Most people are probably aware of the Urban Heat Island (UHI) Effect, i.e. the phenomenon whereby large urban areas are warmer than the surrounding countryside, often by a significant margin.
For some time, sceptics have questioned whether the increasing urbanisation of the planet (and the inevitable urbanisation of some previously rural locations where temperature gauges are located) might create an inaccurate impression as to the extent to which global temperatures are rising. In other words, if temperatures are increasing due in part to urbanisation, might not the claims as to rising temperatures be overdone, and perceived rising temperatures in fact be due more to urbanisation than to rising temperatures per se?
One answer to this question has always been that those responsible for maintaining temperature records carry out adjustments to reflect the impact of the UHI, and that sceptic worries in this regard are unjustified.
A very important tool in the armoury of climate warriors who seek to dismiss suggestions that UHI has any impact on claims of rising worldwide temperatures, is this paper:
“Assessment of Urban Versus Rural In Situ Surface Temperatures in the Contiguous United States: No Difference Found” by Thomas C Peterson, the print edition of which was published on 15th September 2003.i
I think his main conclusion is that stated at the end of the admirably short opening abstract:
Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures. It is postulated that this is due to micro- and local-scale impacts dominating over the mesoscale urban heat island. Industrial sections of towns may well be significantly warmer than rural sites, but urban meteorological observations are more likely to be made within park cool islands than industrial regions.
In arriving at this conclusion, Peterson relied on adjustments that in his view eliminated any risk of UHI bias. Quoting again from the abstract:
All analyses of the impact of urban heat islands (UHIs) on in situ temperature observations suffer from inhomogeneities or biases in the data. These inhomogeneities make urban heat island analyses difficult and can lead to erroneous conclusions. To remove the biases caused by differences in elevation, latitude, time of observation, instrumentation, and nonstandard siting, a variety of adjustments were applied to the data. The resultant data were the most thoroughly homogenized and the homogeneity adjustments were the most rigorously evaluated and thoroughly documented of any large-scale UHI analysis to date.
Even five years ago, Peterson’s 2003 paper had been cited approvingly 285 times. And no wonder. If UHI is a stick with which sceptics try to beat climate warriors, then Peterson has provided a very useful shield to defend against those attacks.
Let’s look at that main conclusion again:
Contrary to generally accepted wisdom, no statistically significant impact of urbanization could be found in annual temperatures.
It’s quite a claim, going as it does against what most people instinctively feel and experience, as well as well as contradicting “generally accepted wisdom”. By the time Peterson wrote his paper in 2003, there had been many studies looking into UHI, and in fairness to him, in arriving at his conclusion, he references a great many of them (below). Having read their conclusions, however, (as set out in Peterson’s paper) I struggle to arrive at the same conclusion as Peterson.
Earlier UHI papers
Cayan and Douglas (1984) found urban-affected heat island temperature increases of 1°–2°C common when comparing linear trends over three to five decades of urban stations with trends at non-urban sites.
Kukla et al. (1986). They looked at the difference in trends between rural and urban data over a 40-yr period for 34 station pairs and concluded that the urban contamination amounted to about 0.12°C decade.
Two different approaches were used by Karl et al. (1988) in an attempt to determine the effect of urbanization on the U.S. climate record. Average annual temperature was found to be 0.11°C warmer in cities of 10, 000 people, 0.32°C warmer in a 100, 000 population city, and 0.91°C warmer in a 1, 000, 000 population city. All of the warming in average temperature comes from minimum temperatures as their annual assessment of daily maximum temperatures indicates that urban sites tend to be cooler than rural during the warmest part of the day.
Using 42 pairs of urban–rural stations in China, Wang et al. (1990) found an average urban heat island of 0.23°C
Gallo et al. (1993) looked at clusters of stations and compared the relationship between the difference in rural and urban temperatures and a vegetation index. Most but not all of their rural–urban differences showed urban stations as warmer.
