Nonlinear principal component analysis: Tropical Indo-Pacific sea surface temperature and sea level pressure

Nonlinear principal component analysis (NLPCA) is a generalization of tradi tional principal component analysis (PCA) that allows for the detection and characterization of low-dimensional nonlinear structure in multivariate da tasets, The authors consider the application of NLPCA to two datasets: trop ical Pacific sea surface temperature (SST) and tropical Indo-Pacific sea le vel pressure (SLP). It is found that for the SST data, the low-dimensional NLPCA approximations characterize the data better than do PCA approximation s of the same dimensionality. In particular. the one-dimensional NLPCA appr oximation characterizes the asymmetry between spatial patterns characterist ic of average Fl Nino and La Nina events, which the 1D PCA approximation ca nnot. The differences between NLPCA and PCA results are more modest for the SLP data. indicating that the lower-dimensional structures of this dataset are nearly linear.