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Revisiting the global seasonal snow classification: An updated dataset for earth system applications

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Author(s): Matthew Sturm, Glenn E. Liston

Document Type:
Publisher: Journal of Hydrometerology
Published Year: 2021
Volume: 22
Pages: 2917 to 2938
DOI Identifier: 10.1175/JHM-D-21-0070.1
ISBN Identifier:
Keywords: Snow Climate classification regimes Snow cover

Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land cover. When introduced in 1995, the finest-resolution global datasets of these variables were on a 0.5° × 0.5° latitude–longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arc-s × 10-arc-s latitude–longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude–longitude (approximately 10 km) gridded meteorological climatologies [1981–2019, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, 5th Generation Land (ERA5-Land)] using MicroMet, a spatially distributed, high-resolution, micrometeorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification, Version 1) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

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Suggested Citations:
In Text Citation:
Sturm and Liston (2021) or (Sturm and Liston, 2021)

References Citation:
Sturm, M. and G.E. Liston, 2021, Revisiting the global seasonal snow classification: An updated dataset for earth system applications: Journal of Hydrometerology, Vol. 22, pp. 2917-2938, doi: 10.1175/JHM-D-21-0070.1.