CLIMATE SCENARIOS FOR URBAN DESIGN: A CASE STUDY OF THE LONDON URBAN HEAT ISLAND

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1 CLIMATE SCENARIOS FOR URBAN DESIGN: A CASE STUDY OF THE LONDON URBAN HEAT ISLAND HACKER, Jacob N. BELCHER, Stephen E. YAU, Raymond M.H. Arup University of Reading and Arup Arup United Kingdom United Kingdom Hong Kong Summary Cities have a climatology that is distinct from their extra-urban ( rural ) surroundings because of the different way the urban land surface stores and releases heat, and to greater or lesser extent, the higher concentration of anthropogenic heat sources, from transport and buildings. Typically, temperatures in urban areas are higher than in the rural surroundings, particularly at night, a phenomenon called the urban heat island (UHI). Heat islands exacerbate the impacts of heatwaves by affecting thermal comfort, human health and ability to passively night cool buildings. Because these factors are dependent on absolute as well as relative temperatures, the UHI also makes cities more vulnerable to the effects of climate change, since current temperatures are already at a higher level. There is also the potential for climate change to affect the frequency and intensity of UHI events due to changes in the underlying climatology. On the other hand, there are measures that urban designers can take to reduce the intensity of the urban island, for example use of shading, greenspace, water and building massing. Further, there is a possibility to beneficially moderate the urban climate during the day to provide a more benign climate in comparison to that in the rural surroundings through design. Modelling work has been carried out through collaboration between Arup and the University of Reading in the UK to better understand the factors affecting the London urban heat island, to assist urban designers in decision making and help formulate adaptation policies to climate change. In this paper two types of model are described: a) an empirical model based on meteorological observations of the UHI, which predicts the statistical characteristics of the urban heat island at a given location; and b) a dynamic thermal model based on the physics of the heat balance at the urban land surface, which predicts the diurnal variation of temperature in an urban area according to land-surface properties. These models are used to examine quantitatively how different meteorological and landuse factors contribute to the urban heat island and what the future impact of climate change on the UHI might be. The ultimate aim of this work is to provide urban designers and policy makers with tools that can be used to produce climate scenarios for urban areas, to assess design options that can beneficial affect the climate of cities and the development of climate change adaptation policies. Keywords Urban heat island, climate change scenarios, downscaling methods, urban design, London 1. Introduction The urban heat island (UHI) is a term used to describe the tendency for temperatures in urban areas to be elevated about those in the extra-urban ( rural ) surroundings. The origins of this effect are known to be the manner in which the urban land surface, which is relatively dry, lacking in vegetation, and highly heterogeneous and irregular, affects the storage and release of heat, principally from the sun but also to a lesser or greater extent, the anthropogenic input of heat from waste heat from buildings and transport [Oke, 1987]. Substantial UHIs have been observed in cities around the world [Voogt 04], including Hong Kong [Nicol & Wong 0] and London [GLA 06]. In this paper we review recent modelling work that has been done to investigate the causative factors of the London UHI and how climate change scenarios can be adjusted to take account of urban-scale effects, which are not typically resolved in global or regional climate models.

