Scenario Projections of Changes in Snow Water Equivalent Due to Possible Climate Changes in Different Regions of the Earth

Abstract

The study was carried out under the international Earth System Model–Snow Model Intercomparison Project on ten experimental well-instrumented snow sites in different parts of the Earth and aimed to examine the ability of the SWAP land surface model, developed by the authors, to reproduce the current characteristics of snow cover dynamics. A procedure was developed for scenario projecting of changes in the characteristics of snow cover formation in the XXI century with the use of series of meteorological forcing data, calculated with the use of atmospheric and oceanic general circulation models. The developed procedure was used to evaluate changes in the characteristics of snow cover formation in the XXI century in the areas of snow site locations in the context of current climate changes.

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REFERENCES

  1. 1

    Gusev, E.M., and Nasonova, O.N., Modelirovanie teplo- i vlagoobmena poverkhnosti sushi s atmosferoi (Modeling Heat and Water Exchange between Land Surface and the Atmosphere), Moscow: Nauka, 2010.

  2. 2

    Gusev, E.M., and Nasonova, O.N., Calculation of snow cover formation under different natural conditions using land surface model SWAP, Led Sneg, 2019, vol. 59, no. 2, pp. 167–181. https://doi.org/10.15356/2076-6734-2019-2-401

    Article  Google Scholar 

  3. 3

    Bartlett, P.A., MacKay, M.D., and Verseghy D.L. Modified snow algorithms in the Canadian land surface scheme: Model runs and sensitivity analysis at three boreal forest stands, Atmos.–Ocean. 2006, vol. 44, no. 3, pp. 207−222. https://doi.org/10.3137/ao.440301

    Article  Google Scholar 

  4. 4

    Brown, R.D., and Mote, P.W., The response of Northern Hemisphere snow cover to a changing climate, J. Clim., 2009, vol. 22, pp. 2124–2145. https://doi.org/10.1175/2008JCLI2665.1

    Article  Google Scholar 

  5. 5

    Climate Change and Water, Technical Paper of the Intergovernmental Panel on Climate Change, Bates, B.C., Kundzewicz, Z.W., Wu, S., and Palutikof, J.P., Geneva: IPCC Secretariat, 2008.

  6. 6

    Derksen, C., and Brown, R. Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections, Geophys. Res. Letters, 2012, vol. 39, pp. 1–6. https://doi.org/10.1029/2012GL053387

    Article  Google Scholar 

  7. 7

    Dirmeyer, P., Gao, X., and Oki, T., The Second Global Soil Wetness Project. Science and Implementation Plan, IGPO Publ. Series, Silver Spring: Int. GEWEX Project Office, 2002. № 37.

    Google Scholar 

  8. 8

    Essery, R., Kontu, A., Lemmetyinen, J., Dumont, M., and Ménard, C.B., A 7-year dataset for driving and evaluating snow models at an Arctic site (Sodankylä, Finland), Geosci. Instrum., Methods Data Syst., 2016, vol. 5, pp. 219−227. https://doi.org/10.5194/gi-5-219-2016

    Article  Google Scholar 

  9. 9

    Flanner, M.G., Shell, K.M., Barlage, M., Perovich, D.K., and Tschudi, M.A., Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008, Nat. Geosci., 2011, vol. 4, pp. 151–155. https://doi.org/10.1038/ngeo1062

    Article  Google Scholar 

  10. 10

    Hosaka, M., Nohara, D., and Kitoh, A., Changes in snow cover and snow water equivalent due to global warming simulated by a 20km-mesh global atmospheric model, SOLA, 2005, vol. 1, pp. 093‒096. https://doi.org/10.2151/sola.2005?025

  11. 11

    Krinner, G., Derksen, C., Essery, R., Flanner, M., Hagemann, S., Clark, M., Hall, A., Rott, H., Brutel-Vuilmet, C., Kim, H., Ménard, C.B., Mudryk, L., Thackeray, C., Wang, L., Arduini, G., Balsamo, G., Bartlett, P., Boike, J., Boone, A., Chéruy, F., Colin, J., Cuntz, M., Dai, Y., Decharme, B., Derry, J., Ducharne, A., Dutra, E., Fang, X., Fierz, C., Ghattas, J., Gusev, Y., Haverd, V., Kontu, A., Lafaysse, M., Law, R., Lawrence, D., Li, W., Marke, T., Marks, D., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Raleigh, M.S., Schaedler, G., Semenov, V., Smirnova, T., Stacke, T., Strasser, U., Svenson, S., Turkov, D., Wang, T., Wever, N., Yuan, H., Zhou, W., ESM-SnowMIP. Assessing models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 2018, vol. 11, pp. 5027–5049.

