ORIGINAL PAPER
Enhancement of urban heat load through social inequalities
on an example of a fictional city King’s Landing
M Žuvela-Aloise1
Received: 15 February 2016 /Revised: 22 July 2016 /Accepted: 1 August 2016 /Published online: 18 August 2016
# The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract The numerical model MUKLIMO_3 is used to
simulate the urban climate of an imaginary city as an illustrative
example to demonstrate that the residential areas with
deprived socio-economic conditions can exhibit an enhanced
heat load at night, and thus more disadvantageous environmental
conditions, compared with the areas of higher socioeconomic
status. The urban climate modelling simulations
differentiate between orographic, natural landscape, building
and social effects, where social differences are introduced by
selection of location, building type and amount of vegetation.
The model results show that the increase of heat load can be
found in the areas inhabited by the poor population as a combined
effect of natural and anthropogenic factors. The
unfavourable location in the city and the building type,
consisting of high density, low housing with high fraction of
pavement and small amount of vegetation contribute to the
formation of excessive heat load. This abstract example shows
that the enhancement of urban heat load can be linked to the
concept of a socially stratified city and is independent of the
historical development of any specific city.
Keywords Urbanheatisland .Urbanclimate .Environmental
justice . Social inequalities . King’s Landing . Urban green
spaces . MUKLIMO_3 model
Introduction
High temperatures, especially long exposure to excessive heat
during heat waves, are associated with severe impacts on human
health and increase in mortality rates (Souch and
Grimmond 2004; Tan et al. 2007; Baccini et al. 2008; Son
et al. 2012). The phenomenon of the urban heat island (UHI)
generates further excess heat within urban areas (Landsberg
1981; Oke 1982), which makes city residents more vulnerable
to climate impacts as the global temperature increases (Gabriel
and Endlicher 2011).
The energy balance in a densely built urban environment is
modified by a number of factors: lack of vegetation, high
absorption of solar radiation on paved surfaces, heat storage
by built-up structures, trapping of long-wave radiation in urban
canyons, reduced circulation of air and the additional
release of anthropogenic heat (Oke et al. 1991). Large cities
have a heterogeneous morphology with an uneven distribution
of built-up and green areas. Different land use properties,
orography, geographical position and regional climate play a
role in forming the local climate. As a result, complex spatial
patterns of urban heat load exist, which leave some areas of
the city more exposed to excessive heat than others. The relationship
between the vegetation and lower surface temperatures
is well established (Gill et al. 2007; Bowler et al. 2010;
Dousset et al. 2011), and the green infrastructure plays a major
role in mitigating the UHI effect and improving the thermal
comfort (Santamouris 2014; Zhang et al. 2014; Georgi and
Dimitriou 2010; Norton et al. 2015).
In addition to counteracting the negative effects of the urban
climate, urban green spaces provide a number of social
and health benefits which are relevant for a higher quality of
life for city residents (Chiesura 2004; Solecki et al. 2005; van
den Berg et al. 2010; Astell-Burt et al. 2013; Qin et al. 2013;
Kabisch and Haase 2014). Epidemiological studies show that
Electronic supplementary material The online version of this article
(doi:10.1007/s00484-016-1230-z) contains supplementary material,
which is available to authorized users.
* M Žuvela-Aloise
maja.zuvela-aloise@zamg.ac.at
1
Zentralanstalt für Meteorologie und Geodynamik (ZAMG),
Vienna, Austria
Int J Biometeorol (2017) 61:527–539
DOI 10.1007/s00484-016-1230-z
access to green spaces has a positive influence on the longevity
of urban citizens and self-reported health (Takano et al.
2002; Maas et al. 2006; Thompson et al. 2012; Besenyi
et al. 2014). However, accessibility of green spaces is often
highly stratified based on socio-economic factors such as income,
race, ethnicity, age, gender or health conditions
(Solecki and Welch 1995; Martin et al. 2004; Barbosa et al.
2007; Wendel et al. 2012; Vaughan et al. 2013; Reyes et al.
2014; Shareck et al. 2014). There is ample evidence that
shows that the amount of vegetation, and in particular tree
cover, is often substantively lower in areas with higher levels
of socio-economic deprivation (Pauleit et al. 2005; Astell-Burt
et al. 2014; Sander and Zhao 2015), which increases the risk
of negative climate impacts for residents with lower income.
The link between an individual’s socio-economic position and
their health has been observed, and the uneven accessibility to
urban green spaces has become recognised as an issue of
environmental justice (Ernstson 2013; Wolch et al. 2014).
Studies have suggested several reasons why green spaces
are differentially distributed within the urban landscape and
why they are frequently less available to the poorer groups.
These include the history of land use development, differences
in the philosophy of park design, evolving ideas about
leisure and recreation, and past social and political situations
(Wolch et al. 2014). Green spaces are often viewed as luxury,
and their inadequate valorisation leads to an inequitable
distribution and access (Wendel et al. 2012; Wolch et al.
