Feb
Urban Heat Island Effect proven to corrupt Aussie climate data
We are delighted to showcase this latest study by Graham Dick that is set to add spice to the already hot debate over Australian surface temperatures. This analysis explodes the mainstream myth that urban heat island (UHI) temperature contamination has no effect on the Aussie temperature record. Australiagate is now a raging torrent bursting through the climate floodgates.
URBAN HEAT-ISLAND CONTENT IN THE AUSTRALIAN TEMPERATURE RECORD
Urban heat-island (UHI) contamination of temperature data is the subject of intensifying investigations around the world. Although dismissed by some as a insignificant 1, there is mounting evidence that the UHI artifact exaggerates apparent global warming trends 4-10.
In Australia, UHI and a number of related factors are the subject of in-depth investigations4, 5, 7, 9, 10. They target specific meteorological (MET) stations in order to identify covert strategies used to manipulate the data. This brief preliminary study, on the other hand, considers the data as it stands. The aim is simply to examine the claim that UHI content has no effect on the national temperature record.
Bureau of Meteorology (BoM) records selected for analysis relate to long record temperature stations (LRTS) 3.
There are just 51 LRTS that have records considered here to be long enough. Thirty-seven have records from 1910 to 2009 inclusive. Three start in 1911, five in 1913 and one in 1914. Two end in 2006 and three in 2008. All station numbers are listed in the Appendix.
Annual LRTS temperatures are averaged to represent mean temperature anomalies for Australia (MTAA) since 1910. They are compared with official MTAA reported in BoM’s Annual Australian Climate Statement 2009 (Fig 1) 2.
The basis for comparison with BoM’s MTAA is simply the difference between the average MTAA for 1910-1959 and 1960-2010 respectively. For BoM MTAA, that difference is estimated from the heights of the decadal means shown in Fig 1.
BoM’s MTAA is compared also with MTAA for (a) two recognised UHI and (b) the LRTS after removing a sample of 8 stations tentatively identified as UHI. They are four major cities and four nominal airports. Depending on MET siting, some airports may register relative cooling 10. However, apparently most airports act as UHI 6, 8. After removing the 8 stations, the residual LRTS subset is referred to here as rural.
Results are represented in Figs 2 to 5 and tabulated below.
Averaged MTAA ( OC ) are:
| Fig | 1910-1959 | 1960-2009 | Warming | |
|---|---|---|---|---|
| #1BoM | 1 | -0.34 | 0.16 | 0.50 |
| #2 LRTS (51 stations | 2 | -0.11 | 0.14 | 0.25 |
| #3 Sydney | 3 | -0.48 | 0.16 | 0.64 |
| #4 Melbourne | 4 | -0.26 | 0.25 | 0.51 |
| #5 LRTS (43 rural stns) | 5 | -0.08 | 0.13 | 0.22 |
The results indicate:
- Warming according to BoM is twice that of LRTS (#1, #2).
- Warming according to BoM mirrors a known UHI, Melbourne (#1, #4).
- UHI effect is greater for Sydney than Melbourne (#3, #4).
- Warming of the LRTS record is reduced by excluding a sample of UHI (#5).
Conclusion
The claim, that UHI content has no effect on the national temperature record, is not supported by this study in which the national temperature trend is represented by long record temperature stations (LRTS). Warming since 1960 of recognised UHI, Sydney and Melbourne, is greater than national warming by a factor of 2 or more. Likewise, BoM’s statement of annual mean temperature anomalies for Australia (MTAA) overstates warming by a factor of 2. That exaggeration of the national trend increases when a small sample of UHI are removed from the LRTS set. Further increase is expected when other suspect stations are confirmed as UHI and removed from consideration. (Quantitative estimates stated here are subject to statistical confirmation.)
Implications
- This study is another indication that BoM’s MTAA (Fig 1) lacks credibility and should not be used as a basis for reporting national temperature trends. Its value, if any, is to indicate trends of UHI like Melbourne.
- Only 8 UHI were excluded to obtain the rural LRTS estimate. They are major cities or nominal airports. Inevitably there are other suspect stations. Based on BoM data, the following stations show UHI or other suspect trends and would be useful to target for closer inspection.
9519, 25509, 29004, 30018, 32004, 32025, 33047, 40043, 44010, 55023, 58012, 61086, 65026, 79023, 84016
- There are just 11 rural LRTS with records back to the 1880′s. Fig 6 shows the MTAA for those extra-long record stations (XLRTS). They warrant further investigation if only to estimate the extent to which their 19th century trend represents record warming 7.
References
- Anderson M 2010 Urban Heat Island Myth is Dead
- Bureau of Meteorology Annual Australian Climate Statement 2009
- Bureau Of Meteorology Climate Data Online
- D’Aleo J and Anthony Watts A 2010 Surface temperature records: Policy driven deception?
