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

== 9.5 Glaciers, Permafrost and Seasonal Snow Cover ==

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=== 9.5.1 Glaciers ===

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==== 9.5.1.1 Observed and Reconstructed Glacier Extent and Mass Changes ====

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===== 9.5.1.1.1 Global glacier contribution =====

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The IPCC’s Fifth Assessment Report (AR5; [[#Vaughan--2013|Vaughan et al., 2013]] ) assessed glacier changes from studies based on the regions defined in the Randolph Glacier Inventory (RGI; RGI version 2.0): a satellite observation-based, global inventory of glacier outlines for the year 2000. Following Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC; [[#Hock--2019b|Hock et al., 2019b]] ; [[#Meredith--2019|Meredith et al., 2019]] ), we report on studies based on RGI version 6.0 ( [[#RGI%20Consortium--2017|RGI Consortium, 2017]] ). Increased volume of satellite observations and the inclusion of detailed regional glacier inventories has resulted in an improved inventory ( [[#RGI%20Consortium--2017|RGI Consortium, 2017]] ). A new consensus estimate for the ice thickness distribution of all glaciers in RGI 6.0 was obtained from an ensemble of five numerical models. However, only one out of five models covered all regions ( [[#Farinotti--2019|Farinotti et al., 2019]] ), and was, where possible, calibrated and validated with the worldwide Glacier Thickness Database (GlaThiDa 3.0: [[#GlaThiDa%20Consortium--2019|GlaThiDa Consortium, 2019]] ; [[#Welty--2020|Welty et al., 2020]] ). The updated inventory shows decreases in estimated glacier volume in the Arctic, High Mountain Asia and Southern Andes, partially compensated by increases in Antarctica. 15% of the total glacier volume is estimated to be below sea level and would not contribute to sea level rise if melted ( [[#Farinotti--2019|Farinotti et al., 2019]] ). Supplementary Material Table 9.SM.2 shows the inventory glacier area and mass for each region in the year 2000.

The SROCC found a globally coherent trend of glacier decline in the last decades, despite large annual variability and regional differences ( ''very high confidence'' ). [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.3|Section 2.3.2.3]] assesses the global glacier mass changes for the whole 20th century (see Table 9.5 for contribution to the sea level budget. Note that the peripheral glaciers in Greenland and Antarctica are added to the ice sheets for the budget). The AR6 assessment is based on [[#Marzeion--2015|Marzeion et al. (2015)]] , using glacier-length reconstructions ( [[#Leclercq--2011|Leclercq et al., 2011]] ) and a glacier model forced by gridded climate observations ( [[#Marzeion--2012|Marzeion et al., 2012]] ), and not considering the estimated mass loss of uncharted glaciers (100 ± 50 Gt yr <sup>–1</sup> ; [[#Parkes--2018|Parkes and Marzeion, 2018]] ). The time series are assumed independent, resulting in larger uncertainty than presented in SROCC (see also [[#9.6.1|Section 9.6.1]] ). The rate of global glacier mass loss (excluding the periphery of ice sheets) for the period 1901–1990 is estimated to be ''very likely'' 210 ± 90 Gt yr <sup>–1</sup> , representing 16 [28 to 7] % of the glacier mass in 1901, in agreement with SROCC within uncertainty estimates.

Since SROCC, new regional estimates for the Andes ( [[#Dussaillant--2019|Dussaillant et al., 2019]] ), High Mountain Asia ( [[#Shean--2020|Shean et al., 2020]] ), Iceland ( [[#Aðalgeirsdóttir--2020|Aðalgeirsdóttir et al., 2020]] ), the European Alps ( [[#Davaze--2020|Davaze et al., 2020]] ; Sommer et al., 2020) and Svalbard ( [[#Schuler--2020|Schuler et al., 2020]] ), two new global ( [[#Ciracì--2020|Ciracì et al., 2020]] ; [[#Hugonnet--2021|Hugonnet et al., 2021]] ) and an ad hoc estimate for the latest glaciological observations ( [[#Zemp--2020|Zemp et al., 2020]] ) have extended the glacier mass change time series up to 2018–2019 (Figure 9.21 and Supplementary Material Table 9.SM.3). A reconciled global estimate for the period 1962–2019 has been compiled by [[#Slater--2021|Slater et al. (2021)]] . However, in contrast to [[#Slater--2021|Slater et al. (2021)]] , after 2000 this assessment is based on the first globally complete and consistent estimate of 21st-century glacier mass change from differencing of digital elevation models ( [[#Hugonnet--2021|Hugonnet et al., 2021]] ) covering 94.7% of glacier area with glacier mass change for each glacier in the inventory produced with unprecedented accuracy. The estimates from [[#Hugonnet--2021|Hugonnet et al. (2021)]] agree within uncertainties with new and previous estimates at global ( [[#Hock--2019b|Hock et al., 2019b]] ; [[#Wouters--2019|Wouters et al., 2019]] ; [[#Zemp--2019|Zemp et al., 2019]] ; [[#Ciracì--2020|Ciracì et al., 2020]] ; [[#Slater--2021|Slater et al., 2021]] ) and regional scale ( [[#Dussaillant--2019|Dussaillant et al., 2019]] ; [[#Aðalgeirsdóttir--2020|Aðalgeirsdóttir et al., 2020]] ; [[#Schuler--2020|Schuler et al., 2020]] ; [[#Shean--2020|Shean et al., 2020]] ). Excluding peripheral glaciers of ice sheets (RGI regions 5 and 19), glacier mass loss rate was ''very likely'' 170 ± 80 Gt yr <sup>–1</sup> for the period 1971 to 2019 (8 [4 to 14] % of 1971 glacier mass) '','' 210 ± 50 Gt yr <sup>–1</sup> over the period 1993–2019 (6 [4 to 8] % of 1993 glacier mass) and 240 ± 40 Gt yr <sup>–1</sup> over the period 2006–2019 (3 [2 to 4] % of 2006 glacier mass; Sections 2.3.2.3 and 9.6.1, Table 9.5, <sup>[[#footnote-001|4]]</sup> and Cross-Chapter Box 9.1). Including the peripheral glaciers of the ice sheets, the global glacier mass loss rate in the period 2000–2019 is ''very likely'' 266 ± 16 Gt yr <sup>–1</sup> (4 [3 to 6] % of glacier mass in 2000) with an increase in the mass loss rate from 240 ± 9 Gt yr <sup>–1</sup> in 2000–2009 to 290 ± 10 Gt yr <sup>–1</sup> in 2010–2019 ( ''high confidence'' ). These estimates are in agreement with SROCC estimate and extend the period to 2018–2019. In summary, new evidence published since SROCC shows that, during the decade 2010–2019, glaciers lost more mass than in any other decade since the beginning of the observational record ( ''very high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.7.1|Section 8.3.1.7.1]] and Figure 9.20).

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[[File:f624bd4a7729400d28f893ddf72f1b43 IPCC_AR6_WGI_Figure_9_20.png]]

'''Figure 9.20''' '''|''' '''Global and regional glacier mass change rate between 1960 and 2019.''' The time series of annual and decadal mean mass change are based on glaciological and geodetic balances ( [[#Zemp--2019|Zemp et al., 2019]] , 2020). Superimposed are the 2002–2019 average rates by ( [[#Ciracì--2020|Ciracì et al., 2020]] ) based on the Gravity Recovery and Climate Experiment (GRACE), 2006–2015 estimated rates as assessed in Special Report on Ocean and Cryosphere in a Changing Climate (SROCC) and the new decadal averages (2000–2009 and 2010–2019) by [[#Hugonnet--2021|Hugonnet et al. (2021)]] . * New regional estimates for the Andes ( [[#Dussaillant--2019|Dussaillant et al., 2019]] ), High Mountain Asia ( [[#Shean--2020|Shean et al., 2020]] ), Iceland ( [[#Aðalgeirsdóttir--2020|Aðalgeirsdóttir et al., 2020]] ), Central Europe ( [[#Sommer--2020|Sommer et al., 2020]] ) and Svalbard ( [[#Schuler--2020|Schuler et al., 2020]] ) are also shown. The uncertainty reported in each study is shown. See Figure 9.2 for the location of each region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

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===== 9.5.1.1.2 Regional glacier changes =====

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A major advance since SROCC is the availability of high-accuracy mass loss estimates for individual glaciers ( [[#Hugonnet--2021|Hugonnet et al., 2021]] ). These results show that, during the last 20 years, the highest regional mass loss rates (&gt;720 kg m <sup>–2</sup> yr <sup>–1</sup> ) were observed in the Southern Andes, New Zealand, Alaska, Central Europe, and Iceland. Meanwhile, the lowest regional mass loss rates (&lt;250 kg m <sup>–2</sup> yr <sup>–1</sup> ) were observed in High Mountain Asia, the Russian Arctic, and the periphery of Antarctica. Glacier mass loss in Alaska (25% of 2000–2019 total mass loss), the periphery of Greenland (13%), Arctic Canada North (11%), Arctic Canada South (10%), the periphery of Antarctica (8%), the Southern Andes (8%) and High Mountain Asia (8%), represent the majority (83%) of the total glacier mass loss during the last 20 years (2000–2019).

