climate impact of urban planning

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INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 28: 19431957 (2008)Published online 19 March 2008 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/joc.1680

Investigating the climatic impact of urban planningstrategies through the use of regional climate modelling:

a case study for Melbourne, Australia

Andrew M. Coutts,* Jason Beringer and Nigel J. TapperSchool of Geography and Environmental Science, Monash University, Melbourne, Vic, 3800, Australia

ABSTRACT: Urban planning is a useful method for improving local climate and human health in cities through

purposefully modifying urban land surface characteristics. This can reduce the potential risks of elevated city temperatures

due to the urban heat island (UHI). Unfortunately, simple tools are not readily available for urban planners to assess the

climatological impacts of various urban development scenarios. Urban modelling could be developed into such a tool to

achieve this. This study attempts to design and evaluate a suitable tool for application in Melbourne, Australia. The Air

Pollution Model (TAPM) was chosen to assess the impact of a long-term urban planning strategy on local climate and the

above canopy UHI in Melbourne. Improvements were made to TAPM by increasing the number of urban land-use classes

in the model and creating a higher resolution land cover database focused on housing density. This modified version of

TAPM showed a good performance in replicating the surface energy balance compared with an observational flux tower

site in suburban Melbourne during summer. TAPM simulated a mean maximum UHI intensity of 34 C at 2 a.m. in

January. A future UHI scenario was then examined (year 2030) using an urban land cover database derived from plans

in the Melbourne 2030 urban planning strategy. Results highlighted specific areas where planning intervention would be

particularly useful to improve local climates, namely activity centres and growth areas. The appropriateness of the use of

TAPM and climate models as a tool in urban planning is also discussed. Copyright 2008 Royal Meteorological Society

KEY WORDS urban planning; urban climate; climate modelling; Melbourne; surface energy balance

Received 18 August 2006; Revised 1 December 2007; Accepted 10 December 2007

1. Introduction

Unplanned and rapid urbanization in cities can often

lead to negative environmental impacts, including mod-

ifications to the local urban climate. The urban heat

island (UHI) phenomenon is often evident in cities

whereby urban areas are warmer than surrounding rural

areas. UHIs may contribute towards elevated tempera-

tures, which can be harmful for vulnerable urban resi-

dents, particularly during summer and heat wave episodes

(Rankin, 1959). Higher incidences of heat-related ill-

nesses including heart disease and even mortality have

been associated with elevated temperatures within urban

areas. Those particularly at risk include the elderly, low-

income earners, and residents in high density, older hous-

ing stock with limited surrounding vegetation (Smoyer-

Tomic et al., 2003). Fortunately, there is sufficient evi-

dence to suggest that urban planning can be a useful

method for improving local climate and human health

(Jackson, 2003; Stone, 2005). In order to reduce negative

climatological impacts, those involved in urban devel-

opment and design must begin to incorporate climate

knowledge into planning strategies.

* Correspondence to: Andrew M. Coutts, School of Geography andEnvironmental Science, Monash University, Wellington Road, Clayton,Victoria, 3800, Australia. E-mail: [email protected]

UHIs form primarily because of high thermal heat

capacity and heat storage of urban surfaces, added

sources of heat from anthropogenic activities, and

reduced evapotranspiration (Oke, 1988). Within the urban

canopy (below maximum building height), urban geome-

try is also important in controlling radiative exchanges

between the walls and floor of urban canyons. Small

sky view factors (SVF) and large height to width ratios

trap radiative energy during the day and limit noctur-

nal cooling. This leads to the development of peak UHI

intensities during the night, as rural areas are allowed

to cool uninhibited. Cloud amount and wind speed areimportant meteorological parameters as they affect long-

wave cooling and ventilation, which serve as surrogate

variables describing the relative roles of radiative and tur-

bulent exchanges in and around the urban region (Morris

and Simmonds, 2000).

While the UHI phenomenon has been well documented

in the climatological literature over the past few decades,

few cities have developed comprehensive strategies to

mitigate its intensity. Reasons for little consideration of

climate related understanding in urban planning include

a lack of knowledge, economic constraints, and com-

munication problems (Eliasson, 2000). Added to thesereasons, planning tools are not often available for plan-

ning authorities to assess the implications of projected

Copyright 2008 Royal Meteorological Society


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1944 A. M. COUTTS ET AL.

land-use change on local climate (Fehrenbach et al.,

2001). According to Eliasson (2000), the development of

such tools based on scientific research that can be incor-

porated into the urban planning framework should be a

challenge and focus for urban climatologists. However,

one such tool that can address the issue of climate impacts

of urban planning strategies, if adequately developed, isclimate modelling, both local and regional. Climate mod-

elling that uses specific treatments at the urban surface

can significantly help in determining the likely impacts of

large scale urbanization on local climate and UHI devel-

opment, improving weather forecasts, estimating energy

consumption, and aiding in urban planning (Kusaka and

Kimura, 2004).

Information at all urban scales (global, regional, local,

and microscale) can be highly beneficial for planners, but

knowledge about climate at the city (regional) and neigh-

bourhood (local) scale is specifically relevant as planning

authorities influence/regulate features at this scale, such

as heights of buildings. For climate models to be a usefultool in aiding sustainable urban planning, it is important

that they are correctly able to simulate the urban climate

at this scale. The urban surface is highly complex and

models require additional inputs, and new and improved

parameterizations, to accurately simulate the urban cli-

mate (Zehnder, 2002). In particular, the high heat storage

of urban landscapes associated with high thermal admit-

tance and radiation trapping, as well as the added sources

of anthropogenic heat, need to be incorporated. Tools like

satellite imagery (such as MODIS) or databases of urban

land-use and land classification (LULC), now provide

finer spatial resolution of the high heterogeneity of urbancharacteristics (albedo, emissivity or heights of buildings)

across cities as input databases for models (Dandou et al.,

2005; Jin and Shepherd, 2005). While accuracy in mod-

elling the urban climate is of prime importance, features

such as ease of use and short running time should also

be important factors, as urban planners require tools that

incorporate such features.

Recent work in regional scale modelling has seen the

development of a number of urban models of varying

degrees of complexity based on two types of param-

eterization schemes. The first type of scheme involves

simple modifications to existing land surface schemes

by modifying or fabricating the parameters of theland surface to broadly behave like an urban surface,

such as increasing roughness lengths and decreasing

albedo (Atkinson, 2003). One simple parameterization

scheme developed by Grimmond and Oke (2002) is called

the Localscale Urban Meteorological Parameterization

Scheme (LUMPS). Using net all-wave radiation, sim-

ple information on surface cover and standard weather

observations, turbulent and storage heat fluxes can be

calculated through a series of linked equations. The equa-

tions include the Objective Hysteresis Model (OHM),

which uses net all-wave radiation and the surface prop-

erties of the site to calculate heat storage (Grimmondet al., 1991. Taha (1999) used a bulk parameterization

approach to better incorporate heat storage and more

explicitly account for urban canopy layer fluxes, which

also included the OHM. Similarly, Dandou et al. (2005)

made modifications to the thermal part of the fifth-

generation Penn state/NCAR Mesoscale Model (MM5)

that incorporated the OHM. The model also included

anthropogenic heating, while modifications were also

made to the dynamical part of the model resulting inacceleration to the diffusion processes during unstable

conditions.

