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The Cryosphere An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Sep 2020

15 Sep 2020

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This preprint is currently under review for the journal TC.

Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling

Rhae Sung Kim1,2, Sujay Kumar1, Carrie Vuyovich1, Paul Houser3, Jessica Lundquist4, Lawrence Mudryk5, Michael Durand6, Ana Barros7, Edward J. Kim1, Barton A. Forman8, Ethan D. Gutmann9, Melissa L. Wrzesien1,2, Camille Garnaud10, Melody Sandells11, Hans-Peter Marshall12, Nicoleta Cristea4, Justin M. Pflug4, Jeremy Johnston3, Yueqian Cao7, David Mocko1,13, and Shugong Wang1,13 Rhae Sung Kim et al.
  • 1Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2Universities Space Research Association, Columbia, MD, USA
  • 3Department of Geography and Geoinformation Sciences, George Mason University, Fairfax, VA, USA
  • 4Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
  • 5Climate Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
  • 6School of Earth Sciences and Byrd Polar and Climate Research Center, The Ohio State University, Columbus, OH, USA
  • 7Civil and Environmental Engineering, Duke, University, Durham, NC, USA
  • 8Civil and Environmental Engineering, University of Maryland, College Park, MD, USA
  • 9National Center for Atmospheric Research, Boulder, Colorado, USA
  • 10Meteorological Research Division, Environment and Climate Change Canada, Dorval, Quebec, Canada
  • 11Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, UK
  • 12Department of Geosciences, Boise State University, Boise, ID, USA
  • 13Science Applications International Corporation, Reston, VA, USA

Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009&ndashl2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

Rhae Sung Kim et al.

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Rhae Sung Kim et al.

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