MultiModel Synthetic superensemble Algorithm for seasonal climate prediction using DEMETER Forecasts

2 In this paper, multi-model superensemble and a new weighted multi-model ensemble approach named synthetic superensemble for long-range climate prediction using coupled DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction) output is presented. Despite the continuous improvement of coupled models, they have serious systematic errors in terms of the mean, the annual cycle and the interannual variability, and consequently the predictive skill of extended forecasts remains quite low. The purpose of the synthetic superensemble approach is to improve long-range prediction skill. The superensemble algorithm entails the division of a time line into two parts, a training phase and a forecast phase. In this technique, the different model forecasts are statistically combined during the training phase using multiple linear regression, with the skill of each ensemble member implicitly factored into the superensemble forecast. The idea of the synthetic algorithm is generating a new data set from actual data set fo