Facilitator: Micael Oliveira
Co-chairs: Sanaa Hobeichi, Ryan Holmes, Tennessee Leeuwenburg, Micael Oliveira (ACCESS-NRI liaison), Yue Sun (NCI liaison)
Acknowledgement of country
Updates from ACCESS-NRI and NCI
ACCESS-NRI 2025-26 Workplan for Machine Learning
New DL models ready to run at NCI
Updates from the Chairs
Highlights from the First Year of the Community.
ML session at AMOS2026
Updates from the community & new community member introductions
Presentation by David Fuchs (Details provided below).
Discussion and follow-up from NRI community workshop (ongoing):
Proposals for interacting/supporting other working groups
Plans for using project nm47 (100TB gdata storage, 700 kSU/quarter) on NCI
See you on Friday!
Details of this month’s presentation:
Presenter: David Fuchs | Senior Scientist, DCCEEW Title: The benefits of lateral connections: towards a stable neural network surrogate for climate model parametrization Abstract: This study compares four neural network (NN) architectures to serve as surrogates for moist convection in a global atmospheric model. The four architectures share a common backbone but differ in implementation, and are tested offline and online in the CAM atmosphere model. A network architecture that enforces a mesh of short and long pathways proves superior to an increase of depth alone, or replacing ResNet shortcuts with DenseNet. An online hybrid climate model run based on this architecture runs stably for 25 years to completion of an atmospheric-only, and 12 years to completion of a coupled ocean-atmosphere scenario. This shows that the learning process loses online numerical stability while reducing online error. Earlier offline learning epochs mastered cloud liquid water while later epochs reduced the error in cloud ice, potentially due to a naive sampling approach. In the coupled ocean-atmosphere scenario, these translated to steep surface temperature increases when earlier training epochs were used as surrogates, which flattened when later training epochs were used. These results emphasize the need to explore further NN architectures as surrogates to existing parameterizations.
This month’s meeting of the Machine Learning for Climate and Weather Working Group will take place this Friday, 7th November, at 2pm (AEDT), and includes a talk from Ajitha Cyriac (CSIRO) - A machine learning approach to downscale sea surface temperature extremes and thermal stress on the Great Barrier Reef
Facilitator: Ryan Holmes
Co-chairs: Tennessee Leeuwenburg, Sanaa Hobeichi, Vassili Kitsios, Micael Oliveira (ACCESS-NRI liaison), and Yue Sun (NCI liaison)
Acknowledgement of country
Updates from ACCESS-NRI and NCI
Feedback collected from the audience of the NCI Deep Learning Model Development in Climate and Weather Studies Webinar (30 Oct) and Exercise session (6 Nov).
NCI dk92 software environment and example notebook update
Updates from the Chairs
Momentum Partnership ML Hackathon - 10-14 November
Updates from the Community & new community member introductions
Presentation by Ajitha Cyriac (CSIRO)(Details provided below).
Discussion (ongoing, dependent on time available in meeting):
Plans for using project nm47 (100TB gdata storage, 875kSU/quarter) on NCI.
Presenter: Ajitha Cyraic | Postdoctoral Research Fellow at CSIRO Title:A machine learning approach to downscale sea surface temperature extremes and thermal stress on the Great Barrier Reef Abstract: In this study, we have successfully developed a machine learning (ML) model based on a super resolution deconvolutional neural network to rapidly downscale SST on the Great Barrier Reef (GBR). When downscaling 80 km data to 10 km resolution, the ML model captures the spatial variability of SST and extreme thermal events significantly better than a conventional interpolation method. We have applied this model to independent datasets from both current and future climates to demonstrate its robustness. We further use this model to downscale thermal stress from five CMIP6 models and analyse coral bleaching risk under different emission scenarios on the GBR. Biography: Ajitha Cyriac is a Postdoctoral Research Fellow at CSIRO based in Perth. Her research focuses on downscaling climate data using ML methods and analyzing coral bleaching risks at Australia’s Marine World Heritage Areas.