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

Viewed

Total article views: 1,529 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,083 389 57 1,529 69 65
  • HTML: 1,083
  • PDF: 389
  • XML: 57
  • Total: 1,529
  • BibTeX: 69
  • EndNote: 65
Views and downloads (calculated since 02 Feb 2024)
Cumulative views and downloads (calculated since 02 Feb 2024)

Viewed (geographical distribution)

Total article views: 1,529 (including HTML, PDF, and XML) Thereof 1,529 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 05 May 2025
Download
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.
Share