Examining Urban Heat Islands – Part 1.

How cities grow

Roy Spencer’s recent work (here and here) and comments by Peter Azlac got me thinking about urban heat islands (UHI) again.  I’ve been musing that so many studies show, measure or quantify UHI in various places, and the likes of Jim Goodridge and Roy Spencer can demonstrate correlation of various measures of urban and human influence vs temperature in (albeit imperfect) studies, while lukewarmers Steven Mosher and Zeke over at Lucia’s cannot? My own personal view is that rural land change and UHI are so indelibly written into the temperature record that we’ll never be sure of deciphering an effect; of course that does not mean we shouldn’t try.

Few people deny that UHI as a phenomenon exists (or at least can exist under the right conditions).  The argument is whether it has any significant impact on the global mean surface temperature.  Skeptics like to point out, as Paul Homewood says here, that warming and cooling trended stations co-exist and that there is an apparent large divergence in the response of stations over a measured period that points to UHI. However, as Steven Mosher puts it:

It is well known that you can look through the data and stations and find “cases” where an urban station looks to have UHI. Doing this is easy. Pick the biggest cities you can. But,  that doesnt get you an answer to the question: “what is the bias in the total record?”  If you like remove those few bad apples and the answer you are left with is indistinguishable from the answer you get with those bad apples left in the record.

Mosh’s approach has tended to use the ‘gold standard’ stations with longer records of 70 years or more, but even some shorter scale studies find no difference overall. Why?

It is not just the ‘bad apples’ that are warming, and not just because of CO2 or global warming (as if!). Changes in land use are starting to gain acceptance as affecting data. The challenge is understanding and proving the effects of human-induced changes and separating them from natural warming and any from CO2. No mean feat.

In The First Measured Century a narrative describes the pace of change in the USA:

At the beginning of the century, 60 percent of the population lived in or around places with fewer than 2,500 inhabitants.

The decades before and after 1900 were a period of enormous transformation in the physical locations of Americans. Demographers typically distinguish two modes of living: urban and rural. In plainer language, people live in the city or they live in the country. Throughout the nineteenth century, the United States had been mostly a nation of farmers, who lived in the country. Indeed, immigrants came to America seeking land that they could farm.

But throughout the nineteenth century, the population living in cities rose faster than the rural population. As the 1800s wore on, more and more Americans moved from the farm to the city, abandoning farming to build new industries in the cities.

In 1880, when a new wave of immigrants began to arrive in the United States, they moved to American cities, not to the countryside as immigrants had for 250 years. Immigrants took jobs in the new industries in the new cities. […] The cities grew at a fabulous pace, some of them doubling in size every decade. By the 1920 census, the urban and rural populations were equal in size, but the rural share would continue to drop for the rest of the twentieth century. […]

Suburban growth (from "The First Measured Century" (PBS) click to view)

The growth of the nation’s suburbs, in contrast, continued throughout the century.

The share of the U.S. population that lived in the suburbs doubled from 1900 to 1950 and doubled again from 1950 to 2000. Frequently, the suburbs of one city expanded until they encountered the suburbs of another, creating urban corridors such as those that connect Chicago and Milwaukee or San Jose and San Francisco. Some of these corridors combined to create even larger configurations. At the end of the century, an urban corridor extended more than 700 miles from Norfolk, Virginia, to Portland, Maine.

In other parts of the world urbanisation is now taking off rapidly. While developed nations have more than 80% of their populations in urban areas, the developing world with 30%, 40%, 50% currently is catching up.  Can attempts to model city growth help?

According to Bettancourt and West (2010) the growth of cities, just like biological growth, displays common characteristics that allow mathematical description.  West also describes their work in a TED video here. Basically city growth is sigmoidal, reaching a stable size at maturity.  Interestingly though, infrastructure growth is also sub-linear (log vs log):

“…doubling the population of any city requires only about an 85% increase in infrastructure, whether that be total road surface, length of electrical cables, water pipes or number of petrol stations.”

This says that population growth is only partially a measure of urban growth.  Yeah, no surprise really.  So we can summarise and generalise about the growth of cities “on average”, but do real cities, over the timescales were dealing with really grow constantly? Actually even looking at the population growth of a few cities suggests not:

Data from US Bureau of Census

OK these were chosen because they showed differences. However, in looking at warming, we’re trying to lump weather station sites into one of three categories: rural/suburban/urban and it is no surprise that sometimes the differences between two stations in the same category are greater than the similarities.  If we look at some of those cities in terms of growth rate, a couple of things show up…

… rapid growth of young towns and cities, declining after 1910 and WWI, the slump of the Great Depression in the 1930s followed by WWII, the post war recovery and baby boom.  These show up too in house-building across the nation and the housing boom in the 1950s is interesting for many reasons, some of which will have to wait for Part 2.

