A new study which statistically analyzed temperature data over the pre-industrial period and the industrial period has rejected the hypothesis that global warming is due to natural variability at confidence levels greater than 99%. The results have been published in the journal Climate Dynamics.
Although there is a large body of evidence to suggest that current global warming is largely due to human activities, much of this has relied on models called general circulation models (GCMs). GCMs are computer-driven models that are key components of global climate models, which as the name suggests are used for modeling climate. Although they are useful tools, some are skeptical as to whether they can really infer connections between anthropogenic factors and global warming. This, coupled with the fact that there has been a tendency to over-rely on them when making assertions, has created a need for empirically based methodologies to complement the GCMs.
Professor Shaun Lovejoy from the McGill University used data from the (mostly) pre-industrial period (1500-1900) and the industrial period (1880-2000), and calculated the probability that global warming since 1880 is due to natural temperature fluctuations, rather than man-made emissions, using statistical analysis.
In order to assess natural variation in climate prior to the industrial period, Lovejoy used both fluctuation-analysis techniques which allow an understanding of temperature variations over different time scales, and multi-proxy climate reconstructions. These reconstructions make use of data derived from sources such as tree rings and ice cores.
To do the same for the industrial period, he used CO2 production from fossil fuel burning as a broad surrogate for all anthropogenic (man-made) forcings. He claimed that this is justified because of the relationship between the emission of greenhouse gases and particulate pollution with global economic activity.
The conclusion drawn from the data was clear- he rejected the natural variability hypothesis with confidence levels of over 99%. It is necessary to understand that rejecting one hypothesis does not prove another- his data therefore does not prove that global warming has an anthropogenic causation. However, the results do enhance the credibility of this ulterior hypothesis.
It is also very important to point out that the confidence levels are likely to be exaggerated since the data used for the pre-industrial era cannot be 100% certain as measurements were taken in an indirect manner, since temperature data was not recorded 500 years ago. Therefore there is a degree of uncertainly in this data, which would inherently affect the statistical confidence. However, this does not mean that his overall conclusion is invalid, and this still remains an important study.
The data generated by the study also allowed Lovejoy to make predictions like those recently published by the International Panel on Climate Change (IPCC). The IPCC predict that if atmospheric CO2 levels double, the climate will increase by between 1.9-4.2oC. Lovejoy’s data complemented this with predictions of temperature rises between 1.5-4.5oC.
While this is just one study, it does add to the ever growing body of evidence that the global warming we are experiencing cannot solely be attributed to natural fluctuations in temperatures.