Compensating errors in inversions for subglacial bed roughness: same steady state, different dynamic response

• Main Authors: Berends, Constantijn J., Van De Wal, Roderik S.W., Van Den Akker, Tim, Lipscomb, William H.
• Other Authors: Sub Dynamics Meteorology, Proceskunde, Sub Algemeen Marine & Atmospheric Res, Marine and Atmospheric Research
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2023
• Subjects: Water Science and Technology; Earth-Surface Processes; Antarctic; Antarc*; Ice Sheet
• Online Access: https://dspace.library.uu.nl/handle/1874/428431

Subglacial bed roughness is one of the main factors controlling the rate of future Antarctic ice-sheet retreat and also one of the most uncertain. A common technique to constrain the bed roughness using ice-sheet models is basal inversion, tuning the roughness to reproduce the observed present-day ice-sheet geometry and/or surface velocity. However, many other factors affecting ice-sheet evolution, such as the englacial temperature and viscosity, the surface and basal mass balance, and the subglacial topography, also contain substantial uncertainties. Using a basal inversion technique intrinsically causes any errors in these other quantities to lead to compensating errors in the inverted bed roughness. Using a set of idealised-geometry experiments, we quantify these compensating errors and investigate their effect on the dynamic response of the ice sheet to a prescribed forcing. We find that relatively small errors in ice viscosity and subglacial topography require substantial compensating errors in the bed roughness in order to produce the same steady-state ice sheet, obscuring the realistic spatial variability in the bed roughness. When subjected to a retreat-inducing forcing, we find that these different parameter combinations, which per definition of the inversion procedure result in the same steady-state geometry, lead to a rate of ice volume loss that can differ by as much as a factor of 2. This implies that ice-sheet models that use basal inversion to initialise their model state can still display a substantial model bias despite having an initial state which is close to the observations.