Dimensionality reduction methods for paleoclimate reconstructions.

Abstract

Paleoclimatology seeks to understand past changes in climate occurred before the instrumental period through paleoclimate archives. These archives consist of natural materials that keep trace of climate changes with different time scales and resolutions. Tree-ring archives are able to provide a timescale of thousands of years with annual resolution. This thesis discusses reconstruction of the past temperature in the period ranging from year 1400 until 1849 on the basis of the information available in a tree-ring dataset consisting of 70 trees located in the United States of America. The temperature data used for calibration and validation come from the HadCRUT4 dataset. The thesis considers past temperature reconstructions based on multiple linear regression models calibrated with instrumental temperature available for the period 1902-1980. Since the number of tree-ring proxies is large compared with the number of observations, standard multiple linear regression is unsuitable thus making necessary to apply dimensionality reduction methods such as principal component regression and partial least squares regression. The methodology developed in the thesis includes corrections to handle for residual serial dependence. The thesis results indicate that (i) key events of the climate forcings are well identified in the reconstructions based on both partial least squares and principal component regression but (ii) the method of partial least squares regression is superior in terms of precision of past temperature predictions.