  We propose a method to merge several paleoclimate time series into one that
exhibits a consensus on the features of the individual times series. The
paleoclimate time series can be noisy, nonuniformly sampled and the dates at
which the paleoclimate is reconstructed can have errors. Bayesian inference is
used to model the various sources of uncertainty and smoothing of the posterior
distribution of the consensus is used to capture its credible features in
different time scales. The technique is demonstrated by analyzing a collection
of six Holocene temperature reconstructions from Finnish Lapland based on
various biological proxies. Although the paper focuses on paleoclimate time
series, the proposed method can be applied in other contexts where one seeks to
infer features that are jointly supported by an ensemble of irregularly sampled
noisy time series.
