Guest article by Dr Michael Chase
Map above: Changes in minimum temperatures (Tmin) in Australia since 1910, according to the Australian Bureau of Meteorology (BoM)
“It was the BEST of tmins, it was the worst of tmins” … apologies to Charles Dickens.
This article is about why the BoM map shown above has warm and cold spots, including a somewhat implausible cooling region near Halls Creek in Western Australia. A four-pronged validation analysis is being mounted against version 2 of ACORN-SAT, intended by the BoM to indicate how air temperatures in Australia have varied from 1910 to the present time. The four prongs are as follows:
· Consistency with near neighbours
· Absence of inhomogeneities
· Adjustments that match non-climatic changes in the raw data
· An adjustment procedure that is not prone to errors
ACORN-SATv2 fails all these checks, and the highly non-uniform map above is one of the results, the hot and cold regions reflecting errors.
The adjustment procedure used by the BoM is to detect and correct non-climatic influences on the measured temperatures, such as from station moves and equipment changes. Errors arise in this procedure from erroneous size estimation and missed detections of genuine non-climatic shifts, false detections, and from the time-varying nature of some non-climatic influences. Going backwards in time from the present the errors accumulate as in a random walk, but the walk is not entirely random, there is a bias towards excessive cooling of early data. The reason for this bias may be that sudden cooling is much more prevalent than sudden warming, but whatever the reason, the bias is definitely a thing.
FIVE OF MANY EXAMPLES
The following figure shows [ACORN – BEST] Tmin data, as 12-month moving averages, for the five towns shown on the map above. BEST stands for Berkeley Earth Surface Temperatures, used here as “reference series”. The BEST data locations used for each town are given in the appendix.
The figure above illustrates the accumulation of ACORN-SATv2 errors, leading to excessive cooling of early data by around 1C, except for Halls Creek, which has excessive warming of early data, explaining the cold spot on the map above.
Adjustment Error Examples
At this point some readers will question the validity of BEST data. The following figure deals with that question for the first two example towns, Rutherglen and Wagga Wagga, both near the ACORN-SAT town of Deniliquin:
In the figure above the data shown are as follows:
· BLUE = RAW – ACORN-SATv2. This shows the adjustments that have been made to raw data.
· RED = RAW – BEST (Albury). This shows the variations of the non-climatic influences on the raw data, such as station moves, equipment changes, and observer errors.
If the ACORN-SATv2 adjustments were correct the blue data would match the moving average of the red data, which it does with great success for the example of Deniliquin Tmin data. In effect ACORN-SATv2 Deniliquin Tmin, and BEST (Albury) Tmin, have jointly validated each other, but things went wrong for ACORN-SATv2 for Rutherglen and Wagga Wagga, as illustrated in the following two analysis figures:
The figures above show examples of ACORN-SATv2 making invalid adjustments, and failing to make adjustments that were needed. The analysis plot for Halls Creek is as follows, an example of incorrect step-size estimation:
The full consequences of the errors in ACORN-SATv2 are not yet known, but they include exaggerated 20th century warming in many locations, and probably the generation of fake temperature records.
Technical notes are given below, further details and examples can be found at: https://diymetanalysis.wordpress.com/
BEST data is used in the validation tests as a “reference series”. A reference series has to have a good approximation to the regional average weather fluctuations, so that its subtraction increases the signal (steps/trends) to noise (weather) ratio. Ideally a reference series must have no more than “small” inhomogeneities. By design as regional averages over many stations, ready availability, and near global coverage, BEST is a very convenient source of reference series, at least for the post 1910 period in Australia. BEST Tmax data for New Zealand appears to fail to match raw data weather fluctuations before around 1942, the extent of this problem is unknown.
The BEST data locations used for the plots in this article are as follows.
· Rutherglen/Wagga/Deniliquin: BEST-Albury, 36.17 S, 147.18 E
· Halls Creek: BEST-Halls Creek, 18.48 S, 128.45 E
· Palmerville: BEST-Palmerville, 16.87 S, 144.00 E
· Boulia: BEST-Mount Isa, 20.09 S, 139.91 E
ACORN-SATv2 daily data (to May 2019) from CSV files was converted to monthly averages, requiring no more than 6 missing days of data in a month. Missing months of data were automatically infilled, up to a maximum gap size of 3 years, using BEST data, and the raw data either side of the gap, for the month in question. The infilling is not essential, but it makes the plots easier on the eye.