Why The "Hockey Stick" Proves Nothing

http://a-sceptical-mind.com/the-hockey-stick-vs-the-ice-cores-2

Forget for the moment that Mann's methods in developing the hockey stick are very much under question, put aside for now that recent measurements have put a big floppy curl on the end of his stick, and try to totally ignore Climategate. Doing all that let us believe the hockey stick IS an accurate representation of the recent past temperature record.

Accepting that, the linked presentation shows why making the leap to the conclusion that it MUST be cause by human made CO2 is complete nonsense with no basis in fact. There have been thousands if not millions of "hockey sticks" much more abrupt than this one in earth's past.

 

The hockey stick is the greatest thing to ever happen to the skeptic movement. The entire warmmonger message was one giant appeal to authority. Lucky for us skeptics Dr. Mann had such a huge ego that he couldn't let it go. If the warmmonger had just moved on we skeptics might have lost. But they so destroyed their own credibility in defending the hockey stick due to Mann's huge ego that they have lost the ability to appeal to authority.

 

A dozen re-evaluations and further work, including at least one skeptic-sponsored study, have all confirmed the hockey stick is real, no matter how much ignoring of reality deniers do.

 

Appeal to numbers is a logical fallacy because you have no reason to believe that the numbers aren't making the same mistake. All temperature reconstructions will fundamentally tend to create a hockey stick because temperature has only been rising during the calibration period. If you understand basic math it is a very simple concept.

 

And your point is?? This proves MAN MADE climate change???? No, because I have shown you a neat little package full of thousands of hockey sticks made by nature.

I am looking at reality friend....you are the one carrying reality into fantasy by saying since something exists concurrent with man, man MUST have caused it.....

 

There's a hockey stick in the Southern Hemisphere too. Are the ignorant enraged? You Betcha!

 

There are hockey sticks everywhere, which is just my point. Since they have been happening all throughout time, they PROVE NOTHING. A fact you seem unable to wrap your brain around...

The "IGNORANT" seem to be well represented on this forum too.

 

There are hockey sticks everywhere, which is just my point. Since they have been happening all throughout time, they PROVE NOTHING. A fact you seem unable to wrap your brain around...

The "IGNORANT" seem to be well represented on this forum too.

 

Mann's paper (which you clearly have not read) made no claim at all about the causes of global warming. The causal evidence is basic physics. And basic physics is something that climate deniers such as yourself (a) don't understand; and (b) don't want to talk about.

So just keep burying your head in the sand, FUD. The icecaps will continue to melt, and we will still be at fault.

 

A reconstruction must fit instrumental era data, or it is by definition not skillful. But that says nothing at all about the size and shape of fluctuations in the pre-instrumental era. You seem to think any temperature rise during the instrumental era proves that the reconstruction is bad. It actually proves the reverse.

 

No I think that if you only have one calibration period you will fall into selection fallacy.

If you have random red noise and only select those that have a sharp uptick at the end you will end up with a graph that is flat with a sharp uptick at the end. You cannot chose and weight your series based on the dependent variable.

 

And that's exactly what you should get! You're recovering actual signal at the end where the uptick is, and you're recovering no signal where there's no signal.

Mr. Statistics strikes again.

 

This, by the way, is exactly why the National Science Foundation looked at McIntyre's alleged "refutation" of the hockey stick, and yawned.

 

There is no signal its red noise. The uptick in some of the series is by chance and in no way related to the dependent variable. Once you select your series based upon correlation to the dependent variable in the correlation period the remaining series are no longer random. They all have on thing in common. They trend the same in the correlation period. So when they are averaged the like correlation periods add together while the rest of the time outside the correlation period is still random and flattens. This is basic math. I really cant understand why you can never grasp it.

 

Warmmonger logic.

Warmmonger: There was no mideival warm period.
Skeptic: Why.
Warmmonger: Becasue the proxy reconsturcitons say so.
Skeptic: But those doing proxy reconstructions truncate or throw out any proxies that show a strong medieval warm period like boreholes and high resolution ice cores.
Warmmonger: Those proxies are truncated or thrown out because they are inaccurate.
Skpetic: Why are they inaccurate.
Warmmonger: Because there was no medieval warm period.
Skpetic: Why?
Warmmonger: Because the proxy reconstructions say so.

 

You are quoting yourself? How sad.

 

OF COURSE IT IS!! You selected that dataset specifically because it was related to the dependent variable. Remember?
The dependent variable is real signal, and you selected that dataset because it reflected that real signal. Otherwise it wouldn't uptick! Hence the final reconstruction shows real signal in the uptick. You forced it to!

Exactly, and exactly correct. Because they now reflect the real signal in the correlation period.

