on climate impacts of a potential expansion of …...on climate impacts of a potential expansion of...

On Climate Impacts of a Potential Expansion of Urban Land in Europe

K. TRUSILOVA, M. JUNG, AND G. CHURKINA

Max-Planck-Institute for Biogeochemistry, Jena, Germany

(Manuscript received 28 August 2008, in final form 30 March 2009)

ABSTRACT

Over the last two decades, a disproportional increase of urban land area in comparison with the population

growth has been observed in many countries of Europe, and this trend is predicted to continue. The

conversion of vegetated land into urban land leads to a higher proportion of impervious surface area,

to decline and change of vegetation cover, to artificial heat sources, and therefore to changes in climate.

This study focuses on the implications of the expansion of urban land for the European climate at the local

and regional scales. Regional climate simulations with the fifth-generation Pennsylvania State University–

NCAR Mesoscale Model (MM5) coupled to the Town Energy Budget model are used to isolate effects

of urban land expansion on temperature and precipitation. The study suggests that the expansion of current

urban land by 40% would lead to an enlargement of regions affected by thermal stress by a factor of 2,

whereas the intensity of the thermal stress does not change significantly. Precipitation in urban areas

would be reduced by 0.2 mm day21 in summer as a result of disturbances of the water cycle caused by

urban surfaces. The area in which precipitation was altered increased nearly linearly with the urban land

increment.

1. Introduction

Urban population is growing at a much faster rate

than the earth’s population as a whole and by larger

annual increments than ever before (World Resources

Institute 1996). Given the future urbanization projec-

tions and estimations of impacts of individual cities on

the environment, it becomes important to investigate

effects of growing urban areas. The patterns of urban

growth remain uncertain and vary among regions and

countries. Whereas small cities may experience popu-

lation densification, large metropolitan areas evolve

by an expansion of less densely populated suburban

land. In developed countries, a general trend toward

less densely populated urban areas has been observed

(European Environment Agency 2006; Brown et al.

2005); according to The Cities Alliance (http://www.

citiesalliance.org), the average built-up area per person

in European cities increased by more than 20% from

1990 to 2000. With the assumption that the population’s

average income and demands for space are not chang-

ing, the growth of urban population would lead to an

expansion of urban land into agricultural and forest

areas. This expansion involves replacing vegetated land

by heterogeneous surfaces partially covered by imper-

vious materials.

Urban areas affect our environment in different ways:

they disturb the water cycle by impervious surfaces,

provide heat accumulation in construction materials,

and serve as sources of air pollution. Previous studies

showed that urbanization largely affects the energy bud-

get of the surface (Grimmond and Oke 1999a; Oke et al.

1999) and air moisture (Mayer et al. 2003; Grimmond and

Oke 1999b). The urban heat island (UHI), one of the

most frequently studied climatic features of cities, was

extensively observed (Bottyan et al. 2005; Alonso et al.

2003; Unger et al. 2001; Klysik and Fortuniak 1999) and

modeled (Trusilova et al. 2008; Lamptey et al. 2005).

Measurement studies on the distribution of summer

precipitation conducted by Mote et al. (2007) and Hand

and Shepherd (2009) identified significant impacts of ur-

banization on precipitation. However, estimates of urban

impacts on precipitation vary greatly. It was found that

increased surface roughness may lead to enhanced con-

vergence (Thielen et al. 2000) and that the UHI may in-

duce a convergence zone that initiates storms (Bornstein

and Lin 2000). Shem and Shepherd (2009) used the

Weather Research and Forecast Model (WRF)–National

Corresponding author address: Kristina Trusilova, MPI-BGC,

Hans-Knoell Str. 10, Jena 07745, Germany.

E-mail: [email protected]

SEPTEMBER 2009 N O T E S A N D C O R R E S P O N D E N C E 1971

DOI: 10.1175/2009JAMC2108.1

� 2009 American Meteorological Society

Centers for Environmental Prediction (NCEP)–Oregon

State University–Air Force–Hydrologic Research Lab-

oratory (Noah) Model coupled atmosphere–land model

to investigate storms observed by Bornstein and Lin. By

varying the size of Atlanta, Georgia, or removing it, they

quantified the impact of surface fluxes and convergence

on downwind precipitation. Rosenfeld et al. (2008) have

conducted a measurement campaign and found that

anthropogenic aerosols suppress precipitation in oro-

graphic clouds. Molders and Olson (2004) performed

model simulations showing that additional moisture

from urban sources contributes to increased downwind

precipitation.

