Detection and attribution methodologies have been developed over the years to delineate anthropogenic from natural drivers of climate change and impacts. A majority of prior attribution studies, which have used climate model simulations and observations or reanalysis datasets, have found evidence for human-induced climate change. This papers tests the hypothesis that Granger causality can be extracted from the bivariate series of globally averaged land surface temperature (GT) observations and observed CO2 in the atmosphere using a reverse cumulative Granger causality test. This proposed extension of the classic Granger causality test is better suited to handle the multisource nature of the data and provides further statistical rigor. The results from this modified test show evidence for Granger causality from a proxy of total radiative forcing (RC), which in this case is a transformation of atmospheric CO2, to GT. Prior literature failed to extract these results via the standard Granger causality test. A forecasting test shows that a holdout set of GT can be better predicted with the addition of lagged RC as a predictor, lending further credibility to the Granger test results. However, since second-order-differenced RC is neither normally distributed nor variance stationary, caution should be exercised in the interpretation of our results.
Bibliographical noteFunding Information:
The authors are grateful to Umberto Triacca for sharing data from a previous study, and to Karsten Steinhaeuser and Shih-Chieh Kao for their valuable input. All computations were done using R and Microsoft Excel. This research funded through the Summer Undergraduate Laboratory Internship (SULI) program of the United Stated Department of Energy (US DOE). The research was performed at the Oak Ridge National Laboratory (ORNL), which in turn is managed by UT-Battelle, LLC, for the US Department of Energy under Contract DE-AC05-00OR22725. The United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allows others to do so, for United States Government purposes. The research of E.K. and A.R.G. was partially supported by the Laboratory Directed Research & Development (LDRD) Program of the Oak Ridge National Laboratory (ORNL) as part of a project on climate extremes and uncertainty led by A.R.G. at ORNL.