Improved Cross-Scale Snow Cover Simulations by Developing a Scale-Aware Ground Snow Cover Fraction Parameterization in the Noah-MP Land Surface Model

Snow cover fraction (SCF) accuracy in land surface models (LSMs) impacts the accuracy of surface albedo and land-atmosphere interactions. However, SCF is a large source of uncertainty, partially because of the scale-dependent nature of snow depletion curves that is not parameterized by LSMs. Using the spatially and temporally complete observationally-informed STC-MODSCAG and Snow Data Assimilation System data sets, we develop a new scale-aware ground SCF parameterization and implement it into the Noah-MP LSM. The new scale-aware parameterization significantly reduces ground SCF errors and the scale-dependence of errors in the western U.S (WUS) compared with the baseline ground SCF formulation. Specifically, the baseline formulation overestimates ground SCF by 4%, 6%, 9%, and 12% at 1-km, 3-km, 13-km, and 25-km resolutions in the WUS, respectively, whereas biases from the enhanced scale-aware scheme are reduced to 0%–2% in box model simulations and do not exhibit a relationship with spatial scales. Noah-MP simulations using the scale-aware parameterization have smaller mean (peak) ground SCF biases than the baseline simulation by 1%–2% (3%–5%), with spatiotemporal variability depending on land cover, topography, and snow depth. Noah-MP simulations using the enhanced scale-aware parameterization remove the baseline WUS surface albedo overestimates of 0.01–0.03 in the 1-km to 25-km resolution simulations, relative to Moderate Resolution Imaging Spectroradiometer retrievals. The Noah-MP ground SCF and surface albedo improvements due to the scale-aware parameterization are found across most land cover classifications and elevations, indicating the enhanced ground SCF scheme can improve simulated snowpack and surface energy budget accuracy across a variety of WUS landscapes.