2014). Rapid urbanisation in many cases leads to an increase
in built-up at the expense of green areas. City growth combined
with urban planning policies to provide energyefficient
housing by densification of the residential areas
can accelerate the loss of urban green spaces (Dallimer
et al. 2011).
While most of the studies relate the unequal distribution of
green spaces and resulting characteristics of urban climate to
the development of a particular city, the question arises whether
there is a general connection between the socio-economic
conditions and negative environmental impacts. Moreover,
there is a serious lack of knowledge in understanding possible
relationships between the socio-economic system and the environmental
issues, which is extremely important for development
of urban planning strategies dealing with the climate
change, such as mitigation or adaptation measures. This study
is intended to demonstrate the link between the socioeconomic
inequality and differences in local climate on an
abstract example in order to show that the relationship between
the low socio-economic status and disadvantageous
environmental conditions in urban area lies beyond the development
of an individual city. An example of a virtual city, that
is realistic enough to capture physical characteristics of an
urban environment, but also simple enough to include only
main features in development of a socially unequal city, is
used. This study postulates that merely the concept of a
socio-economic stratification can lead to an enhancement of
heat load in socially deprived areas.
An illustrative example of an imaginary city called the
King’s Landing is chosen to represent the socially unequal
city. The King’s Landing is the capital of a fictional medieval
land described in the series of fantasy novels A Song of Ice and
Fire (Martin 1996–2011 ). The detailed portrayal of the city in
Martin’s books and a map depicting main features of the natural
landscape and urban structures (King’s Landing Map
2011) allow a precise characterisation of the urban environment.
The fantasy novels are filmed in the TV series Game of
Thrones with the historical city of Dubrovnik, Croatia, chosen
as the filming location. Consequently, Dubrovnik is used in
this study as a reference for the building structure and climate
conditions. The King’s Landing is depicted as a stone-walled
city located on rocky terrain near a river, surrounded by fields
and forest, in a warm and dry climate. The aristocracy and
religious leaders live in the fortresses in elevated areas with
access to parks, while the poor population is situated in the
densely built area located at the foot of the hills.
Urban climate model simulations with the spatial resolution
capable of resolving the temperature gradients on a scale of
building blocks are performed. The orography and land use
characteristics are adjusted to fit the description of the imaginary
city. The thermal stress and human comfort in summer period is
analysed through calculation of perceived temperature and its
diurnal variations for selected weather situations. The climate
indices related to air temperature extremes, such as mean annual
number of hot days and tropical nights, are used to describe the
thermal conditions in urban environment on a climatological
scale taking into account variety of weather conditions. The
urban heat load is therefore used as a general term to describe
thermal conditions in urban environment. The meteorological
data of Dubrovnik are used as a base for the calculation of
climate indices. The setup of the model is described in the
BMethodology^ section. BResults^ section presents the results
of the simulations. Sensitivity tests examine the influence of the
orography, the natural landscape, the walls and the building
distribution (uniform versus socially differentiated) on the urban
climate. A discussion is provided in the BDiscussion^ section,
and the conclusions are given in the BConclusions^ section.
Methodology
Urban climate model MUKLIMO_3 and evaluation
of the climate indices
The urban climate model simulations are performed with the
numerical model MUKLIMO_3 (in German: Mikroskaliges
Urbanes KLImaMOdell in 3-Dimensionen). MUKLIMO_3 is
a non-hydrostatic microscale model based on Reynoldsaveraged
Navier–Stokes (RANS) equations designed to
528 Int J Biometeorol (2017) 61:527–539
simulate atmospheric flow fields in the presence of buildings
(Sievers and Zdunkowski 1985; Sievers 1990; Sievers 1995).
The thermo-dynamical version of the model includes prognostic
equations for air temperature and humidity; the
parameterisation of unresolved buildings using the porous media
approach (Gross 1989); short-wave and long-wave radiation;
balanced heat and moisture budgets in the soil (Sievers
et al. 1983); and a vegetation model based on Siebert et al.
(1992). The model considers friction effects on building surfaces
and turbulence generation due to airflow separation.
Turbulent fluxes of heat, moisture and momentum are calculated
by a first-order closure scheme. The numerical method for
the calculation of short-wave irradiances at the ground level,
the walls and the roof of buildings in an environment with
unresolved built-up is described in Sievers and Früh (2012).
The storage of heat into the urban fabric is simulated by calculation
of the molecular heat fluxes from the outer wall surfaces
into the urban fabric (or vice versa) which depend on the temperature
gradient across the walls of the buildings and the
predefined values of the heat transfer coefficient and the heat
capacity of the walls. The model takes into account the effects
of cloud cover on radiation. However, it does not include cloud
processes, precipitation, horizontal runoff or anthropogenic
heat. The vegetation in the canopy model has three vertical
layers: tree crown, tree trunk and low vegetation. Grid cells
with buildings include only low vegetation.
