Machine Learning for Climate and Weather Working Group Announce

Machine Learning for Climate and Weather Working Group
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Hi everyone,

This month’s meeting of the Machine Learning for Climate and Weather Working Group will take place this Friday, 5th December, at 2pm (AEDT), including a talk from Christian Stassen (Bureau of Meteorology), Machine learning based climate downscaling for the Australia Climate Service

Zoom details: Zoom link
Meeting ID: 831 8730 7759
Password: 123456

Agenda:

Facilitator: Sanaa Hobeichi

Co-chairs: Vassili Kitsios, Tennessee Leeuwenburg, Ryan Holmes, Micael Oliveira (ACCESS-NRI liaison), and Yue Sun (NCI liaison)

  • Acknowledgement of country

  • Updates from ACCESS-NRI

  • Updates from NCI

    • AIFS training benchmark
    • Experimental software environment modules used in Momentum Partnership ML Hackathon, not finalized yet, can be further improved before officially released
      • sda/25.10
      • pet/0.4.0
      • anemoi/25.10
    • Module updates
      • Rapids

  • Updates from the Chairs

    • Feedback on Momentum Partnership ML Hackathon - 10-14 November
    • AMOS 2026 - Session: 10. Advancing weather, climate and ocean research with AI and machine learning

  • Updates from the Community & new community member introductions

  • Presentation by Christian Stassen (BoM) (Details provided below).

  • Discussion (ongoing, dependent on time available in meeting):

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Details of this month’s presentation:

Presenter: Christian Stassen | Modelling Research Scientist | Atmospheric Modelling, Research, Science and Innovation - BoM

Title: Machine learning based climate downscaling for the Australia Climate Service

Abstract: In the first phase of the Australian Climate Service (ACS), two physical models, BARPA and CCAM, were used to dynamically downscale CMIP6 projections over Australia to regional (11km - 17km) and convection-permitting (4km) resolutions. However, the high computational cost of high-resolution physical simulations makes them impractical for large ensembles or extended historical and future periods. Machine learning offers a faster, more cost-effective alternative by leveraging the existing physical model outputs as training data. State-of-the-art generative machine learning models produce realistic, high-fidelity outputs and these approaches can complement and significantly extend the reach of physical downscaling by enabling more global CMIP models, emission pathways, and ensemble members, to be downscaled at convection-permitting resolutions. This will help reducing uncertainty in regional climate projections. This talk will cover plans for the use of ML methods the next phase of ACS climate downscaling.

Biography: Christian is a research scientist at the Bureau of Meteorology working in the Atmospheric Modelling team. He co-leads the climate-ML activity in ACS Work Package 1 - Foundational Climate Modelling.