The glacier mass loss rate from geodetic mass balance assessments in the Southern Andes during 2006–2015 was smaller (720 ± 70 kg m <sup>–2</sup> yr <sup>–1</sup> ; [[#Braun--2019|Braun et al., 2019]] ; [[#Dussaillant--2019|Dussaillant et al., 2019]] ; [[#Hugonnet--2021|Hugonnet et al., 2021]] ) than previously assessed in SROCC (860 ± 160 kg m <sup>–2</sup> yr <sup>–1</sup> ), though within uncertainties. In the Central and Desert regions of the Southern Andes, an increase in mass loss from 2000–2009 to 2010–2018, and a high loss rate in Patagonia for the whole period, are observed ( [[#Dussaillant--2019|Dussaillant et al., 2019]] ). Records of glacier mass loss in Peru ( [[#Seehaus--2019a|Seehaus et al., 2019a]] ) and Bolivia ( [[#Seehaus--2019b|Seehaus et al., 2019b]] ) in the period 2000–2016 show an increase in mass loss towards the end of the observation period. In western North America, outside of Alaska and western Yukon, there was a fourfold increase in mass loss for 2009–2018 (860 ± 320 kg m <sup>–2</sup> yr <sup>–1</sup> ) compared to 2000–2009 (203 ± 214 kg m <sup>–2</sup> yr <sup>–1</sup> ; [[#Menounos--2019|Menounos et al., 2019]] ), and in the Canadian Arctic there was a doubling of mass loss in the last two decades compared with pre-1996 ( [[#Noël--2018|Noël et al., 2018]] ; [[#Cook--2019|Cook et al., 2019]] ). The peripheral glaciers in NE Greenland experienced a 23% increase in mass loss in 1980–2014 compared to the period 1910 to 1978–1987 ( [[#Carrivick--2019|Carrivick et al., 2019]] ). In Iceland, 16 ± 4% of the around 1890 glacier mass has been lost; about half of that loss occurred in the period 1994–2019 ( [[#Aðalgeirsdóttir--2020|Aðalgeirsdóttir et al., 2020]] ). Glacier records starting in 1960 in Norway show that half of the observed glaciers advanced in the 1990s but all have retreated since 2000 ( [[#Andreassen--2020|Andreassen et al., 2020]] ). In Svalbard, glaciers have been losing mass since the 1960s, with a tendency towards more negative mass balance since 2000 ( [[#Deschamps-Berger--2019|Deschamps-Berger et al., 2019]] ; [[#Van%20Pelt--2019|Van Pelt et al., 2019]] ; [[#Morris--2020|Morris et al., 2020]] ; [[#Noël--2020|Noël et al., 2020]] ; [[#Schuler--2020|Schuler et al., 2020]] ). A similar increase in mass loss has been observed for Franz Josef Land in the Russian Arctic ( [[#Zheng--2018|Zheng et al., 2018]] ). Rapid retreat and downwasting throughout the European Alps in the early 21st century is reported ( [[#Sommer--2020|Sommer et al., 2020]] ) and long-term records, although limited, indicate sustained glacier mass loss in High Mountain Asia since around 1850, with increased mass loss in recent decades ( [[#Shean--2020|Shean et al., 2020]] ). In summary, although interannual variability is high in many regions, glacier mass records throughout the world show with ''very high confidence'' that the loss rate has been increasing in the last two decades (see also [[IPCC:Wg1:Chapter:Chapter-8#8.3.1.7.1|Section 8.3.1.7.1]] and 12.4 for regional glacier assessment).

[[IPCC:Wg1:Chapter:Chapter-2#2.3.2.3|Section 2.3.2.3]] assesses that the rate and global character of glacier retreat in the latter part of 20th century, and finds that the first decades of the 21st century appear to be unusual in the context of the Holocene ( ''medium confidence'' ) and the global glacier recession in the beginning of the 21st century to be unprecedented in the last 2000 years ( ''medium confidence'' ). These assessments are supported by regional evidence. New reconstructions of the Patagonian Ice Sheet suggest that 20th-century glacial recession occurred faster than at any time during the Holocene ( [[#Davies--2020|Davies et al., 2020]] ). The reconstructions of glacier variations show that the glaciers in some regions are now smaller than previously recorded: since the mid-16th century in the Mont Blanc and Grindelwald regions of the European Alps ( [[#Nussbaumer--2012|Nussbaumer and Zumbühl, 2012]] ), since the 9th century in Norway ( [[#Nesje--2012|Nesje et al., 2012]] ), and for the past 1800 years in north-west Iceland ( [[#Harning--2016|Harning et al., 2016]] , 2018). In Arctic Canada and Svalbard, many glaciers are now smaller than they have been in at least 4000 years ( [[#Lowell--2013|Lowell et al., 2013]] ; [[#Miller--2013|Miller et al., 2013]] , 2017; [[#Schweinsberg--2017|Schweinsberg et al., 2017]] , 2018) and more than 40,000 years in Baffin Island ( [[#Pendleton--2019|Pendleton et al., 2019]] ). Although the millennial glacier length variation records are incomplete and discontinuous, and glacier fluctuations depend on multiple factors (e.g., temperature, precipitation, topography, internal glacial dynamics), there is a coherent relationship between rising temperatures, negative mass balance and glacier retreat on centennial time scales across most of the world. Glaciological and geodetic observations show that the rates of early 21st-century mass loss are the highest since 1850 ( [[#Zemp--2015|Zemp et al., 2015]] ). For all regions with long-term observations, glacier mass in the decade 2010–2019 was the smallest since at least the beginning of the 20th century ( ''medium confidence'' ).

In contrast to the global glacier mass decline (Figure 9.21, Table 9.5, and Supplementary Material 9.SM.2), a few glaciers have gained mass or advanced due to internal glacier dynamics or locally restricted climatic causes. The SROCC discusses the ‘Karakoram anomaly’ (centred on the western Kunlun range (at about 80°E, 35°N), but also covering part of the Pamir and Karakoram ranges), where glaciers have been close to balance since at least the 1970s, and had a slightly positive mass balance since the 2000s. Since SROCC, new evidence suggests that this anomaly is related to a combination of low-temperature sensitivity of debris-covered glaciers, a decrease of summer air temperatures (Cross-Chapter Box 10.3), and an increase in snowfall, possibly caused by increases in evapotranspiration from irrigated agriculture ( [[#Bonekamp--2019|Bonekamp et al., 2019]] ; [[#de%20Kok--2020|de Kok et al., 2020]] ; [[#Farinotti--2020|Farinotti et al., 2020]] ; [[#Shean--2020|Shean et al., 2020]] ). However, a recent geodetic mass balance estimate suggests substantially increased thinning rates of High Mountain Asian glaciers after about 2010 ( [[#Hugonnet--2021|Hugonnet et al., 2021]] ). There is ''limited evidence'' to assess whether the Karakoram anomaly will persist in coming decades but, due to the projected increase in air temperature throughout the region, its long-term persistence is ''unlikely'' ( ''high confidence'' ) (Cross-Chapter Box 10.3; [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ; [[#de%20Kok--2020|de Kok et al., 2020]] ; [[#Farinotti--2020|Farinotti et al., 2020]] ).

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===== 9.5.1.1.3 Drivers of glacier change =====

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The AR5 ( [[#Masson-Delmotte--2013|Masson-Delmotte et al., 2013]] ) noted that early-to-mid-Holocene glacier minima could be attributed to high summer insolation ( ''high confidence'' ), unlike the current situation. Since AR5, new and improved chronologies of glacier size variations from the end of the last glacial period and the Holocene (e.g., [[#Solomina--2015|Solomina et al., 2015]] , 2016; [[#Eaves--2019|Eaves et al., 2019]] ; [[#Hall--2019|Hall et al., 2019]] ; [[#Marcott--2019|Marcott et al., 2019]] ; [[#Bohleber--2020|Bohleber et al., 2020]] ; [[#Davies--2020|Davies et al., 2020]] ; [[#Palacios--2020|Palacios et al., 2020]] ) confirm the dominant role of orbital forcing for millennial-scale glacier fluctuations, but emphasize the role of other forcings – solar and volcanic activity, ocean circulation, sea ice and internal climate variability – in explaining the regional variability of glacier fluctuations at shorter time scales. [[#Shakun--2015|Shakun et al. (2015)]] demonstrated that, during the last deglacial transition (18–11 ka), the mid-to-low-latitude glacier retreat was driven by an increase in atmospheric CO <sub>2</sub> and global temperature.

In the Northern Hemisphere, where summer insolation decreased during the Holocene ( [[IPCC:Wg1:Chapter:Chapter-2#2.2.1|Section 2.2.1]] ), glaciers generally waxed ( [[#Briner--2016|Briner et al., 2016]] ; [[#Kaufman--2016|Kaufman et al., 2016]] ; [[#Lecavalier--2017|Lecavalier et al., 2017]] ; [[#Zhang--2017|Zhang et al., 2017]] ; [[#Axford--2019|Axford et al., 2019]] ; [[#Geirsdóttir--2019|Geirsdóttir et al., 2019]] ; [[#Larsen--2019|Larsen et al., 2019]] ; [[#Luckman--2020|Luckman et al., 2020]] ). Conversely, in the Southern Hemisphere, where summer insolation increased during the Holocene, glaciers generally waned ( [[#Solomina--2015|Solomina et al., 2015]] ; [[#Kaplan--2016|Kaplan et al., 2016]] ; [[#Reynhout--2019|Reynhout et al., 2019]] ). However, these general global trends were modulated by regional climate variations in temperature and precipitation ( [[#Murari--2014|Murari et al., 2014]] ; [[#Kaplan--2016|Kaplan et al., 2016]] ; [[#Batbaatar--2018|Batbaatar et al., 2018]] ; [[#Saha--2018|Saha et al., 2018]] ) and there are a number of examples of this. A precipitation increase led to a local early Holocene (7–8 ka) glacier maximum in arid Mongolia (Gichginii Range). Glacier advances at about 9 ka in south-west Greenland have been suggested to be a consequence of the freshwater pulse from the Laurentide Ice Sheet, which led to cooling in the Baffin Bay area ( [[#Schweinsberg--2018|Schweinsberg et al., 2018]] ). Lake sediments indicate that the glaciers in the region were smaller than today, or absent between 8.6 and 1.4 ka ( [[#Larocca--2020|Larocca et al., 2020]] ). Glaciers on the Antarctic Peninsula and in Patagonia during the Holocene were strongly affected by the southern westerly winds, sea ice extent, and ocean circulation ( [[#García--2020|García et al., 2020]] ). Recent studies indicate that explosive volcanism can drive glacier advances ( [[#Solomina--2015|Solomina et al., 2015]] , 2016; [[#Schweinsberg--2018|Schweinsberg et al., 2018]] ; [[#Brönnimann--2019|Brönnimann et al., 2019]] ). In summary, on millennial time scales over the Holocene, there is ''high confidence'' that orbital forcing drove hemispheric-scale glacier variations, but new studies provide a nuanced picture of responses to a variety of regional-scale forcings.

( [[IPCC:Wg1:Chapter:Chapter-3#3.4.3.1|Section 3.4.3.1]] assesses new attribution studies for glaciers and finds that human influence is ''very likely'' the main driver of the global, near-universal retreat of glaciers since the 1990s. The SROCC assessed that it is ''very likely'' that atmospheric warming is the primary driver for the global glacier recession. Since SROCC, a study of glaciers in New Zealand used event attribution to confirm a connection between extreme glacier mass loss years and anthropogenic warming ( [[#Vargo--2020|Vargo et al., 2020]] ).

