January 30, 2023
Journal Article

Improving snow albedo modeling in E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau

Abstract

Snow albedo is greatly affected by snow grain properties (e.g., size and shape) and light absorbing particles (LAPs) such as black carbon and dust. The mixing state of LAPs in snow has large impacts on snow albedo reduction. However, most land surface models assume that snow grain shape is spherical and LAPs are externally mixed with snow grains. This study improves the snow radiative transfer model in the E3SM land model (ELM) by considering non-spherical snow grain shapes and internal mixing of dust-snow. A series of ELM simulations with different snow and topography configurations are performed over the Tibetan Plateau. Compared with remote sensing products, the control simulation captures the snow distribution reasonably and the estimated surface radiative forcing ranging from 0 to 21.9 W/m2 is comparable to reported values. Focusing on snow-related processes and surface energy and water cycles, Koch snowflake shape shows the largest difference from spherical shape in spring. Compared to external mixing, internal mixing of LAP-snow can lead to larger snow albedo reduction and snowmelt, which further affect surface energy and water cycles. The individual contributions of non-spherical snow shape, mixing state of LAP-snow, and local topography to the change of snow and surface fluxes have different signs and magnitudes, and their combined effects on net solar radiation range from -29.7 to 12.2 W/m2 in spring. This study advances our understanding of the role of snow grain shape and mixing state of LAP-snow in land surface processes.

Published: January 30, 2023

Citation

Hao D., G. Bisht, K. Rittger, E. Bair, C. He, H. Huang, and C. Dang, et al. 2023. Improving snow albedo modeling in E3SM land model (version 2.0) and assessing its impacts on snow and surface fluxes over the Tibetan Plateau. Geoscientific Model Development 16, no. 1:75-94. PNNL-SA-170799. doi:10.5194/gmd-16-75-2023

Research topics