| description abstract | To improve the depiction of autumn through spring land?atmosphere interactions and feedbacks within regional and global weather, climate, and hydrologic models, a Subgrid SNOW Distribution (SSNOWD) submodel that explicitly includes subgrid snow-depth and snow-cover variability has been developed. 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. 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. In addition, SSNOWD adopts the physically realistic approach of performing separate surface-energy-balance calculations over the snow-covered and snow-free portions of each model grid cell and weighing the resulting fluxes according to these fractional areas. Using a climate version of the Regional Atmospheric Modeling System (ClimRAMS) over a North American domain, SSNOWD was compared with a snow-cover formulation similar to those currently used in most general circulation models. The simulations indicated that accounting for snow-distribution variability has a significant impact on snow-cover evolution and associated energy and moisture fluxes. | |