China’s northern plains were the subject of a UHI analysis by Portman (1993). Using data from 1954 to 1983 and examining how the differences of residuals between each urban station and every rural station changed, the author determined that the mean annual urban warm bias increased 0.19°C during these 30 years.
An analysis of the Barcelona heat island is presented in Moreno-Garcia (1994). In addition to transects, the author examined data from two stations. On an annually averaged basis, the urban site was 0.2°C cooler for daily maximum temperatures and 2.9°C warmer on minimum temperatures. It was noted that the two stations had the same instrumentation, similar elevations, and “similar” distances from the sea
The San Antonio, Texas, heat island was assessed by Boice et al. (1996) using 45 years of data from one San Antonio station and three stations from surrounding small towns. The results indicate that San Antonio’s minimum temperature increased at an average rate of 0.3°C relative to the other stations.
Todhunter (1996) determined that the Minneapolis–St. Paul mean urban heat island in 1989 was 2.1°C.
Using data from three different parts of the world, Camilloni and Barros (1997) determined that the urban–rural temperature difference decreases during periods when rural temperatures are increasing and increases when rural temperatures are decreasing.
Böhm (1998) used data from three urban, three suburban, and three rural stations to examine the Vienna, Austria UHI. He found that the urban effect is strongly influenced by local surroundings and therefore could not be regarded for the city as a whole, with the magnitude varying from 0.2° to 1.6°C. The trend in urban warming varied as well, with two central city stations showing no increase in urban warming while the third had 0.6°C warming in 45 years. In Vienna, the average UHI effect was found to be strongest in winter.
Data from two stations were used by Magee et al. (1999) to determine that the effect of the Fairbanks, Alaska UHI grew by 0.4°C over a 49-yr period, with winter months experiencing a more significant increase of 1.0°C.
An analysis of surface air temperature compared to 0.91-m-deep soil temperature indicated a UHI increase of 0.2°C over the period 1889–1952 for Urbana–Champaign, Illinois (Changnon 1999).
Gallo and Owen (1999) identified clusters of stations in the contiguous United States and compared the relationship between the difference in rural and urban temperatures and a vegetation index. They found seasonal changes in the urban–rural differences that tracked changes in the vegetation index. Most, but not all, of their rural–urban differences showed urban stations as warmer with urban stations averaging 0.38°C warmer than rural.
One city, Tucson, Arizona, was the subject of several different analyses by Comrie (2000), including transects by vehicle-mounted thermistors, spatial examination of in situ data, and comparison of rural and urban temperature time series. The results indicated that Tucson’s UHI warming was ∼3°C over the last century and >2°C of this occurred in the last 30 years.
Using 20 years of data from one urban station and three rural airport stations, Morris et al. (2001) determined that Melbourne’s nocturnal UHI was 1.13°C.
One rural and one urban station were used by Kim and Baik (2002) to determine that Seoul warmed 0.56°C relative to its rural neighbour during the 24-year period 1973–96.
Kalnay and Cai (2003) compared data from 775 urban contiguous U.S. (CONUS) stations with 167 rural stations and found that the urban warmed 0.18°C more than the rural during the 1980s and 1990s.
In fairness, 2 or 3 reports cited by Peterson did not conclude there was a UHI effect. Basically, however, it seems that the weight of scientific evidence, before Peterson 2003 was published, was firmly on the side of there being a substantial UHI. It was the result of numerous studies over lengthy time scales from sites all over the world. Not good news for global climate alarmism. No wonder Peterson has been cited so often since his report emerged!
If the previous research so clearly supported claims that temperature measurements are being distorted by UHI, how did Peterson manage to conclude differently?
