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

=== 9.3.1 Arctic Sea Ice ===

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==== 9.3.1.1 Arctic Sea Ice Coverage ====

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The observed decrease of Arctic sea ice area is a key indicator of large-scale climate change ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] , Cross-Chapter Box 2.2). The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) assesses that sea ice extent, which is the total area of all grid cells with at least 15% sea ice concentration, has declined since 1979 in each month of the year ( ''very high confidence'' ). In contrast to SROCC, we assess changes in sea ice area (the actual area of the ocean covered by sea ice) rather than sea ice extent, because sea ice area is geophysically more relevant and not grid-dependent ( [[#Notz--2014|Notz, 2014]] ; [[#Ivanova--2016|Ivanova et al., 2016]] ; [[#Notz--2016|Notz et al., 2016]] ; Notz and SIMIP Community, 2020). Arctic sea ice area is calculated based on measurements by passive microwave satellite sensors that provide near-continuous measurements of gridded, pan-Arctic sea ice concentration from 1979 onwards. Irreducible uncertainties in the conversion of thermal microwave brightness temperature to sea ice concentration, and choices in algorithm design, cause uncertainties in observed Arctic sea ice area, which are far smaller than the observed sea ice loss (e.g., [[#Comiso--2017a|Comiso et al., 2017a]] ; [[#Niederdrenk--2018|Niederdrenk and Notz, 2018]] ; [[#Alekseeva--2019|Alekseeva et al., 2019]] ; [[#Kern--2019|Kern et al., 2019]] ; [[#Meier--2019|Meier and Stewart, 2019]] ). Sea ice area has decreased in every month of the year from 1979 to the present ( ''very high confidence'' ) (Figure 9.13). The absolute and the relative ice losses are highest in late summer-early autumn ( ''high confidence'' ) (Figure 9.13). Averaged over the decade 2010–2019, the monthly Arctic sea ice area from August to October has been around 2 million km² (or about 25%) smaller than during 1979–1988 ( ''high confidence'' ) (Figure 9.13).

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[[File:dcce997cfc6bf21f871ba4f79af9b161 IPCC_AR6_WGI_Figure_9_13.png]]

'''Figure''' '''9.13 |''' '''Arctic sea ice historical records and Coupled Model Intercomparison Project Phase 6 (CMIP6) projections. (Left)''' Absolute anomaly of monthly-mean Arctic sea ice area during the period 1979 to 2019 relative to the average monthly-mean Arctic sea ice area during the period 1979 to 2008. '''(Right)''' Sea ice concentration in the Arctic for March and September, which usually are the months of maximum and minimum sea ice area, respectively. First column: Satellite-retrieved mean sea ice concentration during the decade 1979–1988. Second column: Satellite-retrieved mean sea ice concentration during the decade 2010–2019. Third column: Absolute change in sea ice concentration between these two decades, with grid lines indicating non-significant differences. Fourth column: Number of available CMIP6 models that simulate a mean sea ice concentration above 15 % for the decade 2045–2054. The average observational record of sea ice area is derived from the UHH sea ice area product ( [[#Doerr--2021|Doerr et al., 2021]] ), based on the average sea ice concentration of OSISAF/CCI (OSI-450 for 1979–2015, OSI-430b for 2016–2019) ( [[#Lavergne--2019|Lavergne et al., 2019]] ), NASA Team (version 1, 1979–2019) ( [[#Cavalieri--1996|Cavalieri et al., 1996]] ) and Bootstrap (version 3, 1979–2019) ( [[#Comiso--2017|Comiso, 2017]] ) that is also used for the figure panels showing observed sea ice concentration. Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

The SROCC discussed the regional distribution of Arctic sea ice loss, and the findings remain valid for the updated time series covering 2019 (Figure 9.13). Sea ice loss in winter is strongest in the Barents Sea, while summer losses occur primarily at the summer sea ice region margins, in particular in the East Siberian, Chukchi, Kara and Beaufort Seas ( [[#Frey--2015|Frey et al., 2015]] ; [[#Chen--2016|Chen et al., 2016]] ; [[#Onarheim--2018|Onarheim et al., 2018]] ; [[#Peng--2018|Peng and Meier, 2018]] ; [[#Maksym--2019|Maksym, 2019]] ). In the Bering Sea, expanding winter sea ice cover was observed until 2017 ( [[#Frey--2015|Frey et al., 2015]] ; [[#Onarheim--2018|Onarheim et al., 2018]] ; [[#Peng--2018|Peng and Meier, 2018]] ), but a marked reduction in sea ice concentration has occurred since then ( ''high confidence'' ) ( [[#Stabeno--2019|Stabeno and Bell, 2019]] ).