The SROCC stated with ''high confidence'' that, besides temperature, other factors, such as precipitation changes or internal glacier dynamics, have modified the temperature-induced glacier response in some regions. Deposition of a thin layer (&lt;2 cm) of light-absorbing particles (e.g., black carbon, brown carbon, algae, mineral dust or volcanic ash) can exert an important control on glacier mass balance, by decreasing surface albedo and thus increasing absorbed shortwave radiation and melt (see also [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.3|Section 7.3.4.3]] ). The SROCC found ''limited evidence'' and ''low agreement'' that this process has had a significant effect on observed long-term glacier changes. Several studies have shown melt increases due to the deposition of light-absorbing particles ( [[#Schmale--2017|Schmale et al., 2017]] ; [[#Wittmann--2017|Wittmann et al., 2017]] ; [[#Sigl--2018|Sigl et al., 2018]] ; [[#Di%20Mauro--2019|Di Mauro et al., 2019]] , 2020; [[#Magalhães--2019|Magalhães et al., 2019]] ; [[#Constantin--2020|Constantin et al., 2020]] ). Conversely, increasingly thick debris cover (&gt;2–5 cm) on retreating glaciers can slow down glacier melt ( [[#Pratap--2015|Pratap et al., 2015]] ; [[#Brun--2016|Brun et al., 2016]] ). Although debris covers only about 4–7% of the total glacier area globally ( [[#Scherler--2018|Scherler et al., 2018]] ; [[#Herreid--2020|Herreid and Pellicciotti, 2020]] ), many glaciers are heavily debris-covered in their lower reaches, especially in High Mountain Asia, the Caucasus, the European Alps, Southern Andes and Alaska, resulting in different responses to warming than similar clean-ice glaciers. A shift in regional meteorological conditions, driven by the location and strength of the upper level zonal wind, has been found to have forced recent high mass loss rates in Western North America ( [[#Menounos--2019|Menounos et al., 2019]] ). High geothermal heat flux areas underneath glaciers and high energy dissipation in the flow of water and ice causes additional mass loss of the glaciers in Iceland ( [[#Jóhannesson--2020|Jóhannesson et al., 2020]] ), accounting for 20% of the mass loss since 1994 (Aðalgeirsdóttir et al. 2020). Glacier lake volume in front of retreating glaciers, has increased globally by around 48% between 1990 and 2018 ( [[#Shugar--2020|Shugar et al., 2020]] ), which can increase both subaqueous melt and calving. In summary, there is ''high confidence'' that non-climatic drivers have and will continue to modulate the first-order temperature response of glaciers in some regions.

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[[File:caba43843191c2254314bf2c24b48612 IPCC_AR6_WGI_Figure_9_21.png]]

'''Figure 9.21''' '''|''' '''Global and regional glacier mass evolution between 1901 and 2100 relative to glacier mass in 2015.''' Reconstructed glacier mass change through the 20th century ( [[#Marzeion--2015|Marzeion et al., 2015]] ) and observed during 1961–2016 ( [[#Zemp--2019|Zemp et al., 2019]] ). Projected (2015–2100) glacier mass evolution is based on the median of three RCP emissions scenarios ( [[#Marzeion--2020|Marzeion et al., 2020]] ). In all cases, uncertainties are the 90% confidence interval. For a better comparison between regions, the maximum relative mass change was set to 200%, although for three regions, the volume changes between 1901 and 2015 exceeded that value. For the Low Latitude, New Zealand, and High Mountain Asia glaciers, the changes were larger than 1000%, 350%, and 250%, respectively. See Figure 9.2 for the location of each region. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

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==== 9.5.1.2 Model Evaluation ====

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Since AR5, glacier mass projections have been coordinatedby the Glacier Model Intercomparison Project (GlacierMIP; [[#Hock--2019a|Hock et al., 2019a]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ). The SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) relied on six global-scale glacier models based on previously published glacier model projections ( [[#Hock--2019a|Hock et al., 2019a]] ). It found with ''high confidence'' that glaciers will lose substantial mass by the end of the century, but assigned ''medium confidence'' to the magnitude and timing of the projected glacier mass loss, because of the simplicity of the models, the limited observations in some regions to calibrate them, and the diverging initial glacier volumes.

Since SROCC, [[#Marzeion--2020|Marzeion et al. (2020)]] projected 21st century global-scale glacier mass changes based on seven global-scale and four regional-scale glacier models (Annex II). All models used the same initial and boundary conditions, forming a more coherent ensemble of projections compared to SROCC. Nevertheless, challenges remain because of scarcity of glacier thickness, surface mass balance (SMB) and frontal ablation data for model calibration, but also due to uncertainties in glacier outlines, surface elevations and ice velocities. The global SMB models are of varying complexity, including mass balance sensitivity approaches (van de Wal and Wild, 2001), temperature-index methods ( [[#Anderson--2012|Anderson and Mackintosh, 2012]] ; [[#Marzeion--2012|Marzeion et al., 2012]] ; [[#Radić--2014|Radić et al., 2014]] ; [[#Huss--2015|Huss and Hock, 2015]] ; [[#Kraaijenbrink--2017|Kraaijenbrink et al., 2017]] ; [[#Maussion--2019|Maussion et al., 2019]] ; [[#Zekollari--2019|Zekollari et al., 2019]] ; [[#Rounce--2020|Rounce et al., 2020]] ) and simplified energy balance calculations ( [[#Sakai--2017|Sakai and Fujita, 2017]] ; [[#Shannon--2019|Shannon et al., 2019]] ). Compared to simpler, empirical parametrizations, full energy-balance models are not necessarily the most appropriate choice for simulating future glacier response to climate change, even at the local scale ( [[#Réveillet--2017|Réveillet et al., 2017]] , 2018), because of parameter and forcing uncertainties. All models account for glacier retreat and advance, but only two models ( [[#Anderson--2012|Anderson and Mackintosh, 2012]] ; [[#Huss--2015|Huss and Hock, 2015]] ) include frontal ablation.

Secondary processes such as debris-cover thickening (e.g., [[#Herreid--2020|Herreid and Pellicciotti, 2020]] ), albedo changes due to light-absorbing particles (e.g., [[#Magalhães--2019|Magalhães et al., 2019]] ; [[#Williamson--2019|Williamson et al., 2019]] ), trends of refreezing and water storage in firn (e.g., [[#Ochwat--2021|Ochwat et al., 2021]] ), dynamic instabilities such as surges (e.g., [[#Thøgersen--2019|Thøgersen et al., 2019]] ) or glacier collapse (e.g., [[#Kääb--2018|Kääb et al., 2018]] ), are not represented in global glacier models, resulting in both underestimated and overestimated sensitivity to warming that is currently not possible to quantify. Furthermore, challenges for future projections are caused by the low-resolution and high-spatial variability at sub-grid scale of the precipitation amount provided by general circulation models (GCMs), which requires downscaling to the spatial scale of a glacier ( [[#Maussion--2019|Maussion et al., 2019]] ; [[#Zekollari--2019|Zekollari et al., 2019]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ). In summary, in agreement with SROCC, progress in global scale glacier modelling efforts allows ''medium confidence'' in the capability of current-generation glacier models to simulate the magnitude and timing of glacier mass changes as a response to climatic forcing.

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==== 9.5.1.3 Projections ====

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The AR5 ( [[#Vaughan--2013|Vaughan et al., 2013]] ) and SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) stated with ''high confidence'' that the world’s glaciers are presently in imbalance due to the warming of recent decades. The observed retreat of glaciers is only a partial response to the already realized warming ( [[#Christian--2018|Christian et al., 2018]] ), and they are committed to losing considerable mass in the future, even without further change in air temperature ( [[#Mernild--2013|Mernild et al., 2013]] ; [[#Trüssel--2013|Trüssel et al., 2013]] ; Zekollari and Huybrechts, 2015; [[#Huss--2016|Huss and Fischer, 2016]] ; [[#Marzeion--2018|Marzeion et al., 2018]] ; [[#Jouvet--2019|Jouvet and Huss, 2019]] ). One model estimates that 36 ± 8 % of global glacier mass is already committed to be lost due to past greenhouse gas emissions ( [[#Marzeion--2018|Marzeion et al., 2018]] ). Although accumulation and ablation instantly determine the SMB, the glacier geometries adjust to changed atmospheric conditions over a longer time ( [[#Zekollari--2020|Zekollari et al., 2020]] ). The adjustment time, often referred to as the response time, is variable from one glacier to another, depending on the glacier geometry (thickness and steepness), SMB and gradient (e.g., [[#Jóhannesson--1989|Jóhannesson et al., 1989]] ; [[#Harrison--2001|Harrison et al., 2001]] ; [[#Lüthi--2009|Lüthi, 2009]] ; [[#Zekollari--2020|Zekollari et al., 2020]] ). Response time is variable: years for smaller and steeper glaciers ( [[#Beedle--2009|Beedle et al., 2009]] ; [[#Lüthi--2010|Lüthi and Bauder, 2010]] ; [[#Rabatel--2013|Rabatel et al., 2013]] ), up to tens or hundreds of years for larger and gentle-sloped glaciers (e.g., [[#Burgess--2004|Burgess and Sharp, 2004]] ; [[#Lüthi--2010|Lüthi et al., 2010]] ; [[#Zekollari--2020|Zekollari et al., 2020]] ). The models indicate that the disequilibrium between the glaciers and present atmospheric conditions (1995 to 2014) reduces and then disappears at around year 2070 ( [[#Marzeion--2020|Marzeion et al., 2020]] ). There is therefore ''very high confidence'' that the disequilibrium of glaciers will persist as warming continues, and that glacierswill continue to lose mass for at least several decades because of their lagged response, even if global temperature is stabilized.

The SROCC assessed that global glacier mass loss by 2100, relative to 2015 will be 18 [ ''likely'' range 11 to 25] % for scenario RCP2.6 and 36 [ ''likely'' range 26 to 47] % for RCP8.5, and that many glaciers will disappear regardless of the emissions scenario ( ''very high confidence'' ). Since SROCC, new results from [[#Marzeion--2020|Marzeion et al. (2020)]] have been published (Box 9.3, Figure 9.21 and Table 9.4, including peripheral glaciers in Greenland and Antarctica). Glaciers will lose 29,000 [9000 to 49,000] Gt and 58,000 [28,000 to 88,000] Gt over the period 2015–2100 for RCP2.6 and RCP8.5, respectively ( ''medium confidence'' ), which represents 18 [5 to 31] % and 36 [16 to 56] % of their early 21st century mass, respectively (Table 9.4). Within uncertainties, these agree with SROCC estimates, although with a slightly smaller mass loss due to the inclusion of models with lower sensitivity to changing climate conditions ( [[#Marzeion--2020|Marzeion et al., 2020]] ). The greatest source of uncertainty in glacier mass loss until the middle of the 21st century is the disagreement between glacier models, with emissions scenario becoming the dominant cause of uncertainty by the end of the 21st century ( [[#Marzeion--2020|Marzeion et al., 2020]] ).

