This is a follow up to my post on Dr. Craig Loehles paper on divergence, The 800lb Gorrilla in the Hockey Stick’s Locker Room. Dr. Loehle demonstrated what will happen to historic temperature trends in tree ring data if we have a non-linear system, citing several papers which refer to an inverse quadratic relationship of tree growth to temperature. Inverse quadratic means that as temp warms, trees first grow more to some limit where additional temperature has the reverse effect, slowing growth. This would explain why tree ring data has so little up or down movement in history and also why extreme tree ring widths are not found in nature (the hockey stick handle).
What we need to understand first is that any noisy signal sorted by correlation is distorted into what you wish to find. In this post I use R to generate ARMA matched tree ring proxies, I inserted an inverse quadratic signal in the proxies and then went looking for it.
Continue reading “Straightening the Hockey Stick Blade” →
Divergence is a serious problem in tree ring climatology reconstructions. Basically divergence means data which years ago vaguely correlated to temperature, diverged from temperature in recent years. So trees which used to be thought of as good thermometers — aren’t. This doesn’t stop scientists from using them however but some creativity in handling this inconvenient data is required. For instance, Mann 08 chopped off the divergent parts and pasted on his own non-divergent data.
In September 2007 Dr. Craig Loehle published a paper on the non-linear nature of tree growth. He holds a PhD in Mathematical Ecology and has published more than 100 papers in applied mathematics and ecology. In short he is the closest thing to an expert we could hope for on tree growth response to temperature. This paper is available on line at the link below.
Continue reading “The 800lb Gorrilla in the Hockey Stick’s Locker Room” →
It’s good to be back. In China the internet is amazingly slow for any websites which might have political meaning due to intense censorship. The censors err on the side of caution. While I wasn’t blocked, after several ten minute logon times to my own blog I gave up posting. However, China’s multi-billion dollar internet censorship program allows my site but our left wing American blogs (Tamino or Real Climate) won’t allow on topic posts which dispute their claims – think about that one.
Continue reading “Comparitive Criticism of Temperature Reconstructions” →
Ok, sorry about the delay in posting. I have been trying to recreate my correlation C++ software in R and it takes about 3X longer while I’m learning.
This post lets everyone see how the hockey stick CPS graphs are made. You can put your own patterns in and see how the actual highly random data which is not temperature can make any graph you want and can fool people into thinking it might be temperature.
If you don’t want to do it yourself you can see this link below.
I made an R script which generates plots like the one below. R is a open source FREE programming software which is easily obtained through a google search for “R”
Continue reading “Build your own HS, Using the Same Data and Math as Michael Mann” →
What happened next? As I discussed in part 1, after realizing what was happening I asked Mann or Gavin at Real Climate for an explanation. In my now old post below.
How to Make a Hockey Stick – Paleoclimatology (What they don’t want you to know) It’s cute how green I was back then, in September. My first comment deletion by Real Climate mom would be proud.
I bring up this post because of a comment I made at the bottom
Continue reading “Hockey Stick Graphs a Series of Epiphanies (Part 2)” →
Ok, it’s time to summarize what I have learned from exploration of proxy data. I went back to Climate Audit and found the exact moment that I learned how hockey sticks were made.
You can see my dm little mind working trying to grasp what has become so obvious now. My first post:
Continue reading “Hockey Stick Graphs a Series of Epiphanies (Part 1)” →
I do love hockey, it is by far my favorite sport. Probably half of my Canadian readers will stop reading when I say that I am a huge Red Wings fan.
If you came here looking for a NHL discussion though, I apologize this post is about how the false mathematics behind the most famous hockey stick temperature curves.
This post like many others here is in response to a request. This time instead of odd linear curves I used GISS (ground instrument) temperature with the 1357 M08 proxies before infilling. Which means “as close as I can get right now to the real data.”
This post clearly shows that actual HS data can be made to support any conclusion. Hockey stick graphs are not temperature whatsoever but are in fact representations of the statistical distortion created by mathematical sorting of random or near random data.
This entire post is actual proxy data from M08. I used CPS methods recommended in the latest hockey stick paper and many before it to produce these graphs.
Continue reading “Famous Hockey Sticks in History” →
If this is your first time here recently see this article Will the Real Hockey Stick Please Stand Up?
The first graph of this post is the final result after the 1209 series were magnified and averaged together. Vertical scale is SD units calibrated from 1850-1995. This is my first post which is based on R software.
The point of this post is just to see the types of data going into the latest hokey hockey stick and what shape the groups provide. If you look at all the graphs after the first one, you might see components which add up to the shape of the final graph. Also, you will notice shapes which you can’t find. The fact that you cannot find certain valleys and peaks means very clearly that individual proxies had substantial influence in creating historic trends.
