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Climate of the Past An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/cp-2019-10
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/cp-2019-10
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 11 Feb 2019

Research article | 11 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Climate of the Past (CP).

How large are temporal representativeness errors in paleoclimatology?

Daniel E. Amrhein Daniel E. Amrhein
  • University of Washington School of Oceanography and Department of Atmospheric Science, USA

Abstract. Ongoing work in paleoclimate reconstruction prioritizes understanding the origins and magnitudes of errors that arise when comparing models and data. One class of such errors arises from assumptions of proxy temporal representativeness – broadly, the time scales over which paleoclimate proxy measurements are associated with climate variables. In the case of estimating time mean values over an interval, errors can arise when the time interval over which data are averaged and the interval that is being studied have different lengths, or if those intervals are offset from one another in time. Because it is challenging to tailor proxy measurements to precise time intervals, such errors are expected to be common in model-data and data-data comparisons, but how large and prevalent they are is unclear. The goal of this work is to provide a framework for first-order quantification of temporal representativity errors and to study the interacting effects of sampling error, archive smoothing (e.g. by bioturbation in sediment cores), chronological offsets and errors (e.g. arising from radiocarbon dating), and the spectral character of the climate process being sampled.

In some cases, particularly for small values of target intervals τx relative to sample intervals τy, errors can be large relative to signals of interest. Errors from mismatches in τx and τy can have magnitudes comparable to those from chronological uncertainty. Archive smoothing can reduce sampling errors by acting as an anti-aliasing filter, but destroys high-frequency climate information. An extension of the approach to paleoclimate time series, which are sequences of time-average values, shows that measurement intervals shorter than the spacing between samples lead to errors, absent compensating effects from archive smoothing. Including these sources of uncertainty will improve accuracy in model-data comparisons and data comparisions and syntheses. Moreover, because sampling procedures emerge as important parameters in uncertainty quantification, reporting salient information about how records are processed and assessments of archive smoothing and chronological uncertainties alongside published data is important to be able to use records to their maximum potential in paleoclimate reconstruction and data assimilation.

Daniel E. Amrhein
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Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Daniel E. Amrhein
Daniel E. Amrhein
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