workshops
EGU 2026 Machine learning and hybrid modelling for carbon cycle science, monitoring and carbon market policy
We are excited to focus a session on the interplay of carbon cycle science, monitoring and carbon markets with machine learning at EGU 2026. While researchers repeatedly warn that current observation-based constraints on the global carbon cycle entail large uncertainties, a need for the quantification of carbon sinks motivated from policy and the carbon credit markets is increasingly answered by innovative solutions from industry. In this session, we aim to bring together the communities, to engage in a rigorous discussion on the current state of carbon cycle measurement and verification and what role machine learning can play.
We are particularly excited to have Christian Igel from the University of Copenhagen as a keynote speaker. He is a distinguished machine learning expert and has been involved in major efforts to map the worlds forest tree height and biomass from remote sensing data.
EGU 2025 Understanding feedbacks between greenhouse gas exchange processes and climate variability using in situ observations, remote sensing, and machine learning
We are excited to co-host a session on greenhouse gas inversions, with conventional and machine learning approaches at EGU25. The session has a large number of high quality talks and poster presentations, and including a keynote talk by Abhishek Chatterjee from NASA JPL on the OCO-2 and OCO-3 progress.
1st Virtual Workshop
We are excited to invite you to the first virtual AI4Carbon Workshop: Machine Learning Meets Atmospheric Transport for Carbon Cycle Research.
Date & Time: November 7th, 2024
Duration: ~2 hours (Start at 5pm CET / 4pm GMT / 11am EST / 8am PST / 11pm ICT)
Workshop Structure
Hour 1: Presentations
Four 15-minute presentations covering atmospheric transport models, inversion, and main challenges.
Speakers
Hour 2: Discussion Session
We will discuss current challenges in atmospheric tracer transport modeling and inversion, and explore how recent advances in AI/ML can facilitate carbon cycle research, including transport model development, data assimilation, and uncertainty quantification. NOTE: We invite all participants to share their perspectives on transport models and inversion methods through a brief pre-workshop survey. Your valuable insights will help guide our discussion and highlight critical areas for development in the field.
Topics for discussion
- Datasets
- Evaluation
- High resolution transport
- Lagrangian vs. Eulerian
- Inverse modeling
- Other trace gases
- Neural Network methods