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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This study aimed to investigate the relationship between the boreal summer Silk Road Pattern (SRP) and the atmospheric heat (<Q1>) over the Tibetan Plateau (TP) region, using 5 reanalysis datasets over the period 1979–2019. Our results indicate an interdecadal change of boreal summer SRP over the Eurasian region, with a regime shift in the spatial structure at around 1997. Meanwhile, the summer <Q1> anomaly also shows a clear interdecadal increasing trend over the TP region, which is highly correlated with the interdecadal variation of the SRP. The impact of the SRP on the summer <Q1> was also investigated. The regime shift of the SRP would have generated circulation anomalies over the Lake Baikal region in 500 hPa, which would have inhibited moisture transport across the eastern boundary of the TP. Meanwhile, Indian Summer Monsoon (ISM) would also transport water vapor through the southern boundary of the TP and increased the contents of water vapor in the TP. Associated with this increase in moisture, the change of vertical motion would result in large plenty of precipitation, which released latent heat and enhanced <Q1> in summer. Thus, the regime shift in summer SRP was an important factor contributing to changes in summer <Q1> over the TP in recent decades.

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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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Snowfall and the subsequent evolution of the snowpack play important roles in the cryospheric and hydrospheric processes that occur on the Tibetan Plateau (TP). Current literature provides scarce evidence covering the sensitivity of solid precipitation to land surface physics schemes and initial and boundary conditions on the TP. Six numerical experiments using the Weather Research and Forecasting (WRF) model were conducted to simulate a snow event over the TP in March 2017. Different land surface physics schemes, that is, Community Land Model (CLM), Noah, and Noah-MP, and initial and boundary conditions provided by atmospheric reanalysis data sets, that is, the National Centers for Environmental Prediction-FNL and ERA-Interim data sets, were applied in sensitivity analyses. The observed near-surface air temperature, snow depth, and snow water equivalent (SWE) values were used to evaluate each model's performance. The results demonstrate that (1) the sensitivity of the near-surface air temperature to land surface physics schemes is greater than it is to both the initial and boundary conditions; (2) the best performance is achieved when applying WRF + CLM with a root-mean-square error of 8.4 °C, a mean absolute deviation of 7.3 °C, a correlation coefficient of 0.75, and a spatial correlation coefficient of ~0.5 to air temperature estimates. A potentially important factor appears to be the advanced parametrization of albedo in the CLM scheme; (3) the advanced land surface schemes in the WRF model describes the physics of cryospheric and hydrospheric processes in detail, and the land surface response is determined by multiple variables and parameters in such schemes. The spatial patterns in such variables and parameters determined the detailed spatial variabilities observed in snow cover and amount and its temporal evolution. The WRF model overestimates, however, the intensity and extent of snow depth and SWE; (4) simulations of solid precipitation are more accurate when applying CLM or Noah-MP + ERA-Interim in WRF; and (5) WRF performance with regard to SWE estimates clearly depends upon the discrimination of lighter from heavier snowfall.@en