Earthly Machine Learning

Citation: Spuler, F. R., Kretschmer, M., Balmaseda, M. A., Kovalchuk, Y., & Shepherd, T. G. (2025). Learning predictable and informative dynamical drivers of extreme precipitation using variational autoencoders. Weather and Climate Dynamics, 6, 995–1014. https://doi.org/10.5194/wcd-6-995-2025
Main Takeaways:
Innovative Machine Learning Approach: The study introduces the Categorical Mixture Model Variational Autoencoder (CMM-VAE), a novel generative machine learning method designed to identify probabilistic atmospheric circulation regimes by combining targeted dimensionality reduction and probabilistic clustering into a single model.
Resolving a Major Forecasting Trade-off: Traditionally, atmospheric regimes are either highly predictable globally but locally uninformative, or highly informative for local impacts but lacking in subseasonal predictability. CMM-VAE resolves this trade-off, successfully identifying patterns that predict local extremes without sacrificing forecast skill at subseasonal lead times.
Targeted Application for Moroccan Rainfall: When applied to extreme winter precipitation in Morocco, the CMM-VAE method successfully disentangled a distinct, highly impactful weather pattern—a Scandinavian blocking coupled with a localized cut-off low—that traditional linear clustering methods failed to isolate.
Linkages to Global Climate Drivers: The weather regimes identified by the model remain physically interpretable and show clear, predictable teleconnections to large-scale, low-frequency climate drivers, notably the Madden-Julian Oscillation (MJO) and the Stratospheric Polar Vortex (SPV).
Enhancing Early Warning Systems: By providing a better representation of regional dynamical drivers, this framework offers significant potential to improve subseasonal-to-seasonal (S2S) forecasts, statistical downscaling, and early-warning systems for severe, localized weather impacts.

Green and intelligent: the role of AI in the climate transition
Citation: Stern, N., Romani, M., Pierfederici, R., Braun, M., Barraclough, D., Lingeswaran, S., Weirich-Benet, E., & Niemann, N. (2025). Green and intelligent: the role of AI in the climate transition. https://doi.org/10.1038/s44168-025-00252-3.
Main Takeaways:
Five Key Areas for Climate Action: Artificial Intelligence can accelerate the net-zero transition across five primary avenues: transforming complex economic systems, innovating technology discovery and resource efficiency, nudging consumer behavior toward sustainable choices, modeling climate systems for better policy, and managing adaptation and resilience.
Significant Emissions Reduction Potential: By applying AI to just three major sectors—power, food (specifically meat and dairy), and mobility (light road vehicles)—global emissions could be reduced by 3.2 to 5.4 GtCO2e annually by 2035.
Net-Positive Climate Impact: The emissions savings generated by AI in these three sectors alone would more than offset the projected 0.4 to 1.6 GtCO2e increase in emissions caused by the energy consumption of all global AI activities and data centers.
Closing the Emissions Gap: Harnessing AI to improve the efficiency and market adoption of low-carbon solutions could push global progress 36% closer to aligning with an ambitious emissions reduction trajectory by 2035.
The Critical Role of Government: Relying solely on market forces to govern AI is risky; an "active state" is essential to direct AI toward public goods, regulate its environmental footprint (like mandating renewable energy for data centers), and ensure equitable deployment so the Global South is not left behind.

Climate Knowledge in Large Language Models
Kuznetsov, I., Grassi, J., Pantiukhin, D., Shapkin, B., Jung, T., & Koldunov, N. (2025). Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research.

LLMs have an internal "map" of the climate, but it is fuzzy: Without access to external tools, Large Language Models (LLMs) can recall the general structure of Earth’s climate—correctly identifying that the tropics are warm and high latitudes are cold. However, their specific numeric predictions are often inaccurate, with average errors ranging from 3°C to 6°C compared to historical weather data.

