An analysis of Australian rural vs non-rural stations’ temperature trends

Australia's weather stations

Guest post by Mike Jonas

 

Temperature change in Australia

The rates of temperature change in Australia at rural and non-rural weather stations are compared. Over those periods in which there were increasing temperatures, the rural stations appear to have warmed at about 60-70% of the warming rate of the non-rural stations.

Data

Data from the Australian Bureau of Meteorology (BOM) is used, and is assumed to be sufficiently accurate for the purpose. It is not known whether any original measurements have been adjusted in the BOM records.

The data values downloaded from BOM are monthly mean max and monthly mean min. These are used in preference to monthly max and monthly min, as being more representative of each month’s temperature.

Weather Stations

47 weather stations were active from 1940 to 2000, ie. from 1940 or earlier to 2000 or later. Of these, 10 were rural.

Station Number Station Name Latitude Longitude Start Year Last Year Is Rural?
3003 BROOME AIRPORT -17.9475 122.2353 1939 2009 No
4020 MARBLE BAR COMPARISON -21.1756 119.7497 1901 2006 Yes
8025 CARNAMAH -29.6889 115.8869 1940 2009 No
8093 MORAWA -29.2103 116.0089 1925 2005 No
9510 BRIDGETOWN COMPARISON -33.9575 116.1375 1907 2009 No
9518 CAPE LEEUWIN -34.3728 115.1358 1907 2009 Yes
9534 DONNYBROOK -33.5719 115.8247 1907 2009 No
9581 MOUNT BARKER -34.625 117.6361 1907 2009 No
10073 KELLERBERRIN -31.6183 117.7217 1910 2009 Yes
10111 NORTHAM -31.6508 116.6586 1907 2009 No
10579 KATANNING COMPARISON -33.6886 117.5553 1907 2009 No
10614 NARROGIN -32.9342 117.1797 1913 2009 No
10648 WANDERING COMPARISON -32.6814 116.6756 1901 2003 No
12038 KALGOORLIE-BOULDER AIRPORT -30.7847 121.4533 1939 2009 No
12071 SALMON GUMS RES.STN. -32.9869 121.6239 1932 2009 Yes
12074 SOUTHERN CROSS -31.2319 119.3281 1907 2007 No
17031 MARREE COMPARISON -29.6481 138.0637 1939 2009 No
17043 OODNADATTA AIRPORT -27.5553 135.4456 1940 2009 No
18012 CEDUNA AMO -32.1297 133.6976 1939 2009 No
18044 KYANCUTTA -33.1332 135.5552 1930 2009 Yes
18070 PORT LINCOLN -34.7225 135.8558 1892 2002 No
21046 SNOWTOWN -33.7844 138.2133 1908 2001 No
26026 ROBE COMPARISON -37.1628 139.756 1884 2009 No
29004 BURKETOWN POST OFFICE -17.7425 139.5475 1890 2009 No
30018 GEORGETOWN POST OFFICE -18.2922 143.5483 1894 2007 No
30045 RICHMOND POST OFFICE -20.7289 143.1425 1893 2009 No
32040 TOWNSVILLE AERO -19.2483 146.7661 1940 2009 No
33047 TE KOWAI EXP STN -21.1642 149.1192 1908 2009 Yes
37010 CAMOOWEAL TOWNSHIP -19.9225 138.1214 1939 2009 No
38003 BOULIA AIRPORT -22.9117 139.9039 1888 2009 No
39039 GAYNDAH POST OFFICE -25.6258 151.6094 1893 2009 No
39083 ROCKHAMPTON AERO -23.3753 150.4775 1939 2009 No
46037 TIBOOBURRA POST OFFICE -29.4345 142.0098 1910 2009 No
55023 GUNNEDAH POOL -30.9841 150.254 1876 2009 No
66037 SYDNEY AIRPORT AMO -33.9411 151.1725 1939 2009 No
66062 SYDNEY (OBSERVATORY HILL) -33.8607 151.205 1859 2009 No
70014 CANBERRA AIRPORT -35.3049 149.2014 1939 2009 No
74128 DENILIQUIN (WILKINSON ST) -35.5269 144.952 1858 2003 No
78031 NHILL -36.3347 141.6367 1897 2008 No
82039 RUTHERGLEN RESEARCH -36.1047 146.5094 1912 2009 Yes
86071 MELBOURNE REGIONAL OFFICE -37.8075 144.97 1855 2009 No
90015 CAPE OTWAY LIGHTHOUSE -38.8556 143.5128 1864 2009 Yes
91057 LOW HEAD (COMPARISON) -41.0567 146.7883 1895 2001 Yes
91104 LAUNCESTON AIRPORT COMPARISON -41.5397 147.2033 1939 2009 No
94029 HOBART (ELLERSLIE ROAD) -42.8897 147.3278 1882 2009 No
200283 WILLIS ISLAND -16.2878 149.9652 1921 2009 Yes
200288 NORFOLK ISLAND AERO -29.0389 167.9408 1939 2009 No

Whether each station was rural or non-rural was determined using Google Earth. The decisions were necessarily subjective. The Google Earth views of the stations are shown in RSelectedStationsGoogleMaps (10.7mb PDF)

Note : Station 9581 Mount Barker is obscured by cloud in the Google Earth view. The population of Mount Barker is over 4,000, and the weather station appears to be in or very near the built-up area, so it is classified as Non-Rural.

