Improved Cross‐Scale Snow Cover Simulations by Developing a Scale‐Aware Ground Snow Cover Fraction Parameterization in the Noah‐MP Land Surface Model - Ronnie Abolafia‐Rosenzweig; Cenlin He; Tzu‐Shun Lin; Michael Barlage; Karl Rittger (Journal of Advances in Modeling Earth Systems)
Abstract 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.