Peterson’s dismissal of the earlier studies relied on adjustments to iron out other factors which he claims contaminated the measurements, thereby distorting the temperatures as recorded, and in effect artificially introducing an otherwise non-existent UHI:
Specifically, careful attention will be paid to adjusting the data to account for the natural effects due to differences in elevation and latitude as well as the artificial effects due to differences in time of observations, differences in instrumentation, and the effects of non-standard siting practices, namely, rooftop installations. Once the data are adjusted for these factors, it will be possible to accurately assess the impact of urbanization on the climate record.
Which is fair enough, so far as it goes, but his data, whilst selected carefully to avoid missing data etc., was very limited:
Quality-controlled mean monthly temperature data for U.S. in situ stations were obtained from the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service/National Climatic Data Center (NOAA/NESDIS/NCDC) archives. The analysis period selected was the same one used by Gallo and Owen (1999), the three years 1989–91. Ending the period in December 1991 allowed the analysis to avoid the confounding influence of the Automated Surface Observing System (ASOS) deployment, which started in 1992. Three years is long enough to produce robust means. A longer period would increase the problem of missing data.
In other words, three years of data from the contiguous US only, provided by sources that not everyone would regard as unbiased or disinterested, was used to overturn decades of research from all over the world, from a variety of data providers.
Also, the way in which rural, urban and suburban are defined might surprise a non-American:
Satellite night-light data are the latest tool used for determining which stations are rural and which are urban. For example, while Hansen et al. (1999) use map-derived rural/urban metadata in their global temperature analyses, Hansen et al. (2001) moved up to satellite-derived night-lights rural/urban metadata. The rural/urban classification metadata used in the analysis presented here was developed by Owen et al. (1998) using night-light data from the Defense Meteorological Satellite Program-Operational Linescan System. Their methodology divided 1-km2 grid boxes throughout the United States into urban, suburban, and rural classifications… Advantages of the Owen et al. metadata include that they are objective (while map based is often subjective) and that night-lights, in the United States at least, are good indicators of urbanization whether residential or industrial. Owen’s et al.’s urban grid boxes had an 84.4% agreement with data from the U.S. Bureau of the Census (1997).
The method used had an 84.4% agreement with data from the US Bureau of the Census – or to put it another way, disagreement of 15.6%. Am I alone in thinking that’s quite a high level of disagreement?
The stations consisted of 40 clusters of stations well distributed around the country with a total of 289 stations (see Fig. 2). The Owen et al. (1998) methodology classified 85 of these stations as rural, 191 as urban, and 13 as suburban.
For example, the Global Historical Climatology Network (GHCN; Peterson and Vose 1997) consists of over 7500 temperature stations around the world that were identified as rural, urban, or an in-between class of small town using information on operational navigation charts and a variety of different atlases. A rural station was any station not associated with a town of over 10, 000 population.
Personally, I think this contaminates his ultimate findings without more ado. It’s not scientific, but living in a rural location (some 3 miles from where I now live, and at the same altitude) I have little doubt that it was distinctly colder than where I live now, in a small town, with a population probably a little under 10,000 (which Peterson would therefore categorise as a rural location). I can’t prove it, but it feels as though it’s the case.
Peterson’s basic conclusion is that once adjustments are made for inhomogeneities, the UHI effectively disappears from the record. In fact, in some respects Peterson goes even further:
Some of the largest cities in the United States were not represented in the 40 clusters. Could the large cities be showing urban warming while the smaller ones do not? To answer that question, the mean urban minus rural temperature difference was calculated for each cluster. An assessment of five of the largest cities—Boston, Massachusetts; Dallas, Texas; Detroit, Michigan; Salt Lake City, Utah; and Seattle, Washington—found that one (Detroit) did not have adequate rural and urban data to be analyzed while all of the rest had homogeneity-adjusted urban temperatures that were cooler than the homogeneity-adjusted temperatures of their rural neighbors.
Here he is actually saying that by the time the adjustments have been made, the countryside is proved to be warmer than cities! Does such a conclusion not ring alarm bells, and undermine the reasoning behind the adjustments? Am I alone in finding this to be a truly astonishing conclusion, apparently accepted by the “consensus” without demur?
