Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators

2026-02-17

AI Summary

This study addresses the challenge of accurately quantifying the rapid radiative feedback response of the atmosphere to abrupt changes in CO₂ concentration without relying on future climate scenarios for model training. To this end, we employ a machine learning weather emulator trained exclusively on historical reanalysis data to simulate the fast precipitation response to sudden CO₂ increases or decreases—without any retraining—and combine these simulations with radiative-convective equilibrium analysis to elucidate the underlying mechanisms. Our approach demonstrates, for the first time, that a purely historical-data-driven machine learning model can effectively capture rapid climate feedbacks under unprecedented greenhouse gas perturbations, thereby overcoming the traditional reliance on Earth system models or future scenario projections. The simulated responses show strong agreement with those from full-physics models, confirming the reliability and potential of this method for studying rapid climate processes.

Technology Category

• Machine Learning: Quantum Machine Learning
• Humans and AI: Human-in-the-loop Machine Learning
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Web Mining and Content Analysis: Machine learning and data science for the Web

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. 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.

Problem

• fast radiative feedbacks
• machine learning emulators
• radiative-convective equilibrium
• precipitation response
• greenhouse gas perturbations

Innovation

• machine learning emulators
• fast radiative feedbacks
• radiative-convective equilibrium
• precipitation response
• Earth System Models