134: Urban heat island (UHI) - implications for climate analysis

 

Fig. 134.1: UHI evidence from six cities in the Southern Hemisphere.

 

In my previous six posts I highlighted six examples of temperature records from major cities in the Southern Hemisphere where the temperature trend appears to be influenced by the urban heat island (UHI) effect that I discussed in Post 127. The result in each case was that the city exhibited significantly more warming than the rest of the state or country that the city was part of (see Fig. 134.1 above). However, the implications of this go beyond what is happening in a few megacities.

If large cities like Jakarta and Buenos Aires are warming because of urban heating, it then follows that smaller towns and cities must as well, only just not as much. As most weather stations are located near sites of human activity or habitation, it then also follows that most of the temperature records are likely to be corrupted to a certain degree by this UHI effect. The problem is determining to what degree that is. 

The six cities I have examined so far are obviously extreme examples and cannot on their own have a significant impact on their regional temperature trends as these regional trends are usually the result of averaging data from dozens of different stations. If they did have a large impact, then the difference between the trends for each city and its region would not be so pronounced. But that does not mean that the wider region is unaffected. 

The regional trends I compared each of the city trends with are also probably affected by the UHI effect, just not as much. That is because most of the data that goes to make up the regional trends comes from stations sited around other, smaller urban developments. So the regional warming is probably overstated as well, just not as much as that of the megacities. It is only because the megacities are so extreme that they stand out from their wider regions, but in reality both trends are likely to exhibit increased warming due to the UHI effect, just not as much as each other, hence the divergence in trends. 

And of course, if the temperature data for all the stations in the region is homogenized, then there is a risk of importing warming trends from the UHI into the surrounding area thereby raising the temperature trends of the wider region more markedly. Either way, the temperature trend for any region is likely to be corrupted to some degree by UHI effects in some of its stations.

But perhaps the biggest issue is the problem of area weighting. Ideally the contribution of each station to the overall regional average should be in proportion to area surrounding that station. So if a country has fifty stations and they are all evenly spaced, then each one should represent 2% of the area and each should make a 2% contribution to the mean temperature for the country. But suppose the station sits in a UHI and the area of the urban region is less than the nominal area that the station is supposed to represent. This is what happens with Jakarta in Indonesia (see Post 131) and for many other UHI stations.

The area of Jakarta is about 660 km², but that is only about 0.035% of the 1,900,000 km² area of Indonesia so the true weighting or contribution of Jakarta Observatorium (Berkeley Earth ID: 155660) to the mean temperature trend of Indonesia should be only 0.035%. But as there are less than sixty reliable temperature records for Indonesia (see Fig. 31.4 in Post 31) that means that each station on average makes a contribution of over 1.7% to the regional temperature trend. So a simple average of all station anomalies would over-represent Jakarta Observatorium (Berkeley Earth ID: 155660) by at least fifty times, and so too would an average based on a gridded method.

So how could we separate the UHI contribution to local warming from other climate drivers like carbon dioxide? One way to go about this might be to compare temperature trends for raw unadjusted temperature data from known urban and rural stations. But the problem here is that we don't know for certain where all these stations are exactly, or what the local geography around each station is. For example, according to its GPS coordinates Perth Regional Office is in the middle of the sea over 8 km off the coast of Perth. This is because the longitude and latitude coordinates for many stations are often only specified to the nearest 0.1° and sometimes the error is even greater. For Perth Regional Office the error is stated as ±0.2° for the longitude. That means its location accuracy is about ± 10 km. That is bigger than the extent of most urban areas. So finding sufficient pure rural stations against which to compare all the others is tough.

The takeaway here is that the urban heat island (UHI) effect is a big problem when interpreting temperature data, but determining how big is also a big problem.

133: UHI #6 - Buenos Aires (Argentina)

The second largest city in South America by population is Buenos Aires in Argentina with a population of over thirteen million people. The largest is Sao Paulo in Brazil. Both could be categorized as urban heat islands (UHIs); Sao Paulo in particular has warmed by about 3°C since 1887 and Buenos Aires by up to 2°C. However, as I have yet to fully analyse temperature data from Brazil it is not possible for me to compare the Sao Paulo data to the temperature change for the wider region, although this is unlikely to be more than about 1°C. So instead I will concentrate on Buenos Aires. Sao Paulo will come later.

A comparison of the temperature trends for Buenos Aires and Argentina shows that temperatures have risen far more in Buenos Aires than they have in Argentina as a whole. In fact as Fig. 133.1 below shows, they have risen almost three times faster in Buenos Aires since 1900 than they have in Argentina. Before 1900 temperatures were stable in both Buenos Aires and Argentina.

Fig. 132.1: The change to the 5-year average temperatures of Buenos Aires (red curve) and Argentina (blue curve) since 1900.

In Post 61 I examined the temperature trends for Argentina. The mean temperature change since 1900 is shown in Fig. 132.2 below and it indicates that Argentina has exhibited only modest warming over the last one hundred years. The best fit for 1901-2000 indicates a temperature rise of about 0.64°C while the 5-year average suggests a rise of about 0.52°C.

Fig. 132.2: The mean temperature change for Argentina since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 2000 and has a positive gradient of +0.64 ± 0.11 °C per century.

The oldest major weather station in Argentina is Buenos Aires Observatorio (Berkeley Earth ID: 151642). It is located in the heart of Buenos Aires and has continuous data stretching back as far as 1856, although there is a break in the data between 2006 and 2011. It is one of only two major stations within 20 km of the city centre, hence its significance as a case study of the urban heat island (UHI) effect. The other is Aeroparque (Berkeley Earth ID: 151640) which only has data from 1961 onwards but also exhibits strong warming.

In contrast to the rest of Argentina, Buenos Aires Observatorio shows strong and continuous warming since 1910 (see Fig. 132.3 below). Before 1910 the temperatures were stable. The best fit for 1901-2000 indicates a temperature rise of about 2.4°C in one hundred years while the 5-year average suggests a rise of 1.77°C.

Fig. 132.3: The mean temperature change for Buenos Aires Observatorio since 1900 relative to its 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 2000 and has a positive gradient of +2.40 ± 0.18 °C per century.

Summary

The following temperature changes were observed from 1901 to 2000.

Argentina: 0.52°C (trend 0.64°C).

Buenos Aires: 1.77°C (trend 2.40°C).

So Buenos Aires has warmed by almost 1.8°C more than the surrounding state of Argentina, or more than three times faster. A classic UHI!

132: UHI #5 - Pretoria (South Africa)

The three largest cities in southern Africa are Kinshasa, Johannesburg and Nairobi. All have populations of more than ten million, so all three could be good contenders as examples of the urban heat island (UHI) effect. Unfortunately in all three cases making a definitive assessment is difficult because these cities do not have data of high enough quality.

