A Technique for the Identification of Inhomogeneities in Canadian Temperature Series

Abstract

A new technique has been developed for the identification of inhomogeneities in Canadian temperature series. The objective is to identify two types of inhomogeneities?nonclimatic steps and trends?in the series of a candidate station in the absence of prior knowledge of the time of site changes and to properly estimate their position in time and their magnitude. This new technique is based on the application of four linear regression models in order to determine whether the tested series is homogeneous, if there is a nonclimatic trend, a step, or trends before and/or after a step. The dependent variable is the series of the candidate station and the independent variables are the series of some neighboring stations. Additional independent variables are used to describe and measure steps and trends existing in the tested series but not in the neighboring series. After the application of each model, the residuals are analyzed in order to determine the fit of the model. If there is significant autocorrelation in the residuals, nonidentified inhomogeneities are suspected in the tested series and a different model is applied to the datasets. A model is finally accepted when the residuals are considered to be random variables. The description of the technique is presented along with some evaluation of its ability to identify inhomogeneities. Results are illustrated through the provision of an example of its application to archived temperature datasets.