Because the aerosol–precipitation feedbacks are still

poorly understood and, thus, rarely included in regional

climate models, we focus only on the effects of land use

change on climate. Thus, the results of this study should

be interpreted with care, keeping in mind that aerosol

effects on surface energy balance and precipitation

formation are not included in the models used here.

Recent studies (Jin et al. 2007; Jin and Shepherd 2005)

have considered methods to include urban effects in

climate models but have not gone beyond proposed

methodologies or into quantitative analysis. Quantita-

tive estimations of regional climate changes caused by

urban land were made for the northeastern United

States (Lamptey et al. 2005) and Europe (Trusilova et al.

2008). These studies have shown that the transformation

of vegetated land into urban land reduces air moisture

content, provides a stronger surface warming, and leads

to shifts in precipitation patterns on the local and re-

gional scales. However, responses of the climate in Eu-

rope to the future expansion of urban land have not yet

been analyzed. The study presented here addresses this

question and aims to provide an estimation of possible

temperature and precipitation changes that result from

an increased fraction of urban land in Europe. This

study is a follow up to work previously published by

Trusilova et al. (2008).

2. Materials and methods

a. Model

To isolate effects of the urban land cover on the climate,

we use the limited-area numerical weather prediction

fifth-generation Pennsylvania State University–National

Center for Atmospheric Research (NCAR) Mesoscale

Model (MM5; Grell et al. 1995) coupled to a single-layer

urban canopy model, the Town Energy Budget model of

Masson (2000). The validation of model results against

temperature and snow-height measurements is de-

scribed in the work of Trusilova et al. (2008). This cou-

pling allowed representation of the impacts of urban

land cover on the atmosphere and resolution of near-

surface processes of heat and moisture transfer suffi-

ciently. Performed model simulations represent responses

of the atmospheric circulation to three different states

of urbanization: 1) no urban land, 2) urban land at the

extent as in 2000–05, and 3) expanded urban land. The

first urbanization state with no urban land is defined as

the reference state. The effects on climate from the

other two are quantified as differences in climate vari-

ables such as temperature and precipitation from the

reference state. To represent mentioned states of ur-

banization, we created corresponding land cover maps,

which include respective fractions of urban land.

b. Mapping of urban areas

Existing land cover databases that include urban land

cover categories define urban land differently. For

this study, we define urban land as artificial surfaces

defined in the Coordination of Information on the

Environment (CORINE) Land Cover 2000 database

(CLC2000; http://etc-lusi.eionet.europa.eu/CLC2000).

There are 11 different classes of artificial surfaces at the

spatial resolution of 250 m. According to the definition

of artificial surfaces in CLC2000, urban land includes

areas mainly occupied by dwellings and buildings, in-

cluding their connected areas (associated lands, road

network, and parking lots), rail networks, airport in-

stallations, river and sea port installations, industrial

livestock-rearing facilities, construction sites, anthro-

pogenic waste dump sites, urban parks, and sport and

leisure facilities.

Three land-cover maps were created to represent the

following states of urbanization: 1) a land cover map that

includes no urban area (NOU map), 2) a land cover map

with urban areas as in 2000–05 (URB map), and 3) a

land cover map that represents expanded urban areas

(EURB map).

The land cover map commonly used in MM5 (MM5

map) was derived from the Global Land Cover Char-

acterization from the U.S. Geological Survey (GLCC-

USGS). This land cover map includes 24 land cover

categories with a single urban land cover class among

them. We updated the mask of urban land class in the

MM5 map for each model simulation. The NOU map

was derived by replacing urban pixels in the MM5 map

with the dominant land cover type of neighbor pixels of

rural land.

To represent the state of urbanization in 2000–05,

an updated mask of urban land was needed because the

MM5 map strongly underestimates urban land in Europe.

