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

=== 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]] .

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==== 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.

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==== 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).

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==== 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.

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