Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect
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‐surf...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2024GL109581 |
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| _version_ | 1849322175477055488 |
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| author | Maria Rugenstein Senne VanLoon Elizabeth A. Barnes |
| author_facet | Maria Rugenstein Senne VanLoon Elizabeth A. Barnes |
| author_sort | Maria Rugenstein |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0bd66aba94fd4cac8d906aeff6a8edaf |
| institution | Kabale University |
| issn | 0094-8276 1944-8007 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geophysical Research Letters |
| spelling | doaj-art-0bd66aba94fd4cac8d906aeff6a8edaf2025-08-20T03:49:31ZengWileyGeophysical Research Letters0094-82761944-80072025-02-01524n/an/a10.1029/2024GL109581Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern EffectMaria Rugenstein0Senne VanLoon1Elizabeth A. Barnes2Colorado State University Fort Collins CO USAColorado State University Fort Collins CO USAColorado State University Fort Collins CO USAAbstract 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.https://doi.org/10.1029/2024GL109581 |
| spellingShingle | Maria Rugenstein Senne VanLoon Elizabeth A. Barnes Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect Geophysical Research Letters |
| title | Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect |
| title_full | Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect |
| title_fullStr | Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect |
| title_full_unstemmed | Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect |
| title_short | Convolutional Neural Networks Trained on Internal Variability Predict Forced Response of TOA Radiation by Learning the Pattern Effect |
| title_sort | convolutional neural networks trained on internal variability predict forced response of toa radiation by learning the pattern effect |
| url | https://doi.org/10.1029/2024GL109581 |
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