REMOVAL OF THE EL-NINO SOUTHERN OSCILLATION SIGNAL FROM THE GRIDDED SURFACE AIR-TEMPERATURE DATA SET

We attempt the removal of the large-scale signal associated with one i mportant component of climate variability, the El Nino-Southern Oscill ation (ENSO) phenomenon, from the global surface air temperature data set in order to facilitate analysis of other potential causes of clima te change. Previous attempts to remove the ENSO signal from climate re cords at the global scale have been based on relatively simple regress ion analysis. We use empirical orthogonal function analysis to identif y characteristic spatial patterns of change in the global surface air temperature field and thus define the ENSO signal. The approach succes sfully identifies two ENSO-related components of the variability in th e global data set, and the removal process results in a significant re duction in variance. Evaluation of the process indicates that attentio n should be paid to seasonality in the ENSO signal. Moreover, it is cl ear that other aspects of the statistical generalization inherent in t he approach have resulted in a residual ENSO signal in the data set. T he remaining signal does, however, appear to be regional in nature and /or only marked in the case of particular ENSO events. We conclude tha t an approach based on empirical orthogonal function analysis presents an effective means of isolating and removing specific climate signals .