We used an urban mask that was derived by merging

urban masks of different land cover databases (Trusilova

1972 J O U R N A L O F A P P L I E D M E T E O R O L O G Y A N D C L I M A T O L O G Y VOLUME 48

et al. 2008): GLCC-USGS, Global LandCover 2000

(from the Joint Research Centre of the European Com-

mission Directorate General; GLC2000), the Moderate

Resolution Imaging Spectroradiometer (MODIS) land

cover types map from the National Aeronautic and

Space Administration, the LandScan population dataset

(LANDSCAN) from the Oak Ridge National Labora-

tory, and nighttime light emissions data (NIGHTLIGHT)

from the Defense Meteorological Satellite Program.

This new urban mask was superimposed on the NOU

map to create the URB map.

For the map of expanded urban land (the EURB

map), we used the MM5 map for vegetated land com-

bined with a new mask of expanded urban land. The

mask of the expanded urban land was created using a

proxy indicator of the urban area extent, the urban

score. The urban score map was calculated using ur-

ban masks from the GLCC-USGS, GLC2000, MODIS,

LANDSCAN, and NIGHTLIGHT datasets with a

spatial resolution of 1 km. In contrast to the other da-

tabases, NIGHTLIGHT and LANDSCAN have con-

tinuous data fields. Thus, appropriate thresholds have to

be set for extracting relevant urban masks from these

datasets.

The CLC2000 database provides a harmonized, reli-

able, and comparable snapshot of land cover for 2000 for

Europe (29 countries) based on high-resolution satellite

data with high geometric quality. The CLC2000 land use

classification was made using not only the satellite im-

ages but also local knowledge about particular land use

at the national level. This last fact distinguishes the

CLC2000 data from other global land use classifications

mentioned above, which are based solely on remote

sensing image data. Therefore, the CLC2000 database

includes multiple urban land use classes that are often

omitted in other databases. We chose the CLC2000 to

validate urban masks of other land cover databases.

Each of the individual urban maps (imap) was com-

pared with CLC2000, and the accuracy of mapping of

the urban class was calculated as the number of pixels

for which CLC2000 and imap agree (both indicate the

urban land cover category for the pixel). The degree of

the match between the urban mask of imap and the

urban mask of CLC2000 (Table 1) was calculated as

P(imap) 5N

urban(imap&CLC2000)

Ntotal

(imap)3 100%, (1)

where imap 5 GLCC-USGSjGLC2000jMODISjNIGHTLIGHTjLANDSCAN, Nurban(imap&CLC2000) 5

number of pixels that are defined as ‘‘urban’’ in both

imap and CLC2000, and Ntotal(imap) 5 total number of

urban pixels in imap only.

From the definition, if the urban masks in imap and

CLC2000 match for all pixels, then P(imap) 5 1; oth-

erwise, P(imap) , 1. Here P(imap) can be understood as

the probability of an urban pixel in imap being urban in

reality. Each urban pixel in imap was set to P(imap).

Given that LANDSCAN and NIGHTLIGHT have con-

tinuous values, P(imap) was calculated for each possible

value. The urban score map (USM) was calculated as the

mean of values P(imap) of all individual imaps for each

pixel in row i and column j:

USMi,j

51

5�

5

imap51P

i,j(imap) 3 100%. (2)

From the definition, the maximum possible value of

USM is 100% when all individual maps agree on the

same urban mask and the minimum value is 0 for pixels

that are not identified as urban by any imap.

The mask of expanded urban land for the EURB map

was derived from the USM by setting a threshold that

masks urban areas that are 2 times as large as in the

URB map. The optimal threshold (thEURB) was cal-

culated by minimizing the difference between the total

urban area in USM(thEURB) and 2 times the total area

of urban mask in the URB map:

UrbanArea[USM(thEURB)]� 2j3 UrbanArea(URB)j ! 0, (3)

where EURB 5 USM(thEURB), thEURB 5 25%, and

UrbanArea(x) or UrbanArea[x] 5 total area (km2) of

urban land in map x 5 EURBjURB.

The total area of urban land contained in the EURB

map is larger than in the URB map and can be inter-

preted as the expanded urban land. The total urban area

in the EURB map accounts for ;180% of CLC2000

total urban area. This is explained by the fact that the

CLC2000 database has a spatial resolution of 250 m and

TABLE 1. Degree of the match of urban masks P between each

individual urban mask (imap) and the reference urban mask

EU-CORINE.