The monthly mean max and monthly mean min temperatures for all these stations were downloaded from the BOM website http://www.bom.gov.au/. The temperatures were downloaded in January and February 2011, but unfortunately the December 2010 temperatures for some of the stations had not then been posted. For those stations, data was in fact complete to November 2010. Note that “Last Year” for stations that are still open is given as 2009, and that 10 of the stations closed between 2000 and 2009. A further 3 stations (29004, 39039, 91104) appear to have closed in 2009.

The data as downloaded is in http://members.westnet.com.au/jonas1/QMeanTemps.pdf
(2.8mb)

Please see the Acknowledgements and Licence information

Product code “IDCJAC0002″ is Monthly Mean Maximum Temperature, and “IDCJAC0004″ is Monthly Mean Minimum Temperature.

Calculations

Temperature trends (least-squares linear fit) over various periods were calculated separately for each calendar month, for each station, using only the monthly temperatures that were given. There was no attempt to fill in any missing data.

For each period, only the stations active for the whole of the period were used (there could be some missing data, but the test was that there was data for the first year of the period or for an earlier year and that there was data for the last year of the period or for a later year).

The overall trend for each period was then taken as the average of all trends for that period – ie, the average across all calendar months and all active stations.

The reasons for using this method are:

  • If an attempt was made to fill in missing data, then there would be a risk that the filling-in method used could introduce errors or biases.
  • Each calculated trend is for one calendar month only (eg. all Januaries), so has no seasonal bias.
  • If trends were calculated on annual averages, instead of by calendar month, then any year with a missing month’s data could not be used, because that would introduce a selection bias.
  • If trends were calculated on all months, instead of by calendar month, then the least-squares algorithm would bias the result towards the hottest and coldest calendar months. The previous issue – a missing month’s data affecting a whole year – also applies.
  • Each calculated trend is for one station only. If station data was averaged before calculating the trend, then it would not be possible to use any month for which any station’s data was missing, because it would introduce a selection bias.
  • The station / calendar month trends for each period were only calculated for stations active in that calendar month over the whole of the period (individual months’ data may still be missing). Thus when trends are averaged for any given period, there is no station selection bias. Note that the number of selected stations can be different for different periods.

It is possible that there may be a geographical bias in the data, ie. the stations used may have different geographical distributions for different periods, or that the geographical distribution of stations is not representative of the region. It is also possible that missing data could affect results, especially if missing values are clustered (eg. same calendar month missing over consecutive years, for a given station).

The full results, where there were at least 5 stations used in each trend average, are given in this table:

Also in: http://members.westnet.com.au/jonas1/RAverageMeanTempTrendsSummary5.pdf (42kb)

and are as follows:

Trends are given in deg C per year. So, for example, the first number, 0.0036, for the period 1910-2000, means that the average trend of Monthly Mean Max temperatures from 1910 to 2000 for Non-Rural stations was 0.0036 deg C per year. (All results are given to 4 decimal places, so there may be rounding errors).

For stations whose Dec 2010 data was missing, the average trend shown to 2010 is the average of the Jan-Nov trends. Stations are only included for periods over which they have both “Max” and “Min” temperature data.

These figures indicate that over those periods in which there were increasing temperatures, the rural stations only warmed at about 60-70% of the warming rate of the non-rural stations.

There are some curious numbers in the results, which could deserve further analysis – for example, the rural trends for 1980-2000 and 1990-2000 are much lower than the non-rural, yet for 2000-2010 they are about the same as the non-rural. Also, there is no warming trend from 1910-1940, a period of quite rapid global warming (see, eg.,

http://www.cru.uea.ac.uk/cru/info/warming/gtc.gif

http://www.cru.uea.ac.uk/cru/info/warming/gtc.gif).

Where there were at least 2 stations used in each trend average, the results are given in

http://members.westnet.com.au/jonas1/RAverageMeanTempTrendsSummary2.pdf (43kb)

and all trend averages are given in

http://members.westnet.com.au/jonas1/RAverageMeanTempTrendsSummary.pdf (45kb)

All of the individual station / calendar-month trends (rounded to 4 dec places) are given in this document:

http://members.westnet.com.au/jonas1/QMeanTempTrends.pdf (734kb)

This too could deserve further analysis, eg. there may be significant summer / winter trend differences.

=============================================

For further information and discussion, see

http://members.westnet.com.au/jonas1/AustraliaTemperatureChangeNotes.pdf

This document includes:

  • List of the 47 weather stations.
  • Google Earth maps used for Rural/Non-Rural identification. [link]
  • BOM website (data source). [link]
  • Source data. [link]
  • Individual station / calendar-month trends. [link]
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74 thoughts on “An analysis of Australian rural vs non-rural stations’ temperature trends

  1. If the temperature record is critical and with billions of dollars at stake – why are stations closing?

  2. Mount Barker in South Australia was a sleepy rural town but has become a dormitory commuter suburb of Adelaide over recent years, particularly after the completion of the freeway and Heysen Tunnels through the Adelaide Hills. As Wiki notes-

    “Mount Barker is an expanding city, home to 10 258 [2] residents that is 33 kilometres up the South Eastern Freeway, east of Adelaide, in South Australia……..

