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. The Snow Ensemble Uncertainty Project (SEUP) is an effortto establish a baseline characterization of snow water equivalent (SWE)uncertainty across North America with the goal of informing global snowobservational needs. An ensemble-based modeling approach, encompassing asuite of current operational models is used to assess the uncertainty inSWE and total snow storage (SWS) estimation over North America during the2009–2017 period. The highest modeled SWE uncertainty is observed inmountainous regions, likely due to the relatively deep snow, forcinguncertainties, and variability between the different models in resolving thesnow processes over complex terrain. This highlights a need forhigh-resolution observations in mountains to capture the high spatial SWEvariability. The greatest SWS is found in Tundra regions where, even thoughthe spatiotemporal variability in modeled SWE is low, there is considerableuncertainty in the SWS estimates due to the large areal extent over whichthose estimates are spread. This highlights the need for high accuracy insnow estimations across the Tundra. In midlatitude boreal forests, largeuncertainties in both SWE and SWS indicate that vegetation–snow impacts area critical area where focused improvements to modeled snow estimationefforts need to be made. Finally, the SEUP results indicate that SWEuncertainty is driving runoff uncertainty, and measurements may be beneficialin reducing uncertainty in SWE and runoff, during the melt season at highlatitudes (e.g., Tundra and Taiga regions) and in the western mountainregions, whereas observations at (or near) peak SWE accumulation are morehelpful over the midlatitudes.

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