Christopher Kadow and colleagues from the MiKlip project INTEGRATION apply a method called ensemble dispersion filtering, on order to improve decadal predictions. The results are presented in the following paper.
Decadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its ensemble average, improves a prediction system. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next.
The study shows that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure applying the average during the model run, called ensemble dispersion filter, results in more accurate results than the standard prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution.
Kadow, C., S. Illing, I. Kröner, U. Ulbrich and U. Cubasch, 2017, Decadal climate predictions improved by ocean ensemble dispersion filtering, J. Adv. Model. Earth Syst., DOI: 10.1002/2016MS000787.