2 (a) (b) Figure 1: Characteristics of the London urban heat island (UHI): (a) The variation in the average maximum UHI intensity for London (which generally occurs in the early hours of the morning) over 24 hours for summer 00; the shaded area shows the range of UHI values for 68 percent of the observation. (b) The spatial pattern of the UHI across London under calm and dry conditions at 00 00hrs for six urban heat island events during the summer of 00 (July 1 st to September th). The area of relatively low UHI to the south-west of the city (dark blue/purple area) is Richmond Park, a large area of greenspace [From GLA 06]. London is the most populace city in the European Union, with a city population of around 8 million and a metropolitan area population of million people, making it one of the world s megacities. The London UHI has been known about since the early nineteenth century and was mapped in some detail in the 1960s [Chandler, 196]. More recently, the modern form of the London UHI and its spatial distribution has been revealed through a programme of monitoring across the city, analysis of meteorological station data, and satellite observations [Graves et al. 01; Wilby 03; GLA 06]. The UHI in London is mainly a night time effect, with minimum temperatures in the city being on average around 3 4 C degrees higher than in the rural surroundings, and broadly speaking shows an intensification towards the centre of the city (figure 1). The UHI is typically largest under warm summer weather, on clear, still nights, for which instantaneous values up to 9 C have been recorded [GLA 06]. Within the general spatial distribution of the UHI (figure 1b) there are also significant local variations according to local land use characteristics, with more densely built up areas showing stronger UHI intensities. Figure 2 shows the variation of the maximum urban heat island intensity with the percentage of continuous urban land use within a 1 km radius centred on the temperature measurement site [Crack 03]. There is an increase in the maximum heat island intensity with urban land use, from 4 o C at % continuous urban to 6 o C at 70% continuous urban. The points are coloured to distinguish measurements made on the inner and outer rings. Higher UHI intensities occur for given percentage of urban fraction on the inner ring than on the outer ring, consistent with the general intensification of the UHI towards the city centre, but within the zones covered by each ring the dependency on the local land use characteristics indicates this to be an important control on the UHI. The understanding that has been built up of the London UHI [GLA 06] is that it is produced by the different urban land surface types altering the vertical heat exchange with the atmosphere. Lateral heat exchange across the heterogeneous land surface also causes a smearing out of the UHI, leading to the type of distribution shown in figure 1b. This is why the UHI tends to be most intense during periods of low-wind speed. During periods of higher wind speed, the UHI intensity tends to be less, and the spatial distribution of higher UHI values elongates to the East, according to the prevailing westerly wind direction. At the present time, it is thought that anthropogenic heat inputs (from transport and

3 buildings) play a relatively minor part in the London UHI, although these affects are important for the UHI of other cities, particular those with areas of intensive use of air conditioning, such as Tokyo [Ichinose et al. 1999]. The general increased prevalence of the use of air conditioning in London due to socio-economic trends and warmer summers under climate change presents the possibility that the anthropogenic contribution to the London UHI may increase in future years. % Continuous Urban Maximum Heat Island Intensity ( C) Figure 2. Relationship between maximum UHI in London with land use and distance from the city centre: a) land use types from the Centre for Ecology and Hydrology land use dataset: white is continuous urban, red is suburban, green is vegetated, blue is water ; the cross hair is centred on the British museum, the small circles indicate the positions of the temperature sensors, and the larger shaded circle indicates a circle of radius 1km, the area used to evaluate the land use around each temperature sensor; b) maximum UHI recorded at the stations show in a) as a function of continuous urban land use, blue symbols are stations on the outer ring of stations and red are stations on the inner ring. [From Crack 03] 2. The urban heat island as a statistical phenomenon To investigate further the characteristics of the London UHI, hourly temperature observations from an urban and a rural site have been examined for the period The urban site was London Heathrow Airport (LHR), which lies in a built up area towards the western edge of London, 24km from the centre. The rural site was Beaufort Park, a country park on the edge of the town of Bracknell, 0km west of central London. These observations are WMO approved sites with sensors mounted 2m above surface. The UHI for LHR is here taken to be the difference in temperature between that site and Beaufort Park. The maximum UHI (UHImax) is the maximum daily value of the UHI. Figure 3 shows the number of occurrences of UHImax values of given magnitude. The figure shows that LHR experiences a significant UHI which is largest in summer. During August there are on average 21 days with a maximum heat island of more than 3 o C, and 8 days of more than o C. Figure 4 shows how the UHI varies during the course of the day (the diurnal variation) when averaged across a number of months. The diurnal cycle show a characteristic shape. During the day the UHI is nearly constant, with a mean value of about 1 o C. Following sunset the heat island intensity builds to a maximum and then reduces following sunrise. Based on these observations, the diurnal UHI amplitude, A, can be approximated by a semi-sinusoidal form (figure ): A t Td T max T d T d t td sin tu t, d, t sd t t su, otherwise, (1) where t is the hour of the day; Td is the constant urban heat island intensity during the day; Tmax is the maximum night time urban heat island; and t su and t sd are sun-up and sun-down times.

