Sheldon Walker seems to be desperate — desperate to believe that global warming exhibited a “slowdown” recently. His latest attempt to prop up his faulty belief is a new post at WUWT titled “Proof that the recent global warming slowdown is statistically significant (at the 99% confidence level)“. He mainly demonstrates that he has a lot to learn about statistics, but isn’t learning and doesn’t know how inadequate is his own knowledge.
His latest attempt amounts to this: estimate the warming rate of global temperature (data from NASA) over 10-year periods (actually, 10 years and 1 month but that’s not really important in this case). Test whether the 10-year+ rate is different from the average rate over the period from January 1970 through January 2017 (0.01782 deg.C/yr). He performs his tests at the 99% confidence level (p-value 0.01) rather than the more customary 95% confidence level (p-value 0.05) in order to have greater confidence in the results. All analyses are performed with Microsoft Excel.
Then he graphs the results, with periods showing significantly faster-than-average warming as red dots and periods showing significantly slower-than-average warming in blue, with periods for which the 10+ year rate is not statistically different from average in gray. And here’s his plot:
His discussion focuses on the set of 5 “final years” in a row near the end for which the warming rate over the 10+ year span is “significantly” slower than the average. This, he believes, establishes the “proof” he promised. He doesn’t say explicitly whether or not he regards the years with “significantly” faster-than-average warming as meaningful, he’s primarily interested in claiming to have proved a slowdown. I wonder whether he actually believes there were both? I wonder whether he wonders why there are so many slowdowns and speed-ups? I wonder whether he has thought about how it is that the 10+ year period ending with 2015 is “significantly” slower than average which that ending with 2017 is “significantly” faster? Those two time spans overlap by 8 years out of 10 … yet one is “significantly” above average, the other “significantly” below average?
If he’s reading this, I wonder whether he wonders why I keep putting the word “significantly” in quotes?
The reason is: what he calls “significant” isn’t.
Computer programs which compute trend lines, including Microsoft Excel, and R, and others, test for statistical significance and also compute uncertainties in the rate of increase or decrease. But they base these calculations on an assumption that the noise, the random part of the data, is what statisticians call white noise. That’s the simplest kind of noise, in which each noise value is uncorrelated with all the other noise values. When the noise really is that way, then their calculations are correct.
But not all noise is the same. Noise values actually can be correlated with nearby noise values and still be just noise, just random. Correlation between noise values is called autocorrelation. It changes the behavior of analysis like fitting a trend line. If the autocorrelation is positive — as it is for global temperature — then the uncertainty in a trend estimate is higher than what you’d calculate for white noise. If the autocorrelation is strong — as it is for monthly average global temperature — the uncertainty in a trend estimate is much higher than what you’d calculate for white noise. And, the statistical significance of a test is much less than what you’d calculate for white noise.
That’s why the values he claims are “significant,” aren’t.
This has been often discussed with regard to global temperature. It’s well known — by those who know what they’re doing. It’s not known by Sheldon Walker.
He has commented here before, specifically about the topic of the non-existent “slowdown” he’s so desperate to believe was real. I’ve posted on that topic, and specifically about his claims regarding the topic. For example, I said in this post:
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.
Sheldon Walker ignored the effect of autocorrelation on the uncertainty of trend estimates and their statistical significance. By doing so, he came to the false conclusion that the had “proved” the non-existent “slowdown” actually existed. A few comments on his blog post have pointed out the need for an autocorrelation correction, but so far he hasn’t addressed that. Sorry, Sheldon, ignoring it won’t make it go away.
IF he learns something from this, then he’ll realize his mistake. IF he’s intellectually honest, he will admit it — not buried in the comment section or as a “correction” at the end of his post, but right at the top of his blog post, announcing in no uncertain terms that his analysis is wrong and his conclusion is wrong.
What are the odds?
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