Bayesian age–depth modelling applied to varve and radiometric dating to optimize the transfer of an existing high-resolution chronology to a new composite sediment profile from Holzmaar (West Eifel Volcanic Field, Germany)

Abstract

Abstract. This study gives an overview of different methods to integrate information from a varve chronology and radiometric measurements in the Bayesian tool Bacon. These techniques will become important for the future as technologies evolve with more sites being revisited for the application of new and high-resolution scanning methods. Thus, the transfer of existing chronologies will become necessary because the recounting of varves will be too time consuming and expensive to be funded. We introduce new sediment cores from Holzmaar (West Eifel Volcanic Field, Germany), a volcanic maar lake with a well-studied varve record. Four different age–depth models have been calculated for the new composite sediment profile (HZM19) using Bayesian modelling with Bacon. All models incorporate new Pb-210 and Cs-137 dates for the top of the record, the latest calibration curve (IntCal20) for radiocarbon ages as well as the new age estimation for the Laacher See Tephra. Model A is based on previously published radiocarbon measurements only, while Models B–D integrate the previously published varve chronology (VT-99) with different approaches. Model B rests upon radiocarbon data, while parameter settings are obtained from sedimentation rates derived from VT-99. Model C is based on radiocarbon dates and on VT-99 as several normal distributed tie points, while Model D is segmented into four sections: sections 1 and 3 are based on VT-99 only, whereas sections 2 and 4 rely on Bacon age–depth models including additional information from VT-99. In terms of accuracy, the parameter-based integration Model B shows little improvement over the non-integrated approach, whereas the tie-point-based integration Model C reflects the complex accumulation history of Holzmaar much better. Only the segmented and parameter-based age integration approach of Model D adapts and improves VT-99 by replacing sections of higher counting errors with Bayesian modelling of radiocarbon ages and thus efficiently makes available the best possible and most precise age–depth model for HZM19. This approach will value all ongoing high-resolution investigations for a better understanding of decadal-scale Holocene environmental and climatic variations.