I don’t think that people entirely appreciate the absurdity of the views of Gavin and Rasmus that consideration of persistence in climate somehow "pitches statistics against physics". If climate scientists are seeking more familiar authority for just how preposterous this claim is, they need look no further than Trenberth , previously discussed on this blog here, which categorically asserts that many statistics will be seriously biased by autocorrelation. The forms of persistence discussed by Trenberth are less severe than the ones considered by Cohn and Lins and Koutsoyannis, but the persistence issue then becomes a matter of degree (rather than of "physics").
Here is an extended excerpt from Trenberth:
Climate is usually regarded as dealing with the average behavior over a relatively long time of the climate system and is not concerned with the daily fluctuations called weather… The focus of many climate studies is the difference between any climatic states that can be distinguished from the climatic noise. This “climatic signal” may arise from influences truly external to the climate system or it may arise from slowly varying modes of the entire climate system…Two other aspects are also important in time series analysis of meteorological parameters…(2) persistence, which gives rise to a lack of independence in the observations. Leith (1973, 1975) discussed the problem of signal-noise ratio in the predictability of climate and showed that the magnitude of the noise was related to persistence in the atmosphere…
However this climatic noise and the persistence, along with the finite size of the samples must be taken into consideration when computing statistics of the circulation or the resulting statistics may be significantly biased…
Trenberth pointed out that”many publications have failed to take note of the potential problems” and goes on to point out severe biases in the estimation of variances, covariances and autocorrelations. He concluded as follows:
“This paper has pointed out the need to take persistence into account in estimating population statistics from a finite sample”.
It’s pretty hard to see a more on-point and more categorical refutation of realclimate’s view of the statistical issues involved with persistence. I’ll probably leave these matters alone for a while, but will conclude by pointing out the following from the Climate Analysis Group, University of Reading:
Climate by definition is the statistics of weather. It therefore makes a lot of sense that climate researchers know something about the important subject of statistics.
It’s too bad that so many climate scientists, who hold themselves out as authorities, actually have such sketchy knowledge of statistics and that, despite this, have generally failed to involve statistical professionals in their work.
Reference: Trenberth, K. , Some effects of finite sample size and persistence on meteorological statistics. Part I: Autocorrelation. Monthly Weather Review, 112, 2359-2368