The impact of model fidelity on seasonal predictive skill

Abstract

The relationship between the quality of a general circulation model's (GCM's) representation of present climate and its predictive skill on seasonal time scales is investigated by a series of GCM experiments. A novel procedure is developed to improve the quality of a GCM's present‐day control climate by applying cyclostationary annually varying run‐time “bias corrections”. Application of this procedure to the Canadian Centre for Climate Modelling and Analysis third generation atmospheric GCM (AGCM3) is shown to result in a significant reduction of time‐mean biases in wind, temperature and humidity fields in its simulation of present‐day climate. Furthermore, it is found that this cyclostationary correction leads to improved variability on seasonal time scales. The ability to improve a GCM's properties in this way allows a careful assessment of the relationship between a model's fidelity and its predictive skill. In this study, the potential predictive skill on seasonal time scales is assessed by performing ensemble simulations with the observed sea surface temperatures and sea‐ice distribution. The analysis indicates that the increase in model fidelity associated with the application of the bias correction results in a general increase in predictive skill on seasonal times scales. To investigate this result further, an additional set of ensemble forecasts are performed with the sign of the bias correction reversed, thereby degrading model fidelity. In this additional experiment the corresponding predictive skill is also degraded. The results of this study have implications with regard to the application and interpretation of model metrics for climate GCMs.