"Climate predictions are increasingly being used to inform policy at national and international level. However, predicting climate and its impacts remains a major challenge, with large uncertainties existing, particularly at a regional level. This project aims to contribute to meeting this challenge through an innovative modelling approach and by enhancing understanding of key uncertainties.A super model is an optimal combination of several models that leads to a model superior to any of the individual models. For low order models this approach, coming from non-linear dynamics and machine-learning concepts, is successful. This exciting approach is being applied to climate modeling in European Union (FP7) and United States of America (DOE) funded projects. This project’s first objective is to strengthen both initiatives, by sharing knowledge and software. This may lead to a super-model constructed from models developed in Europe and the USA. If this controversial approach proves fruitful, then combining a greater number of different models will give even greater gains.Near-term (10-20yr) prediction has potential to improve the response of society, particularly in developing countries, to climate shifts, which can cause famine and disease outbreaks. However, understanding of climate variability on these time-scales is limited and poorly modeled. This is major impediment to near-term climate prediction. The project’s second objective is to better understand uncertainties in Northern Hemisphere climate prediction, by extending an inter-model comparison of Atlantic decadal variability to include the Pacific and also a synthesis of paleo-proxy records.Unfortunately, a gap also exists between predicting climate and its associated impacts. As one step toward closing this gap, the project’s third objective is to quantify uncertainty in key variables for major crops and vector born diseases, such as Malaria and Dengue fever."