Mysterious Warmth at Mackay

By Dr Michael Chase – Re-Blogged From WUWT

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Map above: Changes in maximum temperatures (Tmax) in Australia since 1910, according to the Australian Bureau of Meteorology (BoM)

SCOPE and PURPOSE

At face value this article is about why the region of Australia centred on the city of Mackay in Queensland has appeared to have warmed more than its surroundings since 1910. The short answer is that this hotspot, and almost certainly all the other hot and cool spots on the BoM map shown above, reflect errors in homogenised data. This article starts by revealing clear and irrefutable errors for the specific example of Mackay, with minimal explanation, and then goes on to provide some technical details of the analysis method. The simple validation procedure outlined here is applicable to all homogenised surface air temperature datasets.

ACORN-SATv2 minus BEST

ACORN-SATv2 is the latest (2018) version of homogenised Surface Air Temperatures (SAT) produced by the Australian Bureau of Meteorology (BoM), intended to indicate how air temperatures in Australia have varied from 1910 to the present time. BEST stands for Berkeley Earth Surface Temperatures.

The following figure shows [ACORN – BEST (Cairns)] Tmax data, as 12-month moving averages, for a cluster of 4 nearby ACORN-SATv2 stations:

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These nearby stations are all on or near the coast, so should have very similar climate histories. Thus, the figure above is sufficient on its own to invalidate the ACORN-SATv2 version of Mackay, which has strong warming relative to BEST, not seen in the others, which explains the hot spot on the comedy BoM map shown above. The trend consistency of the other datasets, including BEST (Cairns), and the small size of residual fluctuations in the difference plots, provides strong evidence of their mutual validation.

Diehard BoM fans may want more proof of errors, and the following figure provides it for Mackay Tmax data:

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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 (Cairns). 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. The difference between the blue and red curves shows the adjustment errors, a positive blue-red indicating excessive cooling of raw data. The figure above shows that the raw (red) data only really needs adjustment between 1938 and 1959, and before 1915. There is no justification for the ACORN-SATv2 adjustments in 1995 and 1970, and for the additional adjustment of around 0.5C accumulated between 1960 and 1935. The net error for early data is an over-cooling by around 1.2C.

The figure above shows examples of ACORN-SATv2 making invalid adjustments. The following figure, for the Mackay minimum temperature (Tmin) data, shows an example where there is a failure to make a necessary adjustment:

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In the figure above it can be seen that the accumulated error of around 0.5C in 1910 arises from a failure to detect/correct the onset of a transient non-climatic warming in 1990.

The apparent hotspot in the centre of Australia on the BoM map arises from errors in the ACORN-SATv2 version of Alice Springs. I am working to produce a compilation of all the worst case errors, all of which (so far) involve excessive cooling of early data.

APPENDIX

Technical notes are given here, 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.

ACORN-SATv2 daily data 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.

CONTINUE READING –>

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