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[2602.16090] Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators
Summary
This article explores the use of machine-learning weather emulators to analyze fast radiative feedbacks in the climate system, focusing on their responses to greenhouse gas perturbations.
Why It Matters
Understanding fast radiative feedbacks is crucial for predicting climate responses to greenhouse gas emissions. This research leverages machine learning to enhance climate modeling, potentially improving accuracy in climate predictions and informing policy decisions.
Key Takeaways
- Fast radiative feedbacks operate on weekly timescales and are crucial for understanding climate responses.
- Machine-learning emulators can effectively replicate results from full-physics Earth System Models.
- Historically trained ML models can be used to study climate responses to unprecedented greenhouse gas levels.
Physics > Atmospheric and Oceanic Physics arXiv:2602.16090 (physics) [Submitted on 17 Feb 2026] Title:Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators Authors:Ankur Mahesh, William D. Collins, Travis A. O'Brien, Paul B. Goddard, Sinclaire Zebaze, Shashank Subramanian, James P.C. Duncan, Oliver Watt-Meyer, Boris Bonev, Thorsten Kurth, Karthik Kashinath, Michael S. Pritchard, Da Yang View a PDF of the paper titled Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators, by Ankur Mahesh and 12 other authors View PDF HTML (experimental) Abstract:The response of the climate system to increased greenhouse gases and other radiative perturbations is governed by a combination of fast and slow feedbacks. Slow feedbacks are typically activated in response to changes in ocean temperatures on decadal timescales and manifest as changes in climatic state with no recent historical analogue. However, fast feedbacks are activated in response to rapid atmospheric physical processes on weekly timescales, and they are already operative in the present-day climate. This distinction implies that the physics of fast radiative feedbacks is present in the historical meteorological reanalyses used to train many recent successful machine-learning-based (ML) emulators of weather and climate. In addition, these feedbacks are functional under the historical boundary conditions pertaining to the top-of-atmosphere radiative balance and sea-surface temperatures...
