Nonlinear complex principal component analysis of the tropical Pacific interannual wind variability

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. Copyright 2004 by the American Geophysical Union.