ADJUSTING URBAN BIAS IN THE REGIONAL MEAN SURFACE TEMPERATURE SERIES OF SOUTH KOREA,

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. : () Published online 4 March in Wiley InterScience ( DOI:./joc.88 ADJUSTING URBAN BIAS IN THE REGIONAL MEAN SURFACE TEMPERATURE SERIES OF SOUTH KOREA, YOUNGEUN CHOI, a, * HYUN-SOOK JUNG, b KYUNG-YEUB NAM a and WON-TAE KWON a a Climate Research Laboratory, METRI/KMA, 468 Shindaebang-dong Dongjak-gu Seoul, 6-7, South Korea b Climate Prediction Division, KMA, 468 Shindaebang-dong Dongjak-gu Seoul, 6-7, South Korea Received February Revised 8 November Accepted November ABSTRACT The purpose of this paper is to produce a higher quality regional surface temperature series by removing urban biases in South Korean surface temperatures using statistical procedures. Monthly mean temperatures for 6 stations were obtained for a period of years (968 99). Each station is defined as an urban station or a rural station. Urban (rural) stations are defined as those that have population densities greater (less) than persons per squared kilometer in. Ten urban stations and six rural stations are identified. Again, urban stations are subdivided into two groups according to whether their population totals exceed one million to examine magnitude changes of urban biases with the size of urban areas. Estimates of urban bias magnitude are calculated by averaging the difference between each urban station and every rural station. Estimates of mean urban bias magnitude (Tu r) are calculated by averaging the yearly urban bias estimates. Estimates of the urban trend ( Tu r) are obtained by differencing period means (by doubling the differences obtained between yearly estimates averaged over two 6 year periods, and ). For annual or seasonal mean temperature T i, the adjusted temperature T i is determined. As all estimates of Tu r are greater than zero, it suggests that temperatures in urban stations are warmer than those in rural stations. Estimates of the annual mean magnitude of urban bias range from.5 C for smaller urban stations to.5 C for large urban stations. Also, all estimates of Tu r are positive, indicating an increasing trend in the urban bias time series. Seasonal variations are found in Tu r and Tu r. After adjusting the urban bias, an increasing trend in surface temperature series is still evident. Copyright Royal Meteorological Society. KEY WORDS: urban bias; regional surface temperature series; median rank score; inhomogeneity; climate change; greenhouse gases; South Korea. INTRODUCTION Unusual upward trends of surface temperature over recent decades have received much attention with an increasing interest in climatic change due to natural causes and human-induced CO effects (Jones et al., 989, Parker et al., 994). The IPCC WG Third Assessment Report (Houghton et al., ) concluded that In the light of new evidence and taking into account the remaining uncertainties, most of the observed warming over the last 5 years is likely to have been due to the increase in greenhouse gas concentration. However, there have been suggestions that a proportion of the long-term warming trend may be related to biases introduced not only by local climatic changes, such as the urban effect, but also by some non-climatic factors, such as changes in station location, instrumentation or observation time. Among biases, the urban effect is considered as the most serious source of systematic error identified in land surface climatological measurements, since it is always expected to show warming (Cayan and Douglas, 984; Jones et al., 986, 989, 99; Kukla et al., 986; Karl et al., 988; Karl and Quayle, 989; Downton and Katz, 99). Globally * Correspondence to: Youngeun Choi, Climate Research Laboratory, Meteorological Research Institute, Korea Meteorological Administration, 468, Shindaebang-dong, Dongjak-gu, Seoul 6-7, South Korea; yechoi@metri.re.kr Copyright Royal Meteorological Society

