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

=== 3.4.1 Sea Ice ===

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==== 3.4.1.1 Arctic Sea Ice ====

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The AR5 concluded that ‘anthropogenic forcings are ''very likely'' to have contributed to Arctic sea ice loss since 1979’ ( [[#Bindoff--2013|Bindoff et al., 2013]] ), based on studies showing that models can reproduce the observed decline only when including anthropogenic forcings, and formal attribution studies. Since the beginning of the modern satellite era in 1979, Northern Hemisphere sea ice extent has exhibited significant declines in all months with the largest reduction in September (see [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.1|Section 2.3.2.1.1]] , and Figures 3.20 and 3.21 for more details on observed changes). The recent Arctic sea ice loss during summer is unprecedented since 1850 ( ''high confidence'' ), but as in AR5 and SROCC there remains only ''medium'' ''confidence'' that the recent reduction is unique during at least the past 1000 years due to sparse observations (Sections 2.3.2.1.1 and 9.3.1.1). CMIP5 models also simulate Northern Hemisphere sea ice loss over the satellite era but with large differences among models (e.g., [[#Massonnet--2012|Massonnet et al., 2012]] ; [[#Stroeve--2012|Stroeve et al., 2012]] ). The envelope of simulated ice loss across model simulations encompasses the observed change, although observations fall near the low end of the CMIP5 and CMIP6 distributions of trends (Figure 3.20). CMIP6 models on average better capture the observed Arctic sea ice decline, albeit with large inter-model spread. [[#Notz--2020|Notz et al. (2020)]] found that CMIP6 models better reproduce the sensitivity of Arctic sea ice area to CO <sub>2</sub> emissions and global warming than earlier CMIP models although the models’ underestimation of this sensitivity remains. [[#Davy--2020|Davy and Outten (2020)]] also found that CMIP6 models can simulate the seasonal cycle of Arctic sea ice extent and volume better than CMIP5 models. For the assessment of physical processes associated with changes in Arctic sea ice, see Section 9.3.1.1.

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[[File:c87f4d332e1773e0790526086cdda02f IPCC_AR6_WGI_Figure_3_20.png]]

Figure 3.20 | '''Mean (x-axis) and trend (y-axis) of Arctic sea ice area (SIA) in September (left) and Antarctic SIA in February (right) for 1979–2017 from CMIP5 (upper) and CMIP6 (lower) models.''' All individual models (ensemble means) and the multi-model mean values are compared with the observations (OSISAF, NASA Team, and Bootstrap; see Figure 9.13). Solid line indicates a linear regression slope with corresponding correlation coefficient (r) and p-value provided. Note the different scales used on the y-axis for Arctic and Antarctic SIA. Results remain essentially the same when using sea ice extent (SIE; not shown). Further details on data sources and processing are available in the chapter data table (Table 3.SM.1).

Since AR5, there have been several new detection and attribution studies on Arctic sea ice. While the attribution literature has mostly used sea ice extent (SIE), it is closely proportional to sea ice area (SIA; [[#Notz--2014|Notz, 2014]] ), which is assessed in Chapters 2 and 9 and shown in Figures 3.20 and 3.21. [[#Kirchmeier-Young--2017|Kirchmeier-Young et al. (2017)]] compared the observed time series of the September SIE over the period 1979–2012 with those from different large ensemble simulations which provide a robust sampling of internal climate variability (CanESM2, CESM1, and CMIP5) using an optimal fingerprinting technique. They detected anthropogenic signals which were separable from the response to natural forcing due to solar irradiance variations and volcanic aerosol, supporting previous findings (Figure 3.21; [[#Min--2008b|Min et al., 2008b]] ; [[#Kay--2011|Kay et al., 2011]] ; [[#Notz--2012|Notz and Marotzke, 2012]] ; [[#Notz--2016|Notz and Stroeve, 2016]] ). Using selected CMIP5 models and three independently derived sets of observations, [[#Mueller--2018|Mueller et al. (2018)]] detected fingerprints from greenhouse gases, natural, and other anthropogenic forcings simultaneously in the September Arctic SIE over the period 1953–2012. They further showed that about a quarter of the greenhouse gas induced decrease in SIE has been offset by an increase due to other anthropogenic forcing (mainly aerosols). Similarly, [[#Gagné--2017b|Gagné et al. (2017b)]] suggested that the observed increase in Arctic sea ice concentration over the 1950–1975 period was primarily due to the cooling contribution of anthropogenic aerosol forcing based on single model simulations. [[#Gagné--2017a|Gagné et al. (2017a)]] identified a detectable increase in Arctic SIE in response to volcanic eruptions using CMIP5 models and four observational datasets. [[#Polvani--2020|Polvani et al. (2020)]] suggested that ozone depleting substances played a substantial role in the Arctic sea ice loss over the 1955–2005 period.

