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Atmospheric Environment 45 (2011) 5778e5783

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Atmospheric Environment

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Quantitative estimates of warming by urbanization in South Koreaover the past 55 years (1954e2008)

Maeng-Ki Kim a,*, Seonae Kimb

aDepartment of Atmospheric Science, Kongju National University, Gongju 314-701, Republic of KoreabApplied Meteorology Research Team, Environmental Prediction Research Inc., Daejon 302-831, Republic of Korea

a r t i c l e i n f o

Article history:Received 20 October 2010Received in revised form8 July 2011Accepted 12 July 2011

Keywords:Urban warmingGlobal warmingEmpirical Orthogonal FunctionKoreaUrbanizationMean temperature

* Corresponding author. Tel.: þ82 41 850 8531; faxE-mail address: mkkim@kongju.ac.kr (M.-K. Kim).

1352-2310/$ e see front matter � 2011 Elsevier Ltd.doi:10.1016/j.atmosenv.2011.07.028

a b s t r a c t

The quantitative values of the urban warming effect over city stations in the Korean peninsula wereestimated by using the warming mode of Empirical Orthogonal Function (EOF) analysis of 55 years oftemperature data, from 1954 to 2008. The estimated amount of urban warming was verified by applyingthe multiple linear regression equation with two independent variables: the rate of population growthand the total population.

Through the multiple linear regression equation, we obtained a significance level of 0.05% anda coefficient of determination of 0.60. This means that it is somewhat liable to the estimated effects ofurbanization, in spite of the settings of some supposition. The cities that show great warming due tourbanization are Daegu, Pohang, Seoul, and Incheon, which show values of about 1.35, 1.17, 1.16, and1.10 �C, respectively. The areas that showed urban warming less than 0.2 �C are Chupungnyeong andMokpo. On average, the total temperature increase over South Korea was about 1.37 �C; the amount ofincrease caused by the greenhouse effect is approximately 0.60 �C, and the amount caused by urbanwarming is approximately 0.77 �C.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

The IPCC reported that global mean temperature increasedabout 0.76 (�0.18) from 1906 to 2005. The average temperatureincrease in East Asia is about 1 �C greater than the global average,and the temperature increase in South Korea is estimated to beabout 1.5 �C greater than East Asia (IPCC, 2007). Because SouthKorea industrialized rapidly, the possibility for greater increase inwarming by urbanization is high. On average, urban temperaturesmay be 1e3 �C warmer than rural temperatures, but underappropriate meteorological conditions (calm, cloudless nights inwinter), urban air temperatures can exceed those of surroundingareas by more than 10 �C (Oke, 1981). Globally or regionallyreported annual urbanwarming rates vary in the range from 0.01 �Cto 0.42 �C per decade with different sets of station data, homoge-neity in contrasting techniques of averaging the basic data, anddifferent approaches to how the data is considered (Jones et al.,2008; Choi et al., 2003).

The area-weighted average of the warming effect of urbanheating over land during the 20th century was estimated to be

: þ82 41 856 8527.

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0.06 �C per century globally and approximately 0.06e0.15 �C percentury in the U.S. based on differences in temperature trendsbetween rural and urban stations (Zhou et al., 2004). Regardingurban warming in South Korea, some previous studies haveexamined quantitative estimation of warming values due to theeffect of urban bias in South Korea (Lee and Kang, 1997; Kim et al.,1999; Youn et al., 2004; Oh et al., 2004). These studies showed thatthe warming rate caused by the effect of urban bias in South Koreais as great as 0.3e0.48 �C, indicating that urbanwarming there maybe greater than global urban warming. Thermal differences causedby urbanization can lead to local climate change, such as boundarylayer circulation between urban and non-urban areas, and canincrease the occurrence of extreme climate (Kim and Baik, 2002;Ha et al., 2004; Alexander et al., 2006).

Estimating quantitative temperature increase due to urbaniza-tion is important to predict the warming caused by the greenhouseeffect. The purpose of this paper is to produce estimated values forwarming due to urban bias from observation data in South Korea.

