Assessing the Sensitivity of Snow Depth Simulations to Land Surface Parameterizations within Noah-MP in Northern Xinjiang, China

Abstract

Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes.