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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, e2109089119.
https://doi.org/10.1073/pnas.2109089119

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Lu, Zhenghui1, Autor
Dong, Wenjie1, Autor
Lu, Bo1, Autor
Yuan, Naiming1, Autor
Ma, Zhuguo1, Autor
Bogachev, Mikhail I.1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: 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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Sprache(n): eng - Englisch
 Datum: 2022-03-072022-03-15
 Publikationsstatus: Final veröffentlicht
 Seiten: 9
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: 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
 Art des Abschluß: -

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Titel: Proceedings of the National Academy of Sciences of the United States of America (PNAS)
Genre der Quelle: Zeitschrift, SCI, Scopus, p3
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 119 (11) Artikelnummer: e2109089119 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals410
Publisher: National Academy of Sciences (NAS)