4 Average Number of Days Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 3. The Heathrow Beaufort Park heat island: Average number of days of occurrence in each month of the heat island. Data from The temperature difference must exceed the threshold for at least one hour over the 24-hour diurnal cycle to register a count. The colours indicated difference temperature thresholds as indicated in the legend. UHIMax >3C UHIMax >4C UHIMax >C UHIMax >6C UHIMax >7C 4.0 Seasonal Averaged UHI: Heathrow - Beaufort Park 4.0 Amplitude function, A (for T max = 3. o C, T d = 1 o C) UHI ( C) A(t ) DJF JJA MAM SON 00:00 02:00 04:00 06:00 08:00 :00 12:00 14:00 16:00 18:00 :00 22:00 Figure 4. The seasonally averaged diurnal cycle of the UHI at Heathrow time, t (hour of day) Figure. Hypothesised form of the average diurnal UHI at Heathrow given by equation (1) for Td = 1 C, Tmax = 3. C, t su = 06:00, and t sd = 18:00. 9 Variation of heat island intensity with wind speed Variation of heat island intensity with cloud cover 8 9 Fit is UHI=9.9exp(-0.19CC) heat island intensity (degrees) Fit is UHI=7.exp(-0.2U) + 1 heat island intensity (degrees) U (knots) Cloud cover (oktas) (a) Figure 6. Dependency of the daily maximum UHI at Heathrow with a) daily averaged wind speed, when the daily mean cloud cover is less than 2 oktas and b) with daily mean cloud cover, when wind speed is less than 4 knots. Data from (1 knot = 0.14 ms -1 ) (b)

5 It is known that the London UHI tends to be larger during periods of calm, cloudless conditions. Figure 6 shows the relationship between UHImax and wind speed and cloud cover during conditions conducive to UHI development, taken here to be cloud cover less than 2 oktas and wind speed less than less than 4 knots (2.6 m/s). Whilst there is some scatter in the observations, because other factors influence the heat island intensity, in both cases there is an inverse relationship which can be described by an exponential decay. Based on the above observations regarding the dependency of the UHI on time of day and meteorological conditions, a simple empirical model can be constructed to provide a predictive model of the UHI. The form of this model is: U ( t) Ue C( t) Ce T ( t) A t e T (2) where T is the heat island intensity as a function of hour of the day, t; A(t) is the amplitude function given by equation (1); the two exponential decay terms represent the dependency on wind-speed, U, and cloud cover, C, with respective decay rates U e and C e ; and the last term T is a stochastic term which imparts of degree of randomness into the prediction of the model, to mimic the scatter shown in the data in figure 6. Figure 7 shows the distribution of Heathrow s heat island calculated from the statistical model using hourly observed data for wind speed and cloud cover and compared with the observed values of UHI for The parameters used for this analysis in equations (1) (2) were: Td = 1 C, Tmax = 6 C, U e = 4 knots, C e = 4 oktas), and T was chosen from a Gaussian distribution with zero mean and standard deviation of 0.2 C. The values of t su and t sd are calculated on a daily basis. The statistical model is in reasonable agreement with the measurements, particularly for heat island events stronger than 1 o C. The poorer agreement between the model and the measurements for UHI<1 o C is because the model is designed to capture the strong events: weaker temperature differences are treated with the random temperature fluctuation. No doubt better agreement could be obtained with more tuning, but that does not seem worthwhile given the reasonable agreement for the stronger heat island events of interest here. A statistical model for London s heat island has also been developed by Wilby (03). This model uses synoptic meteorological variables that might for example be obtained from a meteorological forecast model (near-surface wind strength, westerly wind strength, vorticity, relative humidity and 80mb geopotential height) as prognostic variables, whereas the model here uses local meteorological variables, wind strength and cloud cover, that could for example be obtained from a weather station. Although the sets of variables are different, both can be used as indicators of anticyclonic weather conditions, which are those associated with UHI development. Another difference is that Wilby s model makes predictions for daily UHImax where the model here is set up to predict hourly UHI. 0.6 percentage hours data model heat island intensity Figure 7. Distribution of Heathrow s heat island: values calculated with the statistical model and compared with measurements for The horizontal axis gives the magnitude of the UHI and the vertical axis is the fraction of hours (probability) in the year.

