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Earth System Dynamics An interactive open-access journal of the European Geosciences Union

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https://doi.org/10.5194/esd-2018-13
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
13 Mar 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Dynamics (ESD).
Using Network Theory and Machine Learning to predict El Niño
Peter D. Nooteboom1,3, Qing Yi Feng1,3, Cristóbal López2, Emilio Hernández-García2, and Henk A. Dijkstra1,3 1Institute for Marine and Atmospheric Research Utrecht (IMAU), Department of Physics, Utrecht University, The Netherlands
2Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB), University of the Balearic Islands, Spain
3Centre for Complex Systems Studies, Utrecht University, The Netherlands
Abstract. The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag of six months. In this paper, we aim to increase this prediction skill at lags up to one year. The new method to do so combines a classical Autoregressive Integrated Moving Average technique with a modern machine learning approach (through an Artificial Neural Network). The attributes in such a neural network are derived from topological properties of Climate Networks and are tested on both a Zebiak–Cane-type model and observations. For predictions up to six months ahead, the results of the hybrid model give a better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Moreover, results for a twelve-month lead time prediction have a similar skill as the shorter lead time predictions.
Citation: Nooteboom, P. D., Feng, Q. Y., López, C., Hernández-García, E., and Dijkstra, H. A.: Using Network Theory and Machine Learning to predict El Niño, Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2018-13, in review, 2018.
Peter D. Nooteboom et al.
Peter D. Nooteboom et al.
Peter D. Nooteboom et al.

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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 six months ahead. However, this article presents a method which combines network theory and machine learning and predicts El Niño up to one year ahead.
The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern...
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