Climate modelling for net zero carbon emissions

The challenge

Our climate is changing, and new infrastructure needs to be resilient under these new conditions. Our research seeks to forecast these changes and the new landscape of risks from natural disasters in ways that are specific to net-zero infrastructure projects (like wind energy sites or public transport projects), and to assess the effectiveness of net-zero strategies that seek to remove carbon from the air such as afforestation projects.


Why this research is valuable

Our research centres on advanced modelling techniques to understand future risks and the effectiveness of new net-zero projects. While projections of climate change are made at large scales and in the long term, we need local, highly specific data to plan for the future, especially for changing risks of high-impact, low-probability events such as tropical cyclones or bushfires. This requires new statistical methods and new insights tailored to the Australian setting.


Research themes

  1. Artificial Intelligence for Climate Data: The UN IPCC reports are based on enormous quantities of publicly available data and model simulation output. How can we make statistically sound, impactful inferences from this complex spatiotemporal data?
  2. Changing Conditions over the Indo-Pacific: Australia lies between 3 oceans, each with an active role in setting atmospheric conditions. How will oceanic variations (e.g., El NiƱo) respond to varying levels of carbon emissions, and how will that affect storm frequency/intensity, cloud cover, wind speeds, etc?
  3. Accounting for Interactions with the Carbon Cycle: The natural carbon cycle exchanges carbon between the atmosphere, ocean, soil, and organisms. How will these existing systems interact with NetZero infrastructure projects, and can they be leveraged to reduce carbon in the atmosphere?


Want to know more?

This research is closely linked to the work of several researchers at the ARC ITTC for Data Analytics for Resources and the Environment.

Data Analytics for Resources and the Environment

Team members