Representing uncertainty in ocean observations and the ocean model, for coupled ensemble data assimilation and ensemble extended-range prediction
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There is currently a large effort in the development of general circulation model (GCM)-based seasonal to decadal prediction systems to provide climate forecasts. Such techniques are rather complex, technically challenging and still in their infancy. Any weather or climate forecast will be subject to three sources of uncertainty, namely observation uncertainty, the model-component of initial uncertainty, and model uncertainty over the forecast period. The aim of this proposal is to improve the reliability of extended range forecast of weather and climate, mainly focusing on the ocean component of the coupled system. We propose to develop and incorporate various tools based on stochastic physics to improve the reliability of forecasts focusing on a more accurate representation of ocean observations and model uncertainties. The individual impacts of the different developments on the reliability of the forecasts will be quantified to provide estimates of the different sources of uncertainties in the forecasts. The development of reliable extended range forecasts can be extremely beneficial with major economical and societal consequences.
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