Grant: Discovery Grants Program - Individual

The Indian Ocean Dipole (IOD) is a prominent air-sea coupled climate mode of inter-annual variability in the tropical Indian Ocean. It is characterized by an opposite sea surface temperature anomaly (SSTA) in the eastern and western Indian Ocean, with the sea surface wind anomalies over the central equatorial Indian Ocean. Due to the strong SSTA gradient across the Indian Ocean basin, the IOD can significantly modulate the atmospheric circulation, inducing a series of climate anomalies in many areas of the world, including the surrounding area of the Indian Ocean, South America, and East Africa etc., As an aspect of the general cycle of global climate, interacting with similar phenomena like El Nino and Southern Oscillation (ENSO) in the Pacific Ocean, the IOD could also impact Canada's climate.The IOD study began much later than ENSO. Its prediction skill, evaluated by current climate models, is very low and lags behind that of ENSO. It is important, therefore, to explore the reasons for the low prediction ability of the IOD. Is its low skill due to model errors, initialization errors, or simply the intrinsic predictability of IOD? Is it possible to improve the IOD prediction skill and, if so, by how much? This proposal aims to answer these questions, which have not yet been well addressed, by systematically analyzing the actual prediction skill and the potential predictability of the IOD. This will be implemented by constructing a long-term ensemble hindcast over 100 years using a coupled general circulation model and an optimal perturbation technique, initialized using the coupled data assimilation method. A good understanding of IOD predictability, including its dependence on seasons (seasonal barrier) and decades, is possible by analyzing the long-term ensemble hindcast. Further, applying the recently developed framework of potential predictability based on information theory, we expect to explore some important problems associated with the IOD potential predictability, including its quantitative estimate and the precursors needed to control its variation. This proposed program of research will improve understanding of the IOD predictability resulting in important and robust findings that will provide theoretical and scientific guidance to the operational prediction system being developed in the world. The research proposed herein has both theoretical importance and practical significance, and will also make significant contributions to the training of highly qualified personnel in climate modelling, prediction and data assimilation areas that are presently in high demand in Canada and worldwide.