The urban climate model MUKLIMO_3 is selected for the
study because of its capability to simulate the atmospheric
processes in the urban environment on a high spatial resolution
scale (∼100 m), which is necessary to capture the thermal
gradients within the city. The dynamical core of the
MUKLIMO_3 model, based on the RANS equations, is designed
for practical engineering, and the turbulence processes
are parameterised with possible overestimation of the turbulent
mixing. Similar simulations with more spatial detail could
be performed with the microclimate models (e.g. ENVI-met,
RayMan) in a resolved building environment if the modelling
domain has a spatial extent large enough to capture the interaction
between various land use types, and the modelling approach
includes the effects of orography.
The 3D simulation is initialised and driven by a 1D version
of the MUKLIMO_3 model. The 1D model calculates the
daily cycle of temperature, relative humidity and wind for
the reference station located outside of the urban area, taking
into account the geographic position, height, soil type and
land use characteristics of the reference station. At the beginning
of the simulation, soil temperature and moisture are set
constant throughout the soil column. The initial atmospheric
profiles for air temperature, humidity and wind, as well as soil
temperature and moisture are either taken from observations
or tuned to fit different weather conditions in the case where
idealised simulations are performed. The 1D simulation is run
for 24 h after which the values for air temperature, relative
humidity and wind are used to initialise the 3D model taking
into account terrain height and soil type. The 1D model provides
the upper boundary conditions for the entire duration of
the 3D model run by providing hourly values for wind, air
temperature and relative humidity at the top layer of the 3D
model. The lateral sides of the 3D model have free boundary
conditions with horizontal advection terms being zero at the
upstream domain boundaries. Due to the high computational
requirement, the 3D simulations are typically run for 24 h. The
first hour of the 3D model simulation, where the applied initial
vertical profiles from the 1D model are adjusted for the urban
area, is excluded from the analysis. The meteorological fields
given as the output of the 3D model are used for the analysis
of the UHI effect and the calculation of climate indices.
In order to evaluate the mean state of urban climate, particularly
the urban heat load in summer period, the climate indices,
such as mean annual number of summer days (Tmax ≥ 25 °C), hot
days (Tmax ≥ 30 °C) and tropical nights (Tmin ≥ 20 °C), are used.
Additionally, perceived temperature based on the Klima-MichelModell
(Staiger et al. 2012) is calculated for the selected weather
situations based on the MUKLIMO_3 model output. The climate
indices are calculated with the cuboid method (Früh et al.
2011). The cuboid method enables the calculation of heat load
on a longer temporal scale by using a limited number of urban
climate model simulations. Instead of calculating daily simulations
for the 30-year period, the 3D simulations are performed
only for a selected range of weather situations where excessive
heat load in the urban area can occur. Thereafter, the climate
indices are derived by a tri-linear interpolation of modelled
fields, which are weighted based on the real meteorological data
of the mean daily air temperature (T), relative humidity (rh) and
wind speed (v) at a reference station outside of the city. The
selected weather situations can be described by a combination
of the (Tc, rhc, vc) which are named the Bcuboid corners.^ They
have a typical range: Tcmin = 15 °C, Tcmax = 25 °C; rhcmin = 42 %,
rhcmax = 80 %; and vcmin = 0.7 m/s, vcmax = 4 m/s. For clarification,
the climatological records from Central European cities
(Früh et al. 2011; Zuvela-Aloise et al. 2014) show that when
the regional daily mean air temperature is below Tcmin = 15 °C,
the Tmax in the city centre will hardly exceed 25 °C. Therefore,
these situations can be excluded from the calculation of summer
heat load, which greatly reduces the number of simulations necessary
for the climate evaluation. The model work flow is illustrated
in Fig. 1.
The MUKLIMO_3 model has been employed in a number
of urban climate studies in German cities (Früh et al. 2011;
Hoffmann et al. 2014; Buchholz and Kossmann, 2015) and
evaluation of urban heat load on the climatological scale with
the cuboid method was used for the cities in Central Europe
(Bokwa et al. 2015). In case of Vienna, the modelling results
show very good agreement with observations for the heat load
during the day (Zuvela-Aloise et al. 2016), while climate indices
related to minimum temperature are overestimated. The
Int J Biometeorol (2017) 61:527–539 529
same overestimation for the nighttime conditions is not found
in all modelling examples and a detailed validation of the
meteorological fields in the real case applications provided
by the high-resolution model remains a problem due to small
number of monitoring stations, especially when climatological
evaluation is concerned.
Model setup for King’s Landing
The model domain covers an area of 10.1 km × 7.6 km with a
domain size of 168 × 126 × 25 grid points. The horizontal grid
has equidistant grid spacing of 60 m. The vertical resolution
varies from 10 to 50 m with higher resolution near the ground.
The model height is 510 m, while maximum elevation of the
terrain is 100 m. The size of the domain is approximately ten
times larger than the walled city of Dubrovnik. This adjustment
is intended to match the estimated population of the imaginary
city King’s Landing (ca. 500,000 inhabitants), which is approximately
ten times more than the population of the city of
Dubrovnik. The digitalisation scheme of the land use is similar
to the method applied for the reconstruction of the urban climate
of Vienna based on historical maps (Zuvela-Aloise et al.