2 578 Y. CHOI ET AL. or regionally reported annual urban warming rates vary in the range from. to.4 C per decade with different sets of station data, contrasting techniques of averaging the basic data and different approaches to the question of data homogeneity considered (Jones et al., 989). Urbanization effects have been identified not only for large urban areas but also cities of all sizes (Oke, 979; Landsberg, 98; Karl et al., 988). Therefore, quantification of the magnitude of urban bias is considered vital for the detection and monitoring of possible long-term trends associated with increasing concentrations of atmospheric greenhouse gases, even though a precise correction can never be reliably achieved. A few studies have examined the effect of urban bias in surface temperature series for South Korea. Their results suggested a warming rate of..4 C in annual mean surface temperature due to urban effects for the most recent year period in South Korea (Lee and Kang, 997; Kim et al., 999). The purpose of this paper is to produce an urban bias-adjusted surface temperature series in South Korea by estimating quantitatively the magnitude of urban bias in regional surface temperature series using systematic statistical procedures. Inhomogeneity in each surface temperature series was tested using the median-rank score procedures before the magnitude and trend of urban bias were estimated.. GENERAL CHARACTERISTICS OF SOUTH KOREA South Korea sits on a peninsula that stretches south from the eastern tip of Asia toward the Japanese archipelago and is separated from China and Russia to the north. The territory of South Korea lies between 4 E and 8 N and approximately 7% of the country consists of hills and mountains. The north south extent of South Korea, with its low but abundant mountains, brings significant local differences in weather and climate. Temperatures fluctuate greatly between summer and winter. When the Siberian high pressure is prevalent, temperatures fall below C, whereas temperatures of higher than C are not unusual during mid-summer when the North Pacific air mass is dominant. Annual precipitation also varies widely, both from region to region and from season to season: southern coastal areas receive as much as mm, whereas some inland areas receive less than mm per year. Over 5% of the annual precipitation occurs in summer (June August) when the monsoon sets in and stationary fronts are fully developed over the country. Large amounts of rainfall are also brought by typhoons after the rainy spell moves to the north.. DATA AND METHODOLOGY Monthly mean temperatures for 9 stations were obtained for a period of years (968 99). The Korean Meteorological Administration (KMA) has developed a new automated observation system, which replaced the manual observation system in. Therefore, data from were not included in order to reduce homogeneities likely to be introduced into the records. Figure shows maps of station locations and Table I lists some geographic features, including longitude, latitude, height, population density and totals in, and histories of station relocations. All stations selected are located below 5 m elevation; of the 9 stations, eight are set inland and are near the coast... Test of inhomogeneities This study is performed in two steps. The first is to identify inhomogeneities in the surface temperature series and the second is to construct adjusted surface temperature series after removing estimated urban bias. Individual station temperature time series might have large potential inhomogeneities introduced as a result of observation schedule changes, changes in instrument exposures, station relocations or other non-climatic factors (Jones et al., 989). Inhomogeneities can be easily examined by consulting station history information (Karl and Quayle, 988; Portman, 99). KMA () provides operational histories of a station noting changes of observation instruments and station relocations. All thermometers were installed at.5 m above ground level and daily mean temperature is calculated by averaging measurements taken at, 6, 9,,

3 URBAN TEMPERATURE BIAS ADJUSTMENT 579 N 9 N 5 CHINA 4 KOREA JAPAN PACIFIC OCEAN E Sosan Inchon Kunsan Mokpo Chonju Kwangju Chunchon Seoul Suwon Chongju Yosu Sokcho Kangnung CPN Pohang Taegu Ulsan Pusan Tongyong 4 Large urban stations Smaller urban stations Rural stations Excluded E Figure. The geographical location of South Korea and climatological surface stations used for the study. Symbols marking the location of urban and rural stations are indicated in the figure legend: large urban stations ( ), smaller urban stations (ž), rural stations ( ) and excluded ( ), 8, and 4 h local standard time (5 E) in South Korea. KMA () showed that eight out of the 9 stations have experienced a total of station relocations or moves during the observation period. Three stations experienced recent station moves and are excluded from more detailed analysis. An alternative method used for examining discontinuities in surface temperature series of a station is to compare the series of differences calculated between the selected station and its neighbours (Conrad and Pollak, 96; Portman, 99). Station-to-station temperature differences were expressed in terms of a median rank-score procedure, as developed by Portman (99). Application of the median rank-score procedure is summarized in Figure. According to Portman s procedure, the annual mean temperatures of each station to be tested were concurrently compared with annual mean temperatures of all adjacent stations. For example, the annual mean temperature series of Seoul was compared with that of the other remaining stations one by one. The differences of temperature were calculated between every pair of stations and the differences were assigned to a row ( years) column ( stations) matrix. Each row consists of temperature differences calculated, for example, between Seoul and each of the adjacent stations for one year. Each column consists of temperature differences between a given station pair for years (968 99). Within each column vector, elements were ranked in order of magnitude over time. Within each row vector, the elements expressed in terms of rank were then sorted and the median rank-score was retained. Finally, median rankscores were combined to form a unique time series for each station. -to-year differences of median rank-score were then calculated and were fit to a normal distribution. Any year-to-year difference of median rank-score values exceeding a. level of statistical significance was considered to be a signal of possible inhomogeneity in the original temperature time series... Estimation and correction of urban bias To identify the urban bias, stations were classified as an urban station or a rural station using population statistics. Urban (rural) stations were defined as those that had population densities greater (less) than persons per kilometer squared in (Table II): ten urban stations and six rural stations were identified.