The city in southern Africa with the next highest population is Luanda in Angola with a population of eight million people. Luanda does have good temperature data stretching back to 1879 that does appear to show a strong warming trend even though the data after 1980 is fragmented, probably due to the civil war. The problem is that there is very little other good temperature data for Angola (see here for a complete list of stations), so there is no reliable trend for Angola as a region as I showed in Post 82, and so no accurate regional trend with which to compare the Luanda data.

The country in southern Africa with the the best temperature data is South Africa, and while Johannesburg has no high quality weather stations near its centre, the city of Pretoria (which is part of the same conurbation) does, although the temperature record for Pretoria Eendracht (Berkeley Earth ID: 159076) only starts in 1949. Nevertheless, since then the respective temperature trends show that Pretoria has warmed significantly more than South Africa as a whole (see Fig. 132.1 below) with up to 3°C of warming in Pretoria but less than 1°C in South Africa.

Fig. 132.1: The change to the 5-year average temperatures of Pretoria Eendracht (red curve) and South Africa (blue curve) since 1952.

In Post 37 I examined the temperature trends for South Africa. The mean temperature change since 1880 is shown in Fig. 132.2 below and it indicates that South Africa exhibited no significant warming before 1980 but has since warmed by about 0.7°C. In fact the best fit for 1951-2010 indicates a temperature rise of about 1.08°C in 60 years while the 5-year average suggests a rise of about 0.96°C.

Fig. 132.2: The mean temperature change for South Africa since 1857 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1951 to 2010 and has a positive gradient of +1.80 ± 0.14 °C per century.

In contrast to the rest of South Africa, Pretoria Eendracht (Berkeley Earth ID: 159076) shows significant and continuous warming since 1950 (see Fig. 132.3 below). The best fit for 1951-2010 indicates a temperature rise of more than 2.81°C in 60 years while the 5-year average suggests a rise of 2.99°C.

Fig. 132.3: The mean temperature change for Pretoria Eendracht since 1949 relative to its 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1951 to 2010 and has a positive gradient of +4.69 ± 0.24 °C per century.

Summary

The following temperature changes were observed from 1951 to 2010.

South Africa: 0.96°C (trend 1.08°C).

Pretoria: 2.99°C (trend 2.81°C).

So Pretoria has warmed by at about 2°C more than the surrounding state of South Africa, or up to three times faster. A classic UHI!

131: UHI #4 - Jakarta (Indonesia)

Probably the most extreme example of an urban heat island (UHI) in the Southern Hemisphere is Jakarta. I first discussed it when analysing the temperature data of Indonesia for Post 31, but it is so dramatic that it needs further examination. 

Jakarta is the largest city in the Southern Hemisphere with a population of over 33 million. Indonesia has a population of more than 270 million, but this is spread over an archipelago of islands that stretch over 5000 km. The result is that Indonesia has seen no warming over the last one hundred years while Jakarta has warmed by almost 3°C since 1880 (see Fig. 131.1 below).

Fig. 131.1: The change to the 5-year average temperatures of Jakarta (red curve) and Indonesia (blue curve) since 1920.

In Post 31 I examined the temperature trends for Indonesia. The mean temperature change since 1912 is shown in Fig. 131.2 below and it indicates that Indonesia outside of Jakarta has actually cooled slightly over the last one hundred years. The best fit for 1913-2012 indicates a temperature change of -0.08°C while the 5-year average suggests a small rise of about +0.16°C.

Fig. 131.2: The mean temperature change for Indonesia since 1912 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1913 to 2012 and has a slight negative gradient of -0.08 ± 0.04 °C per century.

One of the oldest weather stations in Indonesia is Jakarta Observatorium (Berkeley Earth ID: 155660). It is located in the middle of Jakarta with almost continuous data stretching back as far as 1866, hence its significance as a case study of the urban heat island (UHI) effect. In contrast to the rest of Indonesia, Jakarta Observatorium shows significant and continuous warming since 1870 (see Fig. 131.3 below). The best fit for 1913-2012 indicates a temperature rise of more than 2.16°C in the one hundred years since 1913, while the 5-year average suggests a rise of over 2.35°C.

Fig. 131.3: The mean temperature change for Jakarta Observatorium since 1866 relative to its 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1913 to 2012 and has a positive gradient of +2.16 ± 0.08 °C per century.

It is important to note that while Jakarta Observatorium is the clearest example of a UHI in Indonesia, it is not the only one. Up until 1970 there was a second station in Jakarta (Berkeley Earth ID: 155660) which also exhibited over 1.8°C of warming from 1866 to 1970. But the city of Surabaya (Berkeley Earth ID: 155652) also appears to behave as a UHI. Its population is over twelve million making it the fifth largest city in the Southern Hemisphere. From 1949 to 2013 it appears to have exhibited warming of more than 1.7°C as well (or 2.75°C per century). Yet despite this the rest of Indonesia cooled.

Summary

The following temperature changes were observed from 1913 to 2012.

Indonesia: 0.16°C (trend -0.08°C).

Jakarta: 2.35°C (trend 2.16°C).

So Jakarta has warmed by at least 2°C more than the rest of Indonesia. It has also warmed while Indonesia has not. A classic UHI!

130: UHI #3 - Perth (Western Australia)

The population of Western Australia is only about 2.67 million but two million of that total live in the state capital Perth. So 75% of the state's population live in Perth even though Perth accounts for less than 0.25% of the area of Western Australia. Perhaps this is why temperatures in Perth appear to have risen at more than twice the rate of the rest of the state. By 1990 temperatures in Perth had risen more than 1.5°C since 1900 compared to less than 0.7°C in Western Australia as a whole (see Fig. 130.1 below). That looks like classic urban heat island (UHI) behaviour. The only caveat is that the main weather station for Perth at Perth Regional Office (Berkeley Earth ID: 4321) ceased operations in 1992 just as the UHI was taking off.

Fig. 130.1: The change to the 5-year average temperatures of Perth (red curve) and Western Australia (blue curve) since 1900.

In Post 22 I examined the temperature trends for Western Australia. The mean temperature change since 1900 is shown in Fig. 130.2 below and it indicates that Western Australia has warmed by about 1°C since 1990. The best fit for 1991-1990 indicates a temperature rise of less than 0.64°C in 90 years while the 5-year average suggests a rise of about 0.67°C for the same period.

Fig. 130.2: The mean temperature change for Western Australia since 1900 relative to the 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 1990 and has a positive gradient of +0.71 ± 0.11 °C per century.

