the only relationship I’ve seen between tree growth (or latewood density, I can’t remember) is a U shaped one, where there is an optimum temperature.

you actually mean: “the only relationship that I’ve seen between tree growth (or latewood density, I cant’ remember) and temperature is an inverted U shaped one, where there is an optimum temperature.”

My point is, as has often been noted here, tree rings will be thin in unusually cool (and dry) seasons, thickest in seasons where temperature (and moisture, and light, and whole lot of other variables) are optimal, and thin again at elevated temperatures, especially if drought conditions prevail and the trees are stressed.

Can you please confirm that that is what you meant.

I find the impact of CO2 concentration in the air to be the most interesting, btw, as that’s plant food. How do we get around CO2 causing temperature causing tree-ring width changes? It’s a cart before the horse proposition.

Mark

Now if we want to get into confounding factors and the like, then fine. But that’s a different case from saying there is no relationship.

Yes, I understand that confounding factors are an extreme headache (probably insurmountable). But even beyond that, the only relationship I’ve seen between tree growth (or latewood density, I can’t remember) is a U shaped one, where there is an optimum temperature. Now how does that help in using tree rings as proxies? If there is no linear response between growth (density) and temperature, tree rings are not suitable “thermometers.”

Mark, you and I are likely not so far apart. I just want to get away from the extreme of some people on this site who say that because we didn’t validate the proxies with instruments during the 1200s that we can’t look at the proxies.

That’s not what I’m saying, TCO. Not at all as a matter of fact.

The way the algorithm works is to calculate weights over a correlation period (I do the same thing with radar or communications signals). It is then assumed that the statistics that were present during the “training period” are the same over all time. Flaw #1 as we do not know this, though it is required for the algorithm to work. Then, they take the weights and recombine past proxies, still relying on constant statistics, and perform cross-validations. The cross-validations fail, likely because the statistics are not the same.

The method used by the so-called dendroclimatologists is to select their tree-rings based on current correlations (er, recent). This method suffers simply because we do not know for sure that the correlation that may be strong now, was also strong in the past. The fact that the r2 is low, and near zero, supports a conclusion that the correlation was not the same in the past.

Mark

PS: a more common method in my field is to solve the optimal weight equation w = R^-1 * p (I use Gram-Schmidt), where R is the sample correlation matrix and p is the sample cross correlation vector. These weights are calculated over short blocks to remove the correlated signals, typically noise from a jammer. Obviously, in climatology we want to keep the correlated signals, but the process is the same. We use short blocks in order to guarantee stationarity. If I ran the algorithm adaptively over time in a non-stationary environment, I’d never get a convergence on the weights, and my output would be garbage. This presents a problem in climatology as we are forced to assume the extraneous variables are constant outside of the calibration/verification period when in fact, they may not be. It is a conundrum I do not have a solution for.

Now if we want to get into confounding factors and the like, then fine. But that’s a different case from saying there is no relationship.

Mark, you and I are likely not so far apart. I just want to get away from the extreme of some people on this site who say that because we didn’t validate the proxies with instruments during the 1200s that we can’t look at the proxies. I mean…sure…in some sense that position is right…but if you take that position, you’re sticking your head in the sand and not looking at useful detective work.

As far as the issue of level of precip variation, I think this should be looked at during the instrument period. a. how much of a dependance was seen during this time? b. can one select trees that have less dependance on this (the Lamarch hypothesis). c. was precip variation over the period of the instrumentation period large or small? d. in the end, we may not know (without independant precip proxies or at least extra mixed ones), that some radical precip variation did not take place. But I think one can still do the analysis regardless and just note that a confounding factor was not controlled for. Much of Steve’s criticism does not depend on the issue of a confounding precip variable. They are issues even if we accept linear temp response. e. I would think that the bigger concern is CO2. We KNOW that those conditions are different from the proxy period to the calibration period.

The more I dig in to the tree-ring hypothesis (untested, therefore only hypothesis), the sicker I get about seeing it used as fact.

Mark

I don’t rule out a different system for different species/sites. but if so, you need a lot of good foundational work (more of it) to gibe with such an approach.

No 12. This is the crux of the problem in “dendroclimatology,” IMO. As you know, the whole basis for the tree ring-based temperature reconstructions is that tree growth and/or latewood ring density is positively correlated with temperature. And I’m very suspicious that there is no foundation for the whole premise.