Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION
Here, we implement a variance-based distance metric (D n ) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and tempo...
Published in: | Journal of Geophysical Research: Oceans |
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Online Access: | http://www.osti.gov/servlets/purl/1351197 https://www.osti.gov/biblio/1351197 https://doi.org/10.1002/2016JC012602 |
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ftosti:oai:osti.gov:1351197 2023-07-30T04:06:42+02:00 Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION Urrego Blanco, Jorge Rolando Hunke, Elizabeth Clare Urban, Nathan Mark Jeffery, Nicole Turner, Adrian Keith Langenbrunner, James R. Booker, Jane 2021-07-23 application/pdf http://www.osti.gov/servlets/purl/1351197 https://www.osti.gov/biblio/1351197 https://doi.org/10.1002/2016JC012602 unknown http://www.osti.gov/servlets/purl/1351197 https://www.osti.gov/biblio/1351197 https://doi.org/10.1002/2016JC012602 doi:10.1002/2016JC012602 58 GEOSCIENCES 2021 ftosti https://doi.org/10.1002/2016JC012602 2023-07-11T09:18:05Z Here, we implement a variance-based distance metric (D n ) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness. Other/Unknown Material Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Journal of Geophysical Research: Oceans 122 4 2923 2944 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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58 GEOSCIENCES Urrego Blanco, Jorge Rolando Hunke, Elizabeth Clare Urban, Nathan Mark Jeffery, Nicole Turner, Adrian Keith Langenbrunner, James R. Booker, Jane Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION |
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58 GEOSCIENCES |
description |
Here, we implement a variance-based distance metric (D n ) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal grids improving upon highly aggregated metrics (e.g., total sea ice extent or volume) by capturing the spatial character of model skill. The D n metric is a gamma-distributed statistic that is more general than the χ 2 statistic commonly used to assess model fit, which requires the assumption that the model is unbiased and can only incorporate observational error in the analysis. The D n statistic does not assume that the model is unbiased, and allows the incorporation of multiple observational data sets for the same variable and simultaneously for different variables, along with different types of variances that can characterize uncertainties in both observations and the model. This approach represents a step to establish a systematic framework for probabilistic validation of sea ice models. The methodology is also useful for model tuning by using the D n metric as a cost function and incorporating model parametric uncertainty as part of a scheme to optimize model functionality. We apply this approach to evaluate different configurations of the standalone Los Alamos sea ice model (CICE) encompassing the parametric uncertainty in the model, and to find new sets of model configurations that produce better agreement than previous configurations between model and observational estimates of sea ice concentration and thickness. |
author |
Urrego Blanco, Jorge Rolando Hunke, Elizabeth Clare Urban, Nathan Mark Jeffery, Nicole Turner, Adrian Keith Langenbrunner, James R. Booker, Jane |
author_facet |
Urrego Blanco, Jorge Rolando Hunke, Elizabeth Clare Urban, Nathan Mark Jeffery, Nicole Turner, Adrian Keith Langenbrunner, James R. Booker, Jane |
author_sort |
Urrego Blanco, Jorge Rolando |
title |
Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION |
title_short |
Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION |
title_full |
Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION |
title_fullStr |
Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION |
title_full_unstemmed |
Validation of sea ice models using an uncertainty-based distance metric for multiple model variables: NEW METRIC FOR SEA ICE MODEL VALIDATION |
title_sort |
validation of sea ice models using an uncertainty-based distance metric for multiple model variables: new metric for sea ice model validation |
publishDate |
2021 |
url |
http://www.osti.gov/servlets/purl/1351197 https://www.osti.gov/biblio/1351197 https://doi.org/10.1002/2016JC012602 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1351197 https://www.osti.gov/biblio/1351197 https://doi.org/10.1002/2016JC012602 doi:10.1002/2016JC012602 |
op_doi |
https://doi.org/10.1002/2016JC012602 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
122 |
container_issue |
4 |
container_start_page |
2923 |
op_container_end_page |
2944 |
_version_ |
1772819557182865408 |