An improved projection of climate observations for detection and attribution

Abstract

An important goal of climate research is to determine the causal contribution of human activity to observed changes in the climate system. Methodologically speaking, most climatic causal studies to date have been formulating attribution as a linear regression inference problem. Under this formulation, the inference is often obtained by using the generalized least squares (GLS) estimator after projecting the data on the r leading eigenvectors of the covariance associated with internal variability, which are evaluated from numerical climate models. In this paper, we revisit the problem of obtaining a GLS estimator adapted to this particular situation, in which only the leading eigenvectors of the noise's covariance are assumed to be known. After noting that the eigenvectors associated with the lowest eigenvalues are in general more valuable for inference purposes, we introduce an alternative estimator. Our proposed estimator is shown to outperform the conventional estimator, when using a simulation test bed that represents the 20th century temperature evolution.