Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect

Journal Article · 17 February 2025 · Geophysical Research Letters

DOI: https://doi.org/10.1029/2024GL109581 · OSTI ID:2519717

Rugenstein, Maria  ^[1]; Van Loon, Senne  ^[1]; Barnes, Elizabeth A.  ^[1]

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1. Colorado State University Fort Collins CO USA

Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.

• Sponsoring Organization: USDOE
• OSTI ID: 2519717
• Journal Information: Geophysical Research Letters, Journal Name: Geophysical Research Letters Journal Issue: 4 Vol. 52; ISSN 0094-8276
• Publisher: American Geophysical Union (AGU)Copyright Statement
• Country of Publication: United States
• Language: English