Impact of topography and meteorological forcing on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC)

作者：Libo Wang, Lawrence Mudryk, Joe R. Melton, Colleen Mortimer, Jason N. S. Cole, Gesa Meyer, Paul Bartlett, Mickaël Lalande · 发表于：Geoscientific model development · 年份：2025 · DOI：10.5194/gmd-18-6597-2025 · 被引用次数：3 · 研究领域：Cryospheric studies and observations、Climate change and permafrost、Geology and Paleoclimatology Research

Abstract. Our study evaluates the impacts of an alternate snow cover fraction (SCF) parameterization on snow simulation in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). Three reanalysis-based meteorological datasets are used to drive the model to account for uncertainties in the forcing data. While the default parameterization assumes a simple linear relationship between SCF and snow depth with no dependence on topography, the alternate parameterization accounts for the topographic effects of sub-grid terrain on SCF. We show that the alternate parameterization improves SCF simulated in CLASSIC during winter and spring in mountainous areas for all three choices of meteorological datasets. Annual mean bias, unbiased root mean squared area, and correlation improve by 75 %, 32 %, and 7 % when evaluated with MODIS SCF observations over the Northern Hemisphere. We also demonstrate that the improvements to simulated SCF lead to further improvements in variables related to surface radiation, energy fluxes, and the water cycle. Finally, we link relative biases in the meteorological forcing data to differences in simulated snow water equivalent and SCF. Assessment of simulations with different combinations of SCF parameterizations and meteorological datasets reveals the large impact of meteorological forcing on snow simulation in CLASSIC. Two out of the three meteorological datasets were bias-adjusted using observation-based datasets. However, simulations ...