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

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

doi:10.5194/tc-17-673-2023

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

Journal Article · Fri Feb 10 00:00:00 EST 2023 · The Cryosphere (Online)

DOI:https://doi.org/10.5194/tc-17-673-2023· OSTI ID:1924157

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Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow simulations over the western United States at 0.125° resolution during 2001–2019 using the Snow Telemetry (SNOTEL) in situ networks, MODIS remote sensing products (i.e., MCD43 surface albedo product), the spatially and temporally complete (STC) snow-covered area and grain size (MODSCAG) and MODIS dust and radiative forcing in snow (MODDRFS) products (STC-MODSCAG/STC-MODDRFS), and the snow property inversion from remote sensing (SPIReS) product and two data assimilation products of snow water equivalent and snow depth – i.e., University of Arizona (UA) and SNOw Data Assimilation System (SNODAS). Overall the ELM simulations are consistent with the benchmarking datasets and reproduce the spatio-temporal patterns, interannual variability, and elevation gradients for different snow properties including snow cover fraction (f_sno), surface albedo (α_sur) over snow cover regions, snow water equivalent (SWE), and snow depth (D_sno). However, there are large biases of f_sno with dense forest cover and αsur in the Rocky Mountains and Sierra Nevada in winter, compared to the MODIS products. There are large discrepancies of snow albedo, snow grain size, and light-absorbing particle-induced snow albedo reduction between ELM and the MODIS products, attributed to uncertainties in the aerosol forcing data, snow aging processes in ELM, and remote sensing retrievals. Against UA and SNODAS, ELM has a mean bias of -20.7 mm (-35.9 %) and -20.4 mm (-35.5 %), respectively, for spring, and -13.8 mm (-27.8 %) and -10.2 mm (-22.2 %), respectively, for winter. ELM shows a relatively high correlation with SNOTEL SWE, with mean correlation coefficients of 0.69 but negative mean biases of -122.7 mm. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset dates by 17.3 and 12.4 d, earlier snow end dates by 35.5 and 26.8 d, and shorter snow durations by 52.9 and 39.5 d, respectively. This study underscores the need for diagnosing model biases and improving ELM representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.

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Research Organization:: Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Oceanic and Atmospheric Administration (NOAA); National Aeronautics and Space administration (NASA); National Science Foundation (NSF)

Grant/Contract Number:: Climate Process Team projects; AC05-76RL01830; NOAA-OAR-CPO-2019-2005530; NA19OAR4310243; 80NSSC20K1349, 80NSSC18K0427; NSSC22K0703; 80NSSC21K0997; 80NSSC20K1722

OSTI ID:: 1924157

Alternate ID(s):: OSTI ID: 1964049

Report Number(s):: PNNL-SA-178913

Journal Information:: The Cryosphere (Online), Journal Name: The Cryosphere (Online) Vol. 17 Journal Issue: 2; ISSN 1994-0424

Publisher:: Copernicus GmbHCopyright Statement

Country of Publication:: Germany

Language:: English

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

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