Detecting a global warming signal in hemispheric temperature series: A structural time series analysis

Non-stationary time series such as global and hemispheric temperatures, gre enhouse gas concentrations, solar irradiance, and anthropogenic sulfate aer osols, may contain stochastic trends (the simplest stochastic trend is a ra ndom walk) which, due to their unique patterns, can act as a signal of the influence of other variables on the series in question. Two or more series may share a common stochastic trend, which indicates that either one series causes the behavior of the other or that there is a common driving variabl e. Recent developments in econometrics allow analysts to detect and classif y such trends and analyze relationships among series that contain stochasti c trends. We apply some univariate autoregression based tests to evaluate t he presence of stochastic trends in several time series for temperature and radiative forcing. The temperature and radiative forcing series are found to be of different orders of integration which would cast doubt on the anth ropogenic global warming hypothesis. However, these tests can suffer from s ize distortions when applied to noisy series such as hemispheric temperatur es. We, therefore, use multivariate structural time series techniques to de compose Northern and Southern Hemisphere temperatures into stochastic trend s and autoregressive noise processes. These results show that there are two independent stochastic trends in the data. We investigate the possible ori gins of these trends using a regression method. Radiative forcing due to gr eenhouse gases and solar irradiance can largely explain the common trend. T he second trend, which represents the non-scalar non-stationary differences between the hemispheres, reflects radiative forcing due to tropospheric su lfate aerosols. We find similar results when we use the same techniques to analyze temperature data generated by the Hadley Centre GCM SUL experiment.