Correcting WRF Cold Temperature Bias Over the Qilian Mountains Using MODIS Dynamic Albedo

As a critical parameter influencing the land-atmosphere radiative budget, variations in surface albedo significantly affect regional and global climate systems through complex feedback mechanisms. The Qilian Mountains have experienced notable changes in snow cover and vegetation due to global warming, resulting in substantial alterations in surface albedo and subsequent impacts on the climate system. However, the scarcity of high-altitude data limits observational analysis. Although the Weather Research and Forecasting (WRF) model demonstrates a strong capability in simulating and reproducing climate processes, its default albedo parameters fail to capture recent variations in the majority of the Qilian Mountains, leading to systematic cold bias in temperature simulations. To address this issue, we employ WRF model simulations comparing control (CTL, default parameters) and sensitivity (MOD, MODIS albedo) experiments to assess improvements in cold temperature bias and quantify the impact of albedo on relevant factors. Our results demonstrate that integrating MODIS albedo products effectively reduces the WRF model's systematic cold temperature bias, improving the annual mean bias (MB) by 1.04 °C and the root mean square error (RMSE) by 0.45 °C. This improvement is primarily concentrated in regions and months where the model’s original snow cover—and consequently surface albedo—was most overestimated. The most substantial corrections occur during the core winter and early spring months: MB is reduced by 2.67 °C in January, 2.87 °C in February, 2.25 °C in March, and 1.99 °C in November, with corresponding RMSE reductions of 1.12 °C, 1.69 °C, 1.57 °C, and 0.92 °C. These targeted results underscore a corrective physical mechanism whereby reduced surface albedo increases net radiation, enhancing sensible heat flux and ultimately raising surface air temperatures. This work contributes to the optimization of parameterization in mountain climate simulations and the mechanistic analysis of surface-climate feedbacks.