@ Pat Frank

“I haven’t the paper here just now, but have read that the basis for screening tree ring proxies against local temperatures is that the response of trees is immutable, so that finding a tree that tracks temperatures now implies it has tracked temperature over its entire lifetime.”

Not actually knowing anything about tree ring proxies other than what you said above, my going in assumption has always been that there are so many factors affecting tree ring growth that with regard to temperature they are essentially white (or some color) noise.

As such, if you select a whole bunch of trees and compare a subset of their rings to actual measured temperatures you will inevitably find a tree whose rings are highly correlated with the temperature record. And a whole bunch in the same general area whose rings are not.

How a climate scientist can postulate that ALL the rings of a tree with a subset of rings that are highly correlated with a relatively short thermometer record are equally well correlated with historic temperatures is a mystery to me, given that there are presumably a lot of trees in the same area whose rings are NOT highly correlated, but it is apparently SOP.

I am not technically qualified to reject the idea out of hand, but it sure appears problematical to me, especially since the resulting ‘historical record’ is being taken as gospel and used to justify enormously disruptive political actions.

]]>Wide error bars and a bit of wiggle can’t hide the blade that breaks out to form a hockey stick indeed:

I appreciate the clarification about Antarctic cooling, so this reminds me of Steig too except now the Peninsula has been extended all the way up the coast of South America.

]]>Update from Dennis Bray over there:

“The full raw data set in Excel format with code book is now available at https://www.academia.edu/7454421/CliSci_2013_Data_Set_Excel_Format_with_code_book. The report of descriptive statistics can be found at

https://www.academia.edu/5211187/A_survey_of_the_perceptions_of_climate_scientists_2013

It would be appreciated if any user would share their findings on this blog.”

I agree. But when you talk to a so-called expert they have multiple reasons why “it’s more complicated than that” and therefore common sense effectively doesn’t apply. So having a real but disinterested expert validate common sense is always helpful.

]]>It is puzzling that there are statisticians and/or those who are familiar with statistical techniques and methods that coauthor climate science papers and thus it is difficult to believe the problems with climate science as shown in this thread results from a dearth of people in the field with a lack of statistical backgrounds.

Could it be that these people lack the courage to speak out or can it be that even statistically minded people can have a blind spot when it comes to these selection processes used in temperature and index reconstructions?

]]>I collected all the Abram paper data for the SAM index and Proxies from 1957-1995 into Excel and then into R for my calculations. I standardized all these series by subtracting the series mean and dividing by the standard deviation of the anomaly series. I detrended the series and modeled the residual series for the best fit to an ARMA model by aic score and avoidance of ar and ma coefficients too close to a unit root. The models tested were ARMA(0,0), ARMA(1,0), ARMA(2,0), ARMA(1,1) and ARMA(0,1).

I used the models (red/white noise) from the SAM index and 25 proxies to simulate and calculate the correlation between the SAM index simulation and each of the 25 proxy simulations and the corresponding p.values. From the correlations with the 5 highest r values, I made a weighted composite by the value of the correlation by the method used in the Abram paper and calculated the correlation of this composite with the SAM index simulation. I recorded the 100 r, p.values and the absolute value of the r values. I also used various ranges of trends selected randomly for the proxy simulations. I found that the trends did not change the results. The average absolute value of the correlations was 0.54 and the standard deviation was 0.075. The p.value average was 0.002.

These results show that a selection routine as used in the Abram paper could take series of pure white/red noise and find a composite of 5 selected proxies that results in a significant correlation of this composite with a white noise reference or white noise reference with a trend with a 95% CI range that could approach r=0.69.

Finally I looked at simulations as above except here I drew the 5 best correlations from a selection of 100 simulations instead of 25. I did this to demonstrate how using 25 proxies as in Abram, that well could have been selected from a larger population, can affect the composite of 5 correlations. In this case I obtained an average r=0.65 and a p.value=0.00006 with 95% CI range for r of 0.54 to 0.76.

I am in the process of sending my R code, results and input data to SteveM.

]]>http://klimazwiebel.blogspot.com/2014/06/misrepresentation-of-bray-and-von.html?showComment=1403506008877#c3987302369502607948

“@ Mike R

I would be happy to send the raw data and the code book to anyone that makes a request. Simply send the request by email: dennis.bray@hzg.de. I don’t have time, however, to calculate crosstabs for the entire data set, not at this time, anyway.”

As I mentioned over there, I’m not competent to do it, but I would like to know if the same scientists who are skeptical on one point are skeptical on all, or if a much larger group of scientists are skeptical on at least one critical component of CAGW. Would anyone here like to look at the raw data?

]]>Thank you, Charles. Medical distractions happening here. Apologies.

Geoff.