Articles | Volume 9, issue 3
Earth Syst. Dynam., 9, 969–983, 2018
Earth Syst. Dynam., 9, 969–983, 2018
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 et al.

Related authors

Sedimentary microplankton distributions are shaped by oceanographically connected areas
Peter D. Nooteboom, Peter K. Bijl, Christian Kehl, Erik van Sebille, Martin Ziegler, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 13, 357–371,,, 2022
Short summary

Related subject area

Dynamics of the Earth system: models
Present and future European heat wave magnitudes: climatologies, trends, and their associated uncertainties in GCM-RCM model chains
Changgui Lin, Erik Kjellström, Renate Anna Irma Wilcke, and Deliang Chen
Earth Syst. Dynam., 13, 1197–1214,,, 2022
Short summary
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 13, 1157–1165,,, 2022
Short summary
Estimating the lateral transfer of organic carbon through the European river network using a land surface model
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144,,, 2022
Short summary
Effect of the Atlantic Meridional Overturning Circulation on atmospheric pCO2 variations
Amber Boot, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 13, 1041–1058,,, 2022
Short summary
A methodology for the spatiotemporal identification of compound hazards: wind and precipitation extremes in Great Britain (1979–2019)
Aloïs Tilloy, Bruce D. Malamud, and Amélie Joly-Laugel
Earth Syst. Dynam., 13, 993–1020,,, 2022
Short summary

Cited articles

Akaike, H.: A New Look at the Statistical Model Identification, IEEE T. Automat. Contr., AC-19, 716–723,, 1974.
Aladag, C. H., Egrioglu, E., and Kadilar, C.: Forecasting nonlinear time series with a hybrid methodology, Appl. Math. Lett., 22, 1467–1470,, 2009.
Al-Smadi, A. and Al-Zaben, A.: ARMA Model Order Determination Using Edge Detection: A New Perspective, Circuits, Systems Signal Processing, 24, 723–732, 2005.
Berezin, Y., Gozolchiani, A., Guez, O., and Havlin, S.: Stability of Climate Networks with Time, Sci. Rep.-UK, 2, 1–8,, 2012.
Bergmeir, C. and Benítez, J. M.: On the use of cross-validation for time series predictor evaluation, Inf. Sci. (Ny)., 191, 192–213,, 2012.
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.
Final-revised paper