1. INTRODUCTION
Snow is Earth’s most climatically sensitive land cover type and is
continuing to decline as temperatures warm and precipitation patterns
shift (IPCC et al. , 2019). 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 (Jennings et al. , 2018). Snow cover variability
affects multiple sectors, from water, energy, and forest management to
transportation planning to outdoor recreation and tourism (Hagenstadet al. , 2018). Historically, snow monitoring has focused on
ground-based measurements, with satellite remote sensing adding more
capacity in the past several decades. While ground-based monitoring
networks remain valuable, they do not have the spatial coverage,
representative geospatial characterization, trend analysis, and
predictive capabilities needed by researchers and water managers today
(Milly et al. , 2008). Snow cover products derived from remote
sensing require technical expertise and computing resources to produce
and interpret, thereby limiting their broader usefulness. Here, we
present a suite of snow metrics that may serve as key climate indicators
to support the goals and needs of snow hydrologists, water managers,
climate researchers, and various stakeholder groups.
The objectives of this research were to:
- Produce new snow metrics for assessing past and future changes in snow
cover and snowpacks;
- Demonstrate how these snow-related climate indicators can be used to
identify regions that may be vulnerable to future reductions in snow
cover and warmer winters;
- Provide web-based visualization tools to communicate these snow
metrics.
This suite of snow-related climate indicators is created to address
sector concerns and stakeholder questions such as: Where and how have
the extent/duration/timing of snow cover changed in the past, and how
might it change in the future? How might snowpack storage change over
the next several decades in a particular watershed? Are winter storm
temperatures different from aggregated temperature data, and have winter
storm temperatures in the western US increased in recent years? What has
been the climatological frequency of warm winters for vulnerable
economic sectors such as ski areas? How might the frequency of warm
winters change in mid-century and late-century?
The specific snow metrics include Snow Cover Frequency (SCF), Snow
Disappearance Date (SDD), snowstorm temperature (ST), At-Risk Snow
(ARS), and Frequency of a Warm Winter (FWW). This suite of metrics aims
to provide information to a wide range of researchers and stakeholders,
including hydrologists, climatologists, water and land managers, snow
sports enthusiasts, climate adaptation planners, and others for whom
snow plays a role in decision making.
1.1 Previously developed snow metrics and their
limitations
1.1.1 April 1 Snow Water
Equivalent
The National Resources Conservation Service (NRCS) measures snow water
equivalent along snow courses (survey transects) monthly and at
automated SNOw TELemetry (SNOTEL) stations daily across the western
United States. Snow water equivalent (SWE) is the amount of water
represented by the snowpack (Serreze et al. , 1999). These NRCS
sites were designed to serve as indices for seasonal streamflow
forecasts based on regression-based relationships (Garen, 1992).
Although snow course and SNOTEL data were not intended as climate
indicators, the data have been available for decades allowing
researchers to incorporate SWE measurements into climate research. In
particular, climatologists and hydrologists have used April 1 SWE as an
annual measure of maximum snow accumulation and snow’s total
contribution to the water budget of a watershed. However, of concern is
that April 1 SWE cannot express the effects of midwinter melt events,
which are now common in the maritime snowpacks of Washington, Oregon,
and California (Mote, 2003; Sproles et al. , 2013, 2017; Knowles,
2015; Cooper et al. , 2016; Mote et al. , 2018). At lower
elevation sites, maximum snow accumulation often occurs prior to April
1, thereby introducing a bias to watershed budget calculations (Montoyaet al. , 2014).
The spatial representativeness of SNOTEL and snow course sites is also a
concern when used for purposes outside of streamflow forecasting. For
logistical reasons, snow course and SNOTEL sites occupy a relatively
limited elevation range; thus, high elevation snow and rain-snow
transition zones are under-sampled (Molotch and Bales, 2006; Nolin,
2012; Gleason et al. , 2017). Even with over 800 measurement sites
across the western United States (NRCS
https://www.wcc.nrcs.usda.gov/about/mon_automate.html), the monitoring
network is sparse, with many watersheds having only one or no sites.
Moreover, as the climate continues to warm, these sites, most of which
were installed in the early 1980s, may become unrepresentative of
watershed-scale SWE. This is because the snowline in the mountains
increases during years of warm snow drought and dry snow drought (Cooperet al. , 2016; Sproles et al. , 2017). As such, they may
underestimate trends in snow cover and changes in interannual
variability across the seasonal snow zone. Seasonal drought outlooks
that use April 1 SWE as a key indicator may miss key precipitation
processes leading up to that date. For instance, anomalously low winter
precipitation called a ’dry snow drought’ (Harpold et al. , 2017)
is due to the natural variability of synoptic-scale atmospheric
circulation and has regional impacts across all elevations. This is
important in continental mountain regions where a shift in the storm
track can lead to early and mid-winter dry conditions and low SWE.
In contrast, warmer than average winter temperatures that lead to a
shift from snowfall to rainfall is termed a ’warm snow drought’ (Harpoldet al. , 2017) and may be caused by overall increases in winter
storm temperatures. Warm snow droughts are most pronounced at low
elevations and in maritime snow regions such as the Oregon and
Washington Cascades and the California Sierra Nevada, where storm
temperatures are close to the melting point. These examples demonstrate
that different mechanisms drive warm and dry snow droughts, but there is
no way to know this from April 1 SWE alone.
