Homogenistion

Raw data: annual average minimum temperature from Reno, Nevada 1895 to 2005

Raw data: annual average minimum temperature from Reno, Nevada 1895 to 2005

UPDATE: This page contains errors! Please see the comments for clarification. I have posted a second version of this calculation here.

As I have mentioned on several recent posts, the raw data from even relatively sophisticated climate stations is really rather poor quality. Rather than just ignore this data altogether, researchers have looked at the data and noted that although the actual temperatures might not be correct to within perhaps ±2 °C, the errors in the measurement are likely to have remained constant for a long time. This is because the methods used to take the data have changed only very slowly. So researchers have looked at the data, not to determine the absolute temperature at that station, but in order to determine whether the temperatures have changed. There are many problems inherent in this, so I thought it would be interesting to very explicitly show the kind of thing involved in this endeavour. To show this I have extracted data from a slide that Matt Menne showed in his talk at the Surface Temperature Workshop.

The graph at the head of this article shows the raw data for a single station near Reno, Nevada. Each day a thermometer which records the maximum temperature and the minimum temperature is read and 365 (or 366 in a leap year) measurements of the minimum daily temperature were averaged to produce each data point on the graph. We can see that the year-to-year scatter is rather low. However, two features stand out and I have re-drawn the above graph highlighting these features.

Raw data for the annual mean minimum temperature highlighting significant features.

Raw data for the annual mean minimum temperature highlighting significant features.

The first feature is a dramatic shift in the data: the years following 1937 appear to be between 3 °C and 4 °C colder than the years prior to 1936. This is a pretty obvious artefact and occured because the station was moved from one location to another – micro climates vary by this much even over distances as short as a few metres! (think about how one side of your car can have frost on in the morning but the other side doesn’t!). The second feature is a strikingly linear 4 °C rise in temperature since 1975. This looks strongly like an Urban Heat Island (UHI) effect – a real effect – but not caused by a shift in climate. The question that the ‘homogenisation process’  tries to answer positively is this: can we recover any information at all from the above graph? To try to extract the trend of the data, researchers look at the difference between this station and its ten nearest neighbours – many kilometres away from this station. This difference data is shown below.

Difference between minimum temperatures at Reno and the mean from its 10 nearest neighbours.

Difference between minimum temperatures at Reno and the mean from its 10 nearest neighbours.

If we add this difference data to the raw data, then we should be able to compensate for the local anomalies in the data. The compensated graph is shown below in red with the original data shown in grey. It is pretty clear that the adjusted data is a better representation of the climate-related temperature changes at the Reno, Nevada station than the original data.

Data for the mean annual minimum temperature from temperature adjusted by comparison with its neighbours

Data for the mean annual minimum temperature from temperature adjusted by comparison with its neighbours

The adjusted data do not show a sudden jump in temperature in 1936/1937. And they do not show the real rise in temperatures at the station due to the UHI effect. The data is said to have been homogenised. Now I have  simplified the process a little, but not much. The professionals can make a statistical assessment of the uncertainty associated with the process.

When you look closely at the graphs of the temperature of the Earth versus time you will see that they are all labelled  ‘temperature anomaly’ rather than temperature change. The data from the land surface portion of the Earth’s temperature (around one third of the data) have ALL been adjusted in this way to highlight only changes temperature common to many stations spread over a wide geographical area. The homogenisation analysis extracts from the original data only the portion of it which corresponds to long term trends and rejects artefacts which occur only in a single localised station.

Is this process fair? Well I don’t know. From having talked with the scientists involved in this work I am sure that they are doing the best they can with the data which exists. I am 100% that they are not surreptitiously ‘fixing’ the maths to make the temperature rise for political ends. The rises they observe are a relatively robust signal. Could the whole process be flawed in some unanticipated way? Well its possible, and that’s for you to make up your mind. But I feel obliged to point out that whether you are convinced by this process or not,  there are plenty of other reasons to be concerned about even the possibility that our climate might be changing.

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4 Responses to “Homogenistion”

  1. PaulM Says:

    I have never understood this, and I still don’t.
    Firstly, when you say “add this difference data to the raw data”, I think you mean subtract!

    But this still does not make sense, because (sorry for insulting your intelligence)
    x – (x – (y1+y2..+y10)/10) = (y1+y2..+y10)/10.
    In other words, after subtracting off the difference between Reno and the mean of its neighbours, the resulting data has nothing to do with the Reno data at all – it is just the mean of the neighbours! The Reno data has been discarded completely.

    So the answer to your question “can we recover any information at all from the above graph? ” is a resounding “no”.

    Furthermore, the UHI-affected data in Reno has not been eliminated, it has just been divided out among its ten neighbours, assuming that the same process is used for each of them.

    So, obviously, this can’t be what the climate scientists actually do – even I don’t think climate scientists are that stupid – it must be something more complicated.

  2. protonsforbreakfast Says:

    Those are very good points (and you can insult my intelligence as you like: I take no offence:-)). I was aware that I had oversimplified things, but I didn’t realise it was quite as bad as you pointed out.
    1. When I wrote the post I e-mail Matt Menne and asked him if I had got this right. I hope he will let me know the details of what is
    2. In the talks at Exeter, much of the discussion focussed around identifying ‘Change points’, the sudden (documented or undocumented) shifts in the data. In ‘professional version’ of the homogenisation, I think no correction is applied until a change point is identified. Then the magnitude of the adjustment is made by looking at the neighbouring data. This kind of adjustment is relatively easy.
    3. The more significant problem occurs with the Urban Heat Island type drift. I don’t understand what the procedure is there, butI am hoping that Matt Menne will help.

    Thanks for comments, I really appreciate them.

    M

  3. PaulM Says:

    Yes, I looked at a recent paper of Menne and Williams “Homogenization of Temperature Series via Pairwise Comparisons” and it seemed more to do with identifying abrupt changes than gradual drift. Also it seemed that the method was very complicated – too complicated to figure out in the time I thought I could devote to it!

  4. Homogenisation II « Protons for Breakfast Blog Says:

    […] sensitively extracting trends from meteorological data with large systematic uncertainties. My first post on the subject was rather too simplistic and a  reader was kind enough to point that out. Since then Matt Menne […]

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