Preprints
https://doi.org/10.5194/egusphere-2024-506
https://doi.org/10.5194/egusphere-2024-506
04 Mar 2024
 | 04 Mar 2024
Status: this preprint is open for discussion.

A Global land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: Formulation and evaluation at instrumented sites

Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova

Abstract. Snowpack modulates water storage over extended land regions, and at the same time plays a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) Land Model version 4.1. This new snowpack model, named GLASS ("Global LAnd-Snow Scheme"), includes a refined and dynamical vertical layering snow structure which allows us to track in each snow layer the temporal evolution of snow grain properties, while at the same time limiting the model computational expense, as necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land-atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in-situ observations allow for a comprehensive model evaluation. We find that, when compared to previous version of GFDL snow model, GLASS improves predictions of seasonal snow water equivalent and soil temperature under the snowpack.

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Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-506', Juan Antonio Añel, 28 Mar 2024 reply
    • AC1: 'Reply on CEC1', Enrico Zorzetto, 01 Apr 2024 reply
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 01 Apr 2024 reply
        • AC2: 'Reply on CEC2', Enrico Zorzetto, 01 Apr 2024 reply
          • CEC3: 'Reply on AC2', Juan Antonio Añel, 02 Apr 2024 reply
  • RC1: 'Comment on egusphere-2024-506', Anonymous Referee #1, 30 Apr 2024 reply
Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova

Data sets

A Global Land Snow Scheme (GLASS) v1.0.0 Enrico Zorzetto https://zenodo.org/records/10681526

Model code and software

A Global Land Snow Scheme (GLASS) v1.0.0 Enrico Zorzetto https://zenodo.org/records/10681526

Enrico Zorzetto, Sergey Malyshev, Paul Ginoux, and Elena Shevliakova

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Short summary
We describe a new snow scheme developed for use in global climate models, which simulates the interactions of snowpack with vegetation, atmosphere, and soil. We test the new snow model over a set of sites where in-situ observations are available. We find that, when compared to a simpler snow model, this model improves predictions of seasonal snow and of soil temperature under the snowpack, important variables for simulating both the hydrological cycle and the global climate system.