Improving snow albedo parameterization scheme based on remote sensing data

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Snow albedo is an important parameter for determining the energy budget in high-latitude regions in winter, but it is often overestimated in the model and causes a cold bias of air temperature. To address this issue, we propose a new approach to improve the Canadian Land Surface Scheme (CLASS) snow albedo parameterization in Noah-MP by updating the initial value of vegetation snow albedo based on remote sensing data. The modified CLASS scheme resulted in a 0.04 decrease in root mean squared error (RMSE) of the albedo, and the mean bias was reduced by 0.07, representing relative decreases of 37.3% and 89.1%, respectively. In addition, the simulation errors for the upward radiation and sensible heat flux were significantly reduced. Furthermore, the bias and RMSE of air temperature were reduced by 12.9% and 7.9%, respectively, compared to those in the original CLASS scheme. These qualitative and quantitative analyses demonstrated that our modified CLASS snow albedo parameterization scheme has a robust and positive impact on the simulation of snow cover areas. Our approach provides a preliminary investigation for improving snow albedo estimation, and we plan to conduct a widespread simulation and introduce new vegetation data into the model to improve the estimation of albedo in future research.