Abstract
One conceptual model of weather is that of a series of events which are unconnected. That is, that the weather next week is essentially independent of the weather this week. However, although individual weather systems might be chaotic and unpredictable beyond a week or so, the statistics describing them may be perturbed in a deterministic and predictable way1, particularly by the ocean. In the past, seasonal forceasts of atmospheric variables have largely been based on empirical relationships, which are weak in most areas of the world2. More recently, atmosphere models forced by assumed or predicted ocean conditions have been used3,4. Here a fully coupled global ocean–atmosphere general circulation model is used to make seasonal forecasts of the climate system with a lead time of up to 6 months. Such a model should be able to simulate the predictable perturbations of seasonal climate, but to extract these from the chaotic weather requires an ensemble of model integrations, and hence considerable computer resources. Reliable verification of probabilistic forecasts is difficult, but the results obtained so far, when compared to observations, are encouraging for the prospects for seasonal forecasting. Rainfall predictions for 1997 and the first half of 1998 show a marked increase in the spatial extent of statistically significant anomalies during the present El Niño, and include strong signals over Europe.
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Acknowledgements
The model integrations were carried out on a VPP300 machine donated by Fujitsu Limited. The ocean model was provided by the Max-Planck-Institut für Meteorologie, Hamburg; ocean data assimilation software by the Bureau of Meteorology Research Centre, Melbourne; and coupling software by CERFACS, Toulouse.
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Stockdale, T., Anderson, D., Alves, J. et al. Global seasonal rainfall forecasts using a coupled ocean–atmosphere model. Nature 392, 370–373 (1998). https://doi.org/10.1038/32861
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DOI: https://doi.org/10.1038/32861
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