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Snow and Climate: Feedbacks, Drivers, and Indices of Change

  • Climate Change and Snow/Sea Ice (PJ Kushner, Section Editor)
  • Published:
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Abstract

Purpose of Review

Highlight significant developments that have recently been made to enhance our understanding of how snow responds to climate forcing and the role that snow plays in the climate system.

Recent Findings

Widespread snow loss has occurred in recent decades, with the largest decreases in spring. These changes are primarily driven by temperature and precipitation, but changes in vegetation, light-absorbing impurities, and sea ice also contribute to variability. Changes in snow cover can also affect climate through the snow albedo feedback (SAF). Recently, considerable progress has been made in better understanding the processes contributing to SAF. We also highlight advances in knowledge of how snow variability is linked to large-scale atmospheric changes. Lastly, large-scale snow losses are expected to continue under climate change in all but the coldest climates. These projected changes to snow raise considerable concerns over future freshwater availability in snow-dominated watersheds.

Summary

The results discussed here demonstrate the widespread implications that changes to snow have on the climate system and anthropogenic activity at large.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Robinson D, Frei A. Seasonal variability of northern hemisphere snow extent using visible satellite data. Prof Geogr. 2000;52:307–15.

    Google Scholar 

  2. • Bormann KJ, Brown RD, Derksen C, Painter TH. Estimating snow-cover trends from space. Nat Clim Chang. 2018;8:924–8 This review highlights the uncertainty associated with observing snow cover from satellite-based sensors.

    Google Scholar 

  3. Flanner MG, Shell KM, Barlage M, Perovich DK, Tschudi M a. Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nat Geosci. 2011;4:151–5.

    CAS  Google Scholar 

  4. Serreze MC, Barry RG. Processes and impacts of Arctic amplification: a research synthesis. Glob Planet Chang. 2011;77:85–96.

    Google Scholar 

  5. Qu X, Hall A. On the persistent spread in snow-albedo feedback. Clim Dyn. 2014;42:69–81.

    Google Scholar 

  6. Lawrence DM, Slater AG. The contribution of snow condition trends to future ground climate. Clim Dyn. 2010;34:969–81.

    Google Scholar 

  7. Ruosteenoja K, Markkanen T, Venäläinen A, Räisänen P, Peltola H. Seasonal soil moisture and drought occurrence in Europe in CMIP5 projections for the 21st century. Clim Dyn. 2018;50:1177–92.

    Google Scholar 

  8. Barnett TP, Adam JC, Lettenmaier DP. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature. 2005;438:303–9.

    CAS  Google Scholar 

  9. Mankin JS, Viviroli D, Singh D, Hoekstra AY, Diffenbaugh NS. The potential for snow to supply human water demand in the present and future. Environ Res Lett. 2015;10:114016.

    Google Scholar 

  10. • Sturm M, Goldstein MA, Parr C. Water and life from snow: a trillion dollar science question. Water Resour Res. 2017;53:3534–44 This is a detailed review of the hydrological importance of snow.

    Google Scholar 

  11. Diffenbaugh NS, Pal JS, Trapp RJ, Giorgi F. Fine-scale processes regulate the response of extreme events to global climate change. Proc Natl Acad Sci U S A. 2005;102:15774–8.

    CAS  Google Scholar 

  12. Westerling AL. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos Trans R Soc B Biol Sci. 2016;371:20150178. https://doi.org/10.1098/rstb.2015.0178.

    Article  Google Scholar 

  13. Cohen JL, Furtado JC, Barlow MA, Alexeev VA, Cherry JE. Arctic warming, increasing snow cover and widespread boreal winter cooling. Environ Res Lett. 2012;7:014007.

    Google Scholar 

  14. Cohen J, Screen JA, Furtado JC, et al. Recent Arctic amplification and extreme mid-latitude weather. Nat Geosci. 2014;7:627–37.

    CAS  Google Scholar 

  15. Zou Y, Wang Y, Zhang Y, Koo JH. Arctic sea ice, Eurasia snow, and extreme winter haze in China. Sci Adv 2017;3:e1602751

    Google Scholar 

  16. Liu H, Liu X, Dong B. Influence of central Siberian snow-albedo feedback on the spring east Asian dust cycle and connection with the preceding Winter Arctic Oscillation. J Geophys Res Atmos. 2018;123:13,368–85.

    Google Scholar 

  17. Damm A, Greuell W, Landgren O, Prettenthaler F. Impacts of +2 °C global warming on winter tourism demand in Europe. Clim Serv. 2017;7:31–46.

