Earthly Machine Learning

Citation: Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022). Aligning artificial intelligence with climate change mitigation. Nature Climate Change, 12, 518–527. https://doi.org/10.1038/s41558-022-01377-7
Main Takeaways:
Three Layers of AI's Climate Footprint: The authors propose a framework that splits machine learning's climate impact into three distinct categories — the energy and hardware emissions of computing itself, the immediate effects of specific ML applications, and the broader system-level changes that ML induces across society. The categories that are easiest to measure (like the electricity used to train a model) are likely not the ones with the largest effects, which is why most current discussions of "AI and climate" capture only a sliver of the real picture.
Computing Is a Small Slice — For Now: The entire global ICT sector accounts for roughly 1.4% of global greenhouse gas emissions, and AI workloads are only a fraction of that. But the trajectory is steep: at Facebook, ML training compute has been growing about 150% per year and inference compute about 105% per year, far outpacing efficiency gains. Even striking efficiency wins — like Google's TPU being 30–80 times more energy-efficient than contemporary CPUs or GPUs — can be swamped by raw growth in demand.
The "Internet of Cows" Problem: ML is a general-purpose tool, which means it's just as good at accelerating oil and gas exploration or scaling up cattle farming (an industry already responsible for about 9% of global emissions) as it is at forecasting solar power or optimizing data center cooling. Whether AI is net-positive or net-negative for the climate is genuinely undetermined, and depends on which applications get funded, deployed, and regulated.
System-Level Effects May Dwarf Everything Else: The largest climate impacts of AI may come not from training runs or even individual applications, but from how ML reshapes society — through rebound effects (efficiency gains that drive more consumption), technological lock-in (autonomous cars entrenching private vehicle travel over transit and rail), and ML-powered recommender systems that boost demand for emissions-intensive goods. These effects are the hardest to quantify but potentially the most consequential, and the authors argue they need to be built into climate scenario modeling — something the IEA, EIA, and IPCC's Shared Socioeconomic Pathways largely don't do today.

Machine learning for the physics of climate
Citation: Bracco, A., Brajard, J., Dijkstra, H. A., Hassanzadeh, P., Lessig, C., & Monteleoni, C. (2025). Machine learning for the physics of climate. Nature Reviews Physics, 7, 6–20. https://doi.org/10.1038/s42254-024-00776-3
Main Takeaways:
Breaking the El Niño Spring Barrier: For decades, forecasts of the El Niño Southern Oscillation hit a hard wall at roughly 6 months lead time — a limit known as the spring predictability barrier. Convolutional neural networks trained on a mix of climate model and reanalysis data have shattered this ceiling, delivering skillful forecasts at 17 months out, with newer architectures pushing to 21–24 months. ML models can also now anticipate which type of El Niño will develop (eastern vs. central Pacific), which matters enormously because the two flavors produce very different regional impacts around the world.
Weather Forecasting at a Fraction of the Cost: A new generation of ML weather emulators — Pangu-Weather, GraphCast, FourCastNet, FuXi, NeuralGCM — now match or beat the European Centre's flagship physics-based forecasting system on most variables, including hurricane tracks, while running orders of magnitude faster. They achieve this with surprisingly compressed state representations: roughly 10 vertical atmospheric levels and 0.25° horizontal resolution, compared to 100+ levels and 0.1° in conventional models. The catch is that these models can violate basic physics — geostrophic balance, energy conservation, the butterfly effect — which currently blocks naive extension to climate timescales.
Hybrid Models Are Eating the Climate Stack: Pure ML works for short-range forecasts, but for climate-length runs the field is converging on hybrid architectures that pair a traditional dynamical core with neural-network parameterizations of sub-grid processes like clouds, turbulence, and gravity waves. Google's NeuralGCM exemplifies the approach and already reduces biases in tropical cyclone frequency and tracks. A telling case study on the quasi-biennial oscillation showed that an offline-trained neural network produced unstable, unphysical results — but retraining just two layers online, coupled to the model, recovered the correct physics. Offline-only or online-only training each fail in characteristic ways; the mix is what works.
The Data Wall Is the Real Bottleneck: Climate ML has less than 50 years of dense satellite-era observations to work with, and those observations are heavily biased toward the atmosphere and ocean surface — a single, spatiotemporally correlated realization of one climate. This limits how confidently ML models can extrapolate to warmer, unseen climates, which is exactly what climate projection requires. The path forward involves three parallel bets: hybrid physics-ML models that bake in conservation laws, large-scale "foundation models" for weather and climate trained across simulations and observations together (efforts like ClimaX and AtmoRep are early examples), and rare-event sampling strategies to handle the extremes that matter most for adaptation policy but are by definition underrepresented in any training set.

