Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
Adaptation of the optimal fingerprint method for climate change detection using a well-conditioned covariance matrix estimate
 

Summary: Adaptation of the optimal fingerprint method for climate change
detection using a well-conditioned covariance matrix estimate
Aure´lien Ribes Æ Jean-Marc Azai¨s Æ Serge Planton
Received: 11 April 2008 / Accepted: 18 March 2009 / Published online: 12 May 2009
Ó Springer-Verlag 2009
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

  

Source: Azais, Jean-Marc -Institut de Mathématiques de Toulouse, Université Paul Sabatier

 

Collections: Mathematics