Earthly Machine Learning

Citation:
Rampal, N., González-Abad, J., Addison, H., Baño-Medina, J., Bettolli, M. L., Blasone, V., Booth, B., Coppola, E., Di Gioia, S., Oldham-Dorrington, J., Doury, A., Engelbrecht, F., Fuentes-Franco, R., Gibson, P. B., Glawion, L., Hardy, C., Ivanov, M., Lee, H. K., Legasa, M. N., Olmo, M., Orr, A., Polz, J., Rogers, M. S. J., Schillinger, M., Sharma, S., Soares, P. M. M., Sobolowski, S., Steinkopf, J., Tang, W., Tian, J.-B., Tomé, R., Wang, K.-C., Wang, Y.-C., Watson, P. A. G., Wetherell, T., Widmann, M., & Gutiérrez, J. M. (2026). CORDEX-ML-Bench: A Benchmark for Data-Driven Regional Climate Downscaling—Experiment Design and Overview. WCRP-CORDEX Machine Learning Task Team.
Key Takeaways:
Establishing a Standardized Global Benchmark: The paper introduces CORDEX-ML-Bench, the first coordinated multi-domain, multi-architecture benchmarking framework explicitly designed to standardize machine learning (ML) models for regional climate downscaling. The framework enables researchers to evaluate and compare models consistently using open-source datasets and metrics, focusing on a 20× spatial resolution increase (from ~200 km down to ~10 km grids) for daily precipitation and maximum temperature. The initial benchmark targets three highly diverse geographical regions: the European Alps, New Zealand, and Southern Africa.
The Critical Extrapolation Gap of Historical Training: A foundational finding of the benchmark is that ML models trained strictly on historical climate data systematically underestimate future climate change signals, including extreme warming and intensive precipitation. This reveals a major vulnerability in traditional historical-only statistical downscaling methods and proves that incorporating future climate projections into training datasets (the emulator approach) is vital for producing physically credible long-term projections.
Generative AI Outperforms for Precipitation Extremes: In an evaluation of 40 independently developed ML configurations, generative approaches (such as diffusion models, flow matching, and Generative Adversarial Networks) consistently outperform deterministic regression models at downscaling precipitation. They excel at capturing highly localized spatial variability and heavy-tailed extreme events, whereas deterministic models suffer from spatial "oversmoothing". However, deterministic architectures remain highly competitive for predicting daily maximum temperature.
Balancing Accuracy and Computational Cost: While highly complex diffusion models (like the top-ranked RCMGEM-mv-orog) achieve outstanding accuracy, they are computationally intensive. In contrast, flow-matching and GAN-based models achieve a highly favorable skill-to-compute ratio, lowering inference costs by one to two orders of magnitude. This makes them highly practical solutions for generating large, multi-model ensemble climate projections under restricted computational budgets.

Citation: Henn, B., Bretherton, C. S., Kodunov, N., Lessig, C., Molina, M. J., Arcomano, T., Watt-Meyer, O., Couairon, G., Singh, R., Brunstein, R., Hasson, Y., Jost, A., Brenowitz, N., Manshausen, P., Cresswell-Clay, N., Durran, D., Hall, K. J. C., Yuval, J., Kochkov, D., Hoyer, S., & Lopez-Gomez, I. (2026). AIMIP Phase 1: systematic evaluations of AI weather and climate models. arXiv preprint.
A New Benchmarking Era for AI Climate Models: AIMIP Phase 1 establishes the first systematic intercomparison framework for artificial intelligence weather and climate models (AIWCMs). It defines a common experimental protocol, standardizes CMIP-compatible output formats, and provides an open dataset to evaluate how different AI architectures influence long-term climate simulation behaviors.
Standardized Historical Simulation Protocol: Under the Phase 1 protocol, participating models are trained exclusively on historical ERA5 atmospheric reanalysis data from 1979 to 2014 and run through a 10-year out-of-sample test period (2015–2024). To prevent overfitting, models are forced only by specified sea surface temperatures (SST) and sea ice concentrations (SIC), with direct greenhouse gas inputs (like CO2 concentrations) strictly excluded.
Excellent Representation of Baseline Climate and ENSO: The initial evaluations of the eight participating AI models demonstrate that they represent time-mean climate averages and natural variability patterns—such as the El Niño-Southern Oscillation (ENSO)—just as well as, or in some cases with lower systematic biases than, conventional physically-based climate models like the NOAA GFDL-CM4.
The Out-of-Sample Warming Gap: A primary weakness identified across several AI models is their struggle to accurately replicate global warming trends during the out-of-sample test period (2015–2024). Because greenhouse gases like CO2 are omitted as direct predictors to avoid overfitting, some models fail to translate rising ocean temperatures into the full magnitude of observed atmospheric warming.
Extreme Extrapolation Remains a Challenge: When subjected to extreme, highly out-of-sample sensitivity experiments where sea surface temperatures are uniformly raised by +2 K and +4 K, the AI models diverge significantly. They produce highly inconsistent and sometimes physically implausible responses (such as simulated cooling over land), highlighting that projecting unseen future climates remains a key development challenge for the AI climate modeling community.

