Nonlinear canonical correlation analysis of the tropical Pacific climate variability using a neural network approach

Recent advances in neural network modeling have led to the nonlinear generalization of classical multivariate analysis techniques such as principal component analysis and canonical correlation analysis (CCA). The nonlinear canonical correlation analysis (NLCCA) method is used to study the relationship between the tropical Pacific sea level pressure (SLP) and sea surface temperature (SST) fields. The first mode extracted is a nonlinear El Nino-Southern Oscillation (ENSO) mode, showing the asymmetry between the warm El Nino states and the cool La Nina states. The nonlinearity of the first NLCCA mode is found to increase gradually with time. During 1950-75, the SLP showed no nonlinearity, while the SST revealed weak nonlinearity. During 1976-99, the SLP displayed weak nonlinearity, while the weak nonlinearity in the SST was further enhanced. The second NLCCA mode displays longer timescale fluctuations, again with weak, but noticeable, nonlinearity in the SST but not in the SLP.