Files and links (2)
Published (Version of record)CC BY V4.0, Open Access
Published (Version of record)CC BY V4.0, Open Access
Journal article
Statistical Uncertainty in Paleoclimate Proxy Reconstructions
Geophysical research letters, Vol.48(15), e2021GL092773
16/Aug/2021
Abstract
A quantitative analysis of any environment older than the instrumental record relies on proxies. Uncertainties associated with proxy reconstructions are often underestimated, which can lead to artificial conflict between different proxies, and between data and models. In this paper, using ordinary least squares linear regression as a common example, we describe a simple, robust and generalizable method for quantifying uncertainty in proxy reconstructions. We highlight the primary controls on the magnitude of uncertainty, and compare this simple estimate to equivalent estimates from Bayesian, nonparametric and fiducial statistical frameworks. We discuss when it may be possible to reduce uncertainties, and conclude that the unexplained variance in the calibration must always feature in the uncertainty in the reconstruction. This directs future research toward explaining as much of the variance in the calibration data as possible. We also advocate for a “data-forward” approach, that clearly decouples the presentation of proxy data from plausible environmental inferences.
Details
- Title
- Statistical Uncertainty in Paleoclimate Proxy Reconstructions
- Creators
- H. L. O. McClelland (Corresponding Author) - University of MelbourneItay Halevy - 972WIS_INST___99D. A. Wolf-Gladrow - Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und MeeresforschungD Evans - Goethe University FrankfurtA. S. Bradley - Washington University in St. Louis
- Resource Type
- Journal article
- Publication Details
- Geophysical research letters, Vol.48(15), e2021GL092773; 16/Aug/2021
- Number of pages
- 11
- Language
- English
- DOI
- https://doi.org/10.1029/2021GL092773
- Grant note
- The authors would like to thank Peter Crockford, Sylvia Dee, Allison Jacobel, Wil Leavitt, and Max Lloyd for comments on an earlier draft of the manuscript. Thanks to three anonymous reviewers whose comments greatly improved the manuscript. Thanks to Eli Tziperman for suggesting the nonparametric approach. H. McClelland acknowledges postdoctoral fellowships from the Weizmann Institute of Science, and the Israel Academy of Science and Humanities. I. Halevy acknowledges a Starting Grant from the European Research Council (OOID No. 755053). A. S. Bradley acknowledges funding from the US Department of Energy (DE-SC0014613) and the Simons Foundation (622740).
- Record Identifier
- 993291123703596
Metrics
1 File views/ downloads
7 Record Views