A case study in sustainable AI: Applying carbon-reducing techniques in weather forecasting frameworks
Wednesday, June 24, 2026 3:45 PM to 5:15 PM · 1 hr. 30 min. (Europe/Berlin)
Foyer D-G - 2nd Floor
Women in HPC Poster
Earth, Climate and Weather ModelingML Systems and FrameworksOptimizing for Energy and Performance
Information
Poster is on display and will be presented at the poster pitch session.
Machine Learning (ML) is transforming weather and climate science, increasingly complementing traditional methodologies for forecasting and analysing complex atmospheric phenomena. There is evidence that ML-based solutions can be computationally more efficient than traditional models while achieving comparable accuracy. As minimising the energy consumed by data centres and HPC resources is key to help tackle climate change, leveraging ML for scientific computing applications has the potential to significantly reduce the environmental impact of weather and climate modelling.
We are investigating how a broad set of sustainable AI techniques across data curation, model selection, training and inference can be applied when using domain-specific ML frameworks. This project explores to what extent sustainability is already built-in, and whether the abstractions that these frameworks provide aid or hinder the application of sustainable AI best practices. Driven by the observation that accessibility of these techniques is a blocker to adoption, as considerable expertise and effort can be required to leverage ML within scientific workflows, domain-specific ML frameworks have become popular recently. Examples include ECMWF’s Anemoi and NSF NCAR’s CREDIT, and these have demonstrated the potential to significantly lower the barrier to undertaking research into data-driven weather and climate prediction. These technologies accelerate science by providing tools that abstract away many of the time-consuming details that form part of ML research into configuration files.
Whilst ML models have the potential to offer a step change in simulation efficiency, undertaking ML research can incur significant environmental impacts. There are some potential mitigations, for instance the carbon execution cost can be decreased through green scheduling, and optimising ML workflows to improve both model accuracy and efficiency has been demonstrated to be an effective strategy. However, these efforts can depend very much on location (e.g. low carbon energy). A key objective of this project is to collate appropriate techniques across the community and document their potential impact.
Identifying appropriate metric(s) is a key consideration when exploring the sustainability benefits and challenges of fusing ML with scientific computing. Common approaches include reporting the runtime of a model’s final training run, or the amount of energy or water consumed by one inference run. However, such measurements can be myopic and it is important to consider energy-efficiency and more broadly sustainability for the entire development lifecycle: From data acquisition, preparation, the effective use of resources during (re-)training and tuning hyperparameters, to the integration of a trained model into existing systems and workflows.
This work aims to better understand current best practice, limitations and opportunities around fusing ML with scientific workflows to drive energy efficiency. It is part of the CONTINENTS project, a four-year long EPSRC-funded collaborative programme between EPCC at the University of Edinburgh, the UK’s National Centre for Atmospheric Science (NCAS),and the US National Center for Atmospheric Research (NCAR). This interdisciplinary collaboration aims to bring together world leading centres of HPC research and service provision, atmospheric science experts, and numerical and ML application developers to provide a step change in sustainable climate and weather modelling.
Machine Learning (ML) is transforming weather and climate science, increasingly complementing traditional methodologies for forecasting and analysing complex atmospheric phenomena. There is evidence that ML-based solutions can be computationally more efficient than traditional models while achieving comparable accuracy. As minimising the energy consumed by data centres and HPC resources is key to help tackle climate change, leveraging ML for scientific computing applications has the potential to significantly reduce the environmental impact of weather and climate modelling.
We are investigating how a broad set of sustainable AI techniques across data curation, model selection, training and inference can be applied when using domain-specific ML frameworks. This project explores to what extent sustainability is already built-in, and whether the abstractions that these frameworks provide aid or hinder the application of sustainable AI best practices. Driven by the observation that accessibility of these techniques is a blocker to adoption, as considerable expertise and effort can be required to leverage ML within scientific workflows, domain-specific ML frameworks have become popular recently. Examples include ECMWF’s Anemoi and NSF NCAR’s CREDIT, and these have demonstrated the potential to significantly lower the barrier to undertaking research into data-driven weather and climate prediction. These technologies accelerate science by providing tools that abstract away many of the time-consuming details that form part of ML research into configuration files.
Whilst ML models have the potential to offer a step change in simulation efficiency, undertaking ML research can incur significant environmental impacts. There are some potential mitigations, for instance the carbon execution cost can be decreased through green scheduling, and optimising ML workflows to improve both model accuracy and efficiency has been demonstrated to be an effective strategy. However, these efforts can depend very much on location (e.g. low carbon energy). A key objective of this project is to collate appropriate techniques across the community and document their potential impact.
Identifying appropriate metric(s) is a key consideration when exploring the sustainability benefits and challenges of fusing ML with scientific computing. Common approaches include reporting the runtime of a model’s final training run, or the amount of energy or water consumed by one inference run. However, such measurements can be myopic and it is important to consider energy-efficiency and more broadly sustainability for the entire development lifecycle: From data acquisition, preparation, the effective use of resources during (re-)training and tuning hyperparameters, to the integration of a trained model into existing systems and workflows.
This work aims to better understand current best practice, limitations and opportunities around fusing ML with scientific workflows to drive energy efficiency. It is part of the CONTINENTS project, a four-year long EPSRC-funded collaborative programme between EPCC at the University of Edinburgh, the UK’s National Centre for Atmospheric Science (NCAS),and the US National Center for Atmospheric Research (NCAR). This interdisciplinary collaboration aims to bring together world leading centres of HPC research and service provision, atmospheric science experts, and numerical and ML application developers to provide a step change in sustainable climate and weather modelling.
Format
on-demandon-site