In a previous post I looked at the warming outliers in the Acorn network– those sites that had homogenisation adjustments that created a difference of more than +2 degrees Celsius between the Acorn trend and the raw trend in minima. In all of these six examples, the adjustments had created trends that were not just greater than the raw trends at each site, not just the mean of their Acorn neighbours raw data trends, but greater than the Acorn trends of their neighbours, and in all but one, greater than each of the individual Acorn trends of their neighbours.
In this post I consider the opposite scenario. I look at one cooling outlier, Tarcoola in South Australia, where the cooling adjustments have created a difference in trend of -2.81C per 100 years. There is one other, Forrest in W.A., with an enormous cooling adjustment of around -2.14C, but I have little faith in the accuracy of the data there. Greg Geegman suggested in a comment that if a site that is adjusted downwards is cooled relative to the neighbour group, this may indicate the Percentile Matching algorithm operates iteratively, although Technical Report No. 49 does not mention this. An alternative explanation might be that the algorithm is too sensitive and exaggerates necessary adjustments.
As before, I compare Tarcoola with its neighbours in the Acorn network, using anomalies from the 1961-1990 mean.
Cooler trend than raw. Note the spurious data pre-1930.
Tarcoola appears to need cooling.
Cooler trend than neighbours’ raw
Cooler trend than neighbours Acorn
Tarcoola Acorn trend is also cooler than each of the neighbours’ individual Acorn trends.
So which neighbours were used to make the Tarcoola adjustments?
Both warming and cooling outliers show Acorn adjustments outperforming those of the neighbours. This suggests that the algorithm exaggerates adjustments, both warming and cooling, and needs serious re-examination.