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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.

����ܾ����𳾱�Գٲ�:��The project requires Python programming skills in analysing data.

Supervisors:��Dr. Abhnil Prasad��and��Prof. Steven Sherwood

Large-scale climate modes such as El Niño-Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole significantly influence weather and climate variability across Australia. These modes are typically quantified using Sea Surface Temperature (SST) data from specific regions of the ocean. This project aims to compute these climate indices using simulations from Australia's seasonal forecasting system, ACCESS-S2. ACCESS-S2 is a fully coupled dynamical model operated that provides seasonal climate forecasts. Specifically, the project will derive large-scale climate mode indices from historical SST outputs of ACCESS-S2 and compare these results with satellite-derived SSTs.

The project is expected to commence in July.

����ܾ����𳾱�Գٲ�:��The successful applicant should have strong programming skills in Python.

Supervisors:����and Dr. Sanaa Hobeichi

Global climate models (GCMs) are essential for projecting long-term climate trends, but their coarse spatial resolution limits their ability to capture regional climate variability and extremes. To address this, high-resolution regional downscaling has been carried out over Australia, in collaboration with the Bureau of Meteorology, CSIRO, the NSW Department of Climate Change, Energy, the Environment and Water (DCCEEW), the University of New South Wales, and the University of Queensland. The first phase of project investigated the added value of the four regional downscaled simulations compared to global CMIP6 models. Now, in the second phase, the student will focus on whether bias correction will influence the outcomes of the added value datasets. The student will target specific questions, 1) Does bias correction improve representation of extremes or only the mean climate? 2) Does bias correction alter spatial patterns? 3) Does bias correction affect trend signals? Through this work, the student will develop skills in analysing high-resolution climate datasets, interpreting model outputs, and working with scientific programming tools commonly used in climate science.

Supervisors:��Prof. Jason Evans, Dr. Ulrike Bende-Michl, Dr. Christian Stassen, Dr. Leena Khadke

Experience required: Basic proficiency in Python or another scientific programming language is required. Familiarity with climate data formats (e.g., NetCDF) or experience working with large datasets is beneficial but not essential

Build a tool that benchmarks and visualizes how common scientific array operations scale with dataset size and chunking (NumPy vs xarray eager vs xarray+Dask), producing interactive “scaling curves” and “chunking maps” that teach users how performance changes.

Requirements: A student who is comfortable writing Python, curious about performance, willing to run controlled experiments, and interested in understanding how scientific code behaves as data sizes grow.

Supervisors:��Dr. Sam Green and Dr. Sanaa Hobeichi

Machine learning-based equation discovery has gained traction in many fields but remains largely underexplored in land surface modelling, where it offers the potential to reveal alternative formulations that may complement or improve existing representations of land–atmosphere fluxes. This project will explore equation discovery, in particular the Sparse Identification of Nonlinear Dynamics (SINDy) approach, to select a parsimonious combination of physically inspired candidate terms derived from meteorological forcings measured at FLUXNET sites and infer an interpretable functional equation for land–atmosphere fluxes.
The project will explore both the capabilities and limitations of the ML-based equation discovery approach and its potential to inform improvements in land surface model representations of the examined fluxes.

����ܾ����𳾱�Գٲ�:��The selected student needs to have experience with Python, High-Performance Computing, and GitHub to be considered for this project.

Supervisors:��Dr Sanaa Hobeichi, Dr Ulrike Bende-Michl (BoM), and Prof Gab Abramowitz

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A potentially strong El Niño is currently developing in the tropical Pacific, providing a timely opportunity to evaluate ENSO-based climate predictions using real-world observations. Meanwhile, the climate community has recognized that the traditional Oceanic Niño Index (ONI) is increasingly contaminated by the long-term warming trend, motivating the recent adoption of the Relative Oceanic Niño Index (RONI), which removes the tropical mean warming signal to better isolate ENSO-related interannual variability. This project will compare ONI- and RONI-based reconstructions of South Pacific teleconnections and Australian climate responses using the updated data. In particular, the developing 2026-27 El Niño will serve as a timely out-of-sample test of whether RONI provides a more physically robust predictor of regional climate anomalies in a warming climate.

����ܾ����𳾱�Գٲ�:��Essential programming skills (e.g. Python), and basic knowleage in climate sciences is welcomed.

Supervisors:��Linyuan Sun and Andrea Taschetto

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