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Journal Article

Reconstruction of the El Niño attractor with neural networks


Grieger,  Björn
MPI for Meteorology, Max Planck Society;

Latif,  Mojib
MPI for Meteorology, Max Planck Society;

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Grieger, B., & Latif, M. (1994). Reconstruction of the El Niño attractor with neural networks. Climate Dynamics, 10, 267-276. doi:10.1007/BF00228027.

Cite as: https://hdl.handle.net/21.11116/0000-0001-89AE-1
Based on a combined data set of sea surface temperature, zonal surface wind stress and upper ocean heat content the dynamics of the El Niño phenomenon is investigated. In a reduced phase space spanned by the first four EOFs two different stochastic models are estimated from the data. A nonlinear model represented by a simulated neural network is compared with a linear model obtained with the principal oscillation pattern (POP) analysis. While the linear model is limited to damped oscillations onto a fix point attractor, the nonlinear model recovers a limit cycle attractor. This indicates that the real system is located above the bifurcation point in parameter space supporting self-sustained oscillations. The results are discussed with respect to consistency with current theory. © 1994 Springer-Verlag.