A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.
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The six available DPSs make 15 distinct pairs. For each pair, the corresponding RMSE is calculated between the two hindcasts. These differences are not "errors" but just deviations. We use the term RMSE because it best describes the calculations involved. In the text, we refer to the average RMSE across all pairs.
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The authors gratefully acknowledge the support from the EU FP7 COMBINE Project (Grant Agreement Number 226520). A.B., S.G. and P.J.A. did also receive support from the Italian Ministry of Education, University and Research and Ministry for Environment, Land and Sea through the Project GEMINA. We also wish to thank Dr. G. J. van Oldenborgh for providing some of the data used in this assessment by means of the KNMI Climate Explorer utility. Finally, the insightful comments from two anonymous reviewers are thankfully acknowledged.
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Bellucci, A., Haarsma, R., Gualdi, S. et al. An assessment of a multi-model ensemble of decadal climate predictions. Clim Dyn 44, 2787–2806 (2015). https://doi.org/10.1007/s00382-014-2164-y
- Forecast Skill
- Anomaly Correlation
- Ensemble Spread
- Predictive Skill
- Decadal Prediction