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Observed surface warming induced by urbanization in East China

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Monthly mean surface air temperature data from 463 meteorological stations, including those from the 1981-2007 ordinary and national basic reference surface stations in east China and from the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) Reanalysis, are used to investigate the effect of rapid urbanization on temperature change. These stations are dynamically classified into six categories, namely, metropolis, large city, medium-sized city, small city, suburban, and rural, using satellite-measured nighttime light imagery and population census data. Both observation minus reanalysis (OMR) and urban minus rural (UMR) methods are utilized to detect surface air temperature change induced by urbanization. With objective and dynamic station classification, the observed and reanalyzed temperature changes over rural areas show good agreement, indicating that the reanalysis can effectively capture regional rural temperature trends. The trends of urban heat island (UHI) effects, determined using OMR and UMR approaches, are generally consistent and indicate that rapid urbanization has a significant influence on surface warming over east China. Overall, UHI effects contribute 24.2% to regional average warming trends. The strongest effect of urbanization on annual mean surface air temperature trends occurs over the metropolis and large city stations, with corresponding contributions of about 44% and 35% to total warming, respectively. The UHI trends are 0.398°C and 0.26°C decade-1. The most substantial UHI effect occurred after the early 2000s, implying a significant effect of rapid urbanization on surface air temperature change during this period.
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Observed surface warming induced by urbanization in east China
Xuchao Yang,
1,2
Yiling Hou,
3
and Baode Chen
1
Received 7 December 2010; revised 7 April 2011; accepted 6 May 2011; published 28 July 2011.
[1] Monthly mean surface air temperature data from 463 meteorological stations,
including those from the 19812007 ordinary and national basic reference surface stations
in east China and from the National Centers for Environmental Prediction and National
Center for Atmospheric Research (NCEP/NCAR) Reanalysis, are used to investigate
the effect of rapid urbanization on temperature change. These stations are dynamically
classified into six categories, namely, metropolis, large city, mediumsized city, small city,
suburban, and rural, using satellitemeasured nighttime light imagery and population
census data. Both observation minus reanalysis (OMR) and urban minus rural (UMR)
methods are utilized to detect surface air temperature change induced by urbanization.
With objective and dynamic station classification, the observed and reanalyzed
temperature changes over rural areas show good agreement, indicating that the reanalysis
can effectively capture regional rural temperature trends. The trends of urban heat
island (UHI) effects, determined using OMR and UMR approaches, are generally
consistent and indicate that rapid urbanization has a significant influence on surface
warming over east China. Overall, UHI effects contribute 24.2% to regional average
warming trends. The strongest effect of urbanization on annual mean surface air
temperature trends occurs over the metropolis and large city stations, with corresponding
contributions of about 44% and 35% to total warming, respectively. The UHI trends
are 0.398°C and 0.26°C decade
1
. The most substantial UHI effect occurred after the early
2000s, implying a significant effect of rapid urbanization on surface air temperature
change during this period.
Citation: Yang, X., Y. Hou, and B. Chen (2011), Observed surface warming induced by urbanization in east China, J. Geophys.
Res., 116, D14113, doi:10.1029/2010JD015452.
1. Introduction
[2] Detection and attribution of regional and global cli-
mate change, particularly climate warming stemming from
natural and anthropogenic activities, are the central issues in
current climate change research [Ren et al., 2008]. In gen-
eral, the detection of surface warming caused by enhanced
greenhouse gases (GHG) emission in urban areas is largely
based on observational surface air temperature records. These
records are usually considered inaccurate as they do not
remove the socalled urban heat island (UHI) effect, which
gives rise to longterm warming along with that contributed
by GHG emissions. The UHI effect is mostly regarded as one
of the major errors or sources of uncertainty in current surface
warming studies [Gong and Wang, 2002; Heisler and Brazel,
2010]. Conversely, the effects of landuse and landcover
changes are usually disregarded despite their being firstorder
climate forcing agents; these factors have also played an
important role in alterations in regional and global climates
[Pielke, 2005]. In terms of landuse change, urbanization is
one of the extreme processes [Shepherd, 2005]. As pointed
out by Seto and Shepherd [2009], climate change and
urbanization are two of the most pressing global environ-
mental issues of the 21st century and are becoming increas-
ingly interconnected.
[
3] Within the international climate community, there
currently exist divergent views on urbanization and its degree
of influence on regional and global mean temperature trends.
The fourth assessment report of the Intergovernmental Panel
on Climate Change (IPCC) states that urban heat island
effects are real but local and have a negligible influence on
global warming trends [IPCC, 2007]. Nevertheless, some
research results indicate that this effect may play a more
significant role in temperature trends estimated at multiple
geographic scales; such results should be accorded more
consideration in the mitigation of climate change [ Pielke,
2005; Stone, 2009].
[
4] Investigations on the effect of urbanization on observed
surface air temperature trends are typically conducted by
comparative analysis of stations in urban and rural areas.
In general, either population data [e.g., Easterling et al., 1997;
Hua et al.,2008;Ren et al., 2008] or satellitemeasured
nighttime light data [e.g., Gallo et al., 1999; Hansen et al.,
1
Shanghai Typhoon Institute of China Meteorological Administration,
Shanghai, China.
2
Institute of Meteorological Sciences, Zhejiang Meteorological Bureau,
Hangzhou, China.
3
Shanghai Climate Center, Shanghai, China.
Copyright 2011 by the American Geophysical Union.
01480227/11/2010JD015452
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D14113, doi:10.1029/2010JD015452, 2011
D14113 1of12
2001; Peterson, 2003; Du et al., 2007] are used to classify
meteorological stations into urban and rural types. Recently,
Stone [2007] categorized stations based on both population
and nighttime light data in an analysis of urban and rural
temperature trends over the United States.
[
5] China has experienced rapid economic development
and a dramatic growth in its urban population and city areas
over the past 30 years. Jones et al. [1990] performed a
comparative analysis of mean temperature change obtained
from urban and rural stations in east China and found that
urbanization has had little effect on mean surface tempera-
ture change. Wang et al. [1990] and Portman [1993] indi-
cated that the UHI exerts a significant influence on
temperature trends. On the basis of homogenized tempera-
ture data from 390 national stations, Li et al. [2004] revealed
that the UHI is not a significant contributor to the regional
warming in mainland China. The recent study on northeast
China, in which homogenized temperature data from 187
stations were used, also supports this conclusion [Li et al.,
2010]. In northwest China, Fang et al. [2007] indicated
that the average UHI effect from 1961 to 2000 was only
0.02°C because of the relatively low urbanization level in
this region. On the other hand, some studies have shown that
the effect of the UHI on regional surface air temperature
trends is quite considerable. On the basis of the urban minus
rural (UMR) data of 191 station pairs across China, Hua
et al. [2008] investigated the effect of urbanization on
temperature and showed that the UHI effect was 0.05°C
decade
1
for large cities and 0.03°C decade
1
for medium
sized cities and smalltown stations from 1961 to 2000.
Using 282 stations from two different networks (national
and ordinary weather stations) across northern China, Ren et
al. [2008] comprehensively analyzed the urbanization
effects on observed surface air temperature trends in this
region. The authors showed that the regional average annual
mean temperature series, calculated using the data from 95
national stations, is significantly influenced by urban
warming and that the urban warming trend, determined by
comparing urban data with those from the rural network
(63 sites), was 0.11°C decade
1
over the period 1961 to
2000. Using the data from 322 national stations and ordinary
weather stations, Tang et al. [2008] found that the con-
tributions of urban warming to overall temperature change
are relatively large in southwest China and that the urban
warming rate from 1961 to 2004 was 0.086°C decade
1
for
large and mediumsized cities. Using nighttime light
imagery from the Defense Meteorological Satellite Program
(DMSP) Operational Linescan System (OLS) as a basis, Du
et al. [2007] classified 99 stations in the Yangtze River
Delta into megacity and nonmegacity regions, and found that
the UHI effect was 0.069°C decade
1
from 1961 to 2005. The
UHI that is closely associated with the accelerated develop-
ment of the Yangtze River Delta megacity region since the
1990s may be regarded as a criti cal climate signal. Table 1
summarizes some of the results of the UMR approach to
the UHI effect in China at national and regional scales.
[
6] An important issue presented by the surface air tem-
perature series, particularly in China, is that few weather
stations are located in completely or perfectly rural loca-
tions; the possible underestimation of the UHI effect might
have been a major source of error in the determination
of regional average temperature trends [Ren et al., 2008].
Furthermore, most studies of urbanrelated warming in China,
derived from the difference in temperature trends between a
single city and its adjacent rural stations, usually represent
the characteristics of the stations only at a very small scale and
more regionalscale investigations are needed.
[
7] Kalnay and Cai [2003] introduced a new method for
estimating the effect of urbanization and other land uses on
surface temperature change in the United States. This
method involves subtracting the National Centers for
Environmental Prediction/National Center for Atmospheric
Research (NCEP/NCAR) Reanalysis (NNR) data [Kalnay
et al., 1996] from the observed temperature (observation
minus reanalysis, or OMR). The essence of the OMR method
lies in the fact that the NNR does not assimilate surface
observations of temperature, moisture, and wind over land,
and the surface temperatures are estimated from atmospheric
values so that the NNR is insensitive to land surface prop-
erties and the changes in these properties [Kalnay and Cai,
2003]. Subsequently, Lim et al. [2005] expanded the study
area into the Northern Hemisphere to estimate the sensitivity
of temperature changes to land types and found that desert
and urban areas show the strongest OMR trends. Nuñez
et al. [2008] applied the OMR method to surface stations
in Argentina to estimate the effect of land use changes and
found that the OMR trends show a warming contribution
to mean temperature of 0.07°C decade
1
and a decrease in
Table 1. Results of Recent Studies on the Urban Heat Island (UHI) Effect in China at Regional and National Scales
Study Area Method Time Period UHI Warming (°C decade
1
) References
Southeast China OMR
a
19791998 0.05 Zhou et al. [2004]
East of 110°E over China OMR 19601999 0.12 Zhang et al. [2005]
China OMR 19601999 0.14 Yang et al. [2009]
Mainland China UMR
b
19542001 <0.012 Li et al. [2004]
China UMR 19612000 0.030.05 Hua et al. [2008]
North China UMR 19612000 0.11 Ren et al. [2008]
China Comparison with SST
c
19512004 0.1 Jones et al. [2008]
Northwest China UMR 19612000 0.02 Fang et al. [2007]
Southwest China UMR 19612004 0.086 for large and mediumsized
cities 0.016 for small cities 0.052
for national stations
Tang et al. [2008]
Yangtze River Delta UMR 19612005 0.069 Du et al. [2007]
Northeast China UMR 19542005 0.027 Li et al. [2010]
a
OMR, observation minus reanalysis.
b
UMR, urban minus rural.
