Improving atmosphere-ocean prediction on time scales ranging from days tro seasons: assimilation of Argo data and the construction of ensemble predictions

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This research proposal aims to improve the prediction of the atmosphere-ocean coupled system from days to seasons by: 1) improving oceanic data assimilation techniques; and 2) developing an optimal perturbation technique for multiple predictions (i.e., ensemble prediction). Emphasis will be placed on the assimilation of ocean data generated by Argo buoys and the construction of ensemble climate predictions, as well as the verification of the ensemble predictions. The effort will be focused on assimilation experiments and ensemble predictions using the NCAR (National Center for Atmospheric Research) CCSM4 (Community Climate System Model version 4). Through the proposed program, we expect to improve our ability to predict weather (i.e., short-term forecasts) and climate (long-term forecasts) at multiple time scales, especially in seasonal climate prediction. The expected findings and conclusions may also lead to important theoretical contributions and a deeper understanding of ocean data assimilation and the predictability study of multiple time scales. Thus this research has both strong theoretical components and very practical applications for Canada.