Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data
Probabilistic predictions of the sea level contribution from Antarctica often have large uncertainty intervals. Calibration of model simulations with observations can reduce uncertainties and improve confidence in projections, particularly if this exploits as much of the available information as pos...
| Main Authors: | , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://oro.open.ac.uk/70048/ https://oro.open.ac.uk/70048/1/2100_Wernecke_calibration_cryosphere.pdf https://oro.open.ac.uk/70048/8/Spatial%20probabilistic%20calibration%20of%20a%20high-resolution%20Amundsen%20Sea%20Embayment%20ice%20sheet%20model%20with%20satellite%20altimeter%20data.pdf https://doi.org/10.5194/tc-2019-156 |
| Summary: | Probabilistic predictions of the sea level contribution from Antarctica often have large uncertainty intervals. Calibration of model simulations with observations can reduce uncertainties and improve confidence in projections, particularly if this exploits as much of the available information as possible (such as spatial characteristics), but the necessary statistical treatment is often challenging and can be computationally prohibitive. Ice sheet models with sufficient spatial resolution to resolve grounding line evolution are also computationally expensive. Here we address these challenges by adopting and comparing dimension-reduced calibration approaches based on a principal component decomposition of the adaptive mesh model BISICLES. The effects model parameters have on these principal components are then gathered in statistical emulators to allow for smooth probability density estimates. With the help of a published perturbed parameter ice sheet model ensemble of the Amundsen Sea Embayment (ASE), we show how the use of principal components in combination with spatially resolved observations can improve probabilistic calibrations. In synthetic model experiments (calibrating the model with altered model results) we can identify the correct basal traction and ice viscosity scaling parameters as well as the bedrock map with spatial calibrations. In comparison a simpler calibration against an aggregated observation, the net sea level contribution, imposes only weaker constraints by allowing a wide range of basal traction and viscosity scaling factors. Uncertainties in sea level rise contribution of 50 year simulations from the current state of the ASE can be reduced with satellite observations of recent ice thickness change by nearly 90%; Median and 90% confidence intervals are 18.9 [13.9, 24.8] mm SLE for the proposed spatial calibration ap- proach, 16.8 [7.7, 25.6] mm SLE for the net sea level cal- ibration and 23.1 [-8.4, 94.5] mm SLE for the uncalibrated 35 ensemble. The spatial model behaviour is much more ... |
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