Preprints
https://doi.org/10.5194/gmd-2023-230
https://doi.org/10.5194/gmd-2023-230
Submitted as: development and technical paper
 | 
02 Feb 2024
Submitted as: development and technical paper |  | 02 Feb 2024
Status: a revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Using Deep Learning to integrate paleoclimate and global biogeochemistry over Phanerozoic time

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

Abstract. Databases of 3D paleoclimate model simulations are increasingly used within global biogeochemical models for the Phanerozoic Eon. This improves the accuracy of the surface processes within the biogeochemical models, but the approach is limited by the availability of large numbers of paleoclimate simulations at different pCO2 levels and for different continental configurations. In this paper we apply the Frame Interpolation for Large Motion (FILM) Deep Learning method to a set of paleoclimate model simulations to upscale their time resolution from one model run every ~25 million years to one model run every 1 million year (Myr).

Testing the method on a 5 Myr time-resolution set of continental configurations confirms the accuracy of our approach when reconstructing intermediate frames from configurations separated by up to 40 Myrs. We then apply the method to upscale the paleoclimate datastructure in the SCION climate-biogeochemical model and demonstrate that upscaled outputs for global distributions of surface temperature and runoff follow a logical progression between the original keyframes.

When updated to use the high-time-resolution climate datastructure, the SCION model predicts climate shifts that were not present in the original model outputs due to its previous use of wide-spaced datasets and simple linear interpolation. We conclude that a time resolution of ~10 Myr in paleoclimate simulations is likely sufficient for investigating the long-term carbon cycle, and that Deep Learning methods may be critical in attaining this time-resolution at a reasonable computational expense, as well as for developing new fully-continuous methods in which 3D continental processes—such as species distribution—are able to translate over a moving continental surface in deep time. Nonetheless, the efficacy of Deep Learning methods in interpolating runoff data, compared to that of paleogeography and temperature, is diminished by the heterogeneous distribution of runoff. Consequently, interpolated climates should be confirmed by running a paleoclimate model for any sound scientific conclusions.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Dongyu Zheng, Andrew Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-230', Anonymous Referee #1, 13 Mar 2024
    • AC1: 'Reply on RC1', Dongyu Zheng, 26 Apr 2024
  • RC2: 'Comment on gmd-2023-230', Anonymous Referee #2, 21 Mar 2024
    • AC2: 'Reply on RC2', Dongyu Zheng, 26 Apr 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-230', Anonymous Referee #1, 13 Mar 2024