c
SST, sea surface temperature.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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diurnal temperature range of 0.08°C decade
1
. Recently,
Fall et al. [2010] investigated the sensitivity of temperature
trends to landuse change over the conterminous United
States using the OMR trend from station observations and the
North Amer ican Regional Rea nalysis, which has been devel -
oped as an improvement upon the earlier NCEP/NCAR and
National Centers for Environmental Prediction/Department of
Energy (NCEP/DOE) data in terms of both resolution (32 km
grid increments) and accuracy [Mesinger et al., 2006]. The
results indicat ed that landuse and landcover types are strong
drivers of surface air temperature change. The regions con-
verted into urban areas show a positive (warm) OMR trend,
whereas regions converted into cr oplands display a cool ing
trend (presumably becaus e of irri gation and increased evapo-
ration). Moreover, the results for the areas of OMR warming
and cooling over the United States agree well with those
obtained by Hansen et al. [2001] when they defined urban/
rural stations based on night time light s [Fall et al., 2010].
[
8] Observation minus reanalysis has also been recently
used to estimate the effect of land surface forcing on tem-
perature change in China. By comparing the surface
observation temperature with the NCEP/DOE Reanalysis
data, Zhou et al. [2004] found that the rate of temperature
warming brought about by urbanization in southeast China
was 0.05°C decade
1
from 1979 to 1998. Using similar data
and methods, Zhang et al. [2005] determined the 1960 to 1999
change rate caused by urbanization and landuse change in
China. Their results are as follows: 0.12, 0.2, and 0.03°C
decade
1
for mean, minimum, and maximum temperatures,
respectively. In investigating the sensitivity of temperature
change to land use/cover types in China, Yang et al. [2009]
subtracted the European Centre for MediumRange Weather
Forec asts 40year Reanalysis temperature from the Climatic
Research Unit observational surface air temperature. Their
results revealed that warming in urban land areas reached
0.14°C decade
1
between 1960 and 1999. In a recent study by
Hu et al. [2010], the OMR method was used to assess the
effect of land surface forcing on extreme temperature in
eastern China from 1979 to 2008. They demonstrated that the
landsurface change effect may explain one third of the
observed temperature increase for annual warm nights and
nearly half of the observed decrease for annual cold nights.
Overall, the UHI effect on temperature change, detected using
the OMR method, is more significant than that determined
using the UMR approach (Table 1). However, no detailed
comparisons have been performed to clarify this issue further.
[
9] The inconsistencies in the aforementioned studies may
have resulted from several factors, such as the density of
station network analyzed, the criteria for defining urban
and rural stations, analysis methods, time periods, and the
regional span studied. Most important, except for one or two
Figure 1. Terrain of the study area and locations of the
station s.
Figure 2. Number of pixels of nighttime lights with values larger than 6 for 1992, 2000, and 2007 in
east China.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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studies that utilized satellite nighttime lights to classify sta-
tions [e.g., Du et al., 2007] and data from ordinary weather
stations to analyze the UHI effect [e.g., Ren et al., 2008; Tang
et al., 2008], most of the previous investigations are based on
the records of national reference climate stations and basic
weather stations, the majority of which are located near cities
or towns. The accuracy of the regional average temperature
series obtained from the data of the rural station group may
have been compromised by the urbanization effects. More-
over, Chinas rapid urbanization in the past three decades led
to a quick transition of stations from rural into urban within a
very short period. Note, however, that almost all of the pre-
vious studies did not consider this factor in their UMR
analysis. The type of station remained fixed throughout an
entire analysis period once it was identified as rural or urban.
Thus, disregarding the effect of the conversion of stations
from rural to urban on temperature records may give rise to a
considerable underestimation of the UHI effect.
[
10] East China is densely populated, especially around
the Yangtze River Delta, where dramatic economic devel-
opment and growth have occurred since Chinas reform and
opening up in the late 1970s. This area has been experi-
encing rapid urbanization over the past three decades. With
urban land use area continuously growing and population
increasing, regional UHI problems are becoming increas-
ingly serious. Systematically and quantitatively examining
the urbanization effect on surface warming is therefore an
urgent requirement. In the current study, we attempt to
quantitatively determine the potential magnitude of possible
urbanrelated effects on regionalscale temperature trends
during a rapid urbanization period. To this end, more in
depth observations are made. An objective approach to
dynamically categorizing urban and rural stations is devel-
oped and employed based on the DMSP/OLS nighttime
light data of 19922007, population census data, and geo-
graphical information system (GIS) technology. Finally,
OMR and UMR methods are applied to 463 categorized
stations to examine the UHI effect on temperature change in
east China during 19812007.
[
11] The rest of the paper is organized as follows: section 2
provides geographical information on the study area and a
detailed explanation of the data and analysis approaches,
section 3 presents the results obtained, and section 4 pro-
vides a brief discussion of the results and presents the con-
cluding remarks.
2. Description of the Study Area, Data,
and Analysis Methods
2.1. Study Area
[
12] In general, east China is a geographical and loosely
defined cultural region that covers the eastern coastal area
of China. In the current research, east China is selected as
the one administratively defined by the Chinese govern-
ment, including the provinces of Anhui, Fujian, Jiangsu,
Jiangxi, Shandong, and Zhejiang, as well as the municipality
of Shanghai (Figure 1). This region comprises part of the
North China Plain in the north, the Yangtze River Delta
Plain at the center, and the mountainous area in the south.