Location names matter more than coordinates: The study found that providing geographic context—such as the country, region, or city name—alongside coordinates reduced prediction errors by an average of 27%. This suggests models rely heavily on text associations with place names rather than possessing a precise spatial understanding of latitude and longitude.
Performance struggles with altitude and local trends: Models perform significantly worse in mountainous regions, with errors spiking sharply at elevations above 1500 meters. Furthermore, while LLMs can estimate the global average magnitude of warming, they fail to accurately reproduce the specific local patterns of temperature change that are essential for understanding regional climate dynamics.
Caution is needed for scientific use: The results highlight that while LLMs encode a static snapshot of climatological averages, they lack true physical understanding and struggle with dynamic trends. Consequently, they should not be relied upon as standalone climate databases; reliable applications require connecting them to external, authoritative data sources.

Artificial Intelligence for Atmospheric Sciences: A Research Roadmap
Citation: Zaidan, M. A., Motlagh, N. H., Nurmi, P., Hussein, T., Kulmala, M., Petäjä, T., & Tarkoma, S. (2025). Artificial Intelligence for Atmospheric Sciences: A Research Roadmap.
Revolutionizing Environmental Monitoring: The paper illustrates how AI is transforming atmospheric sciences by bridging the gap between computer science and environmental research. It details how AI processes massive datasets generated by diverse sources—including satellite imagery, ground-based research stations, and low-cost IoT sensors—to improve our understanding of air quality, extreme weather events, and climate change.
Optimizing Infrastructure and Prediction: Current AI applications are already enhancing operational meteorology and Earth system modeling. By utilizing techniques like deep learning and neural networks, researchers can automate sensor calibration, detect anomalies in real-time, and simulate complex climate scenarios with greater speed and efficiency than traditional physical models allow.
A Roadmap for Future Hardware: To handle the escalating demand for data, the authors propose a hardware roadmap that includes self-sustaining and biodegradable sensor networks, CubeSat constellations for high-resolution monitoring, and the adoption of cutting-edge computing paradigms like quantum, neuromorphic, and DNA-based molecular computing.
Next-Generation AI Methodologies: The paper argues for the adoption of advanced AI techniques such as Foundation Models and Generative AI (including Digital Twins of Earth) to predict complex atmospheric phenomena. Crucially, it emphasizes the need for Explainable AI (XAI) and Physics-Informed Machine Learning to solve the "black box" problem, ensuring that AI predictions abide by physical laws and are transparent enough for scientists and policymakers to trust.
From Data to Action: Beyond observation, the research highlights the shift toward actionable insights. This includes automated feedback loops (such as smart HVAC systems responding to air quality data), the integration of citizen science to augment data collection, and the establishment of robust ethical frameworks to manage data privacy and governance in global monitoring networks.

Differentiable and accelerated spherical harmonic and Wigner transforms
Matthew A. Price, Jason D. McEwen
*Journal of Computational Physics (2024)*

* This work introduces novel algorithmic structures for the **accelerated and differentiable computation** of generalized Fourier transforms on the sphere ($S^2$) and the rotation group ($SO(3)$), specifically spherical harmonic and Wigner transforms.
* A key component is a **recursive algorithm for Wigner d-functions** designed to be stable to high harmonic degrees and extremely parallelizable, making the algorithms well-suited for high throughput computing on modern hardware accelerators such as GPUs.
* The transforms support efficient computation of gradients, which is critical for machine learning and other differentiable programming tasks, achieved through a **hybrid automatic and manual differentiation approach** to avoid the memory overhead associated with full automatic differentiation.
* Implemented in the open-source **S2FFT** software code (within the JAX differentiable programming framework), the algorithms support various sampling schemes, including equiangular samplings that admit exact spherical harmonic transforms.
* Benchmarking results demonstrate **up to a 400-fold acceleration** compared to alternative C codes, and the transforms exhibit **very close to optimal linear scaling** when distributed over multiple GPUs, yielding an unprecedented effective linear time complexity (O(L)) given sufficient computational resources.