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Early warning of the Indian Ocean Dipole using climate network analysis
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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931208/ https://www.ncbi.nlm.nih.gov/pubmed/35254900 http://dx.doi.org/10.1073/pnas.2109089119 |
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author | Lu, Zhenghui Dong, Wenjie Lu, Bo Yuan, Naiming Ma, Zhuguo Bogachev, Mikhail I. Kurths, Juergen |
author_facet | Lu, Zhenghui Dong, Wenjie Lu, Bo Yuan, Naiming Ma, Zhuguo Bogachev, Mikhail I. Kurths, Juergen |
author_sort | Lu, Zhenghui |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8931208 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-89312082022-03-19 Early warning of the Indian Ocean Dipole using climate network analysis Lu, Zhenghui Dong, Wenjie Lu, Bo Yuan, Naiming Ma, Zhuguo Bogachev, Mikhail I. Kurths, Juergen Proc Natl Acad Sci U S A Physical Sciences 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. National Academy of Sciences 2022-03-07 2022-03-15 /pmc/articles/PMC8931208/ /pubmed/35254900 http://dx.doi.org/10.1073/pnas.2109089119 Text en Copyright © 2022 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Physical Sciences Lu, Zhenghui Dong, Wenjie Lu, Bo Yuan, Naiming Ma, Zhuguo Bogachev, Mikhail I. Kurths, Juergen Early warning of the Indian Ocean Dipole using climate network analysis |
title | Early warning of the Indian Ocean Dipole using climate network analysis |
title_full | Early warning of the Indian Ocean Dipole using climate network analysis |
title_fullStr | Early warning of the Indian Ocean Dipole using climate network analysis |
title_full_unstemmed | Early warning of the Indian Ocean Dipole using climate network analysis |
title_short | Early warning of the Indian Ocean Dipole using climate network analysis |
title_sort | early warning of the indian ocean dipole using climate network analysis |
topic | Physical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8931208/ https://www.ncbi.nlm.nih.gov/pubmed/35254900 http://dx.doi.org/10.1073/pnas.2109089119 |
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