2. Data and methods

Estimation of the temperature increase caused by the urbani-zation effect and the greenhouse effect requires data for a relativelylong term (Kim et al., 1999). Although the accumulation of weather

Fig. 1. Observation stations in South Korea.

M.-K. Kim, S. Kim / Atmospheric Environment 45 (2011) 5778e5783 5779

data for the Korean peninsula began in the early 1900s, data fromonly four stations are available for 100 years from 1909 to 2008(Table 1). This data is referred to as K0004, which depicts theobservation period and the number of stations. Data from the early1900s deals with fewer stations for evaluating the urbanizationeffect and the greenhouse effect. Since 1969, the Korea Meteoro-logical Administration (KMA) has collected observation data from20 stations, but this period is too short for evaluating the twoeffects. For the case of 12 stations, data collected for 55 years from1954 to 2008 is available. This data is referred to as K5512. Finally,for the case of 20 stations, data collected for 40 years from 1969 to2008 is available and is referred to as K4020. Fig. 1 shows a map ofthe 20 stations used in this study. In the case of K0004, there ismissing data regarding Seoul due to the Korean War.

To consider the effects of urbanization, the total populationinvestigation data was obtained from the National Statistical Officeof Korea. Populationwas investigated in 5-year intervals during the50-year period (1955e2005). Typically, warming by urbanization isestimated using population or population growth (Kim et al., 1999).However, Karl et al. (1988) and Fujibe (2011) suggested thata population of at least 10,000 is enough to enable the effect ofurbanization. Thus, we examined the urbanization effect using thesize of city populations as well as the growth of the population inthis study.

Regarding the order of the study, the first step was to calculatethe temperature growth of K0004, K5512, and K4020 eachaccording to the number of stations and the period of observation.However, the total amount of temperature growth is not thegrowth by urbanization, so the temperature growth of urbanizationshould be extracted from the total amount of temperature growthin order to obtain greenhouse warming. Three sets of data fortemperature growth were performed through EOF Analysis toobtain the eigenvector and time coefficient. By comparing each EOFmode, the three sets with different numbers of stations andobservation periods determined the similar mode of the eigen-vector and time coefficient. This mode, which interprets theincrease of temperature from the three data sets shown incommon, is assumed as a global warmingmode associatedwith thegreenhouse effect. We can figure out the time series to describe thewarmingmode by linear combination between the eigenvector and

Table 1Total linear trend for the periods of 40, 55, and 100 years.

Period station 1969e2008 1954e2008 1909e2008

(�C/40yr) (�C/55yr) (�C/100yr)

(1) Busan 1.27 1.23 1.61(2) Mokpo 0.88 0.60 1.06(3) Seoul 1.75 1.87 2.39(4) Daegu 1.94 2.07 2.40(5) Gangneung 1.43 1.50(6) Jeonju 1.41 1.17(7) Gwangju 1.47 1.26(8) Yoesu 1.20 1.00(9) Ulsan 1.67 1.66(10) Chupungnyeong 0.64 0.42(11) Pohang 1.78 1.88(12) Incheon 2.00 1.78(13) Suwon 2.34(14) Chuncheon 1.14(15) Cheongju 2.27(16) Sokcho 0.88(17) Seosan 0.80(18) Tongyeong 1.13(19) Gunsan 1.04(20) Daejeon 1.75

Mean 1.44 1.37 1.87

time coefficient of the warming mode. The temperature increaseobtained above can be thought of as the temperature growthcaused by the greenhouse effect. Subtracting the temperatureincrease caused by greenhouse effect from the total temperatureincrease yields the temperature increase caused by the urbaniza-tion effect.

The second step was to investigate the correlation with pop-ulation change in order to judge the credibility of warming byurbanization, which is obtained from the global warming mode ofEOF. We set the estimated amount of warming by urbanization asa dependent variable, and the population growth rate and thenumber of people as independent variables. After that, we con-ducted multiple linear regression analysis. We obtained the esti-mation of warming by urbanization by using population data andthen checked the suitability of the temperature increase caused bythe urbanization effect.

3. Results

3.1. Global warming effect and urban warming effect in Korea

Table 1 shows the total temperature growth of the three sets:K0004, K5512, and K4020. K0004 and K5512 show linearity, butlinearity between K5512 and K4020 is not consistent. Kim et al.(1999) similarly mentioned that the amount of temperatureincrease in K4020 is largely controlled by variance in temperaturebetween the first few years and the last few years.

