A guest post by John Goetz
Cross posted from Climate Audit
After I posted GISS Spackle and Caulk, a number of commenters marveled at the symmetry of the histogram (GISS temperature estimate minus actual temperature). Some were dismayed that there was not a clear warming bias in the plot. Others were giddy for the very same reason. A few noted (as I hoped) that the differences tended to be rather large, but most seemed content with the fact GISS could hit the side of a barn from five feet.
No one should be surprised with the shape of the histogram. The “simulation” I performed required that all three months be available in a specific season for a specific station in order to calculate an estimate and compare it to the real value. For example, if summer 1957 was being tested, I needed June, July and August. If August were missing, the GISS algorithm would not be able to estimate June or July, and I would not have a real August to look at either.
With all three months available, I forced symmetry into the result. For every over-estimated August I needed a corresponding under-estimated June or July. The algorithm demanded that if I estimate all three months, their average must match the true average.
However, that is akin to saying that if I flip a coin often enough, the number of heads will be roughly equal to the number of tails. As most of us have experienced, coin flipping can be quite streaky. It is not uncommon to flip eight heads in a row. But having flipped that many heads does not change the probability of the next coin flip.
And so it goes with temperatures. In the actual application of the GISS algorithm, at most one month in a season can be estimated, so symmetry is not guaranteed. If one month is estimated more than another, it might be possible to introduce asymmetry.
As chance would have it, one specific month-year GHCN entry has had its temperature estimated by GISS far more than any other combination in the record. And as luck would have it, we have real GHCN data to compare against those estimates.
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