Raw data shows no warming in Australia

I have the raw measurement data for all 112 stations used by ACORN-SAT.  So I processed the daily minimum and maximum temperatures in exactly the same way as for the homogenised data. Here is the result.

The raw result shows no warming after ~1990. The apparent warming of ACORN-SAT by ~1C is therefore a result of the homogenisation process.  How can that be?

Several ACORN stations are not autonomous but instead are merges of nearby stations. For example Launceston Airport is a merging of the current station, a previous airport location and one in the centre of town. Choices of how to align such offsets between different locations are called homogenisation. Launceston is rejected from the raw Anomaly calculations simply because the current airport site has no coverage between 1961-1990.

Homogenisation consists in looking either manually or statistically for kinks or offsets in temperature data, and then correcting for  them. These can be caused by recorded site moves, instrumentation changes or often for unknown reasons. The corrections are supposed to be determined by near neighbours which in Australia may be 100s of km away. Automated changes are designed to make signals smoother, but this results in a tendency to force all stations to end up following the same trend. That is what homogenisation means. Often these adjustments are based on differences in derived monthly anomalies, which after homogenisation then get converted back into ‘measured’ temperatures. This all seems rather incestuous to me, so I took decided to take a deeper look.

This is Richmond, Queensland which shows no shift in raw temperature anomalies, yet it gets subtly modified by homogenisation thereby increasing a small warming trend. 

The second example is Wilcania which shows a flat anomaly distribution in the raw data but which after ‘homogenisation’ shows a positive warming rrend.

Here is a comparison of the resulting temperature anomalies. The red curve are the annual average anomalies

It is true that several stations indeed show shifts in raw anomalies, mainly due to station re-location, but these should be corrected with a simple offset. The same argument applies in the case that different stations are combined within the same town eg Launceston. However, I now have no doubt that automated ‘pairwise’ algorithms will always exaggerate warming. We see the same bias in GISS, in GHCN, in CRUTEM and especially in Berkeley Earth.

The question is by how much?

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9 Responses to Raw data shows no warming in Australia

  1. Graeme Mochrie says:

    Sounds like a job for a good statistician.

  2. Ron Graf says:

    “I now have no doubt that automated ‘pairwise’ algorithms will always exaggerate warming.”

    I see pairwise logic as just smoothing, which is muddying the water for the real culprit: station moves and corrections of discovered growth of non-climate effects. So each station takes turns getting adjusted for a “legitimate” offset correction. But that warming also then gets smeared to all the other stations until they take their turn to get moved or “fixed.”

  3. Nick Stokes says:

    “Automated changes are designed to make signals smoother, but this results in a tendency to force all stations to end up following the same trend. That is what homogenisation means.”
    That isn’t what it means. But I think the role of neighboring stations is exaggerated. A suspected non-climate change is identified internally in a station series. Then external stations are used to check if they show the same change. If not, then the change is deemed to need correction, and the correction is calculated based on the change within the series. It is not based on transferring trend between stations.

    I illustrated the process with Amberley. It is true there that I used trend over a short period (decade) to quantify the suspect change relative to others, and even adjusted to match to that nearby trend locally. But the Amberley decade trend was so far out of line that I could have adjusted it to zero with much the same effect. I don’t know if PHA uses trend as the quantifier.

    • Ron Graf says:

      Nick, my point is stations should not be adjusted for internal changes at the sensor regardless if they are real as statistically proven real. The only data adjustments that can be scientifically justified are those proven to be correcting a known systematic bias of the entire data set. For example, a particular station’s changing from the “Stevenson box” as the sensor’s enclosure or moving from a different elevation needs to be corrected for only if the study is of that station alone. But in studying a population of stations the only corrections for bias need to be made are for those affecting the population systemically. So the 1910 Australian adoption of the Stevenson box is a legitimate systematic change but random corrections for station moves are not, unless the protocol changes systematically to move sensors from rooftops to 2 meters.

      Also any such corrections needs numerous published studies for validating the quantification of said adjustment.

    • Clive Best says:

      Nick,

      I agree that when there are obvious linear shifts in temperatures then these should be corrected. It is very difficult to justify adjusting non-linear trends because they are different from what is expected by simply overwriting them. GHCN unadjusted values are not what they claim to be. They have already been reduced from daily Tmin/Tmax to monthly Tmin/Tmax and this averaging is undocumented. The same is true for CRUTEM. The only honest procedure is to make available all the original raw instrument measurements. BOM is to be credited for actually doing that.

      Whether I could cope with having 100s of millions of individual temperature readings is another question !

      • Nick Stokes says:

        “It is very difficult to justify adjusting non-linear trends because they are different from what is expected by simply overwriting them.”
        I don’t think anyone does that.

        • “Whether I could cope with having 100s of millions of individual temperature readings is another question !”

          Having a high-resolution on ENSO data is vital. What has generally been considered “noise” in the SOI data appears to be a tidal forcing signal.

          A few scientists at NASA JPL have been complaining about data that has misguidedly filtered to remove tidal signals, wherein if that data had been left unmodified, many climate behaviors would be much easier to reason about.

    • The claim made by Nick about how the adjustments work – parrotting what the GHCN people say, is demonstrably false.

      The much-studied case of Iceland is a good example. Every single station in Iceland shows a sharp cooling in the mid 1960s, and this cooling was discussed and reported at at the time, so it’s clearly real. But the charlatans at GHCN adjust it away anyway.

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