Deducing Multidecadal Anthropogenic Global Warming Trends Using Multiple...

Zhou, J., and K. Tung (2013), Deducing Multidecadal Anthropogenic Global Warming Trends Using Multiple Regression Analysis, J. Atmos. Sci., 70, 3-8, doi:10.1175/JAS-D-12-0208.1.

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. These fluctuations are unimportant as far as their impact on the deduced multidecadal anthropogenic trends is concerned: ENSO and volcano aerosols have very little multidecadal trend. Solar variations do have a secular trend, but it is very small and uncertain. 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). 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. The deduced net anthropogenic global warming trend has been remarkably steady and statistically significant for the past 100 yr.

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Climate Variability and Change Program