From the Royal Meteorological Society. (h/t to reader NJSnowFan)
How much has urbanisation affected United Kingdom temperatures?
Ian L. M. Goddard, Simon F. B. Tett
This study aims to estimate the affect of urbanisation on daily maximum and minimum temperatures in the United Kingdom. Urban fractions were calculated for 10 km × 10 km areas surrounding meteorological weather stations. Using robust regression a linear relationship between urban fraction and temperature difference between station measurements and ERA‐Interim reanalysis temperatures was estimated.
For an urban fraction of 1.0, the daily minimum 2‐m temperature was estimated to increase by 1.90 ± 0.88 K while the daily maximum temperature was not significantly affected by urbanisation. This result was then applied to the whole United Kingdom with a maximum T min urban heat island intensity (UHII) of about 1.7K in London and with many UK cities having T min UHIIs above one degree.
This paper finds through the method of observation minus reanalysis that urbanisation has significantly increased the daily minimum 2‐m temperature in the United Kingdom by up to 1.70 K.
The urban heat island intensity (UHII), which describes increased temperatures in urban areas, has long been known and attempts have been made to quantify it for many years (Mitchell, 1961; Oke, 1982). The urban heat island (UHI) develops through changes to the surface energy balance due to anthropogenic modifications to the land surface. The importance of understanding how these changes will affect the global climate and the potential bias to land temperature records arising from urbanisation has piqued interest in this area of research. Further, due to the consequences of increasing temperatures in urban areas, such as increasing air pollution and mortality rates (Johnson et al., 2005; Stedman, 2004), many studies have attempted to quantify how temperatures in highly urbanised areas will be affected by increasing urbanisation.
Previous studies have generally concluded that urban warming has had a negligible effect on global scale temperature series (Peterson et al., 1999; Parker, 2004). For example, Jones et al.(1990) showed that the urban warming effect corresponds to no more than 0.1 K over the last century. However on regional scales, the affect of urbanisation on temperature may be significant. Specifically in China, where there has been large expansion of urban areas, a significant effect has been estimated. Yan et al. (2010) concluded a large impact of urbanisation up to 0.54 K/decade on local temperature series in Beijing. Whilst Zhou et al.(2004) showed a smaller urban effect of about 0.05 K/decade in south east China.
This effect is not exclusive to Asia, several studies have found similar effects in Europe and parts of the United Kingdom (Emmanuel and Krüger, 2012; Grawe et al., 2013; Trusilova et al., 2008; Chrysanthou et al., 2014). To quantify the UHII, Trusilova et al. 2008 and Grawe et al. (2013) both used atmospheric models to estimate the effect of urbanisation on temperatures in mainland Europe and the greater London area respectively. In Europe, Trusilova et al. (2008) quantified an average increase in the daily minimum temperature (equation/asl2896-math-0001.png) of 1.53 ± 0.49 K and observed that the maximum daily temperature (equation/asl2896-math-0002.png) may increase or decrease depending on local climate. They reported that in cooler climates equation/asl2896-math-0003.png increased due to urbanisation. In the greater London area Grawe et al. (2013) found an average increase in (equation/asl2896-math-0004.png) and (equation/asl2896-math-0005.png) of 1.31 ± 0.30 and 0.57 ± 0.19 K respectively. Further, through the comparison of recorded minimum and maximum daily temperatures between urban and rural sites, Emmanuel and Krüger (2012) found for Glasgow, consistent with other studies, an average increase of 1.6 ± 1.2 and 0.8 ± 2.1 K in equation/asl2896-math-0006.png and equation/asl2896-math-0007.png respectively. The aim of this study is to estimate the impact of urbanisation across the entire United Kingdom.
Previous studies have used varying methods to quantify the impact of urbanisation on temperature. Yan et al. (2010) measured the significance of urbanisation by comparing temperature time series for urban and rural weather stations, observing a greater warming at urban sites. However, it is difficult to classify weather stations as either urban or rural. In their study Yan et al. (2010) used population density as a marker for urbanisation. However, this data is often out of date and can be hard to obtain for rural areas (Wang and Chen, 2016). Satellite data has also been used to asses the urbanisation of an area. Hansen et al.(2001) used satellite measurements of night‐time light emissions to classify weather stations as either urban, semiurban or rural; where a station classed as urban was located in a bright area, a semiurban station was located in a dimly lit area and a rural station in an unlit area. However, a problem with this method is that stations classed as urban may be located inside well lit city parks, where the UHII is reduced by the park cool island (PCI) effect (Cao et al.,2010). The PCI effect, caused by radiative exchanges with vegetation and its surroundings, partially mitigates the development of the UHI (Oliveira et al., 2011). Hence, using night‐time light emission data to characterise stations as urban or nonurban may lead to inaccurately characterising the effects of urban material on temperature. This study aims to deal with the problem of PCI mitigation of the UHI and the issues of urban/rural classification by determining the degree of urbanisation of a given weather station, rather than having discrete classes. This is done through the use of a land cover/land use dataset derived from satellite images to asses the fraction of urban material around weather stations (termed urban fraction).
We next detail the data and methodology used to determine both the degree of urbanisation of weather stations in the United Kingdom and the corresponding urbanisation effect. The results of the analysis are then reported before some discussion of the results and conclusions are given. This study finds there is no significant urban effect on the daily maximum 2‐m temperature but does find a significant increase in the daily minimum 2‐m temperature due to urbanisation.