    Mount Barker has since developed into a large urban centre, which is developing very rapidly. The Mount Barker district council is the area that experienced the fourth largest growth between 1996 and 2006 in South Australia, with an increase of 3,800 new residents (3% growth per year). Mount Barker is ranked fifth for fastest growth in South Australia.[8] During the last 10 years, many new subdivisions have been developed, such as Martin-Dale and Waterford. During this period, there has also been an economic boom in Mount Barker and a number of additional malls and shopping centres,…”

    Consequently, given the location of Mt Barker’s weather station you might notice in the raw data some accelerated warming over the last decade or so.

  3. Tibooburra NOT? rural?
    what!
    any country person can tell you the city temp is at least 5C if not more hotter as you get into a city. and its always a pleasure to leave a city in summer!
    in winter it’s the opposite.
    I wouldnt trust the BoM figures any more.
    theyve been “housekeeping” a bit in the last couple of years..

  4. Can I just ask, I am not the best looking at pages of numbers. I do try but it can be hard at times to actually see what is being referred to in a page of numbers. It may be helpfull to also graph the trends for each set of data and include them in the article for those like myself that really benefit from a very visual representation. :)

  5. I am just a Simple Red-Neck and I have a question. Elsewhere on this blog, the accuracy of temperature measurements was discussed. As I recall, the measurement accuracy was about +/- 0.5 degree C when inherent instrument accuracy and reading accuracy were considered. Since the “trend” here is within the accuracy of the measurements, is the “trend” any trend at all?? Or, are the errors in the measurements random enough and the sample big enough to justify calculations to four decimal places?

    Still, I appreciate the labor involved here and it does seem to use techniques similar to those of the Goracle. It appears to this Humble Red-Neck that there is zip for a trend even with the Goracle’s dubious methods. But, it is worth a chuckle when you start with measurements that are +/- 0.5 and mention possible rounding errors when your calculations are 0.0001.

    Yours in befuddlement,

    Steamboat Jack (Jon Jewett’s evil twin)

  6. Just eyeballing the tables, some of the min values of the slope are *higher* than the max values. (Check out the top line for rural stations)

  7. Dosen’t matter, the fix is in, here in Aus anyway. Crickey, ignore the resession in New Zeland, largely brought about by the new ETS tax and GTS increases. A Price on “carbon” (What Ms. Gillard? Its not a tax, really?), that’ll fix that nasty climate change “carbon” monster. Ms Gillard must have seen the kiddie scare program from the UK. But, as long as footy is on TV, KFC and Crust pizzas are at the end of a phone, she’ll be right mate in Aus!

    In recent weeks I have seen many, almost to vomiting point, pro-AGW aritcles in the MSM and on TV. Many documenaties profiting doom. Many light entertainment programs with “wind farms” in the background. You name it, its on Aussie TV. It is overload! BLECH!

  8. yet for 2000-2010 they are about the same as the non-rural
    ==========================================
    That’s because there has been no increase and exactly what you would expect with no increase.

  9. A single chart containing both series for the study period would be a great way to summarize your results.

  10. Very interesting post. I am going to need to study this in more detail since a quick glance suggests that there has been little warming over the period reviewed.

  11. “Note that “Last Year” for stations that are still open is given as 2009, and that 10 of the stations closed between 2000 and 2009.”
    =========================================================

    Is there any indication as to why they’re closing stations with such good history?

  12. Forgive me, but I don’t understand this post. The only graph is from CRU showing :”massive” global warming from ~1980 to 2010 of ~.45C. Are you going to present a comparison graph of Australia rural and non-rural stations over the same period during which the rural stations warmed by 60-70% of the non-rural? The Weighted Average Trend of these stations is wildly different than the CRU global, if I am reading this correctly. Are you going to point this out? I had to look and look and I don’t know if I understand very well. Also, we don’t know if BOM data are adjusted or not? Why not? Can you ask them and tell us if you got an answer or not? (Forgive me, again, for this request. I can see that the effort that went into this post is very, very extensive. I simply don’t understand its purpose.)

  13. So you’ve managed to observe that global warming is happening and that it’s happening in both rural and non rural areas.

    But this has been well documented for years. That’s why NASA GISS go to great pains to remove any possible influence from Urban Heat Island Effect. They compare urban long term trends to nearby rural trends. They then adjust the urban trend so it matches the rural trend.

    Glad to see WUWT is catching up to what climate science has been saying for years.

  14. “The data as downloaded is in http://members.westnet.com.au/QMeanTemps.pdf (2.8mb)”

    The link does not work.

    “404 Error
    File Not Found
    The file or location you have specified does not exist.
    Possible Reasons
    The address you entered may be misspelt.
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    Click the Back button to try another link.”

  15. Beside rural and non-rural stations, wouldn’t it make sense to have airport stations as a special category?

  16. Super work, brilliant.

    Here deviation (UHI?) between UAH and GISS for continents 1979-2010 as a function of population growth rate:

    AUSTRALIA appear to have rather low divergence UAH-GISS in general 1979-2010. (UAH is basiccaly UHI free).

    However , this is the case for all industrial continents/areas 1979-2010:

    Its the undevelloped areas that shows more heat in ground stations than the UHI-free UAH data.

