Journal
GEOPHYSICAL RESEARCH LETTERS
Volume 48, Issue 15, Pages -Publisher
AMER GEOPHYSICAL UNION
DOI: 10.1029/2021GL092773
Keywords
paleoclimate; paleoceanography; proxies; inverse prediction; calibration; confidence intervals
Categories
Funding
- Weizmann Institute of Science
- Israel Academy of Science and Humanities
- European Research Council [755053]
- US Department of Energy [DE-SC0014613]
- Simons Foundation [622740]
- U.S. Department of Energy (DOE) [DE-SC0014613] Funding Source: U.S. Department of Energy (DOE)
- European Research Council (ERC) [755053] Funding Source: European Research Council (ERC)
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This paper introduces a simple, robust, and generalizable method for quantifying uncertainty in proxy reconstructions, highlighting the primary controls on the magnitude of uncertainty. The discussion includes possible ways to reduce uncertainties and emphasizes the necessity of incorporating unexplained variance in the calibration into the reconstruction's uncertainty.
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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