Snow covers the Arctic land surface for up to nine months each year, and influences the surface energy budget, ground thermal regime, and freshwater budget of the Arctic. Snow also interacts with vegetation, affects biogeochemical activity, and influences migration and access to forage for wildlife, which impacts terrestrial and aquatic ecosystems. Even following the snow cover season, the influence of spring snowmelt timing persists through impacts on river discharge timing and magnitude, surface water, soil moisture, and fire risk. Snow can be characterized by multiple variables. Snow presence (important for the surface energy budget) can be characterized by how much area is covered by snow (snow cover extent - SCE) and how long snow remains on the land surface (snow cover duration - SCD). The amount of snow is more important for determining influences on the ground thermal regime and freshwater budget and timing. There are two main ways to measure snow on the ground: (1) measuring the total depth of snow on ground (this is reported as snow depth and it includes both new and old snow) or (2) measuring the liquid water content of snow from a gauge or from a core sample (this is reported as snow water equivalent - SWE). The SWE from a gauge corresponds to new snow that had fallen in 24 hours. The SWE from a core represents the new and the old snow. The SWE can be thought of as the depth of water that would theoretically result if you melted the entire snowpack instantaneously. Snow depth and SWE can also be estimated from satellite products, reanalyses or models.
In this section, we present an inventory of snow depth, SWE and snow cover data obtained using the three categories:
(1) In-situ measurements
(2) Satellite-derived products
(3) Analyses, reanalyses and reanalysis-driven products.
In the selection, we use datasets that are freely available online, represent an important source of information, cover at least a decade of data, and have supporting documentation. The datasets that provide only snow depth are summarised in the first table, those that provide only SWE are summarized in the second table, while the datasets that provide both snow depth and SWE are presented in the third table. The fourth table focuses on snow cover data.
| name | source | data type | spatial domain | spatial resolution | temporal coverage | time step | data format | |
|---|---|---|---|---|---|---|---|---|
| MSC Snow Depth Observations | MSC/ECCC | Station data | Canada | Point data | Variable (1970-2019) | Daily | CSV; GeoJSON | details |
| Canadian Historical Daily Snow Depth Data | CRD/ECCC | Station data | Canada | Point data; peak of ~2000 stations in 1980s; most data located south of ~55 N; rapid decline in station numbers after 1995 | variable1883-2017; Pan-Canadian coverage after ~1955 | Daily | ASCII; NetCDF | details |
| ISD | NCEI/NOAA | Station data | Global | Point data; ~20,000 stations; NH coverage most dense over mid-latitudes | Variable, 1902 - present | Sub-daily; Daily | ASCII | details |
| ANUSPLIN | CFS/NRCan | Gridded observations | Canada | 10 km x 10 km | 1955-2017 | Monthly | ASCII; NetCDF | details |
| Bennett Townsite, Yukon | Parks Canada | Station data | 1 site | Point data | Since 2015 | Hourly | None | details |
| Yukon Avalanche Association Weather Network | Yukon Avalanche Association | Station data | 2 sites | Point data | Since 2011, 2018 | None | None | details |
| White Pass Railway & River Divisions dataset, Yukon | Yukon Research Centre | Station data | 25 stations, from coast to interior Yukon - Skagway to Dawson and Wernecke | Point data | Earliest start Apr. 1902, latest finish Dec. 1957: median 25 yrs, max. 55.7 yrs | Daily | Excel files | details |
| Kluane Lake Research Station | Arctic Institute of North America/University of Calgary | Station data | Single weather station | Point data | June 2017 to present | 30 min. | CSV; Excel files; NetCDF | details |