Names of database from which the

urban map was derived P(imap) (%)

GLCC 57

GLC2000 47

MODIS 29

LANDSCAN 0–68 (depending on

threshold value)

NIGHTLIGHT 0–72 (depending on

threshold value)


includes a larger number of small urban areas, which are

omitted on the 1-km resolution in the URB and EURB

maps.

The derived urban masks in the URB and EURB

maps were upscaled to the resolution of 10 km of the

model domain (Fig. 1). Because of the upscaling by the

principle of dominant land cover type, multiple small

urban areas were not included into the urban masks with

the coarser resolution. The URB and EURB maps re-

spectively contain 2.8% and 3.9% of land that is classi-

fied as urban (Table 2). The ratio of the total urban area

in the URB map to the total urban area in the EURB

map was 1:1.4, which means that the urban land was

enlarged by 40% of its original size. The increase of the

urban-to-rural land border (perimeter) was ;30%.

c. Modeling protocol

Three sets of model simulations, which make use of

the three different land cover maps, were performed: the

baseline simulation NOU run used the NOU map with

no urban land, the URB run used the URB map, and the

EURB run used the EURB map of expanded urban

land.

The model domain of 361 3 283 grid cells included the

most urbanized areas of Europe, with the grid size of

10 km and 23 vertical s levels. It was nested in an in-

termediate domain with a spatial resolution of 30 km.

At lateral boundaries, the model was constrained every

12 h (at 0000 and 1200 UTC of each simulated day) by the

NCEP final analysis data (FNL ds083.2; http://dss.ucar.

edu/datasets/ds083.2/). The model setup of physical pa-

rameterization schemes is described in detail in the pre-

vious study by Trusilova et al. (2008). This setup was used

for the current study without further modifications.

According to previous investigations, it was found

that urban land is most likely to modify the atmospheric

circulation in winter (Montavez et al. 2000) and in

summer (Bottyan et al. 2005; Unger et al. 2001). Ac-

cording to this finding and in an attempt to reduce large

model computational costs, we performed an ensemble

of six model realizations for January and six realizations

for July over 2000–05 for each model scenario.

d. Analysis of results

Effects of the urban land on the climate were detected

with significance tests of the differences in the near-

surface temperature and precipitation between the URB

and EURB runs and the NOU run. The differences of

climate variables between scenarios are denoted with

indices URB-NOU and EURB-NOU for pairs of URB

or EURB runs and the baseline NOU run, respectively.

FIG. 1. The current-state urban land and the increment of urban land (by 40%) at the spatial

resolution of 10 km.

TABLE 2. Comparison of urban land fraction in the URB and

EURB maps of urban land.

Urban

mask

No. of urban

pixels in the

model domain

Total

area

(km2)

Fraction of urban land

in the total land in the

model domain (%)

URB 1591 159.1 3 103 2.8

EURB 2228 222.8 3 103 3.9


Different significance tests were used for the tempera-

ture and precipitation effects: the Mann–Whitney U test

for analysis of the near-surface temperature and the sign

test for analysis of precipitation differences.

We chose to demonstrate changes of precipitation as

the relative quantity (%) calculated as

DPR 5 PRURB-NOU

/PRNOU

3 100%, (4)

where PRNOU 5 daily precipitation in the baseline

NOU run.

This was done because absolute precipitation amounts

vary strongly over Europe and the same precipitation

change may be insignificant in wet climates whereas it

may be crucial in dry regions. Thus, plotting relative

precipitation change shows the places most affected by

precipitation changes due to urban growth. However, we

still provided quantitative estimates of precipitation

changes in millimeters per day.

To characterize the change in the land area where

climate is affected by urban areas, we used the regional

effect index (REI) suggested by Trusilova et al. (2008).

The REI is calculated for the pair of model runs urb

and NOU (urb 5 URBjEURB) as the ratio of the total

area over which significant differences in the climate

variable x between the two runs were found to the total

area of urban land:

REI(x, urb) 5A

aff rur(x, urb) 1 A

urb

Aurb

, (5)

where Aaff_rur(x, urb) 5 total rural area beyond cities

over which significant differences of x between the urb

and NOU runs were found and Aurb 5 total area of

urban land in the urb run.