We can see that the growth of cities is erratic and variable in time, both in terms of house building and other infrastructure.  It is also spatially variable.  Basically cities and towns, even ones of the same size, cannot necessarily be treated the same across long timescales. It we imagine a single geographical area, perhaps a grid square, which has three urban sites of various sizes and we were able to measure the growth of UHI, it might look something like this:

At Site 1 either there is little change up to 1910 or it does not cause warming; after that a sharp increase stopped by the Great Depression after which a slow increase again.  Site 2 shows no changes until after 1960, while Site 3 undergoes huge change in the initial years. Each has its own story.  In this hypothetical case, note that the average of the warming at these three sites is actually not so different from the global average.

Imagine that these sites co-exist in a grid square with a rural site of which the record looks something like this:

There is actually a 0.34°C/century trend over this record.  Add the three sites and the average for the  grid square without adjustment is increased to 0.7°C/century.

Detecting and removing UHI from station data is complex and is done by comparison with other sites.  The NCDC/NOAA documentation is here and here. Basically this is intended to detect undocumented station moves and “non-climatic artifacts” (such as UHI).

Adjustments are determined by estimating the magnitude of change in pairwise difference series form between the target series and highly correlated neighboring series that have no apparent shifts at the same time as the target.

As an example, here is the overview comparison plot for Lifton, Idaho. The Read-me file says (bold mine):

Stn-vs-net plots display differences between the GHCNMv3 annual average temperature for a station and the annual average temperature of its neighboring stations. The number of neighbors is determined by the most well correlated stations within a radius of about 1500 kilometers. A maximum of 20 stations are used for the calculation of a surrounding network average temperature. The annual average temperature for the station as well as its neighbors is calculated individually as an “anomaly”, a departure from its average when the neighbor overlaps the primary station.

See that bit in bold bothers me. What if warming is already so hard-wired into so many sites,  especially rural sites due to village growth or land use changes, that it is taken for granted that a warming trend is ‘normal’ and that all adjustments take place against that warming baseline anyway?  Roy Spencer certainly argues that Warming in the USHCN is mainly an artifact of adjustments.

But the ISH trend pairs had about 15% better agreement (avg. absolute trend difference of 0.143 C/decade) than did the USHCN trend pairs (avg. absolute trend difference of 0.167 C/decade).

Given the amount of work NOAA has put into the USHCN dataset to increase the agreement between neighboring stations, I don’t have an explanation for this result.

Objective vs Subjective

While scientific methods dictate the necessity of being objective in the treatment of data sources, I wonder if that is what is wrong here.  Perhaps the studies that fail to find an effect of UHI are not paying enough attention to the detail of categorisation.  All apples you say? Would a closer look reveal cookers, desert apples, cider apples and crab apples?

If there is no such thing as a ‘typical’ city or rural station, does being objective make it all too easy to miscategorize?  Maybe a modicum of subjectivity is the reason for the success of skeptic bloggers such as Ed Caryl (A Light in Siberia) and Frank Lansner (RUTI: Coastal Stations) in finding ‘warming artifacts’ of human influence or location.  Perhaps Dr Spencer has succeeded because he looked at US data in a relatively short time period in which it was possible to pin down urban growth, while Jim Goodridge stuck to counties in California.

Averaging and anomalising temperature data for a station and combining it with that of other stations certainly hides a lot of detail. IMO the heterogeneity of urban growth and geographic differences between urban areas means that we must break down data by location and treat it with some subjectivity to see the variability and detect the messages within.

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14 Responses to Examining Urban Heat Islands – Part 1.

  1. I’ve steered clear of UHI until now, and will probably continue to do so. What you find depends largely on what you’re looking for, and that’s the flaw in many (most?) papers and studies seeking to confirm the causes and effects of “global warming” and/or “climate change”.

    I heartily distrust “homogenisation” and “station move” adjustments in temperature data. Data should speak for itself, aided only by appropriate statistical analysis. Leave the damn data alone!

    I’ve done some plotting of GISS and USHCN data for quite a few stations, and what I see is astonishing. It’s generally acknowledged that the 20th century saw a global increase of about 0.8°C, yet many stations worldwide have had large progressive cooling adjustments deducted from the largely unadjusted present through to the late 19th century, amounting to as much as 2.2°C in one case. There is, and cannot be, any justification for such scientific fraud. Bearing in mind that in general, it’s progressive forward negative adjustments for UHI in large towns and cities that should have been applied, the net effect could be as large as 3°C increase over the timeframe. Global warming by adjustment.

    Adjusting my John Maynard Keynes quote on the previous post to fit my argument, “Post Normal Science” seems to be saying “If the data doesn’t fit my hypothesis, I change the data, sir. What would you do, sir?”