In particular, the urban agglomeration in the Yangtze River
Delta has the highest city density and urbanization level
in China. It is composed of Shanghai and 14 prosperous
cities in Jiangsu and Zhejiang provinces where four city
clusters, namely, NanjingZhenjiangYangzhou, Suzhou
WuxiChangzhou, Shanghai, and Hangzhou Bay, form a
belt with a zigzag shape.
2.2. Data
[
13] Monthly mean surface air temperature data from 463
weather and climate stations were used in the study. The
Table 2. Thresholds of Nighttime Lights for Urban Areas at
Different Time Slices Over Each Province
1992 1996 1998 2002 2004
Anhui 6 6 6 7 7
Fujian 12 15 16 18 22
Jiangsu 11 13 15 17 23
Jiangxi 6 6 7 7 9
Shandong 10 11 13 14 18
Shanghai 33 38 47 57 57
Zhejiang 12 14 19 26 30
Figure 3. Changes in the number of station groups derived from nighttime light and population census
data from 1992 to 2007.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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data cover the period 19812007 and include observations
at elevations below 500 m from national reference climate
stations, national basic weather stations, and ordinary weather
stations (Figure 1). Obvious inhomogeneity caused by site
relocation was adjusted for 21 stations.
[
14] In addition, the NNR 2 m air temperature data for
the same period were used. Following the same techniques
and procedures put forward by Kalnay and Cai [2003] in
processing data, we linearly interpolated the NNR temper-
ature data into the data from the observational station sites.
In the analysis, the annual cycles were removed from both
station observations and the NNR data, and only the anoma-
lies were considered, which reduces the effect of systematic
errors in the reanalysis.
[
15] Nighttime light imagery was obtained from the
DMSP/OLS. This sensor is sensitive to the faint nighttime
lights produced by cities, towns, fire, and lightning, making
it unique among environmental remote sensing satellites.
The Version 4 stable nighttime light products (19922007)
with 1 km spatial resolution, downloaded from the National
Geophysical Data Center [http://www.ngdc.noaa.gov/dmsp/
download.html], were used to classify the stations. The
imagery shows lights from cities, towns, and other sites with
persistent lighting, including gas flares. The DMSP/OLS
image has digital number values ranging from 0 to 63. Areas
with zero cloud free observations are represented by the
value 255.
2.3. Classification of Stations
[
16] Classifying stations into different types is a key issue
in UHI research. In the present study, DMSP/OLS nighttime
light data from 1992 to 2007 were employed to dynamically
categorize urban and rural stations. The large nighttime light
value is most likely representative of urban areas. Figure 2
shows the number of pixels with nighttime light values larger
than 6 for 1992, 2000, and 2007 over east China. From 1992
to 2000, the number of pixels with values larger than 6 slightly
increased from about 100,000 to 109,000. However, the
number of pixels with large values grew to more than 300,000
in 2007, indicating that the urbanization process intensified
after 2000.
[
17] He et al. [2006] developed a technique for quickly
and efficiently deriving urban land information from the
nonradiancecalibrated DMSP/OLS nighttime light imag-
ery and statistical data of the administrative, unitbased
urban land area in China. In the present study, we essentially
followed the approach of He et al. [2006] to determine the
urban thresholds under two basic premises:
[
18] (1) The existing statistical data of the administrative,
unitbased urban land area for Shanghai and the other six
provinces can largely reflect the quantitative characteristics
of the urban land in each province as a whole; hence, the
derived total urban land area from the DMSP/OLS imagery
should be close or equal to the urban land area from the
statistical data.
[
19] (2) Within the 1 km resolution DMSP/OLS imagery,
the derived urban land in east China has been continuously
growing year by year, so an urban patch detected in the
earlier DMSP/OLS imagery should remain in later imagery.
[
20] Within a particular province, the nu mber of grids
(i.e., 1 km × 1 km) is accumulated according to the nighttime
light value at the grid from the maximum in a descending
order; once the area (total number of grids) is approximately
equal to the statistical urban land area, the nighttime light
value at the last grid accumulated is defined as the threshold
for an urban area, i.e., a grid with nighttime light value no
less than the threshold. Table 2 provides the nighttime light
thresholds for urban land at different time slices for each
province. The thresholds of urban land are consistent with
the local economic development levels. Shanghai has the
highest level of urbanization. The Yangtze River Delta region
of Jiangsu and Zhejiang and the coastal area in Fujian also
have higher urbanization levels. Nighttime lights over
Shandong are numerous because of its huge population.
Inland regions, such as Anhui and Jiangxi, have relatively
lower urbanization levels.
[
21] To determine the type of station, the means of
nighttime light value over a circular area with the center at
the station were calculated for radii of 1, 2, 3, 4, 5, 6, 7, 8, 9,
10, 15, and 20 km. As observe d, the means of nighttime light
values no longer change much after the radius increases to
7 km. Therefore, the mean of nighttime light values over a
Figure 4. Defense Meteorological Satellite Program Oper-
ational Linescan System (DMSP/OLS) nighttime light imag-
ery and distributi on of stations for six categories in 1992,
1997, 2002, and 2007 in east China.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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circular area with a 7 km radius around the station was used
to identify the type of station. If the mean for a station
exceeds the threshold of its province, the station is catego-
rized as an urban station; otherwise, it is classified as rural.
