Anne W. Nolin1, Eric A. Sproles2,
David Rupp3, Ryan L. Crumley4, Ross
T. Palomaki2, and Eugene Mar5
1Department of Geography, University of Nevada, Reno,
NV 89557, USA
2Department of Earth Sciences, Montana State
University, Bozeman, MT 59717, USA
3Oregon Climate Change Research Institute, College of
Earth, Ocean, and Atmospheric Sciences, Oregon State University,
Corvallis, OR, 97331 USA
4College of Earth, Ocean, and Atmospheric Sciences,
Oregon State University, Corvallis, OR, 97331 USA
5(retired) College of Earth, Ocean, and Atmospheric
Sciences, Oregon State University, Corvallis, OR, 97331 USA
Corresponding Author : Anne W. Nolin, Department of Geography,
University of Nevada, Reno, MS 0154, Reno, NV 89557, USA. Email:
anolin@unr.edu
Abstract (up to 300 words)
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 modeled 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).
Keywords: snow cover, climate change, remote sensing, SNOTEL,
climate model, SnowCloudMetrics