Last month I wrote a post saying:
BEST homogenized rural and non-rural stations then found little difference between the two. Rather than saying, “Oh, that’s what homogenization does,” it said, “Clearly, UHI isn’t a problem for our results.”
This weekend I came across a link on Twitter which makes things even worse. The link has a transcript of an interview with Richard Muller, the head of the BEST project. In it, he says things which are wrong, if not outright dishonest.
The example which stood out the most to me was:
Two more things. The urban heat island effect. That was something we studied I think in a clever and original way. [As opposed to] using all the stations, we could derive the temperature rise based only on rural stations. We got the same answer.
I don't know why Muller felt using only rural stations would be "a clever and original way" of studying the urban heat island (UHI) effect. If you think there is a problem with data taken from urban areas, an obvious thing to do is look at the data for non-urban areas. There is nothing "clever" about that. That's why plenty of other people had thought of doing the same thing, meaning it wasn't "original" either.
But that's a side issue. The main issue is what Muller says here is simply wrong. As I pointed out in my previous post, BEST team member Zeke Hausfather recently said:
To be fair, the separate study that Berkeley did was on homogenized data, so a lack of detectable UHI mainly just indicates that it was effectively removed.
Berkeley Earth, or BEST, did its testing for the UHI effect on a homogenized data set. That homogenized data set was created by taking data from rural and non-rural areas and modifying them to be more like one another. That means BEST used non-rural data to modify its rural data then used the resulting "rural" data to test for the UHI effect. And then BEST's team leader said:
we could derive the temperature rise based only on rural stations. We got the same answer
In what world are results "based only on rural stations" when those "rural stations" were modified using data from non-rural stations?
That's like me saying there is no income inequality between white and black people because after I homogenized the data for white and black people together, I couldn't find a difference between them. Then, having homogenized the data together, I can remove data from one group or the other without my results changing. With such a little trick, I can show income inequality has vanished!
Or, you know, I could not be dishonest. There is no way anyone hearing Muller speak would realize what BEST had done. Anyone hearing BEST calculated results "based only on rural stations" would assume those results were "based only on rural stations." None of them would assume those results were "based only on rural stations which were modified by using non-rural stations." Despite this, Muller has the audacity to say:
I don't think anybody who has responded in the media so far has actually studied our work. We don't expect immediate agreement on such things. What we expect is that by being transparent, open and clear...
Muller's description of how BEST examined the UHI effect was not remotely "transparent, open and clear." Nobody listening to Muller could have possibly guessed what BEST actually did. Even worse, Muller then said:
- by having the data online and the computer programmes so people can see precisely what we did
But to this day, BEST has never published the code it used to test its methodology. That article was written over two years ago, and still, code and data for tests BEST performed are not available. In fact, just a few weeks ago, BEST member Steven Mosher argued against providing it. People wanted to see what tests were performed in order to verify BEST's work, and BEST's response was to refuse.
There are a number of other issues with that interview, but I'm too tired to comment on everything. I just wanted to make sure someone noted the misleading portrayal of BEST's work on UHI by BEST team leader Richard Muller.
It's pretty bad to talk about being open and clear while telling people things you know will mislead them.