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

==== 3.4.1.1 Seasonal Snow Cover ====

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Terrestrial snow cover is a defining characteristic of the Arctic land surface for up to nine months each year, with changes influencing the surface energy budget, ground thermal regime and freshwater budget. Snow cover also interacts with vegetation, influences biogeochemical activity and affects habitats and species, with consequences for ecosystem services. Arctic land areas are almost always completely snow covered in winter, so the transition seasons of autumn and spring are key when characterising variability and change.

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===== 3.4.1.1.1 Extent and duration =====

Dramatic reductions in Arctic (land areas north of 60°N) spring snow cover extent have occurred since satellite charting began in 1967 (Estilow et al., 2015). Declines in May and June of –3.5% (± 1.9%) and –13.4% respectively per decade (± 5.4%) between 1967 and 2018 (relative to the 1981–2010 mean) were determined from multiple datasets based on the methodology of (Mudryk et al., 2017 <sup>[[#fn:r1351|1351]]</sup> ) (Figure 3.10) ( ''high confidence'' ). The loss of spring snow extent is reflected in shorter snow cover duration estimated from surface observations (Bulygina et al., 2011 <sup>[[#fn:r1352|1352]]</sup> ; Brown et al., 2017 <sup>[[#fn:r1353|1353]]</sup> ), satellite data (Wang et al., 2013 <sup>[[#fn:r1354|1354]]</sup> ; Estilow et al., 2015 <sup>[[#fn:r1355|1355]]</sup> ; Anttila et al., 2018 <sup>[[#fn:r1356|1356]]</sup> ), and model-based analyses (Liston and Hiemstra, 2011 <sup>[[#fn:r1357|1357]]</sup> ) ( ''high confidence'' ). These trends range between –0.7 and –3.9 days per decade depending on region and time period, but all spring snow cover duration trends from all datasets are negative (Brown et al., 2017 <sup>[[#fn:r1358|1358]]</sup> ). These same multi-source datasets also identify reductions in autumn snow extent and duration (-0.6 to -1.4 days per decade; summarized in Brown et al., 2017) ( ''high confidence'' ). There is ''low confidence'' in positive October and November snow cover extent trends apparent in a single dataset (Hernández-Henríquez et al., 2015 <sup>[[#fn:r1359|1359]]</sup> ) because they are not replicated in other surface, satellite and model datasets (Brown and Derksen, 2013 <sup>[[#fn:r1360|1360]]</sup> ; Mudryk et al., 2017 <sup>[[#fn:r1361|1361]]</sup> ).

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===== 3.4.1.1.2 Depth and water equivalent =====

Weather station observations across the Russian Arctic identify negative trends in the maximum snow depth between 1966 and 2014 (Bulygina et al., 201 <sup>[[#fn:r1362|1362]]</sup> ; Osokin and Sosnovsky, 2014 <sup>[[#fn:r1363|1363]]</sup> ). There is ''medium confidence'' in this trend because the pointwise nature of these measurements does not capture prevailing conditions across the landscape. Seasonal maximum snow depth trends over the North American Arctic are mixed and largely statistically insignificant (Vincent et al., 2015 <sup>[[#fn:r1364|1364]]</sup> ; Brown et al., 2017 <sup>[[#fn:r1365|1365]]</sup> ). The timing of maximum snow depth has shifted earlier by 2.7 days per decade for the North American Arctic (Brown et al., 2017 <sup>[[#fn:r1366|1366]]</sup> ); comparable analysis is not available for Eurasia. Gridded products from remote sensing and land surface models identify negative trends in snow water equivalent between 1981 and 2016 for both the Eurasian and North American sectors of the Arctic (Brown et al., 2017 <sup>[[#fn:r1367|1367]]</sup> ). While the snow water equivalent anomaly time series show reasonable consistency between products when averaged at the continental scale, considerable inter-dataset variability in the spatial patterns of change (Liston and Hiemstra, 2011 <sup>[[#fn:r1368|1368]]</sup> ; Park et al., 2012 <sup>[[#fn:r1369|1369]]</sup> ; Brown et al., 2017 <sup>[[#fn:r1370|1370]]</sup> ) mean there is only ''medium confidence'' in these trends.

