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

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

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

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

| Highlight paper

|

23 Jul 2018

Research article | Highlight paper |

| 23 Jul 2018

Using network theory and machine learning to predict El Niño

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

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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

AR by Peter Nooteboom on behalf of the Authors (07 Jul 2018)

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.