From the definition of REI(x, urb), it is always greater

than or equal to 1.0, assuming that urban land is always

affected. If no rural land is affected by changes of x and

Aaff_rur(x, urb) / 0, then there is no significant regional

effect on the variable x and REI(x, urb) / 1. If

REI(x, urb) is significantly greater than 1.0, x is altered

on the regional scale. The significance threshold for

REI(x, urb) was set to 0.05, so that REI(x, urb) . 1.05

means urban land in the urb run has a significant effect at

the regional scale.

In addition we calculate the ratio r(x) of total areas

over which the variable x was altered by urban land in

the URB and EURB simulations:

r(x) 5 Aaff tot

(x, URB):Aaff tot

(x, EURB), (6)

where Aaff_tot(x, urb) 5 total area over which signifi-

cant differences between the urb and NOU runs were

found for variable x [Aaff_tot(x, urb) 5 Aaff_rur(x, urb) 1

Aurb(x, urb)]. If the ratio r(x) is close to the ratio of the

urban land expansion (i.e., 1:1.4), then the area over

which x is affected is linearly proportional to the total

urban area. Otherwise, the relationship is not linear.

Quantitative estimates of average impacts of urbani-

zation on climate variables were calculated as the av-

erage over all cities in the model domain. For a detailed

demonstration of urban effects we chose four cities: two

in a temperate zone with cold and humid winters [Berlin,

Germany, (an inland city) and London, United King-

dom (a city located close to the coast)] and two in a drier

temperate climate [Milan, Italy, (a city located close to

the mountains and the coastline) and Madrid, Spain (an

inland city)].

3. Results and discussion

a. Effects of urban growth on near-surfacetemperature

The analysis of the NOU-run and URB-run model

simulations showed that urban land modifies the atmo-

spheric circulation and leads to changes in the near-

surface temperature and precipitation (Trusilova et al.

2008). As the presence of urban land contributed ;18C

to the increase of the minimum diurnal near-surface

temperature Tmin, the subsequent expansion led to a

weakening of this effect. For example, in Milan, the

winter Tmin increment was greater than 2.28C and less

than 1.48C before and after the expansion, respectively

(Table 3). The reason for the Tmin increase can be at-

tributed to the changes in the surface energy balance

between the NOU, URB, and EURB runs: in the EURB

run the ground heat flux was higher during the daytime

than in the URB run. The increase of the latent heat

flux in the EURB run conditioned additional surface

cooling.

Standard deviations for estimates of Tmin increased

from the URB simulation to the EURB simulation. This

indicates that the larger urban surfaces do not directly

imply stronger UHI and may in fact initiate more het-

erogeneous temperature gradients.

Although the difference between TminURB-NOU

and TminEURB-NOU was not statistically significant, the

changes of Tmin in the EURB run were found over

larger suburban areas than in the URB run (Table 4).

The value REI(Tmin, EURB) 5 2.56 for July shows

that significant changes of Tmin were found over the

rural area that were as large as 2.56 2 1.00 5 1.56 times

the total urban area; the expansion of urban land from

159 100 to 222 800 km2 caused the total area affected by

Tmin anomalies to grow from 168 500 to 570 100 km2. In


this case, the total area over which Tmin was altered in-

creased by 238% in response to the 40% increase in ur-

ban land. For January, the increase accounted for 156%.

In winter, the enlargement of urban surface in the

EURB run provided an increase in the maximum diur-

nal near-surface temperature Tmax of the same mag-

nitude as the URB run (Table 3); in both runs the Tmax

increment was less than 0.48C on average. The vari-

ability of Tmax was high over the model domain because

of heterogeneous climate conditions across Europe, and

it resulted in large uncertainties of urban effect esti-

mates on Tmax (Table 3).

In response to the heat storage in artificial materials,

the diurnal temperature peak is more likely to be offset

so that the temperature reaches its highest value Tmax

in a city several hours later than in a rural area. This is

most likely to happen when there is a strong contrast

between urban and rural surface properties (i.e., albedo,

roughness, and heat capacity; Trusilova et al. 2008). For

example, in Madrid Tmax was reduced by 0.038 and

0.548C before and after urban expansion, respectively

(Table 3). In London and Berlin—cities with cold and

humid winters—Tmax changed neither with the presence

of urban land nor with its growth, whereas the Tmax in-

crement in Milan exceeded 0.28C (Table 3; Fig. 2a).

