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 land-

use 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 2004],

including Hong Kong [Nicol & Wong 2005] and London [GLA 2006]. 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.

(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 2000; 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

0200 – 0300hrs for six urban heat island events during the summer of 2000 (July 1st to September

30th). 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 2006].

London is the most populace city in the European Union, with a city population of around 8 million

and a metropolitan area population of 12–14 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, 1965]. 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. 2001; Wilby 2003; GLA 2006].

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 2006].

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 2003]. There is an increase in the maximum heat island intensity with urban

land use, from 4o C at 30% 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 2006] 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

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.

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0 1 2 3 4 5 6 7

Maximum Heat Island Intensity (˚C)

% C

on

tin

uo

us U

rban

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 2003]

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 1993–98. 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,

50km 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 3oC, and 8 days of more than 5

oC.

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 1oC. 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 5):

,,

,,sinmax

otherwiseT

ttttt

ttTTT

tA

d

susd

du

ddd (1)

where t is the hour of the day; dT is the constant urban heat island intensity during the day; maxT is

the maximum night time urban heat island; and tsu and tsd are sun-up and sun-down times.

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Av

era

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mb

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of

Da

ys

UHIMax >3C UHIMax >4C UHIMax >5C UHIMax >6C UHIMax >7C

Figure 3. The Heathrow–

Beaufort Park heat island:

Average number of days of

occurrence in each month of the

heat island. Data from 1993-1999.

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.

Seasonal Averaged UHI: Heathrow - Beaufort Park

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DJF MAM

JJA SON

Figure 4. The seasonally averaged diurnal cycle of

the UHI at Heathrow.

Amplitude function, A (for T m ax = 3.5oC, T d = 1

oC)

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time, t (hour of day)

A(t

)

Figure 5. Hypothesised form of the average diurnal

UHI at Heathrow given by equation (1) for dT =

1°C, maxT = 3.5°C, tsu = 06:00, and tsd = 18:00.

0 2 4 6 8 10 12 14 16 18 201

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U (knots)

heat

isla

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degre

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Variation of heat island intensity with wind speed

Fit is UHI=7.5exp(-0.2U) + 1

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Cloud cover (oktas)

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Variation of heat island intensity with cloud cover

Fit is UHI=9.9exp(-0.19CC)

(a) (b)

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 1994. (1 knot = 0.514 ms-1)

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:

TetAtT ee CtCUtU )()()( (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 Ue and Ce; 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 1996. The parameters used for this analysis in equations (1)–(2) were: dT = 1°C, maxT = 6°C, Ue

= 4 knots, Ce = 4 oktas), and T’ was chosen from a Gaussian distribution with zero mean and standard

deviation of 0.25°C. The values of tsu and tsd are calculated on a daily basis. The statistical model is in

reasonable agreement with the measurements, particularly for heat island events stronger than 1oC.

The poorer agreement between the model and the measurements for UHI<1oC 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 (2003). 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

850mb 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.

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-3 -2 -1 0 1 2 3 4 5 6 7heat is land intensity

pe

rce

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urs

data

model

Figure 7. Distribution of Heathrow’s heat

island: values calculated with the statistical

model and compared with measurements for

1996. The horizontal axis gives the magnitude

of the UHI and the vertical axis is the fraction

of hours (probability) in the year.

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3525th July to 5th August 1995 Heat Wave

Day

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BBP Hourly Temperature

Hourly UHI

Mean Hourly JJA UHI

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Day

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LWC Hourly Temperature

BBP Hourly Temperature

Hourly UHI

Mean Hourly JJA UHI

(a) (b)

Figure 8. Temporal evolution of extreme heat island events in London for a) 17-28 July 1996 and

b) 25 July – 5 August 1995, 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., 2007, 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 time-

dependence: 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.

(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 (QH is the

sensible heat flux, Q* is the net radiation, QS is the ground heat flux and QE 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 2003, Harman & Belcher 2006]. 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:

ASEHF QQQQQQ* , (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: QF is the anthropogenic heat source e.g. from cars,

domestic and industrial fuel combustion; QH is the outgoing sensible heat flux; QE is the outgoing

latent heat flux; QS is the change in heat storage; and QA 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

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 10 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 52o N) and during August

(beginning 10th

August) for a period of three days.

Figure 10a 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 0.5 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 10a 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 0.5 (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 10b 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 10b, 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 10c 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

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 10b).

Figure 10d 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 10oC and night time temperatures reduced by 4

oC. 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.

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Flat cases: effect of surface moisture

dry (Bowen ration = )

Bowen ratio = 2.0

Bowen ratio = 1.0

Bowen ratio = 0.5

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)Flat land surface: effect of surface roughness

roughl =0.01m

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roughl =1.0m

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(a) (b)

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Urban land surface: effect of building height (heavyweight facades and roofs)

Flat-dry

he = 5.0m

he = 10.0m

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Urban land surface: effect of albedo (base case)

alb = 0.20

alb = 0.40

alb = 0.60

alb = 0.80

(c) (d)

Figure 10. 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 20m), and d) effect of changing the surface albedo.

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. 2002]. The UKCIP02 scenarios

describe changes in 15 climate variables under four emissions scenarios (Low, Medium-Low,

Medium-High and High) and three time-slices: 2011 to 2040, called the 2020s; 2041 to 2070, called

the 2050s; and 2071 to 2100, called the 2080s. The scenarios are presented on a 50 km 50 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

50km 50km model grid squares, which include parts of the Greater London area. The main changes

projected for summer are [GLA 2006]: increased summer temperatures of up to 7°C (High emissions,

2080s) and slightly increased diurnal range (around 1°C); small changes in wind speed (<10%);

moderate changes in solar irradiance (of up to 20%), mainly due to low cloud cover; a decrease in

relative humidity and an increase in specific humidity; a decrease in summer precipitation (up to 54%

decrease). Each of these projections has differing levels of uncertainty due to model limitations and

other factors [Hulme et al. 2002; Jenkins & Lowe 2003].

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 2001]. A method to modify hourly weather data consistently across a set of variables

has been developed using timeseries adjustment, called ‘morphing’ [Belcher et al. 2005]. 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. 2005a,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 2080s 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 20% 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 (2003) 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 2080s 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.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

-1 0 1 2 3 4 5 6

heat is land intensity

pe

rce

nta

ge

ho

urs

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,

2020s, 2050s and 2080s.

5. 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 25 – 50km). 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.

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.

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