A probabilistic model of chronological errors in layer-counted climate proxies: applications to annually banded coral archives
- 1Department of Earth Sciences, University of Southern California, 3651 Trousdale Parkway, ZHS 275, Los Angeles, California 90089, USA
- 2Department of Geology, University of Maryland, College Park, Maryland 20742, USA
- 3Department of Earth and Atmospheric Sciences, Georgia Institute of Technology, 311 Ferst Drive, Atlanta, Georgia 30332, USA
- 4Department of Geosciences, University of Arizona, 1040 E. 4th Street, Tucson, Arizona 85721, USA
Abstract. The ability to precisely date climate proxies is central to the reconstruction of past climate variations. To a degree, all climate proxies are affected by age uncertainties, which are seldom quantified. This article proposes a probabilistic age model for proxies based on layer-counted chronologies, and explores its use for annually banded coral archives. The model considers both missing and doubly counted growth increments (represented as independent processes), accommodates various assumptions about error rates, and allows one to quantify the impact of chronological uncertainties on different diagnostics of variability. In the case of a single coral record, we find that time uncertainties primarily affect high-frequency signals but also significantly bias the estimate of decadal signals. We further explore tuning to an independent, tree-ring-based chronology as a way to identify an optimal age model. A synthetic pseudocoral network is used as testing ground to quantify uncertainties in the estimation of spatiotemporal patterns of variability. Even for small error rates, the amplitude of multidecadal variability is systematically overestimated at the expense of interannual variability (El Niño–Southern Oscillation, or ENSO, in this case), artificially flattening its spectrum at periods longer than 10 years. An optimization approach to correct chronological errors in coherent multivariate records is presented and validated in idealized cases, though it is found difficult to apply in practice due to the large number of solutions. We close with a discussion of possible extensions of this model and connections to existing strategies for modeling age uncertainties.