2014). The map of the King’s Landing (King’s Landing Map
2011) is transferred into polygons, and for each polygon area, a
corresponding land use class is defined (Fig. 2).
Sixteen idealised simulations with duration of 24 h for two
opposite wind directions (NW and SE) and the geographical
coordinates of Dubrovnik are calculated. The initial and boundary
conditions for idealised simulations of the cuboid corners
are given by a time-varying 1D vertical profile (up to 3360 m)
of temperature and relative humidity for low and high wind
speed conditions. All simulations are started on July 15 with
1 day spin-up for the 1D boundary model. The 3D simulation is
started on July 16 at 0900 Central European Summer Time
(CEST) and ends on July 17 at 0800 CEST. July 15 is chosen
as a representative day for the calculation of summer heat load
since it has an average sun height and day length for the conditions
in June to August where most summer days occur. The
MUKLIMO_3 simulations
idealized diurnal cycle
for cuboid points (Tc, rhc, vc)
MUKLIMO_3 fields
hourly T, rh, v
daily Tmin, Tmax
climate analysis
time series
daily mean Ti, rhi, vi
tri-linear
interpolat
ion
interpolated fields
Ti,max, TI,min
count threshold
exceedance
climate
indices
Synoptical situation
upper boundary conditions
Atmosphere
(momentum, heat and moisture
equations)
Surface energy balance
radiation
Soil, walls, roofs
(heat and moisture equations)
geogr.
coord.,
date, time
surface
geometry
surface
properties,
incl.
vegetation
soil, wall,
roof
properties
short-wave
long-wave
cuboidmethod
background
climate
(observations)
Soil temp. & moisture at 1 m
depth
Fig. 1 Schematic diagram of the
MUKLIMO_3 model (adapted
from Sievers and Jendritzky
(1986); source: DWD (2015)) and
the calculation of climate indices
using the cuboid method (Früh
et al. 2011)
530 Int J Biometeorol (2017) 61:527–539
water temperature is set to 24 °C and kept constant in all simulations.
The 1D model is initialised with different stability
conditions, cloudiness (from 0 to 0.875), soil moisture, soil
temperature (16 and 23 °C) and indoor temperature (20 and
25 °C). The indoor temperature is constant during the simulations,
and air conditioning is not included. Daily time series
from a Dubrovnik monitoring station are used to calculate 30year
averages of climatic indices (Fig. 3). For each land use
category, a set of model parameters is defined to describe surface
properties and urban structures. The parameters include
the following: fraction of built area (γb), mean building height
(hb), wall area index (wb), fraction of pavement of the non-built
area (v), fraction of tree cover (σt) and fraction of low vegetation
of the remaining surface (σc), height (hc) and leaf area
index (LAIc) of the canopy layer as well as mean height (ht)
and leaf area index (LAIt) of the trees with separated values for
the tree trunk and the tree crown area. The land use classes
and parameters are defined following the Stewart and Oke
(2012) local climate zone (LCZ) classification system
(Table 1). In this case, the model differentiates between
Orography(m)
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100
0 1 2 km0,5
Local Climate Zone (LCZ)
1 2 3 4 5 6 7 8 9 10 A B C D E F G
0 10,5 2 km
Fig. 2 Model orography (left) and land use distribution (right). The classification of local climate zones (LCZs) is given in Table 1
0
5
10
15
20
25
30
-5 0 5 10 15 20 25 30 35 40
RF(%)
Temperature ( C)
0
5
10
15
20
25
10 20 30 40 50 60 70 80 90 100
RF(%)
Relative humidity (%)
0
5
10
15
20
25
30
0 1 2 3 4 5 6 7 8 9 10
RF(%)
Wind speed (m/s)
0%
5%
10%
15%
20%
N
NNE
NE
ENE
ENE
ESE
SE
SSE
S
SSW
SW
WSW
W
WNW
NW
NNW
Total Cuboid
Fig. 3 Relative frequency (RF) distribution of daily mean temperature,
relative humidity, wind speed and wind direction at 1400 CET for the
period 1981–2010 at the reference station Dubrovnik (lat 42° 39′ N, long
18° 05′ E, height 52 m). The values in colour indicate the range used in
the cuboid method (Tcmin ≥ 15 °C) (data source: DHMZ–Croatian
Meteorological and Hydrological Service)
Int J Biometeorol (2017) 61:527–539 531
14 land use categories: 7 built-up classes, 4 vegetation
types, bare surface, rocks and water. The building geometry,
which is not defined by the LCZ classification system,
is estimated from the building structure in Dubrovnik.
Results
In order to illustrate the role of different natural and anthropogenic
factors in the formation of the UHI, the modelling simulations
are split into five stages of gradual city development.
The sensitivity scenarios include (1) orography, (2) natural
landscape, (3) wall, (4) buildings and (5) social effect.
Sensitivity simulations are performed by changing the land
use parameters in idealised simulations for the cuboid corners.
