Scholarships

The Southern Annular Mode (SAM) is the main mode of variability in the Southern Hemisphere. It manifests itself via a poleward shift of the band of strong westerly winds called the jet stream, and has known impacts on rainfall and surface temperatures throughout the Southern Hemisphere. This project will investigate whether the temperature structure in the lower stratosphere just above the tropopause has an influence on the SAM, and if so, tries to quantify its influence. This is based on recent findings in relation to ozone anomalies and sudden stratospheric warmings, which both have been shown to have an impact on surface weather. The project will use existing climate data, and use an idealised climate model to run custom numerical simulations.

Experience: None

Supervisors: Dr Martin Jucker

Australia’s most hail-prone regions are on the east coast from north of Brisbane to south of Sydney. However, the largest hailstone ever recorded in Australia fell in the sub-tropics, just north of Mackay, and the possibility of hail occurrence extends well into the tropics. In particular, a region around Burketown in Queensland shows as a hotspot of hail probability in radar, satellite, and hail-proxy records. In this project, we will investigate hail occurrence in convection-resolving simulations of the atmosphere around Burketown. The student will gain experience in analysing the output from high-resolution weather models, in atmospheric science, and in scientific programming. The project will increase our understanding of the atmospheric conditions leading to hail formation in the (sub-)tropics, a region in which hail occurrence is not well understood.

Experience: To complete this project experience with python is essential and experience with analysing large datasets is a plus.

Supervisors: Dr Tim Raupach

Transitioning from fossil fuels to renewable energy is a key part of Australia’s pledge to reduce greenhouse gas emissions. An energy system with more renewables, however, is susceptible to variations in the weather and climate. Extended periods of cloudy, wind-less weather can mean solar and wind production isn’t enough to meet demand. This can be compounded when hydropower reservoir levels are low. The extent to which solar, wind and hydropower droughts will co-occur in the coming decades is unknown, but is crucial in enabling the energy sector to transition its systems to rely more on renewables. In this project, the student will use climate model data to quantify how likely it is that Australia will experience compound wind, solar and hydropower droughts under different emissions scenarios for the coming century. The results will be used to inform future research into the impacts of climate change on Australia’s energy system.

Experience: Some programming experience (ideally Python or R) is desirable.

Supervisors: Dr Doug Richardson & Dr Anna Ukkola

Leveraging artificial intelligence (AI) to detect weather features, such as atmospheric rivers, fronts, and tropical cyclones, holds great promise in advancing our understanding and prediction of extreme precipitation events. However, to train Artificial Intelligence models effectively, we need a comprehensive database of weather features. In this project, the student will contribute to the development of a comprehensive human-labelled database of atmospheric rivers or/and tropical cyclones and fronts using high-resolution climate model output.

The student will undergo training on how to identify atmospheric rivers by visualizing the high-resolution climate model output. Following this, they will employ suitable data labeling software to draw polygons over the identified atmospheric rivers, generating a meticulously labeled dataset.
By creating this database, the student will be contributing to the development of the training samples needed to train AI models to autonomously detect and classify weather patterns.

Experience: None

Supervisors: Dr Sanaa Hobeichi

The characteristics of numerically simulated clouds and convection depend on the resolution of weather and climate models. Subgrid clouds are parameterized in coarse-resolution models but are often resolved at higher resolutions. Such clouds are essential in understanding shallow convection and can significantly affect the radiation budget if unaccounted for in our current models. This project aims to quantify the characteristics of subgrid clouds by comparing several associated cloud and radiation fields simulated at different resolutions from a numerical weather prediction model to ground-based and available satellite observations. The main objective of the project is to understand how well sub-cloud variability is captured to varying resolutions in model simulations.

Experience: The project requires Python programming skills in analysing data.

Supervisors: Dr Abhnil Prasad, Prof Steven Sherwood

A key understanding of the mechanisms behind past climate changes helps us prepare for the occurrence of similar processes in the future. Using Earth System Climate Models (ESMs), we are now able to simulate the past climate of thousands and millions of years ago. One of the fundamental requirements to simulate past climate is Earth’s orbital parameters, which include obliquity, eccentricity, and precession. Changes in obliquity, axial tilt of the Earth’s axis, have been known to impact the total solar radiation on Northern and Southern Hemisphere during summer, which, in turn, affects the air temperature, sea surface temperature and sea ice over the North and South pole respectively. The student involved in this project will analyse available data of a simulated climate under different astronomical configurations, with a particular focus on the sea ice extent in the Southern Ocean.

Experience: No prior knowledge of past climate changes is crucial here, but students with background in atmospheric/oceanic sciences are preferred. The candidate is required to be familiar with the use of Python, or other data visualization tools.

Supervisors: Dr Himadri Saini &  A/Prof Laurie Menviel

Marine extreme events have a significant impact on marine ecosystems and are predicted to intensify as the ocean warms. However, our understanding of the impacts and nature of marine extremes is mostly limited to studying temperature at the surface, due to a lack of observations. Insights into the response of the ocean to marine extremes can only be gained by examining and contrasting the specific sites which have sufficient long-term observations.

The student will participate in taking a global inventory of long-term observational sites, identifying marine extremes, and exploring the differences between the ocean’s response to extremes in different locations. This project will contribute to our understanding of global subsurface ocean extremes and their impacts on marine ecosystems.

Experience: None

Supervisor: Dr Neil Malan