Articles | Volume 17, issue 13
https://doi.org/10.5194/gmd-17-5413-2024
https://doi.org/10.5194/gmd-17-5413-2024
Development and technical paper
 | 
16 Jul 2024
Development and technical paper |  | 16 Jul 2024

Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon

Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills

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Cited articles

Argaw, D. M. and Kweon, I. S.: Long-term video frame interpolation via feature propagation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3543–3552, 2022. 
Berner, R. A.: Examination of hypotheses for the Permo-Triassic boundary extinction by carbon cycle modeling, P. Natl. Acad. Sci. USA, 99, 4172–4177, https://doi.org/10.1073/pnas.032095199, 2002. 
Chen, G., Cheng, Q., Lyons, T. W., Shen, J., Agterberg, F., Huang, N., and Zhao, M.: Reconstructing Earth's atmospheric oxygenation history using machine learning, Nat. Commun., 13, 5862, https://doi.org/10.1038/s41467-022-33388-5, 2022. 
Cui, C., and Cao, C.: Increased aridity across the Permian–Triassic transition in the mid‐latitude NE Pangea, Geol. J., 56, 6162–6175, 2021. 
Dong, J., Ota, K., and Dong, M.: Video frame interpolation: A comprehensive survey, ACM T. Multim. Comput., 19, 1–31, https://doi.org/10.1145/3556544, 2023. 
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Short summary
This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
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