Guest Post by Willis Eschenbach
I was reading a study just published in Science mag, pay-walled of course. It’s called “The Pace of Shifting Climate in Marine and Terrestrial Ecosystems”, by Burrows et al. (hereinafter B2011). However, I believe that the Supplementary Online Information (SOI) may not be paywalled, and it is here. The paper itself has all kinds of impressive looking graphs and displays, like this one:
I was interested in their error bars on this graph. They were using a 1° x 1° grid size, and given the scarcity of observations in many parts of the world, I wondered how they dealt with the uneven spacing of the ground stations, the lack of data, “infilling”, and other problems with the data itself. I finally found the details regarding how they dealt with uncertainty in their SOI. I was astounded by their error estimation procedure, which was unlike any I’d ever seen.
Here’s what the B2011 SOI says about uncertainty and error bars (emphasis mine):
We do not reflect uncertainty for our estimates or attempt statistical tests because …
Say what? No error bars? No statistical tests? Why not?
The SOI continues with their reason why no error bars. It is because:
… all of our input data include some degree of model-based interpolation. Here we seek only to describe broad regional patterns; more detailed modeling will be required to reflect inherent uncertainty in specific smaller-scale predictions.
So … using model based interpolation somehow buys you a climate indulgence releasing you from needing to calculate error estimates for your work? If you use a model you can just blow off all “statistical tests”? When did that change happen? And more to the point, why didn’t I get the memo?
Also, they’ve modeled the global temperature on a 1° x 1° grid, but they say they need “more detailed modeling” … which brings up two interesting questions. First question is, what will a .5° x 0.5° (“more detailed”) model tell us that a 1° x 1° doesn’t tell us? I don’t get that at all.
The second question is more interesting, viz:
They say they can’t give error estimates now because they are using modeled results … and their proposed cure for this problem is “more detailed modeling”???
I’d rave about this, but it’s a peaceful morning, the sun is out after yesterday’s storm, and my blood is running cool, so let me just say that this is a shabby, childish example of modern climate “science” (and “peer-review”) at its worst. Why does using a model somehow mean you can’t make error estimates or conduct statistical tests?
Sadly, this is all too typical of what passes for climate science these days … and the AGW supporters wonder why their message isn’t getting across?