Abstract
The predictability of Australian rainfall using ocean heat content information is examined. The thermocline, represented by the second unrotated principal component of the 20 °C isotherm depth in the Pacific Ocean, is coupled with the Niño3 index to form the predictive model. The relevance of the subsurface oceanic information is evaluated by comparing results with two alternative approaches, both based on the use of sea surface temperatures. All approaches are applied to predict rainfall available on a 1×1° latitude–longitude grid covering Australia. Results are grouped according to dominant climatic regimes, and evaluated using leave-one-out cross-validation. The skill of each approach is measured using the linear error in probability space (LEPS) score. For a given climatic region, an improvement in skill due to an additional predictor is indicated by a positive shift in the empirical cumulative distribution function (CDF) of the LEPS scores of the constituent grids and by the number of grids that have a statistically significant hindcasting skill. Results show that the addition of thermocline information results in a significant increase in skill of hindcasts for all seasons and in several regions. The thermocline's influence is particularly strong during austral autumn when predictability of rain in the western and northern regions of Australia increased even up to a lag of 18 months. This is an encouraging result considering that prediction in autumn normally experiences a drop in skill due to the spring predictability barrier. By possessing high persistence during the first half of the year, the ocean heat content is able to defy the damping effect of the spring barrier. This study has demonstrated the potential of the thermocline as a direct predictor of Australian rainfall especially at long lead times.
Published Version
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