An efficient surface energy–mass balance model for snow and ice
A comprehensive understanding of the state and dynamics of the land cryosphere and associated sea level rise is not possible without taking into consideration the intrinsic timescales of the continental ice sheets. At the same time, the ice sheet mass balance is the result of seasonal variations in...
| Published in: | The Cryosphere |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2019
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-13-1529-2019 https://www.the-cryosphere.net/13/1529/2019/tc-13-1529-2019.pdf https://doaj.org/article/2e71a95b73e64764ae612d745438cb94 |
| Summary: | A comprehensive understanding of the state and dynamics of the land cryosphere and associated sea level rise is not possible without taking into consideration the intrinsic timescales of the continental ice sheets. At the same time, the ice sheet mass balance is the result of seasonal variations in the meteorological conditions. Simulations of the coupled climate–ice-sheet system thus face the dilemma of skillfully resolving short-lived phenomena, while also being computationally fast enough to run over tens of thousands of years. As a possible solution, we present the BErgen Snow SImulator (BESSI), a surface energy and mass balance model that achieves computational efficiency while simulating all surface and internal fluxes of heat and mass explicitly, based on physical first principles. In its current configuration it covers most land areas of the Northern Hemisphere. Input data are daily values of surface air temperature, total precipitation, and shortwave radiation. The model is calibrated using present-day observations of Greenland firn temperature, cumulative Greenland mass changes, and monthly snow extent over the entire domain. The results of the calibrated simulations are then discussed. Finally, as a first application of the model and to illustrate its numerical efficiency, we present the results of a large ensemble of simulations to assess the model's sensitivity to variations in temperature and precipitation. |
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