CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization
To study the effectiveness of methods to reduce errors for Arctic Sea ice initialization due to underestimation of background error covariance, an advanced ensemble analysis system has been developed. The system integrates the local ensemble transform Kalman filter (LETKF) with the community ice cod...
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Online Access: | https://doi.org/10.3390/jmse9090920 |
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ftmdpi:oai:mdpi.com:/2077-1312/9/9/920/ 2023-08-20T04:03:59+02:00 CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization Xiying Liu Zicheng Sha Chenchen Lu agris 2021-08-24 application/pdf https://doi.org/10.3390/jmse9090920 EN eng Multidisciplinary Digital Publishing Institute Physical Oceanography https://dx.doi.org/10.3390/jmse9090920 https://creativecommons.org/licenses/by/4.0/ Journal of Marine Science and Engineering; Volume 9; Issue 9; Pages: 920 Arctic ensemble Karman filter data assimilation numerical modeling sea ice Text 2021 ftmdpi https://doi.org/10.3390/jmse9090920 2023-08-01T02:31:08Z To study the effectiveness of methods to reduce errors for Arctic Sea ice initialization due to underestimation of background error covariance, an advanced ensemble analysis system has been developed. The system integrates the local ensemble transform Kalman filter (LETKF) with the community ice code (CICE). With a mixed layer ocean model used to compute the sea surface temperature (SST), the experiments on assimilation of observations of sea ice concentration (SIC) have been carried out. Assimilation experiments were performed over a 3-month period from January to March in 1997. The model was sequentially constrained with daily observation data. The effects of observation density, amplification factor for analysis error covariance, and relaxation of disturbance and spread on the results of SIC initialization were studied. It is shown that doubling the density of observation of SIC does not bring significant further improvement on the analysis result; when the ensemble size is doubled, most severe SIC biases in the Labrador, Greenland, Norwegian, and Barents seas are reduced; amplifying the analysis error covariance, relaxing disturbance, and relaxing spread all contribute to improving the reproduction of SIC with amplifying covariance with the largest magnitude. Text Arctic Greenland Sea ice MDPI Open Access Publishing Arctic Greenland Journal of Marine Science and Engineering 9 9 920 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
Arctic ensemble Karman filter data assimilation numerical modeling sea ice |
spellingShingle |
Arctic ensemble Karman filter data assimilation numerical modeling sea ice Xiying Liu Zicheng Sha Chenchen Lu CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization |
topic_facet |
Arctic ensemble Karman filter data assimilation numerical modeling sea ice |
description |
To study the effectiveness of methods to reduce errors for Arctic Sea ice initialization due to underestimation of background error covariance, an advanced ensemble analysis system has been developed. The system integrates the local ensemble transform Kalman filter (LETKF) with the community ice code (CICE). With a mixed layer ocean model used to compute the sea surface temperature (SST), the experiments on assimilation of observations of sea ice concentration (SIC) have been carried out. Assimilation experiments were performed over a 3-month period from January to March in 1997. The model was sequentially constrained with daily observation data. The effects of observation density, amplification factor for analysis error covariance, and relaxation of disturbance and spread on the results of SIC initialization were studied. It is shown that doubling the density of observation of SIC does not bring significant further improvement on the analysis result; when the ensemble size is doubled, most severe SIC biases in the Labrador, Greenland, Norwegian, and Barents seas are reduced; amplifying the analysis error covariance, relaxing disturbance, and relaxing spread all contribute to improving the reproduction of SIC with amplifying covariance with the largest magnitude. |
format |
Text |
author |
Xiying Liu Zicheng Sha Chenchen Lu |
author_facet |
Xiying Liu Zicheng Sha Chenchen Lu |
author_sort |
Xiying Liu |
title |
CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization |
title_short |
CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization |
title_full |
CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization |
title_fullStr |
CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization |
title_full_unstemmed |
CICE-LETKF Ensemble Analysis System with Application to Arctic Sea Ice Initialization |
title_sort |
cice-letkf ensemble analysis system with application to arctic sea ice initialization |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/jmse9090920 |
op_coverage |
agris |
geographic |
Arctic Greenland |
geographic_facet |
Arctic Greenland |
genre |
Arctic Greenland Sea ice |
genre_facet |
Arctic Greenland Sea ice |
op_source |
Journal of Marine Science and Engineering; Volume 9; Issue 9; Pages: 920 |
op_relation |
Physical Oceanography https://dx.doi.org/10.3390/jmse9090920 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/jmse9090920 |
container_title |
Journal of Marine Science and Engineering |
container_volume |
9 |
container_issue |
9 |
container_start_page |
920 |
_version_ |
1774714429907140608 |