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[[File:49a07fca8f0f68d6dc97709134dee999 IPCC_AR6_WGI_Figure_3_21.png]]

Figure 3.21 | '''Seasonal evolution of observed and simulated Arctic (left) and Antarctic (right) sea ice area (SIA) over 1979–2017.''' SIA anomalies relative to the 1979–2000 means from observations '''(OBS from OSISAF, NASA Team, and Bootstrap, top)''' and historical '''(ALL, middle)''' and natural only '''(NAT, bottom)''' simulations from CMIP5 and CMIP6 models. These anomalies are obtained by computing non-overlapping three-year mean SIA anomalies for March (February for Antarctic SIA), June, September, and December separately. CMIP5 historical simulations are extended by using RCP4.5 scenario simulations after 2005 while CMIP6 historical simulations are extended by using SSP2-4.5 scenario simulations after 2014. CMIP5 NAT simulations end in 2012. Numbers in brackets represent the number of models used. The multi-model mean is obtained by taking the ensemble mean for each model first and then averaging over models. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation). Results remain very similar when based on sea ice extent (SIE – not shown). Units: 10 <sup>6</sup> km <sup>2</sup> . Further details on data sources and processing are available in the chapter data table (Table 3.SM.1) and in the caption to Figure 9.13.

Differences in sea ice loss among the models (Figure 3.20) have been attributed to a number of factors (see also Section 9.3.1.1). These factors include the late 20th century simulated sea ice state ( [[#Massonnet--2012|Massonnet et al., 2012]] ), the magnitude of changing ocean heat transport ( [[#Mahlstein--2011|Mahlstein and Knutti, 2011]] ), and the rate of global warming (e.g., [[#Gregory--2002|Gregory et al., 2002]] ; [[#Mahlstein--2012|Mahlstein and Knutti, 2012]] ; [[#Rosenblum--2017|Rosenblum and Eisenman, 2017]] ). Sea ice thermodynamic considerations indicate that the magnitude of sea ice variability and loss depends on ice thickness ( [[#Bitz--2008|Bitz, 2008]] ; [[#Massonnet--2018|Massonnet et al., 2018]] ) and hence the climatology simulated by different models may influence their simulated sea ice trends ( ''medium confidence'' ), as indicated by the regression lines in Figure 3.20.

An important consideration in comparing Arctic sea ice loss in models and observations is the role of internal variability ( ''medium confidence'' ). Using ensemble simulations from a single model, [[#Kay--2011|Kay et al. (2011)]] suggested that internal variability could account for about half of the observed September ice loss. More recently, large ensemble simulations have been performed with many more ensemble members ( [[#Kay--2015|Kay et al., 2015]] ). These enable a more robust characterization of internal variability in the presence of forced anthropogenic change. Using such large ensembles, some studies discussed the influence of internal variability on Arctic sea ice trends ( [[#Swart--2015|Swart et al., 2015]] ). [[#Song--2016|Song et al. (2016)]] also compared the trends in the forced and unforced simulations using multiple climate models and found that internal variability explains about 40% of the observed September sea ice melting trend, supporting previous studies ( [[#Stroeve--2012|Stroeve et al., 2012]] ). Based on the large ensembles of CESM1 and CanESM2, the September Arctic sea ice extent variance first increases and then decreases as SIE declines from its pre-industrial value ( [[#Kirchmeier-Young--2017|Kirchmeier-Young et al., 2017]] ; [[#Mueller--2018|Mueller et al., 2018]] ) consistent with previous work ( [[#Goosse--2009|Goosse et al., 2009]] ), but neither study found a strong sensitivity of detection and attribution results to the change in variability. Further work has indicated that internally-driven summer atmospheric circulation trends with enhanced atmospheric ridges over Greenland and the Arctic Ocean, which project on the negative phase of the North Atlantic Oscillation ( [[#3.7.1|Section 3.7.1]] ), play an important role in the observed Arctic sea ice loss ( [[#Hanna--2015|Hanna et al., 2015]] ; [[#Ding--2017|Ding et al., 2017]] ). A fingerprint analysis using the CESM large ensemble suggests that this internal variability accounts for 40–50% of the observed September Arctic sea ice decline ( [[#Ding--2019|Ding et al., 2019]] ; [[#England--2019|England et al., 2019]] ). Internally-generated decadal tropical variability and associated atmospheric teleconnections were suggested to have contributed to the changing atmospheric circulation in the Arctic and the associated rapid sea ice decline from 2000 to 2014 ( [[#Meehl--2018|Meehl et al., 2018]] ).

