Snow water equivalent (SWE) distribution at fine spatial scales (≤ 10 m) is difficult to estimate due to modeling and observational constraints. However, the distribution of SWE throughout the spring snowmelt season is often correlated to the timing of snow disappearance. Here, we show that snow cover maps generated from PlanetScope’s constellation of Dove Satellites can resolve the 3 m date of snow disappearance across seven alpine domains in California and Colorado. Across a 5-year period (2019 – 2023), the average uncertainty in the date of snow disappearance, or the period of time between the last date of observed snow cover and the first date of observed snow absence, was 3 days. Using a simple shortwave-based snowmelt model calibrated at nearby snow pillows, the PlanetScope date of snow disappearance could be used to reconstruct spring snow water equivalent (SWE). Relative to lidar SWE estimates, the SWE reconstruction had a spatial coefficient of correlation of 0.75, and SWE spatial variability that was biased by 9%, on average. SWE reconstruction biases were then improved to within 0.04 m, on average, by calibrating snowmelt rates to track the spring temporal evolution of fractional snow cover observed by PlanetScope, including fractional snow cover over the full modeling domain, and across domain subsections where snowmelt rates may differ. This study demonstrates the utility of fine-scale and high-frequency optical observations of snow cover, and the simple and annually repeatable connections between snow cover and spring snow water resources in regions with seasonal snowpack.

Global climate models often simulate atmospheric conditions incorrectly due to their coarse grid resolution, flaws in their dynamics, and biases resulting from parameterization schemes. Here we document the magnitude and extent of minimum temperature biases in the CMIP6 model ensemble, relative to ERA5. Bias in the southern Cascadia region (i.e. Pacific Northwestern United States and southwestern British Columbia, Canada, spanning from the coast to the Rocky Mountains) stands out relative to the rest of North America, with some models showing a bias in excess of -10°C in the 1st percentile of daily winter minimum temperature. During the coldest minimum temperature days, the CMIP6 models show an anomalous high in mean sea level pressure in the Northeast Pacific – an atmospheric blocking pattern that is also present in ERA5. While this atmospheric blocking pattern is typically concurrent with cold temperatures across much of North America, terrain barriers such as the Rockies and Cascades prevent the cold air from reaching the Pacific Northwest in observation and reanalysis. Our results suggest that the bias in CMIP6 minimum temperatures is a result of unresolved topography in the Rockies and Cascade mountain ranges, such that the terrain does not adequately block cold air advection from the interior of the continent.