    Google Scholar 

  18. Steiger R, Scott D, Abegg B, Pons M, Aall C. A critical review of climate change risk for ski tourism. Curr Issues Tour. 2017;22:1343–1379.

    Google Scholar 

  19. Groisman P, Karl TR, Knight RW, Stenchikov GL. Changes of snow cover, temperature, and radiative heat balance over the Northern Hemisphere. J Clim. 1994;7:1633–56.

    Google Scholar 

  20. • Mudryk LR, Kushner PJ, Derksen C, Thackeray C. Snow cover response to temperature in observational and climate model ensembles. Geophys Res Lett. 2017. https://doi.org/10.1002/2016GL071789This paper provides the most robust snow cover trend analysis to date by utilizing an ensemble of observational products.

    Google Scholar 

  21. Brown R, Vikhamar-Schuler D, Bulygina O, Derksen C, Luojus K, Mudryk LR, Wang L, Yang D (2017) Chapter 3. Arctic Terrestrial Snow Cover. Snow, Water, Ice and Permafrost in the Arctic (SWIPA) 2017.

  22. Brown RD, Derksen C, Wang L. A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008. J Geophys Res. 2010;115:D16111.

    Google Scholar 

  23. Brown RD, Robinson DA. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosph. 2011;5:219–29.

    Google Scholar 

  24. Derksen C, Brown R. Spring snow cover extent reductions in the 2008-2012 period exceeding climate model projections. Geophys Res Lett. 2012;39:L19504.

    Google Scholar 

  25. Thackeray CW, Fletcher CG, Mudryk LR, Derksen C. Quantifying the uncertainty in historical and future simulations of Northern Hemisphere spring snow cover. J Clim. 2016;29:8647–63.

    Google Scholar 

  26. Estilow TW, Young AH, Robinson DA. A long-term Northern Hemisphere snow cover extent. Earth Syst Sci Data. 2015;7:137–42.

    Google Scholar 

  27. Brown RD, Derksen C. Is Eurasian October snow cover extent increasing? Environ Res Lett. 2013;8:024006.

    Google Scholar 

  28. Mudryk LR, Kushner PJ, Derksen C. Interpreting observed northern hemisphere snow trends with large ensembles of climate simulations. Clim Dyn. 2014;43:345–59.

    Google Scholar 

  29. Kunkel KE, Robinson DA, Champion S, Yin X, Estilow T, Frankson RM. Trends and extremes in northern hemisphere snow characteristics. Curr Clim Chang Rep. 2016;2:65–73.

    Google Scholar 

  30. Mudryk LR, Derksen C, Kushner PJ, Brown R. Characterization of Northern Hemisphere snow water equivalent datasets, 1981-2010. J Clim. 2015;28:8037–51.

    Google Scholar 

  31. Fontrodona Bach A, van der Schrier G, Melsen LA, Klein Tank AMG, Teuling AJ. Widespread and accelerated decrease of observed mean and extreme snow depth over Europe. Geophys Res Lett. 2018;45:12,312–9.

    Google Scholar 

  32. Clow DW. Changes in the timing of snowmelt and streamflow in Colorado: a response to recent warming. J Clim. 2010;23:2293–306.

    Google Scholar 

  33. Kapnick S, Hall A. Causes of recent changes in Western North American snowpack. Clim Dyn. 2012;38:1885–99.

    Google Scholar 

  34. Pederson GT, Betancourt JL, McCabe GJ. Regional patterns and proximal causes of the recent snowpack decline in the Rocky Mountains, U.S. Geophys Res Lett. 2013;40:1811–6.

    Google Scholar 

  35. Mote PW, Rupp DE, Li S, Sharp DJ, Otto F, Uhe PF, et al. Perspectives on the causes of exceptionally low 2015 snowpack in the Western United States. Geophys Res Lett. 2016;43:10980–8.

    Google Scholar 

  36. MMote PW, Li S, Lettenmaier DP, Xiao M, Engel R. Dramatic declines in snowpack in the western US. npj Clim Atmos Sci. 2018;1. https://doi.org/10.1038/s41612-018-0012-1.

  37. Zeng X, Broxton P, Dawson N. Snowpack change from 1982 to 2016 over conterminous United States. Geophys Res Lett. 2018;45:12,940–7.

    Google Scholar 

  38. Siler N, Proistosescu C, Po-Chedley S. Natural variability has slowed the decline in Western U.S. snowpack since the 1980s. Geophys Res Lett. 2019;46:346–55.