6 3 2 th July to th August 199 Heat Wave 3 17 th July to 28 th July 1996 Heat Wave LWC Hourly Temperature LWC Hourly Temperature BBP Hourly Temperature BBP Hourly Temperature Hourly UHI Hourly UHI Mean Hourly JJA UHI Mean Hourly JJA UHI 2 2 Temperature o C 1 Temperature o C Day Day (a) (b) Figure 8. Temporal evolution of extreme heat island events in London for a) July 1996 and b) 2 July August 199, showing temperature at London Weather Centre (central London) and Beaufort Park (extra-urban) and the UHI (difference between the two sites). The average summer diurnal UHI variation is also shown for a comparison. [From Gosling, S., 07, pers. comm.] 3. The urban heat island as a dynamic phenomenon The empirical model developed in the pervious section provides a useful framework for examining how different factors influence the statistical characteristics of the UHI at a given location. It may also be used a predictive model for the statistical characteristic of the UHI for given meteorological conditions. In Section 4 it will be shown how the model can be used to examine the possible influence of climate change on the UHI. However, empirical models of this type have limited ability to make predictions regarding how the UHI at a given site might change if the local or surrounding land-use changes. Such models may also be limited by the fact that they do not include inherent timedependence: in the model the UHI is instantaneously related to the weather conditions at a given time and not on the preceding days. Examination of heat island events in London indicates that the summertime UHI builds over the course of a period of warm weather, typically reaching a maximum on or around the hottest day of the warm spell. Figure 8 shows the development of UHI events at the London Weather Centre (situated at the centre of the city, close to the British Museum) relative to Beaufort Park as the extra-urban reference, over two periods of 11 days centred on an extreme UHI event selected on the basis of having both strong UHI intensity and elevated night-time temperatures. It can be seen in both cases that the nights with strong UHI occur during the period where daily maximum temperatures are rising. In the city night-time temperatures tend to rise in line with the increase in daytime temperatures, whereas those at the extra-urban location do not show a similar increase, in some cases decrease, and also have greater variability. Following the hottest day of the period, the disparity in the night-time temperatures between the two locations reduces leading to a dissipation of the UHI.

7 (a) (b) Figure 9. Schematic of a) the Surface Energy Balance Boundary Layer model (SEBBL) and b) the Urban Boundary Layer energy balance model (UrbanBL). In each case the model solves for the flow of heat to give the temperature, T, within the soil substrate, across the flat surface (Q H is the sensible heat flux, Q * is the net radiation, Q S is the ground heat flux and Q E is that latent heat flux) and in the atmosphere (u is the wind speed, is the temperature and L is the long wave radiation). As explained in the text, the latent heat flux was represented using a Bowen ratio. 3.1 A dynamic thermal model of the UHI In order to investigate the causative processes governing the heat island, a dynamic thermal model which represents essential aspects of the underlying physics has been developed at Reading University [Harman 03, Harman & Belcher 06]. The model is a 1-dimensional column model that solves for the vertical heat balance above an area of land surface. This heat balance can be written mathematically as: Q * Q F Q H Q E Q S Q A, (3) where Q* is the net radiation, which forces the heat balance, composed of the energy gained from the sun and loss due to long-wave radiative cooling: Q F is the anthropogenic heat source e.g. from cars, domestic and industrial fuel combustion; Q H is the outgoing sensible heat flux; Q E is the outgoing latent heat flux; Q S is the change in heat storage; and Q A is the heat transfer by advection. The dynamic model solves the heat balance equation at a fine temporal resolution according to the changing heat inputs (e.g. from solar radiation during the day) to provide a prediction for the land surface temperature and the temperature in the atmosphere above. Here only the vertical heat balance is modelled, so that horizontal heat transfer between areas of differing land surface type and usage are not included. These processes are important, for example producing the variation of the UHI across different land surface types, discussed in Section 1. Potentially horizontal heat transfer can be modelled by coupling the heat balance model to a musicale atmospheric model. However, this has not been done here. The assumption of only vertical heat transport might be considered to correspond in simple terms to situations existing in windless conditions. Two versions of the model have developed: the Surface Energy Balance Boundary Layer model (SEBBL) and the Urban Boundary Layer energy balance model (UrbanBL). These two models are shown schematically in figure 9. The formulation of the two models is similar but the essential difference is that SEBBL models a flat surface whereas UrbanBL models a corrugated land surface composed of repeating street canyons and roofs of given dimension and material properties. Both

