Abstract
Rainfall variability is a crucial element that impinges on the success of sugarcane growing regions around the world. As the scientific community and industry personnel gain more experience at working participatively, the ability of longβrange rainfall forecasts to reduce the risk and uncertainty associated with decisions impacted by rainfall variability has become increasingly recognized. Some important decisions, however, require knowing the chances of rain at early lead times that span the austral autumn period. These types of decisions remain largely unassisted by climate forecasting technologies owing to the boreal spring (austral autumn) persistence barrier. Taking the Australian sugar industry as a case study example, this article explores the capability of a long lead statistical El NiΓ±o Southern Oscillation phenomenon (ENSO) prediction model to reduce the risk associated with decisions that must be made before autumn and are effected by rainfall anomalies postβautumn. Results shown across all regions considered in this study indicated a higher risk of obtaining an aboveβmedian rainfall index when the statistical model predicted La NiΓ±a type conditions to emerge postβspring. For selected regions, this risk was reduced when the model predicted El NiΓ±o type conditions for the same period. In addition, the model would have provided an earlier indication of the likelihood of disruption due to wet harvest conditions in a year that devastated the Australian sugar industry. This benchmark study has highlighted the potential of an ENSO prediction model to aid industry decisions that have previously been made in isolation of probabilistic knowledge about future rainfall conditions. Copyright Β© 2007 Royal Meteorological Society