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'''Table''' '''9.4 |''' '''Projected sea level contributions from global glaciers (including peripheral glaciers in Greenland and Antarctica) by 2100 relative to 2015, for selected Representative Concentration Pathway (RCP) and Shared Socio-economic Pathway (SSP) scenarios.'''

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

|-
| Study

| RCP2.6

| RCP4.5

| RCP8.5

| Notes

|-
| ''IPCC AR5 and SROCC''

( [[#Church--2013b|Church et al., 2013b]] ; [[#Oppenheimer--2019|Oppenheimer et al., 2019]] )

| 0.10

(0.04–0.16) m

| 0.12

(0.06–0.19) m

| 0.17

(0.09–0.25) m

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

|-
| GlacierMIP

[[#Hock--2019a|Hock et al. (2019a)]]

| 0.094

(0.069–0.119) m

| 0.142 (107–177) m

| 0.200

(0.156–0.240) m

| Mean (±1 standard deviation range) contributions

|-
| GlacierMIP

[[#Marzeion--2020|Marzeion et al. (2020)]]

| 0.079

[0.023–0.135] m

| 0.119

[0.053–0.185] m

| 0.159

[0.073–0.245] m

| Median [90% range]

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

|-
| Study

| SSP1-2.6

| SSP2-4.5

| SSP5-8.5

| Notes

|-
| GlacierMIP experimental protocol ( [[#Marzeion--2020|Marzeion et al., 2020]] ) with CMIP6 forcing

| 0.111

(0.077–0.145)

[0.05–0.167] m

| 0.136

(0.096–0.176)

[0.07–0.201] m

| 0.190

(0.133–0.247)

[0.09–0.283] m

| Mean (66% range) [90% range] using 13 GCMs and 2 glacier models <sup>a</sup>

|-
| GlacierMIP ( [[#Marzeion--2020|Marzeion et al., 2020]] ) with AR5 parametric fit: used for rates and post-2100 projections (Supplementary Material 9.SM.4.5)

| 0.102

(0.07 6 – 0 .134) [0.05 9 – 0 .154] m

| 0.128

(0.09 5 – 0 .167) [0.07 6 – 0 .192] m

| 0.171

(0.12 4 – 0 .224) [0.09 8 – 0 .259] m

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

|-
| Emulated ( [[#Marzeion--2020|Marzeion et al., 2020]] ; [[#Edwards--2021|Edwards et al., 2021]] )

| 0.080

(0.05 9 – 0 .101) [0.04 6 – 0 .116] m

| 0.115

(0.09 3 – 0 .137) [0.07 7 – 0 .155] m

| 0.170

(0.14 4 – 0 .196) [0.12 4 – 0 .218] m

| Median (66% range) [90% range] contribution in 2100 relative to 2015 from emulator of GlacierMIP6 used with Chapter 7: Climate Forcing

|}

<sup>a</sup> OGGM ( [[#Maussion--2019|Maussion et al., 2019]] ) and GloGEM ( [[#Huss--2015|Huss and Hock, 2015]] ).

Although the GlacierMIP projections ( [[#Hock--2019a|Hock et al., 2019a]] ; [[#Marzeion--2020|Marzeion et al., 2020]] ) were forced by RCP scenarios, two global glacier models ( [[#Huss--2015|Huss and Hock, 2015]] ; [[#Maussion--2019|Maussion et al., 2019]] ) were also run with 13 GCMs and SSP scenarios (Table 9.4). These results show increased mass loss compared to the RCP forced simulations, although with fewer global glacier models. To enable the glacier contribution to future sea level rise to be estimated under the full range of SSP scenarios ( [[#9.6.3.3|Section 9.6.3.3]] ), the GlacierMIP results are emulated using a Gaussian process model (Box 9.3 and Table 9.4; [[#Edwards--2021|Edwards et al., 2021]] ). The emulated projections show a narrower range than the roughly equivalent RCP projections, which may be explained by not accounting for covariance in the regional uncertainties ( [[#Marzeion--2020|Marzeion et al., 2020]] ) and by the fact that the emulator caps sea level contribution for each region at the volume above floatation estimated by [[#Farinotti--2019|Farinotti et al. (2019)]] (Table 9.SM.2). Comparison of simulated and emulated regional sea level contributions support this explanation. Rates of change and post-2100 sea level projections are estimated with the AR5 parametric fit (Supplementary Material 9.SM.4.5; [[#Church--2013b|Church et al., 2013b]] ) applied to the GlacierMIP results ( [[#Marzeion--2020|Marzeion et al., 2020]] ), and these are also shown in Table 9.4 for comparison.

The mass loss rates vary between regions and there are distinctively different patterns between scenarios ( [[#Marzeion--2020|Marzeion et al., 2020]] ). The global models agree that regions characterized by relatively little glacier-covered area (Low Latitude, Central Europe, Caucasus, Western Canada and USA, North Asia, Scandinavia and New Zealand) will lose nearly all (&gt;80%) glacier mass by 2100 in the RCP8.5 scenario, but their corresponding contribution to sea level rise will be small. A study using detailed ice dynamics for the largest glacier of the European Alps, Great Aletsch Glacier, projects 60% of present ice volume will be lost by 2100 in RCP2.6 and an almost complete wastage of the ice in RCP8.5 ( [[#Jouvet--2019|Jouvet and Huss, 2019]] ). Due to their larger mass, the largest contribution to sea level rise comes from glaciers in the Arctic and Antarctic regions (Antarctic, Arctic Canada, Alaska, Greenland, Svalbard and Russian Arctic), in spite of having the smallest relative mass loss, and it is expected that they will continue to contribute to sea level rise beyond 2100. The regions with intermediate glacier mass (Southern Andes, High Mountain Asia and Iceland) show decreasing mass loss rates for RCP2.6 throughout the 21st century, and increasing rates for RCP8.5 that peak in the mid-to-late 21st century (Figure 9.21). The peak in mass loss rate followed by reduction is due to decreasing glacier volume and stabilizing mass balance ( [[#Marzeion--2020|Marzeion et al., 2020]] ). Vatnajökull, the largest glacier in Iceland, is projected to lose about 50% of its mass by 2300 in extended RCP4.5 and 80–100% in extended RCP8.5 scenarios ( [[#Schmidt--2019|Schmidt et al., 2019]] ). In summary, both global and regional studies agree that glacier mass loss will continue in all regions, with larger mass loss for high-emissions scenarios ( ''high confidence'' ) (see also [[IPCC:Wg1:Chapter:Chapter-8#8.4.1.7.1|Section 8.4.1.7.1]] ).

In AR5 and SROCC, glacier mass loss beyond 2100 was calculated using a parametric fit to available model simulations. In section 9.6.3.5, that same parametric fit is applied to [[#Marzeion--2020|Marzeion et al. (2020)]] projections, resulting in complete glacier mass loss at year 2300 under SSP5-8.5 and 40–100% mass loss under SSP1-2.6. [[#Clark--2016|Clark et al. (2016)]] simulate glacier mass evolution, not including glaciers peripheral to the Antarctic Ice Sheet (AIS), for different warming levels for the next 10,000 years. There is ''limited evidence'' and ''low confidence'' that, at sustained warming levels between 1.5 and 2°C, about 50–60% of glacier mass will remain, predominantly in the polar regions. At sustained warming levels between 2 and 3°C, about 50–60% of glacier mass outside Antarctica will be lost and, at sustained warming levels, between 3 and 5°C, 60–75% of glacier mass outside Antarctica will disappear. Based on [[#Marzeion--2020|Marzeion et al. (2020)]] , there is ''medium confidence'' that nearly all glacier mass in low latitudes, Central Europe, the Caucasus, western Canada and the USA, North Asia, Scandinavia and New Zealand will disappear at this high warming level.

<div id="9.5.2" class="h2-container"></div>

<span id="permafrost-1"></span>
=== 9.5.2 Permafrost ===

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This section focuses on the physical aspects of permafrost (perennially frozen ground) as an element of the climate system, drawing on the assessment of observed global permafrost changes provided in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] , and more specifically model evaluation and projections. The permafrost carbon feedback is assessed in Box 5.1. [[IPCC:Wg1:Chapter:Chapter-12#12.4|Section 12.4]] of this Report provides permafrost information relevant to impacts and risk on regional scales.

<div id="9.5.2.1" class="h3-container"></div>

<span id="observed-and-reconstructed-changes"></span>
==== 9.5.2.1 Observed and Reconstructed Changes ====

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The current extent of the global permafrost region is about 22 ± 3×10 <sup>6</sup> km <sup>2</sup> ( [[#Gruber--2012|Gruber, 2012]] ). Permafrost underlies about 15% of Northern Hemisphere land and more than 50% of the unglacierized land north of 60°N ( [[#Zhang--1999|Zhang et al., 1999]] ; [[#Gruber--2012|Gruber, 2012]] ; [[#Obu--2019|Obu et al., 2019]] ). It is also found in high-altitude areas of mountain ranges in both hemispheres – estimated in SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) as representing about 27–29% of the global permafrost area ( ''medium confidence'' ) and most unglacierized areas in Antarctica ( [[#Vieira--2010|Vieira et al., 2010]] ; [[#Obu--2020|Obu et al., 2020]] ). Ground ice volume in permafrost is variable, reaching up to 90% in syngenetic permafrost deposits ( [[#Kanevskiy--2013|Kanevskiy et al., 2013]] ; [[#Gilbert--2016|Gilbert et al., 2016]] ). The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) reported ''medium confidence'' in the estimation that Earth’s total perennial ground ice volume is equivalent to 2–10 cm of global sea level ( [[#Zhang--2000|Zhang et al., 2000]] ). There is no evidence suggesting that a large part of this volume, if melted, would run off and contribute to global sea level. Therefore, and because of the modest total volume of mobilizable water, the contribution of permafrost thaw to past and future sea level budgets is usually neglected (see [[#9.6.3.2|Section 9.6.3.2]] ).

