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...
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Copernicus Publications
2020
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Online Access: | https://doi.org/10.5194/tc-14-1459-2020 https://www.the-cryosphere.net/14/1459/2020/tc-14-1459-2020.pdf https://doaj.org/article/e9892cc212334400bd02b5b77f4def84 |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:e9892cc212334400bd02b5b77f4def84 2023-05-15T13:23:59+02:00 Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data A. Wernecke T. L. Edwards I. J. Nias P. B. Holden N. R. Edwards 2020-05-01 https://doi.org/10.5194/tc-14-1459-2020 https://www.the-cryosphere.net/14/1459/2020/tc-14-1459-2020.pdf https://doaj.org/article/e9892cc212334400bd02b5b77f4def84 en eng Copernicus Publications doi:10.5194/tc-14-1459-2020 1994-0416 1994-0424 https://www.the-cryosphere.net/14/1459/2020/tc-14-1459-2020.pdf https://doaj.org/article/e9892cc212334400bd02b5b77f4def84 undefined The Cryosphere, Vol 14, Pp 1459-1474 (2020) geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2020 fttriple https://doi.org/10.5194/tc-14-1459-2020 2023-01-22T17:49:51Z 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 (sea level equivalent) for the proposed spatial calibration approach, 16.8 [7.7, 25.6] mm SLE for the net sea level calibration and 23.1 [−8.4, 94.5] mm SLE for the uncalibrated ensemble. The spatial model ... Article in Journal/Newspaper Amundsen Sea Antarc* Antarctica Ice Sheet The Cryosphere Unknown Amundsen Sea The Cryosphere 14 5 1459 1474 |
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Open Polar |
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fttriple |
language |
English |
topic |
geo envir |
spellingShingle |
geo envir A. Wernecke T. L. Edwards I. J. Nias P. B. Holden N. R. Edwards Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data |
topic_facet |
geo envir |
description |
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 (sea level equivalent) for the proposed spatial calibration approach, 16.8 [7.7, 25.6] mm SLE for the net sea level calibration and 23.1 [−8.4, 94.5] mm SLE for the uncalibrated ensemble. The spatial model ... |
format |
Article in Journal/Newspaper |
author |
A. Wernecke T. L. Edwards I. J. Nias P. B. Holden N. R. Edwards |
author_facet |
A. Wernecke T. L. Edwards I. J. Nias P. B. Holden N. R. Edwards |
author_sort |
A. Wernecke |
title |
Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data |
title_short |
Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data |
title_full |
Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data |
title_fullStr |
Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data |
title_full_unstemmed |
Spatial probabilistic calibration of a high-resolution Amundsen Sea Embayment ice sheet model with satellite altimeter data |
title_sort |
spatial probabilistic calibration of a high-resolution amundsen sea embayment ice sheet model with satellite altimeter data |
publisher |
Copernicus Publications |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-14-1459-2020 https://www.the-cryosphere.net/14/1459/2020/tc-14-1459-2020.pdf https://doaj.org/article/e9892cc212334400bd02b5b77f4def84 |
geographic |
Amundsen Sea |
geographic_facet |
Amundsen Sea |
genre |
Amundsen Sea Antarc* Antarctica Ice Sheet The Cryosphere |
genre_facet |
Amundsen Sea Antarc* Antarctica Ice Sheet The Cryosphere |
op_source |
The Cryosphere, Vol 14, Pp 1459-1474 (2020) |
op_relation |
doi:10.5194/tc-14-1459-2020 1994-0416 1994-0424 https://www.the-cryosphere.net/14/1459/2020/tc-14-1459-2020.pdf https://doaj.org/article/e9892cc212334400bd02b5b77f4def84 |
op_rights |
undefined |
op_doi |
https://doi.org/10.5194/tc-14-1459-2020 |
container_title |
The Cryosphere |
container_volume |
14 |
container_issue |
5 |
container_start_page |
1459 |
op_container_end_page |
1474 |
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1766376769551597568 |