Abstract
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 humaninduced
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.