Preprints
https://doi.org/10.5194/egusphere-2022-1213
https://doi.org/10.5194/egusphere-2022-1213
 
16 Nov 2022
16 Nov 2022
Status: this preprint is open for discussion.

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

Denis Felikson1,2, Sophie Nowicki3, Isabel Nias4, Beata Csatho3, Anton Schenk3, Michael Croteau5, and Bryant Loomis5 Denis Felikson et al.
  • 1Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2Goddard Earth Sciences Technology and Research Studies and Investigations II, Morgan State University, Baltimore, MD, USA
  • 3Department of Geology, University at Buffalo, Buffalo, NY, USA
  • 4School of Environmental Sciences, University of Liverpool, Liverpool, UK
  • 5Geodesy and Geophysics Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

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: open (until 11 Jan 2023)

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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.