Australian Temperature Adjustments

If you have seen the articles on the NZ temperature adjustments and Nordic temperature adjustments you might be interested in the Australian data.

The issue with NZ and Nordic data that the raw temperature data for weather stations do not show the temperature increases indicated by the IPCC, raising the question of how the data have been adjusted.

As Prof. Karlen states in the ClimateGate email #1221683947, temperature at many stations has not exceeded early 20th century temperatures:

.. data sets show an increase after the 1970s to the same level as in the late 1930s or lower. None demonstrates the distinct increase IPCC indicates.

Here is the plot of means of Australian raw data for 103 temperature stations, based on the file Aus.tab downloaded from the Australian BoM web site and collated by Steve McIntyre.

fig1

The red line is the annual temperatures from 1910 to 2008 based on a simple average of the data in each year. The temperature is strongly increasing, and there seems to be some sort of glitch around 1940 where temperatures increase suddenly.

The problem with averaging all these stations is that the any tendency in stations to change with latitude introduces bias. That is, if stations are introduced in warmer climates late in the century, the average will be biased to warmer temperatures.

To get around this problem, I have simply normalized each of the station records (i.e. subtracted a station’s mean value) before averaging each year. This puts all of the stations on a even playing field, so to speak, no-matter whether it is normally warm or cold.

The blue line shows the normalized result. The temperatures are only slightly increasing.

Whats more, current day temperature has yet to exceed the peak temperature achieved in 1914 (blue dotted line and marked with blue crosses).

While averages of the raw Australian data appear to be increasing strongly, a simple normalization procedure to remove the bias introduced by station changes virtually eliminates all trace of temperature increases.

One would expect a similar situation in Australia to the Nordic stations, with a lack of individual stations with strongly increasing temperatures.

For interest, here is the figure supporting the temperature curves in IPCC and also published in e.g. Forster, P. et al. 2007: Assessing uncertainty in climate simulation. Nature 4: 63-64.

wrgb1

Here is the data object Aus.tab, and here is the script.R. Save the data object to a directory and then run the R script in that directory.

I have a similar view to Willis Eschenbach on this issue, and don’t claim that the BoM is actually altering the global temperature figures. However, issues in New Zealand and the Fennoscandian region are also found in Australia, proving the point that the compiled data cannot be taken at face value, and the adjustments to get them into the form we usually see need to be comprehensively audited.

Advertisements

0 thoughts on “Australian Temperature Adjustments

  1. I like your approach, but I believe simple normalization leads to an overcorrection. Simple example: assume two stations, both showing a steady warming trend, but covering different decades:Station 1: X X X X X X X X X X Station 2: 0 0 0 0 0 X X X X X(x's mark decades covered)If you simply normalize the station you will create a marked dip after the first five decades, when station two comes in, simply because station 1 will, at this time, already show a strong positive signal, but station 2 actually starts at below zero (because the normalization centers it at its midpoint, making the colder years at the start negative). Averaging both stations will then substantially reduce the long term warming trend that would otherwise be visible in both stations. Now imagine, that over time, more and more stations come in, each of which will reduce the warming signal in the old stations, so that in the end it is no suprise that normalization greatly decreases any warming trend.The problems are even worse when the two stations are not overlapping, as in the following (again assume that both show a clear warming pattern when looked up individually):Station 1: X X X X X O O O O O Station 2: O O O O O X X X X XDue to the normalization you will then get a steady increase for the first five decades, then a quick drop to below 0, and another increase. Again, the normalization procedure has eliminated a clear long term warming trend.Finally, you should note that normalization also affects the range of any signal in the data. If the warming effect is real, than stations with a longer record show a bigger rise over their record. Normalization artificially reduces the range of these stations to that of stations with smaller records, again reducing any clear warming signal.Apologies if I misunderstand what you are doing.

  2. Are you normalizing them to a common base period, or just the mean for each station for every data point? You should do the latter, otherwise everytime a station gets added in (ie is established in a new year) the mean gets closer to zero, which automatically reduces the trend.

  3. I like your approach, but I believe simple normalization leads to an overcorrection. Simple example: assume two stations, both showing a steady warming trend, but covering different decades:

    Station 1: X X X X X X X X X X
    Station 2: 0 0 0 0 0 X X X X X
    (x’s mark decades covered)

    If you simply normalize the station you will create a marked dip after the first five decades, when station two comes in, simply because station 1 will, at this time, already show a strong positive signal, but station 2 actually starts at below zero (because the normalization centers it at its midpoint, making the colder years at the start negative). Averaging both stations will then substantially reduce the long term warming trend that would otherwise be visible in both stations. Now imagine, that over time, more and more stations come in, each of which will reduce the warming signal in the old stations, so that in the end it is no suprise that normalization greatly decreases any warming trend.