    Article  Google Scholar 

  12. 12

    Krysanova, V., and Hattermann, F.F., Intercomparison of climate change impacts in 12 large river basins: overview of methods and summary of results, Clim. Change, 2017, vol. 141, pp. 363–379. https://doi.org/10.1007/s10584-017-1919-y

    Article  Google Scholar 

  13. 13

    Landry, C.C., Buck, K.A., Raleigh, M.S., and Clark, M.P., Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: a new integrative data source to develop and evaluate models of snow and hydrologic processes, Water Resour. Res., 2014, vol. 50, pp. 1773–1788.

    Article  Google Scholar 

  14. 14

    Menard, C.B., Essery, R., Arduini, G., Bartlett, P., Boone, A., Brutel-Vuilmet, C., Burke, E., Cuntz, M., Dai, Y., Decharme, B., Dutra, E., Fang, X., Fierz, C., Gusev, Y., Hagemann, S., Haverd, V., Kim, H., Krinner, G., Lafaysse, M., Marke, T., Nasonova, O., Nitta, T., Niwano, M., Pomeroy, J., Schadler, G., Semenov, V., Smirnova, T., Strasser, U., Swenson, S., Turkov, D., Wever, N., and Yuan, H., Scientific and human errors in a snow model intercomparison, Bull. Am. Meteorol. Soc. https://doi.org/10.1175/BAMS-D-19-0329

  15. 15

    Menard, C.B., Essery, R., Barr, A., Bartlett, P., Derry, J., Dumont, M., Fier, C., Kim, H., Kontu, A., Lejeune, Y., Marks, D., Niwano, M., Raleigh, M., Wang, L., and Wever, N., Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data, Earth Syst. Sci. Data, 2019, vol. 11, pp. 865–880.

    Article  Google Scholar 

  16. 16

    Lesaffre, B., Panel, J.-M., Poncet, D., David, P., and Sudul, M., An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models, Earth Syst. Sci. Data, 2012, vol. 4, pp. 13–21. https://doi.org/10.5194/essd-4-13-2012

    Article  Google Scholar 

  17. 17

    Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J., Elgizouli, I., Emor, S.I., Erda, L., Hibbard, K., Jones, R., Kainuma, M., Kelleher, J., Lamarque, J.F., Manning, M., Matthews, B., Meehl, J., Meyer, L., Mitchell, J., Nakicenovic, N., O’Neill, B., Pichs, R., Riahi, K., Rose, S., Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks, T., van Ypersele, J.P., Zurek, M., Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies, Geneva: IPCC Secretariat, 2008.

    Google Scholar 

  18. 18

    Mote, P.W., Li, S., Lettenmaier, D.P., Xiao, M., and Engel, R., www.nature.com/articles/s41612-018-0012-1#citeas. Accessed December 12, 2020. https://doi.org/10.1038/s41612-018-0012-1

  19. 19

    Mudryk, L.R., Kushner, P.J., Derksen, C., and Thackeray, C., Snow cover response to temperature in observational and climate model ensembles, Geophys. Rev. Lett., 2017, vol. 44, pp. 919–926.

    Article  Google Scholar 

  20. 20

    Nash, J.E., and Sutcliffe, J.V., River flow projecting through conceptual models: 1 A discussion of principles, J. Hydrol., 1970, vol. 10, no. 3, pp. 282–290.