[
22] Furthermore, urban stations classified by the above
mentioned approach were grouped into four types using
nonagricultural population data from China City Statistical
Yearbook, including (1) a small city with a nonagricultural
population of 0.010.1 million, (2) a mediumsized city with
a nonagricultural population of 0.10.5 million, (3) a large
city with a nonagricultural population of 0.51.0 million, and
(4) a metropolis with a nonagricultural population of over
1.0 million. Suburban stations were classified based on their
proximity to a large city or metropolis within a radius of
30 km and a continuous nighttime light patch.
[
23] Figure 3 shows the station number of each group
from 1992 to 2007. Rural stations accounted for more than
60% of the total number of stations in 1992. During the
rapid urbanization in east China over the past two decades,
many rural stations were converted into small cities within a
very short period. The number of small cities exceeded the
number of rural stations in 2004. In 2007, the number of
rural stations accounted for only about 30%.
[
24] Figure 4 shows the classification of stations and the
DMSP/OLS nighttime light imagery in 1992, 1997, 2002,
and 2007 over east China. The most significant urbanization
occurred in the Yangtze River Delta, followed by Jiangsu,
Shandong, and the coastal area.
2.4. Calculation of Time Series of the Surface Air
Temperature Anomalies for Different Station Categories
[
25] In the current research, we primarily considered the
effect of gradually growing urbanization on regional average
temperature series and developed a new method to calculate
the time series of temperature anomalies for each station
category. The surface air temperature anomalies with respect
to the mean annual cycle (based on a 27 year climatology)
for station observations and the NNR were calculated. The
temperature anomalies were then averaged over six station
categories to create six time series according to the dynamic
classification for 19922007. Because the DMSP/OLS night-
time light data were recorded on ly beginning in 1992, the
Figure 5. DMSP/OLS nighttime light imagery of 2007 and annual mean t emperature trends from
(a) station observations (OBS), (b) the NCEP/NCAR Reanalysis (NNR), (c) the observation minus reanal-
ysis (OMR) in °C decade
1
at sites located below 500 m in east China; 0.578°C, 0.438°C, and 0.140°C
decade
1
are the mean trends of all OBS, NNR, and OMR, respectively.
Figure 6. Correlation coefficients between the surface air
temperature for station observations and the NNR; 0.876
is the average.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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categorization applied to stations before 1992 was that used
for 199 2. All trends were calculated using simple linear
regression, and the degree of significance was assessed using
related P values.
2.5. Urban Minus Rural (UMR) Analysis
[
26] Referring to Karl et al. [1988] and considering the
large population in east China, we created a pair of urban
and rural stations by selecting the rural station within a
radius between 50 and 100 km for mediumsized and small
cities and between 100 and 150 km for metropolises, large
cities, and suburban areas. For a given urban station, there
may be several station pairs, and in that case their average is
calculated. Because some rural stations were transformed
into urban stations, the number of station pairs may have
changed in a particular year from 1992 to 2007. The time series
of the surface air temperature anomalies and the UMR were
calculated in a man ner similar to that described in section 2.4.
3. Results
3.1. Temperature Trends
[
27] Figures 5a and 5b show the 27 year surface air
temperature linear trend (in °C decade
1
) of the station
Figure 7. Observational, NNR, and OMR time series of temperature anomalies for each of the station
groups in east China during 19812007; denoted are temperatures from station observations (solid lines
with squares) and NNR (solid lines with dots), OMR (bars), and OMR linear trends (gray lines).
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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records and the NNR for 463 sites located at elevations
below 500 m in east China, as well as the DMSP/OLS
nighttime light imagery in 2007. The station observations
(Figure 5a) indicate that, on average, the mean temperature
in east China reflects an increase by a rate of 0.578°C
decade
1
over the past 27 years, with the warming pattern
closely related to the intensity of the nighttime lights. Sta-
tions in the urban agglomeration of the Yangtze River
Delta, where the nighttime light values are highest, show
the most significant warming trends. Temperatures in
mediumsized city stations with relatively high nighttime
lights also display rapid increases. Conversely, sites with
weak warming trends are mainly rural or small city stations,
which are mostly located in the provinces of Anhui, Jiangxi,
and Fujian, where the economy is less developed and
the nighttime lights are low. A broad range of spatial dis-
parity exists in the temperature linear trends over east
China, with a minimum of 0.183 and a maximum of
1.100°C decade
1
.
[
28] By contrast, the NNR estimation (Figure 5b) shows
that, as a whole, the temperature in east China reflects an
increase at a mean warming rate of 0.438°C decade
1
from
1981 to 2007; spatial difference ranges from 0.225°C to
0.602°C decade
1
. Note that the NNR temperature in the
Yangtze River Delta also shows the strongest warming trend
but cannot reach the amplitude from the station observa-
tions. The spatial disparity in the NNR temperature trends is
substantially less than that in the station observations,
indicating possible effects from atmospheric circulation and
GHG concentration.
[
29] Figure 6 displays the correlation between the surface
temperature from the station observations and from the
NNR at the stations located at elevations below 500 m. The
average correlation of 0.876 implies that the NNR may
identify surface temperature variations caused by atmo-
spheric circulation such as storms, advection of warm/cold
air, and variations in the frequency or track of major storms.
Given that the NNR is not affected by changes in surface
properties, it can be used in the OMR method to estimate the
urbanization effects on temperature change [Kalnay and
Cai, 2003].
3.2. Effect of Urbanization on Surface Air
Temperature Changes Over East China
[
30] Figure 5c shows the difference between the obser-
vational and NNR trends (i.e., OMR). The distribution of
the OMR trends are quite similar to that of the observational
temperature change in Figure 5a, indicating a significant
effect of urbanization on surface warming in east China in the
past 27 years. There are 66 stations showing cooling (nega-
tive) OMR trends, and most of them are located in areas with
low nighttime light values. Other landuse changes such
as croplands with irrigation may have contributed to these
negative OMR trends.