    In fact the area Africa+Latin America + Asia(not Russia) has en average extra heat in ground based GISS data of 0,46 K com pared to UHI free UAH data.
    This is a huge area, this is a huge difference over just 31 years.

    So far at joanne Novas, no alternative explanations than UHI has come up:

    http://joannenova.com.au/2011/02/the-urban-heat-island-effect-could-africa-be-more-affected-than-the-us/

    K.R. Frank

    PS: same data shown as a LOGARTHMIC function: Spot on the same picture:

  17. Hi! Great job!

    But I also think the article could have been a bit easier on the eye, if it contained some graphs. They are massively more illustrative than tables :)..

    Thanks!

  18. It would be interesting to see the same analysis performed for July (winter in Oz). The AGW models, as I understand them, indicate CO2 forcing should have more effect in winter than summer—-But this should be unchanged for rural vs. urban. At a common sense level, it seems that UHI effects should be exaggerated in winter, since cities are filled with buildings, cars,etc, that are aggressively heated during winter—-and that heat is continuously distributed into the atmosphere.

    So a winter month analysis, if UHI is important, might show a greater rural/urban discrepancy.

    Methodology comment: Perhaps given the “subjective” nature of assignment to “rural” and “non-rural”, in the future one might divide three ways: “clearly rural”, “clearly non-rural”, and “unclear”, and perform the analysis for the “clearly…..” categories. This might produce an even stronger signal for a UHI effect.

    Of course, easy for me to say, since I am not doing the hard work, which is much appreciated.

  19. Good stuff – who did this? Anthony? Or Jonas somebody?
    Now all that’s needed is the same analysis for the rest of the world.
    So some of the key facts seem to be
    * Only 10 of 47 BOM sites are rural.
    * For the rural sites the longterm trend is only about 0.4C/century.

  20. Looks like the Aussies had the reverse of the North American temperature trend 1910-1940. Sure would prefer a temp vs. time graph.

  21. “Whether each station was rural or non-rural was determined using Google Earth. The decisions were necessarily subjective.”

    “Temperature trends (least-squares linear fit) over various periods were calculated separately for each calendar month, for each station, using only the monthly temperatures that were given. There was no attempt to fill in any missing data.

    For each period, only the stations active for the whole of the period were used (there could be some missing data, but the test was that there was data for the first year of the period or for an earlier year and that there was data for the last year of the period or for a later year).

    The overall trend for each period was then taken as the average of all trends for that period – ie, the average across all calendar months and all active stations.”

    1. What this means is that the result cannot be replicated. The criteria, objective or subjective, have to be documented.

    2.Your method isnt clear

    I’ll say the same thing I say to Jones or mann. Post your data as used and your code.

    Thanks.

  22. You explained that the decision to classify stations as “rural” or “non-rural” was subjective, based on Google Earth (i.e., satellite photos). So what subjective criteria are different in rural stations as opposed to non-rural stations.
    When I look at the Google Earth photos of some of these station locations, it seems to me that many, if not most, of the stations you have identified as “non-rural” are in locations that in my subjective opinion would consider rural. Places like Salmon Gums or Camooweal — just to pick a couple at random — are dots on the map. There can’t be more than a few hundred people living in either of these places. How could they be considered anything but rural?

  23. SteveE says:
    February 21, 2011 at 7:03 am
    “So you’ve managed to observe that global warming is happening and that it’s happening in both rural and non rural areas.

    But this has been well documented for years. That’s why NASA GISS go to great pains to remove any possible influence from Urban Heat Island Effect. They compare urban long term trends to nearby rural trends. They then adjust the urban trend so it matches the rural trend.”

    LOL, seems the Giss adjustments are actually the opposite, increase the rural so it matches the Urban.

  24. SteveE says:
    February 21, 2011 at 7:03 am

    So you’ve managed to observe that global warming is happening and that it’s happening in both rural and non rural areas.

    But this has been well documented for years. That’s why NASA GISS go to great pains to remove any possible influence from Urban Heat Island Effect. They compare urban long term trends to nearby rural trends. They then adjust the urban trend so it matches the rural trend.

    Glad to see WUWT is catching up to what climate science has been saying for years.

    If you have a long trend for RURAL stations and a long trend for URBAN stations ask yourself why you need to remove the UHI influence from the URBAN stations when you have PRISTINE RURAL data in the first place, and don`t tell me there are not enough rural stations up to the mid 1990`s.

  25. SteveE.

    The groups you cite claim that the increase in UHI over the last 100 years is only 0.01C. This work clearly shows that at least as far as Australia is concerned, the so called “adjustments” for UHI are not accurate. Not even in the same ball park as accurate.

  26. Another point SteveE. Very few people have ever claimed that there has been no warming over the last 100 years. The argument has always been how big the warming is, and what is the cause or causes.

  27. JohnH says:
    February 21, 2011 at 8:49 am

    SteveE says:
    February 21, 2011 at 7:03 am

    You forgot to add that they adjust the wrong way as well, they adjust the past down and the present up.

  28. re observa says…

    there are 2 towns called Mt. Barker in Australia. The article obviously refers to the West Australian one.
    The SA one is somewhat larger than you think, as the suburbs on the other side of the freeway are considered as a different town. From the air it would look like a town of 25,000.

