Abstract. The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation over North America during the 2009&ndashl2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.

Abstract A field campaign was conducted October 30th to November 13th, 2015 with the intention of capturing diurnal soil freeze/thaw state at multiple scales using ground measurements and remote sensing measurements. On four of the five sampling days, we observed a significant difference between morning (frozen scenario) and afternoon (thawed scenario) ground-based measurements of the soil relative permittivity. These results were supported by an in situ soil moisture and temperature network (installed at the scale of a spaceborne passive microwave pixel) which indicated surface soil temperatures fell below 0 °C for the same four sampling dates. Ground-based radiometers appeared to be highly sensitive to F/T conditions of the very surface of the soil and indicated normalized polarization index (NPR) values that were below the defined freezing values during the morning sampling period on all sampling dates. The Scanning L-band Active Passive (SLAP) instrumentation, flown over the study region, showed very good agreement with the ground-based radiometers, with freezing states observed on all four days that the airborne observations covered the fields with ground-based radiometers. The Soil Moisture Active Passive (SMAP) satellite had morning overpasses on three of the sampling days, and indicated frozen conditions on two of those days. It was found that >60% of the in situ network had to indicate surface temperatures below 0 °C before SMAP indicated freezing conditions. This was also true of the SLAP radiometer measurements. The SMAP, SLAP and ground-based radiometer measurements all indicated freezing conditions when soil temperature sensors installed at 5 cm depth were not frozen.