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Wednesday, March 23, 2011

caerbannog says: Comment from Joseph Romm's Climate Progress blog, March 23, 2011, concerning the BEST reanalysis of GISS Temps of global average temperatures

Comment from Joseph Romm's Climate Progress blog, March 23, 2011:

caerbannog says:
Those who think that the BEST project’s additional surface temperature data will change global-average temperature results significantly should have a look at this plot —

The plot shows an ensemble of global-average temperature time-series, where each temperature time-series was computed from a random 1 out of 10 selection of GHCN land temperature stations. For each run, a random-number generator was used to select the stations with a 1 out of 10 probability for each random-number “trial.” Basically, I recomputed global-average temperature results a bunch of times while throwing out 90% of the GHCN data at random each time.

The GHCN stations were selected completely (pseudo)randomly, with no attempt to maintain uniform global coverage.

To provide a basis for comparison, the official GISS land-station results are plotted along with the random “1 out of 10 station” results. For clarity, the GISS temperatures are plotted the foreground (red) scan, as indicated by the legend. (The legend labels for the other scans are just cryptic gobbledygook generated by the program that I wrote — don’t pay any attention to them).

What you can clearly see is that all of the “1 out of 10″ results agree reasonably well with the NASA results. In fact, most of them show a bit more warming than the NASA results do. (So much for idea of NASA “cooking the books” to exaggerate the global-warming trend.)

So if throwing out 90% of the currently available temperature data doesn’t change the results very much, then adding a bunch more data (data that are largely redundant, due to the long temperature-anomaly correlation distances) is highly unlikely to have a noticeable impact on the results.

Expecting the BEST project to upend the current global surface temperature record would be rather like tossing a coin another 1,000 times with the expectation that the head:tail ratio would start to deviate significantly from 50:50.

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