Urban Heat Effect

The recent paper submitted for publication by the Berkeley Earth team would appear to rule out any effect from urban expansion on global temperature anomaly measurements. They selected  data from stations classified by MODIS 500 as being “rural” and then compared resultant  global trends  from these just stations with the full set. They find no effect whatsoever and their conclusion is that urbanisation is a non-issue regarding global warming. Still not fully convinced,  I decided to try a different  approach and identify those stations which show the highest anomaly warming from 1890 to 2010. The use of anomalies to measure global waring is a subtle issue because there will be no difference between  a “hot” city compared to a “rural” station  unless that city has seen a larger differential  increase in the localised heating (urban effect) over time. I therefore  carried out a new study using the station data provided by the UK Met office to identify those stations which have warmed the most since 1890.  The table below shows those stations where the measured temperature anomaly averaged beween  1991 to 2010 (A2) is greater than 1 deg.C  above that measured between 1891 and 1920(A1). DA is the difference (A2-A1) measuring net “warming” over the full period. Each identified station was then classified by hand using the world-gazeteer as being a City/Town/Urban based on the total population.  The results are shown below and ordered with the largest warming effect first. Clearly some of the fastest growing cities , but do they actually effect global measurements ?

Place Country/State A1 A2 DA Type Pop
SAO PAULO BRAZIL -1.58 0.78 2.36 city: 12M
URALSK KAZAKHSTAN -1.74 0.57 2.30 city: 272K
IRKUTSK RUSSIA -1.31 0.96 2.27 city 2.6M
TUNIS TUNISIA -1.16 1.08 2.25 city: 750K
MOSKVA RUSSIA -1.37 0.79 2.16 city: 10.5M
BISMARCK N.DAKO -1.42 0.68 2.10 small: 62K
SVERDLOVSK USSR -1.42 0.65 2.07 city: 1.3M
OMSK RUSSIA -1.38 0.69 2.07 city: 1.2M
BEIJING CHINA -0.87 1.10 1.97 city: 13.3M
HELENA MONTANA -1.23 0.67 1.90 med: 28K
SAN DIEGO CALIFORNI -1.86 -0.01 1.85 city: 1.3M
SAN FRANCISCO CA -1.26 0.58 1.84 city 7.7M
ATYRAY KAZAKHSTAN -1.18 0.65 1.83 city: 162K
HONOLULU HAWAII -1.51 0.32 1.82 city: 1M
BLOCK ISLAND RHODE -1.28 0.49 1.77 small
NEW YORK USA -0.90 0.77 1.67 city 20M
KAGOSHIMA JAPAN -0.79 0.85 1.64 city: 620K
BLUE HILL MASSACHUS -1.32 0.30 1.63 small
JAKARTA/OBS INDONESIA -0.89 0.73 1.62 city: 19M
BOSTON USA -1.37 0.22 1.60 city 6M
GENEVE SWITZERLAND -0.69 0.90 1.59 City: 0.5M
TURUKHANSK RUSSIA -0.75 0.83 1.58 small: 4K
DA-EL-BEIDA ALGERIA -0.99 0.58 1.57 city: 3.3M
SAPPORO JAPAN -0.88 0.68 1.56 city: 2M
SILCHAR INDIA -0.27 1.29 1.56 city: 200K
MADRID/RETIRO SPAIN -0.81 0.75 1.56 City: 6.5M
MAZATLAN SIN. -1.13 0.42 1.55 small
YAKUTSK RUSSIA -0.26 1.29 1.55 city 282K
SORTAVALA RUSSIA -0.81 0.74 1.54 small 20K
ALMATY KAZAKHSTAN -0.71 0.81 1.52 city 1.4M
TASHKENT UKRIANE -0.91 0.61 1.52 city: 2.4M
AOMORI JAPAN -0.88 0.62 1.51 city: 300K
STRASBOURG FRANCE -0.68 0.81 1.49 City 282K
HIROSHIMA JAPAN -0.71 0.78 1.49 city: 1.9M
Wien AUSTRIA -0.70 0.76 1.46 City: 1.7M
KAZALINSK KAZAKHSTAN -0.72 0.74 1.46 small
HAKODATE JAPAN -0.83 0.61 1.44 med: 300K
WILLISTON USA -1.29 0.14 1.43 small
ACCRA GHANA -0.47 0.95 1.42 city: 2.3M
LAGHOUAT ALGERIA -0.65 0.77 1.42 town: 170K
S.PETERSBURG RUSSIA -0.61 0.79 1.40 city: 4.5M
SAENTIS SWITZERLAND -0.61 0.78 1.40 small
F.SEVCENKO KAZAKHSTAN -1.00 0.39 1.39 small
MINUSINSK RUSSIA -0.75 0.63 1.38 medium 61K
ZURICH SWITZERLAND -0.50 0.88 1.38 City: 1.4M
Villacher Alpe/Obir AUSTRIA -0.60 0.77 1.37 small
FARGO USA -0.82 0.54 1.36 medium: 108K
Sonnblick AUSTRIA -0.71 0.64 1.36 small
AKITA JAPAN -0.77 0.57 1.34 med: 300K
SALT LAKE CITY UTAH -0.78 0.54 1.33 city: 2.4M
TOPEKA KANSAS -0.77 0.54 1.32 medium: 127K
NIIGATA JAPAN -0.89 0.42 1.31 city: 800K
BEOGRAD YUGOSLAVIA -0.60 0.71 1.31 City: 1.8M
OITA JAPAN -0.61 0.69 1.31 city: 670K
Koebenhavn DENMARK -0.80 0.50 1.30 City: 1.3M
TOKYO JAPAN -0.73 0.56 1.29 city: 9M
LISBON PORTUGAL -0.80 0.49 1.29 City: 475K
KANSAS CITY USA -0.82 0.46 1.28 city: 500K
VERKHOYANSK RUSSIA -0.43 0.84 1.27 small 1.1K
KAZAN’ RUSSIA -0.33 0.95 1.27 city 1.2M
Kremsmuenster AUSTRIA -0.47 0.79 1.26 snall: 64K
HURON S.DAKOTA -0.76 0.49 1.24 small
Vardoe NORWAY -0.58 0.65 1.23 small
PEORIA USA -0.48 0.75 1.22 ?
LUGANO SWITZERLAND -0.38 0.84 1.22 City: 150K
ASTRAHAN’ RUSSIA -0.65 0.56 1.21 city: 500K
KASSEL GERMANY -0.40 0.80 1.19 Small 1.2K
Klagenfurt AUSTRIA -0.36 0.82 1.18 large 93K
STOCKHOLM SWEDEN -0.22 0.96 1.18 City 1.3M
ATLANTIC CITY USA -0.96 0.20 1.16 medium: 39K
BURLINGTON VERMONT -0.26 0.89 1.15 med 42K
ABERDEEN/DYCE UK -0.52 0.63 1.14 City: 191K
SAINT-LOUIS SENEGAL -1.04 0.09 1.14 city: 183K
NASSAU BAHAMAS -0.47 0.67 1.14 city: 350K
Salzburg AUSTRIA -0.60 0.54 1.14 City: 500K
PORT ELIZABETH SOUTH AFRICA -1.04 0.10 1.14 city 1.3M
TENERIFE CANARIES 0.29 1.42 1.13 town
COLUMBUS OHIO -0.59 0.53 1.13 city: 1M
TOBOL’SK RUSSIA -0.40 0.72 1.12 large: 100K
APARRI PHILIPPINES -0.84 0.27 1.12 med 34K
SALEHARD USSR -0.12 1.00 1.12 medium: 43K
TOLEDO USA -0.32 0.79 1.11 city: 600K
LUANDA ANGOL -1.08 0.04 1.11 city: 3M
Karesuando SWEDEN -0.34 0.75 1.09 small
PERPIGNAN FRANCE -0.57 0.51 1.08 City 112K
IZUHARA JAPAN -0.41 0.66 1.07 small
Kvikkjokk SWEDEN -0.46 0.61 1.07 small
NANTES FRANCE -0.52 0.55 1.07 City: 850K
KOCHI JAPAN -0.74 0.33 1.06 city: 330K
DE BILT NETHERLANDS -0.28 0.78 1.06 town 42K
Akureyri ICELAND -0.52 0.54 1.06 small
EL BAYADH ALGERIA -0.49 0.56 1.05 medium 112K
HOHENPEISSENBERG GERMANY -0.34 0.72 1.05 Small 3.8K
MINNEAPOLIS/ST USA -0.17 0.87 1.05 city: 380K
CURITIBA BRAZIL -0.40 0.64 1.04 city 3M
Oestersund SWEDEN -0.37 0.67 1.04 medium 44K
ARCHANGEL’SK RUSSIA -0.47 0.58 1.04 small
SYRACUSE/HANC USA -0.70 0.34 1.04 city: 700K
NEW PLYMOUTH NEW ZEALAND -0.92 0.11 1.04 small 50K
BURGOS SPAIN -0.51 0.52 1.03 small
AUCKLAND NZ -0.78 0.25 1.03 city: 1.4M
CALCUTTA INDIA -0.82 0.21 1.03 city: 4.4M
BRUSSELS BELGIUM -0.45 0.56 1.02 City 1.1M
MIYAZAKI JAPAN -0.38 0.63 1.01 city: 300K
TAMPA USA -0.45 0.55 1.00 city: 350K

