Steve McIntyre writes about what he considers another “completely worthless” exercise in statistical data mining, writing:
In today’s post, I will look at a new Naturemag climate reconstruction claiming unprecedentedness (h/t Bishop Hill): “Evolution of the Southern Annular Mode during the past millennium” (Abram et al Nature 2014, pdf). Unfortunately, it is marred by precisely the same sort of data mining and spurious multivariate methodology that has been repeatedly identified in Team paleoclimate studies.
The flawed reconstruction has been breathlessly characterized at the Conversation by Guy Williams, an Australian climate academic, as a demonstration that, rather than indicating lower climate sensitivity, the recent increase in Antarctic sea ice is further evidence that things are worse than we thought. Worse it seems than previously imagined even by Australian climate academics.
the apparent paradox of Antarctic sea ice is telling us that it [climate change] is real and that we are contributing to it. The Antarctic canary is alive, but its feathers are increasingly wind-ruffled.
A Quick Review of Multivariate Errors
Let me start by assuming that CA readers understand the basics of multivariate data mining. In an extreme case, if you do a multiple regression of a sine wave against a large enough network of white noise, you can achieve arbitrarily high correlations. (See an early CA post on this here discussing example from Phillips 1998.)
Read the entire post here: http://wp.me/p6iHb-50Y
For information on the Southern Annular Mode, see this: