The impact of urban areas on weather - Wiley Online Library

QUARTERLY JOURNAL OF THE

ROYAL

METEOROLOGICAL

Vol. 132

SOCIETY

JANUARY 2006 Part A

Q. J. R. Meteorol. Soc. (2006), 132, pp. 1–25

No. 614

doi: 10.1256/qj.05.199

The impact of urban areas on weather By C. G. COLLIER∗ Centre for Environmental Systems Research, University of Salford, Greater Manchester, UK (Presidential Address: delivered 15 September 2005)

S UMMARY The industrial revolution led to a rapid development of urban areas. This has continued unremittingly over the last 200 years or so. In most urban areas the surface properties are heterogeneous, which has significant implications for energy budgets, water budgets and weather phenomena within the part of the earth’s atmosphere that humans live. In this paper I discuss the structure of the planetary boundary layer, confining our analysis to the region above the rooftops (canopy layer) up to around the level where clouds form. It is in this part of the atmosphere that most of the weather impacting our lives occurs, and where the buildings of our cities impact the weather. In this review, observations of the structure of the urban atmospheric boundary layer are discussed. In particular the use of Doppler lidar provides measurements above the canopy layer. The impact of high-rise buildings is considered. Urban morphology impacts energy fluxes and airflow leading to phenomena such as the urban heat island and convective rainfall initiation. I discuss in situ surface-based remote sensing and satellite measurements of these effects. Measurements have been used with simple and complex numerical models to understand the complexity and balance of the interactions involved. Cities have been found to be sometimes up to 10 degC warmer than the surrounding rural areas, and to cause large increases in rainfall amounts. However, there are situations in which urban aerosol may suppress precipitation. Although much progress has been made in understanding these impacts, our knowledge remains incomplete. These limitations are identified. As city living becomes even more the norm for large numbers of people, it is imperative that we ensure that urban effects on the weather are included in development plans for the built environment of the future. K EYWORDS: Boundary layer precipitation

Doppler lidar

1.

Heat island

Turbulence

Urban meteorology

Urban

I NTRODUCTION

In 1950 30% of the world’s population lived in urban areas, but by 2000 this figure had increased to 47%, and it is projected to rise to 60% by 2030 (UN 2001). This process of urbanization is very advanced in much of the developed world where at least 75% of the population now live in urban areas, a figure projected to rise to 83% by 2030. The level of urbanization is considerably lower in the less developed regions where 18% of the population lived in urban areas in 1950, increasing to 40% in 2000 and projected to rise to 56% by 2030. In 2000 only 3.7% of the world population resided in cities of 10 million or more inhabitants, whereas 24.8% of the world’s population lived in urban settlements with fewer than 500 000 inhabitants. Furthermore, in 2000 ∗

Corresponding address: Centre for Environmental Systems Research, Built and Human Environment Research Institute, School of Environment and Life Sciences, University of Salford, Salford, Greater Manchester M5 4WT, UK. e-mail: [email protected] c Royal Meteorological Society, 2006.

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52.5% of all urban dwellers lived in settlements with fewer than 500 000 inhabitants (UN 2001). The number of cities with over 5 million inhabitants has increased from 40 in 2001 to 58 in 2005. However, this trend towards the concentration of the population in larger urban settlements has not yet resulted in a significant decline of smaller urban areas. It is therefore important to consider whether smaller settlements as well as the megacities have an impact upon local weather. In the UK it is likely that urbanization will follow global trends for developed countries, modulated by Government policy which will impact developments in both the size of cities and housing densities in and around them. Currently major urban development is planned to extend the Greater London area to cover much more of the south-east of England. It is highly likely that this development, and similar developments elsewhere, will impact local weather. In spite of this, urban areas still only cover approximately 0.2% of the earth’s land surface; yet modelling urban weather and climate is critical for human well-being (Jin and Shepherd 2005). The occurrence of weather phenomena, and the corresponding changes in meteorology, are the largest factors in controlling changes in the chemical state of the atmosphere, referred to as air quality. The lowest layer of the troposphere is the planetary boundary layer (PBL), the lowest part of which is in contact with the earth’s surface. Urbanization impacts the physical and dynamical structure of the PBL, influencing both local weather and the concentration and residence time of pollutants in the atmosphere, which in turn impact air quality. Our ability to accurately predict the PBL structure over different types of land cover and throughout the day and season will require comprehensive observational studies (Dabberdt et al. 2004). Chandler (1976) reviewed the climate of towns in the British Isles 30 years ago. The source of data was surface observations; no satellite data were available nor had any numerical studies been carried out, consequently our knowledge of the atmosphere above cities was extremely limited. Since then there have been major efforts to increase our understanding of the impact of cities on local weather and climate. In this paper I review the impacts of urban development upon local weather. Whilst such impacts have been known for many years, it is often not appreciated that changes to weather brought about by this interaction may approach the magnitude of those changes likely to be induced locally by climate change. I do not consider air pollution dispersion in detail, since air quality itself is determined by the meteorology as well as the pollution sources. While it is well understood that pollution emanates from traffic and industry in and around cities, it is the meteorology that dictates the dispersion mechanisms. I confine our discussion to scales of flow above the scale of individual buildings and street canyons (10–20 m), and to the understanding of flows between the scales associated with groups of buildings (20–500 m) and flows at the city scale (10 km). Primary in our discussions will be the urban heat island (UHI), urban precipitation enhancement and flows induced by land surface heterogeneity.

2.

