Updated to include Dr. Mann’s words.
The last time I did this, R was a brand new language to me. After 6 months messing around in my free time I speak rudimentary R with a C accent. R is a totally free language that anyone can download and learn. This post is a demonstration of the methods behind the Mann08 CPS hockey stick reconstruction. The difference between this and the numerous posts I did before is better R programming and a lot more comments in the code. If you’re serious about understanding vs advocating, you can figure this out. There is not one person I can think of who has ever commented here, incapable of figuring this out.
CPS is composite plus scale, which is an invented method for calibrating proxies to measured temperatures in paleoclimatology reconstructions. In paleoclimatology methods are too often invented to find the signal in the noise – this is not a new problem and it stems from the large signal to noise ration of paleo-data. If you happen to be a paleoclimatologist who does temperature reconstructions, please try your methods on ARIMA data with a known signal before employing it on whatever your proxy is.
I have hundreds of new readers, who didn’t get the day by day experience of my discovery of climatology math. Well some of my early work was a little rough, however it was correct and the specifics still stand uncriticized.
This post was prompted by some people in blogland (despite the complete lack of rational criticism) claiming that my demonstrations of the CPS hockey stick math is faulty rather than the actual hockey stick itself. An oddly reversed situation which could only exist in the new progressive anti-world. Actually, I don’t recall any real criticism of the method or the result other than statements around the AGW crowd that – ‘it’s been proven wrong’.
I’ve cleaned up the code and seriously over-commented it in the hopes that honest people will be able to understand what is going on here. I’ll do a short explanation for it but the primary explanation is in the programming code. To understand it fully you should to read it step by step and run it.
Michael Mann knows full well that this result exists, his explanation is as follows from an RC thread.
Actually, this line of attack is even more disingenuous and/or ill-informed than that. Obviously, if one generates enough red noise surrogate time series (especially when the “redness” is inappropriately inflated, as is often done by the charlatans who make this argument), one can eventually match any target arbitrarily closely.
You can note that this post uses his Actual data rather than red noise data. Dr. Mann’s continued explanation is here.
What this specious line of attack neglects (intentionally, one can safely conclude) is that a screening regression requires independent cross-validation to guard against the selection of false predictors. If a close statistical relationship when training a statistical model arises by chance, as would be the case in such a scenario, then the resulting statistical model will fail when used to make out-of-sample predictions over an independent test period not used in the training of the model. That’s precisely what any serious researchers in this field test for when evaluating the skillfulness of a statistical reconstruction based on any sort of screening regression approach.
So a potentially overstated statistical check is what determines if CPS works. This is in fact false, CPS is incapable of recovering an accurate signal from data. A fact which I will demonstrate in these next few posts. This post however, does not address the statistical validation issue, it does however demonstrate that Dr. Mann is correct that any signal at all can be made using Mann08 math and data. A truth for many of his hockey stick creation methods.
An explanation of Composite Plus Scale (CPS).
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