    Google Scholar 

  39. Mudryk LR, Derksen C, Howell S, Laliberté F, Thackeray C, Sospedra-Alfonso R, et al. Canadian snow and sea ice: historical trends and projections. Cryosph. 2018;12:1157–76.

    Google Scholar 

  40. Smith T, Bookhagen B. Changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Sci Adv. 2018;4:e1701550. https://doi.org/10.1126/sciadv.1701550.

    Article  Google Scholar 

  41. Bintanja R, Andry O. Towards a rain-dominated Arctic. Nat Clim Chang. 2017;7:263–7.

    Google Scholar 

  42. Brown RD, Mote PW. The response of Northern Hemisphere snow cover to a changing climate. J Clim. 2009;22:2124–45.

    Google Scholar 

  43. • Beniston M, Farinotti D, Stoffel M, et al. The European mountain cryosphere: a review of its current state, trends, and future challenges. Cryosph. 2018;12:759–94 This review highlights regional changes to snow across Europe in great detail.

    Google Scholar 

  44. Collins M, Knutti R (2013) Long-term Climate Change: Projections, Commitments and Irreversibility. In: Stocker TF, Qin D, Plattner GK, Tignor M, Allen SK, Doschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Clim. Chang. 2013 Phys. Sci. Basis. Contrib. Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim. Chang. Cambridge University Press, Cambridge, UK, pp 1029–1136.

  45. Marty C, Schlogl S, Bavay M, Lehning M. How much can we save? Impact of different emission scenarios on future snow cover in the Alps. Cryosph. 2017;11:517–29.

    Google Scholar 

  46. Brutel-Vuilmet C, Ménégoz M, Krinner G. An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. Cryosph. 2013;7:67–80.

    Google Scholar 

  47. Mankin JS, Diffenbaugh NS. Influence of temperature and precipitation variability on near-term snow trends. Clim Dyn. 2015;45:1099–116.

    Google Scholar 

  48. Krasting JP, Broccoli AJ, Dixon KW, Lanzante JR. Future changes in Northern Hemisphere snowfall. J Clim. 2013;26:7813–28.

    Google Scholar 

  49. Sospedra-Alfonso R, Merryfield WJ. Influences of temperature and precipitation on historical and future snowpack variability over the Northern Hemisphere in the second generation Canadian Earth System Model. J Clim. 2017;30:4633–56.

    Google Scholar 

  50. Räisänen J. Twenty-first century changes in snowfall climate in Northern Europe in ENSEMBLES regional climate models. Clim Dyn. 2016;46:339–53.

    Google Scholar 

  51. Ashfaq M, Ghosh S, Kao S-C, Bowling LC, Mote P, Touma D, et al. Near-term acceleration of hydroclimatic change in the Western U.S. J Geophys Res Atmos. 2013;118:10,676–93.

    Google Scholar 

  52. • Fyfe JC, Derksen C, Mudryk L, et al. Large near-term projected snowpack loss over the Western United States. Nat Commun. 2017;8:14996 This paper used a large ensemble to better understand the role of natural variability in recent snowpack loss.

    CAS  Google Scholar 

  53. Rhoades AM, Ullrich PA, Zarzycki CM. Projecting 21st century snowpack trends in Western USA mountains using variable-resolution CESM. Clim Dyn. 2018;50:261–88.

    Google Scholar 

  54. Berg N, Hall A. Anthropogenic warming impacts on California snowpack during drought. Geophys Res Lett. 2017;44:2511–8.

    Google Scholar 

  55. Huang X, Hall AD, Berg N. Anthropogenic warming impacts on today’s Sierra Nevada snowpack and flood risk. Geophys Res Lett. 2018;45:6215–22.

    Google Scholar 

  56. Rhoades AM, Jones AD, Ullrich PA. The changing character of the California Sierra Nevada as a natural reservoir. Geophys Res Lett. 2018;45. https://doi.org/10.1029/2018GL080308.

  57. Sun F, Berg N, Hall A, Schwartz M, Walton D. Understanding end-of-century snowpack changes over California’s Sierra Nevada. Geophys Res Lett. 2019;46:933–43.

    Google Scholar 

  58. Lopez-Moreno JI, Gascoin S, Herrero J, et al. Different sensitivities of snowpacks to warming in Mediterranean climate mountain areas. Environ Res Lett. 2017;12.

    Google Scholar 

  59. Donnelly C, Greuell W, Andersson J, Gerten D, Pisacane G, Roudier P, et al. Impacts of climate change on European hydrology at 1.5, 2 and 3 degrees mean global warming above preindustrial level. Clim Chang. 2017;143:13–26.

