Acknowledgements

I would like to express my sincere gratitude to my principal supervisor, Professor David Antoine, Head of the Remote Sensing and Satellite Research Group at Curtin University, for providing me with the opportunity to undertake this research under the Australian Research Council (ARC) Special Research Initiative, Australian Centre for Excellence in Antarctic Science (Project Number: SR200100008). I also gratefully acknowledge the support of the Curtin Higher Degree by Research (HDR) Scholarship, awarded under Curtin Research Project RES-62339, which funded my PhD research program. I am deeply thankful for Professor Antoine’s guidance, encouragement, and consistent support throughout my doctoral studies, which extended well beyond formal supervisory responsibilities. His leadership and mentorship were instrumental in shaping both my research and academic development.

I would like to acknowledge my co-supervisors and collaborators for their valuable contributions to this research. I sincerely thank Professor Peter Strutton (University of Tasmania) and Dr Alex Sengupta (University of New South Wales) for their insightful feedback, constructive comments, and scientific discussions, which significantly strengthened the quality and direction of this work. I am also grateful to Professor Peter Fearns (Curtin University) for his constructive discussions and valuable feedback throughout the research process.

I extend my sincere thanks to Dr Elodie Martinez (IRD, France) for her guidance in understanding deep learning methodologies and their application to Southern Ocean research. I gratefully acknowledge the support of the ISBlue Mobility Scholarship, which enabled my one-month research stay at the University of Western Brittany. I also thank Dr Etienne Pauthenet for providing technical support during my stay, particularly in relation to implementing machine learning approaches for Southern Ocean applications. In addition, I am grateful to Professor Matthieu Lengaigne for his guidance during the climate projection phase of this research.

This research was supported by high-performance computing resources provided by the Pawsey Supercomputing Research Centre, through access to the Nimbus Cloud Computing System, which is funded by the Australian Government and the Government of Western Australia. The use of these facilities was essential for data processing, numerical analysis, and machine learning experiments conducted in this study.

I gratefully acknowledge the data providers that made this research possible, including the European Space Agency (ESA) and Copernicus Marine Service for GlobColour ocean colour products; CCMP for wind data; ARMOR3D mixed-layer depth data; GHRSST sea surface temperature products; CLS sea level anomaly data; CMIP6 climate model simulations; Southern Ocean frontal datasets; and NSIDC sea ice concentration data. Their open-access data products were fundamental to the analyses presented in this thesis.

Finally, I express my deepest appreciation to my family for their continuous encouragement and support throughout my PhD journey. I dedicate this thesis to my beloved wife, Afra Tanjim Shammi, for her patience, kindness, and unwavering support during my doctoral studies.