Choice of observation type affects Bayesian calibration of Greenland Ice Sheet model simulations

• Published in: The Cryosphere
• Main Authors: Felikson, Denis, Nowicki, Sophie, Nias, Isabel, Csatho, Beata, Schenk, Anton, Croteau, Michael J., Loomis, Bryant
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2023
• Subjects: article; Verlagsveröffentlichung; Greenland; Ice Sheet; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-17-4661-2023; https://noa.gwlb.de/receive/cop_mods_00069710; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068085/tc-17-4661-2023.pdf; https://tc.copernicus.org/articles/17/4661/2023/tc-17-4661-2023.pdf

Determining reliable probability distributions for ice sheet mass change over the coming century is critical to refining uncertainties in sea-level rise projections. Bayesian calibration, a method for constraining projection uncertainty using observations, has been previously applied to ice sheet projections but the impact of the chosen observation type on the calibrated posterior probability distributions has not been quantified. Here, we perform three separate Bayesian calibrations to constrain uncertainty in Greenland Ice Sheet (GrIS) simulations of the committed mass loss in 2100 under the current climate, using observations of velocity change, dynamic ice thickness change, and mass change. Comparing the posterior probability distributions shows that the median ice sheet mass change can differ by 119 % for the particular model ensemble that we used, depending on the observation type used in the calibration. More importantly for risk-averse sea-level planning, posterior probabilities of high-end mass change scenarios are highly sensitive to the observation selected for calibration. Furthermore, we show that using mass change observations alone may result in model simulations that overestimate flow acceleration and underestimate dynamic thinning around the margin of the ice sheet. Finally, we look ahead and present ideas for ways to improve Bayesian calibration of ice sheet projections.