Meteorological variables (e.g., air temperature (T2), radiation flux, and precipitation) determine the evolution of glacier mass and characteristics. Observations of these variables are not available with adequate spatial coverage and spatiotemporal resolution over the Tibetan Plateau. Albedo is the key factor of net radiation and is determined by the land cover and snow-related variables. This study focuses on evaluating the performance of the albedo parameterization scheme in WRF coupled with Noah-MP in terms of glacio-meteorological variables, by conducting experiments applying the standard surface albedo scheme with the default vegetation and corrected to ice cover and the modified glacial albedo scheme to the Parlung No. 4 Glacier in the 2016 ablation season. In situ glacio-meteorological element observations and MODIS-retrieved albedo are selected to assess the performance of the model. The key results are as follows. First, compared to the air temperature bias of 1.56 °C in WRF applying the standard surface albedo scheme and the default vegetation cover, realistic land-use categories considerably reduce the model warm bias on the glacier. The model using realistic land-use categories yields similar T2 diurnal patterns to the observations, with a mean bias of only −0.5 °C, no matter which glacial albedo scheme is implemented. Second, the default glacial albedo scheme gives a rather high albedo value of 0.68, causing an apparent underestimation of the net shortwave radiation and net radiation; the modified glacial albedo scheme gives a mean albedo value of 0.35, close to the in situ observations, helping to relieve underestimations of net shortwave radiation and net radiation. Compared with the MODIS albedo of the glacier, WRF applying the default glacial albedo scheme apparently overestimates the albedo with a mean error of 0.18, while WRF applying the modified glacial albedo scheme slightly underestimates the albedo with a mean error of only −0.08. Third, the mean net radiation flux (142 W m−2) and high ground heat flux (182 W m−2) values that were estimated by WRF applying the corrected land cover and the modified glacial albedo scheme result in the heating of the glacier surface and subsurface, causing ice melt and the liquid water content to increase more quickly and preferentially, equating to an estimated ice thickness decrease of 1 m by mid-June in the ablation region. Our study confirms the ability of the WRF model to reproduce glacio-meteorological variables as long as a reasonable glacial albedo scheme and the corrected land cover is applied and provides a theoretical reference for researchers that are committed to further improvement of the glacial albedo scheme.@en

Snowfall and the subsequent evolution of the snowpack have a large effect on the surface energy balance and water cycle of the Tibetan Plateau (TP). The effects of snow cover can be represented by the WRF coupled with a land surface scheme. The widely used Noah scheme is computationally efficient, but its poor representation of albedo needs considerable improvement. In this study, an improved albedo scheme is developed using a satellite-retrieved albedo that takes snow depth and age into account. Numerical experiments were then conducted to simulate a severe snow event in March 2017. The performance of the coupled WRF/Noah model, which implemented the improved albedo scheme, is compared against the model’s performance using the default Noah albedo scheme and against the coupled WRF/CLM that applied CLM albedo scheme. When the improved albedo scheme is implemented, the albedo overestimation in the southeastern TP is reduced, reducing the RMSE of the air temperature by 0.7°C. The improved albedo scheme also attains the highest correlation between the satellite-derived and the model-estimated albedo, which provides for a realistic representation of both the snow water equivalent (SWE) spatial distribution in the heavy snowbelt (SWE > 6 mm) and the maximum SWE in the eastern TP. The underestimated albedo in the coupled WRF/CLM leads to underestimating the regional maximum SWE and a consequent failure to estimate SWE in the heavy snowbelt accurately. Our study demonstrates the feasibility of improving the Noah albedo scheme and provides a theoretical reference for researchers aiming to improve albedo schemes further.

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Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow-related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land-atmosphere interaction estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the Weather Research and Forecasting (WRF) and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. 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 event evolution. The averaged root mean square error (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 our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

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