Permafrost changes mostly refer to changes in extent, temperature and active layer thickness (ALT). The SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ; [[#Meredith--2019|Meredith et al., 2019]] ) reported with ''very high confidence'' that record high permafrost temperatures at the depth of the zero annual amplitude (the depth about 10–20 m below the surface where the seasonal soil temperature cycle vanishes) were attained in recent decades in the Northern circumpolar permafrost region, ''high confidence'' that permafrost has warmed over recent decades in many mountain ranges, and overall ''very high confidence'' that global warming over the last decades has led to widespread permafrost warming. As reported in SROCC, the global (polar and mountain) permafrost temperature has increased at 0.29°C ± 0.12°C near the depth of zero annual amplitude between 2007 and 2016 ( [[#Biskaborn--2019|Biskaborn et al., 2019]] ). Stronger warming has been observed in the continuous permafrost zone (0.39°C ± 0.15°C) compared to the discontinuous zone (0.20°C ± 0.10°C), consistent with the fact that, near the melting point, a large amount of energy is required for melting the ice (Figure 9.22), and because of the reduced effect of Arctic amplification in more southerly locations ( [[#Romanovsky--2017|Romanovsky et al., 2017]] ). This is consistent with longer-term Arctic trends from deep boreholes shown in Figure 2.22. Mountain permafrost temperature trends are heterogeneous, reflecting variations in local conditions such as topography, surface type, soil texture and snow cover, but again, generally weaker warming rates are observed in warmer permafrost at temperatures close to 0°C, particularly when ice content is high (e.g., [[#Mollaret--2019|Mollaret et al., 2019]] ; [[#Noetzli--2019|Noetzli et al., 2019]] ; [[#PERMOS--2019|PERMOS, 2019]] ). In summary, strong variability in recent permafrost temperature trends is linked to local conditions, regionally varying temperature trends, and the thermal state of permafrost itself. However, as discussed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] , there is overall ''high confidence'' in the observed increases in permafrost temperature over the past three to four decades throughout the permafrost regions.

Closer to the surface, the active layer undergoes annual cycles of freeze and thaw. The SROCC reported ''medium confidence'' in ALT increase as a pan-Arctic phenomenon. Recent evidence presented in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] shows pervasive ALT increase in the European and Russian Arctic in the 21st century, and in high elevation areas in Europe and Asia since the mid-1990s. Emergence of a clearer global picture is hampered by: (i) uneven distribution of observing sites; (ii) substantial variability among the existing sites, strongly influenced by local conditions (soil constituents and moisture, snow cover, vegetation); (iii) interannual variability; and (iv) thaw settlement in ice-rich terrain ( [[#Streletskiy--2017|Streletskiy et al., 2017]] ; [[#O’Neill--2019|O’Neill et al., 2019]] ). In summary, in agreement with SROCC and recent evidence presented in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] , there is ''medium confidence'' that ALT increase is a pan-Arctic phenomenon.

There is ''medium confidence'' that the observed acceleration and destabilization of rock glaciers is related to warming temperatures and increase in water content at the permafrost table in recent decades ( [[#Deline--2015|Deline et al., 2015]] ; [[#Cicoira--2019|Cicoira et al., 2019]] ; [[#Marcer--2019|Marcer et al., 2019]] ; [[#PERMOS--2019|PERMOS, 2019]] ; [[#Kenner--2020|Kenner et al., 2020]] ). There is also ''medium confidence'' that observed increases in size and frequency of rock avalanches are linked to permafrost degradation in rock walls ( [[#Ravanel--2017|Ravanel et al., 2017]] ; [[#Patton--2019|Patton et al., 2019]] ; [[#Tapia%20Baldis--2019|Tapia Baldis and Trombotto Liaudat, 2019]] ). In summary, there is ''medium confidence'' that mountain permafrost degradation at high altitude has increased the instability of mountain slopes in the past decade.

The SROCC assessed with ''high confidence'' that the extent of subsea permafrost, formed before submersion on Arctic continental shelves during the last deglaciation, is much reduced compared to older studies that had estimated the entire formerly exposed Arctic shelf area to be underlain by permafrost. This is supported by observations ( [[#Shakhova--2017|Shakhova et al., 2017]] ) that show rapid thaw of recently submerged permafrost on the East Siberian Shelf. A modelling study ( [[#Overduin--2019|Overduin et al., 2019]] ) estimates that 97% of permafrost under Arctic shelves is currently thinning.

Based on multiple studies, there is ''medium confidence'' that widespread retreat of coastal permafrost is accelerating in the Arctic ( [[#Günther--2015|Günther et al., 2015]] ; [[#Cunliffe--2019|Cunliffe et al., 2019]] ; [[#Isaev--2019|Isaev et al., 2019]] ). There is also consistent evidence of complete permafrost thaw in areas of discontinuous and sporadic permafrost since about 1980, but this evidence is geographically scattered ( [[#Camill--2005|Camill, 2005]] ; [[#Kirpotin--2011|Kirpotin et al., 2011]] ; [[#James--2013|James et al., 2013]] ; B.M. [[#Jones--2016|]] [[#Jones--2016|Jones et al., 2016]] ; [[#Borge--2017|Borge et al., 2017]] ; [[#Chasmer--2017|Chasmer and Hopkinson, 2017]] ; [[#Gibson--2018|Gibson et al., 2018]] ). In spite of increasing evidence of landscape changes from site studies and remote sensing, quantifying permafrost extent change remains challenging because it is a subsurface phenomenon that cannot be observed directly ( [[#Jorgenson--2016|Jorgenson and Grosse, 2016]] ; [[#Trofaier--2017|Trofaier et al., 2017]] ). A modelling study for the Qinghai-Tibet Plateau between the 1960s and the 2000s ( [[#Ran--2018|Ran et al., 2018]] ) suggests transition from permafrost to seasonally frozen ground over an area of more than 400,000 km <sup>2</sup> . In summary, there is ''medium confidence'' that complete permafrost thaw in recent decades is a common phenomenon in discontinuous and sporadic permafrost regions. In addition, paleoclimatic evidence presented in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.5|Section 2.3.2.5]] confirms a long-term sensitivity of permafrost extent to climatic variations, although an analysis of North American speleothem records over the last two glacial cycles indicates that this apparent high sensitivity could be a consequence of regional-scale variability ( [[#Batchelor--2019|Batchelor et al., 2019]] ).

There is a lack of formal studies attributing observed permafrost changes (thaw depth, thermal state) or associated landscape changes to anthropogenic forcing. However, the observed Arctic warming has been attributed to anthropogenic forcing (e.g., [[#Najafi--2015|Najafi et al., 2015]] ) and an obvious physical link exists between ground temperatures (and thus permafrost) and surface air temperatures. Therefore, physically consistent and convergent lines of evidence lead to ''medium confidence'' in anthropogenic forcing being the dominant cause of the observed pan-Arctic permafrost changes. Added to this, local permafrost change by soil and ecosystem disturbance is induced by increasing human industrial activities in the Arctic (e.g., [[#Raynolds--2014|Raynolds et al., 2014]] ).

<div id="9.5.2.2" class="h3-container"></div>

<span id="evaluation-of-permafrost-in-climate-models"></span>
==== 9.5.2.2 Evaluation of Permafrost in Climate Models ====

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As stated in AR5 ( [[#Flato--2013|Flato et al., 2013]] ), coupled models contributing to CMIP5 showed large inter-model variability of permafrost extent due to deficiencies in reproducing surface characteristics and processes ( [[#Koven--2013|Koven et al., 2013]] ), particularly thermal properties of the ground and snow. These deficiencies led SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) to express only ''medium confidence'' in the models’ capacity to correctly project the magnitude of future permafrost changes, in spite of ''high confidence'' in the models’ projection of a general thaw depth increase and a substantial loss of shallow permafrost. The SROCC further noted that several types of physical ‘pulse’ disturbances, in particular fire and thermokarst formation, are usually not represented in coupled climate models. This has been discussed in detail in SROCC, which assessed that there is ''high confidence'' that permafrost degradation through fire ( [[#Jones--2015|Jones et al., 2015]] ; [[#Gibson--2018|Gibson et al., 2018]] ) is currently occurring faster in some well-studied regions than during the first half of the 20th century, and ''medium confidence'' that thermokarst formation, to which about 20% of the northern permafrost region is vulnerable ( [[#Olefeldt--2016|Olefeldt et al., 2016]] ), can lead to faster large-scale permafrost degradation in response to climate change.

Since SROCC, dedicated modelling of the evolution of ice- and organic-rich permafrost in the north-east Siberian lowlands ( [[#Nitzbon--2020|Nitzbon et al., 2020]] ) has shown that not representing thermokarst-inducing processes in ice-rich terrain leads to a systematic underestimation of the rapidity and magnitude of permafrost thaw. Simplified inventory-based modelling ( [[#Turetsky--2020|Turetsky et al., 2020]] ) points towards similar conclusions. Although these pulse disturbances still need to be represented in CMIP-type models, there have been many new developments to that type of model since CMIP5 and AR5. Soil freezing and its thermal and hydrological effects are now included in a large number of land-surface modules that are part of the CMIP6 ensemble (S. [[#Chadburn--2015|]] [[#Chadburn--2015|Chadburn et al., 2015]] ; [[#Hagemann--2016|Hagemann et al., 2016]] ; [[#Cuntz--2018|Cuntz and Haverd, 2018]] ; [[#Guimberteau--2018|Guimberteau et al., 2018]] ; [[#Yokohata--2020|Yokohata et al., 2020]] ), sometimes allowing for the effects of excess ice ( [[#Lee--2014|Lee et al., 2014]] ). Improved representation of snow insulation in models has led to more realistic simulated permafrost extents (e.g., [[#Paquin--2015|Paquin and Sushama, 2015]] ). In a post-CMIP5 ensemble of land-surface models driven by observed meteorological conditions ( [[#McGuire--2016|McGuire et al., 2016]] ), inter-model spread was substantially reduced when the ensemble was restricted to models that appropriately represented the effect of snow insulation on the underlying soil (W. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ). More detailed descriptions of high-latitude vegetation characteristics, vegetation dynamics, and snow-vegetation interactions have been included in several models since AR5 (S.E. [[#Chadburn--2015|]] [[#Chadburn--2015|Chadburn et al., 2015]] ; [[#Porada--2016|Porada et al., 2016]] ; [[#Druel--2017|Druel et al., 2017]] ).

A total soil column depth of at least about 10 m is required to adequately represent the dampening effect of seasonal-scale heat exchanges between shallow and deeper ground, and thus to correctly simulate ALT ( [[#Lawrence--2008|Lawrence et al., 2008]] ; [[#Ekici--2015|Ekici et al., 2015]] ). However, many CMIP6 models still have shallower total soil columns ( [[#Burke--2020|Burke et al., 2020]] ) and the proportion of models with deeper total soil columns has not increased since CMIP5 ( [[#Koven--2013|Koven et al., 2013]] ). Another recently identified process, usually not represented in the current (CMIP6) generation of climate models ( [[#Zhu--2019|Zhu et al., 2019]] ), is warming-driven decomposition and burning of organic material that provides strong thermal insulation of underlying ground. Decay of the insulating organic material can lead to increased permafrost thaw, creating a positive feedback loop.

