Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Mahesh, Ankur Collins, William D. O'Brien, Travis A. Goddard, Paul B. Zebaze, Sinclaire Subramanian, Shashank Duncan, James P. C. Watt-Meyer, Oliver Bonev, Boris Kurth, Thorsten Kashinath, Karthik Pritchard, Michael S. Yang, Da |
| author_facet | Mahesh, Ankur Collins, William D. O'Brien, Travis A. Goddard, Paul B. Zebaze, Sinclaire Subramanian, Shashank Duncan, James P. C. Watt-Meyer, Oliver Bonev, Boris Kurth, Thorsten Kashinath, Karthik Pritchard, Michael S. Yang, Da |
| contents | 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. Together, these factors imply that we can use historically trained ML weather emulators to study the response of radiative-convective equilibrium (RCE), and hence the global hydrological cycle, to perturbations in carbon dioxide and other well-mixed greenhouse gases. Without retraining on prospective Earth system conditions, we use ML weather emulators to quantify the fast precipitation response to reduced and elevated carbon dioxed concentrations with no recent historical precedent. We show that the responses from historically trained emulators agree with those produced by full-physics Earth System Models (ESMs). In conclusion, we discuss the prospects for and advantages from using ESMs and ML emulators to study fast processes in global climate. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_16090 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators Mahesh, Ankur Collins, William D. O'Brien, Travis A. Goddard, Paul B. Zebaze, Sinclaire Subramanian, Shashank Duncan, James P. C. Watt-Meyer, Oliver Bonev, Boris Kurth, Thorsten Kashinath, Karthik Pritchard, Michael S. Yang, Da Atmospheric and Oceanic Physics Machine Learning 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. Together, these factors imply that we can use historically trained ML weather emulators to study the response of radiative-convective equilibrium (RCE), and hence the global hydrological cycle, to perturbations in carbon dioxide and other well-mixed greenhouse gases. Without retraining on prospective Earth system conditions, we use ML weather emulators to quantify the fast precipitation response to reduced and elevated carbon dioxed concentrations with no recent historical precedent. We show that the responses from historically trained emulators agree with those produced by full-physics Earth System Models (ESMs). In conclusion, we discuss the prospects for and advantages from using ESMs and ML emulators to study fast processes in global climate. |
| title | Examining Fast Radiatively Driven Responses Using Machine-Learning Weather Emulators |
| topic | Atmospheric and Oceanic Physics Machine Learning |
| url | https://arxiv.org/abs/2602.16090 |