Analysis of each temperature growth by the EOF explains thatthe first modes of the 3-set EOF have a major part of the temper-ature increase with a rate of approximately 90%. Fig. 2 shows thefirst eigenvector (a) and time coefficient (b) of each set determinedby EOF analysis. The absolute values of the eigenvector in the threesets are different, but the pattern is similar, and the differencebecomes less for shorter periods. The time coefficient has verysimilar variability, although its absolute value is different. Becausespatial distribution and time distribution seem to show a similarpattern, regardless of the number of stations and the period ofobservation, this study assumed that this mode is the globalwarming mode. Although the three sets in the case of K0004 are

Fig. 2. (a) The eigenvector values of the first mode of the EOF for 40 (closed rhombus), 55 (closed square), and 100 (closed triangle) years, respectively. (b) Associated time series ofthe first mode for 40 (solid line), 55 (bold dashed line), and 100 (light dashed line) years, respectively. Station names corresponding to station numbers in (a) are indicated in Table 1.

M.-K. Kim, S. Kim / Atmospheric Environment 45 (2011) 5778e57835780

long-term data, these lack representativeness, because they arefrom too few stations and are mostly from large cities. In the case ofK4020, the period of observation was relatively short, so it has theproblem of being easily affected by vibration of a longeperiod cycle.Therefore, we used the results of the K5512 data, which havea suitable period and number of observation stations. As explainedSection 2, we first utilized the eigenvector and time coefficient ofthe global warmingmode (the first mode) to obtain the linear trendof the temperature time series reconstructed from the first mode(RC1 in Table 2) from the K5512 data. But, the linear trendRC1 requires calibration, because RC1 is greater than the totaltemperature warming (TW) in Table 2. This study supposed that

Table 2Greenhouse warming amounts (GW) and Urban warming (UW) for the period from1954 to 2008 for 12 stations in Korea. EV and RC1 are the eigenvector of the firstmode of the EOF and the linear trend of time series reconstructed from the firstmode of the EOF, respectively.

Station EV TW(�C/55yr)

RC1(�C/55yr)

GW(�C/55yr)

UW(�C/55yr)

(1) Busan 0.29 1.23 1.33 0.58 0.65(2) Mokpo 0.28 0.60 1.01 0.44 0.16(3) Seoul 0.29 1.87 1.62 0.71 1.16(4) Daegu 0.29 2.07 1.64 0.72 1.35(5) Gangneung 0.28 1.50 1.45 0.63 0.87(6) Jeonju 0.29 1.17 1.30 0.57 0.60(7) Gwangju 0.29 1.26 1.30 0.57 0.69(8) Yoesu 0.30 1.00 1.19 0.52 0.48(9) Ulsan 0.29 1.66 1.47 0.64 1.02(10) Chupungnyeong 0.27 0.42 0.96 0.42 0.00(11) Pohang 0.29 1.88 1.63 0.71 1.17(12) Incheon 0.29 1.78 1.55 0.68 1.10

Mean 0.29 1.37 1.37 0.60 0.77

Chupungnyeong (which has a population of less than 10,000) is noturbanized, and then referred to the value of the total temperatureincrease as the increased amount due to the greenhouse effect.

In this study, the assumptions are that there is no urbanizationin Chupungnyeong indicating that TWand GW values are the same.Thus, the calibration ratewas the ratio of the TW value to the RC1 ofChupungnyeong. Temperatures during the 55-year period haveincreased 0.42 �C in total, and the RC1 trend during the 55-yearperiod is 0.96 �C, with the ratio of 0.437. The calibrated rate 0.437was multiplied by each RC1 of the stations to obtain GW, and thetotal TW of each observation minus GW is the warming byurbanization (UW). Table 2 shows the total warming of tempera-ture, thewarming by the greenhouse effect, and thewarming of theurbanization effect, respectively. The average of total warming ofthe Korean peninsula is about 1.37 �C; the average of urbanwarming among the total warming is 0.77 �C, higher than green-house warming of 0.60 �C. In particular, large cities such as Seoul,Incheon, and Daegu and the rapidly industrialized cities, such asPohang and Ulsan, showed predominant urban bias. Daegu showedthe greatest value of 1.35 �C, and Pohang, Seoul, and Incheonshowed values of 1.17 �C, 1.16 �C, and 1.10 �C, respectively. On thecontrary, Mokpo showed only a small urbanization effect witha value of 0.16 �C. In contrast, warming trends caused by thegreenhouse effect were similar for all stations.

