Back from a very pleasant Christmas.
A little while ago, I threaded an interesting comment by UC on Tamino’s criticism of Schwartz. As a blog management aside, I like having this kind of thread by others as it was a good comment and it was based on a careful analysis of third party literature. I have no problems threading material like this for discussion as it’s something that’s relevant and analytic and any subsequent discussion was on a relevant thread. My problem with the “thermo” discussions is mostly that too much of it has been too often unfocused, unrelated to primary literature and on irrelevant threads or unthreaded. I wanted to limit the topic until there were thread quality analyses.
Lucia has written a further analysis on the topic, which she’s posted at her own blog here. I’ve transferred some discussion from Unthreaded to the comments below.
Lucia builds on UC’s earlier analysis by making a distinction between how two quite different kinds of error can affect estimation of response time based on temperature data. One type of error occurs from making time averages of temperature data; a second time of error occurs from measurement errors in the temperature data.
Lucia observed that the Tamino-RC analysis argued that the Schwartz analysis was confounded by the first type of error (time averaging). Tamino produced a graphic showing that the GISS and simulated data did not match – presenting this a gotcha against Schwartz. Lucia shows that the effect of this particular class of error does not match the situation: she observes that this would yield a positive intercept for the intercept of time vs log(autocorrelation), whereas the actual result is negative. She observes:
If I were to plot Ln(Rθ) or a physical system with a linear response, that has been measure imprecisely, lack of precision in the measurements results in a negative intercept for the linear regression.
She observes, on the other hand, that the GISS situation nicely matches UC’s plots, thereby suggesting that measurement errors in the temperature data, rather than time averaging bias, accounted for the observed patterns.
My own feeling (and it’s not more than a feeling at this point) was that you couldn’t lean very heavily on response times calculated from the autocorrelations, regardless of whether one liked or disliked the answer.
Prior references on the topic include: Schwartz article; Tamino’s guest post at RC; Lubo comment, UC’s comment here, my report on Schwartz at AGU. In addition, Scafetta and West also consider response times from quite a different point of view (and are criticized by Rasmus at RC.)