The mean temperature anomaly (MTA) for Western Australia shown in Fig. 130.2 above is the result of averaging monthly temperature anomalies from nearly hundred stations as Fig. 130.3 below demonstrates (see here for a full list of all stations). However, before 1900 there are less than ten available stations so the MTA is less reliable and more prone to error from statistical variability. For more details and analysis of the complete data for Western Australia see Post 22.

Fig. 130.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Western Australia in Fig. 130.2.

The oldest weather station in Western Australia is Perth Regional Office (Berkeley Earth ID: 4321). It has data stretching back as far as 1852, and continuous data from 1876 to 1992. In fact it is the only station within Perth with over 480 months of continuous data between 1876 to 1992, hence its significance as a case study of the urban heat island (UHI) effect.

Compared to the rest of Western Australia, Perth Regional Office shows much more significant and continuous warming since 1900 (see Fig. 130.3 below). The best fit for 1901-1990 indicates a temperature rise of more than 1.47°C in 90 years while the 5-year average suggests a rise of over 1.5°C.

Fig. 130.4: The mean temperature change for Perth Regional Office since 1900 relative to its 1961-1990 monthly averages. The best fit is applied to the monthly mean data from 1901 to 1990 and has a positive gradient of +1.63 ± 0.19 °C per century.

Summary

The following temperature changes were observed from 1901 to 1990.

Western Australia: 0.67°C (trend 0.64°C).

Perth: 1.53°C (trend 1.47°C).

So Perth warmed by almost 1°C more than the surrounding state of Western Australia in the ninety years up to 1990, or more than twice as fast. A classic UHI!

129: UHI #2 - Melbourne (Victoria)

The Australian state of Victoria has a total population of 6.7 million, of which 5.1 million live in the city of Melbourne. That means that 76% of the population of Victoria live in its capital city even though Melbourne accounts for only 4.4% of the area of Victoria. It is not really surprising then that the temperature trends for Melbourne and Victoria over the last 100 years are markedly different. In fact while the state of Victoria has cooled slightly for most of the last 120 years, Melbourne has warmed by over 2°C (see Fig. 129.1 below). So like Sydney in the previous post, Melbourne looks like a classic urban heat island (UHI).

Fig. 129.1: The change to the 5-year average temperatures of Melbourne (red curve) and Victoria (blue curve) since 1900.

 

In Post 19 I examined the temperature trends for Victoria. The mean temperature change since 1880 is shown in Fig. 129.2 below and it indicates that Victoria has exhibited no significant warming. In fact the best fit for 1886-2005 indicates that temperatures actually declined very slightly, although the 5-year average over the same period suggests that they may have risen slightly by about 0.47°C with most of this rise occurring after 1990.

Fig. 129.2: The mean temperature change for Victoria since 1880 relative to the 1966-1995 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a slight negative gradient of -0.02 ± 0.08 °C per century.

The mean temperature anomaly (MTA) for Victoria shown in Fig. 129.2 above is the result of averaging monthly temperature anomalies from over fifty stations as Fig. 129.3 below demonstrates (see here for a list of all stations). However, before 1900 there are less than twenty available stations so the MTA is less reliable and more prone to error from statistical variability. For more details and analysis of the complete data for Victoria see Post 19.

Fig. 129.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for Victoria in Fig. 129.2.

The oldest weather stations in Victoria is Melbourne Regional Office (Berkeley Earth ID: 151813). It is located in the heart of Melbourne and has continuous data stretching back as far as 1855. It is also the only major station within 20 km of the city centre that has continuous data extending back before 1940 (the next best is Laverton Aerodrome), hence its significance as a case study of the urban heat island (UHI) effect.

In contrast to the rest of Victoria, Melbourne Regional Office shows significant and continuous warming since 1880 (see Fig. 129.4 below). The best fit for 1886-2005 indicates a temperature rise of more than 1.4°C in 120 years while the 5-year average suggests a rise of over 2.1°C.

Fig. 129.4: The mean temperature change for Melbourne Regional Office since 1880 relative to its 1966-1995 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a positive gradient of +1.23 ± 0.10 °C per century.

Summary

The following temperature changes were observed from 1886 to 2005.

Victoria: 0.47°C (trend -0.02°C).

Melbourne: 2.1°C (trend 1.48°C).

So Melbourne has warmed by at least 1.5°C more than the surrounding state of Victoria, or up to four times faster. A classic UHI!

Given that both Sydney and Melbourne appear to be great examples of UHIs, one might expect the same of similar large cities, Adelaide and Brisbane. Yet this appears not to be the case. Neither of these cities exhibits greater warming than the rest of their respective states even though over 70% of the South Australian population of 1.8 million live in Adelaide. For Brisbane and Queensland, though, the proportion is only 44%, although Brisbane is almost twice the size of Adelaide by population. The reason both Adelaide and Brisbane do not exhibit striking UHI properties could be that they are too small. Adelaide has a population that is less than a quarter of that of Sydney. That said, the population of Perth in Western Australia is just less than two million and yet as the next post will show, it too appears to be an urban heat island (UHI).

128: UHI #1 - Sydney (New South Wales)

In my previous post I explained the concept of the urban heat island (UHI). Unfortunately, finding clear examples in the global temperature records is not so easy. This is not because they don't exist, but because to demonstrate their existence beyond a reasonable doubt requires good data for both the urban area and the wider region within which the UHI sits. 

In the case of the urban data, this needs to be from a station that is located in the heart of the urban area and not on its perimeter such as at the local airport. Finding datasets from such locations is a lot harder than one might think because most weather stations are deliberately sited away from the centre of urban areas. Then the dataset needs to be sufficiently long with no gaps in the record in order for it to exhibit a definite trend over time. 

In the case of the regional data, this too needs to be based on long datasets with no gaps in their records. But in addition, a large number of these datasets are needed in order to establish an accurate trend for the region.

Satisfying these criteria is particularly difficult in the Southern Hemisphere where the data for most countries other than Australia is not particularly good. Nevertheless, I have identified six examples in the Southern Hemisphere so far (excluding Brazil which I have yet to examine in detail) where the quality of the temperature data for the UHI and its host country or state is sufficient to detect unambiguous differences in their temperature trends. Over the following six posts, including this one, I will examine each of these six examples in turn. So for Exhibit #1 I give you Sydney in New South Wales (NSW), Australia.

The city of Sydney has a population of about 5.3 million. That means that 65% of the New South Wales (NSW) population of 8.2 million live in Sydney even though Sydney accounts for only 1.5% of the area of NSW. It is not really surprising then that the temperature trends for Sydney and NSW over the last 100 years are markedly different. For while NSW has barely warmed at all in the last 140 years, Sydney has warmed by almost 2°C (see Fig. 128.1 below).

