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Richard Tol, professor of the economics of climate change, was coordinating lead author for the United Nations Intergovernmental Panel on Climate Change

I think he may have been upset when they pointed out that that he'd swapped a minus sign for a plus sign in his study. When you use the correct sign the economic outlook is less rosy. He ultimately admitted to the mistake and issued a correction to the original paper.

global warming is actually beneficial for the first couple of degrees for humanity as a whole, according to the IPCC even (AR5)

That finding relies on a paper by Richard Tol called “The Economic Effects of Climate Change”. It found that any benefits are sunk after 1C warming. Since we've already warmed by 1C, any further warming will have detrimental effects. The impact is non-linear so things do go down hill quite fast after the next 1C. This was an aggregate of previous studies. Unfortunately "Gremlins intervened" and among other issues, minus signs were dropped from two of the impact studies. The corrected paper is quite a bit less optimistic.

The CO2 based models are still getting it hopelessly wrong.

CMIP3 from the IPCC AR4 is pretty much bang on.

Nein. Consider how many scales are "validated" by Psychology post-docs, either with PCA that they call factor analysis or confirmatory factor analysis. In both cases the measurement structures are wrong (non-continuous data --> a Hessian that's overly inflated, and therefore presumed to be more informative that reality holds): PCA only works for formative measurement structures (i.e., the items cause the latent structure, like the Hollingshead SES scale), whereas nearly all social science measurement assumes a reflexive structure (underlying cause which is reflected by the common answering patterns across items), and can be easily attested to if one reports the Cronbach's alpha measure (a ratio of the signal variance to the total variance: although note that variance is undefined for ordinal variables, from a measurement, not predictive, viewpoint) in the paper. Notice the right wing authoritarianism scale, which claims to be tri-dimensional, with strong Cronbach's alpha reliability, also uses the dimensionality drawn from PCA, on ordinal items, which are mutually exclusive claims, but have never been critiqued. Nearly all scales are drawn up in this fashion. The mathematical test of confirmatory factor analysis actually imposes the reflexive structure, and allows for one to associate specific items with the assumed latent causes: however, the test assumes a multivariate normal joint distribution across the observed information, which is almost never seen in Psychology. This is the reason why the likelihood-ratio test, whose null hypothesis is that the model as specified by the researcher is a good approximation to the data, is nearly universally ignored despite being almost universally found to be significant in applications (look at the kvetching of Les Hayduk about this issue). Part of the issue is the assumption that the items are continuous, which again inflates the information in each item, and misrepresents the nature of the data. Item response theory (or more specifically, multidimensional item response theory) perfectly solves these issues, but it is considered a niche tool for us psychometricians, who can be safely ignored for giving a fuck about what the data says. See the responses of Susan Fiske, an endowed chair Princeton PhD of psychology, as related by Gelman [0].

[0]. http://andrewgelman.com/2016/0...

If CO2 is returned to pre-industrial levels - the ideal goal in addressing global warming

I'm not sure it's ideal to move back to pre-industrial levels. There is some evidence that at least the initial warming was beneficial: https://www.aeaweb.org/article... Some substantial errors were later discovered in that paper, but I think even when corrected there is still some evidence of net benefit for at least the initial warming.

"I agree it's not a problem." Many scientists disagree with you. John Ioannidis and Andrew Gelman come to mind particularly. http://journals.plos.org/plosm... http://andrewgelman.com/

Andrew Gelman, statistics professor and blogger, has characterized the Technology Review article as "horrible" and a "monstrosity" on his blog. He is an MIT graduate. Correlation does not imply causation. It's clickbait, too.

[I have mod points; posting as AC]

Andrew Gelman, a statistics professor with his own blog, has shredded Ashraf and Galor's paper on his blog. This is not science, it's sensationalism.

Money quote: "Everybody wants to be Jared Diamond, that is the problem." He gives some simple counterexamples that illustrate the silliness at play here.

You ignore the strong empirical data regarding the inverse correlation between crop yields and temperature. See, e.g., http://andrewgelman.com/2007/04/climate_change/.

I find your 'ivory tower monk' gag rather strange, given that it was university-based scientists like Norman Borlaug who gave us the green revolution. As opposed to knuckle-dragging hayseeds like yourself.