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Abstract:
Layer-counted proxy records from paleoclimatic archives are subject to considerable dating uncertainties. These uncertainties originate from irregularities in the archive’s deposition process that result, in turn, in errors during the layer counting process. Dating uncertainties can be quantified by assuming a probabilistic model for the relationship between the depth of a sample in the proxy archive and the age of that sample. However, systematic biases in counting or depositional processes can cause the counted chronology to deviate substantially from the true age and possibly corrupt the age model. By synchronizing a given chronology with other, independently dated archives, one can constrain the dating uncertainties and correct potential biases. This can be done by matching the chronology to tie-points obtained by identifying characteristic events which were recorded simultaneously by different archives or with independent methods. However, updating the counted age–depth relationship under the consideration of tie-points is not straightforward and no generally accepted method is presently available for layer-counted archives. A key requirement for such a method is that it should include an appropriate uncertainty-sensitive interpolation between tie-points. Using a Gaussian model to represent a potential bias, we show how tie-points and their uncertainties can be incorporated into a previously suggested Bayesian modeling framework to reflect the general uncertainties of a counted chronology. Both the uncertainty inherent to the tie-points and the age-correlation between the data from different depths in the archive are consistently represented in this approach. We demonstrate the methodology in two applications: first, using synthetic data, and second, applying the methodology to data from the NGRIP ice core, an iconic paleoclimate proxy archive.