[
31] Figure 7 presents the time series of annual mean tem-
perature anomalies from the station observations, the NNR,
and their differences (OMR) averaged over six station groups
(as defined in section 2.4) for the period 19812007. Both the
station and NNR annual mean temperatures show increasing
trends. The NNR agrees well with the station observations in
Table 3. Tempe rature Trends From Station Observations and
NNR, and t he Differences Between the O bservations and NNR
(i.e., OMR) for Different Station Grou ps in East China, 1981
2007, With Statistical Significance at 0.05 (Single Asterisk) and
0.01 (Double Asterisk)
a
Annual
Spring
(MAM)
Summer
(JJA)
Autumn
(SON)
Winter
(DJF)
Rural
OBS 0.502** 0.556** 0.255* 0.446** 0.757**
NNR 0.421** 0.435** 0.252** 0.386* 0.610*
OMR 0.081 0.121 0.003 0.060 0.147
Suburban
OBS 0.651** 0.745** 0.464** 0.614** 0.780**
NNR 0.513** 0.562** 0.350** 0.516** 0.623**
OMR 0.138 0.183 0.114 0.098 0.157
Small city
OBS 0.614** 0.646** 0.356** 0.594** 0.854**
NNR 0.447** 0.467** 0.295** 0.413* 0.612*
OMR 0.167 0.179 0.061 0.181 0.242
Medium city
OBS 0.674** 0.722** 0.424** 0.644** 0.900**
NNR 0.460** 0.471** 0.306** 0.436* 0.627*
OMR 0.214 0.251 0.118 0.208 0.273
Large city
OBS 0.742** 0.836** 0.527** 0.721** 0.883**
NNR 0.482** 0.576** 0.328** 0.448** 0.576*
OMR 0.260 0.260 0.199 0.273 0.307
Metropolis
OBS 0.904** 1.010** 0.689** 0.841** 1.077**
NNR 0.506** 0.525** 0.377** 0.513** 0.611*
OMR 0.398 0.485 0.312 0.328 0.466
a
The unit of measure is °C decade
1
.
Table 4. Urban Minus Rural (UMR) Results for Station Observa-
tions, With Statistical Significance at 0.05 (Single Asterisk) and
0.01 (Double Asterisk)
a
Annual
Spring
(MAM)
Summer
(JJA)
Autumn
(SON)
Winter
(DJF)
Suburban
Urban 0.651** 0.745** 0.464** 0.614** 0.780**
Rural 0.551** 0.652** 0.355** 0.520** 0.759**
UMR 0.100 0.093 0.109 0.094 0.021
Small city
Urban 0.611** 0.646** 0.363** 0.588** 0.840**
Rural 0.534** 0.571** 0.299* 0.495** 0.773**
UMR 0.077 0.075 0.064 0.083 0.067
Medium city
Urban 0.670** 0.714** 0.432** 0.633** 0.878**
Rural 0.535** 0.562** 0.319* 0.507** 0.767**
UMR 0.135 0.152 0.113 0.126 0.111
Large city
Urban 0.802** 0.897** 0.605** 0.772** 0.884**
Rural 0.595** 0.693** 0.372** 0.549** 0.759**
UMR 0.207 0.204 0.233 0.223 0.125
Metropolis
Urban 0.870** 0.925** 0.637** 0.823** 1.011**
Rural 0.585** 0.653** 0.358* 0.550** 0.781**
UMR 0.285 0.272 0.279 0.273 0.230
a
The unit of measure is °C decade
1
.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
8of12
identifying the interannual variability and longterm warm-
ing trends for all the station groups. Nevertheless, the station
observations exhibit a stronger warming trend than does
the NNR. As a result, the OMR shows a positive trend, with
the strongest being in the metropolis group, followed by the
large city, mediumsized city, small city, and suburban
stations. The rural stations show the weakest trend. The
most substantial increase in OMR value occurred after the
early 2000s, implying a significant effect of rapid urbani-
zation on surface air temperature change during this period.
[
32] Table 3 presents the temperature trends from the
station observations and NNR, along with their differences
(OMR). Table 4 shows the temperature trend differences
between urban and rural sites (UMR); the differences were
derived from the station observations. In Tables 3 and 4, the
values for spring, summer, fall, and winter are shown, along
with the annual means for each station group.
[
33] From 1981 to 2007, the largest increase in station
observed annual mean surface temperature occurred at the
metropolis stations with an annual linear trend of 0.904°C
decade
1
. From the sites of large to small cities, the linear
trends show monotonic descent from 0.742°C to 0.614°C
decade
1
. The rural sites display the weakest warming trend
with a rate of 0.502°C decade
1
. The NNR annual mean
temperature changes, which reflect those mainly associated
with changes in circulation and greenhouse warming, gen-
erally exhibit a weaker warming trend than do station
observations. In addition, the NNR trends among various
station groups are nearly uniform, with a low range of
0.421°C to 0.513°C decade
1
(Table 3).
[
34] The annual OMR trends, which may be attributed to
the intense UHI effect in the past 30 years, indicate strong
warming in metropolises and large cities with averages of
0.398°C and 0.260°C decade
1
, contributing 44.027% and
35.040%, respectively, to total warming. The OMR trends
of mediumsized cities, small cities, and suburban areas
display moderate warming with 0.214°C, 0.167°C, and
0.138°C decade
1
, contributing 31.751%, 27.199%, and
21.1980%, respectively, to total warming. The OMR trend
for rural stations shows almost imperceptible warming with
0.081°C decade
1
.
