Regional Climate Model Simulations Suggest Global Products Fail to Capture Mountain Snow

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 among 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 and snow water equivalent, coarse resolution gridded datasets - as is common in global products - may poorly represent actual spatial and temporal variability. Here we compare regional climate model simulations over mountainous regions of North America from the Weather Research and Forecasting (WRF) model with currently available global products, including GLDAS, MERRA, and ERA-Interim. WRF results suggest up to an order of magnitude more snow in the mountains than shown in global products. Hundreds of missing km3 of montane snow storage would cause biases in both the water and energy budgets in the global models. Our results suggest that mountain snow storage estimates must be improved in order to have a better understanding of the global water cycle.