With respect to seasonal changes in the sea ice cover, the winter sea ice loss causes a decrease in the average sea ice age and fraction of multi-year ice, as assessed by SROCC ( ''very high confidence'' ), and also of the ocean area covered intermittently by sea ice ( [[#Bliss--2019|Bliss et al., 2019]] ). In contrast, the seasonal ice zone (covered by sea ice in winter but not in summer) has expanded regionally ( [[#Bliss--2019|Bliss et al., 2019]] ) and over the whole Arctic ( [[#Steele--2015|Steele and Ermold, 2015]] ), because the loss of summer sea ice area is larger than the loss of winter sea ice area. Arctic sea ice retreat includes an earlier onset of surface melt in spring and a later freeze up in autumn, lengthening the open water season in the seasonal sea ice zone ( [[#Stroeve--2018|Stroeve and Notz, 2018]] ). However, there is ''low agreement'' in quantification of regional trends of melt and freeze onset between different observational products ( [[#Bliss--2017|Bliss et al., 2017]] ; [[#Smith--2019|Smith and Jahn, 2019]] ).

Reconstructions of Arctic sea ice coverage put the satellite period changes into centennial context. Direct observational data coverage ( [[#Walsh--2017|Walsh et al., 2017]] ) and model reconstructions ( [[#Brennan--2020|Brennan et al., 2020]] ) warrant ''high confidence'' that the low Arctic sea ice area of summer 2012 is unprecedented since 1850, and that the summer sea ice loss is significant in all Arctic regions except for the Central Arctic ( [[#Cai--2021|Cai et al., 2021]] ). Direct winter observational data coverage before 1953 is too sparse to reliably assess Arctic sea ice area. Since 1953, the years 2015 to 2018 had the four lowest values of maximum Arctic sea ice area, which usually occurs in March ( ''high confidence'' ) (Figure 2.20). Reconstructions of Arctic sea ice area before 1850 remain sparse, and as in SROCC, there remains ''medium confidence'' that the current sea ice levels in late summer are unique during the past 1 kyr ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] ; [[#Kinnard--2011|Kinnard et al., 2011]] ; [[#De%20Vernal--2013b|De Vernal et al., 2013b]] ).

The observed fluctuations and trends of the Arctic sea ice cover arise from a combination of changes in natural external forcing and anthropogenic forcing, internal variability and internal feedbacks (e.g., [[#Notz--2018|Notz and Stroeve, 2018]] ; [[#Halloran--2020|Halloran et al., 2020]] ). New paleo-proxy techniques indicate regional sea ice changes over epochs and millennia and allow possible drivers to be assessed. Biomarker IP25 ( [[#Belt--2007|Belt et al., 2007]] ) together with other sedimentary biomarkers ( [[#Belt--2018|Belt, 2018]] ) provide local temporal information on seasonal sea ice coverage, permanent sea ice coverage and ice-free waters, with occasional ambiguous contrasting results ( [[#Belt--2019|Belt, 2019]] ). These records and other proposed paleo proxies, including bromine in ice cores ( [[#Spolaor--2016|Spolaor et al., 2016]] ), dinocyst assemblages (e.g., [[#De%20Vernal--2013b|De Vernal et al., 2013b]] ) and driftwood (e.g., [[#Funder--2011|Funder et al., 2011]] ), provide evidence of sea ice fluctuations that exceed internal variability ( ''high confidence'' ).

