Optimization of snow-related processes in Noah-MP land surface model over the mid-latitudes of Asian region

Snow plays a critical role in modulating surface energy, water cycles, and climate prediction. Optimizing snow-related parameterizations can enhance the model behaviors in simulating snow-related physical processes and reduce the cold bias observed in winter climate simulations in the Northern Hemisphere. In this study, the topographic complexity and wind speed were incorporated into the parameterization of the snow cover fraction (SCF) and newly fallen snow density (SFD) respectively within the Noah with multi-parameterization (Noah-MP) LSM to optimize the simulation of snow-related processes in the mid-latitude regions of East Asia. A control simulation and three sensitivity experiments were conducted to investigate and quantify the effects of topographic complexity and wind speed on the simulation of snow-related processes and land surface temperature (LST) against MODIS products. The results showed that modifications to the two schemes effectively mitigated the overestimation of snow cover and albedo, and alleviated cold biases in the most of study area. The influence of SFD scheme considering wind speed was more pronounced in regions with more snowfall and higher wind speed, while the SCF scheme considering topographic complexity showed a more widespread effect. The combination of these two modified schemes yielded the best performance. The mean biases of SCF, albedo, and LST over the entire study region with both modified schemes were reduced by 0.126 (∼63 %), 0.044 (∼41 %), and 0.584 °C (∼18 %), respectively. Their RMSEs were reduced by 0.119 (∼36 %), 0.036 (∼22 %), and 0.489 °C (∼10 %), respectively. This study highlights the importance of wind conditions and topographic complexity in the simulations of snow-related characteristics over the mid-latitudes of Asian region.