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Abstract:
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Regression-based climate reconstructions scale one or more noisy proxy records against a (generally) short instrumental data series. Based on that relationship, the indirect information is then used to estimate that particular measure of climate back in time. A well-calibrated proxy record(s), if stationary in its relationship to the target, should faithfully preserve the mean amplitude of the climatic variable. However, it is well established in the statistical literature that traditional regression parameter estimation can lead to substantial amplitude attenuation if the predictors carry significant amounts of noise. This issue is known as "Measurement Error." Climate proxies derived from tree-rings, ice cores, lake sediments, etc., are inherently noisy. Thus all regression-based reconstructions could suffer from this problem, to which we provide a simple solution.
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