realclimate today has a post How Red are My Proxies? which is so weird it’s worthy of Rasmus. (Note: see lsubsequent comment here). They discuss the autocorrelation properties of North American tree ring proxies, something about which I know a lot. They say:
Using data from the North American network of seventy sets of tree rings extending from 1400 to 1980 you obtain an actual one-year AR1 mean autocorrelation factor with a value close to 0.15 (the exact number depends on the proxy series and time period chosen but is always less than about 0.3).
They are nuts. Here’s a histogram of the AR1 coefficients of the 70-series MBH98 tree ring network which we archived in a readable table in connection with our GRL paper. I’ve included a short R script here to calculate AR1 coefficients. The mean autocorrelation was not 0.15, but was 0.4. Out of 70 AR1 coefficients, only three were less than 0.15 and the mean was 0.4. The range of values was from 0.03 to 0.79. Tellingly, the highest AR1 coefficents all belonged to bristlecones.
But it’s even worse than that. If you model the series as ARMA(1,1), the AR1 coefficients increase dramatically with high negative (and nearly always) statistically significant MA1 coefficients. Many of the AR1 coefficients now become close to 1- random walk levels, especially the bristlecones. The statistical properties of this type of series – high AR1 and negative MA1 – are trickier than people think. I’ve posted up notes on them by Ai Deng for example.
I have no idea how realclimate got their results. Their whole post looks completely goofy to me.
The other salient point – and we included this histogram in our Reply to Von Storch discussed , is that the tree ring series in this network have virtually no correlation to gridcell temperature; many of correlations to precipitation and of course the bristlecones have a correlation to CO2 levels.