Peterson justifies his findings, despite the numerous studies whose results have to be explained away, by pointing out that the studies in question failed to discuss or address inhomogeneities. Such inhomogeneities would include a wide range of factors which possibly skew results over a period of time, the main ones being changes in location (whether of latitude, longitude or elevation); changes in observing practices (probably those of greatest concern being changes in the time of once-daily observing and resetting of maximum and minimum thermometers); and changes in instrumentation (the change from one type of thermometer to another can cause an artificial warming or cooling in the data).
He observed that attempts to deal with these issues can themselves be problematic:
The data are inhomogeneous so they need to be adjusted. Yet if the adjustment technique can successfully identify and account for a discontinuity caused by changing from one thermometer to another, the techniques may well identify and compensate for abrupt changes associated with urbanization such as paving nearby grass. Therefore, the inhomogeneity of the data and the approaches to compensate for the inhomogeneities can have strong impacts on assessments of the UHI’s effect on in situ observations.
What Adjustments are Made to Account for Inhomogeneities?
One of the main temperature datasets is maintained by the National Oceanic and Atmospheric Administration (NOAA) in the USA. NOAA’s websiteii is helpfully transparent in this regard, thus helping us to get a handle on the adjustments made. As the introduction to the relevant page on the website makes clear:
There are several factors that are important in monitoring global or U.S. temperature: quality of raw observations, length of record of observations, and the analysis methods used to transform raw data into reliable climate data records by removing existing biases from the data.
Interestingly, NOAA’s website suggests that the main biases for which adjustments need to be made are those relating to sea surface temperatures and in respect of the shift in land-based temperature measurements from afternoon to morning readings. Both biases, and the need for adjustments to be made in respect of them can readily be appreciated. Clearly afternoon readings will generally give a higher temperature than morning readings taken at the same location. If, as seems to be the case, the practice in the US has changed from afternoon readings to morning readings, then without an adjustment, an incorrect perception of cooling could be obtained. I have no problem with an appropriate adjustment being made to reflect that. Similarly, with regard to sea surface temperature measurements:
The most important bias globally was the modification in measured sea surface temperatures associated with the change from ships throwing a bucket over the side, bringing some ocean water on deck, and putting a thermometer in it, to reading the thermometer in the engine coolant water intake. The bucket readings used early in the record were cooler than engine intake observations so the early data have been adjusted warmer to remove that bias. This makes global temperatures indicate less warming than the raw data.
So far so good. What, then do NOAA have to say about UHI adjustments? Surprisingly little:
We identified which GHCN stations were rural and which were urban. Then we created global temperature time series from the rural only stations and compared that to our full dataset. The result was that the two showed almost identical time series (actually the rural showed a little bit more warming) so there apparently was no lingering urban heat island bias in the adjusted GHCN dataset.
This is another defence against allegations that UHI wrongly influences the claims of warming. Essentially, the argument is that if both urban and rural sites show roughly similar warming trends, then the direction of travel of global temperatures is being fairly represented in the statistics. UHI is not artificially increasing the temperature increase. And that is an argument that I can comprehend and accept.
A similar argument is made by Jones et al. in “Urbanization effects in large-scale temperature records, with an emphasis on China” (2008)iii
Global surface temperature trends, based on land and marine data, show warming of about 0.8°C over the last 100 years. This rate of warming is sometimes questioned because of the existence of well-known Urban Heat Islands (UHIs). We show examples of the UHIs at London and Vienna, where city center sites are warmer than surrounding rural locations. Both of these UHIs however do not contribute to warming trends over the 20th century because the influences of the cities on surface temperatures have not changed over this time. In the main part of the paper, for China, we compare a new homogenized station data set with gridded temperature products and attempt to assess possible urban influences using sea surface temperature (SST) data sets for the area east of the Chinese mainland. We show that all the land-based data sets for China agree exceptionally well and that their residual warming compared to the SST series since 1951 is relatively small compared to the large-scale warming. Urban-related warming over China is shown to be about 0.1°C decade over the period 1951–2004, with true climatic warming accounting for 0.81°C over this period.
