Monthly Archives: March 2013

Back to School

Much of what’s wrong with the online discussion of global warming is revealed by a recent reader comment on RealClimate.

Greg Goodman thinks that he’s taking climate scientists to school — he actually “lectures” the RealClimate readership about their supposed need to “dig a bit deeper” into the data on Arctic sea ice (both extent and area). He shows a graph based on some analysis which — unbeknownst to him — actually reveals that he doesn’t know what the hell he’s doing. He thinks he has established the presence of “cyclic variations” of which the climate science community is ignorant, and concludes that climate scientists are missing “important clues” about “internal fluctuations” which, of course, those inadequate computer models just can’t handle.

One would be hard pressed to find a more clear-cut example of hubris.

Climate scientists who study sea ice have been all over the data, every piece of it, but instead of making the mistakes Goodman makes they’ve been as careful and rigorous as their expertise and experience allow. They have certainly dug a whole helluva lot deeper than Greg Goodman has, or probably is capable of. It’s Goodman who needs to go back to school.

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Fact-Checking the Cherry-Pickers: Anthony Watts Edition

Oh the irony.

A post by Anthony Watts at WUWT claims to be a “fact check” about ocean heat content. Alas, Willard Tony didn’t check his “facts.”

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Cherry-Picking is Child’s Play

Anybody can do it.

Fake “skeptics” of global warming do it all the time. One of the latest and most extreme — this one is a real doozy — comes from John Coleman. Of course it’s regurgitated by Anthony Watts.

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A reader recently inquired about using the Theil-Sen slope to estimate trends in temperature data, rather than the more usual least-squares regression. The Theil-Sen estimator is a non-parametric method to estimate a slope (perhaps more properly, a “distribution-free” method) which is robust, i.e., it is resistant to the presence of outliers (extremely variant data values) which can wreak havoc with least-squares regression. It also doesn’t rely on the noise following the normal distribution, it’s truly a distribution-free method. Even when the data are normally distributed and outliers are absent, it’s still competitive with least-squares regression.

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