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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-175
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-175
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Jul 2020

20 Jul 2020

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This preprint is currently under review for the journal TC.

Physics-based modeling of Antarctic snow and firn density

Eric Keenan1, Nander Wever1, Marissa Dattler2, Jan T. M. Lenaerts1, Brooke Medley3, Peter Kuipers Munneke4, and Carleen Reijmer4 Eric Keenan et al.
  • 1Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA
  • 2Department of Atmospheric and Oceanic Sciences, University of Maryland, College Park, MD, USA
  • 3Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, The Netherlands

Abstract. Estimates of snow and firn density are required for satellite altimetry based retrievals of ice sheet mass balance that rely on volume to mass conversions. Therefore, biases and errors in presently used density models confound assessments of ice sheet mass balance, and by extension, ice sheet contribution to sea level rise. Despite this importance, most contemporary firn densification models rely on simplified semi-empirical methods, which are partially reflected by significant modeled density errors when compared to observations. In this study, we present a new, wind-driven, drifting snow compaction scheme that we have implemented into SNOWPACK, a physics-based land surface snow model. We demonstrate high-quality simulation of near-surface Antarctic snow firn density at 122 observed density profiles across the Antarctic ice sheet, as indicated by reduced model biases throughout most of the near-surface firn column when compared to two semi-empirical firn densification models. Because SNOWPACK is physics-based, its performance does not degrade when applied to sites without observations used in the calibration of semi-empirical models, and could therefore better represent firn properties in locations without extensive observations and under future climate scenarios, in which firn properties are expected to diverge from their present state.

Eric Keenan et al.

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Eric Keenan et al.

Model code and software

SNOWPACK model source code used in Keenan et. al., 2020 TCD. Eric Keenan, Nander Wever, Marissa Dattler, Jan T. M. Lenaerts, Brooke Medley, Peter Kuipers Munneke, Carleen Reijmer. https://doi.org/10.5281/zenodo.3891846

Eric Keenan et al.

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Latest update: 04 Aug 2020
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Short summary
Snow density is required to convert observed changes in ice sheet volume into mass, which ultimately drives ice sheet contribution to sea level rise. However, snow properties respond dynamically to wind driven redistribution. Here we include a new wind driven snow density scheme into an existing snow model. Our results demonstrate an improved representation of snow density when compared to observations and can therefore be used to improve retrievals of ice sheet mass balance.
Snow density is required to convert observed changes in ice sheet volume into mass, which...
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