- Eschenbach W 2009 The Smoking Gun At Darwin Zero
- Goetz J 2009 World’s airports continue to run warmer than ROW (rest of world
- Hughes W 2010 Some essential history of IPCC global warming from 20 years ago
- Meyer W 2009 Airports Are Getting Warmer
- Morris CJG 1994 Urban Heat Islands and Climate Change – Melbourne, Australia
- Stewart K 2010 GISS manipulates climate data in Mackay (2nd Edition)
Appendix
Long record temperature stations (LRTS) Fig 2
9510, 9518, 9519, 9534, 9581, 10073, 10111, 10579, 10614, 2074, 13012, 25509, 26026, 29004, 30018, 30045, 32004, 32025, 33047, 36026, 37010, 38003, 39039, 39085, 40043, 40126, 44010, 44026, 46037, 46043, 55023, 58012, 61055, 61086, 64008 , 65026, 66062, 69018, 75031, 75032, 78031, 79023, 80015, 82039, 83025, 84016, 85096, 86071, 89002, 90015, 94029
Excluded stations (Fig 5)
38003, 61055, 66062, 75032, 80015, 86071, 89002, 94029
Extra-long record temperature stations (XLRTS) Fig 6
26026, 46043, 55023, 58012, 64008, 69018, 75031, 83025, 84016, 85096, 90015
Graham Dick, now retired, joined the full-time staff of the School of Optometry, University of New South Wales, Sydney in 1966 and held the position of lecturer for 26 years in the areas of optics, visual impairment and clinical optometry. He has maintained a keen interest in the AGW imbroglio since attending a persuasive lecture by Australia’s Prof Bob Carter in Sydney 3-4 years ago.
Possibly related posts:
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- ScienceNow Daily says 2009 hottest year south of equator. Climate Scientist says hogwash.
- Czechgate: Climate scientists dump world’s second oldest ‘cold’ climate record
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This is good stuff.
The temperature records are available for download from http://www.bom.gov.au.
A while back I downloaded some rural and city data, creating simple trend lines in Excel. But what I was concerned about was the fact that I was playing with whole numbers, i.e., temperate in degrees C. So, is it right to compare Sydney with Dubbo over any time period? How does one normalise the data to reflect the differing nature of the places; their different population densities, different climatic zones? In particular, I wondered if one could normalise by population on its own. Or, normalise by the percentage area under impermeable surfaces like roads, roofs, concrete, buildings etc because the latter do have an effect (stand on a bitumen road in summer in bare feet and similarly on the grassy verge, or a dirt road in the country…..).
I don’t understand the way the pure temperature record is being statistically analyzed but all this concentration on whole number analysis does rather worry me. Anyone explain it to me?
Simon
Simon
Urban heat island effects are summarised nicely by D’Aleo and Watts in Reference #4. On page 34 of their report is a formula relating temperature to population. They also include a great infra-red aerial photo of heat islands.
In the context of the national or global surface temperature record, seemingly the best strategy is not to embed MET stations in populated or industrialised areas in the first place. It makes as much sense to measure the temperature of the kitchen with a thermometer embedded in a hot potato and then adjust for the local hot effect! Same goes for airports. They are inherently riddled with temperature contaminants.
It may not be relevant to your request about data precision, but BoM’s temperature values are given to the first decimal place.
Graham,
Yes, thanks for the reference to page 34 of D’Aleo and Watts. In particular:
One can only assume that, in Oke’s formula for the urban heat-island warming, that population is a suitable surrogate for measuring the effect of “vertical walls, concrete, steal etc”:
UHI = 0.317 ln P, where P = population.What about population density? Or cities that have more concrete, bitumen roads etc than a city of similar population size or density?
At least the article gives me confidence that this is being looked at, measured, and compensated for.
Thanks.
regards
Simon
Simon
In my opinion, D’Aleo and Watts’ report is a landmark study, well organised and comfortable to digest.
Factors contributing to UHI, some of which you mention, are surely innumerable and chaotically variable. A dog’s breakfast, in fact. However, Oke’s empirical equation based on systematic observation is certainly a useful basis for quantifying UHI. It has general application with some variation according to location. For example, I’m just reading this paper: “Evaluation Method of Heat Island Intensity for Coastal Urban Area” by H. Miyazaki 2009. Sorry I don’t have the link.
Presumably, the precision problem you had with BoM’s data is resolved.
Graham,
I agree with you about the importance of Oke’s paper.
That taking into account other innumerable factors will be hard is granted. But perhaps someone might extend Oke’s work, who knows.
I didn’t really have a precision problem with the BOM’s data. By whole numbers I meant unnormalised data (ie degrees Celsius as against degrees Celsius multiplied by 0.37). I did not mean that the temperature values were lacking decimal precision.
Thanks.
Simon