Id Goes Mythbuster on Hockey Sticks- CPS

This post was prompted by a comment by Steve McIntyre at Climate audit. He suggested the use of CPS in my calcs and explained how it works. It is of course important to use the same methods as previous papers to demonstrate the distortion of historic temperatures created by these methods. So I did.

I used the same Northern Hemisphere reconstruction as in my previous post.

Demo of Flawed Hockey Stick Math Using Actual NH Data

A graph of the signal and the calibration range is below.

The red portion in the graph above is the calibration range used. The total graph is the signal stated by Mann08 to be the real NH historic temp within a margin of error. In this example we assume that M08 got the temperature exactly right, insert it into 10,000 red noise proxies and like hound dogs we go look for it with the same methods Mann08 used.

I generated 10,000 series of random red noise data 2008 years long which averages to zero just by its nature. I then added the above northern hemisphere reconstruction to the data so the average made the graph above. The graph is in fact an average of the 10000 series rather than a direct graph of the original data, its accuracy can be confirmed by the 0 to 200AD year tail which has a zero signal because the M08 paper didn’t have any data for this period.

In my last post, the proxy data was calibrated by matching the slope and intercept of a line fitted to each proxy to the slope and intercept of a line fitted to the calibration period. The CPS method matches the standard deviation and mean of each proxy in the calibration period to the standard deviation and mean of the targeted temperature series. This method is employed in this post.

Astute readers will note that this is also scaling and offsetting the data according to a linear method just as my last post did. I don’t care what method you use, eiv, cps, pca whatever these guys can think of. Sorting and discarding data according to a specific period guarantees distortion of the result. In the case of CPS, the distortion is the of the same shape as the other varieties of linear calibration examples I have shown.

The method for cps is

Calculate Standard deviaton and mean for (red) calibration period in the above graph which represents in this case known temperature.

Calculate r correlation value for all series. Higher r means better correlation – new readers.

Calculate and match all series standard deviation and mean in the same calibration period to the standard deviatoin and mean of the calibration range data in the abover graph.

Average the proxies with r values greater than the threshold together.

The first graph uses the above process to sort for r values greater than 0.1 — 3399 series had r >0.1.

The blue line is the original signal which is in this case perfectly known to be the signal we are looking for. By sorting for all proxies with an r > 0.1 and calibrating using the CPS method we get the purple/pink line above. The graphs are not the same clearly, but what is more important to notice is the 1850 to 1995 calibration data fits very well between the graphs, while there is clear demagnification of older historic data. Since the true signal in the data is the blue line we can say that the historic data is not on the same temperature scale the calibration data.

Ok, for those familiar with correlation your first thought might be to increase the r value. After all better correlation matches the calibration period better. Let’s see what happens when we increase the r value to 0.8.

As you can see, the data in the 1850 to 1995 calibration range fits very closely. What happens to the historic data pre-calibration range data is an even more pronounced demagnification and offset of the data. The true representation of temperature actually got worse.

For those who have read my earlier posts, the true zero temperature is shown in the yellow line from 0 to 200 AD. In addition to the strong demagnification of the signal, the graph has an offset of about -0.1 degrees.

What happens if we go extreme and sort to a correlation of r> 0.9.

Again while the fit to the 1850 – 1995 calibration range improved, the historic signal distortion is even worse than before. Both the offset and deamplification are greater. Higher correlation is not the answer.

The next graph shows rather dramatically what happens if I use the same CPS methods on data with no signal whatsoever. There is no real temperature added to the red noise.

After CPS, the purple line below has a clear uprise in temperature in recent times. There is no real temperature in this purple graph, it is 100% distortion.

Conclusions:

I again put a known exact signal in the data, applied reasonable noise and employed the CPS method to recover the data. I was again unsuccessful in creating the original graph.

It doesn’t matter which method you use to sort data by a trend. It is simply not possible to perform sorting based calibration without creating distortions. This is a fundamental problem in the methodology used in paleoclimatology and is a basic understanding of serious scientists around the world.

If these guys really invented multiple magic methods for extracting signal cleanly from noisy data why isn’t it used throughout other sciences? Also, why don’t these geniuses try their methodologies on a known set of data before they publish?.

I don’t think I need to answer that.

Myth busted!