Climate of the Past
Climate of the Past
CP
Articles | Volume 20, issue 2
Articles | Volume 20, issue 2
https://doi.org/10.5194/cp-20-349-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-20-349-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Related subject area

Subject: Climate Modelling | Archive: Marine Archives | Timescale: Cenozoic
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Cited articles

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Bijl, P. K., Schouten, S., Sluijs, A., Reichart, G.-J., Zachos, J. C., and Brinkhuis, H.: Early Palaeogene temperature evolution of the southwest Pacific Ocean, Nature, 461, 776–779, https://doi.org/10.1038/nature08399, 2009. 
Buffan, L., Jones, L. A., Domeier, M., Scotese, C. R., Zahirovic, S., and Varela, S.: Mind the uncertainty: Global plate model choice impacts deep-time palaeobiological studies, Meth. Ecol. Evol., 14, 3007–3019, https://doi.org/10.1111/2041-210X.14204, 2023. 
Burgener, L., Hyland, E., Reich, B. J., and Scotese, C.: Cretaceous climates: Mapping paleo-köppen climatic zones using a bayesian statistical analysis of lithologic, paleontologic, and geochemical proxies, Palaeogeogr. Palaeocl., 613, 111373, https://doi.org/10.1016/j.palaeo.2022.111373, 2023.  
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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.
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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.
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