T HE STRUCTURE OF THE URBAN BOUNDARY LAYER

As air flows from rural to urban areas, an internal boundary layer (IBL), known as the urban boundary layer (UBL) develops downwind of the leading edge of the city (Fig. 1(a)). Above the UBL the flow is characteristic of the upwind ‘rural’ surface. Between the rooftops and the ground region is referred to as the urban canopy layer (UCL) within which are urban street-canyon flows, ducting and trapping of airflow and

IMPACT OF URBAN AREAS

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Figure 1. Structure of the urban boundary layer (UBL) sublayer of the planetary boundary layer (PBL) on the: (a) mesoscale, (b) local scale, and (c) microscale. Angle SVF is the sky-view factor. (After Oke 1987, from Fisher 2002.)

multiple reflections of radiation (Figs. 1(b) and (c)). Above the UCL is the turbulentwake layer within which occur the wakes and IBLs from individual buildings, groups of buildings and plumes of heat, humidity and pollutants. In rural areas this is often referred to as the roughness sub-layer (Raupach 1979) in which conventional flux-profile relationships and Monin–Obukhov similarity theory are likely to be invalid. Roth (2000) concluded from a careful review of many field experiments that this layer extends to about 2.5 to 3 times the height of the buildings. Above the turbulent-wake layer is the urban surface layer (USL, also known as the inertial sub-layer or the constant-flux layer) where the momentum and heat budgets are influenced by the average effects of a larger part of the urban area, and within which individual wakes are not important (Fig. 1(b)). Here turbulent fluxes are constant with height, and landscape-scale energy balance fluxes and Reynolds stresses may be measured. It is largely within and above this layer that this paper considers flow and turbulent characteristics. However, some consideration is given to the coupling between air flow within streets and the well-developed boundary layer aloft. This coupling is complex, and involves the injection of turbulent structures into and out of the UCL (see for example Louka et al. 2000; Britter and Hanna 2003). Above the USL is the urban mixed layer (UML) which extends upwards to the top of the UBL (Fig. 1(a)). The characteristics of the UML are affected by the presence of urban surface heterogeneity larger than the local scale. Hence, the processes present are generally mesoscale phenomena operating over spatial and temporal scales larger than individual buildings and streets. During the day the influence of a large city may extend up to around 1.5 km. At night the UBL may contract to less than 0.3 km in depth as stability increases suppressing vertical transfers through turbulence. Often the nocturnal UBL is weakly convective, driven by several mechanisms including the release of sensible heat from storage in the urban fabric and from anthropogenic sources, and by surface shear and mechanical entrainment of sensible heat

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Figure 2. Sensible-heat flux, QH , as a function of individual model input values, i, with all the other model values held constant at reasonable values. See key for curves relating to: building height, zH , ratio of vertical to horizontal area of buildings, λF , radiative surface temperature, TR , ambient temperature at screen level Ta (giving the vertical temperature gradient) and wind speed u. Each curve relates to the impact on sensible-heat flux of changes in a different input variable while other variables are kept constant. The x-coordinate for each curve relates to the appropriate individual input variability range indicated in the box below the graphs, which replaces the scale 0–24 shown for i. Filled (open) symbols refer to the right- (left-) hand y-axis. (After Voogt and Grimmond 2000; M. G. D. Carraca, personal communication.)

downward from an overlying inversion (see for example Uno et al. 1988). The UBL structure may be carried downwind of the city in an ‘urban plume’ as wide as the city, and for some hundreds of kilometres. In most urban areas the surface is quite heterogeneous, there being significant differences arising from different land use (parks, rivers etc.) and building types (high rise, business quarters etc). This has significant implications for the interpretation of measured energy budgets and weather phenomena, as I discuss later. Temperature may be quite different in the centre of cities compared to the surrounding rural areas; this effect is known as the UHI (see for example Oke 1987). 3.

T URBULENT STRUCTURE AND ENERGY TRANSPORT IN THE UBL

(a) The balance between the impact of thermal and mechanical drivers In the absence of any orography, the roughness of a city is governed mainly by its buildings. Large buildings increase surface drag and wake turbulence, and decrease wind speed (Roth 2000). With strong winds, and therefore a weaker UHI (discussed further in section 4), the mechanical effects of increased surface roughness can be more important than thermal effects (Fast et al. 2005). Figure 2 shows a simple model in which calculated values of sensible heat are related to the height of buildings, the area of the vertical surfaces of the buildings facing the wind, the vertical temperature gradient close to the surface and the wind speed (after Voogt and Grimmond 2000). Additionally, the effects of changes in roughness are manifest as changes in wind field distribution. Increased drag and turbulence in cities


