Articles | Volume 20, issue 2
Research article
 | Highlight paper
21 Feb 2024
Research article | Highlight paper |  | 21 Feb 2024

Bayesian multi-proxy reconstruction of early Eocene latitudinal temperature gradients

Kilian Eichenseer and Lewis A. Jones


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1188', Michiel Baatsen, 27 Jul 2023
    • AC1: 'Reply on RC1', Kilian Eichenseer, 24 Sep 2023
  • RC2: 'Comment on egusphere-2023-1188', Anonymous Referee #2, 30 Jul 2023
    • AC2: 'Reply on RC2', Kilian Eichenseer, 24 Sep 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (02 Oct 2023) by Ran Feng
AR by Kilian Eichenseer on behalf of the Authors (03 Oct 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (15 Oct 2023) by Ran Feng
RR by Michiel Baatsen (21 Dec 2023)
ED: Publish as is (05 Jan 2024) by Ran Feng
AR by Kilian Eichenseer on behalf of the Authors (16 Jan 2024)
Reconstructing large-scale climate patterns from sparse local records is crucial for understanding past climates. Yet, it remains challenging to derive those reconstructions due to the patchiness, uneven spatial distribution, and disparate nature of palaeoclimatic proxy records. In this study, Eichenseer and Jones developed a Bayesian hierarchical model to integrate ecological data with established geochemical proxies into a unified quantitative framework, which bridges the gap in the latitudinal coverage of proxy data. They showed that this framework has the potential to enhance quantitative palaeoclimatic reconstructions especially the latitudinal temperature gradient estimated from datasets with limited spatial sampling.
Short summary
Large-scale palaeoclimate reconstructions are often based on sparse and unevenly sampled records, inviting potential biases. Here, we present a Bayesian hierarchical model that combines geochemical with ecological proxy data to model the latitudinal sea surface temperature gradient. Applying this model to the early Eocene climatic optimum highlights how our integrated approach can improve palaeoclimate reconstructions from datasets with limited sampling.