The model results for perceived temperature and wind field
are shown in Fig. 4, and evaluation of thermal stress on a
typical hot day, representing warm, dry, and weak wind conditions
(Tcmax = 25 °C, rhcmin = 40 %, vcmin = 0.7 m/s) is given
in Table A1, including statistical analysis for each LCZ.
The climate indices related to extreme air temperatures
(mean annual number of hot days and tropical nights) are calculated
to account different weather situations and derive robust
results for the mean state of the urban climate. Spatial
distribution of climate indices and the changes between the
scenarios are illustrated in Fig. 5. Model results including mean
values of climate indices and standard deviation for the given
LCZ, as well as difference between the scenarios and the
statistical significance of change are given in Table A2.
General overview of the modelling results is given in Table 2.
Orographic effect
In the simulation where only features of terrain are considered,
the land surface is elevated 20 m above the sea level and the
relief is flat except for the three hills with the maximum height
of 100 m. The surface is characterised as bare soil (LCZ F)
with 1 % of low vegetation (0.3-m height). No water surfaces
are present.
The results show a uniform distribution of high temperatures
during the day in the low terrain and lower temperatures
on the hill sides. During the night, strong cooling of the free
surface results in temperature inversion, which is visible in the
vertical profile of temperature (Fig. 6). Due to the temperature
inversion, elevated areas are warmer.
Natural landscape effect
In this scenario, a natural surface cover is introduced to the
orography. The land types include forest (LCZ A), scattered
trees or urban park (LCZ B) and scrubs (LCZ C) located on
Table 1 Parameters for land
cover properties in MUKLIMO_3
model following the Stewart and
Oke (2012) local climate zone
(LCZ) classification system
Local climate zone (LCZ) γb
a
(%) hb
a
(m) wb va
(%) σt
a
(%) σc
a
(%) ht (m) hc (m)
1 Compact high-rise 0.60 25 6.67 0.40 0.0 0.01 0 0.3
2 Compact midrise 0.55 15 4.50 0.30 0.0 0.08 0 0.3
3 Compact low-rise 0.55 7 2.33 0.20 0.0 0.13 0 0.3
4 Open high-rise 0.30 25 7.00 0.35 0.0 0.25 0 0.3
5 Open midrise 0.30 15 4.50 0.35 0.0 0.21 0 0.3
6 Open low-rise 0.30 7 2.33 0.35 0.0 0.25 0 0.3
7 Lightweight low-rise 0.75 3 1.80 0.15 0.0 0.03 0 0.3
8 Large low-rise 0.40 7 1.40 0.45 0.0 0.08 0 0.3
9 Sparsely built 0.15 7 2.80 0.10 0.0 0.60 0 0.3
10 Heavy industry 0.25 10 3.00 0.30 0.0 0.14 0 0.3
A Dense trees 0.00 0 0.00 0.00 0.8 0.18 17 0.5
B Scattered trees 0.00 0 0.00 0.00 0.4 0.54 9 0.5
C Bush, scrub 0.00 0 0.00 0.00 0.0 1.00 0 1.5
D Low plants 0.00 0 0.00 0.00 0.0 1.00 0 0.5
E Bare rock or paved 0.00 0 0.00 0.95 0.0 0.01 0 0.3
F Bare soil or sand 0.00 0 0.00 0.00 0.0 0.01 0 0.3
G Water 0.00 0 0.00 −1.00 0.0 0.01 0 0.3
γb fraction of built area, hb mean building height, wb wall area index, v fraction of pavement, σt fraction of tree
cover, σc fraction of low vegetation, ht tree height, hc height of the low vegetation
a
Relative to total grid cell area
„Fig. 4 Land use distribution (left), perceived temperature (°C) and wind
field (m/s) at the reference level (2 m) at 1600 CEST (middle) and 0200
CEST (right) in the idealised simulations with prevailing SE wind, low
mean wind speeds (0.7 m/s), high mean temperature (25 °C) and low
mean relative humidity (40 %)
532 Int J Biometeorol (2017) 61:527–539
1. Orography Day Night
2.Natural landscape
3. Wall
4. Building
5. Social
Local Climate Zone (LCZ)
1 2 3 4 5 6 7 8 9 10 A B C D E F G
0 1 20,5 km
Local Climate Zone (LCZ)
1 2 3 4 5 6 7 8 9 10 A B C D E F G
0 1 20,5 km
Local Climate Zone (LCZ)
1 2 3 4 5 6 7 8 9 10 A B C D E F G
0 1 20,5 km
Local Climate Zone (LCZ)
1 2 3 4 5 6 7 8 9 10 A B C D E F G
0 1 20,5 km
Local Climate Zone (LCZ)
1 2 3 4 5 6 7 8 9 10 A B C D E F G
0 1 20,5 km
Int J Biometeorol (2017) 61:527–539 533
the hillsides, agricultural fields (LCZ D) surrounding the city,
bare rocks (LCZ E) and the sea (LCZ G). The remaining area
is defined as free surface (LCZ F). The roads are included in
the simulation since they are categorised as bare rocks.