Some recent studies evaluated the human contribution to recent record minimum SIE events in the Arctic. Analysing CMIP5 simulations, [[#Zhang--2013|Zhang and Knutson (2013)]] found that the observed 2012 record low in September Arctic SIE is inconsistent with internal climate variability alone. Based on several large ensembles, [[#Kirchmeier-Young--2017|Kirchmeier-Young et al. (2017)]] concluded that the observed 2012 SIE minimum cannot be reproduced in a simulation excluding human influence. [[#Fučkar--2016|Fučkar et al. (2016)]] showed that climate change contributed to the record low March Arctic SIE in 2015, which was accompanied by the record minimum SIE in the Sea of Okhotsk ( [[#Paik--2017|Paik et al., 2017]] ).

Based on the new attribution studies since AR5, we conclude that it is ''very likely'' that anthropogenic forcing mainly due to greenhouse gas increases was the main driver of Arctic sea ice loss since 1979. Increases in anthropogenic aerosols have offset part of the greenhouse gas induced Arctic sea ice loss since the 1950s ( ''medium confidence'' ). Despite large differences in the mean sea ice state in the Arctic, Arctic sea ice loss is captured by all CMIP5 and CMIP6 models. Nonetheless, large inter-model differences in the Arctic sea ice decline remain, limiting our ability to quantify forced changes and internal variability contributions.

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==== 3.4.1.2 Antarctic Sea Ice ====

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AR5 concluded that ‘there is ''low confidence'' in the attribution of the observed increase in Antarctic SIE since 1979’ ( [[#Bindoff--2013|Bindoff et al., 2013]] ) due to the limited understanding of the external forcing contribution as well as the role of internal variability. Based on a difference between the first and last decades, Antarctic sea ice cover exhibited a small increase in summer and winter over the 1979–2017 period ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.2|Section 2.3.2.1.2]] , and Figures 3.20 and 3.21). However, these changes are not statistically significant and starting in late 2016, anomalously low sea ice area has been observed ( [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.2|Section 2.3.2.1.2]] ). The mean hemispheric sea ice changes result from much larger, but partially compensating, regional changes with increases in the western Ross Sea and Weddell Sea and declines in the Bellingshausen and Amundsen Seas ( [[#Hobbs--2016|Hobbs et al., 2016]] ). Observed regional trends have been particularly large in austral autumn (see [[IPCC:Wg1:Chapter:Chapter-2#2.3.2.1.2|Section 2.3.2.1.2]] , and also Section 9.3.2.1 for more details of regional changes and related physical processes). Starting in austral spring of 2016, the ice extent decreased strongly ( [[#Turner--2017|Turner et al., 2017]] ) and has since remained anomalously low (Figure 3.21 and Figure 2.20). This decrease has been associated with anomalous atmospheric conditions associated with teleconnections from warming in the eastern Indian Ocean and a negative Southern Annular Mode ( [[#Chenoli--2017|Chenoli et al., 2017]] ; [[#Stuecker--2017|Stuecker et al., 2017]] ; [[#Schlosser--2018|Schlosser et al., 2018]] ; [[#Meehl--2019|Meehl et al., 2019]] ; [[#Purich--2019|Purich and England, 2019]] ; G. [[#Wang--2019|]] [[#Wang--2019|Wang et al., 2019]] ). A decadal-scale warming of the near-surface ocean that resulted from strengthened westerlies may also have contributed to and helped to sustain the sea ice loss ( [[#Meehl--2019|Meehl et al., 2019]] ). Before satellites and on even longer time scales, very limited observational data and proxy coverage leads to ''low confidence'' in all aspects of Antarctic sea ice (Sections 2.3.2.1.2 and 9.3.2.1).

