Pattern Analysis of SST-Forced Variability in Ensemble GCM Simulations: Examples over Europe and the Tropical Pacific

Abstract

An ensemble of atmospheric general circulation model (GCM) simulations with prescribed sea surface temperature (SST) generates a rich dataset. The main aim here is to advocate and demonstrate an approach to skill and reproducibility based on spatial anomaly patterns. Benefits and applications of this type of analysis include the efficient extraction of the model?s forced variability, guidance on systematic errors in the model?s response to SST forcing, clues to physical mechanisms, and a basis for model output statistics for seasonal forecasting. Some of the possible statistical techniques are illustrated, though the aim is not to provide an exhaustive comparison of the different spatial analysis techniques available. The examples are taken from an ensemble of three GCM integrations forced with observed SST through 1979?88. Boreal summer examples are given for the tropical Pacific and Europe, providing a contrast of a high and a low skill situation, respectively. For model verification, a coupled pattern singular value decomposition analysis is performed between model and observed fields over the analysis domain. Over Europe, a model rainfall pattern is identified that specifies the contrasting rainfall anomalies associated with blocked and westerly summers observed through the period 1979?88, though statistical significance for the result cannot be proven using this small sample size. In the central and western tropical Pacific (CWTP), the leading model (rainfall) and observed (outgoing longwave radiation) modes have near-perfect temporal agreement, but the model?s spatial pattern lacks weight near Indonesia, which may be useful information for model developers. Significant reproducibility of climate anomalies among ensemble members indicates potential seasonal forecast skill, because the similar atmospheric anomalies must derive from a common response to the anomalous SST forcing. To study reproducibility, the cross-covariance among all ensemble members is used to define a model base pattern (referred to as the forced pattern) that maximizes temporal covariance among ensemble members. The close relationship with the principal components of the ensemble mean anomaly is demonstrated. Monte Carlo tests show that the covariances among ensemble members associated with the CWTP and European forced patterns are highly statistically significant. It is suggested that this approach is an efficient way to identify statistically significant reproducibility.