Evaluating the Effect of Single and Combined Climate Modes on Rainfall Predictability
This study attempts to find the effect of past values of El Nino southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on future rainfall. Victoria located at southeast Australia has been chosen as the case study. Many studies have tried to establish the relationships of these large-scale climate indices among the rainfalls of different parts of Australia; unlike the other regions no clear relationship can be found between each individual large-scale climate mode and Victorian rainfall. Past studies considering southeast Australian rainfall predictability could achieve a maximum of 30 % predictability. This study looks into the lagged-time relationships of single and combined climate modes with Victorian spring rainfall using the nonlinear technique Artificial Neural Networks (ANN). Using these climate indices in an ANN model increased the model correlation up to 0.99, 0.98 and 0.30 in the testing set for the three case study stations of Horsham, Melbourne and Orbost in Victoria, Australia respectively. It seems that past values of IOD and ENSO both have a great effect on rainfall forecasting however the effect of IOD is higher in centre and west of Victoria compared to ENSO, while both ENSO and IOD seem to have a strong effect on the east side. This method can be used for other parts of the world where a relationship exists between rainfall and large scale climate modes which could not be established by linear methods.
KeywordsANN ENSO Forecast IOD Rainfall Victoria
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