The inferred sea ice fluctuations over millennia can be related to Northern Hemisphere temperature evolution and give rise to Arctic-wide fluctuations in sea ice coverage in the paleorecord ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] ). On a regional scale, fluctuations include decreased sea ice cover during the Allerød warm period (14.7–12.9 ka) in the Laptev ( [[#Hörner--2016|Hörner et al., 2016]] ) and Bering Seas ( [[#Méheust--2018|Méheust et al., 2018]] ); an extensive sea ice cover during the Younger Dryas (around 12 ka) in the Bering ( [[#Méheust--2018|Méheust et al., 2018]] ), Kara ( [[#Hörner--2018|Hörner et al., 2018]] ), Laptev ( [[#Hörner--2016|Hörner et al., 2016]] ) and Barents ( [[#Belt--2015|Belt et al., 2015]] ) Seas, and at the Yermak Plateau ( [[#Kremer--2018|Kremer et al., 2018]] ); little sea ice during the early Holocene, when Northern Hemisphere summer insolation was higher than today (8000 to 9000 years before present), in the North Icelandic Shelf area ( [[#Cabedo-Sanz--2016|Cabedo-Sanz et al., 2016]] ; [[#Xiao--2017|Xiao et al., 2017]] ), Sea of Okhotsk ( [[#Lo--2018|Lo et al., 2018]] ), Canadian Arctic ( [[#Spolaor--2016|Spolaor et al., 2016]] ), Barents ( [[#Berben--2017|Berben et al., 2017]] ), Bering ( [[#Méheust--2018|Méheust et al., 2018]] ), and Chukchi ( [[#Stein--2017|Stein et al., 2017]] ) Seas, at the Yermak Plateau ( [[#Kremer--2018|Kremer et al., 2018]] ) and north of Greenland ( [[#Funder--2011|Funder et al., 2011]] ); increasing sea ice cover throughout much of the middle and late Holocene around Svalbard ( [[#Knies--2017|Knies et al., 2017]] ), in the North Icelandic Shelf area ( [[#Cabedo-Sanz--2016|Cabedo-Sanz et al., 2016]] ; [[#Harning--2019|Harning et al., 2019]] ; [[#Halloran--2020|Halloran et al., 2020]] ), north of Greenland ( [[#Funder--2011|Funder et al., 2011]] ), and in the Western Greenland ( [[#Kolling--2018|Kolling et al., 2018]] ), Barents ( [[#Belt--2015|Belt et al., 2015]] ; [[#Berben--2017|Berben et al., 2017]] ), Chukchi ( [[#De%20Vernal--2013a|De Vernal et al., 2013a]] ; [[#Stein--2017|Stein et al., 2017]] ) and Laptev ( [[#Hörner--2016|Hörner et al., 2016]] ) Seas. The consistent, Arctic-wide changes give ''high confidence'' in millennial-scale co-variability of the sea ice cover with temperature fluctuation.

The SROCC assessed that approximately half of the satellite-observed Arctic summer sea ice loss is driven by increased concentrations of atmospheric greenhouse gases ( ''medium confidence'' ). Recent attribution studies now allow the strengthened assessment that it is ''very likely'' that more than half of the observed Arctic sea ice loss in summer is anthropogenic ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.1.1|Section 3.4.1.1]] ). This assessment is confirmed by process-based analyses of Arctic sea ice loss not assessed by SROCC. Similar to the paleorecord, the satellite record of Arctic sea ice area from 1979 onwards is strongly and linearly correlated with global mean temperature on decadal and longer time scales (Figures 9.14a,e) (e.g., [[#Gregory--2002|Gregory et al., 2002]] ; [[#Rosenblum--2017|Rosenblum and Eisenman, 2017]] ). The correlation holds across all months with R <sup>2</sup> ranging from 0.61 to 0.81 ( [[#Niederdrenk--2018|Niederdrenk and Notz, 2018]] ). However, in contrast to paleorecords, sea ice fluctuations during the satellite period are only weakly correlated with Northern Hemisphere insolation ( [[#Notz--2012|Notz and Marotzke, 2012]] ); modern Northern Hemisphere sea ice area is more strongly correlated with atmospheric carbon dioxide (CO <sub>2</sub> ) concentration ( [[#Johannessen--2008|Johannessen, 2008]] ; [[#Notz--2012|Notz and Marotzke, 2012]] ) and cumulative anthropogenic CO <sub>2</sub> emissions (Figures 9.14b,f; [[#Zickfeld--2012|Zickfeld et al., 2012]] ; [[#Herrington--2014|Herrington and Zickfeld, 2014]] ; [[#Notz--2016|Notz and Stroeve, 2016]] ). The R <sup>2</sup> values of the correlation between sea ice area and cumulative CO <sub>2</sub> emissions range across all months from 0.76 to 0.92 ( [[#Stroeve--2018|Stroeve and Notz, 2018]] ). In summary, there is ''high confidence'' that satellite-observed Arctic sea ice area is strongly correlated with global mean temperature, CO <sub>2</sub> concentration and cumulative anthropogenic CO <sub>2</sub> emissions.