The statistical analysis revealed no significant differ-

ences between the magnitudes of Tmax induced by urban

land before and after expansion. Thus, the expansion of

urban land does not significantly change urban Tmax.

However, this urban growth leads to an expansion of the

TABLE 4. Differences in the spatial extent of the effects on the near-surface temperature and precipitation from actual (URB map) and

expanded (EURB map) urban land. The ratio of the total urban land in the URB map to the total urban area in the EURB map is 1:1.4.

Here, AreaURB(x) and AreaEURB(x) are the total area over which changes of x are found for the URB and EURB runs, respectively.

Variable x REI(x, URB) AreaURB(x) (km2) REI(x, EURB) AreaEURB(x) (km2)

January

Tmin 1.00 159.1 3 103 1.83 406.7 3 103

Tmax 1.01 161.0 3 103 1.30 289.2 3 103

DTR 1.01 160.1 3 103 1.95 434.5 3 103

PR 6.38 1014.9 3 103 7.29 1623.2 3 103

July

Tmin 1.01 168.5 3 103 2.56 570.1 3 103

Tmax 1.28 203.6 3 103 2.43 541.2 3 103

DTR 1.38 219.3 3 103 2.68 596.9 3 103

PR 5.79 921.7 3 103 6.16 1373.1 3 103

TABLE 3. Effects of urban land and its expansion on the near-surface temperature and precipitation.

Variable

Effect URB-NOU/

EURB-NOU

Effect URB-NOU/EURB-NOU in selected cities

London Berlin Milan Madrid

January

Tmin (C8) 11.24 6 0.78/

11.13 6 0.80

11.05 6 0.31/

10.97 6 0.29

10.29 6 0.56/

10.00 6 0.00

12.21 6 0.37/

11.38 6 0.86

11.81 6 0.31/

11.55 6 0.45

Tmax (C8) 10.30 6 0.50/

10.21 6 0.48

10.00 6 0.00/

10.00 6 0.00

10.00 6 0.00/

10.00 6 0.00

10.87 6 0.66/

10.26 6 0.57

20.03 6 0.14/

20.54 6 0.43

DTR (C8) 20.73 6 0.54/

20.81 6 0.59

20.71 6 0.15/

20.83 6 0.11

20.45 6 0.05/

20.49 6 0.05

21.09 6 0.14/

21.07 6 0.13

22.02 6 0.31/

22.12 6 0.34

PR (mm day21) 20.004 6 0.064/

20.018 6 0.104

10.032 6 0.132/

10.048 6 0.153

10.014 6 0.064/

10.000 6 0.081

20.023 6 0.058/

20.062 6 0.085

20.006 6 0.055/

20.005 6 0.043

July

Tmin (C8) 11.53 6 0.49/

11.22 6 0.58

11.19 6 0.18/

10.88 6 0.22

11.55 6 0.05/

11.38 6 0.04

11.95 6 0.31/

11.04 6 0.48

10.77 6 0.46/

10.14 6 0.40

Tmax (ECEU

region; C8)

10.83 6 0.21/

10.75 6 0.22

10.00 6 0.00/

20.08 6 0.20

10.23 6 0.35/

10.00 6 0.00

— —

Tmax (SEU

region; C8)

21.11 6 0.48/

21.43 6 0.57

— — 10.10 6 0.38/

21.37 6 0.50

20.99 6 0.49/

21.85 6 0.73

DTR (C8) 21.26 6 0.71/

21.49 6 0.81

21.00 6 0.10/

21.23 6 0.12

20.88 6 0.04/

20.92 6 0.07

21.83 6 0.51/

22.46 6 0.51

22.00 6 0.32/

22.61 6 0.51

PR (mm day21) 20.031 6 0.253/

20.115 6 0.466

20.005 6 0.187/

20.050 6 0.135

20.027 6 0.140/

20.011 6 0.156

20.311 6 0.642/

21.199 6 1.372

20.011 6 0.031/

20.021 6 0.044


area of urbanization-modified Tmax by 80% in winter

(Table 4).

