Revising History

This is a topic we have covered here a lot – downward revisions to temperatures decades ago that increase the apparent 20th century warming.  Here is a great example of this from the GISS for Reykjavik, Iceland.  The GISS has revised downwards early 20th century temperatures by as much as 2C, despite Iceland’s Met office crying foul.  It is unclear exactly what justification is being used to adjust the raw data.  Valid reasons include adjustments for changes in the time-of-day of the reading, changes to the instrument’s location or type, and urbanization effects.  It is virtually impossible to imagine changes in the first two categories that would be on the order of magnitude of 2C, and urbanization adjustments would have the opposite sign (e.g. make older readings warmer to match current urban-warming-biased readings).

Arctic stations like these are particularly important to the global metrics because the GISS extrapolates the temperature of the entire Arctic from just a few thermometers.  Changes to one reading at a station like Reykjavik could change the GISS extrapolated temperatures for hundreds of thousands of square miles.

  • Ted Rado

    The idea of “adjusted” data is nauseating. If you have bad or questionable data, you throw it out and get new data.

    Over the years, I have regularly encountered faulty data, which was the result of various measuring problems, and occasionally dishonesty. The first order of business when you receive data is to check it out. Temperature measurements are notoriously difficult due to reradiation from the measuring device. I was once given data on the performance of a turbine generator which, it was argued, proved that my calcs were wrong. Upon digging out my steam Mollier diagram and running some calcs, the data I was given showed a thermodynamic efficiency of 122%. Eureka! The laws of thermo have been circumvented! The problem was obviously faulty temp measurement.

    I learned long ago to ALWAYS check data. If you have doubts, remeasure if possible and throw out the faulty data. How does anyone prove that their “correction” method of faulty data is valid?

  • netdr

    This type of “adjustment” has happened before in the USA.

    It looks like deliberate deception to me.

  • dearieme

    I became a “sceptic” years ago over just such data manipulation. It seemed to combine stupidity with dishonesty.

  • Unjustifiable fiddles to the data ultimately come back and bite the fiddler. It’s counter productive to go down that route. Fortunately there are other data sets that we can compare GISS too, so that we can notice the divergence.