4 58 Y. CHOI ET AL. Table I. Geographical characteristics (longitude, latitude, height), population statistics for stations and history of their relocations Station a Longitude Latitude Height (m) Population density (persons/km ) b Total population b Meteorological station circumstance Four large urban stations Seoul (L) SR in 9 Pusan (C) SR in 94, 96, 94 Taegu (L) None Inchon (C) None Six smaller urban stations Suwon (L) None Mokpo (C) SR in 94, 96, 99, 964 Chongju (L) None Chonju (L) None Ulsan (C) SR in 945, 947 Kangnung (C) None Six rural stations Yosu (C) None Kunsan (C) None Pohang (C) None Chunchon (L) None Sosan (C) None Chupungneung (L) SR in 96 Excluded Sokcho (C) SR in 988 Kwangju (L) SR in 99 Tongyung (C) SR in 99 a L: inland station, C: coastal station, SR: station relocation. b In. Table II. Definition of urban and rural stations Definition Population density persons/km Total population Defined station (#) Urban stations > Large > million Seoul, Pusan, Tague, Inchon (4) Smaller < million Swon, Mokpo, Chonju, Chongju, Kangnung, Ulsan (6) Rural stations < Yosu, Kunsan, Pohang, Chunchon, Sosan, Chupungnyung (6) Urban stations were also subdivided into two groups according to their population totals to examine whether there are magnitude changes of urban bias with the size of urban areas: a group of four large urban stations (population of more than one million) and a group of six smaller urban stations (population of less than one million) were created. The most straightforward method of identifying the effects of urbanization is to compare each urban station with a neighbouring rural station that is considered free of urban influence and industrial growth (Karl et al., 988; Lee and Kang, 997). The application of this procedure of urban bias estimation is presented in Figure.

5 URBAN TEMPERATURE BIAS ADJUSTMENT 58 The annual mean temperatures of each station to be tested (i) are concurrently compared with the annual mean temperatures of all adjacent stations ( j ) All station-to-station temperature differences were expressed in terms of a rank-score parameter. ) Differences of temperature were calculated between every pair of stations ) Temperature differences were assigned to a row column array D (i ) ) Elements of D (i ) were divided into each row vector D (i )k and each column vector D (i )j 4) Within each column vector D (i )j, elements were ranked in order of magnitude over time. Within each row vector D (i )k, the elements (expressed in terms of rank value) were sorted and the median rank value was retained as the rank score for station i in year k. 5) Elements of D (i ) converted to median rank-score values were combined, forming a unique time series for each station i. -to-year differences of median rank score were then calculated for each station i and were fitted to a normal distribution. (Any year-to-year difference exceeding a. level of statistical significance is determined using Student's t-test was considered to be a sign of possible discontinuities) If all values consistently remained above or below the expected median rank-score for a five-year period, then the temperature series for the station i was deemed inhomogeneous. Figure. The steps for testing inhomogeneity in annual temperature time series (after Portman (99)) Unlike most recent urban bias studies, this study estimated the yearly urban bias magnitude by averaging the difference between each urban station and six rural stations. These differences were averaged over three groups of urban stations (ten urban stations: four large urban stations and six smaller urban stations). Estimates of the -year mean urban bias magnitude (Tu r) were calculated by averaging the yearly urban bias estimates over the period Estimates of the -year urban trend ( Tu r) were obtained by differencing period means (by doubling the differences obtained between yearly estimates averaged over two 6 year periods, and ). Finally, an attempt was made to remove the magnitudes and trends of the urban bias from the regionally averaged urban station temperatures. For annual or seasonal mean temperature T i, the adjusted temperature T i was determined from Equation (): T i = T i {Tu r + [( Tu r/)(i 968)]} () where i is any year between 968 and 999. Trends in the year time series of original and adjusted temperatures were estimated by differencing period means. 4. TEST OF HOMOGENEITY The median rank-score method was applied to 6 stations (three having been excluded) and, here, only the results for Seoul have been presented as an example. The annual temperature time series for Seoul during the period is shown in Figure 4(a), the median rank-score series in Figure 4(b), year-to-year difference of the median rank-score series in Figure 4(c), and the standardized difference in Figure 4(d). Significant changes in the year-to-year difference in median rank-score occur in 969, 978 and 99. However, these

