereas those driven by natural-only forced simulations showed a net glacier growth ( [[#Hirabayashi--2016|Hirabayashi et al., 2016]] ). In addition, a study of the role of climate change in glacier retreat using a simple mass-balance model for 37 glaciers worldwide, concluded that observed length changes would not have occurred without anthropogenic climate change, with observed length variations exceeding those associated with internal variability by several standard deviations in many cases ( [[#Roe--2017|Roe et al., 2017]] ). [[#Roe--2021|Roe et al. (2021)]] used the same model to estimate that at least 85% of cumulative glacier mass loss since 1850 is attributable to anthropogenic influence. While [[#Marzeion--2014|Marzeion et al. (2014)]] found that anthropogenic influence contributed only 25 ± 35% of glacier mass loss for the period 1851–2010, their naturally-forced simulations exhibited a substantial negative mass balance, which [[#Roe--2021|Roe et al. (2021)]] argued is unrealistic. Moreover, [[#Marzeion--2014|Marzeion et al. (2014)]] estimated that anthropogenic influence contributed 69 ± 24% of glacier mass loss for the period 1991 to 2010, consistent with a progressively increasing fraction of mass loss attributable to anthropogenic influence found by [[#Roe--2021|Roe et al. (2021)]] .

In summary, considering together the SROCC assessment that atmospheric warming was ''very likely'' the primary driver of glacier recession, the results of Roe et al. (2017, 2021) and our assessment of the dominant role of anthropogenic influence in driving atmospheric warming ( [[#3.3.1|Section 3.3.1]] ), we conclude that human influence is ''very likely'' the main driver of the near-universal retreat of glaciers globally since the 1990s.

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==== 3.4.3.2 Ice Sheets ====

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===== 3.4.3.2.1 Greenland Ice Sheet =====

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The AR5 assessed that it is ''likely'' that anthropogenic forcing contributed to the surface melting of the Greenland Ice Sheet since 1993 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). The SROCC did not directly assess the attribution of Greenland Ice Sheet change to anthropogenic forcing, but it did assess with ''medium confidence'' that summer melting of the Greenland Ice Sheet has increased to a level unprecedented over at least the last 350 years, which is two-to-fivefold the pre-industrial level (see also [[#Trusel--2018|Trusel et al., 2018]] ).

( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.4.1|Section 2.3.2.4.1]] assesses that Greenland Ice Sheet mass loss began in the latter half of the 19th century and that the rate of loss has increased substantially since the turn of the 21st century ( ''high confidence'' ), and also notes that integration of proxy evidence and modelling indicates that the last time the rate of mass loss was similar to the 20th century rate was during the early Holocene. Models of Greenland Ice Sheet evolution are evaluated in detail in Section 9.4.1.2, which assesses that there is overall ''medium confidence'' in these models. Model evaluation of surface mass balance changes over the Greenland Ice Sheet, including regional aspects, is also assessed in Atlas.11.2.3.

Detection and attribution studies of change in the Greenland Ice Sheet remain challenging ( [[#Kjeldsen--2015|Kjeldsen et al., 2015]] ; [[#Bamber--2019|Bamber et al., 2019]] ). This is in part due to the short observational record ( [[#Shepherd--2012|Shepherd et al., 2012]] , 2018, 2020; [[#Bamber--2018|Bamber et al., 2018]] ; [[#Cazenave--2018|Cazenave et al., 2018]] ; [[#Mouginot--2019|Mouginot et al., 2019]] ; [[#Rignot--2019|Rignot et al., 2019]] ) and the challenges this poses to the evaluation of modelling efforts (Section 9.4.1.2). The latter require not only dynamic ice-sheet models, but also appropriate atmospheric and oceanic conditions to use as a boundary forcing to drive the models ( [[#Nowicki--2018|Nowicki and Seroussi, 2018]] ; [[#Barthel--2020|Barthel et al., 2020]] ). Nonetheless, new literature since AR5 finds that ice-sheet mass balance calculations using reanalysis-driven regional model simulations of surface mass balance are found to agree well with the observed decrease in ice-sheet mass over the past twenty years ( [[#Fettweis--2020|Fettweis et al., 2020]] ; [[#Sasgen--2020|Sasgen et al., 2020]] ; [[#Tedesco--2020|Tedesco and Fettweis, 2020]] ), consistent with earlier studies ( [[#Flato--2013|Flato et al., 2013]] ). These studies also show that the exceptional melt events observed in 2012 and 2019 were associated with exceptional atmospheric conditions ( [[#Sasgen--2020|Sasgen et al., 2020]] ; [[#Tedesco--2020|Tedesco and Fettweis, 2020]] ). These results support the finding that increased surface melting is associated with warming, although atmospheric circulation anomalies, including the summer North Atlantic Oscillation (NAO) and variations in snowfall play an important role in driving interannual variations (Section 9.4.1.1; [[#Sasgen--2020|Sasgen et al., 2020]] ; [[#Tedesco--2020|Tedesco and Fettweis, 2020]] ). Further, a coupled ice-sheet-climate model study found emergence of decreased surface mass balance prior to the present day in coastal locations in Greenland, which dominate the integrated surface mass balance ( [[#Fyke--2014|Fyke et al., 2014]] ), suggesting that observed variations in surface mass balance in these regions might be expected to be distinguishable from internal variability. A CMIP6 simulation of the historical period showed stable Greenland surface mass balance up to the 1990s, after which it declined due to increased melt and runoff, consistent with a downscaled reanalysis ( [[#van%20Kampenhout--2020|van Kampenhout et al., 2020]] ). Further, all experts surveyed in a structured expert judgement exercise examining the causes of the increase in mass loss from the Greenland Ice Sheet over the last two decades ( [[#Bamber--2019|Bamber et al., 2019]] ) concluded that external forcing was responsible for at least 50% of the mass loss. A comparison of Greenland Ice Sheet mass loss trends from observations and AR5 model projections for the period 2007–2017 found that the magnitude of the observed surface mass balance trends was at the top of the AR5 assessed range, while mass loss due to changing ice dynamics was near the centre of the AR5 range ( [[#Slater--2020|Slater et al., 2020]] ), providing further evidence of consistent anthropogenically-forced mass loss trends in models and observations.

