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| Titel |
Exploring errors in paleoclimate proxy reconstructions using Monte Carlo simulations: paleotemperature from mollusk and coral geochemistry |
| VerfasserIn |
M. Carré, J. P. Sachs, J. M. Wallace, C. Favier |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1814-9324
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| Digitales Dokument |
URL |
| Erschienen |
In: Climate of the Past ; 8, no. 2 ; Nr. 8, no. 2 (2012-03-09), S.433-450 |
| Datensatznummer |
250005461
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| Publikation (Nr.) |
 copernicus.org/cp-8-433-2012.pdf |
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| Zusammenfassung |
| Quantitative reconstructions of the past climate statistics from geochemical
coral or mollusk records require quantified error bars in order to properly
interpret the amplitude of the climate change and to perform meaningful
comparisons with climate model outputs. We introduce here a more precise
categorization of reconstruction errors, differentiating the error bar due
to the proxy calibration uncertainty from the standard error due to sampling
and variability in the proxy formation process. Then, we propose a numerical
approach based on Monte Carlo simulations with surrogate proxy-derived
climate records. These are produced by perturbing a known time series in a
way that mimics the uncertainty sources in the proxy climate reconstruction.
A freely available algorithm, MoCo, was designed to be parameterized by the
user and to calculate realistic systematic and standard errors of the mean
and the variance of the annual temperature, and of the mean and the variance
of the temperature seasonality reconstructed from marine accretionary
archive geochemistry. In this study, the algorithm is used for sensitivity
experiments in a case study to characterize and quantitatively evaluate the
sensitivity of systematic and standard errors to sampling size, stochastic
uncertainty sources, archive-specific biological limitations, and climate
non-stationarity. The results of the experiments yield an illustrative
example of the range of variations of the standard error and the systematic
error in the reconstruction of climate statistics in the Eastern Tropical
Pacific. Thus, we show that the sample size and the climate variability are
the main sources of the standard error. The experiments allowed the
identification and estimation of systematic bias that would not otherwise be
detected because of limited modern datasets. Our study demonstrates that
numerical simulations based on Monte Carlo analyses are a simple and
powerful approach to improve the understanding of the proxy records. We show
that the standard error for the climate statistics linearly increases with
the climate variability, which means that the accuracy of the error
estimated by MoCo is limited by the climate non-stationarity. |
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