Citation: Glaser, Y., Stopa, J. E., Wolniewicz, L. M., Foster, R., Vandemark, D., Mouche, A., Chapron, B., & Sadowski, P. (2025). WV-Net: A Foundation Model for SAR Ocean Satellite Imagery. Artificial Intelligence for the Earth Systems, e250003. DOI: 10.1175/AIES-D-25-0003.1
Key Takeaways
First Foundation Model for Open-Ocean SAR Imagery: WV-Net represents the first-ever foundation model designed specifically for open-ocean sea surface images, utilizing a massive dataset of nearly 10 million unannotated C-band synthetic aperture radar (SAR) wave mode images collected globally by the Sentinel-1 satellite mission.
Overcoming the Annotation Bottleneck: By leveraging contrastive self-supervised learning (SimCLR), the model learns highly robust, general-purpose representations of complex geophysical signatures directly from raw, unlabeled imagery—bypassing the traditional bottleneck of expensive manual expert annotation.
Outperforming General-Purpose Computer Vision Models: The model's specialized ocean-domain embeddings consistently beat standard models pretrained on natural images (like ImageNet) across key downstream tasks, including estimating wave height, predicting air-sea temperature differences, and identifying 12 distinct atmospheric and oceanic phenomena.
High Data Efficiency & Fine-Tuning Stability: WV-Net scales exceptionally well in data-constrained settings, delivering strong performance with as few as 100 labeled training examples. Additionally, it exhibits greater robustness to hyperparameter selections during fine-tuning, dramatically reducing the need for broad, computationally heavy optimization sweeps.
Optimizing Domain-Specific Augmentations: The researchers discovered that standard computer vision augmentations adapted for radar data (such as mixup, color inversions, rotations, and sharpness adjustments) were crucial to bridging the domain gap, while complex, domain-specific signal filtering (such as random notch filtering) actually degraded model performance.

Citation: Ji, C., Mu, M., Qin, B., Lian, T., Yuan, S., Feng, J., Song, S., Wei, Y., Dai, G., Wang, J., & Fang, X. (2025). Toward skillful forecasting of super El Niño events using a diffusion-based westerly wind burst parameterization. npj Climate and Atmospheric Science (Published in partnership with CECCR at King Abdulaziz University). https://doi.org/10.1038/s41612-025-01158-x
Key Takeaways:
Innovative Generative AI Parameterization: The study introduces a state-of-the-art Denoising Diffusion Probabilistic Model (DDPM) to parameterize westerly wind bursts (WWBs). These wind bursts are critical, episodic atmospheric events that inject wind energy into the Pacific, playing a pivotal role in triggering super El Niños. This new generative AI framework successfully captures the complex, joint modulation of wind bursts by both slow-varying oceanic states and rapid atmospheric processes.
Superior Representation of Wind Burst Physics: Traditional schemes rely heavily on ocean-state indicators like the warm pool eastern edge, which fails to capture high-frequency atmospheric noise. By incorporating multiple physical conditions—Sea Surface Temperature Anomalies (SSTA), Outgoing Longwave Radiation Anomalies (OLRA), and Sea Level Pressure Anomalies (SLPA)—the DDPM-based scheme dramatically improves the simulated frequency, intensity, duration, and spatial distribution of wind bursts compared to observational data.
Drastic Improvements in Super El Niño Intensity Predictions: When coupled online with the Community Earth System Model (CESM), the DDPM scheme significantly outperforms both standard control runs and traditional parameterization schemes. It accurately predicts the absolute amplitude of historic super El Niño events—specifically the 1982/83, 1997/98, and 2015/16 events—by correcting the severe underestimations found in baseline climate models.
Mitigation of Seasonal Phase-Locking Bias: A persistent challenge in climate modeling is "seasonal phase-locking" prediction bias, where models incorrectly project a double-peak warming cycle (peaking in summer, weakening, then re-intensifying in winter). The DDPM scheme overcomes this issue by generating stronger and more realistically eastward-shifted wind stress anomalies, which correctly trigger the positive dynamical feedbacks (such as the Bjerknes feedback) necessary to sustain a steady, natural warming progression toward a single December peak.

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.