Abstract
Complex principal component analysis (CPCA) is a linear multivariate technique commonly applied to complex variables or 2‐dimensional vector fields such as winds or currents. A new nonlinear CPCA (NLCPCA) method has been developed via complex‐valued neural networks. NLCPCA is applied to the tropical Pacific wind field to study the interannual variability. Compared to the CPCA mode 1, the NLCPCA mode 1 is found to explain more variance and reveal the asymmetry in the wind anomalies between El Niño and La Niña states.
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