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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 3 | Copyright
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.

Research article 23 Jul 2018

Research article | 23 Jul 2018

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 Peter D. Nooteboom et al.
  • 1Institute for Marine and Atmospheric Research Utrecht (IMAU), Department of Physics, Utrecht University, Utrecht, the Netherlands
  • 2Instituto de Física Interdisciplinar y Sistemas Complejos (IFISC, CSIC-UIB), University of the Balearic Islands, Balearic Islands, Spain
  • 3Centre for Complex Systems Studies, Utrecht University, Utrecht, the Netherlands

Abstract. The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag time of 6 months. In this paper, we aim to increase this prediction skill at lag times of up to 1 year. The new method 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 knowledge of physical processes and topological properties of climate networks, and they are tested using a Zebiak–Cane-type model and observations. For predictions up to 6 months ahead, the results of the hybrid model give a slightly better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Interestingly, results for a 12-month lead time prediction have a similar skill as the shorter lead time predictions.

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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.
The prediction of the El Niño phenomenon, an increased sea surface temperature in the eastern...
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