Dr Alberto Alberello, School of Mathematics, University of East Anglia
Professor Ian Renfrew, School of Environmental Sciences, University of East Anglia
The Antarctic sea ice seasonal cycle is the Earth’s heartbeat, but current climate models are unable to reproduce its often baffling regional and inter-annual variabilities. The extreme Southern Ocean waves, winds, and currents play a major role in regulating sea ice extent and properties, and, hence, atmosphere-ocean fluxes and ice advance/retreat. This four-year PhD project will develop reliable mathematical models for pancake ice cycle in the presence of waves, winds, and currents, aiming at better predicting the evolution of floe size distribution and deriving a parametrisation for implementation in Earth system models.
The project will use theoretical and numerical methods for solving wave/ice models and kinetic equations mimicking the ice merging/fragmentation process; machine learning techniques will be used for data driven model parametrisation. The research will unfold as follows: (year 1) literature review, wave/ice model in 1D; (year 2) wave/ice model in 2D and kinetic equation models; (year 3) model extension to include ocean currents and wind; (year 4) statistics of extreme events. The parametrisation of the floe size distribution will start in year 2, while the machine learning data driven techniques in year 3. Scientific collaborations with Dr Fabien Montiel (Otago) for the merging/fragmentation ice models and Prof. Marcello Vichi (Cape Town) for field data acquisition, with the possibility of a field trip in the Indian/Southern Ocean, will take place in due course.
The student will take part to the weekly research seminars across the Faculty of Science (Mathematics, Environmental Sciences, COAS) and take mathematics/physics modules of the MAGIC consortium. Other training offered like numerical modelling, use of HPC, scientific dissemination and writing will be available. The student will present their findings to meetings in the UK (UK Sea-ice meeting) and internationally (EGU). This PhD project is part of the ARIES DTP, and as such the student will participate to all its training activities.
We are looking for a highly motivated, enthusiastic, and outstanding candidate holding a degree in Mathematics, Physics, Environmental Sciences, or similar. Experience in mathematical and numerical modelling is highly recommended.