Do these arguments still hold good in 2021?
How Hot Are Urban Heat Islands Today?
Over the last few years, and with increasing shrillness, I have noticed more and more claims that the UHI Effect is very significant indeed. Ironically perhaps, it’s often the likes of the Guardian who make these claims, usually in the context of saying that in effect poor (often BAME) residents of such areas are suffering more from heat waves than are wealthier (often white) people living in the less-affected leafy suburbs. For instance, a recent articleiv in the Guardian stated:
Much of this variability can be traced back to racist housing practices, which concentrated people of color in neighborhoods lacking cooling, green space and tree shade. On a hot day, a neighborhood with little shade can be up to 20F hotter than a more affluent and greener area in the same city.
In fairness, the article doesn’t claim that the UHI is generally quite so extreme, but it does say:
The sweltering heat endured by major American cities is being fueled by vast swaths of concrete and a lack of greenery that can ratchet up temperatures by nearly 9F (5C) compared with surrounding rural areas, new research has found.
It goes on to provide a handy table setting out which US cities are suffering most from UHI effects, with New Orleans at the top at 8.94F down to seven cities (out of 20) tied on 6.97F
The Mayor of London/London Assembly websitev makes similar claims about London:
A changing climate and higher average temperatures combined with increasing urban development and densification is resulting in London getting hotter.
London is experiencing hotter and drier summers that are further impacted by the Urban Heat Island effect (UHI). The UHI can cause London to be up to 10C warmer than neighbouring rural areas. This is a result of the sun’s rays being absorbed by hard surfaces rather than by vegetation such as trees, plants and grass. Radiation from our hard surfaces is released into the air as heat. The UHI reduces the ability for cities to cool and impacts on our own capacity to regulate temperature.
It doesn’t seem to have occurred to those at the Guardian (and elsewhere) who make these claims, that they are simultaneously undermining their claims regarding global warming, and the “climate crisis” since if UHI is as significant an issue as they now claim, then it opens up once more the whole debate as to the impact of UHI on temperature and warming claims. If the UHI Effect is big, and getting bigger, are the adjustments made by the guardians of the temperature records still sufficient? And if they aren’t, then can we trust the claims of a rapidly warming world? Is the reality just one of rapidly warming urban heat islands?
It isn’t just heat. Cities increasingly seem to be suffering from flooding. Of course, this too is regularly being attributed to climate change, for instance in this article in the Guardian: “Flash floods will be more common as climate crisis worsens, say scientists”:
Sadiq Khan, the mayor of London, said his powers were limited on flood defences, but he was dealing urgently with Thames Water on the issue. “We are seeing increasing incidents of extreme weather events linked to climate change,” he said. “This is not the first time in recent weeks that London has been hit by major flooding. Despite having limited powers in the area, it remains a key priority for myself and London’s council leaders that more is done urgently to tackle flooding and the other impacts of climate change. This includes continuing to urge Thames Water to address localised issues with infrastructure that may exacerbate the impact of flooding.
So it’s all down to climate change, then? Maybe, maybe not. Maybe it’s more to do with urbanisation? Interestingly, the BBC ran an articlevi at around the same time as the Guardian, and if one persists far enough, a different story potentially emerges:
Dr Veronica Edmonds-Brown, senior lecturer in aquatic ecology at the University of Hertfordshire said London’s Victorian era drainage system “cannot cope with the huge increase in population”.
Dr Edmonds-Brown said there were several reasons for Sunday’s flooding.
“The first is building on the floodplains of the Thames and River Lea. The second is urbanisation. The more impervious surfaces we install – and we are amid a rapid housing programme at the moment – the worse this situation will get.
