AI4Carbon: Machine Learning for Carbon Cycle Science
A community effort
Announcements
| May 05, 2026 |
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| Jan 15, 2026 |  Please consider submitting an abstract to the EGU26 session Machine learning and hybrid modelling for carbon cycle science, monitoring and carbon market policy – see you in Vienna! | ||
| Jan 10, 2025 |  Please consider submitting an abstract to the EGU25 session Machine Learning for Carbon Cycle Research – see you in Vienna! | ||
| Nov 28, 2024 |  Thanks to everyone who joined our first virtual workshop! We will be back in 2025 with the next steps. Until then, subscribe to our mailing list to stay updated! | ||
| Oct 16, 2024 |  Mark the date! The first AI4Carbon virtual workshop is sheduled for Nov 7th, 2024 – 5pm CET / 4pm GMT / 11am EST / 8am PST / 11pm ICT |
🌍 Advancing AI for Carbon Cycle Science
The AI4Carbon Initiative is a community-driven effort to leverage cutting-edge machine learning for understanding and monitoring carbon cycles. We bridge top-down atmospheric inversion approaches with bottom-up ecosystem modeling through AI, advancing both atmospheric transport science and carbon accounting.
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The Challenge
Artificial intelligence has revolutionized weather prediction and other scientific domains. Yet the carbon cycle—critical for understanding climate and supporting climate policy—remains largely untouched by these AI advances. AI4Carbon works to close this gap by leveraging machine learning across both atmospheric-based (top-down) and ecosystem-based (bottom-up) approaches to carbon science.
🔬 The Top-Down Challenge
Atmospheric inverse modeling relies on coarse-resolution transport models, leading to systematic errors in retrieving surface carbon fluxes. Higher-resolution models exist in research but are computationally prohibitive for operational use.
📊 The Data Gap
Unlike weather prediction, there is no consensus benchmark dataset for training machine learning models on atmospheric CO₂ transport. This lack of standardization hinders progress and collaboration across the research community.
🤝 The Bottom-Up Complement
Ecosystem models and direct measurements provide valuable constraints, but integrating them with atmospheric observations remains challenging. AI can help unify these perspectives by learning patterns across scales and bridging gaps between different data sources.
Our Mission
📈 Build Consensus
Establish benchmark datasets and evaluation frameworks for AI in carbon cycle research.
🔗 Foster Community
Connect researchers across top-down atmospheric science, bottom-up ecosystem modeling, and machine learning through workshops and collaborative projects.
🎯 Drive Impact
Develop and validate AI methods that improve carbon flux estimates and support climate monitoring.
🌐 Enable Action
Provide tools and techniques that support policy-relevant carbon accounting and the Global Greenhouse Gas Watch.
Upcoming Events
Join us at upcoming workshops and conferences to learn more about AI for carbon cycle science:
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Organizing Committee
Advisory Board
Contact
If you are interested in joining the collaborative effort, please reach out to vbenson (at) bgc-jena (dot) mpg (dot) de!