fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models
Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in t...
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ftcopernicus:oai:publications.copernicus.org:gmd93665 2023-05-15T16:40:20+02:00 fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models Koziol, Conrad P. Todd, Joe A. Goldberg, Daniel N. Maddison, James R. 2021-09-24 application/pdf https://doi.org/10.5194/gmd-14-5843-2021 https://gmd.copernicus.org/articles/14/5843/2021/ eng eng doi:10.5194/gmd-14-5843-2021 https://gmd.copernicus.org/articles/14/5843/2021/ eISSN: 1991-9603 Text 2021 ftcopernicus https://doi.org/10.5194/gmd-14-5843-2021 2021-09-27T16:22:26Z Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice sheet models. Text Ice Sheet Copernicus Publications: E-Journals Geoscientific Model Development 14 9 5843 5861 |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
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English |
description |
Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice sheet models. |
format |
Text |
author |
Koziol, Conrad P. Todd, Joe A. Goldberg, Daniel N. Maddison, James R. |
spellingShingle |
Koziol, Conrad P. Todd, Joe A. Goldberg, Daniel N. Maddison, James R. fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
author_facet |
Koziol, Conrad P. Todd, Joe A. Goldberg, Daniel N. Maddison, James R. |
author_sort |
Koziol, Conrad P. |
title |
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
title_short |
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
title_full |
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
title_fullStr |
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
title_full_unstemmed |
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
title_sort |
fenics_ice 1.0: a framework for quantifying initialization uncertainty for time-dependent ice sheet models |
publishDate |
2021 |
url |
https://doi.org/10.5194/gmd-14-5843-2021 https://gmd.copernicus.org/articles/14/5843/2021/ |
genre |
Ice Sheet |
genre_facet |
Ice Sheet |
op_source |
eISSN: 1991-9603 |
op_relation |
doi:10.5194/gmd-14-5843-2021 https://gmd.copernicus.org/articles/14/5843/2021/ |
op_doi |
https://doi.org/10.5194/gmd-14-5843-2021 |
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Geoscientific Model Development |
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14 |
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9 |
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5843 |
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5861 |
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1766030724319674368 |