Abstract

<strong class="journal-contentHeaderColor">Abstract.</strong> Seasonal snowpack dynamics shape the biophysical and societal characteristics of many global regions. However, snowpack accumulation and duration have generally declined in recent decades, largely due to anthropogenic climate change. Mechanistic understanding of snowpack spatiotemporal heterogeneity and climate change impacts will benefit from snow data products that are based on physical principles, simulated at high spatial resolution, and cover large geographic domains. Most existing datasets do not meet these requirements, hindering our ability to understand both contemporary and changing snow regimes and to develop adaptation strategies in regions where snowpack patterns and processes are important components of Earth systems. We developed a computationally efficient process-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry (SNOTEL) sites across the western United States (US), achieving a site-median root-mean-squared error for daily snow water equivalent (SWE) of 64 mm, bias in peak SWE of <span class="inline-formula">−</span>2.6 mm, and bias in snow duration of <span class="inline-formula">−</span>4.5 d when run hourly. Positive biases were found at sites with mean winter temperature above freezing where the estimation of precipitation phase is prone to errors. The model was applied to the western US (a domain covering 3.1 million square kilometers) using newly developed forcing data created by statistically downscaling pre-industrial, historical, and pseudo-global warming climate data from the Weather Research and Forecasting (WRF) model. The resulting product is the SnowClim dataset, a suite of summary climate and snow metrics, including monthly SWE and snow depth, as well as annual maximum SWE and snow cover duration, for the western US at 210 m spatial resolution (Lute et al., 2021). The physical basis, large extent, and high spatial resolution of this dataset enable novel analyses of changing hydroclimate and its implications for natural and human systems.

Highlights

  • Seasonal snowpack shapes the climatic, hydrologic, ecological, economic, and cultural characteristics of many global regions.Snow is an important determinant of the surface energy balance through its effect on land surface albedo, partitioning of sensible and latent heat fluxes, near-surface atmospheric stability, and horizontal energy transport (Cohen, 1994; Rudisill et al, 2021; Stiegler et al, 2016)

  • Ml = Mrain − Mref + Mmelt − Mrunoff + Mcond − Mevap where Ms is the mass of the solid portion of the snowpack, Msnow is the mass of new snowfall, Mref is the mass of liquid water in the snowpack that has been refrozen, Mmelt is the mass of snow that has melted, Mdep is the mass of deposition, Msub is the mass of sublimation, Ml is the mass of the liquid in the snowpack, Mrain is the mass of rain added to the snowpack, Mrunoff is the mass of liquid water that has left the snowpack as runoff, Mcond is the mass of condensation, and Mevap is the mass of evaporation (Fig. 1)

  • Through the development of a new computationally efficient snow model, SnowClim, and novel forcing data, we have overcome the two major hurdles to achieving snow data that meets the criteria outlined in the introduction

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Summary

Introduction

Seasonal snowpack shapes the climatic, hydrologic, ecological, economic, and cultural characteristics of many global regions. Recent decades have seen less precipitation falling as snow, lower peak snow water equivalent (SWE), shorter snow duration, and earlier snowmelt runoff (Choi et al, 2010; Fritze et al, 2011; Knowles et al, 2006; Mote et al, 2018) These developments are expected to continue in the coming decades, resulting in substantial declines (>50%) in seasonal snowpack for areas such as the western US and significant impacts to human and natural systems (Fyfe et al, 2017; Huss et al, 2017; Marshall et al, 2019a; Siirila-Woodburn et al, 2021). The model retains the most important components of physically based models, including the complete energy balance and internal snowpack energetics, while omitting more computationally expensive components such as horizontal transport, multiple layers, and iterative solutions for snow surface temperature Unlike existing models, this simplified physics-based model is efficient enough to be run over sub-continental domains at high spatial resolution. We provide a description of the model and its application to the western US, including parameterization, calibration, climate forcing data preparation, and resultant datasets

Model Overview
Energy Balance
Shortwave Radiation
Longwave Radiation
Turbulent Fluxes
Precipitation heat flux
Ground heat flux
Enhanced single layer approach
Modification for shallow snowpacks
Mass Balance
Accumulation
Liquid water content
Refreezing
Sublimation and condensation
Spatial resolution
Forcing data preparation
Calibration methods
Calibration results
Model results for the Western United States
Discussion and Conclusion
Findings
Code availability

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