6 58 Y. CHOI ET AL. Defining urban and rural stations (Using the census data) Estimates of yearly urban bias magnitude (By averaging the differences of between each urban station and 6 rural station) Estimates of -year mean urban bias magnitude (T u-r ) (By averaging the yearly urban bias estimates over the period ) Estimates of -year mean urban bias trend ( T u-r) (Using the period mean-doubling the differences obtained between yearly estimates averaged over two 6-year period, & ) Removing the magnitude and trends of urban bias from the regionally averaged urban station temperatures Figure. The steps for identifying and correcting urban bias (after Portman (99)) 6 (a) 4 (b) Ann Temp () Median Rank-Score Yr-to-Yr Difference 5 5 (c) Normalized Difference..... (d) Figure 4. Time series for Seoul, : (a) annual mean temperature ( C); (b) median rank-score; (c) year-to-year differences of median rank-score; (d) normalized year-to-year differences potential problem years did not remain consistent for a 5 year period before and after each problem year, so that it could be concluded that the annual temperature series of Seoul did not show any distinct inhomogeneity. Application of the median rank-score procedures to the remaining stations also indicated that the annual mean temperature series might be considered as homogeneous. Using that procedure to screen all data and to identify the most obvious discontinuities prevented the introduction of many large errors in the urban bias calculations. The results of this study remain valid as long as the inhomogeneities were correctly identified and excluded from the analysis.

7 URBAN TEMPERATURE BIAS ADJUSTMENT ANNUAL AND SEASONAL MEAN TEMPERATURE SERIES OF SOUTH KOREA The annual and seasonal mean temperature series for South Korea were constructed by averaging data for 6 stations, each of which had no distinct discontinuities in annual mean temperature time series. Standardized series are presented here with the 5 year moving average highlighting the anomalous trends (Figure 5). In general, an increasing trend was present with different magnitudes for all the series. The increasing trend in the annual mean temperature resulted from persistent positive anomalies since the late 98s. On a seasonal basis, the positive anomalies have become more distinct, and these features were especially true in winter since the late 98s. These trends were all statistically significant except for in summer. Previous studies have already demonstrated that, for both individual stations and for the regional average, the annual temperature series show increasing trends in Korea (Lee and Kang, 997). The mean difference between the first 6 year period and the second 6 year period was.55 C. Seasonally, the second period was.8.6 C warmer than the first period, with the difference largest in winter and smallest in summer (Table III). It is also known that more apparent increasing trends appear in minimum temperature series during the cold seasons than in maximum temperature series in warmer seasons (Landsberg, 98). 6. ESTIMATION OF URBAN BIAS IN ANNUAL AND SEASONAL MEAN TEMPERATURE SERIES The annual and seasonal mean temperature series for each group (a group of urban stations, a group of large urban stations, a group of smaller urban stations, and a group of rural stations) were constructed to examine 4 (a) Spring 4 (b) Summer Anomalies Anomalies (c) Fall 4 (d) Winter Anomalies Anomalies Anomalies 4 - (e) Annual Anomalies 5-year moving average Figure 5. Annual and seasonal mean temperature anomalies (bar) of South Korea constructed by arithmetically averaging data from 6 stations and their 5 year moving averages (line), Spring is MAM, summer is JJA, fall is SON and winter is DJF

8 584 Y. CHOI ET AL. Table III. Mean and trend ( C/year) for annual and seasonal mean temperature series constructed by averaging 6 weather stations (968 99) Spring Summer Fall Winter Annual Mean Trend (984 99) (968 8) (a) Spring 7 6 (b) Summer (c) Fall 4 - (d) Winter (e) Annual Large urban stations Smaller urban stations Rural stations Figure 6. Annual and seasonal mean temperature series for the group of large urban stations, the group of smaller urban stations and the group of rural stations, whether there were any different features among those groups as the size of a city varies (Figure 6). It has been suggested that the magnitude of the difference from the rural areas is greater in metropolitan areas than in small urban areas (Portman, 99). All the temperature series, including the group of rural stations, showed an increasing trend and all three groups of urban station have higher temperatures than the group of rural stations (Table IV). The group of large urban stations has a greater trend in the annual mean temperatures than the group of smaller urban stations. However, the feature is different when the trends are considered on a seasonal basis. The increasing trend is more distinct in the group of larger urban station series than the smaller one for each season except for fall. As the annual and seasonal mean temperature series of South Korea indicated, all the groups showed statistically significant increasing trends except for summer for all groups and in winter for the group of rural stations. Table V lists paired sample t-test results applied to the difference of -year annual and seasonal temperature mean for various pairs of groups. The -year annual