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results in a deeper zone of frictional influence within which wind speeds are reduced compared with those at the same height in the surrounding rural area. If these changes occur on a large enough spatial scale the flow may be modified by the Coriolis force deflecting it anticlockwise in the northern hemisphere (Hunt et al. 2004). Roth and Oke (1993) note that large and organized horizontal and vertical structures in urban areas probably transfer energy through pressure–velocity interactions, and increase transport of locally produced turbulent kinetic energy (TKE) away from the surface. Hence, as noted by Rozoff et al. (2003; see also Segal and Arritt 1992) it is to be expected that feedbacks between the momentum and energy fluxes cause substantial modification to urban circulations. If these modifications are to be modelled successfully, then direct measurements are needed of the spectral statistics of the velocity field, turbulent energy dissipation and sensible-heat flux in the UBL. Surprisingly, perhaps, such measurements are not yet extensively available from urban areas, most likely because of the difficulty of making measurements in and above the UCL. (b) Observations in the UBL Considerable research over many years has exploited radar returns from the clear boundary layer. Echoes from both biological (insects) and particulate targets, and from refractive index inhomogeneities can contribute to clear-air backscatter. Such measurements have revealed considerable information on the structure of the atmospheric boundary layer (Gossard 1990). However, the high-power radars used for this type of research have usually been located away from urban areas. Most field measurements (Oke et al. 1999; Roth 2000; Lemonsu et al. 2004; Rotach et al. 2005) over urban areas have used point or tower measurements, and are mostly restricted to the region close to, or below, the top of the UCL. Such measurements have been used with laboratory studies to verify urban canopy models such as that described by Coceal and Belcher (2004). These models, along with wind-tunnel experiments, have been used to characterize the regions that make up the UBL. However, they do not reflect the wide range of different surface properties found in urban areas. Measurements made using balloon-borne instrumentation are also generally not possible in cities on safety grounds. In recent years sodars (Sound Detection and RAnging: for measuring wind and stability profiles) and Radio Acoustic Sounding Systems (RASS: for measuring temperature profiles), based upon the characteristics of the propagation of sound, have been used to measure above the urban canopy into the UML (Dupont et al. 1999; Mestayer et al. 2005). However, ambient noise in cities, and attenuation problems, have restricted the altitude reached by these systems to typically a few hundred metres. Radar wind profilers and surface-based radiometers have also been used to profile wind and temperature, but with somewhat limited resolution in the PBL. All these remote sensing technologies are useful and have their own place in the spectrum of tools available to the urban meteorologist. Uniquely, Doppler lidar offers a technology capable of measuring winds and turbulence characteristics up to the top of the UML during the day (1–2 km), and sometimes beyond, although as yet this technology has not been extensively deployed in cities. Lidar has the advantage over conventional microwave radar of having a narrow beam width, and therefore no obscuration due to ground-clutter returns. However, it has the disadvantage of having a maximum range restricted to around 10 km and a resolution dependent upon the wavelength used. The ECLAP (Etude de la Couche Limite dans l’Agglomeration Parisienne) experiment investigated the structure of the atmospheric boundary layer over the Paris area

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Figure 3. A vertical cross-section across the rural–urban boundary in West London, illustrating variation of the cloud base at the top of the boundary layer measured by Doppler lidar. The left-hand side of the image is over the rural area to the west, and the right-hand side of the image is over the urban area to the east. The colours represent the strength of Doppler radial velocities in m s−1 towards (positive) and away from (negative) the lidar. (From Collier et al. 2005.)

and its rural suburbs (Dupont et al. 1999). During this experiment measurements were made using mast-mounted instruments, sodars (observing up to 500 m altitude), a frequency doubled Nd –Yag (Neodymium-doped Yttrium Aluminium Garnet) backscatter lidar and a CO2 Doppler lidar (observing up to 4 km altitude). The vertical component of turbulence was shown, largely using sodar data, to be enhanced by the urban area, with the amplitude of this effect strongly dependent upon the meteorological situation. The sensible-heat flux in Paris estimated from the sodar data was found to be generally larger than in the rural suburbs by 25–65 W m−2 , corresponding to some 20–60% increase. Interestingly, the difference in the unstable boundary-layer height between the urban and rural areas, estimated using both lidar and sodar, was found to be less than 100 m most of the time. This result was quite different from the situations observed in the METROMEX experiment (discussed further in section 5) where maximum height differences of 100–200 m were observed, although accompanied by large differences between surface fluxes (Hilebrand and Ackerman 1984). More recently, Collier et al. (2005; see also Davies et al. 2004; Middleton and Davies 2005) using a CO2 Doppler lidar, report convective boundary-layer height differences between urban and rural areas of several hundred metres over West London. The performance of this type of lidar, described by Pearson and Collier (1999) and Bozier et al. (2004), was somewhat different from the ECLAP system as reported by Debas et al. (1998 2000), although the height differences observed are unlikely to be influenced by this difference in performance. Figure 3 shows an example of the variation of the cloud base at the top of the boundary layer across the rural–urban boundary. Betts (2004) described how the mean cloud-base height increases over drier soils with larger surface net radiation, while evaporative fraction increases with soil moisture and decreases with net radiation.

IMPACT OF URBAN AREAS (a)

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(b)

Figure 4. (a) Vertical profile of Richardson number (Ri ) in the rural and urban areas near Northolt, West London, 8 July 2003; the threshold value of 1/4 (see text) is also shown; (b) the associated lidar back-scatter. (F. Davies, personal communication.)

This is consistent with the difference in boundary-layer height observed across the rural (wetter)–urban (drier) boundary. (c) Turbulent structure Kolmogorov (1941) proposed the existence of a range of scales in which the dynamics of the turbulence is controlled by the TKE dissipation rate, ε, alone (known as the inertial range). Hence, as pointed out by Chamecki and Dias (2004) and others, in order to understand the dynamics of turbulent flows it is essential to know ε. Indeed, there is a close link between clouds and the properties of the TKE budget, i.e. the boundary-layer dynamics. The parametrization of the boundary layer in numerical models is an area of considerable research activity but is not reviewed here. However, amongst the approaches being investigated are TKE and mass-flux schemes (Grant and Lock 2004). Schemes may involve the turbulence mixing (roughness) length discussed in subsection 3(d), and stability functions related to the Richardson number (Ri ). As turbulence begins to die down under strongly stratified conditions, a multiplelayer fine structure is observed (see e.g. Browning 1971; James 1980). Sheets form which are opaque to turbulent fluxes, causing a convergence of heat and momentum at the top of the sheet and a divergence at the bottom. Wave instabilities, known as Kelvin– Helmholtz instability, may occur when a critical Ri (a value of 1/4) is reached (Gossard 1990). Figure 4 shows a profile of Ri through the boundary layer and the increase in lidar backscatter associated with a concentration of aerosol brought about by instability occurring in a narrow band indicated by Ri decreasing to close to 1/4. This is illustrative of layered structure in the mixed layer (ML), but note the different occurrence over the urban and rural areas instigated by the spatial variations in shear. In order to investigate the detailed structure of the turbulent motion above the UCL, Davies et al. (2004) compared Doppler lidar-measured turbulent structure functions with those derived using the Von Karman model of isotopic turbulence in the inertial sub-range. Making allowance for the spatial averaging of the lidar pulse volume, the correspondence is comforting (Fig. 5). Hence, estimates of the integral length-scale, the dominant spatial scale of the turbulence above the UCL, can be made from the fit of the model to the observations, giving a range from 250–400 m. In addition, measurements were made of the velocity covariance power spectra, and the corresponding eddy

