Abstract
The occurrence of rainfall over Australia is closely related with several key climate predictors, which are El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Southern Annular Mode (SAM). Some researchers tried to explore the effects of these climate predictors in rainfall variability of different parts of Australia, particularly Western Australia, Queensland and Victoria. Nonetheless, clear association between separate or combined large-scale climate predictors and South Australian spring rainfall is yet to be established. Past studies showed that maximum rainfall predictability was only 20% with isolated/individual effects of ENSO and SAM predictors in this region. The present study further explored these hypotheses. For achieving better predictability of spring rainfall, this paper examined additional two important aspects; relationship between lagged individual climate predictors with spring rainfall as well as linked (multiple combinations of ENSO and SAM predictors) influence of these significant lagged climate indicators on spring rainfall prediction. Multiple regression (MR) modeling was used in this study. Two stations; Tarcoola and Mount Eba were chosen as case study of this region. MR models with combine-lagged climate predictors (SOI-SAM based models) showed better generalization ability for both the model calibration (1957-2009) and model validation (2010-2013) periods for all the stations. Results also demonstrated that rainfall predictability significantly increased using combined climate predictors compared to predictability with individual effects of each predictor. The attained combined climate model predictabilities were 44% for Tarcoola and 40% for Mount Eba during calibration period. The predictabilities were significantly enhanced during model validation; the results are 94% for Tarcoola and 83% for Mount Eba. Whereas, the maximum rainfall predictabilities were limited to 33% and 30% respectively considering the effects of single climate predictors. Therefore, statistical analyses outlined the capabilities of SOI-SAM based combined climate predictors compared to their single/individual influences for predicting South Australian spring rainfall.
Highlights
Rainfall is an outcome of a continuous chain of phenomena involving different ocean currents, air temperature and atmospheric pressure
For evaluating the rainfall predictability, single/individual correlations between south Australian spring rainfall (S-O-N) at any year ‘n’ with lagged monthly values of El Nino Southern Oscillation (ENSO) and Southern Annular Mode (SAM) climate predictors (NINO3, NINO4, NINO3.4 and SOI were chosen as ENSO predictors) from Decn-1-Augn (‘n’ being the year for which spring rainfall is predicted) were investigated
The correlations of rainfall with single predictor within the limits of statistical significance level and multicollinearity among the predictors were chosen for further Multiple regression (MR) analysis
Summary
Rainfall is an outcome of a continuous chain of phenomena involving different ocean currents, air temperature and atmospheric pressure. A reliable forecast of rainfall several months or seasons ahead can be beneficial for the management of land and water resources systems (Anwar et al, 2008), in Australia where the hydroclimatic variability is very high (Peel et al, 2001). Many researchers have tried to establish the relationships between large-scale climate predictors with rainfall in different parts around the world (Niu 2012; Grimm 2011). The variability of Australian rainfall has been linked to several dominant large-scale climate indices based on sea surface temperature (SST) and pressure differences anomalies originate from the Pacific, Indian and Southern Oceans. South Australia is one of the regions where clear association between separate or combined largescale climate predictors and South Australian spring rainfall is yet to be established. Risbey et al (2009) showed that maximum rainfall predictability was only 20% with isolated/individual effects of ENSO and SAM predictors in this region
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