Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1903-2024
https://doi.org/10.5194/gmd-17-1903-2024
Development and technical paper
 | 
01 Mar 2024
Development and technical paper |  | 01 Mar 2024

A novel numerical implementation for the surface energy budget of melting snowpacks and glaciers

Kévin Fourteau, Julien Brondex, Fanny Brun, and Marie Dumont

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Cited articles

Albert, M. R.: Computer models for two-dimensional transient heat conduction, https://apps.dtic.mil/sti/pdfs/ADA134893.pdf (last accessed: 30 November 2023), 1983. a
Anderson, E. A.: A point energy and mass balance model of a snow cover, https://repository.library.noaa.gov/view/noaa/6392 (last accessed: 30 November 2023), 1976. a, b, c
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005. a
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.: One Step at a Time: How Model Time Step Significantly Affects Convection-Permitting Simulations, J. Adv. Model. Earth Sy., 11, 641–658, https://doi.org/10.1029/2018MS001418, 2019. a
Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche warning: Part I: numerical model, Cold Reg. Sci. Tech., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002. a, b, c, d, e, f, g, h, i, j
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Short summary
In this paper, we provide a novel numerical implementation for solving the energy exchanges at the surface of snow and ice. By combining the strong points of previous models, our solution leads to more accurate and robust simulations of the energy exchanges, surface temperature, and melt while preserving a reasonable computation time.
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