The second type of parameterization scheme involves

the inclusion of a separate urban canopy scheme to the

land surface model by incorporating parameters to rep-

resent canyon geometry and interactions between the

walls, rooftops, and roads. A number of variations on

this approach have been developed. Some characteristics

of these included using the drag force approach to repre-

sent the dynamic and turbulent effects of buildings and

vegetation while the thermal modifications of the surface

involve a 3D urban canopy (Dupont et al., 2004; Martilli

et al., 2002). This approach calculates the surface tem-perature of each surface type by taking into account the

interactions of shadowing and radiation trapping effects.

Single level urban canopy models have also been devel-

oped and incorporated into atmospheric models where

the canopy model simulates turbulent fluxes into the

atmosphere at the base of the atmospheric model, param-

eterizing both the surface and the roughness sub-layer

(Kusaka and Kimura, 2004; Masson, 2000). The Town

Energy Balance model (TEB) is one such scheme and

has been shown to simulate the surface energy balance

and climate well compared with observations (Lemonsu

et al., 2004; Masson et al., 2002). A good summary ofurban modelling approaches and developments can be

found in Dandou et al. (2005).

As a result of such modifications and developments,

the ability of climate models to simulate the urban

climate has improved, as has their appropriateness as

a tool that may aid urban planning. For instance, Taha

(1999) modelled effects of increased albedo for all the

LULC types in Atlanta (increasing residential albedo

from 0.16 to 0.29 etc.) and showed a decrease in the air

temperature of about 0.5 C. Atkinson (2003) found that

in London during the day, the albedo, anthropogenic heat,

emissivity, SVF, thermal inertia and surface resistance to

evaporation (SRE) all aided the formation of an UHI tovarying amounts of between 0.2 and 0.8 C. SRE was

the most important factor increasing the UHI intensity

during the day, while the roughness length decreased

intensity. At night, the roughness length, emissivity, SVF

and SRE aided UHI formation by 0.30.75 C, but the

largest effect (2 C) came from anthropogenic heating

(Atkinson, 2003). This kind of information is highly

valuable to urban planners in developing policies for

reducing negative climatic impacts to protect vulnerable

urban dwellers from the risk of exposure to elevated heat

conditions.

Given the growing knowledge and capacity of urbanclimate modelling, this study attempts to investigate the

role of climate modelling as a tool for use in urban

Copyright 2008 Royal Meteorological Society Int. J. Climatol. 28: 19431957 (2008)

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URBAN PLANNING AND CLIMATE IN MELBOURNE 1945

planning and to design and evaluate a suitable tool for

Melbourne, Australia. Through the use of a regional scale

model, The Air Pollution Model (TAPM), the possible

climatic impacts of a long-term urban planning strategy

for Melbourne, were assessed. Future planning directions

of the strategy aim at encouraging a more compact city

by clustering and increasing the amount of housing inestablished urban areas. Continued urbanization follow-

ing existing development patterns is likely to lead to an

intensification of the UHI (Coutts et al., 2007b). Using

a modified version of TAPM, we aimed to model the

regional climate of Melbourne and its subsequent UHI.

Modifications included an improved urban surface param-

eterization and an improved land cover input database.

Results will highlight to urban planners that the UHI is

an issue that needs to be addressed and identify spe-

cific areas/regions where planning intervention may be

required. As well as assessing the impact of the urban

planning strategy, we will comment on the use of regional

scale modelling as a tool in urban planning.

2. Methods

2.1. The urban planning strategy for Melbourne,

Australia

The city of Melbourne, Australia is a rapidly growing city

with an anticipated population increase of up to 1 million

people by the year 2030, requiring the development

of approximately 620 000 new households (Department

of Sustainability and Environment, 2002). In 2002, the

Victorian Government introduced a planning strategy toaccommodate this growth titled Melbourne 2030. The

strategy seeks to achieve a more compact city through

the development of activity centres (built up centres

for business, shopping, working and leisure with forms

of higher density housing) and the establishment of

an urban growth boundary (Figure 1) (Department of

Sustainability and Environment, 2002). The anticipated

development of a more compact city, if not planned

in an informed manner, could lead to an exacerbated

UHI intensity. This will be compounded by increased hot

weather and hazardous climatic conditions through global

warming (IPCC, 2007), which will impact especially on

vulnerable urban dwellers. Melbourne already shows anUHI signature, with a 20-year mean maximum UHI of

2.68 C found at 6 a.m. (Morris et al., 2001). During

summer, anti-cyclonic events often bring warm and dry

North to Northeast airflow over Melbourne, and can

send temperatures in excess of 35 C during the day,

while mean early morning (6 a.m.) UHI intensity during

these conditions has been observed at 3.56 C (Morris

and Simmonds, 2000). These are mean UHI intensities,

suggesting that under optimal conditions (clear skies

and low winds) UHI intensity can be much higher. An

automobile transect across Melbourne in 1992 showed

a peak UHI intensity as high as 7.1

C in the centralbusiness district (CBD) during the evening (9 p.m.)

(Torok et al., 2001).

Unplanned and hasty urban development could com-

promise the overall goal of the Melbourne 2030 strategy,

which aims to achieve a liveable, attractive and prosper-

ous city. Cities that are low density and reliant on private

car transport and strong zoning that separates housing,

employment and services are unsustainable. Rather, a sus-

tainable city is often described in the urban design liter-ature as compact, high density urban form and supported

by a comprehensive transport network, which empha-

sizes connectivity and mixed use developments at critical

nodes (intersecting transport routes) (Mills, 2005). How-

ever, this city model can encourage UHI development

and compromise green-space, potentially threatening the

environmental quality of the city (Pauleit et al., 2005).

Melbourne 2030 aims for a sustainable city and the plan-

ning strategy provided a good opportunity to investigate

the use of regional climate modelling in assessing urban

climate modifications resulting from land-use and plan-

ning policies. Our approach consisted of two scenarios:

(A) a simulation of the current urban climate and UHIintensity in Melbourne and (B) a year 2030 scenario of

increased urbanization based on the Melbourne 2030 key

directions to investigate likely future changes to urban

climate.