[
35] The seasonal mean temperature trends from station
observations and NNR of various station groups (Table 3)
all appear to be most significant in winter, followed by
spring and fall; relatively weak warming is observed in
summer. In the four seasons, the strongest warming trends
in the station observations all occur at the metropolis sta-
tions, while the weakest warming trends occur at rural sta-
tions; the NNR seasonal temperature does not show the
same pattern. In addition, the seasonal OMR trends also
depict strong warming in metropolises and large cities and
weak warming in rural stations. Generally, the OMR trends
in the winter half year are stronger than those in the summer
half year.
[
36] From the difference in observational temperature
trends between urban and rural stations (i.e., UMR, Table 4),
the annual mean UMR trends are 0.285°C decade
1
for
metropolises, 0.207°C decade
1
for large cities, 0.135°C
decade
1
for mediumsized cities, and 0.100°C decade
1
for
suburban sites. These reflect a contribution of 32.759%,
25.810%, 20.149%, and 15.361%, respectively, to total
warming. The lowest annual UMR trend can be seen over
small cities at 0.077°C decade
1
, which accounts for
12.602% of total warming. Similarly, the seasonal mean of
the UMR trends from the station observations are highest in
metropolises and lowest in small cities. Compared with the
OMR trend in Table 3, the UMR results are lower possibly
because the temperature trends of rural stations close to
urban sites are stronger than those reflected by the NNR,
especially in metropolises, large cities, and suburban areas.
The OMR results in Table 3 indicate that a mean warming
of 0.081°C decade
1
occurs over rural stations in east China.
If this value is added to the UMR, the results will be quite
consistent with those of the OMR. Therefore, both methods
Figure 8. Mean OMR (°C decade
1
) at different time slices: (a) 19811990, (b) 19912000, and
(c) 20012007. The DMSP/OLS nighttime light imagery of 2007 is also shown in Figure 8c.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
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indicate that intense urbanization imposes significant effects
on surface air temperature change in east China.
[
37] Of the four seasons, summer reflects the largest con-
tribution of urban warming to total warming from the UMR,
whereas winter shows the smallest contribution. The small
contribution in winter indicates that the UHI is not a major
contributor to the rapid wintertime warming in east China,
which agrees with the result obtained by Ren et al. [2008] for
north China.
[
38] Following the procedure of Kalnay and Cai [2003],
we computed the mean OMR in each decade for every
station (Figure 8). During 20012007, the mean OMR value
for almost all the stations was the largest in comparison with
those of the 1980s and 1990s. The mean OMR value shows
a spatial pattern highly similar to the linear trends of the
observational surface air temperature in Figure 5a and the
OMR trends in Figure 5c. The OMR and the linear obser-
vational temperature trend both show significant surface
warming induced by urbanization in east China, especially
in the Yangtze River Delta and coastal areas, with the most
intensive UHI effect occurring in the 2000s.
[
39] We computed the ratio between the OMR (Figure 5c)
and the observational temperature trends (Figure 5a) for
each station to determine the contribution rate of urbaniza-
tion on observational surface air temperature change in east
China during 19812007 (Figure 9). In the urban agglom-
eration in the Yangtze River Delta, urbanization has con-
tributed more than 40% to climate warming since 1981. The
rapid development of coastal cities in Zhejiang and Fujian
has also contributed to recent warming. The contribution
rates are also relatively large in Shandong Province because
of its huge population. Note that the contributions of the
UHI effect in many stations over Jiangxi Province are also
quite large. Nonetheless, the reasons for such occurrences
require further investigation.
4. Conclusions and Discussion
[40] On the basis of the partly homogeneityadjusted monthly
mean temperature data of 463 stations (including national sta-
tions and ordinary weather stations) located at elevations below
500 m, we conclude that the annual mean temperature over
east China increased at a rate of 0.578°C decade
1
from 1981 to
2007. With an increase rate of 0.438°C decade
1
derived from
the NNR during the same period, the contribution of urbaniza-
tion and other land uses to overall regional warming is deter-
mined to be 24.22%. The results from the OMR method are
generally quite consistent with those from the UMR.
[
41] In the current study, an objective and fast method was
developed to dynamically classify urban and rural stations
based on DMSP/OLS nighttime light data and GIS tech-
nology. Among the nonrural station groups, metropolis
stations exhibit the strongest warming trends in annual and
seasonal mean temperatures, as well as the most significant
UHI effect. Annual mean urbanization warming reaches
0.398°C decade
1
as detected by the OMR and 0.285°C
decade
1
as determined by the UMR, accounting for
44.027% of total warming as measured by the OMR and
32.758% as determined by the UMR. The annual mean UHI
warming rates estimated for the other city station groups are
also significant, with 0.260°C, 0.214°C, and 0.167°C
decade
1
for large cities, mediumsized cities, and small
cities as determined by the OMR and 0.207°C, 0.135°C, and
0.077°C decade
1
, respectively, as measured by the UMR.
