A Probabilistic Model of Chronological Errors in Layer-Counted Climate Proxies

Precisely dating 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. In this talk we describe a new probabilistic age model for proxies based on layer-counted chronologies, and explore its use for annually-banded corals. 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 and bias the estimate of decadal signals in ways that can be corrected.
In multiple dimensions, however, such uncertainties compound in unexpected ways. We illustrate this using a synthetic pseudocoral network, quantifying the effect of chronological uncertainties on 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 (ENSO, in this case), artificially flattening its spectrum at periods longer than 10 years. We close with a discussion of possible extensions of this model and connections to existing strategies for modeling age uncertainties.