In addition to changes in the external forcing, internal variability substantially affects Arctic sea ice, evidenced from both paleorecords (e.g., [[#Chan--2017|Chan et al., 2017]] ; [[#Hörner--2017|Hörner et al., 2017]] ; [[#Kolling--2018|Kolling et al., 2018]] ) and satellites after 1979 (e.g., [[#Notz--2018|Notz and Stroeve, 2018]] ; [[#Roberts--2020|Roberts et al., 2020]] ) ( ''high confidence'' ). Most of the internal variability on annual time scales is related to atmospheric temperature fluctuations, for example linked to cyclone activities ( [[#Wernli--2018|Wernli and Papritz, 2018]] ; [[#Olonscheck--2019|Olonscheck et al., 2019]] ), while multi-decadal internal variability is primarily related to changes in oceanic heat transport ( [[#Zhang--2015|Zhang, 2015]] ; [[#Halloran--2020|Halloran et al., 2020]] ). These mechanisms are represented in current climate models ( [[#Olonscheck--2019|Olonscheck et al., 2019]] ; [[#Halloran--2020|Halloran et al., 2020]] ), but the resulting internal variability of September sea ice area in CMIP5 and CMIP6 models, as given by the ensemble mean standard deviation Σ <sub>SIA,Sep</sub> = 0.5 million km² ( [[#Olonscheck--2017|Olonscheck and Notz, 2017]] ; Notz and SIMIP Community, 2020), exceeds the estimated internal variability for the period 1850 to 1979 from both reanalyses ( Σ <sub>SIA,Sep</sub> = 0.3 million km <sup>2</sup> ) and direct observational reconstructions ( Σ <sub>SIA,Sep</sub> = 0.2 million km <sup>2</sup> ) ( ''medium confidence'' because of limited reliability of longer-term sea ice reconstructions) ( [[#Brennan--2020|Brennan et al., 2020]] ). Internal variability has been estimated to have contributed 30 to 50% of the observed Arctic summer sea ice loss since 1979 ( [[#Kay--2011|Kay et al., 2011]] ; [[#Stroeve--2012|Stroeve et al., 2012]] ; [[#Ding--2017|Ding et al., 2017]] , 2019; [[#England--2019|England et al., 2019]] ). However, this estimate from models might be biased towards internal over forced variability because of the models’ high internal variability and because the CMIP5 simulated September sea ice sensitivity to forcing is lower than observed, even if internal variability is taken into account ( [[#Notz--2016|Notz and Stroeve, 2016]] ; [[#Rosenblum--2017|Rosenblum and Eisenman, 2017]] ). Most CMIP6 models fail to simulate the observed sensitivity of sea ice loss to CO <sub>2</sub> emissions (as a proxy for time) and to temperature simultaneously. However, they better capture the observed sensitivity of sea ice loss to CO <sub>2</sub> emissions than CMIP5 models ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.1|Section 3.4.1]] ; Figure 9.14h; Notz and SIMIP Community, 2020).

