Impact of forcing data and land surface properties on snow simulation in a regional climate model: a case study over the Tianshan Mountains, Central Asia

Abstract

Snow is a key variable that influences hydrological and climatic cycles. Land surface models employing snow physics-modules can simulate the snow accumulation and ablation processes. However, there are still uncertainties in modeling snow resources over complex terrain such as mountains. This study employed the National Center for Atmospheric Research’s Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to run one-year simulations to assess its ability to simulate snow across the Tianshan Mountains. Six tests were conducted based on different reanalysis forcing datasets and different land surface properties. The results indicated that the snow dynamics were reproduced in a snow hydrological year by the WRF/Noah-MP model for all of the tests. The model produced a low bias in snow depth and snow water equivalent (SWE) regardless of the forcing datasets. Additionally, the underestimation of snow depth and SWE could be relatively alleviated by modifying the land cover and vegetation parameters. However, no significant improvement in accuracy was found in the date of snow depth maximum and melt rate. The best performance was achieved using ERA5 with modified land cover and vegetation parameters (mean bias = -4.03 mm and -1.441 mm for snow depth and SWE, respectively). This study highlights the importance of selecting forcing data for snow simulation over the Tianshan Mountains.