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arXiv · 1803.10076

Using Network Theory and Machine Learning to predict El Ni\~no

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

The skill of current predictions of the warm phase of the El Ni\~no 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.

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Peter D. Nooteboom, Qing Yi Feng, Cristóbal López, Emilio Hernández-García, Henk A. Dijkstra. 2018-03-27. Using Network Theory and Machine Learning to predict El Ni\~no. https://doi.org/10.5194/esd-9-969-2018

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