Fig. 128.1: The change to the 5-year average temperatures of Sydney (red curve) and New South Wales (blue curve) since 1900.

In Post 18 I examined the temperature trends for New South Wales. The mean temperature change since 1880 is shown in Fig. 128.2 below and it indicates that NSW has exhibited no significant warming. In fact the best fit for 1886-2005 indicates a temperature rise of less than 0.12°C in 120 years while the 5-year average suggests a rise of about 0.25°C.

Fig. 128.2: The mean temperature change for New South Wales since 1880 relative to the 1965-1994 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a slight positive gradient of +0.099 ± 0.077 °C per century.

The mean temperature anomaly (MTA) for NSW shown in Fig. 128.2 above is the result of averaging monthly temperature anomalies from over one hundred stations as Fig. 128.3 below demonstrates (see here for a list). However, before 1880 there are less than twenty available stations so the MTA is less reliable and more prone to error from statistical variability. For more details and analysis of the complete data for NSW see Post 18.

Fig. 128.3: The number of station records included each month in the mean temperature anomaly (MTA) trend for New South Wales in Fig. 128.2.

One of the oldest weather stations in NSW is Sydney Observatory Hill (Berkeley Earth ID: 151986). It is located in the heart of Sydney, south of the opera house and harbour, and has continuous data stretching back as far as 1859. It is also the only major station within 20 km of the city centre, hence its significance as a case study of the urban heat island (UHI) effect. 

In contrast to the rest of NSW, Sydney Observatory Hill shows significant and continuous warming since 1880 (see Fig. 128.4 below). The best fit for 1886-2005 indicates a temperature rise of more than 1.2°C in 120 years while the 5-year average suggests a rise of over 1.5°C.

Fig. 128.4: The mean temperature change for Sydney Observatory Hill since 1880 relative to its 1965-1994 monthly averages. The best fit is applied to the monthly mean data from 1886 to 2005 and has a positive gradient of +1.01 ± 0.08 °C per century.

Summary

The following temperature changes were observed from 1886 to 2005.

NSW: 0.25°C (trend 0.12°C).

Sydney: 1.5°C (trend 1.2°C).

So Sydney has warmed by at least 1°C more than NSW, or up to ten times faster. A classic UHI!

127: The urban heat island (UHI) effect - an explainer

 

The urban heat island (UHI) effect.

 

The conventional wisdom is that climate change is driven by rising carbon dioxide (CO₂) levels in the atmosphere, and only by CO₂; so the greater the (CO₂) levels the greater the temperature increase (see Fig. 87.3 in Post 87). You may have noticed one direct consequence of this orthodoxy in the way the media these days reports on environmental disasters or extreme weather (floods, droughts, storms, hurricanes, heatwaves, forest fires etc.): they always refer to climate change.

Implicit to this climate change reference is the assumption that all climate change is due to CO₂ even though CO₂ is rarely explicitly mentioned and the causation is rarely demonstrated. Consequently, the solution to all extreme weather events appears to be simple and obvious: cut CO₂ levels in the atmosphere (i.e. Net Zero) and everything will be fine. Except it won't. This is because much of what is happening to local climates has little or nothing to do with CO₂, but it does have a lot to do with other human activities, not least urbanization and industrialization. Central to both of these is the urban heat island (UHI) effect.

The problem when discussing the impact of the UHI effect on climate, and in particular the temperature record, is that it is controversial. This is partly because much of climate science appears to be driven by an anti-fossil fuel dogma that therefore sees any talk of UHIs as at best a distraction from the supposed only true problem, CO₂, and at worst a campaign of disinformation designed to undermine all of climate science and its campaign against CO₂. But it is also partly because UHIs come in many flavours. 

There are those UHIs that just trap more heat by reducing airflows and those that store more solar heat than rural areas by virtue of increased heat capacities. Both of these do not add to the total amount of energy absorbed at the Earth's surface though, so there is no net global temperature increase associated with them. But then there are those UHI processes that do absorb extra heat, either via changes to the albedo of the Earth's surface, or by the emission of large amounts of additional heat through anthropogenic energy use and generation. Both of these certainly do add to global warming but are still largely ignored by climate science. In the following sections I will discuss the relative impact of each of these four types of UHI effect in turn and show that one type in particular can be very significant.

i) Heat trapping

The aspect of the UHI effect that is referred to the most is heat trapping. This is where tall buildings in a city reduce the flow of hot air away from the centre causing the city to retain its heat longer. Perhaps the most obvious example of this is Manhattan in New York City with its dense cluster of tall skyscrapers.

The result of this UHI effect is that the local area of the city stays hotter for longer compared to if the buildings were not there. This is because there is less diffusion of heat to outlying areas, so those areas are less likely to be warmed by the city and the city is less likely to be cooled by heat transfer to the rural areas that surround it. 

However, this does not lead to more global warming because while the city will be hotter for longer than otherwise expected, the surrounding area will be cooler for longer as well because less heat from the urban areas reaches the rural areas. The key point here is that no extra heat is created at the surface of the Earth, it is just prevented from diffusing to colder regions. So the net effect on local mean temperatures is zero. As an example consider the Grand Canyon. It will trap heat in the same way that tall buildings do, but does that mean that it is warming faster than the rest of Arizona? No, and nor does it make Arizona as a whole get any warmer.

This is one reason why climate scientists discount the UHI effect, and in this case they are right, provided that the weather stations used to monitor temperature changes are evenly distributed and their temperature readings are not adjusted. Those, unfortunately, are big IFs, because any bias in station numbers between urban and rural regions compared to their relative areas will affect the the relative contribution of each to the mean global temperature, and we do know that station densities are generally higher in urban areas. So potentially there are more warm urban stations contributing to the global average than there should be and fewer cold rural ones. In an ideal world, though, this should not occur, and so neither would any contribution to global temperatures.

Net effect on global warming: zero.

ii) Increased heat capacity

Probably the second most cited variation of the UHI effect is heat retention where cities heat up and store energy from the Sun during the day and then gradually release it overnight. The net effect of this is that the maximum temperature in the city during the day should be less than expected because of the time it takes the buildings to heat up. This is because the Sun is not just heating up the top layer of the Earth's surface, as would be the case in rural areas; it is also having to heat up large concrete structures with much higher heat capacities. The higher the local heat capacity of these structures, the longer it takes to warm them and the slower, and therefore lower, their temperature rise will be. This in turn means that less infra-red radiation is then radiated back into outer space during the day because the region is cooler than it would be without the buildings, and so there is less heating of the lower atmosphere and less downwelling radiation.

