GREENHOUSE STATISTICS-TIME SERIES ANALYSIS

The relationship global mean temperature - atmospheric concentration o f carbon dioxide is modelled by means of time series analysis as it is used in a non-experimental statistical context. The goal is to test t he hypothesis that the global mean surface air temperature rises due t o the rising atmospheric concentrations of greenhouse gases. The commo n climatological approach to confirm this hypothesis has not yet succe eded because of the overly ambitious model design and the statisticall y less efficient manner of information processing in interpretating th e output of general circulation models. Earlier statistical attempts t o detect the greenhouse signal in the temperature record failed partly because of inefficient modelling. Starting with some naive time serie s models we show that the enhanced greenhouse effect is plausible. Tak ing the long-term natural variability of the climate into account cast s doubt on this claim but properly quantifying the size of the variabi lity restores the significance of the greenhouse parameter. Extending the model to explain part of the shorter term variability by including the influence of the sun, volcanoes and El Nino the hypothesis is aga in but stronger confirmed. A battery of tests reveals that this model describes the observed temperature record (statistically) well. We als o show that the outcomes are robust, i.e. insensitive to changes in th e model. Although statistics cannot constitute a proof of the hypothes is, the results of this paper are strong enough to conclude that at le ast part of the recent high temperatures is, with high probability, ca used by the increase in the atmospheric concentration of carbon dioxid e.