Reconstruction of the El Nino attractor with neural networks
- Univ. of Bremen (Germany)
- Max-Planck-Institute of Meteorology, Hamburg (Germany)
Based on a combined data set of sea surface temperature, zonal surface wind stress and upper ocean heat content the dynamics of the El Nino 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. 21 refs., 10 figs.
- OSTI ID:
- 443823
- Journal Information:
- Climate Dynamics, Vol. 10, Issue 6-7; Other Information: PBD: Sep 1994
- Country of Publication:
- United States
- Language:
- English
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