Investigating the potential of NAO index to forecast droughts in Sicily

• Main Authors: Cancelliere, A., author, Di Mauro, G., author, Bonaccorso, B., author, Rossi, G., author, Colorado State University, publisher
• Format: Text
• Language: English
• Published: Colorado State University. Libraries 2020
• Subjects: North Atlantic; North Atlantic oscillation
• Online Access: https://hdl.handle.net/10217/200689; https://doi.org/10.25675/10217/200689

2007 annual AGU hydrology days was held at Colorado State University on March 19 - March 21, 2007. Includes bibliographical references. Drought monitoring and forecasting is essential for an effective drought preparedness and mitigation. The use of large-scale climatic patterns, such as El Nino Southern Oscillation (ENSO), North Atlantic Oscillation (NAO ) or European Blocking (EB), can potentially improve the forecasting of drought evolution in time and space, provided the influence of such indices on the climatic variability in a region is verified. In the present paper, a stochastic model for the seasonal forecasting of the Standardized Precipitation Index (SPI), developed in previous works, is extended in order to include information from NAO index. In particular SPI forecasts at a generic time horizon M are analytically determined, in terms of conditional expectation, as a function of a finite number of past observations of SPI and NAO, assuming a multivariate normal as the underlying distribution. In addition, an expression of the Mean Square Error (MSE) of prediction is also derived, which allows confidence intervals of prediction to be estimated. The forecasting performance of the model is verified by hindcasting observed SPI values computed on monthly areal average precipitation series observed in Sicily and validation is carried out by repeatedly applying a jackknife scheme. Preliminary results of the comparison between the model based only on the past observations of SPI values and the one that includes also the NAO index, seem to indicate a slight improvement of the latter model. Such results however cannot be considered conclusive and further analyses are needed in order to better assess the use of NAO as a predictor for droughts in Sicily.