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Figure 5. Comparison of lidar measurements of structure function with the Von Karman model over Salford, Greater Manchester at 1317 UTC 2 May 2002. The upper dashed line represents the uncorrected model and the lower thick line the corrected model; crosses are uncorrected lidar measurements and diamonds are the corrected lidar measurements. (From Davies et al. 2004.)

Figure 6. Covariance power spectra and eddy dissipation rates, ε, over Salford, Greater Manchester, derived from line-of-sight measurements made during the SALFEX experiment for three datasets as indicated. The values of ε for the three curves range from 1.1 to 4.3 × 10−3 m2 s−3 . (From Davies et al. 2004.)

dissipation rates are shown in Fig. 6. The slope of the spectra within the inertial sublayer is usually −5/3, although this depends upon the presence of inversion layers and the strength of the turbulence. The fact that the spectrum falls off faster than −5/3 may indicate that the turbulence approaches isotropy∗ locally in the inertial range (Lumley 1965). However, in the case of the lidar measurements the fall off at less negative wave numbers is more likely to be due to the spatial averaging of the data over the beam. Climatologies of urban and rural eddy dissipation rates are needed to evaluate the urban effects. ∗ The velocity difference across a region induced by the mean shear is small compared to the velocities associated with the eddies.


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Figure 7. Normalized peak wavelength versus height normalized by the boundary-layer height derived from Doppler lidar measured covariance spectra: Curve 1 and points are from J. Crowley (personal communication); Curve 2 shows the mean of the measurements made by Caughey and Palmer (1979).

The covariance spectra reveal the transition between the energy-containing eddies at low frequencies (long wavelengths), and the energy-dissipating eddies at the high frequencies (short wavelengths). If the spectra are ‘flat’ this indicates the absence of a preferred length-scale, and the production of energy over a range of eddy scales. Caughey and Palmer (1979) suggested how this transition (occurring at the peak wavelength) might vary through the rural mixed layer (Curve 2 in Fig. 7). The measurements of the peak wavelength are difficult to estimate due to the considerable scatter at the high-frequency end of the spectrum. Figure 7 shows Doppler lidar measurements of the peak wavelength, made both in rural and urban areas, and both within and above the UCL. The original measurements made by Caughey and Palmer were taken at Ashchurch, Worcestershire and in Minnesota (curve 2 Fig. 7). The lidar measurements given in Fig. 7 include measurements made in the urban areas of Salford and West London and in rural Hampshire. It would appear that the transition wavelength is smaller in the mid-part of the UML than indicated from balloon-borne instrument measurements, although, as noted by Caughey and Palmer, their measurements were on occasion significantly contaminated (overestimated by up to 30%) by the balloon motion. The lidar measurements do suggest no significant differences between rural and urban areas in the lower layers, although there is more scatter in the rural points. This conclusion is consistent with the findings of Raupach et al. (1991) that, even when conditions at the surface are greatly altered by roughness elements, the main boundary-layer structure is not fundamentally changed. The entrainment of air into the boundary layer from above, strongly affects the temperature and heat-flux statistics in the upper half of the ML. Here the peak wavelength decreases, i.e. the frequency of the turbulent motion increases, which suggests that the entrained air descends into the ML in the form of discrete plumes. Figure 8 shows four time series of vertical velocity at a height of 400 m, measured in convective conditions over West London using a dual Doppler lidar configuration (Davies et al. 2005). The spread of the time series show the combined effects from instrumental errors on the vertical velocity. This illustrates the difficulty of measuring the vertical velocity

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Figure 8. Time series of derived vertical velocity data on 23 July 2004 over Northolt, West London using four error scenarios: (i) Salford lidar data plus error with QinetiQ lidar data plus error (dotted line); (ii) Salford data minus error with QinetiQ data plus error (dashed line); (iii) Salford data plus error with QinetiQ data minus error (full line); (iv) Salford data minus error with QinetiQ data minus error (dash-dotted line). (From Davies et al. 2005.)

