An assessment of basal melt parameterisations for Antarctic ice shelves

Ocean-induced ice-shelf melt is the highest uncertainty factor in the Antarctic contribution to future sea level. Several parameterisations exist to link oceanic properties to basal melt and force ice-sheet models. Here, we assess the potential of a range of existing basal melt parameterisations to...

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Bibliographic Details
Main Authors: Burgard, Clara, Jourdain, Nicolas C., Reese, Ronja, Jenkins, Adrian, Mathiot, Pierre
Format: Text
Language:English
Published: 2022
Subjects:
Online Access:https://doi.org/10.5194/tc-2022-32
https://tc.copernicus.org/preprints/tc-2022-32/
Description
Summary:Ocean-induced ice-shelf melt is the highest uncertainty factor in the Antarctic contribution to future sea level. Several parameterisations exist to link oceanic properties to basal melt and force ice-sheet models. Here, we assess the potential of a range of existing basal melt parameterisations to emulate basal melt rates simulated by a cavity-resolving ocean model on the circum-Antarctic scale. To do so, we re-tune the parameterisations in a perfect model approach, and compare the melt rates produced by the newly tuned parameterisations to the melt rates simulated by the ocean model. We find that the quadratic dependence of melt to thermal forcing without dependency on the individual ice-shelf slope and the plume parameterisation yield the best compromise, in terms of integrated shelf melt and spatial patterns. The box, PICOP parameterisation and quadratic parameterisations with slope dependency yield basal melt rates further from the model reference. The linear parameterisation cannot be recommended as the resulting integrated ice-shelf melt is comparably furthest from the reference. When using offshore hydrographic input fields in comparison to properties on the continental shelf, all parameterisations perform worse, however the box and the slope-dependent quadratic parameterisations yield the comparably best results. Additionally to the new tuning, we provide uncertainty estimates for the tuned parameters.