I grasp it perfectly. In the correlation period, the reconstruction shows the real signal, which is correct. In the pre-correlation period, the reconstruction shows no-signal noise, which is also correct. The reconstruction is a hockey stick, not because it's random, but because it correctly shows the real signal you used as your selection criterion.

 

Who are these "warmmongers" you speak of? I've never observed anyone make those arguments. They might be overheard in a denier's straw factory.

 

PD you keep referring to this "real signal" what real signal is there in red noise?

Spurious correlation in random data is not a signal.

 

The real signal is in the variable you're correlating to. The variable you're screening the data on. Once you select the data, it's no longer random!

The correlation isn't spurious, it's real. Because the data isn't random any more.

Look at it this way. Suppose I roll a single die 600 times, and get 102 sixes. Then I select those 102 sixes and create a dataset with only sixes in it.

Is that dataset containing only sixes "random" in any sense of the word? Of course not. It's been selected and screened. Just like you selected and screened your random data, to arrive at non-random data.

 

The set of series you are using is no longer random. They all have one thing in common correlation to the dependent variable in the calibration period. But each series is still random and the correlation is still just as spurious.

Of course the correlation is spurious the series is random red noise. It doesn't relate to the dependent variable at all. It is the ultimate spurious correlation.

Anyone who argues that correlation has to be real has really started to lose it. And since I've seen this argument popping up more and more in warmmonger circles they have clearly lost it.

Do you know that the statistics department at Dr. Mann's own university gives an annual spurious correlation award to whoever can identify the most spurious correlation? I dont really know why they let anyone in the world try and claim this award when they need look no further than their own earth science center.

 

And by using non-random series, you have introduced signal into the noise. That's the point. The one who has fallen for the screening fallacy is YOU.

Let's go back to rolling a die again, but this time let's create 1000 series of 10 rolls each. From those 1000 series, we find five series that start 6, 6, 6 and use only them, throwing out the rest. There is now a 100% real signal in the those selected series, even if the generation was random.

And how does Mr. Statistics tell the difference between a real correlation with an r²=.9, and a spurious correlation with an r²=.9? There is no statistical difference. Any time I see deniers try to claim that A ≠ A, I know they've lost it.

 

Regarding this business about hockey-sticks being generated from "red noise", it should be noted that McIntyre screwed up his red-noise generation procedure.

I'll try to explain, in plain-English terms as much as possible, just how he screwed it up.

McIntyre used the R functions acf() and hosking.sim() to generate his red noise. He used those functions to (1)extract the tree-ring data autocorrelation structure, and (2)generate random noise with the same autocorrelation structure as the tree-ring data.

That's a nice idea in principle, but you have to be careful when you implement it. You must ensure that the data that you feed the acf() function contains only noise. If the data contains signal, then the "random noise" generated by acf()/hosking.sim() procedure will be contaminated with signal statistics. As a result, your "random noise" will be useless for evaluating "noise only" performance.

The data that McIntyre fed to the acf() function was tree-ring data that contained not only noise, but also the long-term "hockey-stick" climate signal. You can verify that for yourself by taking the tree-ring chronologies, normalizing them, and simply averaging them together. You will still see an underlying "hockey-stick" trend even when you don't regress the data against the instrumental temperature record. These "simple average" results are noisier, to be sure, but the underlying "hockey stick" signal is still visible.

So if you are going to use the tree-ring data as a model for your noise, you need to pre-process it to remove the "hockey stick" signal first! McIntyre failed to do that (as can be verified by inspecting his R code at http://www.people.fas.harvard.edu/~phuybers/Hockey/Rscript.R).

Because McIntyre failed to do so, his "random noise" was contaminated by long-term signal autocorrelation characteristics, and as a result, his "random noise" had an autocorrelation length that was a significant fraction of the total reconstruction duration. That can introduce "small sample size" issues that will increase the chance that a spurious trend will pop up.

Furthermore, the eigenvalues associated with the "noise hockey-stick" principal components that McIntyre generated were *much* smaller than the eigenvalue associated with Mann "hockey stick" leading principal component (even with the assistance of the "hockey-stick" signal contamination).

Even though the PC's will be reweighted by the regression step (and those weights will likely differ from the eigenvalues), the eigenvalue magnitudes can often tell you **whether it even makes sense to proceed with the regression step**. If your leading PC has a small eigenvalue magnitude (as McIntyre's "noise hockey sticks" did), then that's a good indicator that there may not be much of a coherent signal to extract, and that it may not even make sense to proceed with the regression step.

So in summary:

1) McIntyre's "red noise" was contaminated with hockey-stick signal characteristics.

2) Even with the "hockey stick" contamination, the McIntyre's "red noise" hockey sticks were much smaller in magnitude than Mann's tree-ring hockey stick.

As a result, McIntyre's claim that Mann's method will take random noise and generate a hockey-stick that could be mistaken for a real climate signal is completely invalid.

 

Ooops.