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'''Figure 9.14''' '''|''' '''Monthly mean March (a–d) and September (e–h) sea ice area as a function of global surface air temperature (GSAT) anomaly (a, e); cumulative anthropogenic CO''' <sub>2</sub> '''emissions (b, f); year (c, g) in Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations (shading, ensemble mean as bold line) and in observations (black dots).''' Panels (d) and (h) show the sensitivity of sea ice loss to anthropogenic CO <sub>2</sub> emissions as a function of the modelled sensitivity of GSAT to anthropogenic CO <sub>2</sub> emissions. In panels (d) and (h), the black dot denotes the observed sensitivity, while the shading around it denotes internal variability as inferred from CMIP6 simulations (after Notz and SIMIP Community, 2020). Further details on data sources and processing are available in the chapter data table (Table 9.SM.9).

The SROCC examined the different atmospheric and oceanic processes that caused the observed sea ice loss, with recent studies providing new evidence for the importance of variations in air temperature ( [[#Olonscheck--2019|Olonscheck et al., 2019]] ; [[#Dahlke--2020|Dahlke et al., 2020]] ), wind patterns ( [[#Graham--2019|Graham et al., 2019]] ), oceanic heat flux ( [[#Docquier--2021|Docquier et al., 2021]] ) and riverine heat influx ( [[#Park--2020|Park et al., 2020]] ). As in SROCC, the relative contribution of each physical cause to the sea ice loss cannot be robustly quantified because of disagreement among models ( [[#Burgard--2017|Burgard and Notz, 2017]] ), sparse observations, and limited understanding of the variation of each factor with global mean temperature. This is addressed by new diagnostics available from CMIP6 simulations, which now allow for more detailed analyses of the drivers of sea ice loss at a process level ( [[#Keen--2021|Keen et al., 2021]] ).

In examining temperature thresholds for the loss of Arctic summer sea ice, the Special Report on Global Warming of 1.5°C (SR1.5; [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ) and SROCC assess that a reduction of September mean sea ice area to below 1 million km <sup>2</sup> , practically a sea ice-free Arctic Ocean, is more probable for a global mean warming of 2°C compared to global mean warming of 1.5°C ( ''high confidence'' ). Analyses of CMIP6 simulations (Notz and SIMIP Community, 2020) confirm this result, as they show that, on decadal and longer time scales, Arctic summer sea ice area will remain highly correlated with global mean temperature until the summer sea ice has vanished (Figure 9.14a,e). Quantitatively, existing studies ( [[#Screen--2017|Screen and Williamson, 2017]] ; [[#Jahn--2018|Jahn, 2018]] ; [[#Ridley--2018|Ridley and Blockley, 2018]] ; [[#Sigmond--2018|Sigmond et al., 2018]] ; Notz and SIMIP Community, 2020) also show that, for a warming between 1.5 and 2°C, the Arctic will only be practically sea ice free in September in some years, while at 3°C warming, the Arctic is practically sea ice free in September in most years, with longer practically sea ice-free periods at higher warming levels ( ''medium confidence'' ). However, because of the CMIP5 and CMIP6 models’ generally too low sensitivity of sea ice loss to global warming, there is only ''low confidence'' regarding the specific warming level at which the Arctic Ocean first becomes practically sea ice free ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.2.1|Section 4.3.2.1]] ; Notz and SIMIP Community, 2020).

In contrast, CMIP6 models capture the observed sensitivity of Arctic sea ice area to cumulative anthropogenic CO <sub>2</sub> emissions well, providing ''high confidence'' that the Arctic Ocean will ''likely'' become practically sea ice free in the September mean for the first time for future CO <sub>2</sub> emissions of less than 1000 Gt and before the year 2050 in all SSP scenarios (Notz and SIMIP Community, 2020). This new assessment is consistent with an observation-based projection of a practically sea ice-free Arctic Ocean in September for additional anthropogenic CO <sub>2</sub> emissions of 800 ± 330 GtCO <sub>2</sub> beyond the year 2018 ( [[#Notz--2018|Notz and Stroeve, 2018]] ; [[#Stroeve--2018|Stroeve and Notz, 2018]] ). This estimate may, however, be too high due to neglecting possible future reduction in atmospheric aerosol load that would cause additional warming ( [[#Gagné--2015a|Gagné et al., 2015a]] ; [[#Wang--2018|Wang et al., 2018]] ), and is subject to the same constraints as the carbon budget analysis for global mean temperature (see section 5.5 for details). Based on CMIP6 simulations, it is ''very likely'' that the Arctic Ocean will remain sea ice covered in winter in all scenarios throughout this century (Sections 4.3.2 and 4.4.2).

