Sheldon Walker recently posted at WUWT his view of global warming, in which he objects to the Skeptical Science view of global warming. He takes particular exception to their graphical display of same:
Sheldon Walker prefers what he calls a “contour map.” He takes all possible time intervals, estimates the warming rate for each by linear regression, then produces a graph in which the estimated rate at a particular time, of a particular duration, is indicated by color.
Here’s Sheldon Walker’s view (of the NASA GISS data used in the Skeptical Science graph):
I produced a similar graph:
The colors I’ve used aren’t exactly the same as his, but they’re close, and it’s clear the differences are only cosmetic.
He elaborates on his disbelief thus:
Note that the SkepticalScience view of the warming rate agrees with what the warming rate actually did, when the trend length is greater than 26 years. However, when the trend length is less than 26 years, the SkepticalScience view of the warming rate looks completely bland, and is definitely wrong. Where are the El Nino’s and La Nina’s? Where are the slowdowns and speedups. Do they expect us to believe that global warming proceeds at a uniform constant rate?
To answer your question, Sheldon: we don’t expect you to believe it.
Here are three more contour maps, for three other global temperature data sets:
How would Sheldon Walker interpret these? Do they show “slowdown” episodes? He states explicity:
You now have all of the information that you need to find a Slowdown. Find a contour map with yellow or orange at the top point of the triangular contour map. Now look for any green colour on the map. If you can find any green, then you have found a Slowdown. If you need any help, most 8 year olds are very good at this.
According to which, all three graphs show slowdown episodes. The second one, in particular, shows a slowdown episode which lasts over 20 years.
I wonder whether Sheldon Walker would believe me if I told him I’m quite certain that none of them shows a slowdown.
I wonder what Sheldon Walker will think when I inform him that these “global temperature” graphs aren’t for real global temperature data, they’re for artificial data. The artificial data sets are made from a straight line with the same slope as the NASA data, plus random noise with the same standard deviation as the residuals of the NASA data departures from a straight line.
I repeat: those three graphs are for straight-line-plus-random-noise. The straight line has the same slope as the NASA data. The noise has the same size (standard deviation) and autocorrelation structure as the NASA residuals.
And now to the most important thing.
Climate is the “rules of the game” — weather is the “rolls of the dice.” For the artificial data sets, the rules of the game are straight line increase. That’s the climate. The rolls of the dice are autocorrelated random numbers. That’s the weather. Because the climate — the rules of the game — is straight-line-increase, it’s absolutely certain, no denying it, that these data show no trend change.
But of course the rolls of the dice lead to fluctuations. That’s what weather is all about. It’s not what climate is all about. Those fluctuations cause trend estimates to fluctuate, and sometimes — especially when the time span is short — the estimate can be quite different from the actual trend. The actual trend is the climate, which we know, for certain, no denying it, did not change.
Global warming is about the climate, not about the weather. Except, of course, insofar as weather is influenced by climate.
Sheldon Walker’s method, and his view, include taking any fluctuation of a trend estimate as a slowdown of global warming. It means taking every fluctuation of weather and calling it a change of the climate trend. Most 8 year olds are very good at this.
If you apply some actual statistics to the trend estimates, you’ll find that none of the departures from constant-warming are significant. The statistics can be pretty tricky because the noise isn’t the simplest kind (referred to as “white noise”), it’s autocorrelated noise, and there are other statistical issues too (like “broken trends” and the “multiple testing problem”). But when done right, one finds that (to repeat myself) none of the departures from constant-warming are significant.
Walker’s graphs are an application of the technique which Nicola Scafetta called “multi-scale dynamical analysis” when he applied it to tide gauge data, in an attempt to link sea level changes to lots of stuff other than global warming (in particular, ocean oscillations). Scafetta too failed to do any actual statistics, relying instead on the visual inspection of colored graphs, an “analysis” (actually, lack of analysis) which was rather effectively refuted.
Sheldon Walker has been posting here frequently of late. I’m curious to know what he has learned from this.
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