Roman on How Hockey Sticks are Made
Posted by Jeff Id on September 29, 2009
There is a small discussion hidden in the latest massive thread at CA. TomP made the suggestion that correlation and elimination of non-temperature correlating tree ring data was perfectly ok with him. For those that are a little math savvy and are interested in the problem that creates read my hockey stick temperature distorition posts above. These posts reiterate the conclusioins of VonStorch04 and IMO are much more plainly worded and provide turnkey software code which anyone can run to replicate my results. However, sometimes I read things from other bloggers that just hit the point a heck of a lot better than I’ve written it. This comment by Roman is an excellent example.
If you ever wanted to know how so many papers make hockey sticks, the sorting operation described below is the reason.
The first point quoted is made by TomP.
Rejecting the Schweingruber series [as originally presented] as a good proxy seems reasonable, unless there are doubts about the instrument record. Why it is not a good recent proxy is an important but separate point.
I don’t believe that you have considered the full implication of your argument on the entire reconstruction.
Let’s suppose that you are right and that there are real treemometers which you can identify by comparison to an observed record. Your statement above indicates that there also trees that are not good proxies. Unless, false proxies are a recent phenomenon, the logical conclusion is that there must be a collection of these distributed throughout the entire time period prior to the start of the temperature record since there is no way to identify and exclude those proxies that are not good (unless you know that of course there was no MWP).
So what effect will this have on the reconstruction? Having only good ones in the modern era, we will see that the temperatures have been warming, but we already knew that. The effect the good proxies will have on the early part of the reconstruction will be merely to center it at a particular level of dimensionless chronology units. It will have little or no effect on the quality of the results prior to the actual time at which these known to be good proxies existed.
When we reconstruct the early portion, we will have a mix of good and bad proxies at most time periods. Steve’s sensitivity test shows that the result of including “bad” proxies is to flatten the reconstruction – even in the merged case, the difference is as large as a full unit, of the same order of magnitude as the range of the entire original reconstruction prior to 1800.
The amount of flattening will depend on the relative proportions of good and bad proxies, but the net result will tend towards a hockey stick shape. Any error bars constructed from the fit will seriously underestimate the bias created by the false proxies. Without knowing how prevalent bad proxies are, there is no way to adjust for this bias.
What if the choice of modern proxies is just opportunistic matching to the temperature record? Well, then you get a … hockey stick. But that’s another thread.
This is exactly right and the more data you sort the stronger the hockey stick effect. In Mann08 1209 individual series were sorted. The high series count results in a very flat handle as the noisy data average out and a strong pre-sorted blade on the end. When each series is individually scaled for best fit to temp, the handle of the stick is straightened even further. What drives me crazy is smart people like Tom fall for the ruse – after all, why would we include data which didn’t match temp.
These PhD’s are good at math– why can’t they figure out what I did within a minute (probably seconds) after reading it the first time. I remember trashing the CA thread with comments and staying up until 3 in the morning late last August reading and thinking about the huge implications for climate science. Hell, at the time I was just trying to figure out how real this global warming scare was.
Anyway boys and girls, in the stock market you can’t choose your stocks after the trading day ends. Likewise in science, you don’t get to choose which data fits your conclusion after you collect it.