Pattern Recognition

Scientists pride themselves in the ability to tease informative patterns out of masses of data. And with good reason -- that skill (or aptitude) is one of the traits that leads to insight, and thus publications and professional success.
I don't believe that gazing at "spaghetti graph" reconstructions is the best way to evaluate whether or not the Tiljander data series were used correctly in Mann08 (for links to referred-to papers and posts, see here). That's a question that's better answered by reading her paper (Tiljander03), getting a feel of what her data looks like (graphs here), and thinking about the physical meaning of the varve characteristics that go into "XRD," "lightsum," "darksum," and "thickness."

By weaving these threads together, we can figure out the solution to this puzzle:

Can the Tiljander data series be meaningfully calibrated to the instrumental temperature record, 1850-1995?

The answer is No.

There might be a way to indirectly achieve such a calibration, which was the approach that authors of Kaufman09 took with XRD after belatedly coming to grips with this problem. But there's no feasible direct approach, of the type used in Mann08 and Mann09.

This has proven to be a very contentious point. But there's no good reason it should be seen as such. Truly contentious questions have strong arguments on each side of the issue. The defenders of Mann08 don't even argue for "Yes," but rather for a stance akin to "I don't know, and it doesn't matter."

That's silly.

Knowing that the Tiljander data series were massively contaminated by non-climate signals in the 19th and 20th centuries, we can look for patterns in the reconstructions presented in Mann08 and Mann09.

Let's consider a few cartoons.