Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter
Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an Ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Para...
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ftcopernicus:oai:publications.copernicus.org:tcd84924 2023-05-15T15:03:15+02:00 Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter Zhang, Yong-Fei Bitz, Cecilia M. Anderson, Jeffrey L. Collins, Nancy S. Hoar, Timothy J. Raeder, Kevin D. Blanchard-Wrigglesworth, Edward 2020-05-13 application/pdf https://doi.org/10.5194/tc-2020-96 https://tc.copernicus.org/preprints/tc-2020-96/ eng eng doi:10.5194/tc-2020-96 https://tc.copernicus.org/preprints/tc-2020-96/ eISSN: 1994-0424 Text 2020 ftcopernicus https://doi.org/10.5194/tc-2020-96 2020-07-20T16:22:10Z Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an Ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge compared to requiring the parameter to be uniform everywhere. We compare experiments with both PE and state estimation to experiments with only the latter and found that the benefits of PE mostly occur after the DA period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE’s relevance for improving seasonal predictions of Arctic sea ice. Text Arctic Climate change Sea ice Copernicus Publications: E-Journals Arctic |
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Open Polar |
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Copernicus Publications: E-Journals |
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ftcopernicus |
language |
English |
description |
Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an Ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge compared to requiring the parameter to be uniform everywhere. We compare experiments with both PE and state estimation to experiments with only the latter and found that the benefits of PE mostly occur after the DA period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE’s relevance for improving seasonal predictions of Arctic sea ice. |
format |
Text |
author |
Zhang, Yong-Fei Bitz, Cecilia M. Anderson, Jeffrey L. Collins, Nancy S. Hoar, Timothy J. Raeder, Kevin D. Blanchard-Wrigglesworth, Edward |
spellingShingle |
Zhang, Yong-Fei Bitz, Cecilia M. Anderson, Jeffrey L. Collins, Nancy S. Hoar, Timothy J. Raeder, Kevin D. Blanchard-Wrigglesworth, Edward Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter |
author_facet |
Zhang, Yong-Fei Bitz, Cecilia M. Anderson, Jeffrey L. Collins, Nancy S. Hoar, Timothy J. Raeder, Kevin D. Blanchard-Wrigglesworth, Edward |
author_sort |
Zhang, Yong-Fei |
title |
Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter |
title_short |
Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter |
title_full |
Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter |
title_fullStr |
Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter |
title_full_unstemmed |
Estimating Parameters in a Sea Ice Model using an Ensemble Kalman Filter |
title_sort |
estimating parameters in a sea ice model using an ensemble kalman filter |
publishDate |
2020 |
url |
https://doi.org/10.5194/tc-2020-96 https://tc.copernicus.org/preprints/tc-2020-96/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Climate change Sea ice |
genre_facet |
Arctic Climate change Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-2020-96 https://tc.copernicus.org/preprints/tc-2020-96/ |
op_doi |
https://doi.org/10.5194/tc-2020-96 |
_version_ |
1766335134022238208 |