accurately. Nevertheless, the data indicate that large magnitude downdraughts occur in these atmospheric conditions only infrequently, and have very short durations, i.e. the air descends in discrete plumes. The same is true near the surface where the coupling between the canopy layer and the ML is, similarly, in the form of discrete plumes. This is discussed further in subsection 3(e). (d) The impact of high-rise buildings and urban parks In spite of much work over many years, uncertainties remain in estimating and representing the integrated impact of high-rise buildings upon the UBL. Recent work by Coceal and Belcher (2005) noted that an increase in canopy density is usually associated with a decrease in the mean wind speed in the urban canopy, although this effect may be reversed very near the ground with possible speed-ups if the canopy is especially tall. Taylor et al. (1989) showed that the drag of unresolved orography may be parametrized in numerical weather prediction models through the use of an effective roughness length in a neutrally stratified flow. Grant and Mason (1990) and Wood and Mason (1993) proposed that the pressure force exerted by low hills could be related to the frontal area of the hills. They introduced the idea of a pressure–height scale∗ , Zm , and proposed an expression for the scaling of the pressure force based upon the frontal area of the hill, A, and its surface area, Sh (these definitions later extend to buildings). Numerical model experiments supported the idea that, sufficiently far above the hills, the area-average velocity profile varies approximately logarithmically with height, but with an enhanced, or effective, roughness length, Zoeff , and corresponding surface stress. Blyth et al. (1993) proposed that a similar approach could be used to parametrize sensible- and latent-heat fluxes. Figure 9(a) shows the variation of Zoeff as a function ∗

Wood and Mason define Zm in terms of a height scale hm , which is the height where there is a balance between the different components of the linearized equation for the vertical-velocity perturbations; hence ‘m’ refers to the ‘middle’ layer of the BL.


(a)

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Figure 9. Roughness versus A/Sh (also indicated by λp) for: (a) hills (Wood and Mason 1993), (b) urban area (Grimmond and Oke 1999). See text for details.

of A/Sh derived by Wood and Mason (1993) for low hills. Figure 9(b) shows a similar plot for the roughness length in urban areas associated with different urban geometries represented by A/Sh , where A here is the frontal area of buildings and Sh is the surface area occupied by buildings (Grimmond and Oke 1999). Note the similarities between Figs. 9(a) and (b). However, the degree to which rural representations of turbulence characteristics can be used in urban areas remains unclear. More recently Wood et al. (2001) proposed an alternative to Zoeff , noting the observations made over Scottish lochs by Hignett and Hopwood (1994). These observations showed that in regions of distinct anisotropy in the orography the appropriate roughness length is a strong function of wind direction. However, in convective conditions the roughness length and the eddy dissipation are also functions of the time of day, as discussed by Stull (1988). Therefore care has to be taken in comparing observations with the findings of Wood et al. (2001). Figure 10 shows wind angle relative to the orientation of high-rise buildings in Salford, Greater Manchester, and lidar measurements of eddy dissipation rate (ε), calculated using the covariance technique described by Davies et al. (2004), and plotted as a function of the incident wind direction taken as the deviation from the flow normal to the orientation of the groups of buildings. Although these observations were made on different days, stability conditions were similar (slightly unstable), and the observations were all made during the period 1100–1500 UTC when the temporal variations are at a minimum (see Stull 1988). The wind direction was measured around the middle of the UBL, well above the roughness sub-layer, the position that Belcher et al. (1993) suggest is appropriate for scaling the boundary-layer pressure perturbations. This height is above the region affected by individual buildings (Roth 2000). The observed variation of ε with flow is very similar to that modelled by Wood et al. (2001). It seems that the impact of groups of high-rise buildings on the flow might resemble the impact of low hills. Urban parks, especially if irrigated, may act as ‘oases’ because they are anomalous moisture sources in an otherwise dry area. These have significant impacts upon

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Figure 10. Eddy dissipation ε × 103 m2 s−3 as a function of wind direction incident on a line of high-rise buildings in Salford, Greater Manchester. Observations made between 1100 and 1500 UTC in slightly unstable conditions on a number of different days. The sample figures for selected points are the stability ratio −z/ l, to the first decimal figure, where z is height and l is the Monin–Obukhov length scale.

energy balance (Grimmond and Oke 1995; Best and Clarke 2002; Narita et al. 2002). Such surface heterogeneity may also impact local wind circulations in cities. Segal and Arritt (1992) related the width of a particular land-use patch to sensible-heat flux and the resultant speed of the induced local flow. Groups of high-rise buildings or urban parks may induce these local circulations in synoptic situations with low wind speed, provided that they have a characteristic width of a few kilometres. Further modelling and observational experiments are necessary to disentangle the existence and form of these flows from other effects induced by urban morphologies. Interestingly, Best et al. (2004; see also Best 2005) maintained that there is no need for an atmospheric model to have detailed information about the surface; only the results of the surface computations are needed, namely the fluxes, which are applied as a model boundary condition. This conclusion is supported by Rooney et al. (2005) who found that, for two cities in the UK (Birmingham and Salford) the use of land-use categories removed the need to resolve down to the smallest scale of individual buildings in order to obtain turbulence stresses. Nevertheless, as mentioned earlier, if changes of roughness occur on a large enough scale then the resultant flows may be influenced by the Coriolis force. For cities located close to orography, surface heterogeneity at the city scale, particularly if linked to large orographically induced spatial variations in temperature between the urban and rural areas, may induce low-level airflows having the characteristics of katabatic flows (Haiden and Whiteman 2005). In the UK, Greater Manchester experiences this type of flow, which influences the distribution of fog between the city centre and the suburbs (Collier 1970). More dramatic airflows of this type occur, for example, over the coasts of Greenland and Iceland. The proximity of cities to orography may have considerable impacts upon local weather, related more to the orography rather than to the city morphology. (e) Large-eddy simulation Large-eddy simulation is now used widely for analysing the properties of turbulent flows. Since Mason (1994) reviewed this technique there have been many studies of vertical mixing and related mechanisms under sometimes complex environmental conditions. Recent examples are the study of stably stratified flows near a notched

