Climatic Change

, Volume 47, Issue 4, pp 411-438

First online:

Detecting a Global Warming Signal in Hemispheric Temperature Series: AStructural Time Series Analysis

  • David I. SternAffiliated withCentre for Resource and Environmental Studies, Australian NationalUniversity
  • , Robert K. KaufmannAffiliated withCenter for Energy and Environmental Studies, Boston University

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Non-stationary time series such as global andhemispheric temperatures, greenhouse gasconcentrations, solar irradiance, and anthropogenicsulfate aerosols, may contain stochastic trends (thesimplest stochastic trend is a random walk) which, dueto their unique patterns, can act as a signal of theinfluence of other variables on the series inquestion. Two or more series may share a commonstochastic trend, which indicates that either oneseries causes the behavior of the other or that thereis a common driving variable. Recent developments ineconometrics allow analysts to detect and classifysuch trends and analyze relationships among seriesthat contain stochastic trends. We apply someunivariate autoregression based tests to evaluate thepresence of stochastic trends in several time seriesfor temperature and radiative forcing. The temperatureand radiative forcing series are found to be ofdifferent orders of integration which would cast doubton the anthropogenic global warming hypothesis.However, these tests can suffer from size distortionswhen applied to noisy series such as hemispherictemperatures. We, therefore, use multivariatestructural time series techniques to decomposeNorthern and Southern Hemisphere temperatures intostochastic trends and autoregressive noise processes. These results show that there are two independentstochastic trends in the data. We investigate thepossible origins of these trends using a regressionmethod. Radiative forcing due to greenhouse gases andsolar irradiance can largely explain the common trend.The second trend, which represents the non-scalarnon-stationary differences between the hemispheres,reflects radiative forcing due to tropospheric sulfateaerosols. We find similar results when we use the sametechniques to analyze temperature data generated bythe Hadley Centre GCM SUL experiment.