    Google Scholar 

  60. Jenicek M, Seibert J, Staudinger M. Modeling of future changes in seasonal snowpack and impacts on summer low flows in alpine catchments. Water Resour Res. 2018;54:538–56.

    Google Scholar 

  61. • Musselman KN, Clark MP, Liu C, Ikeda K, Rasmussen R. Slower snowmelt in a warmer world. Nat Clim Chang. 2017;7:214–9 This paper proposes the idea of slower snowmelt in the future, which has major implications for hydrology.

    Google Scholar 

  62. Musselman KN, Lehner F, Ikeda K, Clark MP, Prein AF, Liu C, et al. Projected increases and shifts in rain-on-snow flood risk over Western North America. Nat Clim Chang. 2018;8:808–12. https://doi.org/10.1038/s41558-018-0236-4.

    Article  Google Scholar 

  63. Wachowicz LJ, Mote TL, Henderson GR. A rain on snow climatology and temporal analysis for the eastern United States. Phys Geogr. 2019;00:1–16.

    Google Scholar 

  64. Fyfe JC, Von Salzen K, Gillett NP, Arora VK, Flato GM, McConnell JR. One hundred years of Arctic surface temperature variation due to anthropogenic influence. Sci Rep. 2013;3:1–7.

    Google Scholar 

  65. •• Najafi MR, Zwiers FW, Gillett NP. Attribution of the spring snow cover extent decline in the Northern Hemisphere, Eurasia and North America to anthropogenic influence. Clim Chang. 2016. https://doi.org/10.1007/s10584-016-1632-2This paper applies detection and attribution techniques to find anthropogenic forcing behind observed NH snow cover losses.

    CAS  Google Scholar 

  66. Najafi MR, Zwiers FW, Gillett NP. Attribution of the observed spring snowpack decline in British Columbia to anthropogenic climate change. J Clim. 2017;30:4113–30.

    Google Scholar 

  67. O’Gorman PA, Schneider T. The physical basis for increases in precipitation extremes in simulations of 21st-century climate change. Proc Natl Acad Sci. 2009;106:14773–7.

    Google Scholar 

  68. Kharin VV, Zwiers FW, Zhang X, Wehner M. Changes in temperature and precipitation extremes in the CMIP5 ensemble. Clim Chang. 2013;119:345–57.

    Google Scholar 

  69. Westra S, Alexander LV, Zwiers FW. Global increasing trends in annual maximum daily precipitation. J Clim. 2013;26:3904–18.

    Google Scholar 

  70. Kidd C, Becker A, Huffman GJ, Muller CL, Joe P, Skofronick-Jackson G, et al. So, how much of the earth’s surface is covered by rain gauges? Bull Am Meteorol Soc. 2017;98:69–78.

    Google Scholar 

  71. Serreze MC, Barrett AP, Stroeve J. Recent changes in tropospheric water vapor over the Arctic as assessed from radiosondes and atmospheric reanalyses. J Geophys Res Atmos. 2012;117:1–21.

    Google Scholar 

  72. Henn B, Newman AJ, Livneh B, Daly C, Lundquist JD. An assessment of differences in gridded precipitation datasets in complex terrain. J Hydrol. 2018;556:1205–19.

    Google Scholar 

  73. Callaghan TV, Johansson M, Brown RD, Groisman PY, Labba N, Radionov V, et al. The changing face of arctic snow cover: a synthesis of observed and projected changes. Ambio. 2011;40:17–31.

    Google Scholar 

  74. Hartmann DJ, Klein Tank AMG, Rusticucci M, et al (2013) Observations: atmosphere and surface. Clim Chang 2013 Phys Sci Basis Contrib Work Gr I to Fifth Assess Rep Intergov Panel Clim Chang. https://doi.org/10.1017/CBO9781107415324.008.

  75. Räisänen J. Warmer climate: less or more snow? Clim Dyn. 2008;30:307–19.

    Google Scholar 

  76. Morán-Tejeda E, López-Moreno JI, Beniston M. The changing roles of temperature and precipitation on snowpack variability in Switzerland as a function of altitude. Geophys Res Lett. 2013;40:2131–6.

    Google Scholar 

  77. Sospedra-Alfonso R, Melton JR, Merryfield WJ. Effects of temperature and precipitation on snowpack variability in the Central Rocky Mountains as a function of elevation. Geophys Res Lett. 2015;42:4429–38.

