Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lu, Zhenghui, Dong, Wenjie, Lu, Bo, Yuan, Naiming, Ma, Zhuguo, Bogachev, Mikhail I., Kurths, Juergen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2022
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
_version_ 1784671206395346944
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
work_keys_str_mv AT luzhenghui earlywarningoftheindianoceandipoleusingclimatenetworkanalysis
AT dongwenjie earlywarningoftheindianoceandipoleusingclimatenetworkanalysis
AT lubo earlywarningoftheindianoceandipoleusingclimatenetworkanalysis
AT yuannaiming earlywarningoftheindianoceandipoleusingclimatenetworkanalysis
AT mazhuguo earlywarningoftheindianoceandipoleusingclimatenetworkanalysis
AT bogachevmikhaili earlywarningoftheindianoceandipoleusingclimatenetworkanalysis
AT kurthsjuergen earlywarningoftheindianoceandipoleusingclimatenetworkanalysis