As a global climate phenomenon, the El Ni??o-Southern Oscillation (ENSO) involves the coupling of the ocean and the atmosphere. Most climate prediction studies have, by far, only investigated the teleconnections between global climatic anomalies and the ???surface??? predictors of ENSO. The prediction models resulting from these studies have generally suffered from inadequate, if not the lack of, skill across the so-called boreal ???spring barrier???. This is illustrated in the first part of this thesis where the applicability of the SOI phase for long-lead rainfall projections in Australia is discussed. With the increasing availability of subsurface ocean temperature data, the characteristics of the Pacific Ocean???s heat content and its role in ENSO are now better understood. The second part of this thesis investigated the predictability of ENSO using the thermocline as a predictor. While the persistence and SST-based ENSO hindcasts dropped in skill across the spring barrier, the thermocline-based hindcasts remained skillful even up to a lag of eighteen months. Continuing on the favorable results of ENSO prediction, the third part of this thesis extended the use of the thermocline in the prediction of Australia???s rainfall and streamflow. When compared to models that use ???surface??? predictors, the model that incorporated thermocline information resulted in more skillful projections of rainfall and streamflow especially at long lead-times. More importantly, significant increases in skill of autumn and winter projections demonstrate the ability of the subsurface ocean to retain some climatic memory across the predictability barrier. This resilience can be attributed to the high persistence of the ocean heat content during the first half of the year. Based on weighting, the model averaging exercise also affirmed the superiority of the ???subsurface??? model over the ???surface??? models in terms of streamflow projections. The encouraging findings of this study could have far-reaching implications not only to the science of ENSO prediction but also to the more pragmatic realm of hydrologic forecasting. What this study has demonstrated is an alternative predictor that is suitable for the long range forecasting of ENSO, rainfall and streamflow. With better hydrologic forecasting comes significant improvement in the management of reservoirs which eventually leads to an increase in the reliability and sufficiency of water supply provision.

As a global climate phenomenon, the El Ni??o-Southern Oscillation (ENSO) involves the coupling of the ocean and the atmosphere. Most climate prediction studies have, by far, only investigated the teleconnections between global climatic anomalies and the ???surface??? predictors of ENSO. The prediction models resulting from these studies have generally suffered from inadequate, if not the lack of, skill across the so-called boreal ???spring barrier???. This is illustrated in the first part of this thesis where the applicability of the SOI phase for long-lead rainfall projections in Australia is discussed. With the increasing availability of subsurface ocean temperature data, the characteristics of the Pacific Ocean???s heat content and its role in ENSO are now better understood. The second part of this thesis investigated the predictability of ENSO using the thermocline as a predictor. While the persistence and SST-based ENSO hindcasts dropped in skill across the spring barrier, the thermocline-based hindcasts remained skillful even up to a lag of eighteen months. Continuing on the favorable results of ENSO prediction, the third part of this thesis extended the use of the thermocline in the prediction of Australia???s rainfall and streamflow. When compared to models that use ???surface??? predictors, the model that incorporated thermocline information resulted in more skillful projections of rainfall and streamflow especially at long lead-times. More importantly, significant increases in skill of autumn and winter projections demonstrate the ability of the subsurface ocean to retain some climatic memory across the predictability barrier. This resilience can be attributed to the high persistence of the ocean heat content during the first half of the year. Based on weighting, the model averaging exercise also affirmed the superiority of the ???subsurface??? model over the ???surface??? models in terms of streamflow projections. The encouraging findings of this study could have far-reaching implications not only to the science of ENSO prediction but also to the more pragmatic realm of hydrologic forecasting. What this study has demonstrated is an alternative predictor that is suitable for the long range forecasting of ENSO, rainfall and streamflow. With better hydrologic forecasting comes significant improvement in the management of reservoirs which eventually leads to an increase in the reliability and sufficiency of water supply provision.