We generally find weak and statistically insignificant relationships between monthly, seasonally or annually averaged ΔT max and urban fraction (Figure 3). When ΔT max is averaged annually, the linear relationship between this and urban fraction is insignificant (at a 97.7% confidence level) at 0.25 ± 0.42 K. The strongest relationships are observed in the winter months with December having an urbanisation effect of 0.67 ± 0.34 K. However, this relationship is insignificant for February through to October. The results suggest that urbanisation has had no significant impact on daily maximum temperature across most of the annual cycle.
A significant increase in monthly, seasonally and annually averaged ΔT min is observed in areas of higher urban fraction. For annual average ΔT min, an urbanisation effect of 1.90 ± 0.88 K is found (Figure 3). Stronger relationships are found for ΔT min in the summer months where the maximum UHII reaches 2.17 ± 0.78 K in May.
We have used our results to generate a map of the change in T min due to urban material in the United Kingdom at the 10 km × 10 km scale (Figure 5). We define the UHII as the maximum change in temperature due to urbanisation within the city boundaries and we observe the largest UHII in central London with considerable UHIIs in many other cities. Refer to the Supporting Information for a table of the calculated UHIs of several major cities in the United Kingdom (Table S2).
4 DISCUSSION AND CONCLUSIONS
The observed increase in T min can be attributed to an increased intensity of the UHI during the hours after sunset and into the night. Many studies have previously shown that UHII is maximised during the night (Arifwidodo and Tanaka, 2015; Montávez et al., 2000; Ripley et al., 1996). The intensity is maximised during these hours, as heat absorbed by urban structures will be re‐radiated back into the atmosphere at a slower rate, due to smaller sky views, than natural structures. Further, the increase in impervious surface in an urban area causes a reduction of the latent heat flux and a rise in the sensible heat flux (Zhou et al.,2014). This leads to a difference between the rates at which the urban and natural area will cool during the night, with urban areas sustaining a higher temperature into the night. With minimum temperatures often occurring at night, the slowed rate of cooling in urban areas results in an increase of the observed minimum temperature.
The reduced effect seen in T max may be the result of partial shading (reduced sky‐view factor) in urban areas. If less short wave radiation is absorbed in an urban area than in rural areas, we expect that during the daytime the UHII will be smaller than at night and in some cases has been shown to be negative (Trusilova et al., 2008). Further, the reduced effect may be attributed to higher storage in the day time energy budget of the urban over rural areas. Increased storage leads to less day time sensible heat flux in the urban area causing a reduced increase in temperature. Hence, we observe a smaller difference between the urban and rural temperatures and thus a lower UHII.
The results indicate some seasonal variability in the magnitude of the increase in both T minand T max. Our results for T min agree with previous literature, showing that the UHII is larger in summer than in winter (Kłysik and Fortuniak, 1999; Philandras et al., 1999). This may be due to increased wind and cloud cover in the colder seasons resulting in more mixing of the atmospheric boundary layer and less available short wave radiation. Both of these factors would act to reduce the magnitude of the UHII. Further we observe a significant effect on Tmax only in winter (Figure 3), possibly due to anthropogenic heating leading to a warmer climate in urban areas.
Unlike the studies performed by Wang et al. (2017); Yan et al. (2010); Zhou et al. (2004); Chrysanthou et al. (2014); who performed studies on the rate of warming against urbanisation rate, this study looked only at differences in recorded and reanalysis temperature data and not the rate at which they are changing with respect to one another. Analysis of older land use data sets (CLC 2000, CLC 2006) found no urbanisation changes in the regions around the weather stations used in the study suggesting that there has been no significant urbanisation changes in the United Kingdom since 2000.
In this study, relationships between the urban fraction around weather stations in the United Kingdom and temperature differences between observed and reanalysis values were examined. A small and statistically insignificant relationship was observed for T max. After performing several sensitivity tests, it was found that in almost all cases the result remained insignificant and even when significant, the effect was very weak. This is in contrast to the results for T min where urbanisation has caused significant warming. The results indicate that if an area is 100% urbanised, annual averge T min would have increased by 1.90 ± 0.88 K. The results of the sensitivity tests suggest that whilst this value may be a slight under‐estimate, the significance of the result is robust in most cases. We observe that when considering an area of 400 km2 over 100 km2 the effect may be increased, suggesting that a larger area may influence the UHII more than originally proposed in this study. The relationship found for Tmin in this study is in agreement with the results found by Trusilova et al. (2008) and shows a slightly stronger relationship than that found by Grawe et al. (2013). However, the results from this study show a slight, and largely insignificant, increase in T max due to urbanisation. Whilst the results are likely dependent on the ERA‐interim data used for the analysis, we see that the results are consistent with previous literature, where a weaker relationship between urbanisation and T max than in T min is found (Wang et al., 2017; Trusilova et al., 2008). Albeit, our study does not capture as large an effect in T max as the cited literature.
The full paper: (open access)
This is not unlike what our surfacestations project has found in the USA:
NEW STUDY OF NOAA’S U.S. CLIMATE NETWORK SHOWS A LOWER 30-YEAR TEMPERATURE TREND WHEN HIGH QUALITY TEMPERATURE STATIONS UNPERTURBED BY URBANIZATION ARE CONSIDERED
Figure 4 – Comparisons of 30 year trend for compliant Class 1,2 USHCN stations to non-compliant, Class 3,4,5 USHCN stations to NOAA final adjusted V2.5 USHCN data in the Continental United States