Comparison of Global Mountain Snow Storage Estimates and the Prospect of Improvement with Regional Climate Modeling

Seasonal snow accumulation and melt connects the energy budget and water balance through snowmelt runoff, sublimation, and the snow-albedo feedback. Despite the importance of snow, an estimate of global snow storage is not well constrained, particularly for regions with complex topography. Global datasets, whether from models, satellite products, or reanalyses, are one of the few options for obtaining a large-scale snow storage estimate for mountainous areas. However, for variables with a high degree of spatial variability, such as snow depth or snow water equivalent, gridded datasets may smooth over some of the variations. Here we aim to understand differences in global data products and their estimates of montane snow storage. Total seasonal snow volumes from GLDAS, GlobSnow, MERRA, ERA-Interim, and VIC are averaged over 1980-2010 (where applicable) and compared. Though each comparison dataset is coarse, with resolution ranging from 25 km to 1 degree or more, their estimates are a first step to understanding the amount of seasonal snow storage. Future improvements in modeling will likely allow for a fine scale estimate of global montane snow storage. We also explore the possibility of utilizing fine resolution (~3 km) regional climate model simulations (with the Weather Research and Climate (WRF) model) for estimating montane snow. Preliminary results suggest the coarser datasets underestimate snow storage, with estimates up to an order of magnitude smaller than WRF estimates.