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

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. An edited version of this paper was published by AGU. Copyright 2004 American Geophysical Union.Science, Faculty ofEarth and Ocean Sciences, Department ofReviewedFacult