I worked in a dendrochronology/dendroclimatology lab[1] for several years and can assure you that the researchers involved are well aware of the issue of multip

I worked in a dendrochronology/dendroclimatology lab[1] for several years and can assure you that the researchers involved are well aware of the issue of multiple influences on tree ring growth. There are a couple of ways that they go about dealing with it. NB I was more involved in precipitation reconstructions than temperature reconstructions, but I know enough about the process to discuss it here. The first is experimental design, much like is necessary in any aspect of science: Annual tree growth is definitely a response to a variety of regional, site-specific, and tree-specific factors for any given species in a location. So in order to increase the 'signal to noise' ratio for a given signal (temperature, precip, fire, disease or whatever reconstructions), you look for combinations of species and sites for which the factor you want to study is the limiting growth factor; in other words, other things the tree needs to grow are present in abundance and one is in shortage. If you want to make a precipitation reconstruction, you find a site that has very little soil moisture storage, such as trees basically growing out of fractures in steep bedrock. If you want to study temperature, you pick species like Bristlecone Pine that grow at high elevations (which are typically relatively wet), so that the temperature/length of growing season is the annual limiting growth factor. That sort of thing. The second is that researchers use many other forms of measurements than simply ring width to tease these problems out. For example, many people use oxygen isotopic ratios[2], latewood (the dark outer part of the tree ring) density measurements[3], and other measurements in tree rings, both because of the unique and accessible response of the tree, and because (unlike, say, sediment cores) the tree rings are much more quickly, accurately and reliably dated at the single-year precision. Third, there is a lot of statistical analysis thrown at the problem. I didn't work a lot with this, or see people in my lab do it, but I am aware of researchers doing multivariate statistics techniques such as principal component analysis[4]. I'm sure there is a lot more, but you can do a Scholar search as easily as I can. Fourth, one of the strengths of the multiproxy technique is that many of them are independent, and they all show a lot of correlation to the temperature records during the instrumental calibration period. There is some divergence before this period, but not as much as would be expected otherwise. There is also the problem of weighting, like you mention, and I don't think anyone is comfortable with it but I don't think it's as ad-hoc as you make it out to be. A review (by a statistician) that discusses this is here[5]; I didn't read it in depth. [1] http://www.uark.edu/misc/dendro/ http://www.uark.edu/misc/dendro/ [2] http://link.springer.com/article/10.1007/s00382-013-1674-3 http://link.springer.com/article/10.1007/s00382-013-1674-3 [3] http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00139.1 http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00139.... [4] http://onlinelibrary.wiley.com/doi/10.1111/j.1600-0706.2010.18293.x/abstract http://onlinelibrary.wiley.com/doi/10.1111/j.1600-0706.2010.... [5] http://www.stat.unc.edu/postscript/rs/Sensitivities-Submitted.pdf http://www.stat.unc.edu/postscript/rs/Sensitivities-Submitte...