Earth’s average annual temperature has increased by near 1.50C since the 19th century. This has been analysed principally through computer-based climate models built up from causal hypotheses. The resulting theory of anthropogenic climate change (ACC) has the central hypothesis that observed global warming is driven linearly by rising atmospheric concentrations of greenhouse gases (GHG), especially carbon dioxide (CO2) from human activities. Analysis here adopts a statistical approach that examines warming from the perspective of a researcher in financial markets. The rationale is that climate and markets have much in common as complex, truly global systems with non-linear, hard-to-monitor external influences and multiple feedbacks; each is multidisciplinary; and much of the data in both disciplines is time series, for which it is notoriously difficult to establish cause and effect.
The principal finding is that the central hypothesis of ACC seems spurious, and due to simultaneous rises in global temperature and atmospheric CO2 which independently follow unrelated, time trending variables. ACC is further questioned by the existence of joint test and missing variables problems. Exploring CO2’s limited ability to explain warming by incorporating unsuspected forcers shows that humidity leads temperature and explains most of its increase; further, oceanic oscillations and cereal production are stronger explanators of temperature than CO2.
This statistically-based study adds value to existing physics-based climate models through a complementary analytical perspective that tests the robustness of models to real world data. It concludes that human activity is contributing to global warming, but herding around the forcing role of carbon combustion has seen its influence exaggerated. This has obvious implications for the effectiveness of decarbonisation as a policy to manage global warming.