M
1.Orography
2. Natural landscape
3. Wall
4. Building
5. Social
Day Nigth
ean annual number of hot days
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105 110 115
0 1 20,5 km
Mean annual number of tropical nights
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105110115
0 1 20,5 km
Mean annual number of hot days
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105 110 115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of tropical nights
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105110115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of hot days
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105 110 115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of tropical nights
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105110115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of hot days
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105 110 115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of tropical nights
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105110115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of hot days
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105 110 115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Mean annual number of tropical nights
5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95100105110115
0 1 20,5 km
Difference in days
-50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40 45 50
0 1 20,5 km
Fig. 5 Diurnal and nocturnal urban heat load expressed in mean annual
number of hot days (Tmax ≥ 30 °C) and tropical nights (Tmin ≥ 20 °C) with
absolute values of climate indices (left) and difference in spatial
distribution between the subsequent scenarios (right). Climate indices
are calculated by combining the idealised MUKLIMO_3 urban climate
model simulations with the meteorological data from Dubrovnik weather
station for the period 1981–2010
534 Int J Biometeorol (2017) 61:527–539
The simulated temperatures are in general lower than in the
simulation where only orography is considered. Due to different
properties of surface cover, horizontal temperature gradients
are formed. During the day, agricultural areas warm faster
than the water areas (Fig. 7) and the circulation from the sea
towards inland is enhanced, bypassing the hills as obstacles.
During the night, the water areas cool slower than the land,
and thus, the temperature gradient is eventually reversed with
areas near the sea having higher temperatures.
Wall effect
In the third scenario, the walls surrounding the city are introduced.
The walls are defined as compact high-rise (LCZ 1)
with 60 % of buildings (maximum value defined for this land
use type in the LCZ classification system) and 25-m height
(maximum height of Dubrovnik city walls). The
MUKLIMO_3 model parameterises buildings as a porous media
with lower porosity for higher building density. Therefore,
the flow is reduced, but not completely blocked in presence of
buildings.
The effect of the wall on the temperature distribution is most
pronounced in the vicinity of the walls. During the day, a minor
shadowing effect is visible, producing higher temperatures on
the inner side of the western and northern walls. During the
night, the temperatures along the walls are the highest and the
overall area of the city within the walls is warmer, especially in
the low terrain due to the reduced ventilation. However, since
the walls are only 25 m high, compared to 100-m height of the
hills, they do not significantly affect the elevated areas.
Buildings effect
Free surface areas within the walls and near the roads are
replaced by buildings having identical building geometry.
The typical building type in Dubrovnik (Table 1) most
Table 2 Main effects of
changes in land use and
building structure on the
heat load
Scenario Day Night
1 Orography • High heat load
• Spatially uniform distribution in flat terrain
• Slightly lower in elevated areas
• Low heat load
• Temperature inversion in vertical
• Higher in elevated areas
2 Natural landscape • Decrease due to vegetation and water surfaces
• Formation of land-sea gradient
• Higher in agricultural areas
• Lower near water surfaces
• Increase in low terrain
• Reversal of land-sea gradient
• Lower in agricultural areas
• Higher near water surfaces
3 Wall • Increase in wall area
• Shadowing effect
• Strong increase in wall area
• Increase in areas below the height of
the wall due to reduction of
ventilation
4 Building • Strong increase in built-up areas • Strong increase in built-up areas
• Minor increase in surrounding areas
due to advection of heat
5 Social • Decrease in built-up areas with more
vegetation
• Minor decrease in built-up area with high
density due to shadowing effect
• Minor decrease in built-up areas
with low density and more
vegetation
• Strong increase in built-up areas with
high building density and less
vegetation
0
50
100
150
200
250
300
350
400
450
500
15 20 25 30 35 40
Height(m)
Temperature (°C)
t_1600
t_0200
Fig. 6 Vertical profile of air temperature for daytime (t_1600) and
nighttime conditions (t_0200) in idealised simulation with prevailing
SE wind, low mean wind speeds (0.7 m/s), high mean temperature
(25 °C) and low mean relative humidity (40 %) in the orography scenario
Int J Biometeorol (2017) 61:527–539 535
resembles the compact low-rise (LCZ 3) with three-story
buildings. The LCZ 3 building type is characterised by a high
fraction of built-up area (55 %) and pavement (20 %) and a
small fraction of low vegetation (13 %). There are no trees
simulated on the built-up surfaces.
The modelling results show that the presence of buildings
reduces ventilation and increases heat load in all areas of the
city. During the day, the most pronounced heat excess is found
in the low terrain in the lee-side of the hills and surrounded by
walls, especially in the areas furthest from the sea. This resembles
the pattern of the natural landscape on which the buildings
have been superimposed. During the night, the heat load
in the flat terrain is mostly uniform. Elevated and green areas
have relatively lower heat loads both during the day and night.