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/10.1029/2024MS004655
Main 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.

Citation: Zhu, X. X., Xiong, Z., Wang, Y., Stewart, A. J., Heidler, K., Wang, Y., Yuan, Z., Dujardin, T., Xu, Q., & Shi, Y. (2026). On the foundations of Earth foundation models. Communications Earth & Environment, 7, 103. https://doi.org/10.1038/s43247-025-03127-x
Main Takeaways:
Current Earth AI Models Are Missing the Point: Researchers have identified eleven features that an ideal Earth foundation model must have — including geolocation awareness, multi-sensor integration, physical consistency, and carbon minimization — yet no existing model comes close to checking all eleven boxes. Most models focus on only one or two features, leaving a major gap between what we have and what we actually need to tackle real-world climate and environmental challenges.

The Data Situation Is More Lopsided Than You'd Think: There are now over 1,000 active remote sensing satellites generating nearly 100 petabytes of open satellite data — but labeled datasets used to train AI models account for less than 0.1% of that archive. This massive imbalance is precisely why self-supervised foundation models, which can learn from unlabeled data, are so critical for Earth science going forward.

Weather AI Is Already Dramatically More Efficient — But Incomplete: Models like FourCastNet can generate a week-long global weather forecast in under two seconds on a single GPU, using roughly 12,000 times less energy than traditional forecasting systems. Despite this leap in efficiency, major gaps remain: models struggle beyond two-week forecasts, long-term climate projections drift due to incomplete energy balance, and connecting fine-scale satellite imagery with coarse climate models remains largely unsolved.

What Comes After the Ideal Model: Once a true Earth foundation model exists, the authors argue the most exciting frontier is using it to build an "Earth Embedding" — a compact, unified representation of our entire planet that researchers worldwide could query without ever touching raw satellite data. Beyond that, challenges like machine unlearning (making models forget sensitive imagery), adversarial defenses, and continual learning as the climate itself changes will define the next generation of Earth AI research.

Citation: Olivetti, L., & Messori, G. (2025). Whose weather is it? A fairness framework for data-driven weather forecasting. Environmental Research Letters, 20, 121006. https://doi.org/10.1088/1748-9326/ae21f5
Main Takeaways:
AI Weather Models Aren't Fair to Everyone: The latest generation of AI-powered weather forecasts improves predictions globally — but not equally. Using ECMWF's AIFS model as a case study, the authors show that wealthier and more densely populated areas consistently receive a higher share of forecast improvements compared to poorer and more rural regions, violating basic fairness criteria borrowed from the algorithmic fairness literature.

Two Measurable Fairness Tests — Both Failed: The paper proposes two concrete criteria: statistical parity(improvement rates should be similar across income groups) and conditional independence (a region's GDP or population density should not predict whether it benefits from the new model). Across nearly all tested variables and forecast lead times, AIFS fails both tests at the 0.01 significance level — meaning the disparity is not a statistical fluke.

Extreme Weather Is Where the Gap Hurts Most: For standard temperature and wind forecasts, gaps between rich and poor regions are modest. But for cold extremes, the fairness gap is especially pronounced — precisely the events where accurate early warnings matter most for vulnerable populations with fewer resources to adapt.

Fixing It Is Technically Feasible: Unlike traditional physics-based models, AI weather models offer genuine design levers for fairness. The authors describe two practical approaches: adding penalty terms to the loss function (such as the Hilbert–Schmidt Independence Criterion) to reduce associations with protected variables, and using geographically adaptive weighting that iteratively compensates for emerging performance gaps — without necessarily sacrificing global accuracy.