Climate Distortion in ACORN-SAT, Part 2

Author: Dr. Michael Chase


A recent post dealt with the flaw in ACORN-SAT that it makes the erroneous assumption that all step changes in temperature arise from persistent non-climatic influences. This post illustrates a potential “false alarm” problem with the detection of step changes, that often occur when there is heavier than average rainfall at a weather station. It looks like the algorithms are responding to a transient (several years) cooling of daily maximum temperatures associated with the rainfall and its aftermath, and the analysts are not removing those false detections. Correcting the data for those false alarms cools all years before the event, but the correct thing to do is to make no corrections at all.

The following figure shows rainfall and Tmax data from interior Queensland, an area which responds strongly to rainfall (and clouds), with drops in temperature when rainfall is higher than average:


The stations shown in the figure above are Richmond (red), Camooweal (cyan), Boulia (blue) and Longreach (black). Note how drops in temperature are associated with higher than average rainfall. ACORN-SAT gives a “statistical” step change for Richmond (red) in 1950, exactly when it has a peak in rainfall.

This post will be expanded later to show other examples of ACORN-SAT steps being linked to peaks in rainfall.

A feature of the algorithms that may be contributing to false detections is the use of 10 neighbouring stations to decide on the size of the step. Requiring 10 stations means that a majority of them may be in areas not affected by the local rise in rainfall. The use of medians to decide the size of the step then gives an inconsistency with the temperature change at the station being examined, crossing the threshold involved, and triggering a false detection.

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2 Responses to Climate Distortion in ACORN-SAT, Part 2

  1. MikeR says:

    Hi, Just found out about your blog. Very interesting stuff. It appears you have spent a lot of time poring over the BOM data. Is the above the most blatant example of the BOM getting it wrong?

    I assume you have tried fitting trends for the raw and Acorn anomalies for Richmond. The trend for the raw anomalies from Jan 1910 to Dec 2016 is 0.041 C /.decade while for the Acorn data the trend is less than half that at 0.019 C /decade (see ).

    If this scenario translates to all the other data sets then the Acorn data is significantly underestimating the rate of warming. Who would have thought that?


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