Minor, but statistically significant, change in heat load can be
found in the areas of the city that remained unmodified. In this
case, the heat load is increased through advection of the air
masses between the neighbouring surfaces.
Social effect
In the final scenario, the building structure has been modified
to fit the social conditions described in the books.
Three new building types are introduced within the city:
open high-rise (LCZ 4) to characterise high towers and
religious buildings, open mid-rise (LCZ 5) for the surrounding
large buildings and lightweight low-rise (LCZ
7) as a densely built-up area of the slum with single-story
buildings. The individual buildings for LCZ 7 are lower
(3 m) than typical LCZ 3 buildings (7 m) and have a smaller
surface area (50 m2
, compared to 150 m2
). The fraction
of buildings is higher (75 %), the pavement area is lower
(15 %) and the fraction of low vegetation is smaller (3 %).
High-class buildings (LCZ 4 and 5) are higher than LCZ 3
buildings having heights of 25 and 15 m, respectively. The
surface area of individual buildings is four to five times
larger than LCZ 3 buildings, the fraction of buildings is
lower (30 %) and the area has 20–25 % fraction of low
vegetation.
Changing the building structure induces minor changes
on the heat load during daytime conditions. The model
simulations show a local increase of heat load in elevated
areas for high-rise buildings (LCZ 4), but a minor decrease
in the low terrain (LCZ 7) and on the elevated areas (LCZ
5). During the night, the heat load in elevated areas is
locally reduced compared to the simulation with the equal
buildings. However, the heat load is considerably higher in
the slum area in the low terrain. Since no trees are considered
in any built-up areas, the results are mostly dependent
on the building geometry and relative proportions of builtup,
low-vegetated and paved area.
Discussion
The modelling simulations of the gradual development of
an imaginary city are intended to illustrate the formation of
spatial gradients in urban heat load due to the natural factors
and anthropogenic modifications. The effects of the
main physical processes in urban environment, such as
absorption of solar radiation, reduction of ventilation,
shadowing effect, heat storage, influence of green and water
surfaces, are demonstrated in a simplified form through
the different modelling scenarios. Each scenario helps to
understand processes behind individual land use change
and shows tendencies in heat load induced by the land
use modification. The summary presented in Table 2 can
be used as a simple guidance for urban planners.
In the case of the imaginary city, it is not possible to validate
the modelling results directly. However, the model values
for climate indices are compared with the mean annual number
of hot days and tropical nights in the city of Dubrovnik.
Only one climate monitoring station in the area of Dubrovnik
is available, and it is located in the suburban environment in
the coastal region. The mean annual number of hot days for
the time period 1981–2010 amounts to 29.3, which is comparable
to the modelling results for the LCZ C of 34.8
(Table A2). The observed mean annual number of tropical
nights is 78.3, which is similar to the modelled value of
72.3. The use of land use classification to describe urban
structures increases model uncertainties related to the choice
of land use parameters, such as characteristic building density
and height, fraction of pavement, pervious surfaces and
vegetation. The LCZ classification system was introduced to
limit and standardise the selection of the land use parameters.
However, the range of parameters defined by Stewart and Oke
(2012) allows for variations within one LCZ, which can lead
to minor changes in quantitative evaluation of the heat load.
16
18
20
22
24
26
28
30
32
34
36
0900 1000 1200 1400 1600 1800 2000 2200 0000 0200 0400 0600 0800
Temperature(°C)
Time (h)
t_LCZ D
t_LCZ G
Fig. 7 Diurnal cycle of air temperature at the reference level (2 m) for
agricultural surfaces (LCZ D) and water areas (LCZ G) in idealised
simulation with prevailing SE wind, low mean wind speeds (0.7 m/s),
high mean temperature (25 °C) and low mean relative humidity (40 %) in
the natural landscape scenario
536 Int J Biometeorol (2017) 61:527–539
When calculating atmospheric processes in the urban environment,
the MUKLIMO_3 model takes into account not only
the physical characteristics of the urban structure but also elevation
and advection of the air masses between the
neighbouring surfaces. The heat load for an individual LCZ
depends on the location in the city, relief and the surrounding
land use types. Therefore, the quantitative results for the heat
load for an individual LCZ cannot be directly compared with
the observations from an arbitrary city. The abstract example
should be used to demonstrate general tendencies in urban
heat load modification, and the quantitative values are valid
within the range of model uncertainties and data used in the
simulations.
An imaginary city serves as an illustrative example that
does not deal with any actual city, but the concept of the
socially unequal city. The modelling results point out that
the excessive heat load can be formed in the socially unprivileged
areas and that this might not be merely a coincidence,
but a result of the general expectations how a socially stratified
city looks like. For example, the climate modelling results
might look different if the author of the book suggested that
the area of the poor had broad green alleys, lot of parks and
water surfaces, or was located on a more favourable position
in the city. However, this would not resemble an image of a
slum. Instead, the fantasy books focus on the social relationships
and described physical characteristics are an attempt to
make the city look plausible. Environmental processes and
related climatic consequences are not explicitly considered
in the books nor are intentional; they result from the expected
social conditions.