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Time(sec) Figure 11. Numerical simulation of normalized wavelet power spectrum of u w at x/w, z/H (after Cui et al. 2004). See text for details.

transverse ridge across Salt Lake Valley in the USA by Chen et al. (2004), and the study of turbulent flow in a street canyon by Cui et al. (2004). Cui et al. found that the variations u and w , of horizontal and vertical components of wind velocity, respectively, and variations u w , of momentum flux, are highly intermittent, and are associated with multi-scale turbulent ejection events across the top of the canyon. Figure 11 shows the periods of the dominating modes at the rooftops, identified by means of wavelet analysis as a function of the analysis period. Most of the power is concentrated within the band 4–16 s. Cui et al. concluded that this time-scale fits quite well with that of eddies produced by Kelvin–Helmholtz instability occurring at the rooftops. Recent tower observations by Salmond et al. (2005) using CO2 as a tracer in urban street canyons in Marseille at night, revealed intermittent events or bursts in turbulent fluxes. Wavelet analysis was used to identify and analyse these coherent structures occurring at intervals of around 8 s. Figure 7 suggests that just above the top of the UCL the period of the boundary-layer eddies is around 11 s, as discussed in subsection 3(c). The period then increases to 60–80 s in the middle of the UBL, decreasing towards the top of the UBL. It is not the intention here to review these types of model studies. However, it is important to re-state the view of Mason (1994) that large-scale eddy simulation ‘should by no means eliminate the need for experimental data, but it may well considerably modify data requirements towards studies that support the simulations and enable their application’. This statement is as valid today as it was over 10 years ago. 4.

U RBAN HEAT ISLAND

Most cities are anthropogenic sources of heat. Often there are large areas of asphalt and concrete having albedos and heat capacities that result in the conversion and storage of incoming radiation as sensible heat more effectively than in surrounding rural areas.

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Figure 12.

Idealized diurnal temperature cycles for urban and rural areas (after Oke 1982; Stull 1988).

Thus the lower parts of the UBL are often warmer than the areas surrounding them, this is the UHI (Chandler 1976; Oke 1982). Harmon et al. (2004) and others have shown that the influence of building geometry on the radiation terms of the surface energy balance is a principal cause for surface temperature differences between rural and urban areas. UHIs have been identified in many cities. Whilst the strength of the UHI has been linearly correlated with urban population (Oke 1987), more recent work in Lodz, Poland (Klysik and Fortuniak 1999) has suggested that population magnitude may be a surrogate for the impact of building development density and the occurrence of different types of artificial surfaces. Such a conclusion supports much earlier work reported by Chandler (1976). Best and Clark (2002) found for Reading, England and for London that the ratio of the urban to vegetation fraction within a city also influences the size of the UHI; an increase in vegetation decreases the UHI in a nonlinear way dependent upon the size of the urban area. In addition to these influences Morris et al. (2001) found that for Melbourne, Australia, low wind speeds and little or no cloud resulted in the largest UHI development, although even with complete cloud cover and wind speeds in excess of 5 m s−1 the UHI was still present. As pointed out by Arnfield (2003), over the last 20 years or so it has become evident that there are many types of UHIs displaying different characteristics and controlled by different interacting energy exchange processes. Oke (1976) drew a distinction between the UCL and the UBL heat islands, both manifest through air temperature excess over rural areas (Fig. 12), which may exhibit a marked diurnal variation. However, the use of ground-based thermal remote sensing instrumentation has led to specification of the ground surface UHI, particularly in calm, clear nocturnal conditions. This UHI displays a stronger dependence on microscale site characteristics, particularly street geometry (see e.g. Barring et al. 1985; Eliasson 1996). The use of satellite remote sensing has suggested somewhat different temperature anomalies from the air temperature UHI, which may depend upon viewing angle, target configuration and sampling (Roth et al. 1989). Voogt and Grimmond (2000) found that modelled values of surface sensible-heat flux vary depending upon the method used to determine surface temperature, although this dependency is related to the time of day.

15

IMPACT OF URBAN AREAS 40 38 36 34 32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0

53.50

53.45

53.41

53.36

53.32 -2.36

-2.28

-2.21

-2.13

-2.06

Figure 13. Surface radiative temperatures (◦ C) derived from the MODIS satellite for 1256 UTC 6 August 2003 over Greater Manchester and the surrounding rural area. The area is delineated by lines of latitude and longitude. (M. G. D. Carraca, personal communication. MODIS, NASA.)

Figure 13 shows the surface temperature derived from the MODIS satellite at 1256 UTC 6 August 2003 for the Greater Manchester area, England. An 8 degC temperature difference between the rural and urban area, at the same elevation, is evident to the south-west of the urban area in this summer case. To the east the rural area is about 300 m higher than the urban area to the west, yet the temperature difference is similar to that measured to the south-west. In winter the temperature differences are smaller to the south (2–4 degC), but to the east there is a larger temperature difference (at least 8 degC, sometimes several degrees higher than this) due to the elevated terrain in this direction. The methods used to describe UHIs over the last 20–30 years have remained similar to those originally outlined by Lowry (1977). However, more recent work (Tarleton and Katz 1995; Hawkins et al. 2004) has suggested that in much of the literature the variability of the rural temperature fluctuations has not been sufficiently investigated; therefore estimates of the magnitude of the UHI signatures may have been in error in some cases. Nevertheless, there is considerable evidence that the UHI exists in cities at all latitudes and within all climate regimes (for a review see Arnfield 2003). The UHI thermal anomaly affects the air flowing over the city by modifying the local pressure field and the stability, and increasing turbulence. If synoptic winds are absent or weak, the flow pattern associated with the UHI generally consists of a closed circulation characterized by strong updraught motion at the city centre, convergent flow near the surface and divergent flow aloft. The strength of the UHI circulation is mainly governed by the Froude number (Fr = U/(NL)), where U is the horizontal velocity scale of the flow; N is the buoyancy frequency; and L is the characteristic length scale of the UHI. This circulation may interact with other local flows such as the seabreeze circulation as discussed by Cenedese and Monti (2003). In some situations these thermally induced airflow circulations may have important impacts on the moisture distribution within and around cities, as discussed next (see e.g. Draxler 1986; Balling and Cerceny 1987; Ohasi and Kida 2004).