At night, however, the heating from the Sun stops. The rural areas cool quickly but the urban areas don't because the urban areas have the much higher heat capacity: there is more heat stored that needs to be lost before a new thermal equilibrium without the Sun can be established. So the buildings are now warmer than their rural surroundings but are slowly cooling, acting like large radiators or storage heaters. This means that the city stays warmer for longer, and temperatures within the city are higher at night than they would otherwise be. 

The net effect of this is that temperatures during the day will be lower, but those at night-time will be higher. Overall, though, the effect on the average temperature will be zero as the two changes in temperature cancel due to the fact that the changes in heat absorption will also cancel.

Net effect on global warming: zero.

iii) Increased heat absorption

One consequence of urban development is that it changes the reflectivity of the Earth's surface for incident visible, ultraviolet and near infra-red radiation. This reflectivity is known as the albedo and it is loosely related to the colour of a surface: darker colours tend to absorb more radiation while lighter ones generally reflect more. If the albedo increases, then more radiation is reflected back into space without heating the planet, so ice and snow help to cool the planet (their albedo is over 80%) while dark soil and oceans tend to warm the planet (see Table 14.1 in Post 14 for a list of typical albedos). It therefore follows that if the colour of a surface changes, then so will its albedo, and this can then change the amount of radiation absorbed at the surface. If this absorbed radiation increases, then the Earth will get warmer and the UHI effect is one way this can happen.

In Post 14 I explained that of the average incoming solar radiation of 341 W/m² that the Earth receives, only 161 W/m² is absorbed at its surface, and that greenhouse gases then amplify this with 333 W/m² of additional downwelling radiation. This total absorbed heat of 494 W/m² then dictates the mean surface temperature via the Stefan-Boltzmann law (see Post 12). It therefore follows that if any change occurs at the Earth's surface that increases the 161 W/m² of absorbed radiation, then this will change the downwelling radiation by the same percentage and therefore change the mean surface temperature as well.

The process of urbanization inevitably involves changing the colour and texture of the Earth's surface. It generally means that areas of vegetation are replaced with tarmac and concrete. Buildings with dark roofs absorb more solar radiation than trees and grassland. However the situation is not straightforward because concrete can be very reflective and arable land tends to be very dark. Overall though, there is generally a small decrease in albedo with urbanization, and therefore a small increase in the amount of solar radiation that is absorbed. This will raise the surface temperature of the Earth slightly as well, but because it is small it is not likely to be significant.

One human innovation that can have a big impact on temperature is solar power. Because solar panels are designed to absorb 99% of solar radiation, they will add additional heating to any area where they are installed by reducing the albedo to less than 1%. So they may save on CO₂ emissions but they come with their own drawbacks, particularly if you live near them. And if they are added to roofs of buildings in cities and urban areas, they will substantially warm those areas.

Net effect on global warming: small increase in local temperatures.

iv) Heat production

There is one UHI effect that does significantly affect temperatures though: waste heat. This is where human energy use ends up as waste heat that heats the local environment around where the energy is being used. As I showed first in Post 14 and later in Post 29, this direct anthropogenic surface heating (DASH) can warm suburbs, cities and even whole countries by up to 1°C. But in fact even that warming is small compared to large cities like London. 

In 2013 the total energy use in Greater London from all sources was estimated at over 150,000 GWh. That is equivalent to an average power consumption of over 15 GW throughout the year. As the area of Greater London is about 1569 km², this amounts to a constant power density of 9.6 W/m². In Post 14 I explained how increasing the 161 W/m² of solar radiation absorbed by the Earth's surface by 2.25 W/m² would be sufficient to increase the mean surface temperature by 1°C. But I also explained that any other source of heat that was absorbed or produced at the surface would have the same effect. So 2.25 W/m² of waste heat generated at the surface would also lead to 1°C of warming.

In London the waste heat will amount to 9.6 W/m², more or less the same as the total power usage. This is because, according to the second law of thermodynamics, all energy is destined to end up as heat or entropy eventually. So waste heat is probably responsible for over 4°C of warming in London - not a great shock to people who live there. That is the urban heat island effect (UHI).

Of course not everyone sees it this way. In climate science this warming is dismissed as trivial because it only amounts to 0.028 W/m² of power use when averaged across the entire surface of the Earth, and so it only raises global mean temperatures by about 0.01°C. While this is technically correct, it neglects the uneven distribution of both these heat sources and the weather stations that determine the global temperature. Most weather stations are on land, almost 90% are in the Northern Hemisphere, and most of these are in the USA, Europe and China. So a high proportion are going to be distorted by the UHI effect from waste heat. That is what makes it important.

Net effect on global warming: large increase in large cities and much of Europe and the USA.

Summary

What I have shown here is that most types of urban heat island (UHI) have little or no effect on global warming with one exception: waste heat. This can add several degrees to the local temperatures.

However, even this is not the full story because the existence UHIs of themselves is not the only issue. Just because a small area of the Earth's surface retains or produces more heat than another does not mean that overall temperatures will rise and add to global warming. It is the change in heat retention and emission over time that is important, not the magnitude or difference from the rest of the environment. A UHI has no impact on global warming if its energy usage is not changing over time. Unfortunately in most cases the energy usage has changed, and by a large amount.

In the next six posts I will highlight six extreme examples of UHIs in the Southern Hemisphere. These are all examples of UHIs in large cities where the UHI temperature has increased much faster than that seen in the country or region as a whole, probably due to significant growth in the size, population and energy use in those cities.

125: Queensland revisited - temperature trends STABLE to 1980

In Post 24 I interrogated the temperature data of Queensland. This Australian state had 28 long stations and a further 85 medium stations in its Berkeley Earth (BE) dataset as listed here. Averaging the anomalies from these 113 stations resulted in a mean temperature anomaly (MTA) that appeared to exhibit a warming of 0.7°C since 1900 as shown in Fig. 125.1 below. But is this the true picture? In this post I will show how the data can be reinterpreted, and thus deliver different results without altering the actual data.

Fig. 125.1: The mean temperature change for Queensland since 1887. The best fit is applied to the monthly mean data from 1901 to 2004 and has a positive gradient of +0.74 ± 0.08 °C per century.

The first problem with the data in Fig. 125.1 is that not all the 113 stations used are of equal length. That means that the MTA before 1905 is dependent on data from less than twenty stations rather than over one hundred as was the case in the 1980s, as the graph in Fig. 125.2 below indicates. This suggests that the data after 1920 will be more reliable than the data before.

Fig. 125.2: The number of station records included each month in the mean temperature anomaly (MTA) trend for Queensland in Fig. 125.1.

Then there is the impact of the fitting range. Applying linear regression to the entire range of data is often inappropriate because the data may have different behaviours or trends at different times. This appears to be the case for the data in Fig. 125.1 as data after 1975 is clearly behaving differently to data before that date.

