Non-stationary time series such as global and hemispheric temperatures, greenhouse gas concentrations, solar irradiance, and anthropogenic sulfate aerosols, may contain stochastic trends (the simplest stochastic trend is a random 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 variable. Recent developments in econometrics allow analysts to detect and classify such trends and analyze relationships among series that contain stochastic trends. We apply some univariate autoregression based tests to evaluate the 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 anthropogenic global warming hypothesis. However, these tests can suffer from size distortions when applied to noisy series such as hemispheric temperatures. We, therefore, use multivariate structural time series techniques to decompose Northern and Southern Hemisphere temperatures into stochastic trends and autoregressive noise processes. These results show that there are two independent stochastic trends in the data. We investigate the possible origins of these trends using a regression method. Radiative forcing due to greenhouse gases and solar irradiance can largely explain the common trend. The second trend, which represents the non-scalar non-stationary differences between the hemispheres, reflects radiative forcing due to tropospheric sulfate aerosols. We find similar results when we use the same techniques to analyze temperature data generated by the Hadley Centre GCM SUL experiment.