  29. Jean Parisot : I don’t know why the stations are closing. My understanding is that the stations’ primary purpose is for local weather, not climate, so nothing sinister need be read into the closures. Given the attention being given to climate, however, and the role of weather stations, the closure of a significant number of stations in the last few years does seem a bit odd.

    Michael R : Yes it could be very interesting to graph some of the data. All I need is more time, in short supply at the moment …

    Steamboat Jack : The longer the period, the more valid the four decimal places are. And bear in mind that the exact same trends reported as deg C per century would be only 2 decimal places.

    Jit – yes, what you are looking at are trends. If a “Min” number is higher than a “Max” number, it means that Minimum temperatures have been rising faster than Maximum temperatures. There were seasonal differences too, but I haven’t had time to examine them properly.

    Pyromancer76 : I think a lot more analysis of Australian (and other S.H.) temperatures would be interesting. Does the S.H. Match the reported global trend, or does it go its own separate way? I noted the apparent big difference from global temperatures to 1940, but I don’t have any answers.

    Corey S : Apologies, the correct link is http://members.westnet.com.au/jonas1/QMeanTemps.pdf

    k winterkorn : If I have time (unlikely in the near future), I’ll look at summer vs winter, rural and non-rural. Division into 3 station categories would make sense, but it would also make the number of stations in each category even smaller.

    Steven mosher : The data is posted, see the last link in the post and my note to “Corey S” above. The Google Earth maps used to classify the stations are there too, as are all the individual trends by station / calendar month. Least-squares line-fitting is a well-known process. Let me know if there is anything else you need.

    Peter : In the Google maps, the position of each weather station is given. If it’s among the buildings of a settlement, even if the settlement is small, I have tended to classify it as non-rural. Anthony has made the point before that it isn’t the size of the settlement that matters, but its growth. If the station is well away from the buildings, runways, etc, then I could classify it as rural. But it still in the end can’t be anything but a subjective process.

  30. @SteveE In case you are not trolling, the point is that the magnitude of UHI is much greater then the AGW crowd admits.

  31. Using the bird’s eye view in Bing.com maps for the lat-lon given for Mt Barker shows the area where the met station is located is rural.

  32. Having a play around with WolframAlpha is informative.
    I plugged in the town nearest me (Grantham) and the weather station is RAF Cranwell, showing a warming of 0.036F/year. I then looked for London, where it used the City Airport, a few miles to the East of central London, which is warming at 0.072F/year.

  33. Mike
    I compared the raw temp data (max temps) from 50 long-term w/s throughout Australia and found that 1914 was warmer than 2008. Yet on the official record 2008 is shown to be warmer than 1914.
    I then discovered that when comparing an individual station’s raw data with that station in the BOM’s high-quality climate site network, I found many of the raw temperatures to be adjusted downward for temps prior to 1950. Why?

  34. A coincidence! I was having a jolly good look at BOM last night, and it’s fascinating that their very own Trend Map, when loaded with the 1910 – present data (that’s as far back as it goes) shows a trend of an average maybe 0.5 degrees or a fraction more over the whole Country, in 100+ years!!! Give man’s CO2 is what, 3.5% of total CO2 output, let’s be generous and say Man’s contribution to warming is, say 10 percent? Wooo, so we have warmed Australia by 0.07 degrees? Mmmm, better sell the Silver to fix that…

    http://www.bom.gov.au/cgi-bin/climate/change/trendmaps.cgi?map=tmean&area=aus&season=0112&period=1910

  35. The sample size looks too small. This is basically a sample size of 10 stations. Given the accuracy of the measurements and the random variation of heating and cooling trends between stations, I doubt this reaches statistical significance. You may have just got a couple of unusually low trend rural stations by chance.

    If the purpose of this study is to show that the climate science establishment’s results are invalid due to UHI, then I think this study fails. Of course, it is not clear at all to me that the results of the climate science establishment are themselves statistically significant.

  36. In February 2010, I analysed all 51 of Australia’s Long Record Temperature Stations (LRTS) with the aim of demonstrating a UHI signal, if any, in BoM’s Annual Climate Statement. (LRTS have records covering all, or very nearly all, of 1910-2009.) Following is a summary of the results.

    There are 3 values in each row. The first 2 are the mean temperature anomalies (degC) for 1910-1959 and 1960-2009 respectively. The third value is the difference (warming).
    Annual Australian Climate Statement 2009
    BoM (2009 Report) -0.34 0.16 0.50
    Melbourne -0.26 0.25 0.51
    Sydney -0.48 0.16 0.64
    LRTS (51 stations) -0.11 0.14 0.25
    LRTS (43 rural stations)-0.08 0.13 0.22

    Clearly, national warming as reported by BoM (0.5 degC) is closer to a known heat island, Melbourne, and more than twice that of the national long-standing records.

    BoM’s Reference Climate Station Network, on which its reports are based, overlaps but does not coincide with the LRTS. Even so, the foregoing analysis adds to the disquiet concerning UHI bias in official BoM reports.