So suppose we look just at data after 1920 and only fit to data before 1980. Then the picture changes from that presented in Fig. 125.1. The best fit trend line to the data now rises less steeply by only 0.2°C or so before 1980 (see Fig. 125.3 below), and while there is a larger jump after 1975 of about 0.3°C again, this appears to be temporary as the temperature returns to trend after 2010 (although that may just be a temporary reversal). So changing the interval of the linear regression fit can also change the result, or at least change our perceptions, interpretations and conclusions.

Fig. 125.3: The mean temperature change for Queensland since 1920. The best fit is applied to the monthly mean data from 1921 to 1980 and has a positive gradient of +0.29 ± 0.19 °C per century.

What is more, this temperature rise from 1921 to 1980 seen in Fig. 125.3 is more consistent with that seen for the longest temperature record for the state, Brisbane Regional Office (ID 152224), as shown in Fig. 125.4 below. Unusually, this record shows only modest warming despite coming from the middle of the largest urban area in the state. As I will show in future posts, the urban heat island (UHI) effect, where large urban areas lead to a greater warming of the local environment than is seen in more rural areas, or for the region as a whole, can be a serious issue. It usually results in greater warming for stations in large, dense, urban environments compared to the regional average, not less. But not here.

Fig. 125.4: The mean temperature change for Brisbane since 1887. The best fit is applied to the monthly mean data from 1901 to 2004 and has a positive gradient of +0.22 ± 0.08 °C per century.

All this indicates the difficulties in interpreting temperature data correctly. Not all times in history have equal quality of data, and even if they did, the natural variability in that data means that you need long time intervals to see the true trend. And even then your conclusions will be affected by your choices in how the data is analysed.

So which is the better interpretation of the data, Fig. 125.1 or Fig 125.3? My opinion is Fig. 125.3 because it focuses on the better data. The data analysis also fits to data that is less variable, and therefore more reliable. It is too early to know if the temperature rise after 1975 is part of a trend or whether it is just temporary, so the better approach is to treat it almost as a separate dataset and compare it with what went before. 

The temperature dips in Fig. 125.1 before 1910 are also of questionable veracity. Are they the latter part of an upward trend or are they just just natural variability? Without extra data before 1880 we don't know, and even if that upward trend exists, then why does it exist? Because it can't be caused by rises in CO₂ because those rises were negligible before 1910. In fact CO₂ levels in 1910 are estimated to be less than 300 ppm which is a rise of only 6% since 1800. That is nowhere near enough to produce temperature rises of 1°C or more. In fact it would barely result in rises of 0.1°C (see Fig. 87.3 in Post 87).

But of course not everyone sees things this way. One problem with climate science is the amount of data adjustments that are used to correct for perceived data flaws in the temperature data. But as I have shown repeatedly throughout this blog, those adjustments appear hard to justify from any statistical perspective. The raw data is far more reliable than is often assumed, and this can be evidenced by the repeated behaviours seen in temperature trends based on raw data from neighbouring regions that consistently correlate. Many (but not all) of these adjustments also appear to add warming more often than they reduce it, and so appear to exaggerate the amount of climate change that is occurring.

But perhaps one of the most concerning aspects of temperature adjustments is that they are not permanent. The same data often continues to be readjusted over time, and more often each adjustment makes the claims for the warming trend even greater. As exhibit #1 I give you the Australian Bureau of Meteorology (BoM). According to the BoM the climate of Queensland has warmed by about 1.65°C since 1910 as shown in Fig. 125.5 below. Yet the raw data in Fig. 125.1 suggests that the warming is less than half this value and Fig. 125.3 suggests it may be less than 0.3°C. The problem is that the data shown in Fig. 125.5, which is the official BoM version for July 2022, is rather different from the version published in 2010.

Fig. 125.5: The mean temperature change for Queensland since 1910 according to BoM in 2022. The best fit line has a positive gradient of 1.5 °C per century.

In 2010 the temperature trend for Queensland according to the BoM was as shown in Fig. 125.6 below (h/t Ken's Kingdom). Yes it has twelve years less data, but that is not the only difference. Many of the temperature anomalies before 2010 have rather different values compared to now, and so too does the linear trend which was only 1.0°C per century; this despite there being no change in the 30-year reference period of 1961-1990. Now some of the change in linear trend may be due to the extra data after 2010, but not all. It is quite clear that most of the annual anomalies before 1980 in Fig. 125.6 are larger or less negative than was the case for anomalies for the same year in Fig. 125.5 above, while anomalies for most years after 1980 in Fig. 125.6 have smaller values when compared to the corresponding anomaly in Fig. 125.5.

Fig. 125.6: The mean temperature change for Queensland since 1910 according to BoM in 2010. The best fit line has a positive gradient of 1.0 °C per century.

It may be difficult for some readers to spot the difference because we are talking of changes of less than 0.2°C in the height of the bars, but if we overlay the data from Fig. 125.6 on top of that from Fig. 125.5 the differences become more apparent. This is done in Fig. 125.7 below with the 2010 data from Fig. 125.6 coloured green (for positive vales) or sea-green (for negative values) and being slightly translucent so that the red and blue coloured bars from 2022 can be seen underneath.

Fig. 125.7: A comparison of BoM annual temperature anomalies for Queensland from 2022 (red and blue) and 2020 (green).

What Fig. 125.7 shows quite clearly is that the temperature anomalies before 1980 were up to 0.2°C greater back in 2010, while those after 1980 were up to 0.2°C smaller in value. In other words, the extra adjustments made to the data since 2010 have added up to 0.4°C of warming. And yet neither set of data is comparable to the unadjusted data in Fig. 125.3 where the warming is estimated at less than 0.3°C.

Summary

Re-analysis of the unadjusted Queensland temperature data from Post 24 shows that the state may have warmed by as little as 0.3°C since 1920 (see Fig. 125.3).

The most extreme analysis of the unadjusted data indicates that the warming since 1900 is less than 0.8°C (see Fig. 125.1).

According to the Australian Bureau of Meteorology (BoM) in 2010, there had been 1.0°C of warming from 1910 to 2010 (see Fig. 125.6).

In 2022 the BoM now claims that warming since 1910 has increased to 1.65°C (see 125.5).

Adjustments made to the 1910-2010 data by the BoM since 2010 appear to have added up to 0.4°C of warming (see Fig. 125.7). So up to 60% of the 0.65°C temperature rise claimed by the BoM since 2010 could be due to data readjustments for data before 2010.

45. Review of the year 2020

I started this blog in May, in part to occupy my time during the Covid-19 lockdown. But I was also motivated by a growing dissatisfaction with the quality of data analysis I was witnessing in climate science, and in particular the lack of any objectivity in the way much of the data was being presented and reported. My concerns were twofold. 

