Towards reliable Arctic sea ice prediction using multivariate data assimilation
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation...
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ftawi:oai:epic.awi.de:48980 2023-05-15T14:27:17+02:00 Towards reliable Arctic sea ice prediction using multivariate data assimilation Liu, Jiping Chen, Zhiqiang Hu, Yongyun Zhang, Yuanyuan Ding, Yifan Cheng, Xiao Yang, Qinghua Nerger, Lars Spreen, Gunnar Horton, Radley Inoue, Jun Yang, Chaoyuan Li, Ming 2019 https://epic.awi.de/id/eprint/48980/ https://hdl.handle.net/10013/epic.4601e534-f780-4995-8f2b-71bd3905c736 unknown Elsevier Liu, J. , Chen, Z. , Hu, Y. , Zhang, Y. , Ding, Y. , Cheng, X. , Yang, Q. , Nerger, L. orcid:0000-0002-1908-1010 , Spreen, G. , Horton, R. , Inoue, J. , Yang, C. and Li, M. (2019) Towards reliable Arctic sea ice prediction using multivariate data assimilation , Science Bulletin, 64 , pp. 63-72 . doi:10.1016/j.scib.2018.11.018 <https://doi.org/10.1016/j.scib.2018.11.018> , hdl:10013/epic.4601e534-f780-4995-8f2b-71bd3905c736 EPIC3Science Bulletin, Elsevier, 64, pp. 63-72 Article isiRev 2019 ftawi https://doi.org/10.1016/j.scib.2018.11.018 2021-12-24T15:44:28Z Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the “best” estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models. Article in Journal/Newspaper Arctic Arctic Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Arctic Science Bulletin 64 1 63 72 |
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Open Polar |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
language |
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description |
Rapid declines in Arctic sea ice have captured attention and pose significant challenges to a variety of stakeholders. There is a rising demand for Arctic sea ice prediction at daily to seasonal time scales, which is partly a sea ice initial condition problem. Thus, a multivariate data assimilation that integrates sea ice observations to generate realistic and skillful model initialization is needed to improve predictive skill of Arctic sea ice. Sea ice data assimilation is a relatively new research area. In this review paper, we focus on two challenges for implementing multivariate data assimilation systems for sea ice forecast. First, to address the challenge of limited spatiotemporal coverage and large uncertainties of observations, we discuss sea ice parameters derived from satellite remote sensing that (1) have been utilized for improved model initialization, including concentration, thickness and drift, and (2) are currently under development with the potential for enhancing the predictability of Arctic sea ice, including melt ponds and sea ice leads. Second, to strive to generate the “best” estimate of sea ice initial conditions by combining model simulations/forecasts and observations, we review capabilities and limitations of different data assimilation techniques that have been developed and used to assimilate observed sea ice parameters in dynamical models. |
format |
Article in Journal/Newspaper |
author |
Liu, Jiping Chen, Zhiqiang Hu, Yongyun Zhang, Yuanyuan Ding, Yifan Cheng, Xiao Yang, Qinghua Nerger, Lars Spreen, Gunnar Horton, Radley Inoue, Jun Yang, Chaoyuan Li, Ming |
spellingShingle |
Liu, Jiping Chen, Zhiqiang Hu, Yongyun Zhang, Yuanyuan Ding, Yifan Cheng, Xiao Yang, Qinghua Nerger, Lars Spreen, Gunnar Horton, Radley Inoue, Jun Yang, Chaoyuan Li, Ming Towards reliable Arctic sea ice prediction using multivariate data assimilation |
author_facet |
Liu, Jiping Chen, Zhiqiang Hu, Yongyun Zhang, Yuanyuan Ding, Yifan Cheng, Xiao Yang, Qinghua Nerger, Lars Spreen, Gunnar Horton, Radley Inoue, Jun Yang, Chaoyuan Li, Ming |
author_sort |
Liu, Jiping |
title |
Towards reliable Arctic sea ice prediction using multivariate data assimilation |
title_short |
Towards reliable Arctic sea ice prediction using multivariate data assimilation |
title_full |
Towards reliable Arctic sea ice prediction using multivariate data assimilation |
title_fullStr |
Towards reliable Arctic sea ice prediction using multivariate data assimilation |
title_full_unstemmed |
Towards reliable Arctic sea ice prediction using multivariate data assimilation |
title_sort |
towards reliable arctic sea ice prediction using multivariate data assimilation |
publisher |
Elsevier |
publishDate |
2019 |
url |
https://epic.awi.de/id/eprint/48980/ https://hdl.handle.net/10013/epic.4601e534-f780-4995-8f2b-71bd3905c736 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Sea ice |
genre_facet |
Arctic Arctic Sea ice |
op_source |
EPIC3Science Bulletin, Elsevier, 64, pp. 63-72 |
op_relation |
Liu, J. , Chen, Z. , Hu, Y. , Zhang, Y. , Ding, Y. , Cheng, X. , Yang, Q. , Nerger, L. orcid:0000-0002-1908-1010 , Spreen, G. , Horton, R. , Inoue, J. , Yang, C. and Li, M. (2019) Towards reliable Arctic sea ice prediction using multivariate data assimilation , Science Bulletin, 64 , pp. 63-72 . doi:10.1016/j.scib.2018.11.018 <https://doi.org/10.1016/j.scib.2018.11.018> , hdl:10013/epic.4601e534-f780-4995-8f2b-71bd3905c736 |
op_doi |
https://doi.org/10.1016/j.scib.2018.11.018 |
container_title |
Science Bulletin |
container_volume |
64 |
container_issue |
1 |
container_start_page |
63 |
op_container_end_page |
72 |
_version_ |
1766300923986968576 |