In spite of the aforementioned structural improvements to many models, the simulated current permafrost extent from available CMIP6 models shows no substantial improvement with respect to CMIP5 (see Figure 9.22a). The extent of the region where permafrost is simulated within the top 15 m in the Northern Hemisphere for the 1979–1998 period is characterized by very large scatter in the coupled CMIP5 and CMIP6 historical simulations compared to estimates of the present permafrost extent based on multiple observational lines of evidence ( [[#Zhang--1999|Zhang et al., 1999]] ) and models based on satellite observations and reanalyses ( [[#Gruber--2012|Gruber, 2012]] ; [[#Obu--2019|Obu et al., 2019]] ). Outliers with very low simulated permafrost extent are models that have only a very shallow soil column (leading to an underestimate of thermal inertia at depth) and do not take into account soil water phase changes. These inadequacies lead to an overestimate of seasonal thaw depth, exceeding the total thickness of the models’ soil columns ( [[#Burke--2020|Burke et al., 2020]] ). Excessive simulated permafrost extent can in several cases be traced to insufficient thermal insulation by the winter snow cover ( [[#Burke--2020|Burke et al., 2020]] ).

Figure 9.22a also shows that the corresponding land-atmosphere simulations with prescribed observed sea surface temperatures and sea ice concentrations, and the land-only simulations with prescribed reanalysis-based meteorological forcing, do not provide an improved simulation of the current permafrost extent, although, by construction, they can be expected to exhibit lower land surface climate biases. This further points to deficiencies in the land modules as the main reason for biases, consistent with conclusions drawn from the analysis of CMIP5 output ( [[#Koven--2013|Koven et al., 2013]] ), as reported in SROCC and AR5.

In spite of more realistic description of permafrost-related processes in many coupled climate models, the CMIP6 models still produce a very scattered ensemble of estimates of current permafrost extent, and there is ''high confidence'' that this is strongly linked to deficiencies of the representation of soil processes. Furthermore, current-generation climate models tend to neglect several physical disturbances that can lead to faster permafrost thaw. Because of large uncertainties in the future evolution of these drivers (see SROCC), there is ''limited evidence'' that these shortcomings lead to an underestimate of permafrost degradation rates in response to climate change in the CMIP6 ensemble. In summary, there is ''high confidence'' that coupled models correctly simulate the sign of future permafrost changes linked to surface climate changes, but only ''medium confidence'' in the amplitude and timing of the transient response.

<div id="_idContainer057" class="Basic-Text-Frame"></div>

[[File:8dab1ae218c2f9587eb80c1435a8a0d6 IPCC_AR6_WGI_Figure_9_22.png]]

'''Figure 9.22''' '''|''' '''Simulated versus observed permafrost extent and volume change by warming level. (a)''' Diagnosed Northern Hemisphere permafrost extent (area with perennially frozen ground at 15 m depth, or at the deepest model soil level if this is above 15 m) for 1979–1998, for available Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) models, from the first ensemble member of the historical coupled run, and for CMIP6 Atmospheric Model Intercomparison Project (AMIP) (atmosphere+land surface, prescribed ocean) and land-hist (land only, prescribed atmospheric forcing) runs. Estimates of current permafrost extents based on physical evidence and reanalyses are indicated as black symbols – triangle: [[#Obu--2018|Obu et al. (2018)]] ; star: [[#Zhang--1999|Zhang et al. (1999)]] ; circle: central value and associated range from [[#Gruber--2012|Gruber (2012)]] . '''(b)''' Simulated global permafrost volume change between the surface and 3 m depth as a function of the simulated global surface air temperature (GSAT) change, from the first ensemble members of a selection of scenarios, for available CMIP6 models. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

<div id="9.5.2.3" class="h3-container"></div>

<span id="projected-permafrost-changes"></span>
==== 9.5.2.3 Projected Permafrost Changes ====

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The AR5 ( [[#Collins--2013|Collins et al., 2013]] ) and SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) (based on available CMIP5 output) both expressed ''high confidence'' that future pan-Arctic thaw depth will increase and near-surface permafrost extent will decrease under future global warming, and ''medium confidence'' in the magnitude of the simulated changes because of model deficiencies and the large spread of the results.

The equilibrium sensitivity of permafrost extent to stabilized global mean warming has been inferred (by constraining CMIP5 output with diagnosed relationships between the observed present-day spatial distribution of permafrost and air temperature) to be about 4.0×10 <sup>6</sup> km <sup>2</sup> °C <sup>–1</sup> ( [[#Chadburn--2017|Chadburn et al., 2017]] ) for global surface air temperature (GSAT) changes with respect to the present below about +3°C. This equilibrium permafrost sensitivity, relevant for assessing long-term permafrost changes at a stabilized warming level, is about 20% higher than the transient centennial-scale near-surface permafrost extent sensitivity (diagnosed from seasonal thaw down to 3 m depth) suggested by direct analysis of CMIP5 output ( [[#Slater--2013|Slater and Lawrence, 2013]] ). Compared to these and other studies reported in AR5 and SROCC ( [[#Koven--2013|Koven et al., 2013]] ), the recently suggested equilibrium extent sensitivity to GSAT changes of about 1.5×10 <sup>6</sup> km <sup>2</sup> °C <sup>–1</sup> based on idealized ground temperature modelling ( [[#Liu--2021|Liu et al., 2021]] ) appears unrealistically low.

A strong transient temperature sensitivity of the volume of perennially frozen soil in the top 3 m below the surface is consistently suggested by the available CMIP6 models (Figure 9.22b). Relative to the current volume, the transient sensitivity of the modelled permafrost volume in the top 3 m to GSAT changes (with respect to the 1995–2014 average and up to +3°C change, that is, about up to +4°C with respect to pre-industrial levels) is about 25 ± 5 % °C <sup>–1</sup> ( [[#Burke--2020|Burke et al., 2020]] ), but there is only ''medium confidence'' in this value and 1 standard deviation uncertainty range because of the model deficiencies discussed in 9.5.2.2. It is important to note that permafrost loss will not be limited to the top 3 m, with delayed response of deeper permafrost. The simulated transient temperature sensitivity of permafrost volume is slightly stronger in the SSP1-2.6 scenario than in other SSPs because subsurface temperature lag increases with higher atmospheric warming rates, particularly when ground ice melting induces additional delays.

Due to the role of air temperature as a major driver of permafrost change, SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) expressed ''very high confidence'' that permafrost in high mountain regions is expected to undergo increasing thaw and degradation during the 21st century, with stronger consequences expected for higher greenhouse gas emissions scenarios. Recently published studies (e.g., [[#Zhao--2019|Zhao et al., 2019]] ) support this SROCC assessment.

In summary, based on ''high agreement'' across CMIP6 and older model projections, fundamental process understanding, and paleoclimate evidence, it is ''virtually certain'' that permafrost extent and volume will shrink as global climate warms.

<div id="9.5.3" class="h2-container"></div>

<span id="seasonal-snow-cover"></span>
=== 9.5.3 Seasonal Snow Cover ===

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Mean snow cover extent in January and February, the usual months of maximum extent, covers about 45% of the Northern Hemisphere (NH) land surface – more than 45 million km <sup>2</sup> over the 1967–2014 period ( [[#Estilow--2015|Estilow et al., 2015]] ). In contrast, maximum seasonal snow cover in South America, the dominant ice-free land mass in the Southern Hemisphere in terms of seasonal snow cover extent, remains well below 1 million km <sup>2</sup> ( [[#Foster--2009|Foster et al., 2009]] ) or less than 2% of the Southern Hemisphere land surface.

Terrestrial snow cover is characterized via three variables: (i) areal snow cover extent (SCE); (ii) the time period of continuous snow cover – snow cover duration (SCD) that reflects snow-on and snow-off dates (i.e., the first and last days of observed snow cover); and (iii) snow accumulation – expressed either as snow depth (SD) or snow water equivalent (SWE), the depth of water stored by the snowpack.

Observed large-scale snow cover changes, their attribution to human activity, and their effects on the hydrological cycle are also discussed in [[IPCC:Wg1:Chapter:Chapter-2|Chapter 2]] ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ), [[IPCC:Wg1:Chapter:Chapter-3|Chapter 3]] ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.2|Section 3.4.2]] ) and [[IPCC:Wg1:Chapter:Chapter-8|Chapter 8]] ( [[IPCC:Wg1:Chapter:Chapter-8#8.2.3.1|Section 8.2.3.1]] ) of this Report. The role of snow in the global surface albedo feedback is assessed in [[IPCC:Wg1:Chapter:Chapter-7#7.4.2.3|Section 7.4.2.3]] . The effect of aerosol deposition on snow albedo and associated climate forcing is assessed in [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.3|Section 7.3.4.3]] .

<div id="9.5.3.1" class="h3-container"></div>

<span id="observed-changes-of-seasonal-snow-cover"></span>
==== 9.5.3.1 Observed Changes of Seasonal Snow Cover ====

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The AR5 ( [[#Vaughan--2013|Vaughan et al., 2013]] ) reported that NH SCE in June ''very likely'' decreased by 11.7 [8.8 to 14.6] % per decade over the 1967–2012 period, exceeding the absolute and relative reductions observed in March and April. The AR5 further reported ''very high confidence'' that NH March and April SCE decreased over the 90 years after 1922. The SROCC only assessed snow cover changes for the Arctic and mountain areas. For the Arctic (north of 60°N), SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) expressed ''high confidence'' in SCE decreases of –3.5 ± 1.9% per decade in May and –13.4 ± 5.4% per decade in June, based on a combination of multiple datasets ( [[#Mudryk--2017|Mudryk et al., 2017]] ). Concerning mountain snow cover, SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) reported with ''high confidence'' that mountain snow cover (both in terms of SCE and maximum SWE) has generally declined since the middle of the 20th century at lower elevations. At higher elevations, SROCC reported ''medium confidence'' in generally insignificant snow cover trends (where these were available). The large-scale assessment provided in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] of this Report reports ''very high confidence'' in substantial reductions of NH SCE (particularly in spring) since 1978, and states that there is ''limited evidence'' that this decline extends back to the early 20th century.