[
42] Compared with studies on the UHI warming over other
regions in China (Table 1), our investigat ion adopted muc h
denser da ta sites. As a result, a more eviden t UHI effect on
temperature trend s was obtained . The clarity of results obtained
may be attributed to the availability of more rural observa-
tional sites, reasonable calculation of background warming rate
based on dynamic station classification approach, the choice of
a time period with the most rapid urbanization, and consider-
ation of the growing UHI effect under constant urban devel-
opment. Moreover, there is very good agreement between the
observed and NNR temperature anomaly over rural stations
(Figure 7f), showing that good regiona l background tempera-
ture change was captured for both observations and the NNR
over rural area s. Such results can also be attributed to the
objective and dynam ic cl assification of stations.
[
43] As meteorological stations in China were mostly set
up near cities or towns, and finding rural stations completely
free of UHI effects is difficult, the rural stations selected in
the present study are the only currently available stations
that are relatively less influenced by the UHI effects.
Because of the limited coverage period of the DMSP/OLS
Figure 9. Percentage of contribution from urbanization to
the surface air temperature change during 19812007 and the
difference in DMSP/OLS nighttime lights between 2007 and
1992.
YANG ET AL.: WARMING INDUCED BY UHI D14113D14113
10 of 12
nighttime light data, the growing UHI effect before 1992
was excluded. In effect, the UHI warming trends and their
contributions to the overall warming over east China pro-
vided in this paper can still be regarded as conservative.
[
44] Urban heat islands differ from city to city because of
the varied features and background climatic characteristics
of each site. Note that although the UHI is a reality, what
matters is not the temperature bias introduced by the town
but whether the bias changes over time, which can occur if
the surroundings of the sites change slowly [Strangeways,
2009]. Therefore, detailed information on the sites and their
surroundings is crucial when conducting climate research.
Satellite observations of nighttime light emissions can pro-
vide comparatively objective information on urban devel-
opment but cannot provide a broad range of details on the
physical nature of the sites and their surroundings. The cur-
rent study draws attention to an important issue in the eval-
uation and classification of weather station sites and in the
investigation of the regional UHI effect. Extensive remote
sensing observation of sites and their surroundings will be
needed in future work.
[
45] The investigation of the effects of urbanization and
other landuse changes on local and regionalscale climate
change is an urgent requirement in some regions, such as
China, where rapid urbanization has occurred. However, the
role of land use such as urbanization in climate warming is a
key climaterelated factor that has not been widely covered
by the media. For instance, the mean surface air temperature
trend of seven metropolis stations in Shanghai was 0.961°C
decade
1
from 1981 to 2007, whereas the NNR trend was
0.568°C decade
1
in the same period, indicating an amplifi-
cation of the background warming rate of 0.4°C decade
1
caused by the UHI effect. Therefore, for metropolises and
large cities in east China, the significant contribution of
urbanization to temperature change may be comparable to
that of GHG concentration, suggesting that landsurface
processes can play a vital role in shaping future climate
change [Feddema et al., 2005]. If such trends continue,
certain metropolitan areas may experience a rate of warming
well beyond the range projected by the global climate change
scenarios of IPCC [Stone, 2007]. The increasing divergence
between urban and rural surface temperature trends high-
lights the limitations of the response policy to climate change;
these policies focus only on GHG reduction [Stone, 2009].
Policymakers need to address the impact of land use such as
urbanization and deforestation on climate change in addition
to that of GHG emissions. Serious measures for broadening
the range of management strategies beyond GHG reductions
and a landbased mitigation framework should be included in
the scheme for mitigating climate change [Betts, 2007; Pielke
et al., 2002; Stone, 2009]. The results presented in the current
work suggest that a more complete metrics for the repre-
sentation of anthropogenic contributions to climate change
should be developed. The effects of landsurface conditions
and other processes should be considered as well in climate
change mitigation strategies in east China.
[
46] Ac knowledgments. The autho rs are very grateful to the three
anonymous reviewers for their helpful comments and constructive suggestions,
which led to a significant im proveme nt of the origin al manu script. Thi s
work was supported by the Climate Change Special Foundation of the China
Meteorological Administration (grant CCSF0910), National Natural Science
Foundation of China (grants 41001023 and 40801043), an d Special Fund for
Meteorological Science and Technology of Zhejiang Province, China (grant
KF2008001).
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... China have undergone urbanization (Jiang et al., 2020;. It implied spatial representative error in the trend of temperature, which still need to be resolved. Studies have used methods, such as matchmaking between urban and rural stations, to quantify the effect of urbanization on station temperature observations (Karl et al., 1988;Wen et al., 2019;X. Yang et al., 2011). For example, used 43 reference stations of 763 observation stations to adjust the urbanization bias. Ren et al. (2008) used 282 weather stations to estimate the contribution of urban warming to total annual mean surface air temperature change as 37.9%. Tysa et al. (2019) classified weather stations into six urbanization levels and quan ...
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... With global warming and rapid urbanization, large-scale urban expansion and population growth are accompanied by a series of environmental and ecological problems [1][2][3], such as heat island effect [4][5][6], air pollution [7], and vegetation degradation [8,9]. Rapid urban population growth and limited urban space have led to compact and high-density building layouts in most cities [6,10]. ...
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... Studies have demonstrated that anthropogenic factors-particularly, greenhouse gas emissions-are the primary drivers of observed warming Sun et al., 2014;Wang et al., 2020;Wen et al., 2013), and urbanization mimics the warming effects of increased atmospheric greenhouse gas concentrations (Parker, 2010;Wang & Yan, 2016). This issue is especially pronounced at the local level, as urban development accounts for more than 40% of the detected warming in some areas (Yang et al., 2011). For example, in China, urban expansion since the 1980s has had a disproportionate effect on temperature. ...
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