Bayesian age models and stacks: combining age inferences from radiocarbon and benthic δ18O stratigraphic alignment

Abstract

Abstract. Previously developed software packages that generate probabilistic age models for ocean sediment cores are designed to either interpolate between different age proxies at discrete depths (e.g., radiocarbon, tephra layers, or tie points) or perform a probabilistic stratigraphic alignment to a dated target (e.g., of benthic δ18O) and cannot combine age inferences from both techniques. Furthermore, many radiocarbon dating packages are not specifically designed for marine sediment cores, and the default settings may not accurately reflect the probability of sedimentation rate variability in the deep ocean, thus requiring subjective tuning of the parameter settings. Here we present a new technique for generating Bayesian age models and stacks using ocean sediment core radiocarbon and probabilistic alignment of benthic δ18O data, implemented in a software package named BIGMACS (Bayesian Inference Gaussian Process regression and Multiproxy Alignment of Continuous Signals). BIGMACS constructs multiproxy age models by combining age inferences from both radiocarbon ages and probabilistic benthic δ18O stratigraphic alignment and constrains sedimentation rates using an empirically derived prior model based on 37 14C-dated ocean sediment cores (Lin et al., 2014). BIGMACS also constructs continuous benthic δ18O stacks via a Gaussian process regression, which requires a smaller number of cores than previous stacking methods. This feature allows users to construct stacks for a region that shares a homogeneous deep-water δ18O signal, while leveraging radiocarbon dates across multiple cores. Thus, BIGMACS efficiently generates local or regional stacks with smaller uncertainties in both age and δ18O than previously available techniques. We present two example regional benthic δ18O stacks and demonstrate that the multiproxy age models produced by BIGMACS are more precise than their single-proxy counterparts.