The first was the drip-drip of selective alarmism with an overt confirmation bias that kept appearing in the media with no comparable reporting of events that contradicted that narrative. The worry here is that extreme events that are just part of the natural variation of the climate were being portrayed as the new normal, while events of the opposite extreme were being ignored. It appeared that balance was being sacrificed for publicity.

The second was the over-reliance of much of the climate analysis on complex statistical analysis techniques of doubtful accuracy or veracity. To paraphrase Lord Rutherford: if you need to use complex statistics to see any trends in your data, then you would be better off using better data. Or to put it more simply, if you can't see a trend with simple regression analysis, then the odds are there is no trend to see.

The purpose of this blog has not been to repeat the methods of climate scientists, nor to improve on them. It has merely been to set a benchmark against which their claims can be measured and tested.

My first aim has been to go back to basics, to examine the original temperature data, look for trends in that data, and to apply some basic error analysis to determine how significant those trends really are. Then I have sought to compare what I see in the original data with what climate scientists claim is happening. In most cases I have found that the temperature trends in the real data are significantly less than those reported by climate scientists. In other words, much of the reported temperature rises, particularly in Southern Hemisphere data, result from the data manipulations performed by the climate scientists on the data. This implies that many of the reported temperature rises are an exaggeration.

In addition, I have tried to look at the physics and mathematics underpinning the data in order to test other possible hypotheses that could explain the observed temperature trends that I could detect. Below I have set out a summary of my conclusions so far.

1) The physics and mathematics

There are two alternative theories that I have considered as explanations of the temperature changes. The first is natural variation. The problem here is that in order to conclusively prove this to be the case you need temperature data that extends back in time for dozens of centuries, and we simply do not have that data. Climate scientists have tried to solve this by using proxy data from tree rings and sediments and other biological or geological sources, but in my opinion these are wholly inadequate as they are badly calibrated. The idea that you can measure the average annual temperature of an entire region to an accuracy of better than 0.1 °C simply by measuring the width of a few tree rings, when you have no idea of the degree of linearity of your proxy, or the influence of numerous external variables (e.g. rainfall, soil quality, disease, access to sunlight), is preposterous. But there is another way.

i) Fractals and self-similarity

If you can show that the fluctuations in temperature over different timescales follow a clear pattern, then you can extrapolate back in time. One such pattern is that resulting from fractal behaviour and self-similarity in the temperature record. By self-similarity I mean that every time you average the data you end up with a pattern of fluctuations that looks similar to the one you started with, but with amplitudes and periods that change according to a precise mathematical scaling function.

In Post 9 I applied this analysis to various sets of temperature data from New Zealand. I then repeated it for data from Australia and then again in Post 42 for data from De Bilt in the Netherlands. In virtually all these cases I found a consistent power law for the scaling parameter indicative of a fractal dimension of between 0.20 and 0.30, with most values clustered close to 0.25. The low magnitude of this scaling term suggests that the fluctuations in long term temperatures are much greater in amplitude than conventional statistical analysis would predict. 

For example, in the case of De Bilt it suggests that the standard deviation in the average 100-year temperature is more than 0.2 °C. This means that there is a 16% probability of the mean temperature for any century being more than 0.3°C more (or less) than the mean temperature for the previous century, and therefore a one in six possibility of a 0.6 °C temperature rise in any given century. So a 0.6 °C temperature rise over a century could occur once every 600 years purely because of natural variations in temperature. It also suggests that similar temperature variations that we have seen in temperature data in the last 50 or 100 years might have been repeated frequently in the not so distant past.

ii) Direct anthropogenic surface heating (DASH) and the urban heat island (UHI)

Another possible explanation for any observed rise in temperature is the heating of the environment that occurs due to human industrial activity. All energy use produces waste heat. Not only that, but all energy must end up as heat and entropy in the end. The Second Law of Thermodynamics tells us that. It is therefore inevitable that human activity must heat the local environment. The only question is by how much.

Most discussions in this area focus on what is known as the urban heat island (UHI). This is a phenomenon whereby urban areas either absorb extra solar radiation because of changes made to the surface albedo by urban development (e.g. concrete, tarmac, etc), or tall buildings trap the absorbed heat and reduce the circulation of warm air, thereby concentrating the heat. But there is another contribution that continually gets overlooked - direct anthropogenic surface heating (DASH). 

When humans generate and consume energy they liberate heat or thermal energy. This energy heats up the ground, and the air just above it, in much the same way that radiation from the Sun does. In so doing DASH adds to the heat that is re-emitted from the Earth's surface, and therefore increases the Earth's surface temperature at that location.

In Post 14 I showed that this heating can be significant - up to 1 °C in countries such as Belgium and the Netherlands with high levels of economic output and high population densities. In Post 29 I extended this idea to look at suburban energy usage and found a similar result. 

What this shows is that you don't need to invoke the Greenhouse Effect to find a plausible mechanism via which humans are heating the planet. Simple thermodynamics will suffice. Of course climate scientists dismiss this because they assume that this heat is dissipated uniformly across the Earth's surface - but it isn't. And just as significant is the fact that the majority of weather stations are in places where most people live, and therefore they also tend to be in regions where the direct anthropogenic surface heating (DASH) is most pronounced. So this direct heating effect is magnified in the temperature data.

iii) The data reliability

It is taken as read that the temperature data used to determine the magnitude of the observed global warming is accurate. But is it? Every measurement has an error. In the case of temperature data it appears that these errors are comparable in magnitude to many of the effects climate scientists are trying to measure.

In Post 43 I looked at pairs of stations in the Netherlands that were less than 1.6 km apart. One might expect that most such pairs would exhibit identical datasets for the two stations in the pair, but they don't. In virtually every case the fluctuations in the difference in their monthly average temperatures was about 0.2 °C. While this was consistent with the values one would expect based on error analysis, it does highlight the limits to the accuracy of this data. It also raises questions about how valid techniques such as breakpoint adjustment are, given that these techniques depend on detecting relatively small differences in temperature for data from neighbouring stations.

iv) Temperature correlations between stations

In Post 11 I looked at the product moment correlation coefficients (PMCC) between temperature data from different stations, and compared the correlation coefficients with the station separation. What became apparent was evidence for a strong negative linear relationship between the maximum correlation coefficient for temperature anomalies between pairs of station and their separation. For station separations of less than 500 km positive correlations of better than 0.9 were possible, but this dropped to a maximum correlation of about 0.7 for separations of 1000 km and 0.3 at 2000 km.

