Theoretical and Applied Climatology

, Volume 48, Issue 2–3, pp 63–74 | Cite as

Greenhouse statistics-time series analysis

  • R. S. J. Tol
  • A. F. de Vos
Article
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Revised:

Summary

The relationship global mean temperature — atmospheric concentration of 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 the hypothesis that the global mean surface air temperature rises due to the rising atmospheric concentrations of greenhouse gases.

The common climatological approach to confirm this hypothesis has not yet succeeded because of the overly ambitious model design and the statistically less efficient manner of information processing in interpretating the output of general circulation models. Earlier statistical attempts to detect the greenhouse signal in the temperature record failed partly because of inefficient modelling.

Starting with some naive time series models we show that the enhanced greenhouse effect is plausible. Taking the long-term natural variability of the climate into account casts doubt on this claim but properly quantifying the size of the variability 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 Niño the hypothesis is again but stronger confirmed. A battery of tests reveals that this model describes the observed temperature record (statistically) well. We also show that the outcomes are robust, i.e. insensitive to changes in the model.

Although statistics cannot constitute a proof of the hypothesis, the results of this paper are strong enough to conclude that at least part of the recent high temperatures is, with high probability, caused by the increase in the atmospheric concentration of carbon dioxide.

Keywords

General Circulation Model Time Series Model Temperature Record Atmospheric Concentration Greenhouse Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag 1993

Authors and Affiliations

  • R. S. J. Tol
    • 1
  • A. F. de Vos
    • 2
  1. 1.Institute for Environmental StudiesVrije UniversiteitAmsterdamThe Netherlands
  2. 2.Department of EconometricsVrije UniversiteitAmsterdamThe Netherlands

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