The climate prediction at the seasonal time scale (1-6 months) represents a challenge of the climatic research, with important scientific impacts regarding the identification of the effective long range climate predictors and the definition of the climate predictability.
The research contributes to fill the gap between the instantaneous weather predictions and the climatic predictions.
The seasonal forecasts of the climatic anomalies still suffer from relatively large errors and miss an extensive validation, due to the dependence on different environmental forcings.
CNR-IBIMET developed a wide global climatic database to calibrate, initialize and validate seasonal forecasting techniques: surface and atmospheric reanalysis, ocean surface temperatures, precipitations on land and ocean. It has been conduced a research on the relationship among sea surface temperatures (SST) distribution, tropical monsoons, equatorial storms region and Mid-Latitude seasonal climatic anomalies.
Two seasonal forecasting techniques have been developed: Analogues Technique and Dynamical Technique.
The first one has been developed by CNR-IBIMET on the basis of the proximity of the monthly SST anomalies and their respective tendencies in different years. For each forecasting situation, the most similar is detected on the basis of the above mentioned potential predictors to associate the present forecast to an analogue past situation. These forecasts are generated monthly and 3-monthly. A future perspective will be the enhancement of this technique by means of the integration of further potential predictors.
The second one, in collaboration with the HydroMeteorological Research Centre of Russia, examines the global atmospheric predictability on the seasonal time scale by means of the Global Atmospheric Spectral Model to test its sensitivity and response to perturbations imposed to the SST. The results of the numerical experiments clearly indicate the reliability and the accuracy of the model, making feasible the development of a physical dynamic forecasting system.
The applications of the seasonal forecasts cover many economic, social, environmental relevant fields: insurance, agriculture, industry, catastrophes prevention, energy, transport.
With relevance to these applications the main aim will be to define and develop specific products based on seasonal forecasting information for the end-users.
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