Assessment of Uncertainty Sources in Snow Cover Simulation in the Tibetan Plateau. Issue 18 (16th September 2020) - Jiang, Yingsha; Chen, Fei; Gao, Yanhong; He, Cenlin; Barlage, Michael; Huang, Wubin
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 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°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. Key Points: Optimizing Noah‐MP physics‐scheme options reduces about 8% absolute bias of the annual snow cover fraction (SCF) compared to MODIS SCF Optimizing SCF parameterizations further reduces around 13% SCF absolute bias, which is higher than optimizing physics‐scheme options Contributions of high‐resolution topographically adjusted air temperature to the SCF simulation uncertainty are limited