“The final reason is that our drainage system is not built for the amount of water it is receiving. Due to budget limitations, local authorities are not able to maintain or improve them.”
An articlevii on the website of Japan For Sustainability also suggests that flooding as a result of localised downpours might itself be a function of the UHI, rather than of climate change:
The UHI effect…causes serious weather events such as localized torrential rains. Recently, urban areas in Japan including Tokyo have experienced more incidents of unpredictable, torrential rains, called “guerrilla downpours;” one such event this year caused an accident in which workers in an underground sewage system were swept away and drowned.
UHI phenomena can occur any place with a concentrated population where countermeasures are not being taken. Problems caused by the UHI effect are becoming a common concern among big cities around the world, including those in developing countries.
In the last few days, those well-known climate scientists, Zurich Insurance, have warned, according to the BBC’s Roger Harrabin,viii that proposed planning reforms in the UK, which could make it easier to convert shops into houses and apartments, might lead to more people “suffering from potentially deadly heatwaves”. Not just heatwaves either – floods too. Why?
Well, sloppy thinking prevails. They warn of people suffering from climate change, but the article then says:
It warned that many properties in built-up areas were affected by the “urban heat island” effect, where temperatures are hotter than outlying areas. It added these properties were also at risk from flash floods caused by heavy downpours on concrete surfaces.
Not climate change then. As with increasing heat, it’s a problem of urbanisation. What, then are the trends with regard to urbanisation?
What follows is a limited tour d’horizon, and I cheerfully admit using Wikipedia as a ready source.ix According to it:
It is predicted that by 2050 about 64% of the developing world and 86% of the developed world will be urbanized. That is equivalent to approximately 3 billion urbanites by 2050, much of which will occur in Africa and Asia. Notably, the United Nations has also recently projected that nearly all global population growth from 2017 to 2030 will be by cities, with about 1.1 billion new urbanites over the next 10 years.
According to Wikipedia, urbanisation brings costs and benefits, both to individual health, and to the planet. Although healthcare is often better in urban areas, especially in developing countries, less active lifestyles can lead to increased obesity, and rapid unplanned flight from rural areas to cities can lead to the growth of slums and shanty towns. In some countries, urban areas are also often much more prone to violence and drugs.
Environmentally, some argue that urbanisation is beneficial, for example by leading to reduced birth rates and a reduction in “slash and burn” farming methods. On the other hand:
However, existing infrastructure and city planning practices are not sustainable. In July 2013 a report issued by the United Nations Department of Economic and Social Affairswarned that with 2.4 billion more people by 2050, the amount of food produced will have to increase by 70%, straining food resources, especially in countries already facing food insecurity due to changing environmental conditions. The mix of changing environmental conditions and the growing population of urban regions, according to UN experts, will strain basic sanitation systems and health care, and potentially cause a humanitarian and environmental disaster.
In the long-run, urbanisation looks set to continue, possibly at an increasing rate. This is a remarkable prospect, given that we already have a situation where the population of the world’s largest city (Tokyo), at 37-39 million (depending on how you define it) is around the same as the population of the whole of Canada.
I’m not a scientist, and I certainly don’t seek to argue that climate change isn’t happening. I do, however, ask some questions, and seek some balance in the debate. I suspect that in an increasingly urbanised world, urbanisation may be causing as many problems as are caused by increasing greenhouse gas emissions. I wonder if UHI temperature adjustments are adequate to deal with the significantly increasing urban temperatures. And finally, I think all this points to the need to concentrate on adaptation rather than mitigation. In cities in particular, there are steps that can be taken to minimise the impact of UHI. It is my belief that, in a world where financial resources are limited, and Governments’ budgets are already strained by the impact of the Covid pandemic, money would be better spent on taking those (adaptation) steps than on attempting, futilely, to reduce greenhouse gas emissions.