There is an indication that CMIP6 simulations of Arctic sea ice have improved relative to CMIP5 ( [[IPCC:Wg1:Chapter:Chapter-3#3.4.1.1|Section 3.4.1.1]] ), but detailed evaluation studies exist mainly for CMIP5 models. These studies found that CMIP5 model projections and reanalyses show a large spread of simulated regional Arctic sea ice concentration ( [[#Laliberté--2016|Laliberté et al., 2016]] ; [[#Chevallier--2017|Chevallier et al., 2017]] ), which remains true for CMIP6 models ( [[#Shu--2020|Shu et al., 2020]] ; [[#Wei--2020|Wei et al., 2020]] ). In addition, both CMIP5 and CMIP6 models show a large spread in the simulated seasonal cycle of Arctic sea ice area, with too high a sea ice area in March in the ensemble mean (Notz and SIMIP Community, 2020). The CMIP5 models have also had difficulty simulating realistic landfast sea ice ( [[#Laliberté--2018|Laliberté et al., 2018]] ). These findings imply that both CMIP5 and CMIP6 models do not realistically capture the regional and seasonal processes governing observed Arctic sea ice evolution, causing ''low confidence'' in the models’ projections of future regional sea ice evolution, including updated projections for shipping routes across the Northern Sea Route and Northwest Passage ( [[#Wei--2020|Wei et al., 2020]] ).

The CMIP5 models also have issues with capturing the seasonal cycle of observed changes in Arctic sea ice drift speed, which affects their simulation of regional sea ice concentration patterns. Direct measurements of Arctic sea ice from drift buoys and satellites show that drift speed of Arctic sea ice has increased over the satellite period in all seasons (e.g., [[#Rampal--2009|Rampal et al., 2009]] ; [[#Docquier--2017|Docquier et al., 2017]] ). In summer, CMIP5 models show a slowdown of Arctic sea ice drift rather than the observed acceleration ( [[#Tandon--2018|Tandon et al., 2018]] ). In winter, CMIP5 models generally capture the observed acceleration of Arctic drift speed. The drift acceleration is primarily caused by the decrease in concentration and thickness in the observational record ( [[#Rampal--2009|Rampal et al., 2009]] ; [[#Spreen--2011|Spreen et al., 2011]] ; Olason and [[#Notz--2014|Notz, 2014]] ; [[#Docquier--2017|Docquier et al., 2017]] ) and, for winter, in CMIP5 models ( [[#Tandon--2018|Tandon et al., 2018]] ). Changes in wind speed are less important for the observed large-scale changes ( [[#Spreen--2011|Spreen et al., 2011]] ; [[#Vihma--2012|Vihma et al., 2012]] ; Olason and [[#Notz--2014|Notz, 2014]] ; [[#Docquier--2017|Docquier et al., 2017]] ; [[#Tandon--2018|Tandon et al., 2018]] ). In summary, there is ''high confidence'' that Arctic sea ice drift has accelerated because of the decrease in sea ice concentration and thickness.

