Global Snow-Water-Equivalent Depth Coefficient-of-Variation Classification

• Main Author: Glen E. Liston
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center
• Subjects: Snow Depth; Models/Analyses; Arctic
• Online Access: https://doi.org/10.5065/D6GF0RMX

This data set provides a global distribution of nine subgrid snow-depth-variability categories and a coefficient of variation applicable to each category, as the result of the Subgrid SNOW Distribution (SSNOWD) submodel that defines subgrid snow-depth and snow-cover variability. This data set provides the distribution of those nine categories and their coefficient-of-variation values on a global, 2.5 arc-min latitude-longitude (approximately 5-km) grid. The time period covered is 1 January 1994 through 1 January 2004. The SSNOWD submodel was formulated to improve the depiction of autumn through spring land-atmosphere interactions and feedbacks within global weather, climate, and hydrologic models. From both atmospheric and hydrologic perspectives, the subgrid snow-depth distribution is an important quantity to account for within large-scale models. In the natural system, these subgrid snow-depth distributions are largely responsible for the mosaic of snow-covered and snow-free areas that develop as the snow melts, and the impacts of these fractional areas must be quantified in order to realistically simulate grid-averaged surface fluxes. SSNOWD's formulation incorporates observational studies showing that snow distributions can be described by a lognormal distribution and the snow-depth coefficient-of-variation (CV). Using an understanding of the physical processes that lead to these observed snow-depth variations, a global distribution of nine subgrid snow-depth-variability categories was developed, and coefficient-of-variation values were assigned to each category based on published measurements. Data are in binary format.