2.2. The air pollution model (TAPM) and urban

modifications

Selecting an appropriate model as a tool for urban climate

impact assessment and use by urban planners is likely to

depend on a number of parameters. The accuracy of the

model must be sufficient to robustly simulate the urban

climate yet not overly complex, computationally expen-sive, and should be user-friendly. Dandou et al. (2005)

suggested that despite the simplicity of their bulk urban

parameterization scheme, improvements in results were

comparable with that produced by the complex canopy

scheme of Martilli et al. (2002). The ease of use is likely

to be important, and inputs of surface characteristics into

the model should be simply described and readily avail-

able, such as through easily obtainable data on types

of surface cover, vegetation cover, albedo, mean build-

ing height, anthropogenic heating, and dwelling density.

TAPM (Hurley, 2005) was selected for this study as bene-

fits included the ability to conduct year-long simulations;

the ability to run simulations without surface observa-tional inputs; the ease of a PC-based interface for use in

Windows operating systems; user-defined surface cover

databases; and a range of methods for analysing outputs.

Therefore, TAPM has the potential to be adopted as an

urban planning tool.

The meteorological component of TAPM is an incom-

pressible, non-hydrostatic, primitive equation meteoro-

logical model with a terrain following vertical coordi-

nate for 3D simulations and a 3D nestable, eularian

grid (Hurley, 2005). As described in Hurley (2005), the

prognostic meteorological component solves approxima-

tions to the fundamental fluid dynamics equations, andrather than requiring site specific observations, flows such

as sea breezes and terrain flows are predicted against


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Figure 1. The Melbourne 2030 compact city with the location of activity centres and the urban growth boundary (Department of Sustainability

and Environment, 2003) (State of Victoria, Department of Sustainability and Environment, 2003). This figure is available in colour online at

www.interscience.wiley.com/ijoc

a background of larger scale meteorology provided by

global synoptic analysis. The vertical fluxes are rep-

resented by a gradient diffusion approach, including a

counter-gradient term for heat flux from turbulence terms

determined by solving equations for turbulent kinetic

energy and eddy dissipation rate. TAPM includes a soil-

vegetation-atmosphere transfer (SVAT) scheme, which is

used at the model surface, and a radiative flux parame-

terization at both the model surface and at upper levels

in the atmosphere.

For urban land surfaces in TAPM, temperature andspecific humidity are calculated depending on the frac-

tion of urban surface cover following similar approaches

for non-urban surfaces, except that the surface properties

(albedo, thermal conductivity) are given appropriate

urban values. The anthropogenic heat flux is also included

in the surface flux equations (Hurley, 2005). A number

of validation studies and evaluations have been con-

ducted on TAPM, including one in Melbourne (Hurley

et al., 2003). For the period July 1997 to June 1998,

model verification was completed using eight monitor-

ing sites across Melbourne. Results showed that the 10-

m winds and screen level temperature were predicted

very well with a low average Root Mean Square Error(RMSE) and a high index of agreement (IOA) (Hur-

ley et al., 2003). However, TAPM only incorporated a


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single homogeneous urban surface class for the entire

urban region with single values for land surface parame-

ters such as albedo and thermal conductivity derived from

the literature.

The surface energy balance is simulated in TAPM and

considered fundamental to an understanding of boundary

layer climates and is basic to an understanding of suchfeatures as thermodynamic behaviour of air and surface

temperature and humidity, and the dynamics of local

airflow (Oke, 1988). Hence, models need to be able to

replicate the partitioning of available energy adequately

in order to robustly simulate the resultant climate. The

urban surface energy balance is given by (Oke, 1988):

Q +QF = QH +QE +QS

where Q is net radiation, QF is anthropogenic heating,

QH is sensible heat flux, QE is the latent heat flux and

QS

is the storage heat flux.

TAPM was modified to improve simulations of urban

environments by incorporating four urban land surface

types (low, medium and high density, and CBD) replac-

ing the existing single urban surface. Surface parameters

in TAPM for the medium density surface type were speci-

fied using information from an observational site located

in suburban Melbourne (Coutts et al., 2007b). The site

was located in Preston, north of Melbourne (145047,

374357) in a sprawling, moderately developed hous-

ing area consisting largely of detached dwellings, typical

of the Melbourne urban landscape (Figure 2). Site char-acterization showed a plan impervious surface cover of

62%, which included a plan building area of 45% and had

a mean height to width ratio of 0.42. Surface character-

istics observed at the site included fraction of urban and

vegetation cover (determined from aerial photography);

mean building height; anthropogenic heat flux (deter-

mined using population, energy, and transport databases);

roughness length; and albedo (Table I) and were available

as model input parameters.

Surface energy balance measurements from the Pre-

ston site were used to evaluate the performance of the

model for simulated medium density housing in Preston.

Observations were taken from instruments mounted on

a tall tower at a height of 40 m using the eddy cor-

relation technique (Baldocchi et al., 1988) to measure

local scale fluxes (102 104 m) of sensible and latent

Figure 2. The medium density observational study site in Preston, located north of Melbourne CBD. This figure is available in colour online atwww.interscience.wiley.com/ijoc

Table I. The original urban surface characteristics from Preston are given along with the values assigned in the model for

each level of urban density: fraction of urban cover u; albedo u; anthropogenic heat flux Au (W.m2); thermal conductivity

ku (W.m1.K1); roughness length z0u (m); building height zH (m); fraction of non-urban area covered by vegetation f; leaf

area index LAI; minimum stomatal resistance rsi (s1.m1).

u u Au ku zou zH f LAI rsi

Observed (Preston) 0.62 0.15 9 12 0.4 12

TAPM (3.0.2) default 0.5 0.15 30 4.6 1 10 0.75 2 100

Urban (low) 0.5 0.17 10 15 0.4 8 0.75 2 100

Urban (medium) 0.65 0.15 15 25 0.6 12 0.75 2 100

Urban (high) 0.8 0.13 20 40 0.8 16 0.75 2 100Urban (CBD) 0.95 0.1 40 60 2 100 0.75 2 100


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heat and momentum. Radiation sensors measured each

component of the radiation balance, giving net radiation.

The storage heat flux was calculated as a residual of the

energy balance equation. Observations of temperature,

vapour pressure, wind speed, and friction velocity were

also conducted. Model outputs matching the location of

the Preston site were compared with the observed sur-face energy balance and meteorological parameters and

provided information to evaluate how well TAPM repli-

cated the surface energy balance and simulated the local

urban climate at the observational site. This site was one

of three urban flux sites operating at various times and

locations in Melbourne during 2003 2004 (Coutts et al.,

2007b).

The urban land surface characteristics for Urban

(medium) (Table I) were set to be very similar to the

medium density observational site described (Preston).

The low, high, and CBD urban land surface characteris-

tics were then assigned with reference to the values of

Urban (medium) (Table I) using expert knowledge of theMelbourne urban landscape from the observational cam-

paign (Coutts et al., 2007b) and other literature values.