The SR1.5 assessed with ''high confidence'' that there is no hysteresis in the loss of Arctic summer sea ice. In addition, there is no tipping point or critical threshold in global mean temperature beyond which the loss of summer sea ice becomes self-accelerating and irreversible ( ''high confidence'' ). This is because stabilizing feedbacks during winter related to increased heat loss through thin ice and thin snow, and increased emission of longwave radiation from open water, dominate over the amplifying ice albedo feedback (see [[IPCC:Wg1:Chapter:Chapter-7#7.4.2|Section 7.4.2]] for details on the individual feedbacks; e.g., [[#Eisenman--2012|Eisenman, 2012]] ; [[#Wagner--2015|Wagner and Eisenman, 2015]] ; [[#Notz--2018|Notz and Stroeve, 2018]] ). Observed and modelled Arctic summer sea ice and global mean temperature are linked with little temporal delay, and the summer sea ice loss is reversible on decadal time scales ( [[#Armour--2011|Armour et al., 2011]] ; [[#Ridley--2012|Ridley et al., 2012]] ; [[#Li--2013|Li et al., 2013]] ; [[#Jahn--2018|Jahn, 2018]] ). The loss of winter sea ice is reversible as well, but the loss of winter sea ice area per degree of warming in CMIP5 and CMIP6 projections increases as the ice retreats from the continental shore lines, because these limit the possible areal fluctuations ( ''high confidence'' ) ( [[IPCC:Wg1:Chapter:Chapter-4#4.3.2.1|Section 4.3.2.1]] ; [[#Bathiany--2016|Bathiany et al., 2016]] , 2020; [[#Meccia--2020|Meccia et al., 2020]] ).

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==== 9.3.1.2 Arctic Sea Ice Volume and Thickness ====

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The SROCC assessed with ''very high confidence'' that Arctic sea ice has become thinner over the satellite period from 1979 onwards, and this assessment is confirmed for the updated time series ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] ). Sea ice area has also decreased substantially over this period ( [[#9.3.1.1|Section 9.3.1.1]] ), leading to the assessment that Arctic sea ice volume has also decreased with ''very high confidence'' over the satellite period since 1979. There is, however, only ''low confidence'' in quantitative estimates of the sea ice volume loss over this period because of a lack of reliable, long-term, pan-Arctic observations and substantial spread in available reanalyses ( [[#Chevallier--2017|Chevallier et al., 2017]] ). Current best estimates from reanalyses suggest a reduction of September Arctic sea ice volume of 55 to 65% over the period 1979–2010, and of about 72% over the period 1979–2016, with the latter deemed a conservative estimate ( [[#Schweiger--2019|Schweiger et al., 2019]] ).

For the more recent past, ice thickness can be directly estimated from satellite retrievals of sea ice freeboard ( [[#Kwok--2015|Kwok and Cunningham, 2015]] ; [[#Kwok--2018|Kwok, 2018]] ). Based on these retrievals, there is ''medium confidence'' that Arctic sea ice volume has decreased since 2003. There is ''low confidence'' in the amount of decrease over this period and over the CryoSat-2 period from 2011 onwards, primarily because of snow-induced uncertainties in the retrieval algorithms, the shortness of the record, and the small identified trend (e.g., [[#Bunzel--2018|Bunzel et al., 2018]] ; [[#Petty--2018|Petty et al., 2018]] , 2020).

Observations of regional changes in sea ice thickness vary in quality. Analysis of submarine data in the central Arctic Ocean suggests that its sea ice has thinned by about 75 cm compared to the mid-1970s ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] ). For smaller regions, data are too sparse to allow for quantitative estimates of long-term trends ( [[#King--2017|King et al., 2017]] ; [[#Rösel--2018|Rösel et al., 2018]] ), but a clear thinning signal over 10 to 20 years has been found for sea ice in the Fram Strait ( [[#Spreen--2020|Spreen et al., 2020]] ), north of Canada ( [[#Haas--2017|Haas et al., 2017]] ) and for landfast ice in the Kongsfjorden/Svalbard Arctic border ( [[#Pavlova--2019|Pavlova et al., 2019]] ). The CMIP5 models and reanalyses fail to capture the observed distribution ( [[#Stroeve--2014|Stroeve et al., 2014]] ; [[#Shu--2015|Shu et al., 2015]] ) and evolution ( [[#Chevallier--2017|Chevallier et al., 2017]] ) of Arctic sea ice thickness. Most CMIP6 models do not capture the observed spatial distribution of sea ice thickness realistically ( [[#Wei--2020|Wei et al., 2020]] ). This leads to ''low confidence'' in estimates of thickness from reanalyses and from CMIP5 and CMIP6 models and in their projections of sea ice volume.

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