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).