    Article  Google Scholar 

  21. 21

    Navarro-Racines, C.E., Tarapues-Montenegro, J.E., and Ramírez-Villegas, J.A., Bias-correction in the CCAFS-Climate Portal: A description of methodologies. Decision and Policy Analysis (DAPA) Research Area. International Center for Tropical Agriculture (CIAT). Cali, Colombia. 2015. URL: http:// http://ccafs-climate.org/downloads/docs/BC_methods_explaining_v2.pdf. Accessed Dec. 4, 2020.

  22. 22

    Niwano, M., Aoki, T., Kuchiki, K., Hosaka, M., and Kodama, Y., Snow metamorphism and albedo process (SMAP) model for climate studies: model validation using meteorological and snow impurity data measured at Sapporo, Japan, J. Geophys. Res.: Earth Surf., 2012, vol. 117, pp. 1–18. https://doi.org/10.1029/2011JF002239

    Article  Google Scholar 

  23. 23

    Qu, X. and Hall, A., On the persistent spread in snow-albedo feedback, Clim. Dyn., 2014, vol. 42, pp. 69–81. https://doi.org/10.1007/s00382-013-1774-0

    Article  Google Scholar 

  24. 24

    Räisänen, J., Warmer climate: Less or more snow? Clim. Dyn., 2008, vol. 30, pp. 307–319. https://doi.org/10.1007/s00382-007-0289-y

    Article  Google Scholar 

  25. 25

    Ramirez-Villegas, J., Challinor, A., Thornton, P., and Jarvis, A., Implications of regional improvement in global climate models for agricultural impact research, Environ. Res. Lett., 2013, vol. 8, no. 2, p. 024018. https://doi.org/10.1088/1748-9326/8/2/024018

    Article  Google Scholar 

  26. 26

    Rasmus, S., Räisänen, J., and Lehning, M., Estimating snow conditions in Finland in the late 21st century using the SNOWPACK model with regional climate scenario data as input, Ann. Glaciol., 2004, vol. 38, pp. 238–244.

    Article  Google Scholar 

  27. 27

    Reba, M.L., Marks, D., Seyfried, M., Winstral, A., Kumar, M., and Flerchinger, G., A long-term data set for hydrologic modeling in a snow-dominated mountain catchment, Water Resour. Res., 2011, vol. 47, pp. 1–7. https://doi.org/10.1029/2010WR010030

    Article  Google Scholar 

  28. 28

    Schmucki, E., Marty, C., Fierz, C., and Lehning, M., Simulations of 21st century snow response to climate change in Switzerland from a set of RCMs, Int. J. Climatol., vol. 35, no. 11, pp. 3262–3273. https://doi.org/10.1002/joc.4205

  29. 29

    Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., and Schewe, J., The inter-sectoral impact model intercomparison project (ISI-MIP): project framework, Proc. Natl. Acad. Sci. USA, 2014, vol. 111, no. 9, pp. 3228–3232. https://doi.org/10.1073/pnas.1312330110

    Article  Google Scholar 

  30. 30

    WSL Institute for Snow and Avalanche Research SLF. Weissfluhjoch dataset for ESM-SnowMIP. 2017. https://doi.org/10.16904/16. URL:http://www.envidat.ch/dataset/snowmip. Accessed December 3, 2020.

  31. 31

    Zhao, M. and Dirmeyer, P., Production and analysis of GSWP-2 near-surface meteorology data sets, COLA Technical Report. Calverton: Center for Ocean-Land-Atmosphere Studies, 2003, no. 159.

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ACKNOWLEDGMENTS

The authors are grateful to the organizers of ESM-SnowMIP Project and, personally, R. Essery and  C.B. Mé-nard (Edinburg University, Great Britain), who provided data for model simulations.

Funding

This study was supported by the Russian Science Foundation, project no. 16-17-10 039.

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Correspondence to E. M. Gusev.

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Translated by G. Krichevets

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Gusev, E.M., Nasonova, O.N., Kovalev, E.E. et al. Scenario Projections of Changes in Snow Water Equivalent Due to Possible Climate Changes in Different Regions of the Earth. Water Resour 48, 133–145 (2021). https://doi.org/10.1134/S0097807821010176

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Keywords:

  • ESM-SnowMIP project
  • models of snow cover formation
  • land surface model SWAP
  • snow water equivalent
  • RCP-scenarios of climate change