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| Titel |
Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions |
| VerfasserIn |
C. M. Ammann, M. G. Genton, B. Li |
| 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 ; 6, no. 2 ; Nr. 6, no. 2 (2010-04-20), S.273-279 |
| Datensatznummer |
250003449
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| Publikation (Nr.) |
 copernicus.org/cp-6-273-2010.pdf |
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| Zusammenfassung |
| 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" (Fuller, 1987; Carroll et al.,
2006). Climate proxies derived from tree-rings, ice cores, lake sediments,
etc., are inherently noisy and thus all regression-based reconstructions
could suffer from this problem. Some recent applications attempt to ward off
amplitude attenuation, but implementations are often complex (Lee et
al., 2008) or require additional information, e.g. from climate models
(Hegerl et al., 2006, 2007). Here we explain the cause
of the problem and propose an easy, generally applicable, data-driven
strategy to effectively correct for attenuation (Fuller, 1987;
Carroll et al., 2006), even at annual resolution. The impact is illustrated
in the context of a Northern Hemisphere mean temperature
reconstruction. An inescapable trade-off for achieving an unbiased
reconstruction is an increase in variance, but for many climate applications
the change in mean is a core interest. |
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