A glacial systems model configured for large ensemble analysis of Antarctic deglaciation

• Published in: The Cryosphere
• Main Authors: Briggs, R., Pollard, D., Tarasov, L.
• Format: Text
• Language: English
• Published: 2018
• Subjects: Antarctic; Filchner Ice Shelf; Antarc*; Ice Sheet; Ice Shelf; Ice Shelves; University of Newfoundland
• Online Access: https://doi.org/10.5194/tc-7-1949-2013; https://tc.copernicus.org/articles/7/1949/2013/

This article describes the Memorial University of Newfoundland/Penn State University (MUN/PSU) glacial systems model (GSM) that has been developed specifically for large-ensemble data-constrained analysis of past Antarctic Ice Sheet evolution. Our approach emphasizes the introduction of a large set of model parameters to explicitly account for the uncertainties inherent in the modelling of such a complex system. At the core of the GSM is a 3-D thermo-mechanically coupled ice sheet model that solves both the shallow ice and shallow shelf approximations. This enables the different stress regimes of ice sheet, ice shelves, and ice streams to be represented. The grounding line is modelled through an analytical sub-grid flux parameterization. To this dynamical core the following have been added: a heavily parameterized basal drag component; a visco-elastic isostatic adjustment solver; a diverse set of climate forcings (to remove any reliance on any single method); tidewater and ice shelf calving functionality; and a new physically motivated, empirically-derived sub-ice-shelf melt (SSM) component. To assess the accuracy of the latter, we compare predicted SSM values against a compilation of published observations. Within parametric and observational uncertainties, computed SSM for the present-day ice sheet is in accord with observations for all but the Filchner ice shelf. The GSM has 31 ensemble parameters that are varied to account (in part) for the uncertainty in the ice physics, the climate forcing, and the ice–ocean interaction. We document the parameters and parametric sensitivity of the model to motivate the choice of ensemble parameters in a quest to approximately bound reality (within the limits of 31 parameters).