Conclusions
The study shows the potential development of the UHI during
the summer period in the imaginary city of King’s Landing
using an urban climate model MUKLIMO_3 and meteorological
data for the city of Dubrovnik, Croatia. The model results
show that the excessive heat load can be found in the areas
inhabited by the poor population as a combined effect of natural
and anthropogenic factors. Dense, low-rise, built-up areas in
low terrain have higher temperatures in comparison to elevated
areas with higher buildings, lower building density and more
vegetation. The spatio-temporal variability of the heat load pattern
is formed by both natural landscape and human modifications.
During the day, the temperatures are highest inland,
which can be associated with the natural characteristics of the
land-sea distribution. During the night, the temperature gradient
is reversed, with higher temperatures towards the sea. However,
within the city walls, the building characteristics dominate with
the area of the socially disadvantageous residents exhibiting the
highest heat load due to unfavourable location, high building
density and low amount of vegetation.
The modelling simulations show that there are two factors
which contribute to the formation of the heat load in socially
unprivileged areas: (1) unfavourable location, in this case location
under the hill in the low terrain surrounded by the high city
walls, which prevent good ventilation, and (2) the building
type, consisting of high density, low housing with high fraction
of pavement and less vegetation. The UHI effect is less pronounced
in the elevated areas which have naturally better conditions
and, additionally, less building density and more vegetation.
In an abstract sense, the concept of a socially stratified
city suggests that the areas that are naturally advantageous are
likely to be inhabited by the higher classes and maintain good
quality due to careful planning. In contrast, the less favourable
areas are expected to be inhabited by lower social classes and
the quality can further be deteriorated by inappropriate planning
and lack of mechanisms that could compensate for the naturally
disadvantageous conditions. The study shows a basic mechanism
that relates lower social standards with negative environmental
impacts. In this sense, the King Landing’s effect is defined
as the enhancement of heat load in an urban environment
by socio-economic stratification.
This study is intended to demonstrate the relationship between
the low socio-economic status and disadvantageous environmental
conditions on an abstract example in order to help
urban planners to identify the problems and develop appropriate
urban planning strategies. In the case of cities where the
green spaces are predominantly available to the high-income
population and value of green infrastructure is regulated
through the real-estate prices, the general incentives for urban
greening, as a strategies to adapt to climate change, might not
necessarily lead to the desired outcome. Instead, the result can
even be a gentrification and a displacement of the residents for
which the green spaces were intended (Wolch et al. 2014).
Therefore, urban planning strategies should consider the
socio-economic structure, in order to appropriately address the
environmental problems. Using the mapping of urban climate
and physically based models, it is possible to identify the critical
zones in which the intervention are necessary and to which
extent they need to be applied to improve the environmental
conditions prior to undertaking large infrastructural investments.
Together with empirical research of benefits of such
measures (Bowler et al. 2010) and taking into account sociodemographic
status, user preferences, cultural perceptions and
barriers (Kabisch and Haase 2014; Wendel et al. 2012), this
information could help to develop appropriate solutions and
equalise the social and environmental disparities.
In reality, the socio-economic system and the urban climate
are more complex than shown in this study. The expected deprivation
of urban green spaces in low-income neighbourhoods
is not always found (Abercrombie et al. 2008), and even an
overall increase in urban green spaces is reported in many cities
(Kabisch and Haase 2013). Additionally, the relationship between
green space and change in health is not always clear
Int J Biometeorol (2017) 61:527–539 537
(Wolfe et al. 2014) and a better understanding is needed
(Lachowycz and Jones 2013). The background climate conditions
and the terrain morphology can modify the heat load
characteristics for different LCZs. Therefore, the simple and
somewhat exaggerated example presented in this study should
not be used for urban planning purposes without considering
the particularities of the individual city.
Nevertheless, the study points out an important mechanism
that might be present in many cities but is masked by the
complex dynamics of the city as a system. Understanding
relationships between different elements of the system and
raising awareness are primary steps in addressing environmental
and socio-economic problems related to climate
change. Using popular media or social games are an effective
way in addressing the public at large and can help people to
communicate the trade-offs between mitigation and adaptation
measures in an urban environment (Juhola et al. 2013).
For this reason, the popular, but instructive, example can be
used for educative purposes in order to increase the interest in
problems of climate change in urban environments.
Acknowledgments This study was supported by the Austrian Federal
Ministry of Science, Research and Economy (BMWFW) as a preparatory
work for the training course in MUKLIMO_3 modelling tool. The author
would like to thank the Deutscher Wetterdienst (DWD) for making available
the MUKLIMO_3 model and for their continuous support, the
Croatian Meteorological and Hydrological Service (DHMZ) for providing
the meteorological data for Dubrovnik, and colleagues at the ZAMG
for sharing the enthusiasm and help in editing the English writing.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
appropriate credit to the original author(s) and the source, provide a link
to the Creative Commons license, and indicate if changes were made.
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