Bayesian Chronology Modelling for Paleoclimate Archives
Doctoral Researcher:
Marcus J. A. Plath, Alfred Wegener Institute - Helmholtz Centre for Polar and Marine Research, Bremerhaven marcus.plath@awi.de
Supervisors:
- Dr. Florian Adolphi, Marine Geochemistry, Alfred Wegener Institute - Helmholtz Centre for Polar and Marine Research, Bremerhaven. florian.adolphi@awi.de
- Prof. Dr. Peter Zaspel, Scientific Computing and High Performance Computing, University of Wuppertal. zaspel@uni-wuppertal.de
Location: Bremen
Disciplines: Data Science, Statistics, Geochronology
Keywords: Bayesian Modelling, geochronology, sediment cores, ice cores
Motivation: Past changes of the Earth system offer a unique way to study its dynamics and feedbacks under different conditions. For this purpose, records of past environmental changes from different parts of the world must be jointly interpreted. However, a key challenge for this endeavour is to place all records in one consistent and precise chronological framework. In this project, we aim to construct a tool that allows the construction of such a framework by combining different sources of stratigraphic information using Bayesian methods. Ultimately, this will contribute to an improved understanding of the dynamics of the Earth system.
Aim: Our project is at the interface of data-science, statistics and geochronology. It aims to develop and apply the cutting-edge methodological framework needed to fuse chronologic information from different Earth components into one consistent picture (e.g. data from polar ice cores and marine sediment cores). Different Bayesian statistical models will be combined to synthesize absolute and relative age information across published timeseries in a flexible and extendable way. This will not only result in a new approach to investigate complex systems in a data-driven way, but also in a consistently dated network of past environmental changes.
(1) Combine and extend existing Bayesian statistical models [1,2] in order to include new prior knowledge and parameters ,e.g. the effect of bioturbation on sedimentation processes [3] as well as novel proxies.
(2) Benchmarking of different samplers and parameter studies.
(3) Coupling of chronology models and extension to multiple sites and records.
References
[1] Blaauw, M. & Andrés Christen, J. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Anal. 6; 10.1214/11-BA618 (2011).
[2] Ramsey, C. B. Deposition models for chronological records. Quaternary Science Reviews 27, 42–60; 10.1016/j.quascirev.2007.01.019 (2008).
[3] Lougheed, B. C. Using sedimentological priors to improve 14 C calibration of bioturbated sediment archives. Radiocarbon 64, 135–151; 10.1017/RDC.2021.116 (2022).
