Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions
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 st...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2010-04-01
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| Series: | Climate of the Past |
| Online Access: | http://www.clim-past.net/6/273/2010/cp-6-273-2010.pdf |
| _version_ | 1811267520045252608 |
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| author | C. M. Ammann M. G. Genton B. Li |
| author_facet | C. M. Ammann M. G. Genton B. Li |
| author_sort | C. M. Ammann |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T21:03:59Z |
| format | Article |
| id | doaj.art-96940cd58ca849e295cc2a88be879491 |
| institution | Directory Open Access Journal |
| issn | 1814-9324 1814-9332 |
| language | English |
| last_indexed | 2024-04-12T21:03:59Z |
| publishDate | 2010-04-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Climate of the Past |
| spelling | doaj.art-96940cd58ca849e295cc2a88be8794912022-12-22T03:16:45ZengCopernicus PublicationsClimate of the Past1814-93241814-93322010-04-016227327910.5194/cp-6-273-2010Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructionsC. M. AmmannM. G. GentonB. LiRegression-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.http://www.clim-past.net/6/273/2010/cp-6-273-2010.pdf |
| spellingShingle | C. M. Ammann M. G. Genton B. Li Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions Climate of the Past |
| title | Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions |
| title_full | Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions |
| title_fullStr | Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions |
| title_full_unstemmed | Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions |
| title_short | Technical Note: Correcting for signal attenuation from noisy proxy data in climate reconstructions |
| title_sort | technical note correcting for signal attenuation from noisy proxy data in climate reconstructions |
| url | http://www.clim-past.net/6/273/2010/cp-6-273-2010.pdf |
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