AAPP PhD Projects and Scholarships
A number of PhD research projects related to the research program of the AAPP will be advertised below and on the University of Tasmania Graduate Research website. Applicants will be able to apply for Stipend Scholarships and fee waivers from the University of Tasmania or from other sources. If successful, applicants will also receive a top-up scholarship of $6,000 per annum for 3.5 years. This scholarship is funded from the Australian Government as part of the Antarctic Science Collaboration Initiative program through the Australian Antarctic Program Partnership (AAPP).
If you are interested in undertaking a PhD with the AAPP, please check this page frequently for opportunities or contact any of our researchers directly.
The biogeochemistry of trace elements in the Southern Ocean: MISO-GEOTRACES section from Australia to Antarctica
Project 5: Biogeochemistry
The Southern Ocean influences climate, sea level, biogeochemical cycles and marine productivity on global scales. Observations suggest that rapid change is already underway in the Southern Ocean, but the measurements are sparse and hence the nature, causes and implications of Southern Ocean change are not yet understood. This project will contribute to a multi-disciplinary observational program measuring a comprehensive suite of physical and biogeochemical variables along a full depth repeat hydrographic section extending from western Australia to the Antarctic sea ice edge.
The candidate will join a research team on a 59-day voyage (‘MISO’ project) of the Marine National Facility’s Research Vessel ‘Investigator’ in early 2024 that will study the marine biogeochemistry of trace elements and their isotopes (TEIs) along the I9S section (~115oE), a signature field program of the Australian Antarctic Program Partnership (AAPP). Following the fieldwork, the candidate will participate in laboratory analyses and experiments using state-of-the-art facilities and instrumentation to determine the distributions, physico-chemical forms and sufficiency of micronutrient trace elements in the Southern Ocean, focussing on elements that have been rarely studied in this region. This will be expanded to investigate trace metal/carbon and trace metal/nutrient (e.g., Cd/P, Zn/Si) relationships across different Southern Ocean water masses, how they vary seasonally and spatially, and may change under future environmental conditions. In the latter stages, this project will feed vital information on the prevalence and flux of trace elements into biogeochemical and ecosystem models of the region.
Our observational strategy has strong collaborative activity under the auspices of the international GEOTRACES program (international study of global marine biogeochemical cycles of trace elements and their isotopes). This research will provide the critical information on trace elements biogeochemistry for ocean productivity and marine ecosystem health, providing the science for predicting a key factor in the future impact of the oceans on climate.
Ice shelf deep learning
Project 3: Ice shelves
Deep learning with artificial neural networks has evolved rapidly and become a widely applied tool for the study of Earth surface processes. In glaciology, neural networks have been used to emulate physically advanced glacier models, speeding up the computational simulation by several orders of magnitude. This efficiency gain makes deep learning a promising new tool for assessment of ice shelves, which are the floating extension of the Antarctic Ice Sheet.
This project will use machine learning to understand how ice shelves in Antarctica behave and interact with the ocean and climate. The student will simulate ice shelves using the Instructed Glacier Model which emulates the physics of ice shelves. Upon configuration and training, the emulator will be used to investigate ice shelves across Antarctica with the aim of identifying which ones are fragile and which ones are stable over the coming decades and century.
Specifically, the research will:
- Use a physics-informed emulator to simulate the flow of ice shelves in East Antarctica
- Explore the emulator model's ability to reproduce ice shelves as observed
- Assess ice shelf stability of East Antarctic ice shelves under different environmental forcing scenarios
With a modern approach and broad scope, the research will advance our understanding of ice shelves, which have disappeared almost entirely in Greenland (where climate is warmer) but still play a major role for the stability of the Antarctic Ice Sheet.
Southern Ocean aerosol and clouds
Project 1: Atmosphere
This project will focus on understanding the formation of aerosol and cloud over the Southern Ocean, and their role in the region's energy balance and precipitation. Most aerosol over the Southern Ocean are formed from breaking waves or emissions from marine microorganisms. Cloud droplets and ice crystals form around these aerosol particles. Most current generation climate models struggle to simulate aerosol and cloud properties accurately in the region. This has significant consequences for our understanding of current and future climate not just over the Southern Ocean, but globally as well. Because of this, the biological, chemical, and physical processes that govern aerosol and cloud formation over the Southern Ocean are of significant interest to the international community.
A variety of approaches are needed to tackle the problem. These approaches include 1) collecting and analysing surface observations of aerosols, clouds, precipitation and radiation collected from ships and land stations; 2) performing sensitivity tests and evaluation of regional and global climate model simulations; 3) analysing and evaluating remote sensing observations from satellite platforms of cloud properties; and 4) using machine learning with inputs from a variety of surface and satellite observations as well as climate models and reanalyses to better understand physical processes and make improved predictions. This project has the flexibility to focus on one or several of these approaches depending on the skills and interests of the candidate.
The outcome of this project will be an improved understanding of the drivers and processes that govern Southern Ocean aerosol and cloud formation and their role in climate, with opportunities to translate this knowledge into climate models improvements.
Modelling cloud-aerosol interaction
Project 1: Atmosphere
The project will seek to better understand aerosol-cloud interaction in pristine clouds at Kennaook/Cape Grim using both field campaign data and a regional atmospheric model
Kennaook/Cape Grim, located on the northwest coast of Tasmania, is considered an excellent location to observe 'baseline' air, i.e. pristine Southern Ocean airmasses (Gras & Keywood, 2017). Such pristine airmasses provide an excellent opportunity to understand atmospheric processes away from the influence of human activity, which can help us understand how our weather and climate is changing.
Climate and weather models still produce large significant Southern Ocean radiation biases that have been attributed to the poor simulation of aerosol-cloud processes in such pristine environments (Fiddes et al., 2022; Mallet et al., 2023). One of the primary reasons for this problem is the limited availability of observations in the area, leading to a lack of understanding of crucial cloud formation processes specific to the Southern Ocean (McFarquhar et al., 2020).
Beginning in April 2024, the Cloud and Precipitation Experiment at Kennaook (Cape-K) campaign will begin, through to September 2025 (Mace et al., 2023). This campaign will be complemented by a comparison voyage of the RV Investigator and a potential flight campaign. These extensive and co-ordinated field campaigns, targeting aerosol-cloud interaction, provide a rare opportunity for processes understanding, model evaluation and development across a range of seasons.
1. What are the microphysical and aerosol properties of different cloud types in pristine Southern Ocean airmasses?
2. Can a high-resolution model represent these microphysical and aerosol properties?
3. Can we improve the representation of these cloud properties by improving the aerosol and cloud microphysics in the model, or with machine learning?
4. What is the seasonal dependence of cloud properties with respect to aerosol sources and can we replicate this in an atmospheric model?