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Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble land surface modeling
  • +18
  • Rhae Sung Kim,
  • Sujay Kumar,
  • Carrie Vuyovich,
  • Paul R Houser,
  • Jessica D Lundquist,
  • Lawrence Mudryk,
  • Michael Durand,
  • Ana P. Barros,
  • Edward J Kim,
  • Barton A Forman,
  • Ethan D. Gutmann,
  • Melissa L Wrzesien,
  • Camille Garnaud,
  • Melody Sandells,
  • Hans-Peter Marshall,
  • Nicoleta C Cristea,
  • Justin Pflug,
  • Jeremy Johnston,
  • Yueqian Cao,
  • David M. Mocko,
  • Shugong Wang
Rhae Sung Kim
NASA GSFC

Corresponding Author:[email protected]

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Sujay Kumar
NASA GSFC
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Carrie Vuyovich
NASA Goddard Space Flight Center
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Paul R Houser
George Mason University
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Jessica D Lundquist
University of Washington
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Lawrence Mudryk
Environment and Climate Change Canada
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Michael Durand
Ohio State University
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Ana P. Barros
Duke University
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Edward J Kim
NASA Goddard Space Flight Center
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Barton A Forman
University of Maryland, College Park
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Ethan D. Gutmann
National Center for Atmospheric Research (UCAR)
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Melissa L Wrzesien
NASA Goddard Space Flight Center
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Camille Garnaud
Environment and Climate Change Canada
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Melody Sandells
Northumbria University
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Hans-Peter Marshall
Boise State University
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Nicoleta C Cristea
University of Washington
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Justin Pflug
University of Washington
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Jeremy Johnston
George Mason University
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Yueqian Cao
Duke University
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David M. Mocko
NASA Goddard Space Flight Center
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Shugong Wang
Goddard Space Flight Center, NASA
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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 during the 2009-2017 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.
17 Feb 2021Published in The Cryosphere volume 15 issue 2 on pages 771-791. 10.5194/tc-15-771-2021