CMIP5 climate models generally simulate Antarctic sea ice loss over the satellite era since 1979 ( [[#Mahlstein--2013|Mahlstein et al., 2013]] ; [[#Turner--2013|Turner et al., 2013]] ) in contrast to the observed change, and CMIP6 models also simulate Antarctic ice loss ( [[#Roach--2020|Roach et al., 2020]] ; Figure 3.20 and 3.21). A number of studies have suggested that this discrepancy may be in part due to the role of internal variability in the observed change ( [[#Mahlstein--2013|Mahlstein et al., 2013]] ; [[#Polvani--2013|Polvani and Smith, 2013]] ; [[#Zunz--2013|Zunz et al., 2013]] ; [[#Meehl--2016c|Meehl et al., 2016c]] ; [[#Turner--2016|Turner et al., 2016]] ), including teleconnections associated with tropical Pacific variability ( [[#Meehl--2016c|Meehl et al., 2016c]] ) and changing surface conditions resulting from multi-decadal ocean circulation variations ( [[#Singh--2019|Singh et al., 2019]] ). However, when the spatial pattern is considered, trends in the summer and autumn (from 1979–2005) appear outside the range of internal variability ( [[#Hobbs--2015|Hobbs et al., 2015]] ). This suggests that the models may exhibit an unrealistic simulation of the Antarctic sea ice forced response or the internal variability of the system. Discrepancies among the models in simulated sea ice variability ( [[#Zunz--2013|Zunz et al., 2013]] ), the sea ice climatological state ( [[#Roach--2018|Roach et al., 2018]] ), upper ocean temperature trends ( [[#Schneider--2018|Schneider and Deser, 2018]] ), Southern Hemisphere westerly wind trends ( [[#Purich--2016|Purich et al., 2016]] ), or the sea ice response to Southern Annular Mode variations ( [[#Ferreira--2014|Ferreira et al., 2014]] ; [[#Holland--2017|Holland et al., 2017]] ; [[#Kostov--2017|Kostov et al., 2017]] ; [[#Landrum--2017|Landrum et al., 2017]] ) may all play some role in explaining these differences with the observed trends. Increased fresh water fluxes caused by mass loss of the Antarctic Ice Sheet (either by melting at the front of ice shelves or via iceberg calving) have been suggested as a possible mechanism driving the multi-decadal Antarctic sea ice expansion ( [[#Bintanja--2015|Bintanja et al., 2015]] ; [[#Pauling--2016|Pauling et al., 2016]] ) but there is a lack of consensus on this mechanism’s impacts ( [[#Pauling--2017|Pauling et al., 2017]] ). A recent study based on a decadal prediction system suggests that initializing the state of the Antarctic Bottom Water cell allows the system to reproduce the observed Antarctic sea ice increase ( [[#Zhang--2017|Zhang et al., 2017]] ), consistent with the suggestion that multi-decadal variability associated with variations in deep convection has contributed to the observed increase in Antarctic sea ice since 1979 ( [[#Latif--2013|Latif et al., 2013]] ; [[#Zhang--2017|Zhang et al., 2017]] ; L. [[#Zhang--2019|]] [[#Zhang--2019|Zhang et al., 2019]] ) (see also Section 9.3.2.1).

There have been several studies that aimed to identify causes of the observed Antarctic SIE changes. [[#Gagné--2015|Gagné et al. (2015)]] assessed the consistency of observed and simulated changes in Antarctic SIE for an extended period using recovered satellite-based estimates, and found that the observed trends since the mid-1960s are not inconsistent with model simulated trends. Studies based on the satellite period also indicate that the observed trends are largely within the range of simulated internal variability ( [[#Hobbs--2016|Hobbs et al., 2016]] ). A few distinct factors that led to the weak signal-to-noise ratio in Antarctic SIE trends have been further identified, which include large multi-decadal variability ( [[#Monselesan--2015|Monselesan et al., 2015]] ), the short observational record (e.g., [[#Abram--2013|Abram et al., 2013]] ), and the limited model performance at representing the complex Antarctic climate system as discussed above ( [[#Bintanja--2013|Bintanja et al., 2013]] ; [[#Uotila--2014|Uotila et al., 2014]] ). The short period of comprehensive satellite observations, beginning in 1979, makes it challenging to set the observed increase between 1979 and 2015, or the subsequent decrease, in a long-term context, and to assess whether the difference in trend between observations and models, which mostly simulate long-term decreases, is systematic or a rare expression of internal variability on decadal to multi-decadal time scales.

In conclusion, the observed small increase in Antarctic sea ice extent during the satellite era is not generally captured by global climate models, and there is ''low confidence'' in attributing the causes of the change.

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