New Santer et al. Paper on Satellites vs. Models: Even Cherry Picking Ends with Model Failure

stratosphere-clouds(the following is mostly based upon information provided by Dr. John Christy)

Dr. John Christy’s congressional testimonies on 8 Dec 2015 and 2 Feb 2016 in which he stated that climate models over-forecast climate warming by a factor of 2.5 to 3, apparently struck a nerve in Climate Consensus land.

In a recently published paper in J. Climate entitled Comparing Tropospheric Warming in Climate Models and Satellite Data, Santer et al. use a combination of lesser-known satellite datasets and neglect of radiosonde data to reduce the model bias to only 1.7 times too much warming.

Wow. Stop the presses.

Part of the new paper’s obfuscation is a supposed stratospheric correction to the mid-tropospheric temperature channel the satellite datasets use. Of course, Christy’s comparisons between models and satellite data are always apples-to-apples, so the small influence of the stratosphere on the MT channel is included in both satellite and climate model data. The stratospheric correction really isn’t needed in the tropics, where the model-observation bias is the largest, because there is virtually no stratospheric influence on the MT channel there.

Another obfuscation is the reference the authors make to previously-published radiosonde comparisons:

“we do not compare model results with radiosonde-based atmospheric temperature measurements, as has been done in a number of previous studies (Gaffen et al. 2000; Hegerl and Wallace 2002; Thorne et al. 2007, 2011; Santer et al. 2008; Lott et al. 2013).”

Conveniently omitted from the list are the most extensive radiosonde comparisons published (Christy, J.R., R.W. Spencer and W.B Norris, 2011: The role of remote sensing in monitoring global bulk tropospheric temperatures. Int. J. Remote Sens. 32, 671-685, and references therein). This is the same kind of marginalization I have experienced in my previous research life in satellite rainfall estimation. By publishing a paper and ignoring the published work of others, they can marginalize your influence on the research community at large. They also keep people from finding information that might undermine the case they are trying to build in their paper.

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    Amber

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    All that Global Greening and only a 1.7 times bias of climate models ? Think of the possibilities if we can help it get even warmer ! Come on China start cranking out more CO2 . So glad the science fiction is settled .

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