| name | source | data type | spatial domain | spatial resolution | temporal coverage | time step | data format | |
|---|---|---|---|---|---|---|---|---|
| CanSWE | CRD/ECCC | Station data | Canada | Point data; Transects, network peaks at ~2000 surveys in period 1965 -1985; most data located south of ~55 N | 1928 - 2020 | Daily | NetCDF | details |
| AMSR-E Historical Algorithm | NSIDC | Satellite data | The Northern Hemisphere | 25 km EASE (equal area) | 2002-06-19 to 2010-08-27 | Daily; Weekly; Monthly | HDF | details |
| AMSR-E Operational Algorithm | NSIDC | Satellite data | The Northern Hemisphere | 25 km EASE (equal area) | 2010-08-27 to 2011-10-3 | Daily; Weekly; Monthly | HDF | details |
| GlobSnow | ESA | Gridded hybrid data: observations, satellite | The Northern Hemisphere | 25 km EASE1 (equal area) | 1979-2018 | Daily; Weekly; Monthly | Matlab; NetCDF | details |
| Snow CCI | ESA | Gridded hybrid data: observations, satellite | The Northern Hemisphere | 25 km; 12.5 km EASE2 | 1979-2018 | Daily | NetCDF | details |
| name | source | data type | spatial domain | spatial resolution | temporal coverage | time step | data format | |
|---|---|---|---|---|---|---|---|---|
| Crocus-ERA5 | Météo-France | Model based on reanalyses | Global | 0.25ᵒ x 0.25ᵒ | Jan 1981-- present (ongoing, annual updates) | Daily | NetCDF | details |
| ERA5-Land | ECMWF | Land surface reanalysis/model | Global | 0.1° x 0.1° (9 km) | Jan 1950-- present (ongoing, ~3 month latency) | Hourly; Monthly | GRIB; NetCDF | details |
| NARR | NCEP | Regional reanalysis | North America | 32 km x 32 km | 1979/01/01 to May 31, 2021 | 3h; Daily; Monthly | GRIB; NetCDF | details |
| RDRSv2.1 | CCMEP/ECCC | Regional reanalysis | North America | 10 km x 10 km | 2000 - 2017,(1980 - 1999 pending) | Hourly | RPN | details |
| NLDAS LSM | NASA | Land surface reanalysis/model | Central North America (25-53 North). | 0.125 deg. | 1979 - present | Hourly; Monthly | GRIB | details |
| ERA5 | ECMWF | Global atmospheric reanalysis | Global | 0.25ᵒ x 0.25ᵒ | 1950 - present | Hourly; Monthly | GRIB; NetCDF | details |
| ASRv2 | Byrd Polar Research Center/The Ohio State University; UCAR/NCAR | Regional reanalysis | Arctic | 15 km x 15 km | 2000/01 to 2016/12 | 3h; Monthly | NetCDF | details |
| MERRA-2 | NASA | Global atmospheric reanalysis | Global | 0.625ᵒ x 0.5ᵒ | 1980-present | Hourly; Daily; Monthly | NetCDF | details |
| JRA-55 | JMA | Global atmospheric reanalysis | Global | 0.6258ᵒ x 0.6258ᵒ | 1958 - present | 3h; 6h; Monthly | GRIB | details |
| 20CRv3 | CIRES; NOAA; DOE | Global atmospheric reanalysis | Global | T254 (approximately 75 km at the equator) | 20CRv3.SI is available for years 1836-1980 and 20CRv3.MO is available for years 1981-2015 | 3h; Daily; Monthly | NetCDF | details |
| CFSR + CFSv2 | NCEP | Global atmospheric reanalysis | Global | 0.5° x 0.5° | 1979-2010 (CFSR)2011-present (CFSv2) | Hourly; 6h; Monthly | GRIB | details |
| GLDAS-2.0 (Noah LSM) | NASA | Land surface reanalysis/model | Global | 0.25ᵒ x 0.25ᵒ | 1948-2014 | 3h; Monthly | GeoTIFF; KMZ; NetCDF | details |
| Liston and Hiemstra (2011) dataset | UCAR/NCAR | Model based on reanalyses | Northern Hemisphere land area, north of ~55 N | 10 km x 10 km | 1979-2009 | 3h | CTL; Gdat | details |
| USDA snow courses | NRCS/US Dept. of Agriculture | Station data | Western US, Canada, and Alaska | Point data; 1,111 courses concentrated in mountains of western USA, Canada and Alaska | 1935-- | Monthly | None | details |
| Yukon Snow Survey Network | Department of Environment/Yukon Government | Station data | Yukon-wide; 57 active sites, 28 discontinued | Point data | Variable(~1975-05-01 - present) | One or several times per year | CSV | details |
| Northwest Territories dataset | Department of Environment and Natural Resources/GNWT | Station data | Northwest Territories Wide; approx 67 sites. | Point data | Information not available | One or several times per year | None | details |
