"I’ve just completed Mike’s Nature trick of adding in the real temps to each series for the last 20 years (ie from 1981 onwards) amd from 1961 for Keith’s to hide the decline."
"I’ve just completed Mike’s Nature trick of adding in the real temps to each series for the last 20 years (ie from 1981 onwards) amd from 1961 for Keith’s to hide the decline."
I have often seen people use "trick" meaning "useful change" and "hide" meaning "doesn't show incorrect results". Without context there is no way to tell. Such a statement is somewhat worrying, but not exactly blatant in any way.
The volume of leaked correspondence does begin to form a context, but before the heavy and severe claim of scientific misconduct is really substantiated it's going to require scientific analysis of the fault. Cherrypicked quotes appearing on a news site is often known as researcher bias and it's just as big a flaw.
> Scientists often use the term "trick" to refer to a "a good way to deal with a problem", rather than something that is "secret", and so there is nothing problematic in this at all. As for the ‘decline’, it is well known that Keith Briffa’s maximum latewood tree ring density proxy diverges from the temperature records after 1960 (this is more commonly known as the "divergence problem" - see e.g. the recent discussion in this paper) and has been discussed in the literature since Briffa et al in Nature in 1998 (Nature, 391, 678-682). Those authors have always recommend not using the post 1960 part of their reconstruction, and so while ‘hiding’ is probably a poor choice of words (since it is ‘hidden’ in plain sight), not using the data in the plot is completely appropriate, as is further research to understand why this happens.
I have no doubt that this is exactly what the author meant by "hide". That doesn't mean that it was a good idea.
If something is both Important and True, then the fact that the current data seems not to show the truth clearly might be thought a good reason to fix it. I mean, it's not like other people have to do this -- we can assume that everyone else's results are straight, so that means that there's something wrong with the data, and we can just fix that up so that it provides the correct correlation with what we already know is true...
Sure, it looks like it's pretty damning. But what is the context, and what did he actually do?