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.

40. Belgium and Luxembourg - temperature trends 1°C WARMING

In the next few posts I am going to take a look at the temperature trends in a few countries in western Europe, starting with Belgium. The unique feature of these countries is that they have some of the longest instrumental temperature records in the world.

Fig. 40.1: The temperature trend for Brussels since 1794. The best fit is applied to all the data and has a positive gradient of +0.67 ± 0.05 °C per century. The monthly temperature changes are defined relative to the 1976-2005 monthly averages.

 

The longest temperature record in Belgium comes, not surprisingly, from Brussels, and extends back to 1794 (see Fig. 40.1 above). That is the good news. The bad news is that there are no other temperature records with significant temperature data before 1973. Four records do have a couple of years of data in the early 1940s. But this data is probably not very reliable as there is then a thirty year gap to the rest of the data, and the early data was clearly collected under conditions of wartime occupation. The only other significant dataset comes from Luxembourg to the south of Belgium (see Fig. 40.2 below) which extends back to 1878.

The blue data in Fig. 40.1 above is the monthly temperature anomaly for Brussels, i.e. the amount by which each month's mean reading deviated from a reference value for that month for that station. Those monthly reference temperatures (MRT) were calculated by averaging all equivalent months (i.e. January or February etc.) in that dataset over the period 1976-2005. This is a later period than that used for most previous blog posts (most use 1961-1990) and is solely because of the lack of data before 1973. The monthly reference temperatures (MRT) are then subtracted from the raw monthly data to generate the monthly anomaly data. For a longer explanation of this process see Post 38 and Post 4.

It can be seen from the anomaly data in Fig. 40.1 that the range of anomaly values can be up to 12 °C, with the extreme negative values being more extreme than the extreme positive ones. These extreme negative values almost always correspond to severe winters; the winter of 1942 was particularly bad with two consecutive months (January and February) recording monthly means that were over 6 °C below normal. In the middle of a Nazi occupation I suspect that was really grim. Overall, though, this suggests that extreme winter cold spells are much deeper and longer lasting than prolonged summer heatwaves.

The other main feature of the data in Fig. 40.1 is the overall upward trend. Apart from a significant dip around 1890, this is almost continuous, and is illustrated more clearly by the 5-year moving average (yellow curve). Overall the mean temperature in Brussels rises by over 1 °C, as indicated by the red best fit line, from 1794 to 2013. However, as I pointed out in Post 14, the growth in energy usage in Belgium over the same period would be expected to raise temperatures by around 0.98 °C anyway. This would appear to indicate, that while the temperature rise is probably man-made, it is in all likelihood not entirely due to the emission of carbon dioxide and the Greenhouse Effect. 

It may be tempting to also discount this temperature record for Brussels as being an aberration or anomaly from the norm. However, if we compare it to the data for Luxembourg shown in Fig. 40.2 below, we see similar trends and features. There is a similar temperature rise after 1985, similar peaks in the 5-year moving average around 1947 and 1960, and a similar trough around 1890. The gradients of the best fit lines are similar in both cases as well, although the uncertainty for the Luxembourg best fit is much greater at almost ±0.16 °C. This, though, is partly due to the shorter time span of the Luxembourg data. 

Fig. 40.2: The temperature trend for Luxembourg since 1878. The best fit is applied to the interval 1895-2004 and has a positive gradient of +0.49 ± 0.16 °C per century. The monthly temperature changes are defined relative to the 1976-2005 monthly averages.

If we now look at the remaining data for Belgium and Luxembourg we see that there are an additional fourteen medium stations with temperature records that contain at least 480 months of data (see here for a list). Most of this data is for the period 1973-2013. The locations of the two long stations (Brussels and Luxembourg) and the fourteen medium stations are shown on the map in Fig. 40.3 below.

 

Fig. 40.3: The locations of long stations (large squares) and medium stations (small diamonds) in Belgium and Luxembourg. Those stations with a high warming trend are marked in red.

The map in Fig. 40.3 indicates that the two long stations with over 1200 months of data and the fourteen medium stations with over 480 months of data are distributed fairly evenly across Belgium and Luxembourg. This is important because it means that we probably don't need to resort to complex weighted averages when finding the overall temperature trend. A simple mean will suffice. In which case, combining the anomalies for the sixteen stations indicated in Fig. 40.3 gives the overall trend shown in Fig. 40.4 below.

Fig. 40.4: The temperature trend for Belgium and Luxembourg since 1794. The best fit is applied to the interval 1895-2004 and has a positive gradient of +0.52 ± 0.15 °C per century. The monthly temperature changes are defined relative to the 1976-2005 monthly averages.

 

As can be clearly seen, the overall trend for the whole of Belgium is not that different from that illustrated for Brussels in Fig. 40.1, but then why would it be? Over 80% of the trend in Fig. 40.4 is entirely due to the data from two stations: Brussels and Luxembourg. This is shown graphically in Fig. 40.5 below.

 

Fig. 40.5: The number of sets of station data included each month in the temperature trend for Belgium and Luxembourg.

 

Finally, if we compare these results using the raw data with those produced by Berkeley Earth which used adjusted data, we see broad similarities but some notable differences.

 

Fig. 40.6: Temperature trends for all long and medium stations in Belgium and Luxembourg since 1794 derived by aggregating and averaging the Berkeley Earth adjusted data. The best fit linear trend line (in red) is for the period 1801-1980 and has a gradient of +0.28 ± 0.03 °C/century.
 

