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Comparing paleoclimate time series is complicated by a variety of typicalfeatures, including irregular sampling, age model uncertainty (e.g., errors dueto interpolation between radiocarbon sampling points) and time uncertainty(uncertainty in calibration), which, taken together, result in unequal anduncertain observation times of the individual time series to be correlated.Several methods have been proposed to approximate the joint probabilitydistribution needed to estimate correlations, most of which rely either oninterpolation or temporal downsampling.  Here, we compare the performance of some popular approximation methods usingsynthetic data resembling common properties of real world marine sedimentrecords. Correlations are determined by estimating the parameters of abivariate Gaussian model from the data using Markov Chain Monte Carlo sampling.We complement our pseudoproxy experiments by applying the same methodology to apair of marine benthic oxygen records from the Atlantic Ocean.  We find that methods based upon interpolation yield better results in termsof precision and accuracy than those which reduce the number of observations.In all cases, the specific characteristics of the studied time series are,however, more important than the choice of a particular interpolation method.Relevant features include the number of observations, the persistence of eachrecord, and the imposed coupling strength between the paired series. In most ofour pseudoproxy experiments, uncertainty in observation times introduces lessadditional uncertainty than unequal sampling and errors in observation timesdo. Thus, it can be reasonable to rely on published time scales as long ascalibration uncertainties are not known.

Correlating Paleoclimate Time Series: Sources of Uncertainty and Potential Pitfalls