Continue reading “Building a ‘Hokey’ stick” →
I won’ t abuse you with my venting today. Mostly pictures. Pretty interesting ones I think.
I used the CPS method today with actual proxies from M08. Instead of the infilled data with the fake hockey stick glued on the end I used the original original 1357 series before processing. I also chopped off the 95 Luterbacher series which aren’t really proxies, they are temperature. It didn’t make any big difference to the shape of graphs in my results but I don’t like em.
Continue reading “Will the Real Hockey Stick Please Stand Up?” →
I ran across a link to a letter from Michael Mann to some magazine or website on CA. It sent my blood pressure through the roof. Those who come visit my site know the fallacy of the hockey stick well by now. This is the link HERE.
What gives me some peace though is everyone who has looked, knows his work will fall the way of the flat earth in time. He will be remembered not as a great intellect of our time but rather as a sloppy scientist who had a huge impact on world government.
I deleted the post because I agree with Chris H, Demesure and the others below. Besides this guy is going to discredit himself, I don’t need to help him. In the future I will redouble my efforts to keep my irritation in check but trust me, it’s still there.
Ok, just a quick post done at the suggesion of Craig Loehle. He asked what would happen if we use CPS without r sorting. This led to my breaking down the distortion into two modes. One mode is the r correlation sort, which is well known to create a HS out of most anything yet is still used. The second mode is created by data scaling. This led to some revealing graphs.
See my previous post if you haven’t seen the background of this experiment.
Id Goes Mythbuster on Hockey Sticks- CPS
Same data as my last post 10,000 random series using CPS sorting.
Continue reading “Breakdown of Hockey Stick Distortions in 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.
Continue reading “Id Goes Mythbuster on Hockey Sticks- CPS” →
I got a bit of a surprise while continuing my calcs using red noise. This time I used a real reconstruction from the NH data.
Last night I spent about 5 hours on more red noise plus signal analysis. I found some incredible things regarding the methodology of statistically sorting and calibrating proxy data. While others have examined and published papers on how hockey sticks can be created from red noise, I am taking this to the next step. What I have done here is to impart artificial known signals in the red noise and go look for it.
Last night I expanded that work to put in my red noise data the CPS northern hemisphere composite data from Mann08 and then used a similar methodologies to his to go look for it.
If you are not familiar with my other work go here first.
Simple Statistical Evidence Why Hockey Stick Temp Graphs are Bent!!
Below is the one curve M08 provided as a result from his proxies.
This is one continuous curve but I colored the end red to show the 1850-1995 calibration range used below in all calculations. This curve is actually an average of 10,000 red noise graphs with the signal added in. It is a near perfect match to the original and can be verified by the quality of the tail from 0 to 200 AD. There is a 0 signal and the average of the red noise is basically 0 here.
Continue reading “Demo of Flawed Hockey Stick Math Using Actual NH Data” →
We found out last week that Shweingruber MXD series had some very unusual things done to it. Series were cut off at 1960 and new fake data was pasted on the ends. The fake data of course correlated well to temperature and over 90% of the series passed correlation. The comments regarding the data warned that data after 1960, temperature records were mathematically incorporated in the series.
The values after 1960 are a combination of information from high-frequency MXD variations and low-frequency instrumental temperature variations. We recommend, therefore, that the post-1960 values be deleted or ignored in any analysis that might be biased by the inclusion of this observed temperature information, such as the calibration of these data to form a climate reconstruction, or comparision of these data with instrumental climate observations for the purpose of assessing the ability of these data to represent temperature variability.
I got this gorgeous quote from Climate Audit which was copied from the original data site. What they are saying is don’t correlate the data because you may accept too much of it, leading to criticisms. However, the added temp info didn’t really help too much.
The line to the right shows the difference between the actual series data and the data used. The blue line to the right of the yellow DOES NOT EXIST because it is faked in using a BS statistical process. The purple line is the actual data with the information to the right incorporating temperature to try and help it out.
Continue reading “MXD Tree Removal Service (Choppin Wood off A Hockey Stick)” →
This is a continuation of my posts on selective sorting of data and why it absolutely is a false representation of temperature. If this is your first time on this subject start at this link The Flaw in the Math Behind Every Hockey Stick
Paleoclimatology uses a statistical sorting technique to generate hockey stick graphs which demonstrate to the world that our current temperature has a higher upslope than any time in recent history. This is yet another demonstration of how that conclusion is false!! The data used in this post is random, a known temperature signal has been physically added with a + sign to the random data
As promised before but a bit late, I have worked on some red noise examples to explore how variations in signal noise affect the overall weighting of temperature in hockey stick style calibrations.
Continue reading “Simple Statistical Evidence Why Hockey Stick Temp Graphs are Bent!!” →