2. DESCRIPTIONS, DATA, AND EXAMPLES OF THE NEW SNOW METRICS
The new snow metrics described below are meant to improve upon and
augment established snow metrics. The snow metrics described in the
following section are intended to add value through extended spatial
coverage, improved spatial resolution, more nuanced interpretation,
improved data access, and user-driven flexibility.
2.1 Snow Cover Frequency (SCF; Global)
Snow Cover Frequency (SCF) is a global, satellite-derived, gridded
product representing the observed frequency of snow cover in a grid
cell. It is computed as the number of days that snow is observed on the
land surface divided by the number of valid observations for a specified
period (Eq. 1). We use the global, 500-m, daily, gridded snow cover
product, version 6 from NASA’s Moderate Resolution SpectroRadiometer
(MODIS/Terra MOD10A1). Because the MOD10A1 product is daily and global,
it allows the user to compute SCF anywhere globally for the MODIS period
of record (February 2000–present). It is statistically valid for any
period of 30 days or longer. In related work, Crumley et al. (2020) have
created a version of this SCF metric for the Northern Hemisphere Water
Year (WY, October 1–September
30) for WY 2001–2019. Their SCF snow metric is available as a web-based
product (SnowCloudMetrics.app) developed using the Google Earth Engine
(GEE) framework. In its current form, the app approach has somewhat
limited flexibility for user-defined temporal and spatial subsetting.
Even greater flexibility is gained if a user is willing to run the SCF
code within GEE (i.e., as a GEE developer). In this case, users can
select and compute SCF for any sub-annual set of sequential days within
the MOD10A1 record. Users can also spatially subset SCF by user-defined
polygon, US state boundary or Canadian province, elevation range, or
USGS watershed. For instance, a user can draw a polygon around the
California Sierra Nevada range and compute a regional SCF. The code used
to produce the SCF metric, as described here, is available to users who
wish to compute this product on their own using GEE.
Here, we present both a global and a regional example of SCF using MODIS
data. Figure 1a shows global SCF for WY 2015. Over the western US, one
can see significant differences in SCF between the two years; 2015 was a
record low snow year for the western United States, and 2017 snowpack
was anomalously high over the region. Figures 1b and 1c take a closer
look, focusing on SCF over the California Sierra Nevada for WY 2015 and
WY 2017.
In another GEE application of the SCF metric, Sproles et al. (2018)
found that SCF was valuable for monthly streamflow forecasting. They
used SCF in their web-based, cloud-computing tool SnowCloudHydro, which
combines basin-scale SCF with a simple hydrologic model for use in
snow-dominated and data-sparse watersheds. Crumley et al. (2020) noted a
strong association between measured SNOTEL-derived SCF and MODIS-derived
SCF (using SnowCloudMetrics). We are not claiming that SCF is analogous
to SWE though this may be the case in much of the western US, especially
for elevations where snowpacks are very temperature sensitive. Indeed,
in cold regions such as the Arctic boreal region, any relationship
between SCF and SWE might be weak because the cold, thin snow cover can
persist for many months but have a low SWE. However, we note that the
SCF metric is valuable in these regions because it can detect
interannual variability in the snow line elevation in the spring months
(Verbyla et al. , 2017). This is important for wildlife such as
Dall sheep who produce lambs at that time and for whom late-season snow
cover reduces forage access, and for caribou who migrate long distances
(Verbyla et al. , 2017; Mahoney et al. , 2018; Van De Kerket al. , 2018; Boelman et al. , 2019).
2.2 Snow Disappearance Date (SDD, Northern Hemisphere
extent)
The Snow Disappearance Date is a global, satellite-derived, gridded
product that maps the last day of the WY when snow is last detected in a
pixel. As with SCF, SDD also uses the global, 500-m, daily, gridded snow
cover product from MODIS. Starting on the last day of each WY, the
algorithm searches back in the WY for the longest period without snow
after a minimum of 5 days of snow cover (accounting for cloudy days).
Crumley et al. (2020) computed SDD for WY 2001–2019. Like SCF, SDD has
been used in wildlife studies for locations where spring snow
disappearance affects the survival of young (Van De Kerk et al. ,
2018). To date, SDD has not been as widely used as SCF. Still, we
anticipate its value for areas where snow disappearance date has been
related to the onset of the wildfire season (Westerling et al. ,
2006b) and where spring vegetation phenology varies with snow cover
(Huang et al. , 2018; Xie et al. , 2020).
With our revised metric, a user can compute SDD globally in about 20
seconds or use the polygon drawing tool to select a region such as the
California Sierra Nevada. Figure 2a shows global SDD for WY2015 and SDD
subset by polygon for the Sierra Nevada (Figures 2b and 2c), providing
the ability to compare a low snow year (WY 2015) with a high snow year
(WY2017).
At this time, SDD spatial coverage is for the Northern Hemisphere only.
In addition to SDD through the SnowCloudMetrics.app, users can also
download the SDD code and run it independently using Google Earth
Engine. SDD is still computed only for the Northern Hemisphere WY, but
users have greater spatial subsetting options, similar to those for SCF,
as described above.
