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  Early warning of the Indian Ocean Dipole using climate network analysis

Lu, Z., Dong, W., Lu, B., Yuan, N., Ma, Z., Bogachev, M. I., & Kurths, J. (2022). Early warning of the Indian Ocean Dipole using climate network analysis. Proceedings of the National Academy of Sciences of the United States of America (PNAS), 119(11):. doi:10.1073/pnas.2109089119.

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資料種別: 学術論文

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27025oa.pdf (出版社版), 3MB
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27025oa.pdf
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 作成者:
Lu, Zhenghui1, 著者
Dong, Wenjie1, 著者
Lu, Bo1, 著者
Yuan, Naiming1, 著者
Ma, Zhuguo1, 著者
Bogachev, Mikhail I.1, 著者
Kurths, Jürgen2, 著者              
所属:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 要旨: In recent years, the Indian Ocean Dipole (IOD) has received much attention in light of its substantial impacts on both the climate system and humanity. Due to its complexity, however, a reliable prediction of the IOD is still a great challenge. In this study, climate network analysis was employed to investigate whether there are early warning signals prior to the start of IOD events. An enhanced seesaw tendency in sea surface temperature (SST) among a large number of grid points between the dipole regions in the tropical Indian Ocean was revealed in boreal winter, which can be used to forewarn the potential occurrence of the IOD in the coming year. We combined this insight with the indicator of the December equatorial zonal wind in the tropical Indian Ocean to propose a network-based predictor that clearly outperforms the current dynamic models. Of the 15 IOD events over the past 37 y (1984 to 2020), 11 events were correctly predicted from December of the previous year, i.e., a hit rate of higher than 70%, and the false alarm rate was around 35%. This network-based approach suggests a perspective for better understanding and predicting the IOD.

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言語: eng - 英語
 日付: 2022-03-072022-03-15
 出版の状態: Finally published
 ページ: 9
 出版情報: -
 目次: -
 査読: 査読あり
 識別子(DOI, ISBNなど): DOI: 10.1073/pnas.2109089119
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Climate impacts
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Oceans
Research topic keyword: Weather
Model / method: Nonlinear Data Analysis
Working Group: Network- and machine-learning-based prediction of extreme events
OATYPE: Hybrid Open Access
 学位: -

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出版物 1

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出版物名: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
種別: 学術雑誌, SCI, Scopus, p3
 著者・編者:
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出版社, 出版地: -
ページ: - 巻号: 119 (11) 通巻号: e2109089119 開始・終了ページ: - 識別子(ISBN, ISSN, DOIなど): CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals410
Publisher: National Academy of Sciences (NAS)