Evaluation of climate predictability for multiple climate models at various time scales.

The predictability of the Pacific North American (PNA) pattern is evaluated on time scales from days to months using state-of-the-art dynamical multiple model ensembles including the Canadian Historical Forecast Project (HFP2) ensemble, the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual prediction (DEMETER) ensemble, and the Ensemble Based Predictions of Climate Changes and their Impacts (ENSEMBLES). Some interesting findings in this study include (i) Multiple-model ensemble (MME) skill was better than skill from most of the individual models (ii) both actual prediction skill and potential predictability increased as the averaging time scale increased from days to months (iii) There is no significant difference in actual skill between coupled and uncoupled models, in contrast with the potential predictability where coupled models performed better than uncoupled models (iv) relative entropy (RE[subscript]A) is an effective measure in characterizing the potential predictability of individual predictions, whereas the mutual information (MI) is a reliable indicator of overall prediction skill (v) Compared with conventional potential predictability measures of the signal-to-noise ratio, the MI-based measures characterized more potential predictability when the ensemble spread varied over initial conditions. It is also confirmed that from monthly to seasonal time scales, the potential predictability of PNA is teleconnected with ENSO. The predictive skill on intra-seasonal time scales in the tropics is linked to Madden-Julian Oscillations (MJO). Using recently developed framework of potential predictability, information-based and ensemble based predictability measures were explored on multiple time scales for MJO predictability. Results show that there is no significant difference in the simulation of MJO in coupled (CanCM3) and uncoupled (GCM3) models. Both models simulated the tropical low frequency variability reasonably well compared with observations with some positive bias i