    Google Scholar 

  78. Bond TC, Doherty SJ, Fahey DW, Forster PM, Berntsen T, DeAngelo BJ, et al. Bounding the role of black carbon in the climate system: a scientific assessment. J Geophys Res Atmos. 2013;118:5380–552.

    CAS  Google Scholar 

  79. •• Skiles SM, Flanner M, Cook JM, Dumont M, Painter TH. Radiative forcing by light-absorbing particles in snow. Nat Clim Chang. 2018;8:964–71 This review summarizes the radiative impact that light-absorbing impurities such as black carbon and dust have on snow.

    Google Scholar 

  80. • Gleason KE, McConnell JR, Arienzo MM, Chellman N, Calvin WM. Four-fold increase in solar forcing on snow in Western U.S. burned forests since 1999. Nat Commun. 2019;10:1–8 This paper links increases in wildfires to earlier snowmelt across parts of the Western US.

    CAS  Google Scholar 

  81. Mahowald NM, Kloster S, Engelstaedter S, Moore JK, Mukhopadhyay S, McConnell JR, et al. Observed 20th century desert dust variability: impact on climateand biogeochemistry. Atmos Chem Phys. 2010;10:10875–93.

    CAS  Google Scholar 

  82. Painter TH, Deems JS, Belnap J, Hamlet AF, Landry CC, Udall B. Response of Colorado River runoff to dust radiative forcing in snow. Proc Natl Acad Sci. 2010;107:17125–30.

    CAS  Google Scholar 

  83. Skiles SM, Painter TH, Deems JS, Bryant AC, Landry CC. Dust radiative forcing in snow of the upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates. Water Resour Res. 2012;48:1–11.

    Google Scholar 

  84. Painter TH, McKenzie Skiles S, Deems JS, Tyler Brandt W, Dozier J. Variation in rising limb of Colorado River snowmelt runoff hydrograph controlled by dust radiative forcing in snow. Geophys Res Lett. 2017;45:797–808.

    Google Scholar 

  85. Di Mauro B, Garzonio R, Rossini M, et al. Saharan dust events in the European Alps: role on snowmelt and geochemical characterization. Cryosph. 2019;13:1147–65.

    Google Scholar 

  86. Stroeve JC, Kattsov V, Barrett A, Serreze M, Pavlova T, Holland M, et al. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophys Res Lett. 2012;39:1–7.

    Google Scholar 

  87. Stroeve J, Notz D. Changing state of Arctic sea ice across all seasons. Environ Res Lett Res Lett. 2018;13:103001.

    Google Scholar 

  88. Kopec BG, Feng X, Michel FA, Posmentier ES. Influence of sea ice on Arctic precipitation. Proc Natl Acad Sci. 2015;113:46–51.

    Google Scholar 

  89. Bintanja R, Selten FM. Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat. Nature. 2014;509:479–82.

    CAS  Google Scholar 

  90. Sturm M, Stuefer S. Wind-blown flux rates derived from drifts at arctic snow fences. J Glaciol. 2013;59:21–34.

    Google Scholar 

  91. Bokhorst S, Pedersen SH, Brucker L, Anisimov O, Bjerke JW, Brown RD, et al. Changing Arctic snow cover: a review of recent developments and assessment of future needs for observations, modelling, and impacts. Ambio. 2016;45:516–37. https://doi.org/10.1007/s13280-016-0770-0.

    Article  Google Scholar 

  92. Gouttevin I, Menegoz M, Dominé F, Krinner G, Koven C, Ciais P, et al. How the insulating properties of snow affect soil carbon distribution in the continental pan-Arctic area. J Geophys Res Biogeosci. 2012;117:1–11.

    Google Scholar 

  93. Druel A, Peylin P, Krinner G, Ciais P, Viovy N, Peregon A, et al. Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0). Geosci Model Dev. 2017;10:4693–722.

    Google Scholar 

  94. Myers-Smith IH, Elmendorf SC, Beck PSA, et al. Climate sensitivity of shrub growth across the tundra biome. Nat Clim Chang. 2015;5:887–91.

    Google Scholar 

  95. Ju J, Masek JG. The vegetation greenness trend in Canada and US Alaska from 1984-2012 Landsat data. Remote Sens Environ. 2016;176:1–16.

    Google Scholar 

  96. Loranty MM, Berner LT, Goetz SJ, Jin Y, Randerson JT. Vegetation controls on northern high latitude snow-albedo feedback: observations and CMIP5 model simulations. Glob Chang Biol. 2014;20:594–606.