    Mike Jonas’ study differs in the method of analysis and selection of stations. As other readers here have urged, it would be really useful if Mike could summarise his findings, perhaps in tabular form as here, and wind up with a definitive conclusion.

    http://www.bom.gov.au/climate/data/index.shtml

    http://www.bom.gov.au/announcements/media_releases/climate/change/20100105.shtml

  37. In my previous comment, the second paragraph should read:

    There are 3 values in each row. The first 2 are the mean temperature anomalies (degC) for 1910-1959 and 1960-2009 respectively. The third value is the difference (warming).

    BoM (2009 Statement) -0.34 0.16 0.50
    Melbourne -0.26 0.25 0.51
    Sydney -0.48 0.16 0.64
    LRTS (51 stations) -0.11 0.14 0.25
    LRTS (43 rural stations)-0.08 0.13 0.22

  38. The amateur American study mentioned above seems better designed and based on much more data. It was also mentioned on JC’s ‘Defunding IPCC’ post, here: http://judithcurry.com/2011/02/19/u-s-to-kill-funding-for-the-ipcc/#comment-45205

    Cross-post:

    ferd berple | February 21, 2011 at 12:43 pm | Reply | Reply w/ Link |

    Here is what a father and son team were able to do without funding. It looks a lot more meaningful than what we are seeing from the IPCC and well worth watching.

    Brian H | February 21, 2011 at 5:47 pm | Reply | Reply w/ Link |
    Excellent stuff! Zero increase rural since 1900. So the AGW/CRU/GISS numbers in the range of .7°+ are basically 50% composed of the 1.5°C UHI increase.

    Heh. Precisely what we Deniers have been scurrilously alleging. Put that in your bungholes and smoke it, Mike and Phil et al.!

    P.S. Attempted to put the link on T&N, but it seems to be in periodic purge mode, or something — no comments box. Reloading from the Home page doesn’t help.

  39. @Steamboat Jack says:
    February 21, 2011 at 5:40 am

    “Steamboat Jack (Jon Jewett’s evil twin)” I knew a Jon Jewett once; did you ever work for a major oil company, like in the 1980s?

  40. #
    Ian George says:
    February 21, 2011 at 12:42 pm

    I then discovered that when comparing an individual station’s raw data with that station in the BOM’s high-quality climate site network, I found many of the raw temperatures to be adjusted downward for temps prior to 1950. Why?
    #
    Scott says:
    February 21, 2011 at 1:18 pm

    The data you can download from the BOM site is modified. Shamefully they call it ‘raw data’

    Ian: The “why” is to produce a positive slope, and apparency of warming.
    Scott: here’s a correction: “ShamefullyShamelessly they call it ‘raw data’ ”
    There, all fixed! ;)

  41. Mindbuilder,

    would you like to show us how many stations were researched to come up with the homogenization adjustment that is so popular in the official temp series data?

  42. This is pretty poor methodology. Most of these stations ought to be classified rural, exceptions being Sydney Canberra Melbourne Rockhampton Launceston Hobart and possibly a few others depending on specific site characteristics. Unless the result holds up with a more sensible station classification, it doesnt mean anything

  43. “Looks like the Aussies had the reverse of the North American temperature trend 1910-1940. Sure would prefer a temp vs. time graph”

    That is net surprising if you analyse the CRU data.

    Very little data from 1900-1940 was actually used and 4years of data was picked from another station in the same grid.
    e.g. Kalgoorlie was filled with 1890′s figures 200km away

    Geraldton was filled in the 1890′s from Hamlem pool (200km away)

    Hall’s Creek used only 1899 from the old town 12km away

    Then after climategate the Met office released the CRU2010 details that for Australia purport to be “Based on the original temperature observations sourced from records held by the Australian Bureau of Meteorology”.

    That was real eyeopener as that showed less warming/cooling on the sites I have checked and lower numbers for 2007,2008,2009.

    So which is the original data?

    The old Bom site or the CRU2010v data?

    The only way to find out is to go back thought the original paper forms IMO.

  44. SteveE says:
    February 21, 2011 at 7:03 am

    So you’ve managed to observe that global warming is happening and that it’s happening in both rural and non rural areas.

    But this has been well documented for years. That’s why NASA GISS go to great pains to remove any possible influence from Urban Heat Island Effect. They compare urban long term trends to nearby rural trends. They then adjust the urban trend so it matches the rural trend.

    E.M. Smith has explained why this is a superficial view of what’s done to bias the record.

  45. I don’t see a way to enter anything in tips and notes, so here goes.

    The Wolfram Alpha weather data is summarized in the link. They list:
    Among current principal sources for WeatherData are:
    Citizen Weather Observer Program. “Citizen Weather Observer Program (CWOP).” 2008. »
    National Oceanic and Atmospheric Administration. “National Weather Service.” 2008. »
    United States National Climatic Data Center. “Global Surface Summary of Day.” 2008. »
    United States National Climatic Data Center. “Integrated Surface Database.” 2008. »

    Does anyone believe they realize the data might not be so good? I noted to them that they need to plot error bars and the source(s) of the data. They pointed out the Sources link at the bottom of the temp chart. (For example, in WolframAlpha, you can type in “temperature Houston” and get a temp record for Houston. http://www.wolframalpha.com/input/?i=Houston+temperature

    The data source list is here:

    http://reference.wolfram.com/mathematica/note/WeatherDataSourceInformation.html

  46. This is pretty poor methodology. Most of these stations ought to be classified rural

    No, it is good reasoning, although the terminology is poor.

