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.