A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams
Ice sheet models are the main tool to generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet mo...
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ftdoajarticles:oai:doaj.org/article:9e0c184d845443ffb329917c7ef0eafe 2023-11-05T03:33:31+01:00 A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams B. Recinos D. Goldberg J. R. Maddison J. Todd 2023-10-01T00:00:00Z https://doi.org/10.5194/tc-17-4241-2023 https://doaj.org/article/9e0c184d845443ffb329917c7ef0eafe EN eng Copernicus Publications https://tc.copernicus.org/articles/17/4241/2023/tc-17-4241-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-4241-2023 1994-0416 1994-0424 https://doaj.org/article/9e0c184d845443ffb329917c7ef0eafe The Cryosphere, Vol 17, Pp 4241-4266 (2023) Environmental sciences GE1-350 Geology QE1-996.5 article 2023 ftdoajarticles https://doi.org/10.5194/tc-17-4241-2023 2023-10-08T00:36:02Z Ice sheet models are the main tool to generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model, and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea level rise contribution or volume above flotation (VAF) due to the choice of different regularization strengths (prior strengths), sliding laws, and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L -curve analysis, as is typically done in ice sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularization suggested by the L curves is too strong, and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our two-member “sample” of observed velocity fields. Article in Journal/Newspaper Antarc* Antarctic Antarctica Ice Sheet The Cryosphere West Antarctica Directory of Open Access Journals: DOAJ Articles The Cryosphere 17 10 4241 4266 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 B. Recinos D. Goldberg J. R. Maddison J. Todd A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Ice sheet models are the main tool to generate forecasts of ice sheet mass loss, a significant contributor to sea level rise; thus, knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model, and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea level rise contribution or volume above flotation (VAF) due to the choice of different regularization strengths (prior strengths), sliding laws, and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L -curve analysis, as is typically done in ice sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularization suggested by the L curves is too strong, and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our two-member “sample” of observed velocity fields. |
format |
Article in Journal/Newspaper |
author |
B. Recinos D. Goldberg J. R. Maddison J. Todd |
author_facet |
B. Recinos D. Goldberg J. R. Maddison J. Todd |
author_sort |
B. Recinos |
title |
A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams |
title_short |
A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams |
title_full |
A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams |
title_fullStr |
A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams |
title_full_unstemmed |
A framework for time-dependent ice sheet uncertainty quantification, applied to three West Antarctic ice streams |
title_sort |
framework for time-dependent ice sheet uncertainty quantification, applied to three west antarctic ice streams |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-4241-2023 https://doaj.org/article/9e0c184d845443ffb329917c7ef0eafe |
genre |
Antarc* Antarctic Antarctica Ice Sheet The Cryosphere West Antarctica |
genre_facet |
Antarc* Antarctic Antarctica Ice Sheet The Cryosphere West Antarctica |
op_source |
The Cryosphere, Vol 17, Pp 4241-4266 (2023) |
op_relation |
https://tc.copernicus.org/articles/17/4241/2023/tc-17-4241-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-4241-2023 1994-0416 1994-0424 https://doaj.org/article/9e0c184d845443ffb329917c7ef0eafe |
op_doi |
https://doi.org/10.5194/tc-17-4241-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
10 |
container_start_page |
4241 |
op_container_end_page |
4266 |
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1781699877194956800 |