That graphic says it all for me. The variation between those time series is greater than variation within them. For me it just makes it all noise.

Mark

The data that Jan Esper used are ONLY ring-width. Again, as I have told you in earlier e-mails, maximum density and ring-width data are doing quite different things in this region of the world. However, they compliment each other very nicely when combined using multiple regression to develop a reconstruction. More discussion on this is detailed in an earlier paper:
http://freespace.virgin.net/rob.dendro/pdf/Wilson%20and%20Luckman%202003.pdf

I didn’t pick up on the fact that Rob’s citation here stated that the RW and MXD series were “naturally orthogonal” in this article, so it’s pretty obvious in retrospect that the fit is going to be “improved” merely because of the orthogonality of the RW and MXD series, regardless of anything else. In the post in question , I presented the following graphic showing differences between the Esper Alberta data, which I had only just obtained and the Luckman-Wilson reconstruction, which is shown again below (I observed at the time that the differences between the two versions at merely one site seemed inconsistent with Mann’s confident claim of knowing past temperature within 0.2 deg C):

When we re-visit this particular graphic, here’s another take on this in light of subsequent experiments: the greater low-frequency content of the Esper reconstruction here is undoubtedly due in considerable measure to the fact that he “ONLY” used RW data. Rob’s OLS regression onto two orthogonal series (RW and MXD) surely has the impact of attenuating low-frequency content. It’s hard to say how much attenuation takes place. These issues are quite independent of whether RW and MXD are measuring different months and are mathematical in nature.

Condition____RW_____MXD
Hot_________small___high
Cold________small___low
Good________large___moderate

Since the assumption is that these are treeline sites, temperature limited, the two most common conditions would be Cold and Goo. I can’t see how that is orthogonal … which may be my own lack of understanding.

I also didn’t understand Rob’s post. He says it makes sense that they are orthogonal, since the times of significant correlation only overlap in June. He also says that he was surprised that they were orthogonal. But the correlation over the entire summer (May to August) is what they are looking at, and I assume that there is a positive correlation (although not significant) in the other months for both variables. I say this because Rob says:

The correlations of the MXD and RW chronologies with May-August temperatures are 0.64 and 0.39 respectively.

which is higher than the correlation with the individual months.

w.

Is it your position that all temperature reconstructions from ring-widths are useless?

I try to avoid generalizing and “useless” is not a word that I usually use. I try to discuss particular studies as much as possible. The tree ring information is a vast and interesting data set. I think that one can be critical of existing statistical practices without saying that all the information is useless. I thought that Naurzbaev et al 2004 and Miller et al 2006 were interesting and novel applications of tree ring data to temperature reconstruction. We brought these studies to the attention of the NAS panel, which, in turn, cited them both favorably.

I have a much better impression of the precipitation reconstructions than the temperature reconstructions, but I haven’t studied them in detail. Cook, Woodhosue et al, a precipitation reconstruction, had good verification r2 statistics as well as good RE statistics – so that gives me a much better impression.