  2. Bloke down the pub says:

    It’s a pity we only had one satelite orbiting the Earth at the start of the nineteenth century. It’ll take a while yet before the ground based temperature record can be looked at in comparison to the space based record with any long term perspective.

  3. Many people don’t know many counties in the US shrunk since 1900 and some grew very little.

    There is a correlation between low growth / shrinking counties and stations cooling or warming.

    I’m using Mosher’s BEST data.

    1956 Stations with data in 2011 and 1900.

    1320 were warming and 636 were cooling.

    1213 of those I could match to the table of US Counties.

    1089 distinct counties.

    562 of those counties had more warming stations than cooling.

    496 had more cooling stations than warming.

    31 had an equal number of cooling and warming stations.

    Warming Counties had a mean temperature change of .0692C/decade.

    Warming counties had a mean population increase of 174,361.

    Warming counties on average grew by 648% from 1900 to 2011.

    Cooling counties had a mean temperature change of -.0573C/decade.

    Cooling counties had a mean population increase of 39,060.

    Cooling counties on average grew by 194% for 1900 to 2011.

    “Equal” counties had a mean temperature change of .0119C/decade.

    “Equal” counties had a mean population increase of 86,469.

    “Equal” counties on average grew by 512% from 1900 to 2011.


  4. Lars P. says:

    Thanks for the post Verity, I trust UHI is very often the hidden reason behind much of the warming. Since the 60s world population doubled, since 1800 multiplied 7 fold. As each person who has a car thermometer can see the UHI when driving in and out of cities this should be a wide understood phenomenon.
    Furthermore if one would put several such UHI islands one aside the other it is logical that the resulted UHI island will have in the end a litle higher UHI value then each of the components. This would imply that a city growing will have also a higher UHI value – so it will create a growing trend.
    Now considering a group of 4 times the same UHI – the same city – the new formed city will have 4 time the population, but the UHI island also formed will not have 4 times the UHI effect, not sure it will even double.
    The UHI effect can grow very fast for small locations growing, and then slowlier for greater agglomerations.
    I think city grow and UHI effect could be easy modeled and would show a non-linear dependency to city size in number of population – possible a logarithmic dependency, but it is logical that few persons who would like to prove global warming would really investigate this in a proper manner.
    The recent Berkeley paper did not found a UHI signature, but I would think that you are right with the assumption that the whole database is UHI contaminated and it is difficult to separate the elements which are not.
    As you correctly point RUTI is doing a better effort in cleaning up the database.
    As the discussion continues, I think Berkeley also found a UHI signature even if it was not recognized as such.
    In the Berkeley UHi paper there is a comparison done between all minus rural. The paper finds surprisingly a slight downwards trend :

    “We observe the opposite of an urban heating effect over the period 1950 to 2010, with a
    slope of -0.19 ± 0.19 °C/100yr. This is not statistically consistent with prior estimates, but it
    does verify that the effect is very small, and almost insignificant on the scale of the observed
    warming (1.9 ± 0.1 °C/100yr since 1950 in the land average from figure 5A).”

    The result is surprising at first view, but it is perfectly consistent with UHI and a fully contaminated UHI database.
    The stations identified by Berkeley as “very rural” as was also shown at WUWT contain a huge number of airports and other locations which cannot be clasified as very rural, so this group is also UHI contaminated.
    In the 20th century there was a huge urbanization process and an interesting demographic evolution. Many big cities have been formed and a big part of the world reached over 90% urbanisation and ceased to grow. We have seen this especially in the developed countries.
    The slow growth of big cities and smaller in absolute values UHI increase for cities from a certain size would explain a smaller UHI increase for an urban group that contains big cities, in comparison with a UHI contaminated average of small locations.
    A small city growing from 5000 to 10000 or 20000 inhabitants may create a UHI increase of 2-3-4 degrees, whereas growing the same city to 100000 will add only another 1 degree. From 100000 to 120000 will be maybe 0.1 or non discernable.
    Comparing a set of such 5000 people locations with 1000000 people locations and substracting the temperatures from the second group, as Berkeley did, we would certainly find a negative trend.
    This is why, Berkeley found for “non-rural” selection a smaller UHI trend which would look like a UHI cooling for big cities.
    Which means the negative trend that has been found is evidence of UHI increase in trend for the “other” group, but not the complete value from the database which is even greater.

  5. Verity Jones says:

    I’m with you on the ‘homogenisation’ and ‘station move’ adjustments. I see no need for homogenisation – by the time you get to weighted averages in grid squares and larger, you have averages of averages. Data is noisy, it is uncertain. Live with it. The stations move adjustments I can understand a need for; “distrust” night be too strong a word, but they do make me feel uneasy. Personally that is one area where I’d like to see openness and justification however onerous that would be. It’s not that I’m thinking “fraud”, more sort of overzealous adjustment. I’d be more in favour of “if in doubt don’t adjust”.

