Nigel Persaud Dons His Eyeshade and Audits the Auditor

Some time ago Nigel Persaud took up the trade of auditor and inquired about this and that.  Somebunny known here and abouts took up the challenge, only to find that careful examination showed that most of the inquiries were, shall Eli say it, perhaps about nothing at all, but that there were a couple of lacuna, things missing.  They eventually were noted in the appropriate place.

On the scale of errors, there are blunders, there are errors, there is over clever data selection, and there is ignorance.  There might be more, Eli will await word from Willard, but blunders occupy a special and deep circle of academic hell.

One of the auditors, Ross McKitrick, has an impressive case of the blunders.  Tim Lambert made a hobby of finding them.  There was, of course the famous confusion of degrees with radians in Michaels and McKitrick 2004 (MM04) and much much more.

A bunch of the lab mice, Rasmus Benestad, Dana Nuccitelli, Stephan Lewandowsky, Katherine Hayhoe, Hans Olav Hygen, Rob van Dorland, and John Cook have taken MM04 under the microscope as an example of, well, pretty much all of category of errors discussed in their recent paper.  They further respond to the McKitrick beasts wails of hurt in a recent Real Climate post.

There is one crucial point that McKitrick seems to have missed, which is that nearby temperature trends are related because the trend varies smoothly over space.

An important point made in (Benestad et al., 2015) was that a large portion of the data in the analysis of McKitrick and Michaels (2004) came from within the same country and involved common information for the economic statistics (GDP, etc). In technical terms, we say that there were dependencies within the sample of data points.

Bob Grumbine had pretty well nailed this over a decade ago after looking at the original version of MM04

He was fooling around with correlating per capita income with the observed temperature changes. He concluded that the warming was a figment of climatologists imaginations, as there was a correlation between money and warming. ‘Obviously’ this had to be due to wealth creating the warming in the dataset, rather than any climate change—his conclusion.

Along the way he:
1) selected a subset of temperature records
1a) without using a random method
1b) without paying attention to spatial distribution
1c) without ensuring that the records were far enough apart to be independant—ok, I shouldn’t say ‘he’ did it, because he didn’t. He blindly took a selection that his student made and which was—to my eyes—distributed quite peculiarly.

2) Treated the records as being independant (I know William knows this, but for some other folks: Surface temperature records are correlated across fairly substantial distances—a few hundred km. This is what makes paleoreconstructions possible, and what makes it possible to initialize global numerical weather prediction models with so few observations.)

3) Ignored that we do expect, and have reason to expect that the warming will be higher in higher latitudes

4) Ignored that the wealthy countries are at higher latitudes

Hence my calling it fooling around rather than work or study. He was, he said, submitting that pile of tripe* to a journal. *pile of tripe being my term, not his.

and

His main conclusion was regarding climate change—namely that there isn’t any. His secondary conclusion was that climate people studying climate data were idiots. Neither of those is a statement of economics, so my knowledge of economics is irrelevant (though, in matter of fact, it is far greater than his knowledge of climate; this says little, as his displayed level doesn’t challenge a bright jr. high student.).

Now this discussion of McKitrick and Michaels stirred a memory in Eli's rememberer, a comment that Steve Mosher had made when a follow on paper to MM04 and MM07 was being featured by Judith Curry.

I downloaded his data. In his data package he has a spreadsheet named MMJGR07.csv.
This contains his input data of things like population, GDP etc.

In line 195 he has the following data

Latitude = -42.5
Longitude = -7.5
Population in 1979 =56.242
Population in 1989 = 57.358
Population in 1999 = 59.11
Land = 240940 In his code he performs the following calculation

SURFACE PROCESSES: % growth population, income, GDP & Coal use // land is in sq km, pop is in millions; scale popden to persons/km2 // gdp is in trillions; gdpden is in $millions/km2

generate p79 = 1000000*pop79/land
generate p99 = 1000000*pop99/land

So, at latitude -42,5, Longitude -7.5 he has a 1979 population of 56 million people and 240940 sq km and a population density in the middle of the ocean that is higher than 50% of the places on land. Weird.

A few others looked at the spread sheet and saw that well in the words of another McKitrick was spreading the population and GDP of France across a couple of small islands in the Pacific.

WebHubTelescope summed it up

Whether it is getting radians and degrees mixed up, or doing elementary sanity checks on the data, this stuff isn’t that hard to verify for quality. Could it be that some people just don’t have the feel for the data? Or that they rely too much on blindly shoving numbers into stats packages? McKitrick’s paper has that sheen of mathematical formalism that can obscure the fact that he lacks some the skill of a practical analyst. Beats me as to his real skill level, or that he is just sloppy. 

 As far as Eli can see this "event" was only discussed in one other place, Marcel Crok's blog by Jos Hagelaars. 

Today Eli went and downloaded the file.  Just a quick pass through shows that of the 25/469 stations south of -40.0 latitude, 4 are UK territories and are associated with the population and GDP of the UK and the south pacific data is dominated by french territories.  Oh yeah, the Faroes have the population of Denmark.

Said file is available on request with a donation to the Ancient Bunny Fund.