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Abstract:

Most seasonal forecasts of Atlantic tropical storm numbers are produced using statistical-empirical models. However, forecasts can also be made using numerical models which encode the laws of physics, here referred to as "dynamical models". Based on 12 years of re-forecasts and 2 years of real-time forecasts, we show that the so-called EUROSIP (EUROpean Seasonal to Inter-annual Prediction) multi-model ensemble of coupled ocean atmosphere models has substantial skill in probabilistic prediction of the number of Atlantic tropical storms. The EUROSIP real-time forecasts correctly distinguished between the exceptional year of 2005 and the average hurricane year of 2006. These results have implications for the reliability of climate change predictions of tropical cyclone activity using similar dynamically-based coupled ocean-atmosphere models.

Abstract:

Sensitivity experiments using a coupled model initialized from analysed atmospheric and oceanic observations are used to investigate the potential for interannual-to-decadal predictability. The potential for extending seasonal predictions to longer time scales is explored using the same coupled model configuration and initialization procedure as used for seasonal prediction. It is found that, despite model drift, climatic signals on interannual-to-decadal time scales appear to be detectable. Two climatic states have been chosen: one starting in 1965, i.e. ahead of a period of global cooling, and the other in 1994, ahead of a period of global warming. The impact of initial conditions and of the different levels of greenhouse gases are isolated in order to gain insights into the source of predictability.

Abstract:

Recent developments in dynamical seasonal forecasting of potential relevance to agricultural management are discussed. These developments emphasize the importance of using a fully probabilistic approach at all stages of the forecasting process, from the dynamical ocean-atmosphere models used to predict climate variability at seasonal and interannual time scales, through the models used to downscale the global output to finer scales, to the end-user forecast models. The final goal is to create an end-to-end multi-scale (both in space and time) integrated prediction system that provides skilful, useful predictions of variables with socio-economic interest. Multi-model ensemble predictions made with the leading European global coupled climate models as part of the DEMETER (Development of a European Multi-model Ensemble system for seasonal to inTERannual prediction) project are used as an example to illustrate the potential of producing useful probabilistic predictions of seasonal climate fluctuations and of applying them to crop yield forecasting. © Inter-Research 2006.