caa a22 4500 445799900 CHVBK 20180317145137.0 cr unu---uuuuu 170323e20110601xx s 000 0 eng 10.1007/s00704-010-0342-3 doi (NATIONALLICENCE)springer-10.1007/s00704-010-0342-3 Exploring Granger causality between global average observed time series of carbon dioxide and temperature [Elektronische Daten] [Evan Kodra, Snigdhansu Chatterjee, Auroop Ganguly] 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. The Author(s), 2010 Kodra Evan Statistics, Operations and Management Science, University of Tennessee, 331 SMC, 916, Volunteer Blvd, 37996-0532, Knoxville, TN, USA aut Chatterjee Snigdhansu School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church Street S.E., 55455, Minneapolis, MN, USA aut Ganguly Auroop Geographic Information Science and Technology Group, Computer Science and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd, 37831-6017, Oak Ridge, TN, USA aut Theoretical and Applied Climatology Springer Vienna 104/3-4(2011-06-01), 325-335 0177-798X 104:3-4<325 2011 104 704 https://doi.org/10.1007/s00704-010-0342-3 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00704-010-0342-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kodra Evan Statistics, Operations and Management Science, University of Tennessee, 331 SMC, 916, Volunteer Blvd, 37996-0532, Knoxville, TN, USA aut NATIONALLICENCE 700 1- Chatterjee Snigdhansu School of Statistics, University of Minnesota, 313 Ford Hall, 224 Church Street S.E., 55455, Minneapolis, MN, USA aut NATIONALLICENCE 700 1- Ganguly Auroop Geographic Information Science and Technology Group, Computer Science and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Rd, 37831-6017, Oak Ridge, TN, USA aut NATIONALLICENCE 773 0- Theoretical and Applied Climatology Springer Vienna 104/3-4(2011-06-01), 325-335 0177-798X 104:3-4<325 2011 104 704 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer