A reader recently asked about a post on WUWT which, to everyone’s shocking surprise, claims to contradict man-made global warming. It claims that temperature records (at individual stations) follow step changes rather than linear increase, so the theory of anthropogenic global warming (AGW) is wrong.
The idea is silly. For one thing, greenhouse-gas theory doesn’t imply that temperature must follow a linear pattern. Particularly during the period of focus (1960 to 2010), claiming that “Warming during 1960-2010 was clearly a non-linear process” is simply stating the obvious, whether one is talking about “at station level” (as the blog post does), or about global averages, or the output of compute models. There is a time span (say, about 1975 to the present) when global temperature does follow an approximately linear trend, but that doesn’t mean that individual station records have to. The presentation doesn’t even establish its central claim, that individual stations are going through step changes rather than linear changes. Perhaps silliest of all, there’s no disputing that something is causing earth to warm, and the notion that step change (at individual stations) rather than linear increase somehow singles out greenhouse-gas warming (as impossible), or negates the radiative properties of CO2, is ludicrous. Even if the central claim were true, it hardly disproves “anthropogenic global warming (AGW) claims.”
Yet perhaps the simplest point is that the post just fails to demonstrate its central claim. Let’s look at some data:
Let’s apply the same regime shift detection tool (from Radionov) used by the blog post’s author. It indicates 6 regime shifts over the 100-year period. Each of the regime shifts is strongly statistically significant, there’s absolutely no doubt about it.
This illustrates one of the most important lessons in statistics: just because your model is overwhelmingly statistically significant, that doesn’t mean your model is correct. It only shows that the null hypothesis is wrong.
The null hypothesis is “no trend at all.” The regime-shift detection tool has succesfully disproved that. Congratulations. But is has not shown that the data follow a series of step changes. The fact of the matter is that the regime shift detection tool will respond to just about any trend, no matter what its form. Even if the trend is perfectly linear.
The other fact of the matter is that for these data, the trend is perfectly linear. This is artificial data, and that’s how I designed it — a linear trend plus random noise.
Ironically, the shift-change model actually has smaller residuals. But it also has more parameters (quite a few more in fact), and according the the Akaike information criterion (AIC) the linear model is better. But the difference in AIC is small and says the linear model is only slightly better! It’s even possible (not very likely, but easily possible) for the opposite result to have occured. The step-change model is nothing more nor less than regressing onto step functions — which make excellent (and very generally applicable) model functions in general. The fact that they fit this (or any other) data well is neither a surprise nor a big deal.
Sure, it’s easy to claim I know what the trend is when I create artificial data. What say we look at one of the stations exhibited in the blog post, Malacca in southeast Asia:
The data in the GHCN version 3 are nearly identical. All the shifts are strongly statistically significant. No doubt about it.
But we can also model these data using two linear segments:
Once again, both models mimic the data well. Both are strongly statistically significant. Both disprove the null hypothesis that there’s no trend. The step-changes model has smaller residuals, but the two-straight-lines model has lower AIC (better model).
It’s bad enough to make a claim without demonstrating its correctness — one that apparently hasn’t even been properly tested. It’s worse to attach false significance to it, that the claim somehow contradicts anthropogenic global warming, which wouldn’t follow even if it were demonstrated.
It’s pretty clear — in fact it’s bloody obvious — the author simply applied an analysis method, misinterpreted the result, then concluded what he wanted to conclude for no other reason than that’s what he wanted. That’s what passes for science at the WUWT blog.