This is a reposting of the third of five responses I made regarding various aspects of Jim Steele's 1/7/15 WattsUpWithThat 'essay' - in light of Steele's recent comment, I think it's only fair to bring it up again.
Here I take a closer look at what Steele said about USHCN temperature data, in particular examining the way he misrepresents California temperature trends. Also Victor Venema explains some of the reasons why temperature data must be adjusted.
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Tuesday, January 27, 2015
Peter Miesler Helps Expose USHCN Homogenization Insanity - WUWT
Although it'll be a few days before I'm ready to comment on the USHCN itself and the way Mr. Steele puts their data to work, (too many other things going on, for now I offer the following reading list courtesy of VV at VV).
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Victor Venema at Variable Variability
put together a good reading list of articles that look into temperature adjustment:
Climatologists have manipulated data to REDUCE global warming
Variable Variability
Phil Plait at Bad Astronomy comment on the Telegraph piece: No, Adjusting Temperature Measurements Is Not a Scandal
John Timmer at Ars Technica is also fed up with being served the same story about some upward adjusted stations every year: Temperature data is not “the biggest scientific scandal ever” Do we have to go through this every year?
The astronomer behind And Then There's Physics writes why the removal of non-climatic effects makes sense. In the comments he talks about adjustments made to astronomical data. Probably every numerical observational discipline of science performs data processing to improve the accuracy of their analysis.
Steven Mosher, a climate "sceptic" who has studied the temperature record in detail and is no longer sceptical about that reminds of all the adjustments demanded by the "sceptics".
Nick Stokes, an Australian scientist, has a beautiful post that explains the small adjustments to the land surface temperature in more detail.
My two most recent posts were about some reasons for temperature trend biases: Temperature bias from the village heat island and Changes in screen design leading to temperature trend biases
You may also be interested in the posts on how homogenization methods work (Statistical homogenisation for dummies) and how they are validated (New article: Benchmarking homogenisation algorithms for monthly data)
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For now I want to focus on two more fundamental issues: 1) Why does Mr. Steele rely on a few graphs based on a data set he also belittles as next to worthless?
2) Why doesn't he allow the copious observational evidence to guide his convictions - rather than obsessing over fractions in a very complex data set?