Since SROCC, progress has been made in characterizing seasonal NH snow cover changes through the combined analysis of datasets from multiple sources (surface observations, remote sensing, land surface models and reanalysis products). A recent combined dataset ( [[#Mudryk--2020|Mudryk et al., 2020]] ) identified negative NH SCE trends in all months between 1981 and 2018, exceeding –50 × 10 <sup>3</sup> km <sup>2</sup> yr <sup>–1</sup> in November, December, March and May (Figure 9.23a,b). The loss of spring SCE is also reflected in earlier spring snow melt, derived from surface observations ( [[#Bulygina--2011|Bulygina et al., 2011]] ; [[#Brown--2017|Brown et al., 2017]] ), satellite observations ( [[#Wang--2013|Wang et al., 2013]] ; [[#Estilow--2015|Estilow et al., 2015]] ; [[#Anttila--2018|Anttila et al., 2018]] ), and model-based analyses ( [[#Liston--2011|Liston and Hiemstra, 2011]] ). There is considerable inter-dataset and regional variability, but the continental-scale trends of snow-off dates from these datasets are consistently negative ( [[#Brown--2017|Brown et al., 2017]] ; [[#Kouki--2019|Kouki et al., 2019]] ).

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[[File:425f3c95af5739044b1949cc796b6e5b IPCC_AR6_WGI_Figure_9_23.png]]

'''Figure 9.23''' '''|''' '''Observed monthly Northern Hemisphere snow cover (a) trends and (b) anomalies, and snow mass (c) trends and (d) anomalies.''' From the observation-based ensemble discussed in the text ( [[#Mudryk--2020|Mudryk et al., 2020]] ). Trends and anomalies are calculated over the 1981–2018 period. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

Satellite-derived estimates of NH SCE compiled within the National Oceanic and Atmospheric Administration Climate Data Record (NOAA CDR) snow chart extend back to 1967, providing one of the longest environmental data records from spaceborne measurements ( [[#Estilow--2015|Estilow et al., 2015]] ). Continental trends from these coarse resolution estimates (about 200 km) show declining snow cover during the spring period, consistent with surface warming ( [[#Hernández-Henríquez--2015|Hernández-Henríquez et al., 2015]] ; [[#Mudryk--2017|Mudryk et al., 2017]] ). Therefore, as assessed in [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] , there is ''very high confidence'' that the NH spring SCE has been decreasing since 1978.

Hemispheric reconstructions with simple snow models and in situ observations have extended a pre-satellite record to precede the satellite record and extend back to 1922 ( [[#Brown--2011|Brown and Robinson, 2011]] ), putting the satellite era in historical context. This study, also assessed in AR5, suggests an increase in North American spring (March–April) SCE from 1915 to about 1950, followed by a decrease of the same total magnitude afterwards. In Eurasia, a negative trend in April is visible over the entire 1922–2010 period of record, while in March, a step decrease at about 1985 separates two periods with insignificant trends. Overall, combining March and April, consistency between the continental trends since 1950, and agreement in sign with the NOAA satellite record since 1967, provides ''high confidence'' in Northern Hemisphere spring snow cover decrease since about 1950. Analysis of paleoclimate records ( [[#Pederson--2011|Pederson et al., 2011]] ; [[#Belmecheri--2016|Belmecheri et al., 2016]] ) suggests that recent snowpack reductions in western North America are exceptional on a millennial time scale ( ''medium confidence'' ).

Recent remote sensing global-scale studies ( [[#Hammond--2018|Hammond et al., 2018]] ; [[#Notarnicola--2020|Notarnicola, 2020]] ) report that, since 2000, snow cover area and/or duration decreased in 78% of global mountain areas ( [[#Notarnicola--2020|Notarnicola, 2020]] ). Due to the shortness of these records and high spatial variability, they only provide ''limited evidence'' in ''medium agreement'' that snow cover area and duration changes over that recent period are more consistently negative at higher (&gt;4000 m) than at lower elevations, and do not alter the ''high confidence'' in longer-term mountain snow cover decrease at lower elevations since the middle of the 20th century that was already reported in SROCC.

As assessed in detail in [[IPCC:Wg1:Chapter:Chapter-3#3.4.2|Section 3.4.2]] , it is ''very likely'' that anthropogenic influence contributed to the observed reductions in Northern Hemisphere spring snow cover since the mid-20th century. The reasons for this assessment are: (i) physical consistency of the observed spring snowpack and surface temperature changes in observations and models; (ii) the strong observed hemispheric and regional spring SCE and SWE trends; and (iii) the general attribution of hemispheric temperature changes to human influence. Consistent between multiple observational products and historical climate model simulations, the observed NH SCE sensitivity to NH land (&gt;30°N) warming ( [[#Mudryk--2017|Mudryk et al., 2017]] ) is approximately –1.9×10 <sup>6</sup> km <sup>2</sup> °C <sup>–1</sup> (95% confidence range of ±0.9×10 <sup>6</sup> km <sup>2</sup> °C <sup>–1</sup> ) throughout the snow season.

Compared to numerous studies on spring SCE changes, less attention has been paid to changes in NH snow cover during the onset period in the autumn, a challenging period to retrieve snow information from optical satellite imagery due to persistent clouds and decreased solar illumination at higher latitudes. Positive trends in October and November SCE in the NOAA CDR ( [[#Hernández-Henríquez--2015|Hernández-Henríquez et al., 2015]] ) are not replicated in other surface, satellite, and model datasets ( [[#Brown--2013|Brown and Derksen, 2013]] ; [[#Peng--2013|Peng et al., 2013]] ; [[#Hori--2017|Hori et al., 2017]] ; [[#Mudryk--2017|Mudryk et al., 2017]] ). The positive trends from the NOAA CDR are also inconsistent with later autumn snow-on dates since 1980 (–0.6 to –1.4 days per decade), based on historical surface observations, model-derived analyses and independent satellite datasets (updated from [[#Derksen--2017|Derksen et al., 2017]] ). The SCE trend sensitivity to surface temperature forcing in the NOAA CDR is anomalous compared to other datasets during October and November ( [[#Mudryk--2017|Mudryk et al., 2017]] ). There is therefore ''medium confidence'' that the NH SCE trend for the 1981–2016 period was also negative during these two months ( [[#Mudryk--2020|Mudryk et al., 2020]] ).

In the low-to-mid latitude (18°S–40°S) South American Andes, a dry-season snow cover decrease of about 12% per decade has been reported for the 1986–2018 period ( [[#Cordero--2019|Cordero et al., 2019]] ), linked to El Niño–Southern Oscillation (ENSO) changes dominant in the northern part, and an additional influence of poleward migration of the westerly wind zone in the southern part of the study area. Further south, long-term warming has been identified as the dominant cause of observed winter snow cover reduction over the 1972–2016 period at about 53°S in Brunswick Peninsula ( [[#Aguirre--2018|Aguirre et al., 2018]] ).

The AR5 ( [[#Hock--2019b|Hock et al., 2019b]] ) reported on SWE and SD in situ observations mostly from mountain areas, the majority of which showed negative trends over their respective observational periods. However, AR5 did not provide an assessment of large-scale snow mass changes across the Northern Hemisphere. The SROCC attributed ''medium confidence'' to reports of negative SWE trends in the Russian Arctic between 1966 and 2014, and stated that seasonal maximum SD trends in the North American Arctic were mostly insignificant and inconsistently positive or negative. It further attributed ''medium confidence'' to gridded products that suggest negative pan-Arctic SWE trends between 1981 and 2016, and ''high confidence'' in a general decline of mountain snow mass at lower elevations, albeit with regional variations.

Since AR5, the number of global or hemispheric-scale gridded SWE products has substantially increased. A validation and intercomparison ( [[#Mortimer--2020|Mortimer et al., 2020]] ) of datasets – derived from: (i) reanalysis-based products; (ii) a combined surface observation – passive microwave remote sensing product; and (iii) stand-alone passive microwave products – has led to better understanding of the strengths and limitations of each. These gridded products consistently identify negative trends in maximum pre-melt SWE across the 1981–2016 period over Eurasia and North America (Figure 9.23c,d; [[#Mudryk--2020|Mudryk et al., 2020]] ). To further constrain SWE uncertainty, [[#Pulliainen--2020|Pulliainen et al. (2020)]] implemented a bias correction based on snow course observations which yielded a current best estimate for the average 1980–2018 March SWE over NH non-alpine land north of 40°N of 2938 [ ''likely'' range 2846–3062] Gt. Using this method, the bias-corrected GlobSnow v3.0 dataset suggests a 4.6 Gt yr <sup>–1</sup> decrease of March SWE over this 39-year period across North America, and a negligible trend across Eurasia. These SWE trends are consistent with the continental SCE trends over this period, as assessed above, but strong regional and temporal variability only allows ''medium confidence'' in the signs and magnitudes of these trends. However, there is ''high confidence'' in a general decline of NH spring SWE since 1981 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ). In the longer term (see also [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ), annual maximum SD trends from site measurements confirm mostly negative trends in North America ( [[#Kunkel--2016|Kunkel et al., 2016]] ) between 1960–1961 and 2014–2015, and strong spatial variability in Eurasia ( [[#Zhong--2018|Zhong et al., 2018]] ) between 1966 and 2012, with spatial patterns bearing some resemblance to the shorter satellite-based trends reported by [[#Pulliainen--2020|Pulliainen et al. (2020)]] . However, over this longer period, the Eurasian measurements ( [[#Zhong--2018|Zhong et al., 2018]] ) exhibit, on average, a positive trend. On the Qinghai-Tibet Plateau, site measurements between 1961 and 2010 ( [[#Xu--2017|Xu et al., 2017]] ) suggest a shift from an initial increase of spring SD until about 1980 to a decreasing trend afterwards.

Concerning the assessment of SWE trends in mountainous regions, SROCC noted a need for observations spanning several decades because of very strong temporal variability. Moreover, determining SWE trends in mountain regions is challenging because the coarse resolution (typically 25 to 50 km) of gridded SWE products is inadequate in areas of mountainous terrain ( [[#Snauffer--2016|Snauffer et al., 2016]] ). Based on a compilation of a large number of studies of SWE trends in mountain regions, SROCC noted strong regional variations, but a general consistency in greater reductions in SWE at lower elevations associated with shifts from solid to liquid precipitation. A recent synthesis of snow observations in the European Alps ( [[#Matiu--2021|Matiu et al., 2021]] ) shows a 1971–2019 seasonal (November to May) SD trend of –8.4% per decade, along with negative maximum SD and seasonal snow cover duration trends. The trends are stronger and more significant during transitional seasons and at transitional (from no snow to snow) altitudes, and exhibit strong regional variations, consistent with earlier reports for the Swiss and Austrian Alps ( [[#Schöner--2019|Schöner et al., 2019]] ) and the Pyrenees (López‐Moreno et al., 2020).

