Statistical separation of observed global and European climate data into natural and anthropogenic signals

Observed global and European spatiotemporal related fields of surface air temperature, mean-sea-level pressure and precipitation are analyzed statistically with respect to their response to external forcing factors such 
Observed global and European spatiotemporal related fields of surface air temperature, mean-sea-level pressure and precipitation are analyzed statistically with respect to their response to external forcing factors such as anthropogenic greenhouse gases, anthropogenic sulfate aerosol, solar variations and explosive volcanism, and known internal climate mechanisms such as the El Niño-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). As a first step, a principal component analysis (PCA) is applied to the observed spatiotemporal related fields to obtain spatial patterns with linear independent temporal structure. In a second step, the time series of each of the spatial patterns is subject to a stepwise regression analysis in order to separate it into signals of the external forcing factors and internal climate mechanisms as listed above as well as the residuals. Finally a back-transformation leads to the spatiotemporally related patterns of all these signals being intercompared. Two kinds of significance tests are applied to the anthropogenic signals. First, it is tested whether the anthropogenic signal is significant compared with the complete residual variance including natural variability. This test answers the question whether a significant anthropogenic climate change is visible in the observed data. As a second test the anthropogenic signal is tested with respect to the climate noise component only. This test answers the question whether the anthropogenic signal is significant among others in the observed data. Using both tests, regions can be specified where the anthropogenic influence is visible (second test) and regions where the anthropogenic influence has already significantly changed climate (first test).
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Metadaten
Author:Tim Staeger, Jürgen Grieser, Christian-Dietrich Schönwiese
URN:urn:nbn:de:hebis:30-14872
Document Type:Article
Language:English
Date of Publication (online):2005/09/14
Year of first Publication:2003
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2005/09/14
Tag:Climate change ; Greenhouse effect ; Observed climate signals ; Principal component analysis
Source:Climate Research, Vol. 24: 3-13, 2003
HeBIS PPN:197124429
Institutes:Geowissenschaften
Dewey Decimal Classification:550 Geowissenschaften
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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