Articles | Volume 9, issue 3
Earth Syst. Dynam., 9, 969–983, 2018
https://doi.org/10.5194/esd-9-969-2018
Earth Syst. Dynam., 9, 969–983, 2018
https://doi.org/10.5194/esd-9-969-2018

Research article 23 Jul 2018

Research article | 23 Jul 2018

Using network theory and machine learning to predict El Niño

Peter D. Nooteboom et al.

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (17 Jun 2018) by Ben Kravitz
AR by Peter Nooteboom on behalf of the Authors (22 Jun 2018)  Author's response    Manuscript
ED: Publish as is (26 Jun 2018) by Ben Kravitz
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Short summary
The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern Pacific, fascinates people for a long time. El Niño is associated with natural disasters, such as droughts and floods. Current methods can make a reliable prediction of this phenomenon up to 6 months ahead. However, this article presents a method which combines network theory and machine learning which predicts El Niño up to 1 year ahead.
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