I don’t think so. Correlation between A and B doesn’t mean A causes B, and says even less about why and how A influences B. But after hearing about all the wonderful liberties that CRU “scientists” took with the raw data that were all too happy to shred instead of sharing with all the different kinds of researchers out there (particularly given how historic and consequential this research was supposed to be for all mankind), I really doubt whether much of this has anything to do with science. Going by the plots up above, I don’t see a correlation between temperature and sunspot activity either.
Seems to me the statisticians here have been the good guys. The “scientists” decided what the answer was going to be, built their models, set the parameters using statistical methods they did not really understand, and then did everything they could to prevent others from trying to replicate them, including destruction of data.
Scientists were using fundamentally statistical methods that they either misused or misunderstood (fixing the data and building predictive models based on the fixed data), doing it poorly, and then using the models as 90% of the proof of their AGW hypothesis. They never tested their models against a null-hypothesis. Nor did they assess their models against simple alternatives (say, linear regression of temperature as a function of time or a “no-change” hypothesis, or the very simple sunspot model). Nor did they test their models on future data (data the models had not been optimized on).
It was usually a statisticians who insisted the models should have included confidence intervals (not the nonsense the IPCC puts out using multiple AGW models to predict and then using the variance across models) and make useful predictions about reality before we blindly accepted them and who were gauche enough to point out that the AGW scientists had not succeeded in either regard.
And, it was statisticians who busted what appeared to be scientific fraud—Mann’s hockey stick happened only because Mann changed the normalization step in principal components analysis with the effect that PCA became a data dredging tool looking through hundreds of tree-ring records for just those trees that showed temperature rises in the 20th century. See also Briggs recent analysis of the adjustments that were made to normalize the Darwin Station temperature data.
As to the “beautiful” science behind AGW, nothing in the science requires that climate respond to CO2 increases in a non-linear manner. We cannot desribe complex, positive feedback systems from fundamentals well enough to reach that conclusion. The way the AGW scientists decided to do that was to build many-parametered, positive-feedback CO2, finite element computer models and set the parameters from past data. Then when the models predicted the past data thay say “see, it was the CO2 all the time.” This is just another form of Post Hoc Ergo Propter Hoc. A model that fits data is just that—a fit model. It does not show causation. Yet that was the inference the AGW scientists would have us make.
So I see the fundamental technical problem here as statistical. Physics does not say enough about the system so that we could derive the non-linear relationship between CO2 and temperature from first principles. So the proof, if proof there is, must be statistical in nature.
So it was natural that much of the skeptics attack would be lead by statisticans.