However, in order to replicate the storage capacity of

a complex 3D urban surface in a one-dimensional sur-

face scheme the thermal conductivity was substantially

increased in the model. Using the value of a component

material such as concrete in bulk model parameterizations

does not capture the full influence of the heterogeneous

urban landscape or the effects of the urban canopy. Sug-

awara et al. (2001) created a thermal property param-

eter (combining the product of specific heat and ther-

mal conductivity) that better represented urban surfaces.This parameter was determined to be much larger than a

homogenous surface type such as asphalt and concluded

that the value should be a few times larger than the com-

ponent material in bulk urban models that do not deal

with canyon shape explicitly (Sugawara et al., 2001). In

TAPM, the thermal conductivity value for the land sur-

face was modified in order to match the storage heat

flux results from TAPM with the observational results at

the medium density site in Preston during January. The

thermal conductivity needed to be increased well above

realistic values before the surface began behaving simi-

larly to a real urban surface, identifying the importance

of canyon geometry in trapping and storing energy.

2.3. Model configuration and database development

Model scenarios for the current and year 2030 scenarios

were performed for January during the Austral summer,

as urban residents are exposed to higher ambient tempera-

tures at this time. Large scale synoptic analyses were used

to force the model between the periods 1997 and 2004.

Synoptic scale forcing meteorology was provided from

the 6-hourly Limited Area Prediction Systems (LAPS)

(Puri et al., 1998) at a 0.75 grid spacing. These 8 years

of January simulations were conducted so that modelleddifferences were due to land surface changes and not

due to year-to-year climate variability. Moreover, the

same forcing data were used for each experiment. The

modified TAPM version 3.0.2 was configured with three

nested grids of 110 110 horizontal points with the inner

grid encompassing the Melbourne metropolitan area at a

grid spacing of 1000 m centred at 1459E and 3759S.

The middle and outer grid spacings were 3000 m and

10 000 m, respectively, and 25 vertical grid levels wereselected with the highest level at 8000 m. Other databases

of terrain height (9-s DEM), sea surface temperature and

soil classification data, were also used in the scenarios

(Hurley, 2005).

In order to run the scenarios described, relevant

land surface databases were developed at a suitable

resolution for input into TAPM. For the current day

scenario (Scenario A) a vegetation (land-use) database

was obtained that provided recent vegetation cover (1988)

(Geoscience Australia, 2003). In addition, a surface

database of low, medium, and high density areas, as

well as the CBD, was constructed. Information on census

districts for the entire Melbourne metropolitan area werecollected (Australian Bureau of Statistics (ABS), 2001)

and the dwelling density calculated for each district

(dwellings per km2). This information was converted to

mean dwelling density for 0.01 decimal degree grid cells

(approximately 1 km). Plans for Melbourne 2030 aim to

increase the average housing density significantly from

1000 dwellings per km2 to an average of 1500 dwellings

per km2 (Department of Sustainability and Environment,

2002). Therefore, high density areas were deemed to be

greater than 15 dwellings per hectare, medium density

areas between 10 and 15 dwellings per hectare and low

density areas less than 10 dwellings per hectare (thoughgreater than 1) (Figure 3). This database was overlain on

the vegetation database and used as input into TAPM.

The database for the Melbourne 2030 scenario (Sce-

nario B) was based on the documents key directions

as discussed earlier. Taking the current urban density

database, the urban growth boundary was added and those

areas not currently developed within the urban growth

boundary were assigned to the low density class. The

location of the proposed 26 Principal, 82 Major, and 10

Specialized activity centres were then added, by assum-

ing that the surrounding housing for a 1-km radius would

be high density (within walking distance). Housing within

another 1-km radius was anticipated to increase to at least

medium density while existing high density housing areas

and the CBD areas remained as such (Figure 3).

3. Results and Discussion

3.1. Evaluating TAPM against urban surface energy

balance observations

Using the new land surface database of the current Mel-

bourne urban landscape, TAPM was run for the month

of January and compared with the observations at the

medium density observational site (Preston) (Figure 4).TAPM showed a good performance in replicating the

diurnal course and monthly mean surface energy balance


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Figure 3. Land surface database of current density scenario A (left) and the Melbourne 2030 scenario B with the urban growth boundary (right).

This figure is available in colour online at www.interscience.wiley.com/ijoc

Figure 4. Comparison of the observed (dashed line) and modelled (solid line) diurnal surface energy balance (observed height 40 m and model

level 50 m) for location (145047, 374357) and corresponding grid point (043, 084) for the month of January 2004. Regression (fit) equations

were Q (y = 1.032x + 58.626); QH (y = 1.137x + 30.017); QE (y = 0.789x + 18.819); QS (y = 0.833x + 14.007). This figure is available

in colour online at www.interscience.wiley.com/ijoc

for the month of January 2004. The evaporative flux was

replicated well by the model, only showing an overesti-

mation in the afternoon. The storage heat flux was also

well replicated, although some discrepancy was evident

in both Q

and QH. This is caused by an overestimationof incoming solar radiation due to the inability of the

model to capture cloudy skies and poor air quality (which

can reflect and scatter incoming short wave radiation),

so monthly averages of Q were overestimated. This

extra energy led to additional partitioning into QH. On

the majority of the January days, the TAPM model per-

formed well. The model also captured important featuresof urban energy balance partitioning (Figure 4). These

included the hysteresis pattern in QS, showing a peak


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approximately 12 h before the peak in Q. The peak

was not as evident in the observations during this month

as what was generally seen during the observational cam-

paign (Coutts et al., 2007b). The asymmetry in QH was

also evident with the peak occurring later in the after-

noon. Importantly, both QH and QE remained positive

into the evening, supported by heat storage release fromthe urban fabric, and remained slightly positive through-

out the night.

Despite the discrepancy in Q and QH, the model

accurately simulated temperature, relative humidity, and

wind speed (Figure 5). Slight discrepancies were seen

in the diurnal temperature plot, with a reduced lag in

temperature approaching its maximum and the nighttime

temperatures were underestimated, due to underestima-

tion of nighttime heat storage release. Table II gives the

January 2004 monthly comparison of modelled meteoro-

logical variables and their associated error for the model

grid point corresponding with the measurement tower

location, compiled following Willmott (1981). Statistical

comparisons are also given for the surface energy balance

components.

Changes in urban surface characteristics influence how

net radiation is partitioned into each of the surface energy

balance components, so the flux ratios and how they

vary between density classes were of particular interest(Figure 6). Also, while the summer month (January) was

of primary interest, there was also observational data

available for a full year (Coutts et al., 2007b) and it

was possible to see how well the model reproduced

the partitioning of the urban surface energy balance

seasonally (Figure 6). Therefore, TAPM was also run

from August 2003 to July 2004 corresponding with the

year-long observational study. Naturally, partitioning in

January was good as the model parameters for the urban

surface characteristics were adjusted to match this data,

yet over the course of the year, the model did not

capture QS and QH well. A reasonable replication

Figure 5. Comparison of observed (left) and modelled (right) temperature, relative humidity, and wind speed (observed height 40 m, model level

50 m) for location (145047, 374357) and corresponding grid point (043, 084) for the month of January 2004.