16

Figure 14.

C. G. COLLIER

Precipitation in Atlanta induced by the urban heat island (UHI): (a) by month, and (b) by hour (local time; from Dixon and Mote 2003).

5.

C ONVECTIVE INITIATION BY URBAN AREAS

Over the last 20 years or so many observational studies have indicated that rainfall patterns in and downwind of cities are modified (for a review see Shepherd 2005). An early study by Atkinson (1968) noted the possible impact of London’s urban area on rainfall. The Metropolitan Meteorological Experiment (METROMEX) investigated the enhancement of convective storms in the vicinity of St Louis, Missouri from 1971 to 1975 (Huff and Vogel 1978; Changnon 1980, 1981; Changnon and Huff 1986). This experiment confirmed that deep, moist convection is enhanced up to some 40 km downwind of St Louis. This enhancement was attributed mostly to modifications of the airflow due to the UHI and the increased roughness of the urban area. However, the possibility that the presence of increased giant cloud condensation nuclei (CCN) concentrations originating from the urban area could also play a role was not excluded. More recent work has confirmed similar effects relating to: Phoenix, Arizona (Balling and Brazel 1988); Atlanta, Georgia (Bornstein and Lin 2000); New York (Bornstein and LeRoy 1990); Mexico City (Jauregui and Romeles 1996); and Bucharest (Tumanov et al. 1999). Cooper and Eichinger (1994) show an example of lidar observations over Mexico City illustrating the occurrence of large plumes and eddies formed at the surface and growing by thermal convection to the top of the UML. Many of these studies have revealed the detailed climatology of UHI-induced precipitation events. For example, Fig. 14 shows the number of such events by hour of the day and by month for Atlanta derived by Dixon and Mote (2003). Here most events occur in July with a diurnal peak just after local midnight. Hand (2005) developed a climatology of shower initiation in the United Kingdom based upon radar data. He noted that the frequency of showers in light wind conditions in summer revealed the impact of the London heat island effect to the south and east of the city.


17

Figure 15. Regional Atmospheric Modelling System (RAMS) evolution of the first model level (48 m) heat island on 8 July 1999, St. Louis. The centre of the image is at the centre of the city. Colours represent temperature, and wind velocity is shown by the arrows with sample vectors beneath each frame. (From Rozoff et al. 2003.)

The Tropical Rainfall Measuring Mission (TRMM) space-borne precipitation radar has offered new opportunities to identify potential UHI-induced rainfall signatures. Shepherd et al. (2002) investigated the UHI-induced precipitation signature of Atlanta using such data, and concluded that the TRMM radar offered a new approach to identifying these signatures. However, the pitfalls which need to be avoided in these studies were discussed thoroughly by Diem et al. (2004). These include: the accuracy of the satellite-derived rainfall rates; the temporal sampling of the TRMM satellite; considerations of spatial scale of the data; inappropriate ensemble averaging; and comparisons with unrealistic ground-based derived precipitation totals. Nevertheless, the utility of space-based radar should not be underestimated in seeking urban signatures on the weather. Modelling studies have supported observations of this urban-induced convection. For example, Thielen et al. (2000) used a simple parametrization for sensible- and latentheat flux and urban roughness within a three-dimensional model. They demonstrated that variations in the surface parameters, especially sensible-heat flux, affected the development of precipitation over Paris. More recently Rozoff et al. (2003) used a storm-resolving version of the Regional Atmospheric Modelling System (RAMS) to simulate the urban atmosphere over St Louis, Missouri. They showed that the UHI plays the largest role in initiating deep, moist convection downwind of the city. Figure 15 is an example of the RAMS evolution of the first model level (48 m altitude) heat island, and the associated wind field.

18

C. G. COLLIER

Surface convergence is enhanced on the leeward side of the city. However, the UHI circulation may also lead to drying over suburban areas away from the city centre as indicated by Ohashi and Kida (2004) using a mesoscale model. These comprehensive modelling studies offer insight into the balance between different physical processes as they impact urban-induced signatures which are present in a range of synoptic situations. However, more limited model approaches also provide complimentary understanding. Thielen and Gadian (1997) used a three-dimensional numerical model with comprehensive microphysics initialized with varying surface fluxes, to demonstrate the impact of the morphology of Manchester on rainfall generation. They showed that the urban area significantly increased the rainfall compared with a reference case (constant surface flux). A climatology of frequency of occurrence of showers in the British Isles compiled by Hand (2005) clearly shows the UHI effect of London in summer. 6.

