Articles | Volume 17, issue 2
https://doi.org/10.5194/tc-17-673-2023
https://doi.org/10.5194/tc-17-673-2023
Research article
 | 
10 Feb 2023
Research article |  | 10 Feb 2023

Evaluation of E3SM land model snow simulations over the western United States

Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung

Data sets

MODIS daily snow data (STC-MODSCAG/STC-MODDRFS and SPIReS) over the Western US from 2001-2019 D. Hao https://doi.org/10.5281/zenodo.7194703

Daily 4\,km gridded SWE and snow depth from assimilated in-situ and modeled data over the conterminous US, version 1 P. Broxton, X. Zeng, and N. Dawson https://doi.org/10.5067/0GGPB220EX6A

Snow Data Assimilation System (SNODAS) Data Products at NSIDC, Version 1 National Operational Hydrologic Remote Sensing Center https://doi.org/10.7265/N5TB14TC

Model code and software

daleihao/E3SM: ELM-SNOW (v1.1.0.0) D. Hao https://doi.org/10.5281/zenodo.6324131

edwardbair/SPIRES E. Bair https://github.com/edwardbair/SPIRES

daleihao/snow_evaluation_ELM: Codes for the ELM snow evaluation study (Version v1) D. Hao https://doi.org/10.5281/zenodo.7607813

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Short summary
We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.