1.1.2 Snow Disappearance
Date
A second previously developed snow metric is the Snow Disappearance Date
(SDD). The presence or absence of snow affects landscape albedo, which
in turn controls the energy balance. In snowy climates, SDD signifies
the onset of spring and the start of the growing season. The elevational
progression of snow disappearance in spring affects Arctic wildlife,
with late spring, low elevation snow having a negative impact on
populations of caribou and Dall sheep (Mahoney et al. , 2018;
Boelman et al. , 2019). In the western United States, SDD is
associated with wildfire activity (Westerling et al. , 2006a) in
the sense that SDD reflects the end of snowmelt and the onset of
seasonal declines in soil moisture and fine fuel moisture content.
Lundquist et al. (2013) used SDD in their comparison of forested and
open sites where they showed that, in locations with relatively warmer
winters, forests lose their snow cover earlier than in open areas.
Snow disappearance date has been measured using ground-based and
remotely-sensed measurements. In their study on spring snow
disappearance, Foster et al. (1992) used satellite remote sensing and
station-based data to map snow cover and snow disappearance for
locations in Alaska, Canada, Scandinavia, and Siberia. In the studies
using station data, the manner in which SDD was determined is not always
clear. In his Arctic tundra studies, Foster (1992) defined SDD as the
first day of the calendar year when station-based snow depth
measurements dropped below 1” (2.5cm). In their 2013 meta-analysis,
Lundquist et al. (2013) listed SDD as the first day of the calendar year
with no snow as reported from station data (though they didn’t provide a
measurement threshold). Lundquist and Lott (2010) used near-surface soil
temperature measurements to detect the presence and absence of snow
cover, which can be used to identify the SDD.
Such varied uses and ways of recording SDD indicate both the importance
of this snow metric and a need for a spatially consistent approach to
its measurement. Station-based data are inherently limited in their
spatial representation of SDD, though they are critical for calibration
and validation of remote sensing measurements of SDD. Temporal factors
affecting SDD are also essential to consider. Transient snowfall events
can influence SDD detection and can be important for hydrology and
wildlife. The variable nature of spring meteorology means that it is not
uncommon for spring melt to be followed by spring snowstorms that can
drive the actual snow disappearance date to be days or even weeks later.
1.1.3 Snow cover Absence and Snow
cover
Persistence
Several researchers have used remotely sensed snow cover to create
metrics such as snow cover absence, SA (Wayand et al. , 2018),
snow persistence, SP, and snow season, SS (Hammond et al., 2018). Wayand
et al. (2018) developed SA using high-resolution remote sensing data
from Landsat-8 and Sentinel-2 to map snow-free areas during winter as a
way to detect snow removal by wind erosion and avalanches in mountainous
areas. The authors also created a snow cover persistence index that
mapped snow-covered areas during summer as a way to identify wind- and
avalanche deposited snow. Hammond et al. (2018) developed a remote
sensing-based snow cover persistence metric though their definition of
SP is ”the fraction of time that snow is present on the ground.” The
advantage of SP, SS, and SA is that they can be computed using remote
sensing data and produced at 10-30 m spatial scales, thus capturing
high-resolution snow processes such as the effects of redistribution and
boundaries between intermittent and seasonal snow zones (Moore et
al. , 2015). However, to date, these metrics have not been produced
daily or globally. There is a trade-off between temporal and spatial
resolution satellite data. Finer spatial scale data such from Landsat 8
have a 16-day revisit time so even with two satellites there are long
gaps in coverage. Such gaps can miss important events such as snowfall
and snowmelt events.
1.1.4 |
Climatologically-based Snow
Metrics
At-risk Snow
First defined by Nolin and Daly (2006), “At-risk” snow (ARS) is when
snowfall is at risk of turning to rainfall under climate warming
conditions. Nolin and Daly used the PRISM 4-km gridded climate data
(Daly et al. , 1993) and a decision tree approach to classify grid
cells as either at-risk or not at-risk, based on monthly mean air
temperature for the months of December, January, and February. The
study’s geographic scope covered only the Pacific Northwest (Washington,
Oregon, Idaho, and western Montana), where snowfall is often close to
the melting point. Their approach used the 0°C monthly mean temperature
threshold to partition between rain and snow. The monthly mean 0°C
threshold was selected because, though spatial and temporal differences
exist, both thermodynamically and practically, it represents a shift
from snowfall and snow accumulation to rainfall and declining snowpacks.
Their methodology assumed that locations that might warm to that 0°C
monthly mean temperature threshold would be at-risk of converting from
snowfall to rainfall. For instance, a grid cell with a climatological
mean monthly temperature of -2°C for any one of the core winter months
(Dec-Feb) was classified as ARS for a +2°C warming scenario. This
data-driven approach was simple but did not consider possible changes in
atmospheric circulation and storm patterns. Moreover, the 4-km
grid-scale was too coarse to address changes at more local scales,
especially in mountain regions where a 4-km grid cell can span a wide
range of elevations and temperatures.
Frequency of a Warm Winter
Nolin and Daly (2006) also developed the Frequency of a Warm Winter
(FWW) metric and as with ARS, Nolin and Daly used the PRISM gridded
climate data product and monthly mean temperatures for December,
January, and February. FWW is computed as the number of winters out of
30 consecutive winters that meet the ”warm winter” criterion. A warm
winter is defined as one in which the mean monthly temperature exceeds
0°C during any one of the three core winter months (December-February).
Thus if three winters out of 30 winters are classified as warm in a
given grid cell, the frequency is 0.1 or 10%. To demonstrate possible
impacts of changes in FWW, Nolin and Daly (2006) tabulated the
historical and future FWW values for selected ski areas in the study
region. All ski areas in the region showed increases in FWW for
incremental temperature increases, but the 4-km spatial resolution
introduced uncertainty due to the range of elevations in a grid cell.