8 models have essentially three components: (1) a diffusion model for the transport of heat in the substrate, which calculates the vertical profile of temperature in the substrate; (2) the flow of heat, momentum and long wave radiation in the atmospheric boundary layer to give the temperature and wind profile there; and (3) the budget of heat at the land surface, which forms the interface between the atmosphere and the substrate (the surface energy balance). In UrbanBL, each of the four facets of the land surface (the two walls, the roof and road surface) has a separate surface energy balance that takes into account the incoming radiation, accounting for shadowing and reflections, and the penetration of heat into the substrate (the interior temperature of the buildings is held fixed). Each of the four urban facets can have different material properties (heat capacity, conductivity, albedo and emissivity) and the height, width and spacing of the buildings can be varied. In both models, initial vertical profiles of temperature in the atmosphere and substrate are specified. A geostrophic wind speed and the latitude of the site form the other inputs. 3.2 Modelling results Figure shows results from a number of experiments carried out with the model. In all cases the model was run with parameters for London Heathrow Airport (latitude 2 o N) and during August (beginning th August) for a period of three days. Figure a shows results from the flat-surface model (SEBBL) to examine the role of surface moisture. Moisture is represented in the model though the use of a Bowen ratio. This approach follows the observation that the latent heat flux tends to be a fixed fraction of the sensible heat flux during the day, and zero at night [Oke 1987]. The Bowen ratio is defined as the ratio of the sensible to latent heat flux during the day. Hence a perfectly dry surface has an infinite Bowen ratio. Vegetated surfaces tend to have a Bowen ratio of about 0. to 1.0; an open body of water has a small Bowen ratio, say 0.1; urban areas are typically quite dry with high Bowen ratio ratios, typically lying in the range 1 to 8 [Grimmond, pers. comm.]. Figure a shows the evolution of the near surface air temperature produced by the model for a flat surface, SEBBL, for values of Bowen ratio of infinity (perfectly dry), 2.0 (semi-dry), 1.0 (lightly vegetated) and 0. (heavily vegetated). The main effect of surface moisture is to generally reduce the sensible heat flux during the day and thereby reduce daytime temperatures. This change in the heat balance has the knock-on effect of reducing night-time temperatures, but not by as much as during the day. Hence the diurnal range in the model is also reduced by surface moisture. Figure b shows results where the land surface is again kept flat, but its aerodynamic roughness is varied. Urban areas are typically more irregular, making them aerodynamically rougher than vegetated surfaces. Surface roughness is often parameterised using a parameter called the roughness length. An aerodynamically smooth surface would be say tarmac, whereas a field of crops or a forest canopy is aerodynamically rough. Over very smooth water the roughness length is about 0.01 m, over grass and low vegetation it is 0.1 m and over urban areas it has a typical value of 1 m. The results, which are shown in figure b, demonstrate that as the roughness length increases, the daytime temperatures increase and the night time temperatures decrease. The diurnal range is consequently increased. The reason is that increased roughness increases the efficiency of heat transport away from the surface, enhancing the heating of the air from the ground during the day (the ground warms from solar heating), and cooling of the air from the ground at night (the ground surface cools from emissions of infrared thermal radiation). Figure c shows results from the urban surface model UrbanBL to examine the role of building height. In these simulations, the building facades and roof materials were given the same thermal properties as the street. As the building height is increased, the thermal mass of the land surface increases, because its surface area becomes larger, but other effects also come into play: an increase in

9 the effective surface roughness and the reduction of the sky view through which the street and walls cool by long-wave radiation to the sky. As the building height is increased the near-surface air temperature shows a reduced diurnal temperature range. In particular the urban surface cools much less at night than the flat surface, due to combination of increased thermal storage and reduced sky view factor. These processes more than counteract the effect of increased surface roughness (explicitly modelled in UrbanBL) which tends to reduce night time temperatures (figure b). Figure d shows a set of simulations, again with the urban surface, in which the albedo (the fraction of sunlight reflected from the surface) has been varied between 0.2 and 0.8 (the maximum possible value is 1). As the albedo is increased air temperatures decrease during the day because less heat is absorbed by the surface. This effect is predicted to be quite large, resulting in daytime air temperatures being reduced by up to o C and night time temperatures reduced by 4 o C. In practice, care needs to be taken when employing high albedo surfaces on building facades, as they can lead to glare and an increase in the radiant field causing outdoor thermal discomfort. Another possibility is to use so called cool materials that are not necessarily lightly coloured or highly reflective but absorb a smaller portion of sunlight. 