Melting and freezing under Antarctic ice shelves from a combination of ice-sheet modelling and observations
ABSTRACT Ice-shelf basal melting is the largest contributor to the negative mass balance of the Antarctic ice sheet. However, current implementations of ice/ocean interactions in ice-sheet models disagree with the distribution of sub-shelf melt and freezing rates revealed by recent observational stu...
| Published in: | Journal of Glaciology |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Cambridge University Press (CUP)
2017
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| Subjects: | |
| Online Access: | http://dx.doi.org/10.1017/jog.2017.42 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143017000429 |
| Summary: | ABSTRACT Ice-shelf basal melting is the largest contributor to the negative mass balance of the Antarctic ice sheet. However, current implementations of ice/ocean interactions in ice-sheet models disagree with the distribution of sub-shelf melt and freezing rates revealed by recent observational studies. Here we present a novel combination of a continental-scale ice flow model and a calibration technique to derive the spatial distribution of basal melting and freezing rates for the whole Antarctic ice-shelf system. The modelled ice-sheet equilibrium state is evaluated against topographic and velocity observations. Our high-resolution (10-km spacing) simulation predicts an equilibrium ice-shelf basal mass balance of −1648.7 Gt a −1 that increases to −1917.0 Gt a −1 when the observed ice-shelf thinning rates are taken into account. Our estimates reproduce the complexity of the basal mass balance of Antarctic ice shelves, providing a reference for parameterisations of sub-shelf ocean/ice interactions in continental ice-sheet models. We perform a sensitivity analysis to assess the effects of variations in the model set-up, showing that the retrieved estimates of basal melting and freezing rates are largely insensitive to changes in the internal model parameters, but respond strongly to a reduction of model resolution and the uncertainty in the input datasets. |
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