Date of Award


Degree Type


Degree Name

Master of Science in Oceanography



First Advisor

Mark Wimbush


This thesis analyzes three observational data sets related to the El Niño/Southern Oscillation (ENSO) phenomenon to determine if ENSO can be considered as a low-dimensional chaotic system. In order to test this hypothesis, I apply Barahona and Poon's [1996] method for detecting nonlinear determinism in short, noisecontaminated time series, and calculate the correlation dimension, Dc. A slightly modified version of the Vallis [1986] model is used to provide a context for interpreting the results, and to validate the computations done on the observational data.

When applied to observational ENSO data, the Barahona and Poon algorithm indicates that low-order nonlinear mapping functions have predictive power which is not significantly different from linear models. In contrast, when the algorithm is applied to correspondingly sampled data from the Vallis model, the algorithm shows the presence of nonlinear determinism, even when these data are strongly contaminated with noise.

There is a weak convergence of the correlation dimension in the observational data to a Dc value between 8 and 10. This indicates that the phase-space dimension of ENSO is at least 8.

These results suggest that either (1) ENSO is not governed by low-dimensional nonlinear dynamics, or (2) noise related to local physical processes overwhelms the ENSO chaotic signal in the observational data.