A supermodel to enhance climate prediction

We present the first results from a supermodel constructed using three state-of-the-art earth system models: NorESM, CESM, MPIESM. A supermodel is an interactive ensemble in which models are optimally combined so that the systematic errors of the individual models compensate to achieve a model with superior performance. In the supermodel, the individual models are synchronized every month using data assimilation to handle the discrepancies of grid, resolution and variable representativity between the models. In particular, we assimilate a pseudo sea surface temperature (SST) that is computed as a weighted combination of the SST of the individual models. The synchronization of the models distinguishes this approach from the standard multi-model ensemble approach in which model outputs are combined a-posteriori. The data assimilation method used is the Ensemble Optimal Interpolation (EnOI) scheme, for which the covariance matrices are constructed from preindustrial control simulations of the individual models. The performances of a first version of the supermodel based on equal weights is compared to the individual models performances for the period 1980 to 2010. Synchronisation of the surface ocean is achieved in most places and dynamical regimes such as ENSO are occurring in phase. The biases of each model are reduced and the pathway of the Gulf Stream improved. The variability of the supermodel is not larger than in the super ensemble mean, but it is shown with an idealized model that the deflation is cause by a misconstruction of the pseudo observation and can be counteracted by perturbing them.  The Perspectives for performing predictions and climate change experiments with the supermodel method are presented and discussed.