A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model
Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence ev...
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ftunivnotexas:info:ark/67531/metadc835362 2023-05-15T18:18:07+02:00 A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model Covey, C Brandon, S Bremer, P T Domyancis, D Garaizar, X Johannesson, G Klein, R Klein, S A Lucas, D D Tannahill, J Zhang, Y United States. Department of Energy. 2011-10-27 PDF-file: 40 pages; size: 3.2 Mbytes Text https://doi.org/10.2172/1035301 http://digital.library.unt.edu/ark:/67531/metadc835362/ English eng Lawrence Livermore National Laboratory rep-no: LLNL-TR-509454 grantno: W-7405-ENG-48 doi:10.2172/1035301 osti: 1035301 http://digital.library.unt.edu/ark:/67531/metadc835362/ ark: ark:/67531/metadc835362 Weather Clouds Carbon Dioxide Seas Climates Sensitivity Simulation Boundary Conditions Climate Models 54 Environmental Sciences Metrics Boundary Layers Accuracy Algorithms Report 2011 ftunivnotexas https://doi.org/10.2172/1035301 2016-12-03T23:11:22Z Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide. Report Sea ice University of North Texas: UNT Digital Library |
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Open Polar |
collection |
University of North Texas: UNT Digital Library |
op_collection_id |
ftunivnotexas |
language |
English |
topic |
Weather Clouds Carbon Dioxide Seas Climates Sensitivity Simulation Boundary Conditions Climate Models 54 Environmental Sciences Metrics Boundary Layers Accuracy Algorithms |
spellingShingle |
Weather Clouds Carbon Dioxide Seas Climates Sensitivity Simulation Boundary Conditions Climate Models 54 Environmental Sciences Metrics Boundary Layers Accuracy Algorithms Covey, C Brandon, S Bremer, P T Domyancis, D Garaizar, X Johannesson, G Klein, R Klein, S A Lucas, D D Tannahill, J Zhang, Y A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model |
topic_facet |
Weather Clouds Carbon Dioxide Seas Climates Sensitivity Simulation Boundary Conditions Climate Models 54 Environmental Sciences Metrics Boundary Layers Accuracy Algorithms |
description |
Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide. |
author2 |
United States. Department of Energy. |
format |
Report |
author |
Covey, C Brandon, S Bremer, P T Domyancis, D Garaizar, X Johannesson, G Klein, R Klein, S A Lucas, D D Tannahill, J Zhang, Y |
author_facet |
Covey, C Brandon, S Bremer, P T Domyancis, D Garaizar, X Johannesson, G Klein, R Klein, S A Lucas, D D Tannahill, J Zhang, Y |
author_sort |
Covey, C |
title |
A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model |
title_short |
A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model |
title_full |
A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model |
title_fullStr |
A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model |
title_full_unstemmed |
A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model |
title_sort |
new ensemble of perturbed-input-parameter simulations by the community atmosphere model |
publisher |
Lawrence Livermore National Laboratory |
publishDate |
2011 |
url |
https://doi.org/10.2172/1035301 http://digital.library.unt.edu/ark:/67531/metadc835362/ |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
rep-no: LLNL-TR-509454 grantno: W-7405-ENG-48 doi:10.2172/1035301 osti: 1035301 http://digital.library.unt.edu/ark:/67531/metadc835362/ ark: ark:/67531/metadc835362 |
op_doi |
https://doi.org/10.2172/1035301 |
_version_ |
1766194545811259392 |