An observation-based formulation of snow cover fraction and its evaluation over large North American river basins

[1] Snow cover strongly interacts with climate through snow albedo feedbacks. However, global climate models still are not adequate in representing snow cover fraction (SCF), i.e., the fraction of a model grid cell covered by snow. Through an analysis of the advanced very high resolution radiometer (AVHRR) derived SCF and the Canadian Meteorological Centre (CMC) gridded snow depth and snow water equivalent (SWE), we found that the SCF–snow depth relationship varies with seasons, which may be approximated by variations in snow density. We then added snow density to an existing SCF formulation to reflect the variations in the SCF–snow depth relationship with seasons. The reconstructed SCF with the gridded snow depth and SWE by employing this snow density–dependent SCF formulation agrees better with the AVHRR-derived SCF than other formulations. The default SCF formulation in the National Center for Atmospheric Research community land model (CLM), driven by observed near-surface meteorological forcings, simulates a smaller SCF and a shallower snow depth than observations. Implementation of the new SCF formulation into the NCAR CLM greatly improves the simulations of SCF, snow depth, and SWE in most North American (NA) river basins. The new SCF formulation increases SCF by 20–40%, decreases net solar radiation by up to 20 W m , and decreases surface temperature by up to 4 K in most midlatitude regions in winter and at high latitudes in spring. The new scheme reproduces the observed SCF, snow depth, and SWE in terms of interannual variability and interbasin variability in most NA river basins except for the mountainous Columbia and Colorado River basins. It produces SCF trends similar to that of AVHRR. However, it produces greater decreasing trends in ablation seasons and smaller increasing trends in accumulation seasons than those of the CMC snow depth and SWE.