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

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 forced by the dataset without bias correction outperform relative to simulations forced by datasets with bias correction, suggesting that there are large uncertainties in the observation-based datasets and/or methods used for bias correction. This study underscores the importance of accounting for topographic effects of sub-grid terrain and accurate meteorological forcing on snow simulation in land surface models.