I had a conversation with a co-worker the other day about global warming. Not surprisingly, I get into this conversation a lot. I made mention that a significant minority of the data that is used to decipher our warming is actually modified or estimated. My co-worker was shocked at this so I wanted to share it with my readers.
The best analysis of this process that I have seen is written by Steve McIntyre over at Climate Audit in two separate discussions. From February 9th:
Unfortunately, the previous year’s December temperature is not included in the first year of a scribal record, so it must be estimated. And because December must be estimated, the resulting DJF is an estimate, as is the resulting annual temperature.
As we later learned, Hansen’s estimation algorithm enables him to estimate an annual average when up to six monthly averages are missing.
From his April 6th article:
One can readily see that as new temperatures are added to the record, the average monthly temperatures will change. Because those average monthly temperatures change, the estimated quarterly temperatures will change, as will the estimated annual averages.
On average 20% of the historical record was modified 16 times in the last 2 1/2 years. The largest single jump was 0.27 C. This occurred between the Oct 13, 2006 and Jan 15, 2007 records when Aug 2006 changed from an anomoly of +0.43C to +0.70C, a change of nearly 68%.
If one compares the latest file with the one from Sep 24, 2005, it can be seen that the earliest and latest years are adjusted lower today than in 2005, while the middle years are adjusted higher. However, this is purely coincidence. If one compares the file from Aug. 2007 with the latest file, it appears the earliest temperatures have been adjusted downward, leading to an overall upward trend. Surely other comparisons will yield a downward tend. It is by pure chance that we have selected two endpoint datasets that appear to have no effect on the tend.
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Filed under: Weather science