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.
Reconstructing large-scale climate patterns from sparse local records is crucial for...
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.
Large-scale palaeoclimate reconstructions are often based on sparse and unevenly sampled...