Table II. Statistical comparison between variables for the observational location (145047, 374357) and corresponding model

grid point (043, 084) for the month of January 2004 of temperature T (C); wind speed WS (m/s); specific humidity q (g/kg);

friction velocity u (m/s); sensible heat flux QH (W.m2); latent heat flux QE (W.m

2); and storage heat flux QS (W.m2).

T WS q u Q QH QE QS

n 744 744 744 744 744 744 744 744

O 16.35 4.74 7.21 0.40 146.26 88.01 40.81 17.43

P 16.00 4.27 6.93 0.44 209.58 130.06 51.00 28.52

sO 3.76 2.33 1.73 0.21 267.22 116.34 45.58 127.61

sP 4.03 1.92 1.38 0.23 307.93 151.91 47.88 131.87

CORR 0.89 0.79 0.73 0.79 0.90 0.87 0.75 0.81

RMSE 1.84 1.50 1.21 0.15 151.10 87.24 34.61 81.71

RMSES 0.39 0.94 0.75 0.07 63.90 44.95 14.03 24.06

RMSEU 1.80 1.17 0.95 0.13 136.92 74.76 31.64 78.09

MAE 0.35 0.47 0.27 0.04 63.33 42.05 10.19 11.09

d 0.94 0.86 0.83 0.87 0.93 0.89 0.85 0.89

r2 0.80 0.63 0.53 0.63 0.80 0.76 0.56 0.65

n, number of observations; O, observations; P, predicted values; sO sP, observed and predicted standard deviations; CORR, Pearman Correlation;

RMSE, Root Mean Square Error; RMSES, Systematic RMSE; RMSEU, Unsystematic RMSE; d, Index of Agreement; r2, Coefficient of

determination (Willmott, 1981).


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Figure 6. Mean monthly plots of daytime fractions of Q for each energy balance component and the Bowen Ratio. CBD, HIGH, MEDIUM, and

LOW correspond to each of the urban classes and OBSERVATIONS correspond to the measured data from Preston. The Bowen Ratio (QH/QE)

is not shown for the CBD as it was significantly higher (20).

of observations for the evaporative fraction (QE/Q)

was seen over the course of the year. Figure 6 also

demonstrates the differences in partitioning of energy

between each of the urban land surface classes in the

model and shows that the influence of changing the landsurface values alters energy balance partitioning. The

modelled data for these urban classes were not verified

against any observations.

Some differences in energy partitioning over the course

of the year could result from a number of uncertain-

ties. Actual deep soil volumetric moisture contents were

not available to initialize the model and we found

that there was a mismatch in the seasonal course of

QE/Q between the observations and the model out-

put (Figure 6). In the model, moisture contents were

the lowest over the Austral summer months (Decem-

ber February). Rainfall in Melbourne during Februaryand March 2004, however, was well below average, so it

was likely that deep soil volumetric moisture contents

were also below average at this time, leading to the

reduced energy partitioning into QE. More accurate val-

ues of monthly soil moisture content could improve this

result. As expected, QE/Q decreased with increasing

urban density as the vegetated surface cover was replacedwith greater impervious surface cover, restricting evapo-

transpiration. Generally, the partitioning of energy into

QE was acceptable over the course of the year and

responded well to the changes in surface cover.

The modelled Urban (medium) heat storage fraction

(QS/Q) during the summer period generally showed

a slight underestimation compared with the observations,

but were much improved compared with earlier versions

of TAPM. The substantial increases of the values for

thermal conductivity in the model were large enough to

capture the significant energy storage by the 3D urban

landscape. Comparing each of the densities, the amountof heat storage increased with increasing density. How-

ever, absorption of energy by the urban surface in the


DOI: 10.1002/joc


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model appeared to saturate (reach a maximum absorptive

capacity) when increasing thermal conductivity. There-

fore, despite the increasing thermal conductivity with

urban density, the 1D land surface in the model did not

capture the full influence of the 3D canyon morphology

on heat storage.

During winter, the land surface scheme did not repli-cate the high heat absorption (QS/Q

) by the urban

fabric that was seen in the observations (Figure 6). In

most Northern Hemisphere energy balance studies, a

decrease in heat storage is seen during winter, following

the reduction in Q, as well as added surface moisture

for increased QE/Q (Grimmond, 1992), a pattern that

TAPM did replicate. However, in this case, it may not

be that TAPM inaccurately represented urban heat stor-

age, but rather the uncertainty may lie in the observations.

Spronken-Smith et al. (2006) found in Christchurch, New

Zealand that under settled anti-cyclonic conditions, a

strong inversion can occur that can severely restrict tur-

bulent mixing and influence the above canopy flux mea-

surements. As the observations in Melbourne calculated

QS as a residual of the eddy correlation technique, it

could be that the observational results over emphasize

the importance of heat storage during stable wintertime

conditions and is an area that requires further study.

On account of the slightly underestimated QS/Q,

the sensible heating fraction (QH/Q) during the sum-

mertime for the Urban (medium) density was also slightly

higher than observed. The partitioning of energy was very

similar for each urban density during summer, though all

sites were slightly higher than the observations. This is

often why above canopy temperatures are similar acrossan urban area during the day, as higher density sites

absorb more energy and QS/Q increases, restricting

the availability for atmospheric heating, which sometimes

aids Urban Cool Island (UCI) development in combina-

tion with shading (Morris and Simmonds, 2000). The

Bowen ratio (QH/QE) throughout the year was well

replicated compared with Urban (medium) and increased

with higher urban density (Figure 6). The Bowen ratio

from the model results also preceded the observations

again as a result of the lack of input data for the soil

moisture content and the influence of this on QE.

The model was not able to accurately replicate thepartitioning of energy outside of the summer months.

However, as TAPM was replicating the partitioning of

energy and meteorological parameters at the surface rea-

sonably well in January, it can be used for the scenarios

described with a good degree of confidence. While a

crude method of parameterizing the model to behave

more like an urban surface was used, and direct validation

was not completed on the energy balance partitioning,

the model has vastly improved on the performance of

TAPM version 2.0 before the modifications were made

(data not shown). Additionally, the model was only eval-

uated for the medium density urban class, so there maybe limitations in the models applicability to other urban

density classes. The lack of an urban canopy scheme

could also limit the models capacity to accurately repli-

cate urban heat storage across density classes. There is

obvious scope for a specific urban parameterization in

TAPM.

