Representation of vegetation effects on the snow-covered albedo in the Noah land surface model with multiple physics options

Abstract. Snow albedo plays a critical role in calculating the energy budget, but parameterization of the snow surface albedo is still under great uncertainty. It varies with snow grain size, snow cover thickness, snow age, forest shading factor and other variables. Snow albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, the vegetation effect rarely exerts on the surface albedo in winter in East Asia with only these parameters. Thus, we investigated the vegetation effects on the snow-covered albedo from observations and evaluated the model improvement by considering such effect. We found that calculation of albedo without proper reflection of the vegetation effect is mainly responsible for the large positive bias in winter. Therefore, we developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the winter albedo. As a result, the Noah-MP's performance in albedo has been significantly improved – RMSE is reduced by approximately 73%.