16 Nov 2022
 | 16 Nov 2022

Choice of observation type affects Bayesian calibration of ice sheet model projections

Denis Felikson, Sophie Nowicki, Isabel Nias, Beata Csatho, Anton Schenk, Michael Croteau, and Bryant Loomis

Abstract. Determining reliable probability distributions for ice sheet mass change over the coming century is critical to improving 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 projections using observations of velocity change, dynamic thickness change, and mass change. Comparing the posterior probability distributions shows that the maximum a posteriori ice sheet mass change can differ by 130 % 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. Finally, we show that using mass change observations alone may result in projections that overestimate flow acceleration and underestimate dynamic thinning around the margin of the ice sheet.

Denis Felikson et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-1213', Anonymous Referee #1, 30 Dec 2022
  • RC2: 'Comment on egusphere-2022-1213', Anonymous Referee #2, 10 Feb 2023

Denis Felikson et al.


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Short summary
We calibrate uncertainty in projections of the Greenland Ice Sheet using velocity change, dynamic thickness change, and mass change observations. We find that the type of observation that is chosen can lead to significantly different calibrated probability distributions. Further work is required to understand how to best calibrate ice sheet projections because this will improve probability distributions of projected sea-level rise, which is crucial for coastal planning and adaptation.