In summer, changes of Tmax were similar across two

geographical regions: 1) eastern and central Europe (the

ECEU region) and 2) southern Europe (the SEU re-

gion). For both the URB and EURB runs, within the

ECEU region Tmax increased by more than 0.78C,

whereas within the SEU region Tmax was reduced by

more than 1.18C (Table 3). For example, in Berlin

(ECEU) the Tmax increment was greater than 0.28C

before the expansion and in Madrid (SEU) the change

of Tmax from the baseline was negative (Table 3).

Similar to the winter simulations, the diurnal tempera-

ture peak was delayed by several hours responding to

the urban heat storage. The heat storage as well as the

sensible heat flux was larger in the expanded cities than

in the cities of the URB run (Fig. 2). A case of the urban

cooling effect in Madrid in July 2005 was also demon-

strated by Trusilova et al. (2008).

In the URB and EURB runs changes of Tmax were

detected in urban areas and in the close suburban sur-

roundings, thus indicating no strong regional-scale ef-

fects (Table 4). From the analysis of the magnitude and

extent of TmaxURB-NOU and TmaxEURB-NOU, we found

that the expansion of urban land does not significantly

change urban Tmax. The urban expansion by 40%

provided 166% increase of the area of urbanization-

modified Tmax in summer.

The changes in Tmin and Tmax caused the diurnal

temperature range (DTR) to decrease within urban

land and its surroundings (Figs. 3a,b). DTRURB-NOU and

DTREURB-NOU decreased by more than 0.78C in January

and 1.28C in July (Table 3). For four chosen cities—Berlin,

London, Milan, and Madrid—the DTR decreased; how-

ever, for Madrid and Milan this reduction was stronger

then for London and Berlin in both seasons (Table 3).

Because the urbanization-induced effects on Tmin and

Tmax were not significantly different between the URB

and EURB runs, DTR also did not change significantly

with the expansion of the urban land. The area of

urbanization-modified DTR was enlarged by 170% and

172% in January and July, respectively (Table 4).

The assumed 40% expansion of urban land did not

significantly contribute to the magnitude of urbanization-

induced thermal stress but did result in a considerable

increase of area over which temperature regimes were

affected by the urban land use. The analyzed cases

showed that the area of urbanization-affected climate

increased by more than 100% in response to the 40%

urban land increment. This nonlinear proportion suggests

that growing cities alter thermal regimes over distances

larger than may be expected.

FIG. 2. The near-surface temperature and the energy balance of the urban surface in the reference run (NOU) and before (URB) and after

(EURB) expansion. The data are 30-day averages of model output for (left) Milan in January 2005 and (right) Berlin in July 2005.


b. Effects of urban growth on precipitation

Both model simulations that include urban land

produced less precipitation (PRURB-NOU , 0 and

PREURB-NOU , 0) than the baseline simulation (Figs. 3c,d),

with a stronger average reduction of precipitation over

the expanded urban land (Table 3). The high spatial

variability of precipitation rates across the model do-

main produced large uncertainties in estimates of pre-

cipitation response to urban land.

Although the simulated effects PRURB-NOU and

PREURB-NOU were of the same order of magnitude in

winter, the expansion of urban land caused a greater

change in summer precipitation (Table 3). Whereas ur-

ban land provided some deficit of urban precipitation all

over the model domain on average, some large cities

such as London and Berlin showed enhanced precipi-

tation in winter (Table 3). This local increase was mostly

found in the temperate zone in which winters are cold

and humid and the thermal perturbation of the bound-

ary layer initiated by the UHI may lead to enhanced

convection (Bornstein and Lin 2000).

In dry climates within large urban areas such as

Madrid and Milan, winter precipitation was reduced

because of changes in the hydrological cycle. Because

the low-level moisture is one of the most important

factors for UHI-induced precipitation (Dixon and Mote

2003), the large surface runoff in urban areas leads to

FIG. 3. Spatial distribution of statistically significant differences of diurnal temperature range (8C) between EURB-run and NOU-run

for (a) January and (b) July simulations. Spatial distribution of statistically significant precipitation differences (%) between EURB-run

and NOU-run for (c) January and (d) July simulations.


reduced surface evaporation and a deficit of moisture

availability for convective precipitation formation. As

evidence, the latent heat flux in the URB and EURB

runs was very small (Fig. 2) as compared with the base-

line simulation. Because of the larger size of urban areas

in the EURB-run, the total water surface runoff was also

larger than in the URB run and resulted in a stronger

deficit of surface evaporation.