3.2. Verification of urban warming

This stage validates the amount of urbanization obtained fromthe eigen mode of the EOF using population change. Through thetotal population investigation progressed in 5-year intervals, thenumber of each urban population from 1955 to 2005 was obtained.

M.-K. Kim, S. Kim / Atmospheric Environment 45 (2011) 5778e5783 5781

The population data for each urban area can utilize only during51years for each urban area, so it cannot be directly related to thetemperature increase by urbanization for the last 55 years.However, considering that industrialization occurred mainly in the1960s, most of the temperature increase caused by urbanizationover the last 55 years would have occurred after the 1960s. Thus,assuming that the amount of temperature increase caused byurbanization can be expressed by population change after the1960s, the regression equation formula of increase betweenincrease of temperature by population and urbanization can beexpressed, as follows, using the mode of global warming. Thedependent variable is urbanization warming (TND), and indepen-dent variables are the average population during the total period(POPM) and the population increase rate (POPR), respectively.

TND ¼ aþ b1*POPR þ b2*POPM (1)

In formula (1), a, b1, and b2 are regression coefficients ofmultiple linear regression. However, methods of expressing thepopulation increase rate can be diverse. Karl et al. (1988) said thatthe difference between urban and suburban is proportional to the0.45 square of city population from the analysis, utilizing about1000 observation post data in the U.S. As Karl et al. indicated, 0.45square is a feasible value as an average in case the data are from

Fig. 3. The various types of population increase rates for 12 stations in Korea. (a), (b), (c), andobtained from Eqs. (2), (3) and (4), respectively.

enough observation posts. However, in the case of Korea, thefeasibility of the method is questionable, because there are fewerobservation posts. Therefore, in our study, the following populationincrease rate quotient of the three cases was used to investigate theappropriateness of the regression equation regarding each case.

POPR1 ¼ POP2� POP150

(2)

POPR2 ¼ POP20:45 � POP10:45

50(3)

POPR3 ¼ POP2POP1

� 10050

(4)

In the above equations, POP1, POP2, POPR1, POPR2, and POPR3express the population in 1955, the population in 2005, the growthrate of the population, the square root of the increasing rate of thepopulation, and the population percent growth rate, respectively.Also, the 50 indicates the total period of population data. Theunits of Eqs. (2), (3), and (4) are population/1yr, population0.45/1yr,and %/yr. In the case of Chupungnyeong, the population wasrecorded after 1995, but the population was excluded for 2005,because of its substantially low population of 2614. In Fig. 3, the

(d) indicate the mean population, and the increase rate of population for various types

Table 4Contribution amounts of POPM and POPR3 to UWE. UWE indicates the estimatedurban warming obtained from multiple linear regression Eq. (5) in testing.

Station POPR3 POPM UWE

① ② aþ①þ②

(1) Busan 0.40 0.14 0.67(2) Mokpo 0.25 0.01 0.40(3) Seoul 0.74 0.38 1.25(4) Daegu 0.60 0.10 0.82(5) Gangneung 0.51 0.01 0.65(6) Jeonju 0.59 0.03 0.75(7) Gwangju 0.72 0.05 0.90(8) Yoesu 0.45 0.01 0.59(9) Ulsan 0.66 0.04 0.83(10) Chupungnyeong 0.00 0.00 0.13(11) Pohang 1.10 0.02 1.25(12) Incheon 0.94 0.09 1.16

Mean 0.58 0.07 0.78

M.-K. Kim, S. Kim / Atmospheric Environment 45 (2011) 5778e57835782

calculated population growth rate is based on Eqs. (2), (3), and (4)for each city. POPM is in proportion to POPR1 and POPR2, whilePOPR3 shows the population growth relatively well. Regardingpopulation growth indicated by POPR3, it showed the greatestvalue in order of Seoul, Pohang, and Incheon, and the least value inorder of Yeosu, Mokpo, and Chupungnyeong.