3.2. Modelling UHI intensity and the impact of

Melbourne 2030

TAPM was configured as described in Section 2.3 and

run for eight Januaries from 1997 to 2004 to provide an

ensemble average for current summertime conditions and

then again for the 2030 planning scenario. The current

scenario (A) showed a mean nighttime (0200) UHI of

approximately 34 C in the CBD, reducing as distance

from the CBD increased (Figure 7(a)). Variability was

high with anomalous warmer and cooler areas seen across

the metropolitan area corresponding with urban density

class. The modelled UHI intensity was similar in range

to those previously observed in Melbourne (Morris and

Simmonds, 2000; Morris et al., 2001). During the day

(1400) the current Scenario (A) screen level UHI was notas intense as at 0200, but still showed an UHI intensity

of 12 C, with temperatures being more uniform across

the region (Figure 7(b)). The CBD was not warmer than

the surrounding urban area. Temperatures away from the

coast to the north and east of Port Phillip Bay showed

higher values as a result of mesoscale airflows and a

regional sea breeze. During the night, the lower wind

speeds reduced the influence of the regional flows and the

urban density more strongly controlled the development

of the UHI. The modelled UHI also varied with synoptic

conditions that supported maximum UHI development

under conditions of anti-cyclonic highs centred just eastof Melbourne, low wind speeds and cloudless skies

(Morris and Simmonds, 2000). The Melbourne 2030

scenario (B) revealed a slightly modified UHI pattern

from the current scenario (A) (Figure 7(c) and (d)).

While the maximum intensity of the UHI did not increase,

the areal extent of elevated temperatures expanded. The

nighttime UHI reduced in its spatial variability, becoming

more uniform across the urban area similar to that of the

daytime UHI.

Analysing the difference in screen level tempera-

ture between the current and Melbourne 2030 scenarios

allows specific areas of significant warming to be iden-

tified and is what urban planners are most interested in.The extent of change in the UHI resulting from planning

strategies shows areas that are particularly vulnerable.

This information can be used for improved planning deci-

sions. The greatest temperature increases during night-

time maximum UHI intensity (Figure 8) were seen in

areas where development replaced pasture land and in

new activity centres. Temperatures in other areas of Mel-

bourne also appeared to respond significantly to increases

in housing density especially along the edge of the current

urban-rural boundary. Some of these areas are located

along transport links and growth areas designated for

concentrated expansion as outlined in Melbourne 2030.While these areas are likely to show the greatest increase

in temperatures in 2030, temperatures were only seen to


DOI: 10.1002/joc


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Figure 7. Spatial variability in mean screen level temperatures for the Melbourne area at 02 : 00 (2 a.m.) and 14 : 00 (2 p.m.) h for each scenario:

A Current development at 02 : 00 (a) and 14 : 00 (b); B Melbourne 2030 planned development at 02 : 00 (c) and 14 : 00 (d). This figure is

available in colour online at www.interscience.wiley.com/ijoc

increase to levels currently seen within the CBD. Initia-

tives that can help reduce temperature increases can be

more easily incorporated into newly developing regions,

rather than in existing urban development. Therefore,these growth areas and new or minimally developed exist-

ing activity centres could provide excellent opportunities

for UHI mitigation strategies to be put in place.

During the day, some portions of Melbourne to the

west and north showed elevated temperatures following

the planned development (Figure 9). Interestingly, during

the day a large fraction of the urban area, mainly where

development increases from low to higher densities, actu-

ally showed a very slight decrease (largely insignificant)

in temperature due to the increased heat storage limiting

the amount of energy available for atmospheric heating

and reducing temperatures. The areas of greatest tempera-ture increase were the planned growth areas where devel-

opment will replace existing natural landscapes. While it

may seem that these mean temperatures are not high,

during periods in summer of extreme heat, temperatures

can be much higher. While higher nighttime tempera-

tures from restricted nocturnal cooling in urban areas maynot seem like a significant problem, extended periods of

warmer temperatures can limit nighttime recovery from

daily heat stress. Inland activity centres that do not feel

the effects of the cooling sea breeze would especially

benefit from UHI mitigation measures.

The planned increase in urban density through the

establishment of an urban growth boundary and the

development of activity centres in Melbourne will likely

lead to a more intense UHI during the night, while during

the day this is less significant. Coutts et al. (2007b) in

their observational study in Melbourne found that during

the summer across three urban sites of varying urbandensity, all sites showed a mean daytime Bowen ratio

of over 2 and the daily Bowen ratio was sometimes


DOI: 10.1002/joc


12/15


Figure 8. Change in mean nighttime (02 : 00) screen level temperature change from the current urban development, to that proposed by the

Melbourne 2030 planning strategy. Areas within the contours are statistically significant at the 95% confidence level. This figure is available in

colour online at www.interscience.wiley.com/ijoc

Figure 9. Change in mean daytime (14 : 00) screen level temperature from the current urban development, to that proposed by the Melbourne

2030 planning strategy. Areas within the contours are statistically significant at the 95% confidence level. This figure is available in colour

online at www.interscience.wiley.com/ijoc


DOI: 10.1002/joc


13/15


in excess of 5. The increase in Bowen ratios with

increasing urban density modelled in this study were

not found in the observational results as evaporative

fluxes were very similar across all housing densities

despite varying vegetation cover. This was a result of

poor moisture availability in response to water restrictions

when observations were conducted (Coutts et al., 2007b).Therefore during the summer, the entire Melbourne

region experienced warm, dry and hence unfavourable

climatic conditions. Adoption of the Melbourne 2030

strategy is not likely to increase Bowen ratios across

the city significantly, but there will be an extension of

warm and dry conditions over longer periods of the

day as well as an extension of the seasonal exposure to

unfavourable conditions along with an increased spatial

extent, especially if water restrictions remain tight.

In this study, the effect of heat trapping and storage in

the urban environment was replicated in the simulations

by significantly increasing the thermal conductivity. The

3D nature and complexity of the urban landscape wasnot explicitly included in the model, unlike new urban

canopy schemes. As a result, the model could not deliver

within-canopy temperatures, which could possibly be

greater than the modelled temperatures in this study.

Modelled screen level temperatures were also slightly

underestimated during the evening and night due to an

underestimation of the slow release of heat stored in

the urban fabric due to complex canyon morphology

(including walls). Finally, the Melbourne 2030 scenario

(B) only accounts for climatic impacts from land cover

change. Mean global temperatures over the last 100 years

(1906 2005; 100-year linear trend) have increased by0.74 C (0.18 C) largely as a result of carbon dioxide

(CO2) emissions (IPCC, 2007), so projected global

temperature rises (0.2 C per decade for the next two

decades (IPCC, 2007)) coupled with heating from further

urban development will lead to further increases in urban

temperatures. Also, the frequency of extreme warm days

and nights has increased since 1961 (Plummer et al.,

1999). Urban areas themselves are a significant source of

CO2 mostly from vehicles emissions with local annual

emissions from urban Melbourne as high as 84.9 t

CO2 ha1 y1 (Coutts et al., 2007a). Urban planning

measures such as energy-efficient buildings and increased

public transport use would help contribute to combating

greenhouse gas emissions.

