Zaved Khan (Bureau of Meteorology).
Modelling seasonal rainfall forecasts forced with improved predictive ocean surface temperature
Seasonal rainfall forecasts are in high demand for users such as irrigators and water managers in decision making and risk management. Both statistical and dynamical models are widely used to generate probabilistic rainfall forecasts in advance for a season. Statistical prediction systems establish a stationary relationship between the predictor and the predictand variables. On the other hand, dynamical models are based on the laws of physics and thus they can capture non-linear interactions of the atmosphere, land and ocean. In the case of seasonal forecasts, these models use a two-tiered process by predicting global Sea Surface Temperatures (SSTs) first; an atmospheric general circulation model (GCM) is subsequently forced by the pre-forecast SST to make a seasonal prediction. Improvement in predicted SST is therefore significant for issuing better concurrent seasonal rainfall forecasts. For the purpose of improving seasonal SST forecasting, this research work presents a multimodel combination approach which considers intermodel dependency between the multiple participating GCMs. Then the statistical techniques – Bayesian Joint Probability (BJP) and Bayesian Model Averaging (BMA) are applied to translate seasonal rainfall forecasts using six predicted SSTA indices over the Indian and Pacific Oceans. The BJP-BMA approach shows encouraging results derived from improved SSTA indices, although no upper atmosphere predictor variable is considered. In addition, the global climate model – ACCESS is used to issue concurrent seasonal rainfall prediction on a global scale from the improved forecast SST. The results indicate that there is merit in formulating global seasonal rainfall forecasts from the predictive uncertainty reduced SST, rather than relying on a single model predicted SST.
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