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New snow metrics for a warming world
  • +3
  • Anne Nolin,
  • Eric Sproles,
  • David Rupp,
  • Ryan Crumley,
  • Ross Palomaki,
  • Eugene Mar
Anne Nolin
University of Nevada Reno
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Eric Sproles
Montana State University Bozeman
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David Rupp
Oregon State University
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Ryan Crumley
Oregon State University
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Ross Palomaki
Montana State University Bozeman
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Eugene Mar
Oregon State University
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Abstract

Snow is Earth’s most climatically sensitive land cover type. Air temperature and moisture availability are first-order controls on snowfall. Maximum snowfall occurs at temperatures very near 0°C, so even a slight increase in temperature will shift a snowy winter to one with midseason rainfall and melt events. Traditional snow metrics are not able to adequately capture the changing nature of snow cover. For example, April 1 snow water equivalent (SWE, the amount of water represented by the snowpack) is used as a streamflow predictor. Still, it cannot express the effects of midwinter melt events, now expected in warming snow climates. The multiple impacts of a changing snowpack require a suite of climate indicators derived from readily measured or modelled data that serve as proxies for relevant snow-related and climate-driven processes. Such indicators need to be simple enough to “tell the story” of snowpack changes over space and time, but not overly simplistic as to be conflated with other variables or, conversely, overly complicated in their interpretation. This paper describes a targeted set of spatially explicit, multi-temporal snow metrics for multiple sectors, stakeholders, and scientists. They include metrics based on satellite data from NASA’s Moderate Resolution Imaging Spectroradiometer, meteorological observations and snow data from ground-based stations, and climate model output. We describe and provide examples for Snow Cover Frequency (SCF), Snow Disappearance Date (SDD), snowstorm temperature (ST), At-Risk Snow (ARS), and Frequency of a Warm Winter (FWW).

Peer review status:Published

31 Oct 2020Submitted to Hydrological Processes
31 Oct 2020Submission Checks Completed
31 Oct 2020Assigned to Editor
31 Oct 2020Reviewer(s) Assigned
21 Dec 2020Review(s) Completed, Editorial Evaluation Pending
22 Dec 2020Editorial Decision: Revise Major
30 May 20211st Revision Received
31 May 2021Submission Checks Completed
31 May 2021Assigned to Editor
31 May 2021Reviewer(s) Assigned
31 May 2021Review(s) Completed, Editorial Evaluation Pending
31 May 2021Editorial Decision: Accept
04 Jun 2021Published in Hydrological Processes. 10.1002/hyp.14262