Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model
Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a q...
Published in: | Journal of Geophysical Research: Oceans |
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ftosti:oai:osti.gov:1325643 2023-07-30T04:06:41+02:00 Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model Urrego Blanco, Jorge Rolando Urban, Nathan Mark Hunke, Elizabeth Clare Turner, Adrian Keith Jeffery, Nicole 2022-05-23 application/pdf http://www.osti.gov/servlets/purl/1325643 https://www.osti.gov/biblio/1325643 https://doi.org/10.1002/2015JC011558 unknown http://www.osti.gov/servlets/purl/1325643 https://www.osti.gov/biblio/1325643 https://doi.org/10.1002/2015JC011558 doi:10.1002/2015JC011558 58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES 2022 ftosti https://doi.org/10.1002/2015JC011558 2023-07-11T09:15:22Z Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model. Other/Unknown Material Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Journal of Geophysical Research: Oceans 121 4 2709 2732 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES |
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58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES Urrego Blanco, Jorge Rolando Urban, Nathan Mark Hunke, Elizabeth Clare Turner, Adrian Keith Jeffery, Nicole Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model |
topic_facet |
58 GEOSCIENCES 54 ENVIRONMENTAL SCIENCES |
description |
Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. Lastly, it is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model. |
author |
Urrego Blanco, Jorge Rolando Urban, Nathan Mark Hunke, Elizabeth Clare Turner, Adrian Keith Jeffery, Nicole |
author_facet |
Urrego Blanco, Jorge Rolando Urban, Nathan Mark Hunke, Elizabeth Clare Turner, Adrian Keith Jeffery, Nicole |
author_sort |
Urrego Blanco, Jorge Rolando |
title |
Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model |
title_short |
Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model |
title_full |
Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model |
title_fullStr |
Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model |
title_full_unstemmed |
Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model |
title_sort |
uncertainty quantification and global sensitivity analysis of the los alamos sea ice model |
publishDate |
2022 |
url |
http://www.osti.gov/servlets/purl/1325643 https://www.osti.gov/biblio/1325643 https://doi.org/10.1002/2015JC011558 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1325643 https://www.osti.gov/biblio/1325643 https://doi.org/10.1002/2015JC011558 doi:10.1002/2015JC011558 |
op_doi |
https://doi.org/10.1002/2015JC011558 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
121 |
container_issue |
4 |
container_start_page |
2709 |
op_container_end_page |
2732 |
_version_ |
1772819547323105280 |