4. Conclusions

Simulations of the changes in climate resulting from the

proposed land cover changes identified in the directions

of the Melbourne 2030 plan showed that continued

increases in density would result in an increased intensity

of the nighttime Melbourne UHI. Growth areas and

particular activity centres were predicted to have the

greatest temperature increases. During the day, the impactof changes in urban development was not seen to

increase the peak daytime temperature due to increased

storage limiting the amount of sensible heating of the

atmosphere. Yet, existing urban climates during summer

days can already be unfavourable with high Bowen

ratios regularly observed across varying densities of the

city (Coutts et al., 2007b). These results demonstrate

the utility of regional scale climate modelling as a

tool for climate impact assessment and show the abilityto determine likely climate modifications from simple

land-use changes based on planning directions. The

use of TAPM for the Melbourne urban landscape was

adequate for January, and identified that continued urban

development in Melbourne could lead to higher diurnal

exposure to warmer temperatures. Modelling results such

as these are an excellent way to present and convey

information and issues to environmental planners.

Planning in urban areas to ameliorate, and limit the

development of degraded local climates has been known

for decades (Aron, 1984; Oke, 1984; Oke, 2005), yet pol-

icy development in this area is still lacking despite calls

for improvements. The concept of sustainable settlementsis recognized within Melbourne 2030 with initiatives

such as those under the direction of A greener city,

including reducing the impacts of storm-water on bays

and catchments, and the management of water resources

(Department of Sustainability and Environment, 2002).

Melbourne 2030 currently notes concern for issues such

as global warming and a livable city but an assessment

of the impact of a more compact city on climate had not

been undertaken. Our analysis should persuade the devel-

opment of new policies for UHI mitigation by planners.

This work may be opportune since the Melbourne 2030

plan is due for review in 2007. Some initiatives alreadyexist that aid in reducing UHI intensity include energy-

efficient buildings and encouraging a shift in travel from

private vehicles to public transport, which will reduce

anthropogenic heat emissions. While this is good, a com-

prehensive UHI mitigation strategy for Melbourne is

required and it is hoped that this study will prompt the

Melbourne 2030 planning group to act and encourage the

implementation of UHI mitigation initiatives. It would be

a great opportunity for the Victorian Government, who

wish to lead by example in environmental management

(Department of Sustainability and Environment, 2002).

Regional scale modelling of urban climate is a pow-

erful tool and the use of TAPM as a model for usein urban planning has both benefits and shortfalls. As

TAPM is now set up for Melbourne, further summertime

scenarios could be conducted to investigate the poten-

tial of mitigation strategies such as alterations in surface

albedo or the effect of increasing vegetation cover. Also,

any type of urban spatial configuration at the neighbour-

hood scale could also be easily modelled. This study has

demonstrated the potential for TAPM to become a rig-

orous model for use in urban planning. However, much

improvement is still required before it could be com-

monly used. Operating the model for other Australian

or international cities may not be feasible without somemodification of surface parameters (requiring local field

observations) or development of new parameterizations.


DOI: 10.1002/joc


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Also, as the winter performance of the model was poor,

further improvements to TAPM would be required before

it could be used as a year round urban climate model

for climate impact assessment or forecasting. On account

of the high spatial resolution used in these scenarios,

compute time was long and could be improved on high-

performance machines.TAPM does not currently have any urban canopy

scheme and so does not explicitly resolve canyon geom-

etry effects and hence there is a lot of room for improve-

ments through new urban parameterizations. This high-

lights the need for further developments in urban climate

modelling within the Australian modelling community,

especially as the focus on extreme temperatures in urban

regions grows. The inclusion of an urban canopy scheme

would also then permit modelling of diurnal tempera-

ture scenarios, rather than just the monthly averages for

January presented here. A complex urban canopy model

with the simplicity and ease of use of TAPM would bean ideal product. The UHI was predicted well compared

with previous observed ranges of UHI intensity. Future

work could also involve coupling TAPM with a water

use model such as Aquacycle (Mitchell et al., 2001),

which could allow investigation of climate interaction

with human water consumption and irrigation, which is

very important (and ever growing in importance), in the

Melbourne urban landscape.

Despite the ease of use of the PC-based TAPM,

substantial resources are required to both understand

and run the model, and to develop the urban databases

for their application in the simulations. The potentialoperation of climate models directly by urban planners

may be unachievable currently and therefore climate

impact studies of urban development scenarios are best

out-sourced to urban climatologists. While continued

model improvements and validation are still needed, even

the best urban climate model would need to be run by

those who know how to use it. An inter-disciplinary

and team-based approach is imperative in order for

this to be effective (Oke, 2005). As a result, planners

and climatologists must work together utilizing their

full knowledge and allowing the development of more

accurate urban climate predictions.

Acknowledgements

Thanks to Peter Hurley for assistance and the conduction

of modifications to the model. Thanks to Peter Wallace of

P. G. Wallace Communications for permission of the use

of the communications tower for the field observations

and to Christopher Barker for assistance in setting up

and maintenance of the towers and equipment. The loan

of instrumentation by Lindsay Hutley (Charles DarwinUniversity) and Russell Jaycock (James Cook University)

is also greatly appreciated.

References

Aron J. 1984. Climatic consciousnessa conscious change in theAustralian architectural environment. Energy and Buildings 7:7175.

Atkinson BW. 2003. Numerical modeling of Urban heat-islandintensity. Boundary-Layer Meteorology 109: 285310.

Australian Bureau of Statistics (ABS). 2001. 2001 Census, Australia.Baldocchi DD, Hicks BB, Meyers TP. 1988. Measuring biosphere-

atmosphere exchanges of biologically related gases with microm-eteorological methods (in special feature: gas exchange in a newdimension). Ecology 69: 1331 1340.

Coutts AM, Beringer J, Tapper NJ. 2007a. Characteristics influencingthe variability of urban CO2 fluxes in Melbourne, Australia. Atmospheric Environment 41: 51 62.

Coutts AM, Beringer J, Tapper NJ. 2007b. Impact of increasing urbandensity on local climate: spatial and temporal variations in thesurface energy balance in Melbourne, Australia. Journal of AppliedMeteorology 46: 477493.

Dandou A, Tombrou M, Akylas E, Soulakellis N, Bossioli E. 2005.Development and evaluation of an urban parameterization schemein the Penn State/NCAR Mesoscale Model (MM5). Journal ofGeophysical Research-Atmospheres 110: 10102.

Department of Sustainability and Environment. 2002. Melbourne 2030:Planning for Sustainable Growth, State of Victoria, 192.

