Steve: I’d be more inclined to say that this shows that this stuff doesn’t show anything.

ROTFL. Money well spent!

]]>(Chuckle). In my career times, we banned geological statements that commenced “It all depends…” and also mathematical explanations that started with a written triple integral before speech.

The serious answer to yopur question is that I want accepted proxy methods to have valid error ranges that overlap with a good degree of confidence; a scientific attempt to explain isolated, unexpected outliers; a resolution of detail commensurate with actual rather than interpolated sampling frequency; proxies that do not drift unexplainaby from each other with the passage of time; a calibration statistic that exceeds the variance of the test period; a demonstration that the proxy is different to noise; and a quantification and sensitivity test of the most likely hypothesised or actual interferences.

That’s just a start, but it is not unusual.

]]>6 Geoff Sherrington says:

August 10th, 2008 at 6:22 am

The answer is dependent upon how much resolution of detail you want within your millenia of samples.

]]>Re boreholes, the Russians did a lot of temperature work on the Kola Peninsula and found difficulty in both measuring and correlating boreholes close together. IIRC, they did not recommend climate temperature reconstructions.

Where does increasing sophistication in statistics meet altered sensitivity in reconstructions of proxies in general? Are we there yet?

]]>Craig, you’re preaching to the choir. The underlying assumption that tree rings widths/densities are linear in annualized temperature (after age correction) over 1000 years is an extraordinary claim. Yet Mann and others make this assumption about their proxies without proof. Other climate scientists including NAS appear to agree or doesn’t care as Mann et al gets the “right answer”.

You’ve suggested an interesting point about “a purely physical process like boreholes or something.” I will suggest that the readers consider an inconsistent linear set of equations Ax~B, where A is an ill-conditioned matrix to the point that the max to min singular value ratio is greater than 1e7. Would anyone suggest that the solution for x be unique or the “correct answer”?

For the borehole temperature reconstructions of Hugo Beltrami and et al, B is the vector of borehole temperatures vs depth, the elements of x consist of the temperature reconstruction plus a slope and intercept, and the columns of A are generated from the heat conduction physics equation. Noting again, that the A matrix generated from the physics equations is ill-conditioned. Seems like the physics are suggesting something??

In the borehole literature, the x or temperature reconstruction is solved by performing a truncated least squares fit (svd psuedoinverse) and throwing out singular values until (drum roll) the hockey stick is generated. These results have been in peer reviewed literature for 20 years. Recently, to avoid the appearance of just throwing out singular values, the latest literature performs a ridge regression (RegEM) where singular values from the ill-conditioned matrix are thrown away such that they “optimally” trade off the norm of the residual for the norm of the x vector. Not clear why anyone would think that this answer is the “correct temperature reconstruction” or even that this is the correct answer plus noise. Now, if I was determining the “optimal” feed recipe for my chickens, one might have something.

]]>**Steve:** I’d be more inclined to say that this shows that this stuff doesn’t show anything.