S UPPRESSION OF CONVECTION BY URBAN AEROSOL

Some 15 years ago Coakley et al. (1987) used space-borne instrumentation to measure the characteristics of the ships’ tracks evident in marine stratocumulus. Similar observations using in situ aircraft measurements were made by Radke et al. (1989). It was clear that the effluent from ship funnels was changing the microstructure of clouds by redistributing cloud water into a larger number of smaller droplets. The presence of aerosols, specifically CCN and ice nuclei, are closely associated with the formation of precipitation. Indeed, it was suspected for many years that large concentrations of CCN from burning vegetation may nucleate so many small cloud droplets that they coalesce inefficiently into raindrops (Kaufman and Fraser 1997). The formation of a high concentration of small cloud droplets then leads to an increased cloud albedo and the suppression of precipitation. Rosenfeld (1999) provided the first conclusive evidence of this process using analyses from the TRMM satellite. With regard to the impact of aerosols generated in urban areas from whatever source, Rosenfeld (2000), again using TRMM with Advanced Very High Resolution (AVHRR) satellite data, provided evidence that such pollution also inhibits precipitation generation from clouds that have temperatures at their tops of around −10 ◦ C over large areas. The precipitating area was defined using the satellite-derived cloud droplet effective radius threshold of 14 μm identified by Rosenfeld and Gutman (1994). An example of the suppression of precipitation by urban and industrial pollution is shown in Fig. 16. Recently Givati and Rosenfeld (2004) provided evidence of the existence of this precipitation suppression in the climate record for major coastal urban areas in California and Israel. The suppression amounts to 15–25% of the annual precipitation, and occurs mainly in the relatively shallow orographic clouds within the cold air mass of cyclones. It was proposed that air pollution aerosols incorporated in orographic clouds slow down cloud-droplet coalescence and riming on ice hydrometeors, and therefore delay the conversion of cloud water into precipitation. While this mechanism is quite plausible, further verification in other regions is needed. Recent work using a large-eddy model described by Johnson et al. (2004) indicates how aerosols may absorb solar radiation, leading to a decrease of low-cloud cover and liquid-water path, resulting in a positive radiative forcing on a climate scale. In the UK, Brown (2004) published the percentage change in UK rainfall for 1971– 2000 compared to that for 1961–1990 showing a decrease in rainfall across northwest England and Yorkshire of about 5%, which he attributed to climate change.


19

Figure 16. Suppression of rain and snow by urban and industrial pollution as shown by visible and infrared satellite data (VIRS). In area 1 pollution tracks in the clouds over South Australia due to reduced droplets are shown in yellow. The TRMM precipitation radar shows precipitation as white patches only outside the pollution tracks in areas 1 and 3. In areas 1 and 3 the VIRS retrieved effective radius exceeds the 14 μm precipitation threshold, but does not do so in area 2. The TMI (passive microwave imager) across A–B–C shows that ample water exists in the polluted clouds as well in the non-polluted clouds. (From Rosenfeld 2000.)

Figure 17. Plan Position Indicator (PPI) of lidar (10 μm wavelength) backscatter at 15 degrees elevation over Greater Manchester and the rural surrounds on 2 May 2002. The range at which the lidar beam intersects cloud is indicated by the brown colour. The backscatter is much larger over the centre of the urban area. (K. E. Bozier, personal communication.)

20

C. G. COLLIER

Government statistics (Defra 2004) indicate that between 1971 and 2000 the amount of PM10 (particulates with diameters less than 10 μm) in the atmosphere increased by an average of 5–10%. An example of lidar backscatter measurements (Fig. 17), proportional to target aerosol of the same type, over Greater Manchester and the surrounding rural area shows a large concentrations of aerosol over the urban areas. It seems possible therefore that the reduction of rainfall could relate to the urban pollution rather than climate change. However, there remain uncertainties in the role of urban aerosols. Jin et al. (2005) suggest that aerosol variations may not be a primary reason for urban modifications to rainfall. Dynamic forcing caused by convergence generated by urban roughness or by UHI destabilization of the boundary layer may be more important. As stressed by Shepherd (2005), it is likely that new observing systems will be needed to resolve the role of aerosols and aspects of the influence of surface heterogeneity.

7.

C ONCLUDING REMARKS

Urban areas will continue to grow, and existing cities will undergo regeneration. It is important to appreciate the impact of the urban morphology upon weather and climate, and apply this knowledge in urban planning. Failure to do so could result in deterioration of the environment in which many of us live. The way in which the built environment changes the atmosphere is complex. It is only comparatively recently that computer simulations and extensive field measurements of urban flows, both within and above the urban canopy, have revealed this complexity. However, our knowledge of that part of the atmosphere between the tops of the buildings and the top of the UML remains incomplete. Numerical modelling and laboratory-based techniques are increasingly being deployed to improve our knowledge of this region. Nevertheless, observations of the real atmosphere are essential to complement and guide these studies if we are to ensure the validity of the conclusions drawn from them. Remote sensing technologies must play a major role in providing the observations needed, but care must be taken to understand the true nature of what is actually measured and the system performance characteristics if these technologies are not to mislead us. Given that urban development will continue, and no doubt accelerate, in many areas, it is imperative that urban effects on the weather are included in planning the built environment of the future. In the UK very significant urban development plans to the east and north of London into the south Midlands, and across Merseyside, Greater Manchester, south Yorkshire and Humberside have been announced by the Office of the Deputy Prime Minister. Investigation of the impacts of such development is an important aspect of ensuring an optimal environment for human well-being. The impact of urban areas can be at least as large locally as those impacts being predicted as a consequence of climate change.

ACKNOWLEDGEMENTS

The help of the following colleagues at the University of Salford is gratefully acknowledged: Dr Karen Bozier, Maria da Graca Carraca, Jenny Crowley and Dr Fay Davies. Thanks are also due to Dr Daniel Rosenfeld (Hebrew University, Jerusalem, Israel) and Dr Chris Rozoff (Colorado State University, USA) for the provision of information illustrating their research.


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