SEMIC: An efficient surface energy and mass balance model applied to the Greenland ice sheet

• Main Authors: Krapp, Mario, Robinson, Alexander, Ganopolski, Andrey
• Format: Article in Journal/Newspaper
• Language: English
• Published: München : European Geopyhsical Union 2017
• Subjects: ddc:550; climate modeling; diurnal variation; freeze-thaw cycle; future prospect; glacier mass balance; ice sheet; model validation; parameterization; regional climate; snowpack; surface energy; surface temperature; glacier; Greenland; Ice Sheet; Prospect Glacier
• Online Access: https://oa.tib.eu/renate/handle/123456789/673; https://doi.org/10.34657/1264

We present SEMIC, a Surface Energy and Mass balance model of Intermediate Complexity for snow- and ice-covered surfaces such as the Greenland ice sheet. SEMIC is fast enough for glacial cycle applications, making it a suitable replacement for simpler methods such as the positive degree day (PDD) method often used in ice sheet modelling. Our model explicitly calculates the main processes involved in the surface energy and mass balance, while maintaining a simple interface and requiring minimal data input to drive it. In this novel approach, we parameterise diurnal temperature variations in order to more realistically capture the daily thaw–freeze cycles that characterise the ice sheet mass balance. We show how to derive optimal model parameters for SEMIC specifically to reproduce surface characteristics and day-to-day variations similar to the regional climate model MAR (Modèle Atmosphérique Régional, version 2) and its incorporated multilayer snowpack model SISVAT (Soil Ice Snow Vegetation Atmosphere Transfer). A validation test shows that SEMIC simulates future changes in surface temperature and surface mass balance in good agreement with the more sophisticated multilayer snowpack model SISVAT included in MAR. With this paper, we present a physically based surface model to the ice sheet modelling community that is general enough to be used with in situ observations, climate model, or reanalysis data, and that is at the same time computationally fast enough for long-term integrations, such as glacial cycles or future climate change scenarios.