by Roy W. Spencer, Ph. D.
(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.
John Christy provides this additional input:
My testimony in Dec 2015 and Feb 2016 included all observational datasets in their latest versions at that time. Santer et al. neglected the independent datasets generated from balloon measurements. The brand new “hot” satellite dataset (NOAAv4.0) used by Santer et al. to my knowledge has no documentation.
Here is my testimony of 2 Feb 2016 (pg 5):
“I’ve shown here that for the global bulk atmosphere, the models overwarm the atmosphere by a factor of about 2.5. As a further note, if one focuses on the tropics, the models show an even stronger greenhouse warming in this layer … the models over-warm the tropical atmosphere by a factor of approximately 3.”
Even when we use the latest satellite datasets used by Santer, these are the results which back up my testimony.
Global MT trends (1979-2015, C/decade) & magnification factor models vs. dataset:
___UWein(2) +0.090 2.38x radiosonde
_____RATPAC +0.087 2.47x radiosonde
_______UNSW +0.092 2.33x radiosonde
____UAHv6.0 +0.072 2.97x satellite
____RSSv4.0 +0.129 1.66x satellite
___NOAAv4.0 +0.136 1.57x satellite
________ERA +0.082 2.25x reanalysis
The range of model warming rate magnification versus observational datasets goes from 1.6x (NOAAv4.0) to 3.0x with median value of 2.3x for models warming faster than the observations.
Tropical MT trends (1979-2015, C/decade) & magnification factor models vs. dataset:
___UWein(2) +0.095 2.85x radiosonde
_____RATPAC +0.068 3.96x radiosonde
_______UNSW +0.073 3.69x radiosonde
____UAHv6.0 +0.065 4.14x satellite
____RSSv4.0 +0.137 1.98x satellite
___NOAAv4.0 +0.160 1.69x satellite
________ERA +0.082 3.31x reanalysis
Range goes from 1.7 (NOAAv4.0) to 4.1 with a median value of 3.3 for the models warming faster than the observations.
Therefore, the testimony of 2 Feb 2016 is corroborated by the evidence.
Overall, it looks to me like Santer et al. twist themselves into a pretzel by cherry picking data, using a new hot satellite dataset that appears to be undocumented, ignores independent (radiosonde) evidence (since it does not support their desired conclusion), and still arrives at a substantial 1.7x average bias in the climate models warming rates.