Snow Ensemble Uncertainty Project (SEUP): Quantification of snow water equivalent uncertainty across North America via ensemble-based land surface modeling

Well-characterized snow water equivalent (SWE) uncertainty is critical in seasonal to decadal climate prediction systems as well as for planning future snow satellite missions. In this study, an ensemble-based land surface modeling approach is applied to characterize the sources and regions of high model uncertainty across North America. Four different land surface models of varying complexity are driven using three different forcing datasets. Model simulations are conducted across North American over multiple winter snow seasons (2009-2017) at a 5-km spatial resolution using the NASA Land Information System (LIS). The relatively coarse resolution meteorological inputs are then spatially downscaled using a mix of lapse-rate, slope-aspect, and climatology-based approaches. The Hydrological Modeling and Analysis Platform (HyMAP) in LIS is the used to derive estimates of streamflow. In this study, we characterize the dominant factors that govern the spatial variability and seasonality of SWE uncertainty by using latent factor analysis. The contributing role of various landscapes and precipitation regimes on the modeled SWE estimates will also be quantified. The ensemble-based land surface modeling will provide when and where the models agree and disagree. The results of the study are expected to guide the selection of sites for future snow field campaigns as well as provide useful guidance towards the planning of future satellite missions.