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- Statistical separation of observed global and European climate data into natural and anthropogenic signals (2003)
- 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).
- Statistical analyses for the purpose of an early detection of global and regional climate change due to the anthropogenic greenhouse effect (2000)
- The assumption that mankind is able to have an in uence on global or regional climate, respectively, due to the emission of greenhouse gases, is often discussed. This assumption is both very important and very obscure. In consequence, it is necessary to clarify definitively which meteorological elements (climate parameters) are in uencend by the anthropogenic climate impact, and to which extent in which regions of the world. In addition, to be able to interprete such an information properly, it is also necessary to know the magnitude of the different climate signals due to natural variability (for example due to volcanic or solar activity) and the magnitide of stochastic climate noise. The usual tool of climatologists, general circulation models (GCM) suffer from the problem that they are at least quantitatively uncertain with regard to the regional patterns of the behaviour of climate elements and from the lack of accurate information about long-term (decadal and centennial) forcing. In contrast to that, statistical methods as used in this study have the advantage to test hypotheses directly based on observational data. So, we focus to the very reality of climate variability as it has occurred in the past. We apply two strategies of time series analyis with regard to the observed climate variables under consideration. First, each time series is splitted into its variation components. This procedure is called 'structure-oriented time series separation'. The second strategy called 'cause-oriented time series separation' matches various time series representing various forcing mechanisms with those representing the climate behaviour (climate elements). In this way it can be assessed which part of observed climate variability can be explained by this (combined) forcing and which part remains unexplained.