    Google Scholar 

  97. Randall DA, Cess RD, Blanchet JP, Chalita S, Colman R, Dazlich DA, et al. Analysis of snow feedbacks in 14 general circulation models. J Geophys Res. 1994;99:757–71.

    Google Scholar 

  98. Hall A. The role of surface albedo feedback in climate. J Clim. 2004;17:1550–68.

    Google Scholar 

  99. Qu X, Hall A. What controls the strength of snow-albedo feedback? J Clim. 2007;20:3971–81.

    Google Scholar 

  100. Thackeray CW, Fletcher CG. Snow albedo feedback: current knowledge, importance, outstanding issues and future directions. Prog Phys Geogr. 2016;40:392–408.

    Google Scholar 

  101. Colman RA. Surface albedo feedbacks from climate variability and change. J Geophys Res Atmos. 2013;118:2827–34.

    Google Scholar 

  102. Bony S, Colman R, Kattsov VM, Allan RP, Bretherton CS, Dufresne JL, et al. How well do we understand and evaluate climate change feedback processes? J Clim. 2006;19:3445–82.

    Google Scholar 

  103. Soden BJ, Held IM, Colman RC, Shell KM, Kiehl JT, Shields C a. Quantifying climate feedbacks using radiative kernels. J Clim. 2008;21:3504–20.

    Google Scholar 

  104. Dessler AE. Observations of climate feedbacks over 2000-10 and comparisons to climate models. J Clim. 2013;26:333–42.

    Google Scholar 

  105. Pithan F, Mauritsen T. Arctic amplification dominated by temperature feedbacks in contemporary climate models. Nat Geosci. 2014;7:181–4.

    CAS  Google Scholar 

  106. Walsh JE. Intensified warming of the Arctic: causes and impacts on middle latitudes. Glob Planet Chang. 2014;117:52–63.

    Google Scholar 

  107. Boeke RC, Taylor PC. Seasonal energy exchange in sea ice retreat regions contributes to differences in projected Arctic warming. Nat Commun. 2018;9:1–14.

    CAS  Google Scholar 

  108. Screen JA, Simmonds I. The central role of diminishing sea ice in recent Arctic temperature amplification. Nature. 2010;464:1334–7.

    CAS  Google Scholar 

  109. Graversen RG, Langen PL, Mauritsen T. Polar amplification in CCSM4: contributions from the lapse rate and surface albedo feedbacks. J Clim. 2014;27:4433–50.

    Google Scholar 

  110. Pepin N, Bradley RS, Diaz HF, et al. Elevation-dependent warming in mountain regions of the world. Nat Clim Chang. 2015;5:424–30.

    Google Scholar 

  111. Palazzi E, Mortarini L, Terzago S, von Hardenberg J. Elevation-dependent warming in global climate model simulations at high spatial resolution. Clim Dyn. 2019;52:2685–702.

    Google Scholar 

  112. Fletcher CG, Thackeray CW, Burgers TM. Evaluating biases in simulated snow albedo feedback in two generations of climate models. J Geophys Res Atmos. 2015;120:12–26.

    Google Scholar 

  113. Wetherald R, Manabe S. The effects of changing the solar constant on the climate of a general circulation model. J Atmos Sci. 1975;32:2044–59.

    Google Scholar 

  114. Lian MS, Cess RD. Energy balance climate models: a reappraisal of ice-albedo feedback. J Atmos Sci. 1977;34:1058–62.

    Google Scholar 

  115. Guo H, Wang X, Wang T, Ma Y, Ryder J, Zhang T, Liu D, Ding J, Li Y, Piao S. Spring snow‐albedo feedback analysis over the Third Pole: results from satellite observation and CMIP5 model simulations. J Geophys Res Atmos. 2018:123:750–763.

    Google Scholar 

  116. Thackeray CW, Qu X, Hall A. Why do models produce spread in snow albedo feedback? Geophys Res Lett. 2018;45:6223–31.

    Google Scholar 

  117. Wang L, Cole JNS, Bartlett P, Verseghy D, Derksen C, Brown R, et al. Investigating the spread in surface albedo for snow-covered forests in CMIP5 models. J Geophys Res Atmos. 2016;121:1–16.

    CAS  Google Scholar 

  118. Thackeray CW, Fletcher CG, Derksen C. The influence of canopy snow parameterizations on snow albedo feedback in boreal forest regions. J Geophys Res Atmos. 2014;119:9810–21.

    Google Scholar 

  119. Thackeray CW, Fletcher CG, Derksen C. Quantifying the skill of CMIP5 models in simulating seasonal albedo and snow cover evolution. J Geophys Res Atmos. 2015;120:5831–49.

