Improving Australian Rainfall Prediction Using Sea Surface Salinity

This study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer (December-February, DJF) rainfall over northeast Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150oE-165oW and 10oS-10oN), and SSSI (50oE-95oE and 10oS-10oN)] co-vary with Australian rainfall, particularly over the Northeast. Composite analysis based on high (low) SSS events in SSSP and SSSI region is performed to understand the physical links between the SSS and the atmospheric moisture originating from the regions of anomalously high (low) SSS and precipitation over Australia. The composites show the signature of co-occurring La Nina and negative Indian Ocean dipole (co-occurring El Nino and positive Indian Ocean dipole) with anomalously wet (dry) conditions over Australia. During the high (low) SSS events of SSSP and SSSI regions, the convergence (divergence) of incoming moisture flux results in anomalously wet (dry) conditions over Australia with a positive (negative) soil moisture anomaly. Furthermore, we show from the random forest regression analysis that the El Nino Southern Oscillation is the most important precursor for the Australian rainfall, followed by the SSS of the western Pacific warm pool (SSSP). The random forest regression also predicts Australian rainfall, and this prediction is improved by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.