Evaluation of snow processes over the Western United States in E3SM land model

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, 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 particles 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, respectively. Compared to the snow phenology of STC-MODSCAG and SPIReS, ELM shows delayed snow accumulation onset date by 17.3 and 12.4 days, earlier snow end date by 35.5 and 26.8 days, and shorter snow duration by 52.9 and 39.5 days. 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.

A cleaner snow future mitigates Northern Hemisphere snowpack loss from warming

“…Our previous study showed that ELM can well capture the snow distribution in the TP, compared to the MODIS remote sensing data, supporting the reliability of the results 28 . ELM simulations can reproduce the spatio-temporal pattern, interannual variability and elevation gradient for different snow properties over the WUS 58 . The simulations are in line with Snow Telemetry field measurements, MODIS remote sensing products, and data assimilation products.…”

“…The model enhancements allow ELM to be used to investigate uncertainties related to model configurations on future snow projections. Our previous study showed that ELM can well capture the spatio-temporal distribution and interannual variability of snow properties and timing compared to field measurements, remote sensing observations, and data assimilated products 28,58 . These confirm the effectiveness of simulating snow processes in ELM.…”