Abstract

Snow cover over the Tibetan Plateau (TP) plays an important role in Asian climate. State-of-the-art models, however, show significant simulation biases. In this study, we assess the main uncertainty associated with model physics in snow cover modeling over the TP using ground-based observations a... Show moreSnow cover over the Tibetan Plateau (TP) plays an important role in Asian climate. State-of-the-art models, however, show significant simulation biases. In this study, we assess the main uncertainty associated with model physics in snow cover modeling over the TP using ground-based observations and high-resolution snow cover satellite products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FengYun-3B (FY3B). We first conducted 10-km simulations using the Noah with multiparameterization (Noah-MP) land surface model by optimizing physics-scheme options, which reduces 8.2% absolute bias of annual snow cover fraction (SCF) compared with the default model settings. Then, five SCF parameterizations in Noah-MP were optimized and assessed, with three of them further reducing the annual SCF biases from around 15% to less than 2%. Thus, optimizing SCF parameterizations appears to be more important than optimizing physics-scheme options in reducing the uncertainty of snow modeling. As a result of improved SCF, the positive bias of simulated surface albedo decreases significantly compared to the GLASS albedo data, particularly in high-elevation regions. This substantially enhances the absorbed solar radiation and further reduces the annual mean biases of ground temperature from -3.5 to -0.8 degrees C and snow depth from 4.2 to 0.2 mm. However, the optimized model still overestimates SCF in the western TP and underestimates SCF in the eastern TP. Further analysis using a higher-resolution (4 km) simulation driven by topographically adjusted air temperature shows slight improvement, suggesting a rather limited contribution of the finer-scale land surface characteristics to SCF uncertainty. Show less