Jan 7, 2013

Deducing Multidecadal Anthropogenic Global Warming Trends Using Multiple Regression Analysis

Jiansong Zhou and Ka-Kit Tung
AMS Journal of the Atmospheric Sciences
  • States that, to unmask the anthropogenic global warming trend imbedded in the climate data, multiple linear regression analysis is often employed to filter out short-term fluctuations caused by El Niño–Southern Oscillation (ENSO), volcano aerosols, and solar forcing
  • States that these fluctuations, however, are unimportant as far as their impact on the deduced multidecadal anthropogenic trends is concerned: ENSO and volcano aerosols have very little multidecadal trend
  • States that what is important, but is left out of all multiple regression analysis of global warming so far, is a long-period oscillation called the Atlantic multidecadal oscillation (AMO)
  • Finds that when the AMO index is included as a regressor (i.e., explanatory variable), the deduced multidecadal anthropogenic global warming trend is so impacted that previously deduced anthropogenic warming rates need to be substantially revised
  • Concludes that the deduced net anthropogenic global warming trend has been remarkably steady and statistically significant for the past 100 yr