Preprints
https://doi.org/10.5194/gmd-2021-407
https://doi.org/10.5194/gmd-2021-407
Submitted as: model description paper
16 Dec 2021
Submitted as: model description paper | 16 Dec 2021
Status: a revised version of this preprint is currently under review for the journal GMD.

SnowClim v1.0: High-resolution snow model and data for the western United States

Abby C. Lute1,a, John Abatzoglou2, and Timothy Link3 Abby C. Lute et al.
  • 1Water Resources Program, University of Idaho, Moscow, ID, 83844, USA
  • 2Management of Complex Systems, University of California, Merced, CA, 95343, USA
  • 3Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID, 83844, USA
  • anow at: Geophysical Fluid Dynamics Laboratory, National Oceanic and Atmospheric Administration/Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, 08540, USA

Abstract. 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, that are simulated at high spatial resolution, and that cover large geographic domains. 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 physics-based snow model, SnowClim, that can be run in the cloud. The model was evaluated and calibrated at Snowpack Telemetry sites across the western United States (US), achieving a site-median root mean square error for daily snow water equivalent of 62 mm, bias in peak snow water equivalent of −9.6 mm, and bias in snow duration of 1.2 days 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 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 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 will enable novel analyses of changing hydroclimate and its implications for natural and human systems.

Abby C. Lute et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on gmd-2021-407', Shiheng Duan, 21 Dec 2021
    • AC1: 'Reply on CC1', Abigail Lute, 22 Dec 2021
  • CEC1: 'Comment on gmd-2021-407', Juan Antonio Añel, 03 Jan 2022
    • AC2: 'Reply on CEC1', Abigail Lute, 05 Jan 2022
      • CEC2: 'Reply on AC2', Juan Antonio Añel, 05 Jan 2022
        • AC3: 'Reply on CEC2', Abigail Lute, 05 Jan 2022
  • RC1: 'Comment on gmd-2021-407', Anonymous Referee #1, 12 Jan 2022
    • AC4: 'Reply on RC1', Abigail Lute, 28 Mar 2022
  • RC2: 'Comment on gmd-2021-407', Anonymous Referee #2, 28 Jan 2022
    • AC5: 'Reply on RC2', Abigail Lute, 28 Mar 2022

Abby C. Lute et al.

Data sets

SnowClim Data A. C. Lute, John Abatzoglou, Timothy Link https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/

Model code and software

SnowClim Model A. C. Lute, John Abatzoglou, Timothy Link https://www.hydroshare.org/resource/dc3a40e067bf416d82d87c664d2edcc7/

Abby C. Lute et al.

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Short summary
We developed a snow model that can be used to quantify snowpack over large areas with a high degree of spatial detail. We ran the model over the western United States, creating a snow and climate dataset for three time periods. Compared to observations of snowpack, the model captured the key aspects of snow across time and space. The model and dataset will be useful in understanding historical and future changes in snowpack, with relevance to water resources, agriculture, and ecosystems.