Time series properties of global climate variables: detection and attribution of climate change

Several time series investigations of global climate change have been published, but the time series properties of the variables has received little attention with a few exceptions in the case of global temperature series. We focus on the presence or absence of stochastic trends. We use three different tests to determine the presence of stochastic trends in a selected group of global climate change data for the longest time series available. The test results indicate that the radiative forcing due to changes in the atmospheric concentrations of CO2, CH4, CFCs, and N2O, emissions of SOX, CO2, CH4, and CFCs and solar irradiance contain a unit root while most tests indicate that temperature does not. The concentration of stratospheric sulfate aerosols emitted by volcanoes is stationary. The radiative forcing variables cannot be aggregated into a deterministic trend which might explain the changes in temperature. Taken at face value our statistical tests would indicate that climate change has taken place over the last 140 years but that this is not due to anthropogenic forcing. However, the noisiness of the temperature series makes it difficult for the univariate tests we use to detect the presence of a stochastic trend. We demonstrate that multivariate cointegration analysis can attribute the observed climate change directly to natural and anthropogenic forcing factors in a statistically significant manner between 1860 and 1994.