Combining the Berkeley Earth adjusted anomaly data for the same sixteen station records as in Fig. 40.4 and taking the mean value yields the two trends shown in Fig. 40.6 above: one trend for the 12-month average (in black) and a second for the 10-year average (in orange). For temperature data after 1860 the two trends are very similar to those published by Berkeley Earth and shown in Fig. 40.7 below, with the curves exhibiting similar patterns of peaks and troughs in the two figures. This does appear to validate our initial assumption that weighted averages are unnecessary when combining these temperature records due to their even geographical spacing. However, the Berkeley Earth data before 1860 looks slightly different, and quite frankly is unlikely to be very reliable, given that it is based on only one temperature record, or for the curve before 1794, on no local data at all. 

Fig. 40.7: The temperature trend for Belgium since 1760 according to Berkeley Earth.

Finally, if we look at the difference between the raw data shown in Fig. 40.4 and the Berkeley Earth adjusted data presented in Fig. 40.6 we see that while the overall net adjustments Berkeley Earth made to the data in this instance are small and result in a slightly negative contribution to the trend, there were still large corrections made to segments of the data before 1930 that in effect attempt to "flatten the curve". These do not appear to have a significant impact on the overall trend though.

Fig. 40.8: The contribution of Berkeley Earth (BE) adjustments to the anomaly data after smoothing with a 12-month moving average. The linear best fit to the data is for the period 1831-2010 (red line) and the gradient is -0.048 ± 0.009 °C per century. The orange curve represents the contribution made to the BE adjustment curve by breakpoint adjustments only.

Conclusions

It is clear from Fig. 40.4 that there has been a large degree of warming in Belgium and Luxembourg over the last 200 years. It is likely, given the agreement between the data from the two longest temperature records and their significant spatial separation, that this warming is a feature of the entire region, and is not localized to just one area (or maybe two) of the country, although given the lack of data before 1973, that is not a certainty. The magnitude of this warming is probably in excess of 1 °C. However, this temperature rise is only what one would expect from the growth of industrial energy use over this period (for Belgium it should be about 0.98 °C) as explained in Post 14. It is also less than the 1.5 °C we are told to expect for anthropogenic global warming (AGW) in the Northern Hemisphere as claimed by the IPCC and the HadCRUT4 data. Consequently, it does not really add support to the theory that carbon dioxide is the primary driver of warming, otherwise the warming should be much larger.

29. Lateral thought #1 - suburban heating

Question

How much does the average home heat up its environment?

This is really a question that ties in with what I wrote on Post 14. Surface Heating, but I think it illustrates the point at a level that most people can relate to.
 

Answer

Well, we know from Trenberth et al. that the average power of solar radiation incident on the Earth's surface is approximately 161 W/m² (see Fig. 14.1). We also know that this leads to a mean surface temperature for the Earth of about 288 K. We also know from Post 12 (black body radiation and Planck's law) that the emitted surface radiation density scales as T ⁴, where T is the absolute temperature of the surface measured in kelvins, and the emitted radiation must balance the incoming radiation. In other words, both incoming and outgoing surface radiation densities will be proportional to T ⁴. For the outgoing radiation the constant of proportionality will be the Stefan-Boltzmann constant, while for the incoming radiation it will be the Stefan-Boltzmann constant divided by the feedback amplification factor. It therefore follows that if the mean surface temperature were to increase by 1 K to 289 K, then the quantity T ⁴ must increase by 1.40 %. And there are two main ways that this could be achieved. 

The first is to increase the feedback or radiative forcing through an increase in the strength of the Greenhouse Effect. This is what most climate scientists concentrate on, and what they believe is responsible for any temperature changes. But the second possibility is to increase the radiation power absorbed by the surface before the feedback amplification occurs. This could happen if the strength of the Sun's output changes, but more realistically it will happen whenever extra heat is liberated at the surface of the Earth. The amount of heat required to do this will be 1.40 % of 161 W/m², or 2.25 W/m². So an increase of 2.25 W/m² in the incident surface energy density will result in a 1 °C temperature rise (see Post 13 - Case 2).

As I pointed out in Post 14, a major source for such additional heat liberation at the Earth's surface is energy generation and consumption by humans, often for industrial needs. This leads to direct anthropogenic surface heating (DASH) that can raise the temperature of whole countries by as much as 1 °C. But it is not just industry that can significantly heat the local environment.

Consider a typical home. The average household in the UK uses at least 10,000 kWh of energy per year. That equates to an average rate of usage of energy of 1.14 kW throughout the year.

The average land area of homes in the UK is at most 500 m². Most modern housing developments have more than 30 new homes per hectare (see PPG3 guidance paragraphs 57-58); older suburban developments are generally a lot less dense than this; inner city flats and terraced houses are clearly a lot more.

All of this means that the power density for heat escaping from homes will be at least 2.28 W/m² (i.e 1140 ÷ 500). In other words, the energy used by a typical household is more than sufficient to increase the local surface temperature by more than 1 °C. And remember, all this heating has got nothing to do with CO₂ emissions. Nor does this calculation include the energy consumption of commercial buildings, industry or transport.


Conclusion

The energy used by the average household in the UK each day raises the temperature of their local environment by at least 1 °C compared to pre-industrial levels. That will be true irrespective of the source of the energy. Renewables will not help. Nor will cutting your level of CO₂ emissions. This is all down to heat, entropy and thermodynamics.