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Climate indices are very effective predictors for forecasting seasonal rainfall. For any rainfall forecasting approach, it is necessary to understand the behavior of potential climate indices with rainfall variability. At present, rainfall forecasting using climate indices is one of the most reliable method to predict rainfall variability in many parts of the world. But, most of the studies have concentration on concurrent relationship between climate indices and seasonal rainfall. While a very few studies were conducted on concentration of lagged relationship between climate indices and seasonal rainfall. This study explores the significant correlation among lagged climate indices with autumn rainfall for south coast of Western Australia. As several climate indices such as Indian Ocean Dipole (IOD), El Nino Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Southern Annular Mode (SAM), Blocking highs, Enso Modoki Index (EMI) are responsible for rainfall variability in Australia; therefore, this study evaluates the major climate indices and their interaction in generating rainfall variability in south coast of Western Australia. In the south coast part of Western Australia, two stations (Albany and Mount Barker) were considered for this study. From the single statistical correlation analysis, it was found that DMI (IOD indicator), SOI, Nino3.4, Nino3 and Nino4 (ENSO indicators) have significant correlation with autumn rainfall in Albany and Mount Barker. However, for these two stations EMI did not show any significant correlation with autumn rainfall. A time series analysis approach (Auto Regressive Integrated Moving Average-ARIMA) was conducted using climate indices, which showed significant correlation with autumn rainfall. In ARIMA model, lagged DMINino3; lagged DMI-Nino4; Lagged DMI-Nino3.4 and Lagged DMI-SOI were chosen as independent variables (predictors) as it has showed significant correlation. From the ARIMA model analysis, it was evident that lagged DMI-Nino3 models showed highest predictability that is 56% and 33% for Albany and Mount Barker respectively. Finally, among those models, statistically significant models that showed high performance in predictability were suggested to forecast long-term rainfall for this region.

Application of lagged climate indices for forecasting autumn rainfall in South Coast region of Western Australia using ARIMA model