This is a follow-up post to the previous post on the pending paper:
“Assessing the consistency between short-term global temperature trends in observations and climate model projections"
by Patrick Michaels, Chip Knappenberger, John Christy, Chad Herman, Lucia Liljegren and James Annan
I'm calling it the Knappenberger study because the only hard information I have is Chip's talk at the ICCC meeting. But James Annan has confirmed that Chip's plots, if not the language, are from the paper.
Fallacy is likely because, as I showed in the previous post, the picture presented there is considerably different after just four months of new readings. Scientific truth should at least be durable enough to outlast the publication process.
The major fallacy
Chip's talk did not provide an explicit measure of the statistical significance of their claim of non-warming, despite hints that this was the aim. The main message we're meant to take, according to James, is
"the obs are near the bottom end of the model range"
Amd that's certainly what the plots suggest - the indices are scraping against that big black 95% level. This is explicit in Chip's slide 11:
"In the HadCRUT, RSS, and UAH observed datasets, the current trends of length 8, 12, and 13 years are expected from the models to occur with a probability of less than 1 in 20. "
But here's the fallacy - that 95% range is not a measure of expected spread of the observations. It expresses the likelihood that a model output will be that far from the central measure of this particular selection of models. It measures computational variability and may include some measure of spread of model bias. But it includes nothing of the variability of actual measured weather.
The GISS etc indices of course include measurement uncertainty, which the models don't have. But they also include lots of physical effects which it is well-known that the models can't predict - eg volcanoes, ENSO etc. There haven't been big vocanoes lately, but small ones have an effect too. And that's the main reason why this particular graph looks wobbly as new data arrives. Weather variability is not there, and it's big.