[Insert Figure 2]
2.3 | Snowstorm Temperature (ST, western US
extent)
Snowstorm temperature is the mean
daily temperature on days when snow accumulation occurred (Hu and Nolin,
2019, 2020). This is our only snow metric that is derived from SNOTEL
station data. A storm day is determined based on measured changes in
SWE. Spatial coverage is for the western United States, excluding
Alaska. The snowstorm temperature data set is composed of 33 years of
daily meteorological data (1984–2016) from 567 SNOTEL sites and a
homogenized daily temperature dataset (TopoWx). The SNOTEL data were
used to determine dates on which a snowstorm occurred and whether the
snow water equivalent increased, decreased, or stayed the same. Storm
day is defined as one with a measurable positive change in
precipitation. SWE-gaining and SWE-losing days were recorded. TopoWx
temperature data (Oyler et al. , 2015) were used to produce the
dry-day and storm-day data for each of the 573 SNOTEL sites.
Disaggregating winter storm days from non-storm (dry) days turns out to
have a major impact on the interpretation of temperature trends. For
instance, Hu and Nolin (2020) found that storm days are warming at
roughly twice the rate of dry days. They also found that there were
significantly fewer storm days in November. Such a trend indicates not
only a delay in the start of the wet season but with declining spring
snowpacks (Mote et al. , 2005, 2018), means a lengthening of the
preceding dry season.
2.4 “At-Risk” Snow (ARS, conterminous US
extent)
At-Risk Snow is computed in a manner similar to that in Nolin and Daly
(2006) except that we use climate model output in this updated version
of the ARS metric. ARS is computed using the gridded NASA Earth Exchange
Downscaled Climate Projections (NEX-DCP30) 30 arcsec (approximately
800-m) dataset (Thrasher et al. , 2013), which covers the
conterminous United States. We compute mean temperature from their
average monthly maximum and minimum temperature data from 33 downscaled
climate models and four Representative Concentration Pathways (RCPs)
(Meinshausen et al. , 2011). The data set includes retrospective
model runs covering the historical period from 1950 to 2005, and
prospective model runs for 2006 to 2099. The spatial domain is the
conterminous United States (Figure 3). The ARS gridded map product
depicts model grid cells where December–February monthly mean
temperatures will reach the 0°C threshold.
We have produced a fully functional code running on GEE that allows the
user to compute At-Risk Snow for any range of years in the period
covered by the NASA NEX-DCP30 downscaled climate data. Users can
spatially and temporally subset, visualize, explore, and download the
FWW data of interest. A user can filter by emissions scenario, global
climate model, spatial extent, and period. A user can select from any of
four CMIP5 emissions scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) and
from numerous downscaled (800-m) model output (23 models for RCP2.6; 33
models for RCP4.5; 17 models for RCP6.0; and 31 models for RCP8.5). If
desired, a user can subset by watershed using the USGS hydrologic unit
codes (HUC levels 2-12).
[Insert Figure 3]
2.5 Frequency of a Warm Winter (FWW, conterminous US
extent)
As with the At-Risk Snow metric, FWW uses the monthly maximum and
minimum temperature data from NEX-DCP30, computing mean temperature from
their average. The definition of FWW is the same as in Nolin and Daly
(2006), but the geographic scope extends to cover the conterminous US at
800-m spatial resolution. The temporal range is 1950–2005 for historic
FWW and 2006–2099 for future FWW.
For users to compute FWW, we have produced code that runs on GEE. This
allows the user to calculate FWW for any range of years in the period
covered by the NASA NEX-DCP30 downscaled climate data. As with ARS,
users can spatially and temporally subset, visualize, explore, and
download the FWW data of interest. A user can filter by emissions
scenario, global climate model, spatial extent, and range of years. If
desired, users can subset by watershed using the USGS hydrologic unit
codes (HUC levels 2-12). When computing FWW, we recommend that users
specify a range of at least 30 years so that the calculated values are
the empirical equivalent of a statistical probability. For instance, a
user can compare FWW for 1979-2009 (historical) with FWW for 2035-2069
(mid-century) or 2079-2099 (late-century). The 800-m gridded data are
sufficiently fine spatial resolution that users can explore projected
FWW changes for spatial extent as large as the conterminous United
States and as small as a headwater catchment, an urban area, and even
along an elevation gradient in a ski area.
Figure 4 shows an example of FWW for the conterminous United States and
selected ski areas, for three time periods: historic (1970-1999) and
RCP8.5 mid-century (2035-2064), and RCP8.5 late-century (2070-2099).
In a recent application of FWW, Nolin assisted the city of Whitefish,
Montana with their Climate Action Plan (City of Whitefish, 2018). The
winter economy of Whitefish depends on the success of the nearby ski
area, Whitefish Mountain Resort. For this report, Non subset and
computed FWW for the lower, mid- and summit elevations of the ski area
for different climate scenarios and for mid-century and late-century.
This is a straightforward example of the use of the FWW metric for
non-scientists seeking climate change information at the
local-to-regional scale. Table 1 gives FWW values for the selected ski
areas shown in Figure 4. These represent major ski resorts in a maritime
snow climate (Squaw Valley, CA; Mt. Bachelor, OR), northern Rocky
Mountains (Big Sky, MT), central Rocky Mountains (Vail, CO), and
northern Appalachian Mountains (Killington, VT).
[Insert Figure 4]
[Insert Table 1]