In summary, since AR5, intercomparison, dataset blending of gridded products, and bias correction using snow course measurements contributed to an improved estimate of the average 1980–2018 March SWE over NH non-alpine land north of 40°N of 2938 [ ''likely'' range 2846–3062] Gt, with ''medium confidence'' in the magnitudes of continental-scale trends over that period. However, there is ''high confidence'' in a general decline of NH spring SWE since 1981 ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.2|Section 2.3.2.2]] ). In mountain areas, in situ observations tend to suggest that annual maximum SWE reductions are generally stronger at elevation bands where shifts from solid to liquid precipitation affected the snow mass.

<div id="9.5.3.2" class="h3-container"></div>

<span id="evaluation-of-seasonal-snow-in-climate-models"></span>
==== 9.5.3.2 Evaluation of Seasonal Snow in Climate Models ====

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Building on AR5 ( [[#Flato--2013|Flato et al., 2013]] ) and subsequent published work, SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) stated that CMIP5 models tended to underestimate the observed decrease of Northern Hemisphere spring SCE due to inappropriate parametrization of snow processes, misrepresentation of the snow-albedo feedback, underestimated temperature sensitivity, and biased climatological spring snow cover. Since AR5, progress in the observation, description and understanding of snow microstructure ( [[#Kinar--2015|Kinar and Pomeroy, 2015]] ; [[#Calonne--2017|Calonne et al., 2017]] ) and its links to physical (thermal and radiative) properties ( [[#Löwe--2013|Löwe et al., 2013]] ; [[#Calonne--2014|Calonne et al., 2014]] ) has prompted efforts to represent physical properties as a function of the evolving snow microstructure in models ( [[#Carmagnola--2014|Carmagnola et al., 2014]] ; [[#Calonne--2015|Calonne et al., 2015]] ). However, even state-of-the-art snow models intended for meteorological and climate applications still struggle to correctly represent the time evolution of the snow thermal properties, particularly of cold and dry tundra snow ( [[#Domine--2016|Domine et al., 2016]] ). Moreover, most, if not all, CMIP6 climate models do not explicitly represent the darkening of snow by deposition of black carbon and other light-absorbing aerosol species known to influence snow melt rates ( [[IPCC:Wg1:Chapter:Chapter-7#7.3.4.3|Section 7.3.4.3]] ). Regardless of these shortcomings, snow modules of climate models continue to be improved. Recent progress includes the incorporation of multiple energy balances within the canopy and between sub-grid tiles with different snow heights ( [[#Aas--2017|Aas et al., 2017]] ; [[#Boone--2017|Boone et al., 2017]] ) and inclusion of advanced specific snow models in coupled climate models ( [[#Niwano--2018|Niwano et al., 2018]] ; [[#Voldoire--2019|Voldoire et al., 2019]] ), opening the prospect of future progress in quantifying snow-related feedbacks in a changing climate. Recently developed multi-physics snow models ( [[#Essery--2015|Essery, 2015]] ; [[#Lafaysse--2017|Lafaysse et al., 2017]] ), which are able to emulate the behaviour of a large number of models in a broad range of climates, allow model shortcomings and key parameter uncertainties, for example, concerning snow masking by vegetation or snow thermal conductivity, to be identified. Guidance for future model improvement can be provided by improved diagnostics, such as a concise metric of snow insulation (A.G. [[#Slater--2017|]] [[#Slater--2017|Slater et al., 2017]] ), which builds on an observed relation between effective seasonal mean SD and the dampening of winter season temperature decrease within the soil, and allows an efficient quantification of inaccuracies in the simulated snow insulation effect.

There is ''high confidence'' that large inter-model variations in the snow-cover sensitivity to temperature can largely be explained by inaccuracies in the simulated snow-albedo feedback ( [[#Qu--2014|Qu and Hall, 2014]] ); a multi-model sub-ensemble of CMIP5 models that simulate a correct magnitude of this feedback presents a 40% reduced spread in the projected 21st century Northern Hemisphere land warming trend ( [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ). Errors of the simulated feedback strength were linked to: (i) systematic positive albedo biases over the boreal forest belt, mostly due to unrealistic treatment of vegetation masking ( [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ); (ii) inaccurate prescribed tree cover fraction and inappropriate parametrization of leaf area index in some models ( [[#Loranty--2014|Loranty et al., 2014]] ; L. [[#Wang--2016|]] [[#Wang--2016|]] [[#Wang--2016|Wang et al., 2016]] ); and (iii) low spatial resolution leading to inaccuracies in the strength of the simulated snow albedo feedback in mountainous regions ( [[#Letcher--2015|Letcher and Minder, 2015]] ). Although the representation of snow-albedo feedback improved in many CMIP5 models over CMIP3, some models deteriorated ( [[#Thackeray--2018|Thackeray et al., 2018]] ).

Analysis of the available CMIP6 historical simulations for the 1981–2014 period shows that, on average, CMIP6 models simulate well the observed SCE ( [[#Mudryk--2020|Mudryk et al., 2020]] ), except for outliers and a median low bias during the winter months (Figure 9.24a). This is an improvement over CMIP5 ( [[#Mudryk--2020|Mudryk et al., 2020]] ), where many snow-related biases were linked to inadequacies of the vegetation masking of snow cover over the boreal forests ( [[#Thackeray--2015|Thackeray et al., 2015]] ). A comparison between CMIP5 and CMIP6 results ( [[#Mudryk--2020|Mudryk et al., 2020]] ) shows that there is no notable progress in the quality of the representation of the observed 1981–2014 monthly snow cover trends.

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[[File:34d7c3435878107860cd421521aace20 IPCC_AR6_WGI_Figure_9_24.png]]

'''Figure 9.24''' '''|''' '''Simulated Coupled Model Intercomparison Project Phase 6 (CMIP6) and observed snow cover extent (SCE). (a)''' Simulated CMIP6 and observed ( [[#Mudryk--2020|Mudryk et al., 2020]] ) SCE (in millions of km <sup>2</sup> ) for 1981–2014. Boxes and whiskers with outliers represent monthly mean values for the individual CMIP6 models averaged over 1981–2014, with the red bar indicating the median of the CMIP6 multi-model ensemble for that period. The observed interannual distribution over the period is represented in green, with the yellow bar indicating the median. '''(b)''' Spring (March to May) Northern Hemisphere SCE against global surface air temperature (GSAT) (relative to the 1995–2014 average) for the CMIP6 Tier 1 scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), with linear regressions. Each data point is the mean for one CMIP6 simulation (first ensemble member for each available model) in the corresponding temperature bin. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

<div id="9.5.3.3" class="h3-container"></div>

<span id="projected-snow-cover-changes"></span>
==== 9.5.3.3 Projected Snow Cover Changes ====

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The AR5 ( [[#Collins--2013|Collins et al., 2013]] ) stated that substantial NH spring snow cover reductions at the end of the 21st century were ''very likely'' under strong emissions scenarios, and expressed ''medium confidence'' in the projected geographic patterns of annual maximum SWE changes. Based on studies using downscaled CMIP5 or regional climate model output, either directly or via snowpack models driven by such output, SROCC ( [[#Hock--2019b|Hock et al., 2019b]] ) reported ''likely'' SD or mass decreases at lower elevations in many mountain ranges over the 21st century and ''high confidence'' in smaller future changes at higher elevations.

Since AR5, one study ( [[#Brown--2017|Brown et al., 2017]] ), applying a method developed by [[#de%20Elía--2013|de Elía et al. (2013)]] to a CMIP5 sub-ensemble, suggested that over most of the Northern Hemisphere, the projected decrease of SCD will exceed natural variability before this will be the case for annual maximum SWE. The same study reports that, over large parts of Eastern and Western North America and Europe, forced SCD changes are projected to exceed natural variability in the 2020s in spring and autumn, while the signals tend to emerge later in the Arctic regions and particularly late, after 2060, in Eastern Siberia under the RCP8.5 scenario. [[#Thackeray--2016|Thackeray and Fletcher (2016)]] have shown that inter-model spread in projected spring SCE trends could be reduced through improved simulation of spring season warming because of the tight coupling between temperature and SCE linked to the snow-albedo feedback ( [[#Qu--2014|Qu and Hall, 2014]] ; [[#Thackeray--2016|Thackeray and Fletcher, 2016]] ).

Across all emissions scenarios, and with negligible scenario dependence (Figure 9.24b), CMIP6 models consistently (all models and all months) simulate Northern Hemisphere snow cover decrease in response to future GSAT change over the 21st century ( [[#Mudryk--2020|Mudryk et al., 2020]] ). The simulated SCE decrease is close to a linear function of global temperature change for all months (shown in Figure 9.24b for spring, with ''medium confidence'' in an average sensitivity of about –8% per °C of GSAT increase), except when snow cover vanishes. This occurs at about +2°C of GSAT change above the 1995–2014 level (that is, about +3°C above the pre-industrial level) for the months of July and August, and at about +3°C above the 1995–2014 level for June and September. Possible effects of such changes on the hydrological cycle are assessed in [[IPCC:Wg1:Chapter:Chapter-8#8.2.3.1|Section 8.2.3.1]] .

In summary, consistent projections from all generations of global climate models, elementary process understanding and strong covariance between snow cover and temperature on several time scales make it ''virtually certain'' that future Northern Hemisphere snow cover extent and duration will continue to decrease as global climate continues to warm, and process understanding strongly suggests that this also applies to Southern Hemisphere seasonal snow cover ( ''high confidence'' ).

Seasonal snow cover, by definition, has a clear annual cycle with usually complete disappearance in spring and summer and re-formation in autumn or winter. Therefore, there is ''very high confidence'' that the current and projected changes to seasonal snow cover are reversible ( [[#Verfaillie--2018|Verfaillie et al., 2018]] ). In the case of global or regional cooling, abrupt large-scale snow-cover changes, with a transition from seasonal to persistent snow cover due to a strong snow-albedo feedback, are a typical feature of glacial inceptions (e.g., [[#Baum--2003|Baum and Crowley, 2003]] ; [[#Calov--2005|Calov et al., 2005]] ), and these can be irreversible on centennial or longer time scales because of this feedback. In summary, based on physical understanding and the absence of occurrence of such events in climate model projections, abrupt future changes of seasonal snow cover on large scales in the absence of concomitant abrupt atmospheric change as a driver appear ''very unlikely'' in the context of current and projected warming.

<div id="9.6" class="h1-container"></div>

<span id="sea-level-change"></span>