There are indeed many large cities in the list which have grown over the last century, particularly Sao Paolo and Beijing.So at first glance this looks like direct evidence that urban warming could well be skewing the overall result. To investigate this possibility I excluded all the cities in the above list with a population greater than 0.4 million and recalculated the global average anomalies over the full time span. The result is surprising – there is no effect – see figure 1! It is indeed true that urban warming does not systematically effect the overall trend in global temperatures.

Figure 1: Comparison of the land station data CRUTEM3 with and without the warmest cities listed in the table.

So the conclusion is more or less the same as the BEST result. Observed increases in global temperature (anomalies) are not  effected by large urban areas.  I am convinced.

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2 Responses to Urban Heat Effect

  1. pochas says:

    Clive,

    Would you care to comment at Roy Spencer’s blog on possible differences between your work and his?

    http://www.drroyspencer.com/2012/03/mckitrick-michaels-were-right-more-evidence-of-spurious-warming-in-the-ipcc-surface-temperature-dataset

  2. feliksch says:

    To locate weather-stations is not easy.
    AFAIK Saentis and Sonnblick are mountaintops; Villacher Alpe and Hohenpeissenberg are hills, Kremsmuenster is within the walls of a monastery, Salzburg and Lugano seem to be airports as may be Vienna (or hill). The population numbers are not really reliable.

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