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Abstract

Snow albedo is an essential factor in the land surface energy balance and the water cycle. It is usually parameterized as functions of snow-related variables in land surface models (LSMs). However, comparing with albedo schemes in the CLM and Noah-MP LSMs, the default snow albedo scheme in the widely used Noah LSM shows evident drawbacks in land-atmosphere interactions simulations during an extreme snow process on the complex topographic Tibetan Plateau (TP). We firstly demonstrate that the improved Noah snow albedo scheme includes MODIS albedo products and explicit considers snow depth as an additional factor. It performs well in relation to near-surface meteorological elements estimates during an extreme snow process. Then, we comprehensively evaluate the performance of the improved snow albedo scheme in WRF coupled with Noah LSM in simulating the additional eight heavy snow events on the TP against in-situ observations, MODIS albedo and IMS snow cover products. It reveals that the improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates, by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow cover. The averaged RMSE relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27% (5%), 32% (69%), 13% (17%) and 21% (108%) respectively. These results demonstrate the strong potential of the improved snow albedo parameterization scheme for heavy snow events simulations on the TP.