Atmospheric Transport Modeling of CO2 With Neural Networks

Citation: Benson, V., Bastos, A., Reimers, C., Winkler, A. J., Yang, F., & Reichstein, M. (2025). Atmospheric transport modeling of CO2 with neural networks. Journal of Advances in Modeling Earth Systems, 17, e2024MS004655. https://doi.org/10.1029/2024MS004655Main Takeaways:A New Benchmark for AI Carbon Tracking: The authors introduce CarbonBench, the first systematic benchmark dataset designed specifically for training and evaluating machine learning emulators of Eulerian atmospheric transport. Built from CarbonTracker CT2022 inversions and ObsPack station observations, it ships at three resolutions (the coarsest being 5.625° × 10 vertical levels × 6h) and is engineered to plug directly into modern deep learning pipelines — opening atmospheric carbon modeling to the broader ML community.SwinTransformer Wins, Decisively: Of the four architectures tested (UNet, GraphCast, SFNO, and SwinTransformer), the SwinTransformer reaches near-perfect emulation with a 90-day R² above 0.99 and stays stable in physically plausible forward runs for over three years — a regime where neural PDE solvers typically blow up. At measurement stations, it actually captures the seasonal cycle in Svalbard better than TM5, the conventional model it was trained to emulate, possibly due to differences in boundary layer transport near the poles.Physics Tricks Were the Unlock: Out of the box, the neural networks were unstable — especially the mesh-based UNet and GraphCast. Two simple physics-aware adjustments fixed this across all four architectures: centering the CO2 input field at each timestep to remove the covariate shift from steadily rising atmospheric CO2 (called CentFlux), and a post-hoc mass fixer that rescales predicted mass to match the surface flux budget. The result is mass conservation with RMSE of just 0.00058 PgC against a total atmospheric carbon mass of ~865 PgC — effectively negligible.Speed Isn't the Selling Point (Yet): Unlike AI weather models, which famously outpace numerical forecasting by orders of magnitude, the SwinTransformer is not significantly faster than TM5 at this resolution — about 1.5 seconds for a 30-day run on an A40 GPU versus a few minutes for TM5 on 24 CPUs. The real promise lies elsewhere: the networks are fully differentiable (useful for inverse modeling of surface fluxes), natively support batched ensembles, and scale better to high resolution where conventional solvers become prohibitively expensive — exactly the regime where current CO2 inversions struggle most.