There were also clear differences between the behaviour of the raw anomaly data and the Berkeley Earth adjusted data. The Berkeley Earth adjustments appear to reduce the scatter in the correlations for the 12-month averaged data, but do so at the expense of the quality of the monthly data. This suggests that these adjustments may be making the data less reliable not more so. The improvement in the scatter of the Berkeley Earth 12-month averaged data is also curious. Is it because it is this data that is used to determine the adjustments and not the monthly data, or is this not the case and instead there is some other reason? And what of the scatter in the data? Can we use this to measure the quality and reliability of the original data? This clearly warrants further study.

Fig. 45.1: Correlations (PMCC) for the period 1971-2010 between temperature anomalies for all stations in New Zealand with a minimum overlap of 200 months. Three datasets were studied: a) the monthly anomalies; b) the 12-month average of the monthly anomalies; c) the 5-year average of the monthly anomalies. Also studied were the equivalent for the Berkeley Earth adjusted data.

2) The data

Over the last eight months I have analysed most of the temperature data in the Southern Hemisphere as well as all the data in Europe that predates 1850. The results are summarized below.

i) Antarctica

In Post 4 I showed that the temperature at the South Pole has been stable since the 1950s. There is no instrumental temperature data before 1956 and there are only two stations of note near the South Pole (Amundsen-Scott and Vostok). Both show stable or negative trends.

Then in Post 30 I looked at the temperature data from the periphery of the continent. This I divided into three geographical regions: the Atlantic coast, the Pacific coast and the Peninsula. The first two only have data from about 1950 onwards. In both cases the temperature data is also stable with no statistically significant trend either upwards or downwards. Only the Peninsula exhibited a strong and statistically significant upward trend of about 2 °C since 1945.

ii) New Zealand

Fig. 45.2: Average warming trend of for long and medium stations in New Zealand. The best fit to the data has a gradient of +0.27 ± 0.04 °C per century.

In Posts 6-9 I looked at the temperature data from New Zealand. Although the country only has about 27 long or medium length temperature records, with only ten having data before 1880, there is sufficient data before 1930 to suggest temperatures in this period were almost comparable to those of today. The difference is less than 0.3 °C.

iii) Australia

Fig. 45.3: The temperature trend for Australia since 1853. The best fit is applied to the interval 1871-2010 and has a gradient of 0.24 ± 0.04 °C per century.

The temperature trend for Australia (see Post 26) is very similar to that of New Zealand. Most states and territories exhibited high temperatures in the latter part of the 19th century that then declined before increasing in the latter quarter of the 20th century. The exceptions were Queensland (see Post 24) and Western Australia (see Post 22), but this was largely due to an absence of data before 1900. While there is much less temperature data for Australia before 1900 compared to the latter part of the 20th century, there is sufficient to indicate that, as in New Zealand, temperatures in the late 19th century were similar to those of the present day.

iv) Indonesia

Fig. 45.4: The temperature trend for Indonesia since 1840. The best fit is applied to the interval 1908-2002 and has a negative gradient of -0.03 ± 0.04 °C per century.

The temperature data for Indonesia is complicated by the lack of quality data before 1960 (see Post 31). The temperature trend after 1960 is the average of between 33 and 53 different datasets, but between 1910 and 1960 it generally comprises less than ten. Nevertheless, this is sufficient data to suggest that temperatures in the first half of the 20th century were greater than those in the latter half. This is despite the data from Jakarta Observatorium which exhibits an overall warming trend of nearly 3 °C from 1870 to 2010 (see Fig. 31.1 in Post 31).

It is also worth noting that the temperature data from Papua New Guinea (see Post 32) is similar to that for Indonesia for the period from 1940 onwards. Unfortunately Papua New Guinea only has one significant dataset that predates 1940, so conclusions regarding the temperature trend in this earlier time period are difficult to ascertain.

v) South Pacific

Most of the temperature data from the South Pacific comes from the various islands in the western half of the ocean. This data exhibits little if any warming, but does exhibit large fluctuations in temperature over the course of the 20th century (see Post 33). The eastern half of the South Pacific, on the other hand, exhibits a small but discernible negative temperature trend of between -0.1 and -0.2 °C per century (see Post 34).

vi) South America

Fig. 45.5: The temperature trend for South America since 1832. The best fit is applied to the interval 1900-1999 and has a gradient of +0.54 ± 0.05 °C per century.

In Post 35 I analysed over 300 of the longest temperature records from South America, including over 20 with more than 100 years of data. The overall trend suggests that temperatures fluctuated significantly before 1900 and have risen by about 0.5 °C since. The high temperatures seen before 1850 are exclusively due to the data from Rio de Janeiro and so may not be representative of the region as a whole.

vii) Southern Africa

Fig. 45.6: The temperature trend for South Africa since 1840. The best fit is applied to the interval 1857-1976 and has a gradient of +0.017 ± 0.056 °C per century.

In Posts 37-39 I looked at the temperature trends for South Africa, Botswana and Namibia. Botswana and Namibia were both found to have less than four usable sets of station data before 1960 and only about 10-12 afterwards. South Africa had much more data, but the general trends were the same. Before 1980 the temperature trends were stable or perhaps slightly negative, but after 1980 there was a sudden rise of between 0.5 °C and 2 °C in all three trends, with the largest being found in Botswana. This does not correlate with accepted theories on global warming (the rises in temperature are too large and too sudden, and do not correlate with rises in atmospheric carbon dioxide), and so the exact origin of these rises appears to be unexplained.

 

viii) Europe

Fig. 45.7: The temperature trend for Europe since 1700. The best fit is applied to the interval 1731-1980 and has a positive gradient of +0.10 ± 0.04 °C per century.

In Post 44 I used the 109 longest temperature records to determine the temperature trend in Europe since 1700. The resulting data suggests that temperatures were stable from 1700 to 1980 (they rose by less than 0.25 °C), and then rose suddenly by about 0.8 °C after 1986. The reason for this change is unclear, but one possibility is that it has occurred due to a significant improvement in air quality that reduced the amount of particulates in the atmosphere. These particulates, that may have been present in earlier years, could have induced a cooling that compensated for the underlying warming trend. Once removed, the temperature then rebounded. Even if this is true, it suggests a maximum warming of about 1 °C since 1700, much of which could be the result of direct anthropogenic surface heating (DASH) as discussed in Post 14. In countries such as Belgium and the Netherlands the temperature rise is even less than that expected from such surface heating. It is also much less than that expected from an enhanced Greenhouse Effect due to increasing carbon dioxide levels in the atmosphere (i.e. about 1.5 °C in the Northern Hemisphere since 1910). In fact the total temperature rise should exceed 2.5 °C. So here is the BIG question? Where has all that missing temperature rise gone?