Maximum Triviality Reconstruction
Posted by Jeff Condon on April 27, 2009
I did this reconstruction of Antarctic temperatures some time ago. It is a true Voroni area weighted reconstruction using only surface station data. Instead of infilling NA values, this reconstruction simply uses the closest station weighted by the area of the polygon. Many advocates have suggested that I am a denier yet I’ve known about this simple evidence that the Antarctic isn’t warming at 0.12C/Decade +/-0.07 for some time. The fact that I haven’t presented it yet is because of the lack of complete data for the trend calcs at some stations. This lack of trend results in extreme slopes at certain stations, so I don’t like this recon as much as others. This is despite the fact that this recon presents the lowest average trend of any reconstructions – denier food. Still it’s not bad though simply because it represents the least fooled around with reconstruction I know of.
The Antarctic temperature distribution shows the difference. Here’s the most simple reconstruction.
It’s a lot more colorful than the closest station reconstruction which infilled missing data. This next plot is from my previous post using the next closest station to infill missing values.
The number of polygons and reduction of extreme trends caused by missing data is apparent. That’s why I prefer ‘this previous’ reconstruction despite the higher trend. Still this recon may actually be a more correct trend because there are error factors which occur from infilling missing values. Either way, we know to a high degree of certainty, the Antarctic is not warming as Steig et al. and RealClimate claim. Below is the trend from 1967 onward.
This is a pretty strong negative trend. You would think these baselines would tend to slow down the AGW scientists, but apparently thermometers aren’t as reliable as RegEM.
The next plot is the distribution of temps in the Antarctic.
Now that plot is very colorful with deep red’s and blues adjacent to each other. Temperature trends certainly didn’t vary by that much. They are an artifact of the missing data. In this case, if the data is missing at random from the trend’s perspective, the average trend could be close.
Next I’ll do RegEM with no islands or peninsula, then I think it’s time for a summary.