Choice of observation type affects Bayesian calibration of ice sheet model projections
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 p...
| Main Authors: | , , , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2022
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/egusphere-2022-1213 https://noa.gwlb.de/receive/cop_mods_00063480 https://egusphere.copernicus.org/preprints/egusphere-2022-1213/egusphere-2022-1213.pdf |
| Summary: | 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. |
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