Granger Causality Analyses for Climatic Attribution

Abstract

This review paper focuses on the application of the Granger causality technique to the study of the causes of recent global warming (a case of climatic attribution). A concise but comprehensive review is performed and particular attention is paid to the direct role of anthropogenic and natural forcings, and to the influence of patterns of natural variability. By analyzing both in-sample and out-of-sample results, clear evidences are obtained (e.g., the major role of greenhousegases radiative forcing in driving temperature, a recent causal decoupling between solar irradiance and temperature itself) together with interesting prospects of further research.

A non-linear Granger-causality framework to investigate climate–vegetation dynamics

“…To this end, statistical tests have been proposed and applied both in the econometric literature as well as in Granger-causality studies in the context of climate science. These kinds of tests, which compare out-of-sample prediction errors, are available for models for which parameter estimation is done through ordinary least squares or maximum likelihood estimation (Attanasio et al, 2013). Moreover, the asymptotic and finitesample properties of a battery of tests for comparing forecasting accuracies of different models have been studied and, more recently, further tests aiming specifically at nested models have been proposed (Clark and McCracken, 2001).…”

“…In climate, relations between variables are highly non-linear and tend to become even more non-linear as the temporal resolution of the data becomes finer (Attanasio et al, 2013). Therefore, it would be convenient to have at our disposal a statistical test to assess the significance of any quantitative evidence of climate (Granger) causing vegetation anomalies.…”

“…An alternative approach for comparing the predictive performance of different models is to use resampling methods such as the bootstrap or schemes such as 5×2 cross-validation (Dietterich, 1998). Methods based on the bootstrap have been used before in Granger-causality studies with climate data (Diks and Mudelsee, 2000;Attanasio et al, 2013). However, these results need to be interpreted with care because, by increasing the number of bootstrap samples, the power of any paired test (such as the Wilcoxon signed rank test) to detect significant differences between the error distributions of both models (full and baseline) increases as well.…”

A multi-approach strategy in climate attribution studies: Is it possible to apply a robustness framework?

“…Granger causality analyses, if conducted inside a genuine predictive landscape, lead to establish their major role, even in comparison with natural forcings and drivers of natural variability: see Attanasio et al (2012) and Pasini et al (2012), Stern and Kaufmann (2014) for specific results, and Attanasio et al (2013) for a brief review of attribution studies via Granger causality analyses.…”

On the role of sulfates in recent global warming: a Granger causality analysis

“…In particular, neural network modelling (Pasini et al, 2006) and Granger causality analyses have been used for attribution studies at global level: see Attanasio et al (2013) for a recent review and Attanasio et al (2012), Pasini et al (2012), Triacca et al (2013), Stern and Kaufmann (2014) for specific investigations on attribution of global temperature. It is worthwhile to note that the results of these papers generally confirm the major role of GHGs in driving the recent global warming.…”