Abstract
Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons.
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Cite this article
APA 7
Beucler, T., Neelin, J. D., Su, H., Asthana, S., Bretherton, C., Chapman, W., Christopoulos, C., Clark, S. K., Grover, A., Lopez-Gomez, I., Schneider, T., Subel, A., & Watt-Meyer, O. (2026). Artificial intelligence pathways from weather to climate. https://omanscience.com/en/articles/artificial-intelligence-pathways-from-weather-to-climate
MLA 9
Beucler, Tom, et al. "Artificial intelligence pathways from weather to climate." https://omanscience.com/en/articles/artificial-intelligence-pathways-from-weather-to-climate.
Chicago (author–date)
Beucler, Tom, J. David Neelin, Hui Su, Shivanshi Asthana, Chris Bretherton, Will Chapman, Costa Christopoulos, Spencer K. Clark, Aditya Grover, Ignacio Lopez-Gomez, Tapio Schneider, Adam Subel, and Oliver Watt-Meyer. 2026. "Artificial intelligence pathways from weather to climate." https://omanscience.com/en/articles/artificial-intelligence-pathways-from-weather-to-climate.
Harvard
Beucler, T., Neelin, J. D., Su, H., Asthana, S., Bretherton, C., Chapman, W., Christopoulos, C., Clark, S. K., Grover, A., Lopez-Gomez, I., Schneider, T., Subel, A. and Watt-Meyer, O. (2026) 'Artificial intelligence pathways from weather to climate', Available at: https://omanscience.com/en/articles/artificial-intelligence-pathways-from-weather-to-climate.
Vancouver
Beucler T, Neelin JD, Su H, Asthana S, Bretherton C, Chapman W, et al. Artificial intelligence pathways from weather to climate. https://omanscience.com/en/articles/artificial-intelligence-pathways-from-weather-to-climate
IEEE
T. Beucler, J. D. Neelin, H. Su, S. Asthana, C. Bretherton, W. Chapman, C. Christopoulos, S. K. Clark, A. Grover, I. Lopez-Gomez, T. Schneider, A. Subel, and O. Watt-Meyer, "Artificial intelligence pathways from weather to climate," https://omanscience.com/en/articles/artificial-intelligence-pathways-from-weather-to-climate.