An assessment of basal melt parameterisations for Antarctic ice shelves

• Published in: The Cryosphere
• Main Authors: Burgard, Clara, Jourdain, Nicolas C., Reese, Ronja, Jenkins, Adrian, Mathiot, Pierre
• Format: Text
• Language: English
• Published: 2022
• Subjects: Antarctic; The Antarctic; Antarc*; Ice Sheet; Ice Shelf; Ice Shelves
• Online Access: https://doi.org/10.5194/tc-16-4931-2022; https://tc.copernicus.org/articles/16/4931/2022/

Ocean-induced ice-shelf melt is one of the largest uncertainty factors in the Antarctic contribution to future sea-level rise. Several parameterisations exist, linking oceanic properties in front of the ice shelf to melt at the base of the ice shelf, to force ice-sheet models. Here, we assess the potential of a range of these 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 perform two cross-validations, over time and over ice shelves respectively, and re-tune the parameterisations in a perfect-model approach, to 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 parameterisation, which separates the sub-shelf circulation into boxes, the PICOP parameterisation, which combines the box and plume parameterisation, and quadratic parameterisations with dependency on the ice slope 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. In addition to the new tuning, we provide uncertainty estimates for the tuned parameters.