This was interesting for the true schadenfreude value of it all.
And thank God for the person at East Anglia University who either hacked or leaked this information:
The temperature data that the “hockey stick” researchers used is a useless mess.
The first red flag in research is when someone won’t share their data. These guys wouldn’t and this is likely why.
The second red flag is that others are not getting similar results looking at similar data (in that case more correct data).
The third red flag is that their “models” couldn’t predict current weather patterns (meaning that the models were not effective).
The fourth red flag is that Al Gore made this his life’s work. And in my opinion Al Gore is a neat mix of not very bright and completely bat*^%# crazy.
I’m not saying that global warming is not a potential problem (although I think global cooling has worse implications for humanity outside of longer ski seasons).
The main thing is that these guys who are claiming that we’re on the brink of catastrophe are idiots who should have similar credibility as that Korean clone guy.
UPDATE: Here’s a comment from my very smart friend Frank:
Here’s my favorite quote from the email chain you sent:
“The problem is that the synthetics are incorporated at 2.5-degrees, NO IDEA why, so saying they affect particular 0.5-degree cells is harder than it should be. So we’ll just gloss over that entirely ;0)
“ARGH. Just went back to check on synthetic production. Apparently – I have no memory of this at all – we’re not doing observed rain days! It’s all synthetic from 1990 onwards. So I’m going to need conditionals in the update program to handle that.”
Just so you know…”conditionals” are an oft-used term for Bayesian update probabilities to handle data that comes from expert opinion rather than fact.
Translation into English: it looks like their data (at least their rain data, but maybe more) after 1990 (when temperatures started going down again) were “synthetic” or…faked. And in order to compensate for that, they used probabilities adjustments that their temperatures weren’t right. That would mean their models would have a standard error dependent on how strong they thought their guesses were.
In practice, what that means is the same guys making up the temperature data were making up the number representing how sure they were of their made up data (Bayesian prior standard deviation).
That calls into question their entire model. Totally bogus.”