10 October 2022
About the research project
A major disadvantage of many current machine learning approaches to modelling physical systems is the “black box” nature of the resulting outputs. More generally, approaches such as deep learning convolution neural networks assume statistical stationarity of the data and are predicated on there being a sufficiently long record for training. However, this is not the case for the Earth’s climate as it undergoes rapid anthropogenic change. Recently a new class of machine learning (ML) tools have been developed that allow the determination of optimal stochastic models, which by construction contain the dynamically relevant covariances between model inputs and external covariates (forcings) for application to problems where the dimensionality of the problem far exceeds the number of available data instances, for which climate is a paradigmatic example. In this way extremely high-dimensional data may be factorised while identifying the representative underlying evolving dynamics. Once determined, dynamical systems methods may be applied to characterise the dynamics of the resulting reduced order models and associated regime behaviour (metastability).
Recent studies of extremes in the tropospheric circulation of the Northern Hemisphere, for example the 2010 Russian heatwave, have shown that transitions between these regime states may be characterised in terms of the local Lyapunov exponents to reveal the local attractor dimension and, crucially, (non)hyperbolic dynamics. To date no comparable studies have been carried out on the circulation dynamics of the Southern Hemisphere. Many open questions remain in relation to how changes in hyperbolicity (i.e., alignment of local Lyapunov vectors) translate into the observed dynamics of the high dimensional physical system.
This project applies novel approaches for deriving predictive causal stochastic models constructed directly from data to better characterise the causes of recently observed changes in the structure of synoptic variability in the Southern Hemisphere tropospheric circulation and associated impacts on the Antarctic climate.
Primary SupervisorMeet Prof Andrew Bassom
Applicants will be considered for a Research Training Program (RTP) scholarship or Tasmania Graduate Research Scholarship (TGRS) which, if successful, provides:
- a living allowance stipend of $28,854 per annum (2022 rate, indexed annually) for 3.5 years
- a relocation allowance of up to $2,000
- a tuition fees offset covering the cost of tuition fees for up to four years (domestic applicants only)
If successful, international applicants will receive a University of Tasmania Fees Offset for up to four years.
As part of the application process you may indicate if you do not wish to be considered for scholarship funding.
Applicants should review the Higher Degree by Research minimum entry requirements.
Additional eligibility criteria specific to this project/scholarship:
- Applicants must be able to undertake the project on-campus
The project is competitively assessed and awarded. Selection is based on academic merit and suitability to the project as determined by the College.
Additional essential selection criteria specific to this project:
- Strong background in applied mathematics and/or physics
Additional desirable selection criteria specific to this project:
- Some training in fluid dynamics and geophysical fluids in particular
There is a three-step application process:
- Select your project, and check you meet the eligibility and selection criteria;
- Contact the Primary Supervisor, Prof Andrew Bassom to discuss your suitability and the project's requirements; and
- Submit an application by the closing date listed above.
- Copy and paste the title of the project from this advertisement into your application. If you don’t correctly do this your application may be rejected.
- As part of your application, you will be required to submit a covering letter, a CV including 2 x referees and your project research proposal.
Following the application closing date applications will be assessed within the College. Applicants should expect to receive notification of the outcome by email by the advertised outcome date.