    The problems are even worse when the two stations are not overlapping, as in the following (again assume that both show a clear warming pattern when looked up individually):

    Station 1: X X X X X O O O O O
    Station 2: O O O O O X X X X X

    Due to the normalization you will then get a steady increase for the first five decades, then a quick drop to below 0, and another increase. Again, the normalization procedure has eliminated a clear long term warming trend.

    Finally, you should note that normalization also affects the range of any signal in the data. If the warming effect is real, than stations with a longer record show a bigger rise over their record. Normalization artificially reduces the range of these stations to that of stations with smaller records, again reducing any clear warming signal.

    Apologies if I misunderstand what you are doing.

  4. Are you normalizing them to a common base period, or just the mean for each station for every data point? You should do the latter, otherwise everytime a station gets added in (ie is established in a new year) the mean gets closer to zero, which automatically reduces the trend.

  5. Paz makes a good point. The normalization for any given station is to define a 'base period' and then subtract the given temp reading from that base average. For stations operating for the whole period would be the average over that whole period, then the trend would be preserved as is.For stations introduced in the middle of the series however, the base period is tricky. if you do the whole remaining period, then you are going to shift the year 1 in the opposite direction of any trend from year 1 to the average remaining years, in effect reducing the effective trend. Alternatives are:1 year base period – assume year 1 is 'zero'5 year base period – average out a few years to avoid an outlyer effect 10 year base period etc.ANother way to normalize is to check the numbers for the nearest stations and to set the 'offset from mean' to equate to the avg offset of nearby stations.Also, what about Urban Heat island effect? If that is not taken into account, it throws a lot of this off.

  6. Paz makes a good point. The normalization for any given station is to define a ‘base period’ and then subtract the given temp reading from that base average. For stations operating for the whole period would be the average over that whole period, then the trend would be preserved as is.

    For stations introduced in the middle of the series however, the base period is tricky. if you do the whole remaining period, then you are going to shift the year 1 in the opposite direction of any trend from year 1 to the average remaining years, in effect reducing the effective trend.

    Alternatives are:
    1 year base period – assume year 1 is ‘zero’
    5 year base period – average out a few years to avoid an outlyer effect
    10 year base period etc.
    ANother way to normalize is to check the numbers for the nearest stations and to set the ‘offset from mean’ to equate to the avg offset of nearby stations.

    Also, what about Urban Heat island effect? If that is not taken into account, it throws a lot of this off.

  7. As a query …are the temperatures adjusted to Mean Sea Level (MSL) ..which if they are, would result in many having bits added on to the raw data.If not how can one construct a data set that is creating an an average temerature for a region if the vakles arrived at are dependant upon the height above MSLof the gauges used..which can vary enormously

  8. As a query …are the temperatures adjusted to Mean Sea Level (MSL) ..which if they are, would result in many having bits added on to the raw data.

    If not how can one construct a data set that is creating an an average temerature for a region if the vakles arrived at are dependant upon the height above MSLof the gauges used..which can vary enormously

  9. Does one dare to guess that the sharp rise in temperatures c. 1940 was due to the building of new air force bases in northern Australia?

  10. Does one dare to guess that the sharp rise in temperatures c. 1940 was due to the building of new air force bases in northern Australia?

    • Probably that sort of thing. But this is only with the average, not with the difference normalization method I am proposing.

  11. Probably that sort of thing. But this is only with the average, not with the difference normalization method I am proposing.

  12. I have used the same technique to do a similar analysis with the NZ data and could not replicate NIWA's data, even when I used NASA GISS data that had been “adjusted”.

  13. I have used the same technique to do a similar analysis with the NZ data and could not replicate NIWA’s data, even when I used NASA GISS data that had been “adjusted”.

  14. I have used the same technique to do a similar analysis with the NZ data and could not replicate NIWA's data, even when I used NASA GISS data that had been “adjusted”.

  15. Pingback: link do strony

  16. Pingback: zobacz tutaj

  17. Pingback: zobacz tutaj

  18. Pingback: tutaj

  19. Pingback: zobacz

  20. Pingback: polecam

  21. Pingback: zobacz oferte

  22. Pingback: polecam link

  23. Pingback: obsluga informatyczna bytom

  24. Pingback: oprogramowanie sklepu internetowego

  25. Pingback: witryna www

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s