3 Flat cases: effect of surface moisture 3 Flat land surface: effect of surface roughness temperature (atmosphere first model level) ( o C) 2 1 dry (Bowen ration = ) Bowen ratio = 2.0 Bowen ratio = 1.0 Bowen ratio = time (days) temperature (atmosphere first model level) ( o C) 2 1 roughl =0.01m roughl =0.m roughl =1.0m roughl =.0m time (days) (a) (b) Urban land surface: effect of building height (heavyweight facades and roofs) 3 3 Urban land surface: effect of albedo (base case) temperature (atmosphere first model level) ( o C) 2 1 Flat-dry he =.0m he =.0m he =.0m time (days) (c) temperature (atmosphere first model level) ( o C) 2 1 alb = 0. alb = 0.40 alb = 0.60 alb = time (days) Figure. Modelling results from the dynamic thermal models. SEBBL: a) flat surface with varying degrees of moisture (parameterised using a Bowen ratio; low Bowen ration is moist and high Bowen ratio is dry), b) flat surfaces with differing aerodynamic roughness (expressed as roughness length); and from UrbanBL: c) effect of building height (building spacing and width are kept fixed at m), and d) effect of changing the surface albedo. (d)

10 4. Climate change and the UHI A set of regional climate change models has been developed by the UK government for climate change adaptation studies called the UKCIP02 scenarios [Hulme et al. 02]. The UKCIP02 scenarios describe changes in 1 climate variables under four emissions scenarios (Low, Medium-Low, Medium-High and High) and three time-slices: 11 to 40, called the s; 41 to 70, called the 0s; and 71 to, called the 80s. The scenarios are presented on a 0 km 0 km grid. In common with most climate change models, the regional climate model underpinning the UKCIP02 scenarios (HadRM3H) did not include a different land surface parameterisation of the London land surface; a single (rural) land surface type was used for all land areas. Although land surface parameterisation schemes are under development to model the impact of urban areas, the ability of such schemes to capture the dynamics of the UHI, elucidated above, has not yet been established. Here changes in London s climate have been obtained by taking an average of the four UKCIP02 0km0km model grid squares, which include parts of the Greater London area. The main changes projected for summer are [GLA 06]: increased summer temperatures of up to 7 C (High emissions, 80s) and slightly increased diurnal range (around 1 C); small changes in wind speed (<%); moderate changes in solar irradiance (of up to %), mainly due to low cloud cover; a decrease in relative humidity and an increase in specific humidity; a decrease in summer precipitation (up to 4% decrease). Each of these projections has differing levels of uncertainty due to model limitations and other factors [Hulme et al. 02; Jenkins & Lowe 03]. The UKCIP02 scenarios give changes for the monthly average values of the variables. In order to make detailed use of the scenarios in urban design both spatial and temporal downscaling needs to be carried out [IPCC 01]. A method to modify hourly weather data consistently across a set of variables has been developed using timeseries adjustment, called morphing [Belcher et al. 0]. This method has been used to adjust weather years used by building designers to model the impacts of climate change on the thermal and energy performance of buildings [Hacker et al. 0a,b]. In order to assess how climate change might affect the London UHI, the statistical model developed in Section 2 has been applied to timeseries of hourly wind speed and cloud cover data for Heathrow airport morphed under the UKCIP02 scenarios. The results for the 80s under the High scenario (the most extreme case in terms of projected changes in UKCIP02) are shown in figure 11. The model indicates some small changes to the UHI distribution. For example heat islands of 4 C are seen for 181 hours a year, compared with 148 in the present day. This change is attributed to the decrease in average summer cloud cover leading to an increase in the number of days with conditions conducive to UHI development. The change in the frequency is relatively small because the decrease in summer cloud cover is relatively small (around % decrease). The statistical model developed in section 2 caps the maximum possible UHI, presumed to be a function of the local land use type. The model cannot, therefore, say anything about a possible increase in the size of this maximum. Wilby (03) found using a statistical model for the London UHI which does not impose this constraint (described in section 2) an increase of the average UHI of 0.04 C per decade up to the 80s under the Medium-High Emissions scenario. This is a relatively small trend compared to the 0.33 C per decade regional warming projected for SE England under climate change. Both Wilby s analysis and that presented here suggests, therefore, only modest changes in the magnitude of the UHI under climate change. It is however possible that dynamic changes not captured by the statistical models will lead to changes in the UHI and this is an area of active research.