Department of Sustainability and Environment. 2003. Page 1 ofAddendum: Figure of Activity Centers and Principle PublicTransport Network 2003 Only, State of Victoria, 2.

Dupont S, Otte TL, Ching JKS. 2004. Simulation of meteorologicalfields within and above urban and rural canopies with a mesoscalemodel (MM5). Boundary-Layer Meteorology 113: 111158.

Eliasson I. 2000. The use of climate knowledge in urban planning. Landscape and Urban Planning 48: 31 44.

Fehrenbach U, Scherer D, Parlow E. 2001. Automated classification ofplanning objectives for the consideration of climate and air qualityin urban and regional planning for the example of the region ofBasel/Switzerland. Atmospheric Environment 35: 5605 5615.

Geoscience Australia. 2003. Vegetation Post-European Settlement,1988. National Mapping Division.

Grimmond CSB. 1992. The suburban energy balance: methodologicalconsiderations and results for a mid-latitude west coast city underwinter and spring conditions. International Journal of Climatology

12: 481497.Grimmond CSB, Oke TR. 2002. Turbulent heat fluxes in urban areas:

observations and a local-scale urban meteorological parameterizationscheme (LUMPS). Journal of Applied Meteorology 41: 792810.

Grimmond CSB, Cleugh HA, Oke TR. 1991. An objective urban heat-storage model and its comparison with other schemes. Atmospheric Environment Part B-Urban Atmosphere 25: 311326.

Hurley PJ. 2005. CSIRO Atmospheric Research Technical Paper No.71, 54.

Hurley P, Manins P, Lee S, Boyle R, Ng YL, Dewundege P. 2003.Year-long, high-resolution, urban airshed modeling: verification ofTAPM predictions of smog and particles in Melbourne, Australia. Atmospheric Environment 37: 1899 1910.

IPCC. 2007. Summary for Policymakers. In Climate Change 2007:The Physical Science Basis. Contribution of Working Group I to theFourth Assessment Report of the Intergovernmental Panel on ClimateChange, Solomon S, Qin D, Manning M, Chen Z, Marquis M,

Averyt KB, Tignor M, Miller HL (eds). Cambridge University Press:Cambridge and New York.

Jackson LE. 2003. The relationship of urban design to human healthand condition. Landscape and Urban Planning 64: 191200.

Jin M, Shepherd JM. 2005. Inclusion of urban landscape in a climatemodelHow can satellite data help? Bulletin of the American Meteorological Society 86: 681689.

Kusaka H, Kimura F. 2004. Coupling a single-layer urban canopymodel with a simple atmospheric model: impact on urban heatisland simulation for an idealized case. Journal of the MeteorologicalSociety of Japan 82: 67 80.

Lemonsu A, Grimmond CSB, Masson V. 2004. Modeling the surfaceenergy balance of the core of an old Mediterranean city: Marseille. Journal of Applied Meteorology 43: 312327.

Martilli A, Clappier A, Rotach MW. 2002. An urban surfaceexchange parameterisation for mesoscale models. Boundary-LayerMeteorology 104: 261304.

Masson V. 2000. A physically-based scheme for the urban energybudget in atmospheric models. Boundary-Layer Meteorology 94:357397.


DOI: 10.1002/joc


15/15


Masson V, Grimmond CSB, Oke TR. 2002. Evaluation of the TownEnergy Balance (TEB) scheme with direct measurements fromdry districts in two cities. Journal of Applied Meteorology 41:10111026.

Mills G. 2005. Progress toward sustainable settlements: a role for urbanclimatology. Theoretical and Applied Climatology 84: 6976.

Mitchell VG, Mein RG, McMahon TA. 2001. Journal of environmentalmodeling and software. Modeling the Urban Water Cycle 16:615629.

Morris CJG, Simmonds I. 2000. Associations between varyingmagnitudes of the urban heat island and the synoptic climatologyin Melbourne, Australia. International Journal of Climatology 20:19311954.

Morris CJG, Simmonds I, Plummer N. 2001. Quantification of theinfluences of wind and cloud on the nocturnal urban heat islandof a large city. Journal of Applied Meteorology 40: 169182.

Oke TR. 1984. Towards a prescription for the greater use of climaticprinciples in settlement planning. Energy and Buildings 7: 110.

Oke TR. 1988. The Urban energy balance. Progress in PhysicalGeography 12: 471508.

Oke TR. 2005. Towards better scientific communication in urbanclimate. Theoretical and Applied Climatology 84: 179190.

Pauleit S, Ennos AR, Golding Y. 2005. Modeling the environmentalimpacts of urban land use and land cover changea study inMerseyside, UK. Landscape and Urban Planning 71: 295310.

Plummer N, Salinger MJ, Nicholls N, Suppiah R, Hennessy KJ,Leighton RM, Trewin B, Page CM, Lough JM. 1999. Changes inclimate extremes over the Australian region and New Zealand duringthe twentieth century. Climatic Change 42: 183202.

Puri K, Dietachmayer GS, Mills GA, Davidson NE, Bowen RA,Logan LW. 1998. The new BMRC limited area prediction system,LAPS. Australian Meteorological Magazine 47: 203223.

Rankin DW. 1959. Mortality associated with heat wave conditionsin the Melbourne metropolitan area, January and February, 1959. Australian Meteorological Magazine 26: 96 98.

Smoyer-Tomic KE, Kuhn R, Hudson A. 2003. Heat Wave Hazards: anoverview of heat Wave impacts in Canada. Natural Hazards 28:463485.

Spronken-Smith RA, Kossmann M, Zawar-Reza P. 2006. Where doesall the energy go? Surface energy partitioning in suburbanChristchurch under stable wintertime conditions. Theoretical and Applied Climatology 84: 137150.

Stone B. 2005. Urban heat and air pollutionan emerging role forplanners in the climate change debate. Journal of the AmericanPlanning Association 71: 13 25.

Sugawara H, Narita K, Mikami T. 2001. Estimation of effectivethermal property parameter on a heterogeneous Urban surface. Journal of the Meteorological Society of Japan 79: 1169 1181.

Taha H. 1999. Modifying a mesoscale meteorological model to betterincorporate urban heat storage: A bulk-parameterization approach. Journal of Applied Meteorology 38: 466.

Torok SJ, Morris CJG, Skinner C, Plummer N. 2001. Urban heat islandfeatures of southeast Australian towns. Australian MeteorologicalMagazine 50: 113.

Willmott CJ. 1981. On the validation of models. Physical Geography2: 184194.

Zehnder JA. 2002. Simple modifications to improve fifth-generation

Pennsylvania State University-National Center for AtmosphericResearch Mesoscale Model performance for the Phoenix,Arizona, metropolitan area. Journal of Applied Meteorology 41:971979.


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