    The issue is not whether a site is “rural” or “urban”. The issue is whether the land use in the nearby area has changed over the last 100 years.

    A site out of town, but by the international airport, is not “rural” for climate purposes. No matter how few people live nearby. The UHI effects are what matter, not the population.

    Do try to keep up with the actual issues. The pretence that because a site not inside a town means it is “rural” is a key feature in the inflated land temperature values we get from GISS etc.

  47. Mt Barker is non-rural, but it’s also non-urban. It’s only around 30kms from Adelaide but nestled in the heart of the Adelaide Hills. It is elevated well above adelaide though and gets a lot more rain and a constant temperature of 2 degrees less than adelaide. (Yes I Live near by)

  48. Heavily OT, but I am anxious about the folks in New Zealand whether some have suffered from the earthquake of this morning.
    Are you OK?

  49. The coordinates of Mt Barker in the first table make it the West Australian town, not the SA town.

    MOUNT BARKER ,01/1886, -34.6250, 117.6361 BoM #009581

    The 2006 census population was 1622 people. The given coordinates place the weather station about 1,600 km west of the edge of town. There have been up to 3 station moves since recording began in 1886.

  50. Darren Parker says:
    February 21, 2011 at 9:48 pm

    Mt Barker is non-rural, but it’s also non-urban. It’s only around 30kms from Adelaide but nestled in the heart of the Adelaide Hills. It is elevated well above adelaide though and gets a lot more rain and a constant temperature of 2 degrees less than adelaide. (Yes I Live near by)
    ——————————————–
    I thought it was established that it was Mt Barker in WA?

    I commend the researchers, but suggest that more work is needed to bring this up to publishable standard. Going back to taws (there’s one for the international English brigade) we need to know whether, and if so how and why, the BOM data has been adjusted.

    It is good to see that people are out there working on this stuff.

  51. If you still have the data in suitable form, it might be interesting to sort those stations elevated more than 150 m asl and compare trends of the low ones with the high ones. On a smaller set of data, I found this to be the most significant extraneous factor in the last 40 years.

    Coastal below 150 m average trend deg C/yr Tmax 0.0084 Tmin 0.003
    Inland above 150 m average trend deg C/yr Tmax0.0219 Tmin 0.0206

    The other extraneous factor that needs investigation is the change from Hg to electronic temperature sensors. This started in the late 1980s. It might help explain your convergence of rural with non-rural in the decades after this.

  52. Ed Waage : I think a few of the stations’ classifications are marginal, and there are a number of other interesting features worth pursuing, but right now I’m not putting any more time into it other than responding to some comments. The data is readily available, so anyone can work on it, but be prepared for it to be very time-consuming!

    Ian George : I have no idea why the temperatures have been adjusted. If I do get going on the next phase of this exercise, it would be nice to have access to the raw data. Do you have a link? When looking at temperature trends in Oz, I think you should look at minimums [minima?] as well as maximums. Eyeballing the trends as posted, it looks like minimum temperatures went up much more than maximums soon after 1970, but that the trend reversed around 2000. More work needed …..

    Mindbuilder : Yes the sample size is small, and probably too small to reach firm conclusions (which is why I said “… appear to …”). I certainly don’t want to try to read into it any more than is justifiable. I don’t think that my study proves anything, but combined with other tests for UH (such as have been reported on WUWT in the past and by Graham here) it does suggest that a proper evaluation of UH is needed.

    Graham : It seems that however one looks at the data, there is an indication of UH. But as I said above, I’m not sure that the sample size that was available to me is large enough to come to a definitive conclusion.

    Bill : Anthony has shown that it not the absolute size of a settlement that matters. Its growth or encroachment on the weather station is a more important factor. (Mooloo has put it well a few comments down from yours).

    Darren Parker : It seems there are two Mt Barkers. The relevant one here is at (-34.625, 117.6361) just N of Albany, WA.

  53. Oodnadatta is clasified as non-rural, it has a scattered population of 280, it is in the middle of the Simpson desert, has Finke, an Aboriginal township 130 miles (8 hours) to the north, Coober Pedy, a mining town 130 miles (5 hours) to the southwest, Birdsville 450 miles (2 days if you are lucky) to the east and Kalbarri 1100 miles (5 days) to the west. If that isn’t rural I don’t know what is.

  54. The classic study of Barrow, AK, showed that a mere couple thousand population can cause a significant UHI effect. It depends on the details of the site and the changes that have occurred.

    With that in mind, without a detailed site analysis I suspect many stations classified as rural, really aren’t. Truly rural stations are probably quite rare. A proper analysis would eliminate ALL non-rural stations, even if it eliminated almost all of them.

  55. None of this UHI research matters if the satellites show the same warming as the thermometers. Do they? Are they a little different than the thermometers? Are they a lot different? Are the satellites calibrated against the ground stations from time to time to account for the drift or change in the satellite instruments? Calibrating the satellites against the ground stations would be a stupid thing to do if you wanted to use the satellites as evidence to corroborate the ground stations. Is there anybody doing measurements in the satellite community who we can trust not to redo the calculations as many times as it takes to get the “right” answer?