From results of Ryoo andMoon (1995), the population of Pohang(11) grew rapidly, since POSCO(Pohang Iron & Steel Co, Ltd) wasestablished in the mid-1970s. Its population growth rate becameabout 18.7%/yr. Incheon (12) also accelerated the populationgrowth in the late 1960s, as the region emerged as a fast-growingexport hub due to the development of various industries. In thecase of Busan (1), the average of population is high because pop-ulation remained high from 1955 to 2005, but its populationgrowth rate is rather low. Through substitution of POPR of theregression Eq. (1) regarding Eqs. (2), (3), and (4), the fidelity of theregression equation was investigated. The regression coefficientsobtained through multiple linear regression, significance level, andthe coefficient of determination are obtained by using each index ofpopulation growth, and are arranged in Table 3. In the case of usingPOPR1, the coefficient of determination showed a small value ofabout 0.34, but POPR2 and POPR3 showed comparatively goodfidelities of about 0.55 and 0.60, respectively. The result of POPR2was inappropriate because the regression coefficient for theaverage population (b2) was negative. The value of a contributes totemperature growth regardless of population and growth rate,which becomes more appropriate for smaller amounts. In the caseof POPR3, it showed the lowest value of 0.13. By the results pre-sented above, POPR3 was determined as the best fitting multiplelinear regression model, since it was deemed better than POP1 andPOPR2. Eq. (5) showed significance in a significance level of 0.05%as a result of the F-test, which means the estimated effect ofurbanization is somewhat reliable despite previously establishedassumptions.

TND ¼ 0:130þ 0:060 POPR3þ 0:000618 POPM (5)

Table 4 presents values calculated by the multiple linearregression Eq. (5) for examining the size of each term of theequation during the past 55 years that contribute to urban bias. Theincreased value of the urbanization warming effect (UWE) is duemostly to the contribution caused by population percent growthrate, while the contribution caused by the average population didnot play a big role. Pohang showed the greatest contribution with1.10 due to the population percent growth rate, and next wasIncheon with 0.94. Large cities and newly industrialized citiesshowed relatively great contribution by POPR3 values. Seoulshowed the greatest value in contribution by average populationwith 0.38, and next Busan and Daegu showed 0.14 and 0.10. Fig. 4shows the relationship between UWE obtained by the regressionEq. (5) and UW obtained by the global warming mode of the EOF.Except in Daegu, most cities matched fairly well. In the case ofDaegu, the UW value appeared greater than the UWE value,

Table 3The results of variance analysis of the multiple linear regression Eq. (1) for variousPOPR types of Eq. (2), (3), and (4), respectively. a, b1, and b2 indicate the regressioncoefficients of Eq. (1). F and R2 indicate F statistics and the coefficient of determi-nation, respectively.

POPR1 POPR2 POPR3

a 0.734 0.224 0.130b1 0.361 0.147 0.060b2 �0.00854 �0.00198 0.000618F 2.06 4.97 5.99R2 0.339 0.554 0.599

because Daegu has a heating effect caused by its geographicalcharacteristics as the basin.

3.3. Comparison with previous studies

Lots of studies on urbanization have been performed over Korea(Kim et al., 2000; Choi et al., 2003; Oh et al., 2004), China (Zhouet al., 2004; Jones et al., 2008; Ren et al., 2008), and Japan(Fujibe, 2009, 2011; Sakano et al., 2011). Previous studies for theKorean peninsula shows large uncertainty with the range from0.071 �C per decade to 0.226 �C per decade, which is mainly due tothe different methods, data period, and the number of station used.Our estimated warming (0.171 �C per decade) by urbanization iswithin the range of previous studies. On the other hand, urban-related warming over China is shown to be smaller than about0.1 �C per decade (Jones et al., 2008) or 0.05 �C per decade(Zhou et al. (2004), while urban warming for large city and smallcity group are estimated by about 0.16 and 0.07 �C per decade(Ren et al., 2008). Fujibe’s review paper (2011) showed that stationsin densely inhabited areas have an warming trend of 0.12 �C perdecade compared to an warming trend of 0.03e0.05 �C per decade

Fig. 4. Scatter diagram of the urban warming (x axis) versus the estimated urbanwarming amounts (y axis) obtained from multiple linear regression formula (5) for55 years from 1954 to 2008 for 12 stations in Korea. The coefficient of determinationand the significance level of regression equation are 0.60 and 0.05%, respectively.

