Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017
Abstract In an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general c...
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Online Access: | http://dx.doi.org/10.1017/jog.2019.55 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143019000558 |
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crcambridgeupr:10.1017/jog.2019.55 2024-10-13T14:04:08+00:00 Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 Mu, Longjiang Liang, Xi Yang, Qinghua Liu, Jiping Zheng, Fei 2019 http://dx.doi.org/10.1017/jog.2019.55 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143019000558 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by-nc-nd/4.0/ Journal of Glaciology volume 65, issue 253, page 813-821 ISSN 0022-1430 1727-5652 journal-article 2019 crcambridgeupr https://doi.org/10.1017/jog.2019.55 2024-10-02T04:01:03Z Abstract In an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system. Article in Journal/Newspaper Arctic Beaufort Sea Journal of Glaciology Sea ice Cambridge University Press Arctic Journal of Glaciology 65 253 813 821 |
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Open Polar |
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Cambridge University Press |
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crcambridgeupr |
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English |
description |
Abstract In an effort to improve the reliability of Arctic sea-ice predictions, an ensemble-based Arctic Ice Ocean Prediction System (ArcIOPS) has been developed to meet operational demands. The system is based on a regional Arctic configuration of the Massachusetts Institute of Technology general circulation model. A localized error subspace transform ensemble Kalman filter is used to assimilate the weekly merged CryoSat-2 and Soil Moisture and Ocean Salinity sea-ice thickness data together with the daily Advanced Microwave Scanning Radiometer 2 (AMSR2) sea-ice concentration data. The weather forecasts from the Global Forecast System of the National Centers for Environmental Prediction drive the sea ice–ocean coupled model. The ensemble mean sea-ice forecasts were used to facilitate the Chinese National Arctic Research Expedition in summer 2017. The forecasted sea-ice concentration is evaluated against AMSR2 and Special Sensor Microwave Imager/Sounder sea-ice concentration data. The forecasted sea-ice thickness is compared to the in-situ observations and the Pan-Arctic Ice-Ocean Modeling and Assimilation System. These comparisons show the promising potential of ArcIOPS for operational Arctic sea-ice forecasts. Nevertheless, the forecast bias in the Beaufort Sea calls for a delicate parameter calibration and a better design of the assimilation system. |
format |
Article in Journal/Newspaper |
author |
Mu, Longjiang Liang, Xi Yang, Qinghua Liu, Jiping Zheng, Fei |
spellingShingle |
Mu, Longjiang Liang, Xi Yang, Qinghua Liu, Jiping Zheng, Fei Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 |
author_facet |
Mu, Longjiang Liang, Xi Yang, Qinghua Liu, Jiping Zheng, Fei |
author_sort |
Mu, Longjiang |
title |
Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 |
title_short |
Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 |
title_full |
Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 |
title_fullStr |
Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 |
title_full_unstemmed |
Arctic Ice Ocean Prediction System: evaluating sea-ice forecasts during Xuelong's first trans-Arctic Passage in summer 2017 |
title_sort |
arctic ice ocean prediction system: evaluating sea-ice forecasts during xuelong's first trans-arctic passage in summer 2017 |
publisher |
Cambridge University Press (CUP) |
publishDate |
2019 |
url |
http://dx.doi.org/10.1017/jog.2019.55 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143019000558 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Beaufort Sea Journal of Glaciology Sea ice |
genre_facet |
Arctic Beaufort Sea Journal of Glaciology Sea ice |
op_source |
Journal of Glaciology volume 65, issue 253, page 813-821 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
op_doi |
https://doi.org/10.1017/jog.2019.55 |
container_title |
Journal of Glaciology |
container_volume |
65 |
container_issue |
253 |
container_start_page |
813 |
op_container_end_page |
821 |
_version_ |
1812809286570475520 |