The resolution and potential value of Australian seasonal rainfall forecasts based on the five phases of the Southern Oscillation Index
Andrew L. Vizard A B and Garry A. Anderson AA Faculty of Veterinary Science, University of Melbourne, 250 Princes Highway, Werribee, Vic. 3030, Australia.
B Corresponding author. Email: a.vizard@unimelb.edu.au
Crop and Pasture Science 60(3) 230-239 https://doi.org/10.1071/CP08277
Submitted: 20 August 2008 Accepted: 12 December 2008 Published: 16 March 2009
Abstract
We assess the resolution of the Southern Oscillation Index (SOI) seasonal rainfall forecasting system and calculate the loss in potential value of the forecasting system using a cost/loss model. Forecasts of the probability of a ‘dry’ autumn, winter, spring, and summer were obtained for 226 towns across Australia, based on the 5 phases of the SOI. For every town the variance ratio, the observed forecast variance as a proportion of the variance of a perfect forecasting system, was calculated for each season. Value score curves, showing the expected value of the forecasts as a proportion of the expected value of perfect information, were calculated for every town for each season. Maps of variance ratio and maps of mean value scores across Australia were produced by ordinary kriging. In all seasons and regions the SOI forecasting system had a variance ratio of less than 0.20, indicating that resolution and skill were never high. Variance ratios greater than 0.10 only occurred in parts of south-eastern Australia and the Cape York region during spring and in the Townsville region during summer. The variance ratio was less than 0.05 for the majority of Australia during autumn, winter, and summer.
The mean value scores for actions that are only triggered by a large shift in the forecast from climatology were uniformly close to zero in all seasons and regions, indicating that little or no value can be derived in such cases. Actions triggered by a moderate shift of the forecast were also generally associated with low value scores. Mean value scores above 0.20 were limited to actions with a decision threshold close to climatology and only occurred in parts of south-eastern Australia and the Cape York region during spring and in the Townsville region during summer. We conclude that the imperfect resolution of the SOI forecasting system has a substantial effect on potential value. The forecasting system can potentially deliver value to users with actions that are triggered by a small shift in the forecast from climatology, especially in eastern Australia during spring, but not to users with actions that are only triggered by a large shift of the forecasts from climatology.
Additional keywords: ENSO, climate, forecasting, risk, agriculture.
Acknowledgments
We thank Alice O’Connor for her instructive advice on preparation of maps using ArcGIS and ArcMap.
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