Ocean-driven melting of ice shelves in Antarctic and Greenland regions is accelerating, with substantial implications for future sea level rise and global warming. However, the ice shelf response to changing ocean temperatures is poorly understood due, in part, to lack of knowledge of the fine-scale ocean processes that bring heat and salt towards the ice. Double-diffusive layering is an interesting fluid dynamics phenomenon that has been observed around Antarctica and Greenland which may help to bring this heat and salt towards the ice shelves, thereby influencing melt rates. This project will apply novel unsupervised machine learning methods to state-of-the-art ocean simulations and observations to identify the processes and regions where double-diffusive layering occurs. Particular focus will be placed on quantifying the transport of ocean heat and salt in these layers, which has important implications for ice shelf melting in future climate scenarios.
Ocean-driven melting of ice shelves in Antarctic and Greenland regions is accelerating, with substantial implications for future sea level rise and global warming. However, the ice shelf response to changing ocean temperatures is poorly understood due, in part, to lack of knowledge of the fine-scale ocean processes that bring heat and salt towards the ice. Double-diffusive layering is an interesting fluid dynamics phenomenon that has been observed around Antarctica and Greenland which may help to bring this heat and salt towards the ice shelves, thereby influencing melt rates. This project will apply novel unsupervised machine learning methods to state-of-the-art ocean simulations and observations to identify the processes and regions where double-diffusive layering occurs. Particular focus will be placed on quantifying the transport of ocean heat and salt in these layers, which has important implications for ice shelf melting in future climate scenarios.
Supervisors: Taimoor Sohail (t.sohail@unsw.edu.au), Cat Vreugdenhil (cat.vreugdenhil@unimelb.edu.au) and Bishakdatta Gayen (bishakhdatta.gayen@unimelb.edu.au)
Location: University of Melbourne.
Requirements: Some programming experience (particularly Python) is a plus but not required.
When: Flexible and to be run, depending on availability, on a full-time basis over the summer or winter break, or part-time for the equivalent of six weeks full time work throughout the academic year.