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...