Drawing together the evidence from the continued and strengthened observed mass loss, the agreement between anthropogenically forced climate simulations and observations, and historical and paleo evidence for the unusualness of the observed rate of surface melting and mass loss, we assess 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, and that there is ''medium confidence'' in an anthropogenic contribution to recent overall mass loss from Greenland.

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===== 3.4.3.2.2 Antarctic Ice Sheet =====

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AR5 assessed that there was ''low confidence'' in attributing the causes of the observed mass loss from the Antarctic Ice Sheet since 1993 ( [[#Bindoff--2013|Bindoff et al., 2013]] ). The SROCC assessed that there is ''medium agreement'' but ''limited evidence'' of anthropogenic forcing of Antarctic mass balance through both surface mass balance and glacier dynamics. It further assessed that Antarctic ice loss is dominated by acceleration, retreat and rapid thinning of the major West Antarctic Ice Sheet outlet glaciers ( ''very high confidence'' ), driven by melting of ice shelves by warm ocean waters ( ''high confidence'' ). Based on updated observations, [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] assesses that there is ''very high confidence'' that the Antarctic Ice Sheet lost mass between 1992 and 2017, and that there is ''medium confidence'' that this mass loss has accelerated. Models of Antarctic Ice Sheet evolution are evaluated in detail in Section 9.4.2.2, which assesses that there is ''medium confidence'' in many ice-sheet processes in Antarctic Ice Sheet models, but ''low confidence'' in the ocean forcing affecting basal melt rates. CMIP5 and CMIP6 models perform similarly in their simulation of Antarctic surface mass balance (Section 9.4.2.2, [[#Gorte--2020|Gorte et al., 2020]] ). Model evaluation of surface mass balance over the Antarctic Ice Sheet, including regional aspects, is also assessed in Atlas.11.1.3.

Ice discharge around the West Antarctic Ice Sheet is strongly influenced by variability in basal melt ( [[#Jenkins--2018|Jenkins et al., 2018]] ; [[#Hoffman--2019|Hoffman et al., 2019]] ), in particular at decadal and longer time scales ( [[#Snow--2017|Snow et al., 2017]] ). Basal melt rate variability can be induced by wind-driven ocean current changes, which may partly be of anthropogenic origin via greenhouse gas forcing ( [[#Holland--2019|Holland et al., 2019]] ). Moreover, ice discharge losses from the Antarctic Ice Sheet over the 2007–2017 period are close to the centre of the model-based range projected in AR5 ( [[#Slater--2020|Slater et al., 2020]] ). However, expert opinion differs as to whether recent Antarctic ice loss from the West Antarctic Ice Sheet has been driven primarily by external forcing or by internal variability, and there is no consensus ( [[#Bamber--2019|Bamber et al., 2019]] ). Anthropogenic influence on the Antarctic surface mass balance, which is expected to partially compensate for ice discharge losses through increases in snowfall, is currently masked by strong natural variability ( [[#Previdi--2016|Previdi and Polvani, 2016]] ; [[#Bodart--2019|Bodart and Bingham, 2019]] ), and observations suggest that it has been close to zero over recent years (see further discussion in Section 9.4.2.1; [[#Slater--2020|Slater et al., 2020]] ).

Overall, there is ''medium agreement'' but ''limited evidence'' of anthropogenic influence on Antarctic mass balance through changes in ice discharge.

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