EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Echo-State Networks for Predicting ENSO Beyond One Year

forough hassanibesheli1,2, Niklas Boers2,3, and Jurgen Kurths1,2
forough hassanibesheli et al.
  • 1Humboldt university of Berlin, Physics, Germany (
  • 2Potsdam Institute for Climate Impact Research (PIK)
  • 3Department of Mathematics and Computer Science, Free University Berlin, Germany

Most forecasting schemes in the geosciences, and in particular for predicting weather and
climate indices such as the El Niño Southern Oscillation (ENSO), rely on process-based
numerical models [1]. Although statistical modelling[2] and prediction approaches also have
a long history, more recently, different machine learning techniques have been used to predict
climatic time series. One of the supervised machine learning algorithm which is suited for
temporal and sequential data processing and prediction is given by recurrent neural networks
(RNNs)[3]. In this study we develop a RNN-based method that (1) can learn the dynamics
of a stochastic time series without requiring access to a huge amount of data for training, and
(2) has comparatively simple structure and efficient training procedure. Since this algorithm
is suitable for investigating complex nonlinear time series such as climate time series, we
apply it to different ENSO indices. We demonstrate that our model can capture key features
of the complex system dynamics underlying ENSO variability, and that it can accurately
forecast ENSO for longer lead times in comparison to other recent studies[4].



[1] P. Bauer, A. Thorpe, and G. Brunet, “The quiet revolution of numerical weather prediction,”
Nature, vol. 525, no. 7567, pp. 47–55, 2015.

[2] D. Kondrashov, S. Kravtsov, A. W. Robertson, and M. Ghil, “A hierarchy of data-based enso
models,” Journal of climate, vol. 18, no. 21, pp. 4425–4444, 2005.

[3] L. R. Medsker and L. Jain, “Recurrent neural networks,” Design and Applications, vol. 5, 2001.

[4] Y.-G. Ham, J.-H. Kim, and J.-J. Luo, “Deep learning for multi-year enso forecasts,” Nature,
vol. 573, no. 7775, pp. 568–572, 2019.

How to cite: hassanibesheli, F., Boers, N., and Kurths, J.: Echo-State Networks for Predicting ENSO Beyond One Year, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4826,, 2021.

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