M.-K. Kim, S. Kim / Atmospheric Environment 45 (2011) 5778e5783 5783

over even sparsely populated sites over Japan. It is very difficult tocompare the current study with previous studies quantitativelybecause of different periods, different methods, and differentgrouping. However, our estimated urban warming for large cityseems to be over 0.2 �C per decade, larger than in China and Japan,indicating that rapid industrialization has been processing.

4. Conclusions and discussion

In this study, we separate urban warming and greenhousewarming in South Korea using the global warming mode obtainedfrom the EOF analysis. The quantitative values of the greenhouseeffect and urban warming effect were estimated by using thetemperature data of the past 55 years from 12 stations. Regardingthe mode obtained by EOF analysis, we could estimate the green-house effect and urban warming effect. To verify these effects, weperformed multiple linear regression analysis by setting the esti-mated amount of warming by urbanization as the dependentvariable, and population growth rate and average population as theindependent variables.

Among the pairs of eigenvectors and time coefficients obtainedby EOF analysis regarding three different sets of K0004, K5512,K4020 according to the number of observation posts and the periodof observation, we obtained the first mode, which showed similarvolatility. Among the three sets, we restored the relatively suitableK5512 and obtained the greenhouse effect mode (RC1). After cali-brating RC1, the total warming value was separated into urbanwarming due to the greenhouse effect and the urban warming dueto the urbanization effect. Among the average total temperaturegrowth of about 1.37, the amount due to the greenhouse effect isestimated as approximately 0.60, and the amount due to the urbanwarming effect is estimated as approximately 0.77.

With respect to population change, we checked the validity ofthe urbanwarming obtained by using the EOF eigenmode.We haveperformed the multiple linear regression analysis by using not onlythe suggested indices on population growth, but also the averagepopulation during total period. Results show that both the pop-ulation percent growth rate and average population as dependentvariables is the best fit to explain the urban warming. In this case,we obtained a significance level of 0.05% and a coefficient ofdetermination of 0.60. Therefore, the multiple linear regressionequation explained the warming value by urbanization well. Thecities that showed the greatest warming due to urbanization areDaegu, Pohang, Seoul, and Incheon (in order), with respectivevalues of 1.35, 1.17, 1.16, and 1.10. The areas that showed urbanwarming less than 0.2�C were Chupungnyeong and Mokpo.

Through the regression equation, we recognized that urbanwarming depends more on the population percent growth ratethan the average population. In particular, in the case of Pohang andIncheon (which have rapidly increasing populations due to rapidindustrialization), the degree of urbanization was great. However,in the case of Busan (which has a large and steady population), thedegree of urbanizationwas weak. In other words, the rising trend oftemperature appeared stronger in newly industrialized cities morethan in large cities. With concerns over global warming risingrapidly, East Asia, and especially the Korean peninsula, faces theserious problem that the amount of increasing temperature isgreat. Thus, South Korea needs a study quantitatively estimatingthe urbanwarming effect, which accelerates global warming, usingvarious methods. When we predict the global warming effect, thecorrect estimation of the urban warming effect can reduce errorsabout warming caused by the greenhouse effect. The warmer

conditions in many cities result in greater energy and resourceconsumption by the inhabitants to offset the effect, andmake urbanpopulations more vulnerable to heat waves and other extremeconditions (Grimmond, 2007). Also, through a model that canpredict the degree of urbanization, we should be able to prevent theoccurrence of extreme climate.

Acknowledgements

This work was funded by the Korean Meteorological Adminis-tration Research and Development Program under GrantRACS_2010e2015. We thank the reviewers for helpful commentsand suggestions that have much improved this paper.

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