    Google Scholar 

  120. Slater AG, Schlosser A, Desborough CE, et al. The representation of snow in land surface schemes: results from PILPS 2 (d). J Hydrometeorol. 2001;2:7–25.

    Google Scholar 

  121. Verseghy D, Brown R, Wang L. Evaluation of CLASS Snow Simulation over Eastern Canada. J Hydrometeorol. 2017;18:1205–25.

    Google Scholar 

  122. Letcher TW, Minder JR. Characterization of the simulated regional snow albedo feedback using a regional climate model over complex terrain. J Clim. 2015;28:7576–95.

    Google Scholar 

  123. Winter KJPM, Kotlarski S, Scherrer SC, Schär C. The alpine snow-albedo feedback in regional climate models. Clim Dyn. 2017;48:1109–24.

    Google Scholar 

  124. Walton DB, Hall A, Berg N, Schwartz M, Sun F. Incorporating snow albedo feedback into downscaled temperature and snow cover projections for California’s Sierra Nevada. J Clim. 2017;30:1417–38.

    Google Scholar 

  125. Cess RD, Potter GL, Zhang MH, Blanchet J, Chalita S, Colman R, et al. Interpretation of snow-climate feedback as produced by 17 general circulation models. Science (80-). 1991;253:888–92.

    CAS  Google Scholar 

  126. Cohen J, Entekhabi D. Eurasian snow cover variability and Northern Hemisphere climate predictability. Geophys Res Lett. 1999;26:345–8.

    Google Scholar 

  127. Saito K, Cohen J, Entekhabi D. Evolution of atmospheric response to early-season Eurasian snow cover anomalies. Mon Weather Rev. 2001;129:2746–60.

    Google Scholar 

  128. Gong G, Entekhabi D, Cohen J. Modeled Northern Hemisphere winter climate response to realistic Siberian snow anomalies. J Clim. 2003;16:3917–31.

    Google Scholar 

  129. Fletcher CG, Hardiman SC, Kushner PJ, Cohen J. The dynamical response to snow cover perturbations in a large ensemble of atmospheric GCM integrations. J Clim. 2009;22:1208–22.

    Google Scholar 

  130. Allen RJ, Zender CS. Forcing of the Arctic Oscillation by Eurasian snow cover. J Clim. 2011;24:6528–39.

    Google Scholar 

  131. Peings Y, Saint-Martin D, Douville H. A numerical sensitivity study of the influence of Siberian snow on the northern annular mode. J Clim. 2012;25:592–607.

    Google Scholar 

  132. Henderson GR, Leathers DJ, Hanson B. Circulation response to Eurasian versus North American anomalous snow scenarios in the Northern Hemisphere with an AGCM coupled to a slab ocean model. J Clim. 2013;26:1502–15.

    Google Scholar 

  133. Hardiman SC, Kushner PJ, Cohen J. Investigating the ability of general circulation models to capture the effects of Eurasian snow cover on winter climate. J Geophys Res Atmos. 2008;113:1–9.

    Google Scholar 

  134. Furtado JC, Cohen JL, Butler AH, Riddle EE, Kumar A. Eurasian snow cover variability and links to winter climate in the CMIP5 models. Clim Dyn. 2015;45:2591–605.

    Google Scholar 

  135. •• Henderson GR, Peings Y, Furtado JC, Kushner PJ. Snow–atmosphere coupling in the Northern Hemisphere. Nat Clim Chang. 2018;8:954–63 This review extensively details the possible ways in which snow may influence atmospheric variability.

    Google Scholar 

  136. Peings Y, Brun E, Mauvais V, Douville H. How stationary is the relationship between Siberian snow and Arctic Oscillation over the 20th century? Geophys Res Lett. 2013;40:183–8.

    Google Scholar 

  137. Peings Y, Douville H, Colin J, Saint-Martin D, Magnusdottir G. Snow–(N)AO teleconnection and its modulation by the quasi-biennial oscillation. J Clim. 2017;30:10211–35.

    Google Scholar 

  138. Blandford HF. On the connection of the Himalaya snowfall with dry winds and seasons of drought in India. Proc R Soc Lond. 1884;37:3–22. https://doi.org/10.1098/rspl.1884.0003.

    Article  Google Scholar 

  139. Hahn DG, Shukla J. An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. J Atmos Sci. 1976;33:2461–2.

    Google Scholar 

  140. Kripalani RH, Kulkarni A, Sabade SS, Khandekar ML. Indian monsoon variability in a global warming scenario. Nat Hazards. 2003;29:189–206.

