Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate |
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Authors: | Aurélien Ribes Jean-Marc Azaïs Serge Planton |
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Institution: | (1) CNRM-GAME, Météo France-CNRS, 42 av G Coriolis, 31057 Toulouse, France;(2) Université de Toulouse, UPS, IMT, LSP, 118 Route de Narbonne, 31062 Toulouse, France |
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Abstract: | The “optimal fingerprint” method, usually used for detection and attribution studies, requires to know, or, in practice, to
estimate the covariance matrix of the internal climate variability. In this work, a new adaptation of the “optimal fingerprints”
method is presented. The main goal is to allow the use of a covariance matrix estimate based on an observation dataset in
which the number of years used for covariance estimation is close to the number of observed time series. Our adaptation is
based on the use of a regularized estimate of the covariance matrix, that is well-conditioned, and asymptotically more precise,
in the sense of the mean square error. This method is shown to be more powerful than the basic “guess pattern fingerprint”,
and than the classical use of a pseudo-inverted truncation of the empirical covariance matrix. The construction of the detection
test is achieved by using a bootstrap technique particularly well-suited to estimate the internal climate variability in real
world observations. In order to validate the efficiency of the detection algorithm with climate data, the methodology presented
here is first applied with pseudo-observations derived from transient regional climate change scenarios covering the 1960–2099
period. It is then used to perform a formal detection study of climate change over France, analyzing homogenized observed
temperature series from 1900 to 2006. In this case, the estimation of the covariance matrix is only based on a part of the
observation dataset. This new approach allows the confirmation and extension of previous results regarding the detection of
an anthropogenic climate change signal over the country. |
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Keywords: | Anthropogenic climate change Detection Optimal fingerprints Covariance matrix estimation |
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