| Scotty Creek Research Site, Northwest Territories | Bill Quinton/Wilfred Laurier University | Station data | Scotty Creek, Deh Tah region of Northwest Territories | Point Data | Since 1995 | None | None | details |
| Trail Valley Creek Research Site, Northwest Territories. | Dr. Phil Marsh/Wilfred Laurier University | Station data | Trail Valley Creek, Northwest Territories | Point data | Since 1991: not every year included | Annual | None | details |
| Havikpak Creek Research Site, Northwest Territories | Dr. Phil Marsh/Wilfred Laurier University | Station data | Havikpak Creek, Northwest Territories | Point data | Since 1991: not every year included | Annual | None | details |
| Yukon Water Resources Meteorological Network | Yukon Government | Station data | Of a total of 4 active AWSs, 2 have 30+ yr records - Tagish (09AA-M1), Withers Lake (09DB-M1). | Point data | Installed 1989, 1991 | None | None | details |
| Wolf Creek Research Basin, Yukon | Dr. Sean Carey/McMaster University | Station data | Wolf Creek, Yukon | Point data | Various: observatory has been operating since 1992 Note, though, that some individual instrumentation arrays have been more permanent, than others- the latter more sporadic, to support specific projects. | Monthly | None | details |
| Baker Creek Research Site, Northwest Territories | Chris Spence/ECCC; University of Saskatchewan; Carleton University | Station data | Baker Creek, Northwest Territories | Point data | from 2004 | Annual | None | details |
| name | source | data type | spatial domain | spatial resolution | temporal coverage | time step | data format | |
|---|---|---|---|---|---|---|---|---|
| ERA5-Land | ECMWF | Land surface reanalysis/model | Global | 0.1° x 0.1° (9 km) | Jan 1950-- present (ongoing, ~3 month latency) | Hourly; Monthly | GRIB; NetCDF | details |
| NARR | NCEP | Regional reanalysis | North America | 32 km x 32 km | 1979/01/01 to May 31, 2021 | 3h; Daily; Monthly | GRIB; NetCDF | details |
| ASRv2 | Byrd Polar Research Center/The Ohio State University; UCAR/NCAR | Regional reanalysis | Arctic | 15 km x 15 km | 2000/01 to 2016/12 | 3h; Monthly | NetCDF | details |
| MERRA-2 | NASA | Global atmospheric reanalysis | Global | ½° latitude x ⅝° longitude | 1980-present | Hourly; Daily; Monthly | NetCDF | details |
| 20CRv3 | CIRES; NOAA; DOE | Global atmospheric reanalysis | Global | T254 (approximately 75 km at the equator) | 20CRv3.SI is available for years 1836-1980 and 20CRv3.MO is available for years 1981-2015 | 3h; Daily; Monthly | NetCDF | details |
| CFSR + CFSv2 | NCEP | Global atmospheric reanalysis | Global | 0.5° x 0.5° | 1979-2010(CFSR)2011-present (CFSv2) | Hourly; 6h; Monthly | GRIB | details |
| MODIS Terra / Aqua | NSIDC; NASA | Interpreted/diagnosed from optical imagery | Global | 500 m (nominal) on sinusoidal grid 0.05° Climate Modelling Grid | Terra: 24 Feb. 2000 - present Aqua: 4 Jul. 2002 - present | Sub-daily; Daily; Monthly | GeoTIFF; HDF | details |
| NOAA Climate Data Record of Northern Hemisphere Snow Cover Extent version 1 | NCEI/NOAA; Rutgers U. | Interpreted/diagnosed from optical imagery | Northern Hemisphere | 89 x 89 grid: cell size varies with latitude, from ∼10,700 km² near lat. 0° to ∼41,800 km² near lat. 90°N | Record begins in 1966 (but more reliable from 1970) | Weekly | NetCDF | details |
| Rutgers Northern Hemisphere 24 km Weekly Snow Cover Extent | Rutgers U.; NSIDC | Interpreted/diagnosed from optical imagery | Northern Hemisphere | 1024 x 1024 grid: Cell areas range from ~159 km2 at the equator to ~651 km2 near the pole | Sept 1980 - present | Weekly | NetCDF | details |
Scales of snow data
While many processes combine at a variety of scales to influence the spatial heterogeneity of snow, in the following description we roughly divide snow depth and SWE datasets into two groups based on a single scale break: L ~ 1 km:
- Datasets constructed from point data or grouped data representative up to a spatial scale of several hundred metres.
- Datasets representative of spatial scales L >1 km.