    Google Scholar 

  141. Zhang T, Wang T, Krinner G, Wang X, Gasser T, Peng S, et al. The weakening relationship between Eurasian spring snow cover and Indian summer monsoon rainfall. Sci Adv. 2019;5:1–8.

    Google Scholar 

  142. Peings Y, Douville H. Influence of the Eurasian snow cover on the Indian summer monsoon variability in observed climatologies and CMIP3 simulations. Clim Dyn. 2010;34:643–60.

    Google Scholar 

  143. Turner AG, Slingo JM. Using idealized snow forcing to test teleconnections with the Indian summer monsoon in the Hadley Centre GCM. Clim Dyn. 2011;36:1717–35.

    Google Scholar 

  144. Senan R, Orsolini YJ, Weisheimer A, Vitart F, Balsamo G, Stockdale TN, et al. Impact of springtime Himalayan–Tibetan Plateau snowpack on the onset of the Indian summer monsoon in coupled seasonal forecasts. Clim Dyn. 2016;47:2709–25.

    Google Scholar 

  145. Xu L, Dirmeyer P. Snow-atmosphere coupling strength in a global atmospheric model. Geophys Res Lett. 2011;38:1–5.

    Google Scholar 

  146. Halder S, Dirmeyer PA. Relation of Eurasian snow cover and Indian summer monsoon rainfall : importance of the delayed hydrological effect. J Clim. 2017;30:1273–89.

    Google Scholar 

  147. Robock A, Mu M, Vinnikov K. Land surface conditions over Eurasia and Indian summer monsoon rainfall. J Geophys Res. 2003;108. https://doi.org/10.1029/2002JD002286.

  148. Koster RD, Suarez MJ. Soil moisture memory in climate models. J Hydrometeorol. 2002;2:558–70.

    Google Scholar 

  149. Shi HX, Wang CH. Projected 21st century changes in snow water equivalent over Northern Hemisphere landmasses from the CMIP5 model ensemble. Cryosph. 2015;9:1943–53.

    Google Scholar 

  150. Marx A, Kumar R, Thober S, Rakovec O, Wanders N, Zink M, et al. Climate change alters low flows in Europe under global warming of 1.5, 2, and 3°C. Hydrol Earth Syst Sci. 2018;22:1017–32.

    Google Scholar 

  151. Brun E, Vionnet V, Boone A, Decharme B, Peings Y, Valette R, et al. Simulation of northern Eurasian local snow depth, mass and density using a detailed snowpack model and meteorological reanalysis. J Hydrometeorol. 2013;14:203–19.

    Google Scholar 

  152. Girotto M, Essery R, Musselman K (2019) New terrestrial snow observations and modelling and importance in climate change. Submitted to Curr Clim Chang Rep.

  153. Wrzesien ML, Durand MT, Pavelsky TM, Kapnick SB, Zhang Y, Guo J, et al. A new estimate of North American Mountain snow accumulation from regional climate model simulations. Geophys Res Lett. 2018;45:1423–32.

    Google Scholar 

  154. • Krinner G, Derksen C, Essery R, et al. ESM-SnowMIP: assessing models and quantifying snow-related climate feedbacks. Geosci Model Dev. 2018;11:5027–49 This paper details a series of proposed model experiments that will go a long way to improving our understanding of snow-climate interactions.

    Google Scholar 

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Acknowledgments

We would like to thank Lawrence Mudryk for providing the data to reproduce Fig. 1. We also thank the editor and two anonymous reviewers for their helpful comments. On behalf of all authors, the corresponding author states that there is no conflict of interest.

Funding

C.W.T. and A.H. would like to thank the funding from the National Science Foundation grant (#1543268) titled “Reducing Uncertainty Surrounding Climate Change Using Emergent Constraints.”

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Authors and Affiliations

  1. Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, Los Angeles, CA, USA

    Chad W. Thackeray & Alex Hall

  2. Climate Research Division, Environment and Climate Change Canada, Toronto, Canada

    Chris Derksen

  3. Department of Geography & Environmental Management, University of Waterloo, Waterloo, Canada

    Christopher G. Fletcher

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  1. Chad W. Thackeray
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  2. Chris Derksen
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Correspondence to Chad W. Thackeray.

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Thackeray, C.W., Derksen, C., Fletcher, C.G. et al. Snow and Climate: Feedbacks, Drivers, and Indices of Change. Curr Clim Change Rep 5, 322–333 (2019). https://doi.org/10.1007/s40641-019-00143-w

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