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===== 3.4.1.1.3 Drivers =====

Despite uncertainties due to sparse observations (Cowtan and Way, 2014 <sup>[[#fn:r1371|1371]]</sup> ), surface temperature has increased across Arctic land areas in recent decades (Hawkins and Sutton, 2012 <sup>[[#fn:r1372|1372]]</sup> ; Fyfe et al., 2013 <sup>[[#fn:r1373|1373]]</sup> ), driving reductions in Arctic snow extent and duration ( ''high confidence'' ) ''.'' Changes in Arctic snow extent can be directly related to extratropical temperature increases (Brutel-Vuilmet et al., 2013 <sup>[[#fn:r1374|1374]]</sup> ; Thackeray et al., 2016 <sup>[[#fn:r1375|1375]]</sup> ; Mudryk et al., 2017 <sup>[[#fn:r1376|1376]]</sup> ). Based on multiple historical datasets, there is a consistent temperature sensitivity for Arctic snow extent, with approximately 800,000 km 2 of snow cover lost per degrees Celsius warming in spring (Brown and Derksen, 2013 <sup>[[#fn:r1377|1377]]</sup> ; Brown et al., 2017), and 700,000–800,000 km 2 lost in autumn (Derksen and Brown, 2012 <sup>[[#fn:r1379|1379]]</sup> ; Brown and Derksen, 2013 <sup>[[#fn:r1380|1380]]</sup> ) ( ''high confidence'' ).

There is ''high confidence'' that darkening of snow through the deposition of black carbon and other light absorbing particles enhances snow melt (Bullard et al., 2016 <sup>[[#fn:r1381|1381]]</sup> ; Skiles et al., 2018 <sup>[[#fn:r1382|1382]]</sup> ; Boy et al., 2019 <sup>[[#fn:r1383|1383]]</sup> ). The global direct radiative forcing for black carbon in seasonal snow and over sea ice is estimated to be 0.04 W m –2 , but the effective forcing can be up to threefold greater at regional scales due to the enhanced albedo feedback triggered by the initial darkening (Bond et al., 2013). Lawrence et al. (2011) <sup>[[#fn:r1393|1393]]</sup> estimate the present-day radiative effect of black carbon and dust in land-based snow to be 0.083 W m –2 , only marginally greater than the simulated 1850 effect (0.075 W m –2 ) due to offsetting effects from increased black carbon emissions and reductions in dust darkening ( ''medium confidence'' ). Kylling et al. (2018) <sup>[[#fn:r1394|1394]]</sup> estimate a surface radiative effect of 0.292 W m –2 caused by dust deposition (largely transported from Asia) to Arctic snow, approximately half of the black carbon central scenario estimate of Flanner et al. (2007) <sup>[[#fn:r1395|1395]]</sup> . The forcing from brown carbon deposited in snow (associated with both combustion and secondary organic carbon) is estimated to be 0.09−0.25 W m –2 , with the range due to assumptions of particle absorptivity (Lin et al., 2014 <sup>[[#fn:r1396|1396]]</sup> ) ( ''low confidence'' ).

Precipitation remains a sparse and highly uncertain measurement over Arctic land areas: ''in situ'' datasets remain uncertain (Yang, 2014 <sup>[[#fn:r1397|1397]]</sup> ) and are largely regional (Kononova, 2012 <sup>[[#fn:r1398|1398]]</sup> ; Vincent et al., 2015 <sup>[[#fn:r1399|1399]]</sup> ). Atmospheric reanalyses show increases in Arctic precipitation in recent decades (Lique et al., 2016 <sup>[[#fn:r1400|1400]]</sup> ; Vihma et al., 2016 <sup>[[#fn:r1401|1401]]</sup> ), but there remains ''low confidence'' in reanalysis-based closure of the Arctic freshwater budget due to a wide spread between available reanalysis derived precipitation estimates (Lindsay et al., 2014 <sup>[[#fn:r1484|1484]]</sup> ). Despite improved process understanding, estimates of sublimation loss during blowing snow events remain a key uncertainty in the mass budget of the Arctic snowpack (Sturm and Stuefer, 2013 <sup>[[#fn:r1485|1485]]</sup> ).

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