The strongest reduction of precipitation in July was

found in northern Italy and accounted for 0.41 6 0.35

and 0.67 6 0.59 mm day21 before and after the expan-

sion, respectively. The majority of cities all over Europe

showed some precipitation reduction in the dry season

that was amplified by the cities’ expansion. For example,

in London and in Madrid the summer precipitation

deficit was amplified with the growth of urban areas

(Table 3). In a similar way, Kaufmann et al. (2007) found

a causal relationship from temporal and spatial patterns

of urbanization to temporal and spatial patterns of

precipitation during the dry season in the Pearl River

delta of China. The authors suggested that urbanization-

related changes in surface hydrology led to the precipi-

tation deficit.

REI values indicate that the land area over which cities

influence precipitation has increased with urban growth

(Table 4). The increment of the area of precipitation

change and the magnitude of precipitation suggest that

the expansion of urban land has almost a linear effect on

precipitation. However, the intensified urban drought in

summer may be evidence of the high sensitivity of con-

vective precipitation to the growth of urban land.

4. Summary and outlook

This study suggests that the size of urban areas has

a significant influence on near-surface temperatures

and summer precipitation. Our numerical simulations

showed that the expansion of existing urban areas leads

to a disproportional enlargement of the area over which

near-surface temperature is affected. The proportion of

land over which the diurnal temperature range was

significantly affected grew by more than a factor of 2 in

response to the 40% urban land increase. Thus, the rural

areas adjacent to cities are very sensitive to the urban ex-

pansion because rural temperatures are strongly influ-

enced by thermal regimes of cities.

A strong reduction of summer precipitation was at-

tributed to the lack of surface evaporation, which af-

fected the formation of convective precipitation. The

area over which precipitation was affected increased in

almost linear proportion to the urban land increment.

We found that the diurnal temperature range was

reduced by 0.08 C8 in January and by 0.23 C8 in July as a

result of the expansion of urban areas. This relatively

small effect becomes important when we take into ac-

count the growing proportion of land over which it oc-

curs. The maximum reduction of summer precipitation

provided by cities’ growth was greater than 0.2 mm day21

(6 mm month21) in the area of northern Italy. This re-

duction was local in character but could potentially af-

fect many city’s inhabitants and suburban agricultural

lands. However, the estimation of precipitation changes

from this study should be taken with care because the

precipitation microphysics was not resolved explicitly

because of the coarse resolution of the model; extreme

highly localized precipitation events are likely to be

omitted in this estimation.

These findings suggest that local modifications of land

cover such as a switch from vegetated to urban land may

significantly alter temperature and precipitation of areas

much larger than the cities themselves. Therefore, ur-

banization may cause significant effects on climate at the

regional scale and thus should not be neglected in re-

gional climate forecasts by climate scientists or policy

makers when cities in the region of interest are expected

to grow.

In this study we have focused on one of the most obvious

urbanization-driven modifications of our environment—

changes in land cover—and its implications for temper-

ature and precipitation. As was pointed out in the review

by Shepherd (2005), other shortcomings of urbaniza-

tion such as air pollution may also affect urban and

rural environments. Air pollution causes significant im-

pacts on cloud and precipitation formation (Dixon

and Mote 2003; Rosenfeld 2000; Huff and Changnon

1973) through disturbances in cloud droplet nucleation

and on the surface–atmosphere energy budget through

shielding shortwave radiation (Stanhill and Kalma

1995). These effects should be taken into account in

future research when their mechanisms can be better

understood and parameterized within weather/climate

models.

Acknowledgments. We thank the Max-Planck-

Gesellschaft for providing the scholarship for Kristina

Trusilova, DKRZ (Deutsches Klimarechenzentrum

GmbH) for providing computer facilities, and the MM5

development team at NCAR for the model support. We

especially thank Dr. J. Schumacher for consulting on the

statistics and Prof. S. Grimmond for fruitful discussions

and useful suggestions on the modeling work.

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