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All of the stations showed warming, but only 17 out of 106 locations had trends which cannot be explained as arising from intrinsic climate fluctuations when tested against any of the three null models.

This is a very interesting article, at least in terms of its statistical models. I am not aware of anyone else in all of climate science who has been testing models with this level of sophistication. That the article exists at all is a very good sign for the discipline.

However, there is a glaring problem with their selection of data to test. They statistically test each station individually for warming, while observing that all of them display warming. To see the problem, imagine doing the same for household income during a recession. By its very definition, one would expect that the average household income would be declining during a recession. But they don't test the average. Instead, this team (in effect) tests each individual household for a trend. Many of the individual households show no significant trend. So what? It's still a recession.

I guess I just don't see the point of all of this effort. Global warming is global, by definition. How many of their stations did not show a significant trend merely because their time series were too short? The article is silent on this crucial question.

Still, I am extremely pleased that they are exploring the use of some of the more exotic statistical models in the toolshed. These are "exotic" only in the sense that they were developed only over the last thirty years, and therefore are unfamiliar to many scientists. Mathematically speaking, they are both sound and informative. They need to be used more often. Done properly, they will shed a lot of light on the temporal characteristics of climate data series.

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