I’ve not seen an example which works, and I’d think that if there were one, warmers would trot it out and make it well known.

If the data gathered by such a method didn’t support their conclusions, there’s no quarentee that they would “trot it out”. It might go into the censored directory, along with other inconvenient results.

Hey, I was just giving the theory. I don’t agree with it for various reasons including the same reason you ask about. And while there might indeed be ways to separate out the temperature signal from other signals, I’ve not seen an example which works, and I’d think that if there were one, warmers would trot it out and make it well known.

BTW, for people wanting to be taken seriously. It’s important to be able to articulate accurately (and dispassionately) the position of those you disagree with. If you can’t do that, take a break and work on your knowledge base until you can. If you present a position which is clearly not what your opponent believes then you’ll never convince anyone in your opponent’s camp. Further you’ll cause your allies to raise their eyebrows and downgrade your reliability.

OTOH, if you do state correctly and clearly what your opponent believes, your statements of why you disagree with that position will be taken seriously.

I believe that the theory is that the ring-widths contain a relatively high-frequency local signal, a low frequency global signal and some sort of noise of various sorts. The idea is to filter out the local signal and use the spatial coverage to attenuate the noise, assuming it will vary locally. You’re then supposed to be left with a good representation of the global signal.

Yes, I agree that this is the central point. I accept the idea that the high frequency local signal is a function of many variables like temperature, moisture, sun light, fertilization, etc.– or maybe more accurately, it a composite signal of several high frequency signals. And I think its very reasonable to assume that there is a low frequency signal that is global (there might even be low frequency signals that are local — but lets set this aside for now).

It seems to me that the low frequency signal is also a composite signal. So to convert trees into thermometers, you would need to remove the local signal, remove the noise, and then separate the remaining signal into its component parts and select the one that represented temperature variation. I could accept such a process. But how do you know which component to select particularly if there are multiple variables of similar strength? This really isn’t a rhetorical question on my part. I’m simply wondering if there is a workable method for doing this.

It is data selection to show a certain chart that the author had in mind from the beginning. Science is based on explanation and replication. This is certainly not that at all.

I believe that the theory is that the ring-widths contain a relatively high-frequency local signal, a low frequency global signal and some sort of noise of various sorts. The idea is to filter out the local signal and use the spatial coverage to attenuate the noise, assuming it will vary locally. You’re then supposed to be left with a good representation of the global signal.

If that is the case, then one should – with sufficient spatial coverage – be able to do the reverse. That is to say, extract the high frequency component (which is supposed to be local only conditions) and use these to generate a global average temperature using methods similar to GISS or HADCRU. If the theory is correct then the correlation to GISS/HADCRUT etc should be reasonable, right? Does anyone know if this has been tried? Does anyone want to try and do it? (sorry, it’s beyond my skills)