Snow data characterized by spatial scales <1 km are influenced by wind redistribution and local scale topography or vegetation (e.g., wind-related drifting of snow or deep vs shallow snow in low vs high relief; Brown et al. 2010; Mott et al. 2018). The influence of these processes begins to reduce at scales of several kilometres as these effects are averaged out and the primary source of spatial variability becomes the distribution of synoptic scale events (i.e. where and when snowfall occurs in a given year, and where and when melt occurs). The relative influence of these different processes means that comparisons between point data and gridded output is frequently not possible as they sample fundamentally different variability.
The above separation is appropriate for regions of non-complex topography. In mountainous regions, the terrain has more intrinsically complex variability on smaller scales (e.g., small scale variability in slope and aspect). Furthermore, the larger-scale aspects of mountains act to alter the local weather patterns as well: e.g., precipitation is preferentially deposited on windward slopes, while leeward slopes experience precipitation shadows; the leeward slopes also experience wind-related warming due to adiabatic effects, while the higher elevations experience much colder temperatures, which together lead to complex spatial patterns in precipitation phase and melt. Because these effects operate at a hierarchy of spatial scales, mountainous regions (i.e. defined by complex topography, high terrain slope, high elevations, or a combination of these), intrinsically require finely resolved observational data and are not well represented by most gridded data presently available.
Appropriate use of data:
Based on the above information, we recommend separate consideration of small (<1 km) vs large (>1 km) scale snow depth and SWE data sets. Typically, datasets based on point data or small-scale spatial data will be temporally and spatially discontinuous. Datasets based on spatial scales > 1 km, even if based on point information, have typically been merged into more spatially or temporally continuous formats. This merging process will average out some of the variability due to fine-scale processes yielding products more consistent with large scale gridded datasets. Gridded datasets based on satellite data or reanalysis or model output will also be fully or near-fully continuous, both spatially and temporally.
Uses of point data: for the very specific locations these data represent, they should reflect accurate amounts of snow depth or SWE (excepting standard measurement errors and/or recording errors) and its local scale variability to the extent it is temporally sampled. Overall, observations are too spatially and temporally sparse in the Canadian North to properly assess whether one gridded/modelled dataset is better or worse than another for this region, particularly for SWE and snow depth.
Uses of gridded data: depending on their sources, these data may or may not reflect accurate amounts of snow and may or may not represent snow variability accurately as follows:
- Merged from point/in situ observations to larger scales (e.g., ANUSPLIN snow depth): should accurately reflect amounts and larger-scale variability (although may depend on the quantity of measurements incorporated in the process of merging to larger scales).
- Satellite/in situ blended SWE data (e.g., GlobSnow all versions and Snow CCI all versions): should reflect accurate amounts and larger-scale variability depending on the local amount of in-situ data ingested – this are less in more northern regions.
- Snow presence is reliably detected by satellites with optical sensors (versus passive microwave sensors used to estimate SWE or snow depth). Hence, visible satellite observations from MODIS, IMS, or integrated data sets like the NOAA Climate Data Record can be used to validate snow cover fraction and snow extent over the Canadian North (e.g., Wang et al. 2005).
- Reanalysis/offline historically forced models: will not necessarily reflect accurate amounts (due to poorly constrained snow mass balance), but has been shown to accurately reflect spatial and temporal variability (excepting temporal inhomogeneities in some datasets; known inhomogeneities are noted for particular datasets). Different datasets tend to have uncorrelated error components, therefore averaging multiple datasets together will typically yield more accurate results, but systematic biases present in all datasets would still remain. This result (lower RMSE, better correlation) was shown for Northern Hemisphere SWE in Mortimer et al. (2020). While the validation data used for this study includes locations in tundra and other land classes present in northern Europe/Siberia and Canada/Alaska, the majority of stations are situated in more southern locations. Therefore, conclusions are true for the hemispheric view, and could potentially apply to the Canadian North. A similar method can be used with focus on the Canadian North and also on other variables (e.g., snow depth, snow cover) to confirm the findings for this region.
- Climate model output: will not necessarily reflect accurate amounts (again due to poor constraints on the balance of snowfall, melt and sublimation); will not reflect historical spatial or temporal variability due to the strong influence of natural variability on the distribution of synoptic scale events (typical climate models are not configured to replicate historical natural variability). Output should represent the climatological spatial pattern of snow, and may represent long-term trends in regions and over periods of time for which climate forcing dominates the trend signal over the influence of natural variability.
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