    That’s really interesting, and good to find your blog. I had seen your comments at Mosh’s but not looked at your site before. Very interesting; I will investigate further.

    I’ve stayed away from BEST data. I’m limping along with an old laptop that can just about handle the GHCN data in Excel at a snails pace – I really need to work with smaller sections now. I have no background in programming – wouldn’t know where to start.

    Valuable insights – thank you. I would agree that’s very likely to be the case. I couldn’t have explained it any better!

    • Adjustment during analysis is one thing – there may be justification for it, though I’ve yet to see any convincing case for it. Adjustment of raw data is at best undesirable, at worst bordering on fraud. How can there be any justification for adjusting past temperatures by as much as 2.2 degrees? If adjustment has to be done, then the unadjusted data should always be available. In the case of CRU temperature data, the original data “got lost”. Presumably that data was on magnetic tape. How can you “lose” an entire archive? I didn’t know the motley CRU were allowed to keep dogs in their “gold standard” offices, but perhaps “dog ate my data” is part of the “Urban Myth Island”.

      What’s the justification for homogenisation? If a rural station records a lower temperature that a nearby urban one, which way should the adjustment be done? There’s evidence that far from eliminating UHI effect, some adjustments in Australia and New Zealand have spread it around by adjusting rural stations to match the urban, and not vice-versa.

      There’s something of an agenda with UHI. First Phil Jones publishes a paper “proving” it’s a myth, then it turns out much of the information on station moves has “got lost” or even didn’t exist (sound familiar? I am an old cynic, ain’t I?), then he publishes one which shows that it exists (as everyone knowledgeable already knew), but downplays the extent and size.

      If “inconvenient data” dilutes your message, then mention it and attempt to explain why it’s not important. At least you’ll get marks for honesty. Leave it out and you’ll get challenged, and the very challenge (amongst critics) may be construed as debunking, and often is. Shakun et. al. may have found that out recently. Why did their proxy plot end 6000 years ago, when their archived data extended much later?
      Back in the 70s and 80s, the “Beeb” (BBC) weather forecasters often mentioned the 2 or 3 degree effect in cities, especially London. In the late 90s and since, no mention at all, even thought the charts showed the effect clearly. An “inconvenient truth”.? They’ve just started mentioning it once more, but rarely. A national broadcaster with an agenda is undesirable, but all seem to have one, more or less.

  6. Pascvaks says:

    “We have met the enemy and he and she is us.”

    There are so many variables. So many changes over the 20th Century. Population growth, industrialization, wars, draught, tropical (ENSO) shifts, pollution here there and everywhere, plastic, jet exhausts, trains, ships, autos, chemical this and that (including a few spills), going from dirt roads to concrete highways to asphalt interstates – M-ways – and Autobahns. And then there’s the urban sprawl of concrete, asphalt, shingles, and glass. Oh, yes, and all the kids, parents, and long-lived grandparents, and a few great grandparents.

    No wonder some among us think it’s all just too much.

    There doesn’t seem to be any end in sight. China, India, Brazil, South Africia, and others want to rise to the top of the heap too. No wonder some of the some among us think the only reasonable solution looks a lot like a ‘world government’ of a certain persuasion, with the power to ‘make things happen for the good of all’.

    But, empires do collapse. World economies do go bust. Plagues do kill millions upon millions. And there’s always the great reducer, the old political solution of last resort, the one we seem to resort to more and more, war. Not to worry. The Earth will cool again. The jungle will swallow the concrete, asphalt, and steel.

    PS: 😉

    • Verity Jones says:

      Profound as ever and very much echoing some of my thoughts, perhaps on more melancholy days.

    • Very few things or thoughts are totally new; your last words have occurred to many in the past, and some committed their thoughts to paper:


      I met a traveller from an antique land,
      Who said—“Two vast and trunkless legs of stone
      Stand in the desert. . . . Near them, on the sand,
      Half sunk a shattered visage lies, whose frown,
      And wrinkled lip, and sneer of cold command,
      Tell that its sculptor well those passions read
      Which yet survive, stamped on these lifeless things,
      The hand that mocked them, and the heart that fed;
      And on the pedestal, these words appear:
      My name is Ozymandias, King of Kings;
      Look on my Works, ye Mighty, and despair!
      Nothing beside remains. Round the decay
      Of that colossal Wreck, boundless and bare
      The lone and level sands stretch far away.”

      Percy Bysshe Shelley 1792–1822

      Remember also Hitler’s “1000 year Reich” – was he using an early climate model for his “projection”? IPPC authors over-estimate “climate sensitivity”. Hitler underestimated the free world’s sensitivity.

      • Verity Jones says:

        Lovely. There’s a nice meter to that. There is something rather wonderful about painting pictures with words – whether prose or poetry.

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