Statistical uncertainty in paleoclimate proxy reconstructions
Statistical uncertainty in paleoclimate proxy reconstructions
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.
McClelland, H. L. O.
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Halevy, I.
a9132941-7750-47e2-b22e-284536a93b77
Wolf‐Gladrow, D. A.
87c29592-0b4d-415c-bedc-6be7d62fecd9
Evans, D.
878c65c7-eab9-4362-896b-166e165eb94b
Bradley, A. S.
994697ed-30da-4605-a091-86372a03cc69
15 July 2021
McClelland, H. L. O.
1ae4f3ff-10b7-4f6f-bc5b-4722be7c8d81
Halevy, I.
a9132941-7750-47e2-b22e-284536a93b77
Wolf‐Gladrow, D. A.
87c29592-0b4d-415c-bedc-6be7d62fecd9
Evans, D.
878c65c7-eab9-4362-896b-166e165eb94b
Bradley, A. S.
994697ed-30da-4605-a091-86372a03cc69
McClelland, H. L. O., Halevy, I., Wolf‐Gladrow, D. A., Evans, D. and Bradley, A. S.
(2021)
Statistical uncertainty in paleoclimate proxy reconstructions.
Geophysical Research Letters, 48 (15), [e2021GL092773].
(doi:10.1029/2021GL092773).
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.
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Published date: 15 July 2021
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Local EPrints ID: 502479
URI: http://eprints.soton.ac.uk/id/eprint/502479
ISSN: 0094-8276
PURE UUID: 1d677049-8f6f-479b-a304-982e453976d4
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Date deposited: 26 Jun 2025 17:14
Last modified: 27 Jun 2025 02:09
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Author:
H. L. O. McClelland
Author:
I. Halevy
Author:
D. A. Wolf‐Gladrow
Author:
D. Evans
Author:
A. S. Bradley
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