Using network theory and machine learning  to predict El Niño

Nooteboom, Peter D.; Feng, Qing Yi; López, Cristóbal; Hernández-García, Emilio; Dijkstra, Henk A.

doi:https://doi.org/10.5194/esd-9-969-2018

Articles | Volume 9, issue 3

Earth Syst. Dynam., 9, 969–983, 2018

https://doi.org/10.5194/esd-9-969-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Earth Syst. Dynam., 9, 969–983, 2018

https://doi.org/10.5194/esd-9-969-2018

© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 3

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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Final revised paper (published on 23 Jul 2018)
Preprint (discussion started on 13 Mar 2018)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of ``Using Network Theory and Machine Learning to predict {El Ni\~{n}o}''', Robert Link, 28 Mar 2018
- AC1: 'Interactive comment on “Using Network Theory and Machine Learning to predict El Niño” by Peter Nooteboom et al.', Peter Nooteboom, 06 Jun 2018
RC2: 'Review of Nooteboom et al', Anonymous Referee #2, 13 Apr 2018
- AC2: 'Reply', Peter Nooteboom, 06 Jun 2018

Peer-review completion

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

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.