11 percentage hours heat island intensity Figure 11 Distribution of the London UHI intensity at Heathrow predicted by the statistical mode for the UKCIP02 High emissions scenario for the present day, s, 0s and 80s.. Conclusions In this paper, we have investigated in detail some of the properties of the London urban heat island using hourly meteorological observations. It has been shown that the statistical properties of the UHI at a particular location can be predicted using a relatively simple model based on cloud cover and wind speed. However, analysis of the time development of UHI events suggests that there may be some essential level of time-dependence and hysteresis, which is not included in the statistical model. In particular, the contrast between urban and rural locations appears to build through a heat wave as temperatures are rising. Dynamic thermal modelling of urban temperatures under different land use scenarios indicates similar patterns of behaviour, with urban day and night time temperatures building day on day, but also with a high degree of sensitivity to various factors, including availability of surface moisture, surface roughness, building geometry, and street, façade and roofing material properties. These results indicate that the observed UHI observed across London is likely to be due to the amalgamation of numerous local UHIs, produced by particular types of urban land surface. These results also indicate the potential for designers to beneficially influence the UHI, at a local microclimate scale and on the larger city macroscale, if land-surface adaptations can be affected in sufficient number. Regional climate models (RCMs) used to formulate climate change scenarios for use in urban planning and design typically do not include urban land surface parameterisations, assigning urban areas a rural land surface characteristic. Any attempt to include the urban land surface in regional climate models is challenging, because the scales of interest in the urban environment are much less than those of the RCM resolution (typically 2 0km). This means that little information is currently available as to how urban climates may change relative to the extra-urban surroundings under climate change. In order to effectively implement climate change adaptation programmes, in both new build and retrofit, designers need good quality climate data, for both the present and projections for the future. The existence of the UHI and it s complexity across the heterogeneous urban land surface of cities present challenges to the provision of accurate climate scenarios. Examples of statistical and dynamical modelling approaches to downscale climate change scenarios from regional climate models have been presented. The importance of cities as the home for the majority of the world s population, increasingly so under urbanisation trends, means that understanding and modelling of urban climate is likely to be an important and developing aspect of managing the impacts of climate change.

12 Acknowledgements This work has been supported by the Greater London Authority (GLA), the UK Department of Trade and Industry, and Arup. We are grateful to Prof. Glenn McGregor, Simon Gosling and Prof. Sue Grimmond from King s College London, and Alex Nickson from the GLA, for helpful discussions, and to Robert Slater at Arup for assistance in preparing the figures in this paper. The meterological data used in the study was supplied by the British Atmospheric Data centre. References Belcher, S.E., Hacker, J.N., Powell, D.S. (0) Constructing design weather data for future climates. Building Services Engineering Research and Technology Vol 26(1) pp Chandler, TJ (196) The climate of London. Hutchinson & Co, London. Crack, R. (03) Parameters Controlling the Spatial Distribution and Temporal Development of London s Heat Island. MSc Thesis. University of Reading. GLA (06) London s Urban Heat Island: A summary for decision makers. Greater London Authority. Graves, H, Watkins, R, Westbury, P & Littlefair, P. (01) Cooling buildings in London: Overcoming the heat island. Building Research Establishment, Gartson, UK. Hacker JN, Belcher SE & Connell RK (0a) Beating the Heat: keeping UK buildings cool in a warming climate. UKCIP Briefing Report. Principal authors: UKCIP, Oxford. Available online: Hacker JN, Holmes MJ, Belcher SB & Davies GD (0b) Climate change and the indoor environment: impacts and adaptation. CIBSE TM36. Chartered Institution of Building Services Engineers, London. Harman, I.N. & Belcher, S.E. (07) The surface energy balance and boundary layer over urban street canyons. Quart J Roy Meteorol Soc 132, Harman, I.N. (03) The energy balance of urban areas. PhD Thesis, University of Reading. Hulme, M., Jenkins, G.J., Lu, X., Turnpenny, J.R., Mitchell, T.D., Jones, R.G., Lowe, J., Murphy, J.M., Hassell, D., Boorman, P., McDonald, R. and Hills, S. (02) Climate change scenarios for the United Kingdom: The UKCIP02 Scientific Report, Principal authors: Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK 1pp. Ichinose, T., K. Shimodozono & Hanaki, K. (1999). "Impact of anthropogenic heat on urban climate in Tokyo." Atmospheric Environment Vol 33(24-2): IPCC 01. Climate change 01: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the IPCC Third Assessment Report. Cambridge University Press. Jenkins, G. & Lowe, J. (03) Handling uncertainties in the UKCIP02 scenarios of climate change. Hadley Centre TN44. Nichol, J. & Wong, M.S. (0) Modeling urban environmental quality in an tropical city. Landscape and Urban Planning Vol 73, pp Oke, TR (1987) Boundary layer climates. Routledge. Voogt, J (04) Urban Heat Islands: Hotter Cities Action Bioscience Wilby, R.L. (03) Past and projected trends in London s urban heat island. Weather. Vol. 8, pp