  56. old44 : It might seem odd that a little remote place like Oodnadatta is classified non-rural, but Station 17043 is Oodnadatta AIRPORT. The weather station there appears to be right alongside where the planes taxi in and out (see the Google Maps link). The test isn’t whether the place is a major urban centre, but whether the temperature record there is likely to be contaminated by development.

  57. Mike Jonas says:
    February 21, 2011 at 11:17 pm
    You are correct about minimums – they seem to be increasing (maybe due to cloud cover or increasing CO2? – just speculating.). Of course, more cloud cover can cool daytime max temps.
    Link to raw data is:

    http://www.bom.gov.au/climate/data/

    Link to high-quality data sites (which I presume they use for their official data sets is:

    http://reg.bom.gov.au/cgi-bin/climate/hqsites/site_networks.cgi?variable=maxT&period=annual&state=aus

  58. Ian George : Thanks for the links. The “raw data” is what I used, but I don’t think it is stated anywhere that it is raw – I assumed that it could have been adjusted (but that it was the best available).

    I see their high quality climate sites are classified urban or rural, with only 9 of the 100-odd sites being classified urban. Port Macquarie (Bellevue Gardens) and Bathurst Agricultural Station are among the urban sites, yet Brisbane Aero and Canberra Airport are classified rural.

  59. Geoff Sherrington : Apoologies, I missed your comment earlier. The individual station trends by period are all in
    http://members.westnet.com.au/jonas1/QMeanTempTrends.pdf (734kb)
    if you want to sort them by altitude.
    The trends are rounded to 4 dec places but hopefully that will be sufficient.
    Let me know if you need a different format.

    Interesting about the change from Hg to electronic temperature sensors from the late 1980s. All temperature analyses are in trouble if that made a significant difference.

  60. Mike, I’m in the middle of a similar project, using several hundred stations. Like you, I’m using Google earth to define Pristine, Rural with sensor away from town, rural with sensor within town (and population from several past censuses). I have the advantage that I’ve visited very many of these places over the last 55 years and I have retained some local knowledge. Indeed, I was part of setting up some stations.

    It is likely that the USA categories of rural and non-rural based on population are too insensitive. I’m listing populations from 10 people upwards , hoping to end up with composite graphs of Tmax and Tmin trends, dissected into whatever other factors appear influential. Warwick Huhes did this a few years ago. One of the factors I’ve so far found for non-urban sites is that airports seldom affect the trend. Too few flights, aircraft too small, often dirt strips with no asphalt.

    Because I’m so tied up with this, I suggested the look at trends versus altitude. It’s not that I’m lazy, just thought you might be able to do it with a flick of a familar wrist on the data. You might be surprised to find how large (and how inexplicable) it is.

    BTW, I too made an error with Mt Barker WA. The recording station is 1,600 m, not km, west of the edge of town.

    Finally, re the changeover from Hg to electronic sensors, this was about the time of a WMO get together on data gathering, 1990 or so. If you look at various spaghetti maps like Darwin below, you often see that the various data adjusters seem to come to closest concordance about 1990, then diverge again.

  61. In the Google maps, the position of each weather station is given. If it’s among the buildings of a settlement, even if the settlement is small, I have tended to classify it as non-rural. Anthony has made the point before that it isn’t the size of the settlement that matters, but its growth. If the station is well away from the buildings, runways, etc, then I could classify it as rural. But it still in the end can’t be anything but a subjective process.

    I have a couple of hopefully constructive thoughts about this. It sounds like what you are trying to say is that the difference between the rural/non-rural classifications is in the distance between the weather station and other man-made features. If that is the case, I think you should say that, because the use of the titles “rural” and “non-rural” is misleading. More importantly, shouldn’t you show us exactly how you made the rural/non-rural classifications? How far does a weather station need to be from other mad-made features so it becomes a “rural” station as opposed to a “non-rural” station? “Well away” could mean 10m to one person and 1 km to another. And how was that distance determined? Is there research that informed that decision?

    Using that set of criteria, could a properly sited weather station in the middle of park in a large urban area be classified as “rural” if there are no neighboring structures? If I put a weather station in the middle of the Great Lawn in Central Park (in New York), would that be a rural station?

  62. Peter – the process was imprecise. In nearly all cases, the classification was pretty obvious, but some of them did come down to a subjective decision. The basic criterion was whether it looked like the station might have been affected by nearby development after it first went into operation. An airport therefore immediately put the station into “non-rural”. But a position next to a lighthouse, for example, would still qualify as “rural”. I don’t have to know whether airports do or don’t affect temperatures, the argument is that they might therefore a non-rural classification is the safe one. I accept earlier comments that “rural” and”non-rural” are not fully descriptive either, but they or “rural” and “urban” are the terms that seem to be generally used, and “possibly affected by nearby development or activity” and “probably not affected by nearby development or activity” seemed a bit unwieldy.

    There were some other factors that I couldn’t allow for, such as stations moving or having changed equipment, as I didn’t have that information. Another issue that I didn’t address explicitly is this: I don’t know for sure whether the Australian temperatures used in the “official” historical global temperature calculations (GISS, Hadcrut etc) were indeed the ones I accessed, or whether the “official” history uses some other set – for example the BOM “high quality” stations. In that case, comparison of the rural trends to the “high quality” stations’ trends might have been more useful.

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