Evaluation of E3SM land model snow simulations over the western United States
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Published:2023-02-10
Issue:2
Volume:17
Page:673-697
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ISSN:1994-0424
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Container-title:The Cryosphere
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language:en
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Short-container-title:The Cryosphere
Author:
Hao DaleiORCID, Bisht GautamORCID, Rittger KarlORCID, Stillinger TimboORCID, Bair EdwardORCID, Gu YuORCID, Leung L. RubyORCID
Abstract
Abstract. 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 (fsno), surface albedo (αsur) over snow cover regions, snow water equivalent (SWE), and snow depth (Dsno). However, there are large biases of fsno 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.
Funder
National Oceanic and Atmospheric Administration Earth Sciences Division Office of Science
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
Reference98 articles.
1. Anttila, K., Manninen, T., Jääskeläinen, E., Riihelä, A., and Lahtinen, P.: The Role of Climate and Land Use in the Changes in Surface Albedo Prior to Snow Melt and the Timing of Melt Season of Seasonal Snow in Northern Land Areas of 40∘ N–80∘ N during 1982–2015,
Remote Sens., 10, 1619, https://doi.org/10.3390/rs10101619, 2018. 2. Bair, E. H., Rittger, K., Davis, R. E., Painter, T. H., and Dozier, J.: Validating reconstruction of snow water equivalent in California's Sierra
Nevada using measurements from the NASA Airborne Snow Observatory, Water Resour. Res., 52, 8437–8460, 2016. 3. Bair, E. H., Rittger, K., Skiles, S. M., and Dozier, J.: An Examination of
Snow Albedo Estimates From MODIS and Their Impact on Snow Water Equivalent
Reconstruction, Water Resour. Res., 55, 7826–7842, 2019. 4. Bair, E.: edwardbair/SPIRES, GitHub [code],
https://github.com/edwardbair/SPIRES, last access: 4 February 2023. 5. Bair, E., Stillinger, T., Rittger, K., and Skiles, M.: COVID-19 lockdowns
show reduced pollution on snow and ice in the Indus River Basin, P. Natl. Acad. Sci. USA, 118, e2101174118, https://doi.org/10.1073/pnas.2101174118, 2021a.
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