Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| Predictability and error growth dynamics of the Kuroshio Extension state transition process in an eddy-resolving regional ocean model | |
Geng, Yu1,2; Wang, Qiang3; Mu, Mu1,4; Zhang, Kun1,5
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| 2020-09-01 | |
| Source Publication | OCEAN MODELLING
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| ISSN | 1463-5003 |
| Volume | 153Pages:13 |
| Corresponding Author | Wang, Qiang(wangq@hhu.edu.cn) |
| Abstract | Based on an eddy-resolving Regional Ocean Modeling System (ROMS) and the conditional nonlinear optimal perturbation (CNOP) method, we set up a nonlinear optimization system to investigate the effects of the fastest -growing initial errors on the prediction of the Kuroshio Extension (KE) transition process from the stable to the unstable state. The results reveal that the larger values of the CNOP-type fastest-growing initial errors are mainly located upstream of the KE over the upper 500 m. Furthermore, the dynamic mechanism of the error growth is explored in terms of error movement, maintenance and development during the KE transition period. The results indicate that the errors are transported eastward from the south of Japan to the KE's first quasi-stationary meander by linear advection. The nonlinear advection slows down the moving speed of these errors, leading to their long-term existence in the first quasi-stationary meander of the KE. In this situation, errors have enough time to absorb energy from the background field continuously through barotropic energy conversion. Ultimately, errors grow rapidly and further induce the overestimation of the KE strength. |
| Keyword | Kuroshio Extension Predictability Initial error |
| DOI | 10.1016/j.ocemod.2020.101659 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Qingdao National Laboratory for Marine Science and Technology[QNLM2016ORP0107] ; Fundamental Research Funds for the Central Universities[B200201011] ; National Natural Science Foundation of China[41576015] ; National Natural Science Foundation of China[41806013] ; National Natural Science Foundation of China[41906003] ; NSFC Innovative Group[41421005] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA20060502] ; National Programme on Global Change and Air-Sea Interaction[GASI-IPOVAI-06] ; NSFC-Shandong Joint Fund for Marine Science Research Centers[U1606402] ; High Performance Computing Center at the Institute of Oceanology, Chinese Academy of Sciences |
| WOS Research Area | Meteorology & Atmospheric Sciences ; Oceanography |
| WOS Subject | Meteorology & Atmospheric Sciences ; Oceanography |
| WOS ID | WOS:000564119200005 |
| Publisher | ELSEVIER SCI LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/168535 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Wang, Qiang |
| Affiliation | 1.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Hohai Univ, Coll Oceanog, Nanjing 210098, Peoples R China 4.Fudan Univ, Inst Atmospher Sci, Shanghai 200438, Peoples R China 5.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China |
| First Author Affilication | Institute of Oceanology, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Geng, Yu,Wang, Qiang,Mu, Mu,et al. Predictability and error growth dynamics of the Kuroshio Extension state transition process in an eddy-resolving regional ocean model[J]. OCEAN MODELLING,2020,153:13. |
| APA | Geng, Yu,Wang, Qiang,Mu, Mu,&Zhang, Kun.(2020).Predictability and error growth dynamics of the Kuroshio Extension state transition process in an eddy-resolving regional ocean model.OCEAN MODELLING,153,13. |
| MLA | Geng, Yu,et al."Predictability and error growth dynamics of the Kuroshio Extension state transition process in an eddy-resolving regional ocean model".OCEAN MODELLING 153(2020):13. |
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| Predictability and e（11651KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View | |
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