The way he treats the endpoint of his dataset; the mean of his proxies peaks in 1966 and drops rapidly to the end in 1980 (see below):

1964 0.100322801
1965 0.142321563
1966 0.170178093
1967 0.157816547
1968 0.143066997
1969 0.130265684
1970 0.138856536
1971 0.151107518
1972 0.12857254
1973 0.113799023
1974 0.089954119
1975 0.05341691
1976 0.015333634
1977 0.003300072
1978 0.010647498
1979 -0.008464229
1980 -0.009645872

Hadcrut3 shows an increase of ~0.2ºC over the same timeframe (and ~0.6ºC to the present day).

The 30′s appear not to be very warm in comparison:

1932 -0.012193721
1933 -0.011362548
1934 -0.011035132
1935 0.035464209
1936 0.021091592
1937 0.01574397
1938 0.021998722
1939 0.017378256
1940 0.009740916

Based on that data I would say that Loehle represents the period 1930-1980 extremely poorly!

Steve M or anybody: what causes the Moberg-style graph in this post to have a huge uptick at the end and Loehle 2007 to have a downtick? Loehle 2007 seems to better represent the 1970s cooling, though the warm 1930s don’t seem to make an appearance.

Might I suggest the reason for you getting a red X?

Network filtering software is the likely culprit. Sites that are mainly for images are likely being blocked.

Upload yur stuff. Post. Easier than statistics. or dendroendrochronology

So says you.:) I kept getting a thing with a little red x in it. I’ll try again.

Whoops, ignore #13…

Steve, not to nag, but it would make the blog more intelligible for your readers if you preserved the original post numbers whe you moderate…

Steve: I’m sorry about that, but the mechanics of snipping and leaving is sufficiently more time-consuming than deleting and I’m already swamped that I’m going to do the quickest thing on many occasions. Sorry about that. If you suggest to WordPress that they add a Clear button beside their Delete button, I’d use it and preserve the order.

#17 Exactly why JEG said that the proxies should always be weighted according to the amount of instrumental variation they explain. If Ababneh bcp explains zero, its weight in the recon would be zero. If it’s 0.1, it’s weighting would be 0.1. Better proxies get more weight because they’re more credible.

The question is why are measurements of xyz a proxy for temperature–pick your favorite xyz. Your statement does not directly address the importance of distinguishing local variance from global variance.

A “teleconnection” to global variance requires intermediate physical processes which draw into question the linearity assumptions of the technique. Conversely, a correlation with local variance is meaningful so much as your knowledge of the local temperature is accurate.

Loehle claims no specialized knowledge of whether timeseries xyz is a proxy for temperature. He draws from peer-reviewed studies where others have claimed such specialized knowledge to identify and to calibrate the timeseries.

Consequently the variance-weighting approach JEG mentions has no direct meaning within the logic of Loehle’s approach which takes the calibrated timeseries as an a priori given. Thus, JEG’s suggestion is a gross misunderstanding both of what is taking place in the Loehle approach and in what is taking place in the MBH approach.