9 URBAN TEMPERATURE BIAS ADJUSTMENT 585 Table IV. Trends ( C/year) for annual and seasonal mean temperature series for each group (large urban, smaller urban and rural stations) Spring Summer Fall Winter Annual Large stations Smaller stations Rural stations Table V. Paired sample t-test results for -year annual and seasonal mean temperature for each group (urban, large urban, smaller urban and rural stations) Spring Summer Fall Winter Annual Urban stations minus rural stations Large stations minus rural stations Smaller stations minus rural stations Large-smaller stations significant at. level. mean temperature for the group of urban station was.4 C higher than the rural group and the difference was statistically significant. Also, the -year annual temperature mean of the larger urban group is.5 C higher than the rural group, and that of the smaller urban group is.5 C higher than the rural one. On a seasonal basis, the group of urban stations is.7 C warmer than the group of rural stations in summer and.47 C warmer in fall. The magnitudes of the -year seasonal temperature mean in the group of large urban stations are greater than the rural group (except for summer) and the smaller urban group. Differences of the -year annual and seasonal mean will be used as Tu r estimates of -year mean urban bias magnitude below. Table VI lists estimates of the -year mean magnitude and trend of urban bias in annual and seasonal temperatures for three urban groups: all ten stations, four large urban stations and six smaller urban stations. The results agree with those found for different parts of the world, as urban warming effects are clearly evident in South Korea (Balling and Idso, 989; Jones et al., 99). As all estimates of Tu r are greater than zero, this suggests that temperatures in urban stations are warmer than those in rural stations. The magnitudes are, however, relatively small compared with those from other regions, such as the USA and China (Cayan and Douglas, 984; Portman, 99). This difference might result from the fact that the rural stations sampled for this study have experienced more urbanization than rural stations used for regions in other areas. Table VI. Estimates of -year mean magnitude (Tu r and trend ( Tu r) of urban bias in South Korea, Estimates are for three urban station groups relative to a group of rural stations Spring Summer Fall Winter Annual Urban stations Tu r Tu r Large urban stations Tu r Tu r Smaller urban stations Tu r Tu r

10 586 Y. CHOI ET AL. All estimates of Tu r are positive, indicating an increasing trend in the urban bias time series. Seasonal variations are found in Tu r and Tu r. For urban stations, the maximum Tu r occurs in fall and the minimum Tu r occurs in summer. It is known that the intensity of urban heat islands is strongest during fall and weakest during summer in Korea (Boo and Oh, ). For all three groups of urban stations, Tu r has its largest variation during winter. However, owing to a larger variation, there are some instances of temperatures of individual urban stations being cooler than those of the rural stations. Therefore, it is recommended that the urban bias estimates obtained from regionally averaged quantities not be used to adjust temperatures of the individual urban station. Estimates of urban bias calculated for each year, , are plotted as annual and seasonal time series in Figure 7. As mentioned earlier, all of these yearly estimates are greater than zero and have increased since the 98s. As indicated by the spacing between the curves in Figure 7, differences of bias between the group of large urban stations and the group of smaller urban stations are greater during spring and fall than during summer and winter. Also, the interannual variations of urban bias appear to be highly correlated, because all estimates were calculated relative to the temperature of the rural stations. Based on the results presented so far, it appears that the magnitude of urban bias in surface temperature is greater for the group of large urban stations than for the group of smaller urban stations. This result implies larger cities create a greater urban bias... (a) Spring.. (b) Summer s (c) Fall... s (d) Winter s. s.. (e) Annual s Large urban stations Smaller urban stations Urban stations Figure 7. Estimated yearly urban bias of annual and seasonal mean temperature series for the group of large urban stations, the group of smaller urban stations and the group of urban stations (large plus smaller urban stations),

11 URBAN TEMPERATURE BIAS ADJUSTMENT CORRECTION OF URBAN BIAS Figure 8 presents annual and seasonal mean temperature series for two groups of urban stations (larger and smaller urban stations) and rural stations before and after the correction of urban bias. Estimates of the -year trends in the original and the adjusted temperature series for the group of large urban stations, the group of smaller urban stations, and the group of rural stations are listed in Table VII. The largest trends are found in the original temperature series of the urban stations, whereas trends in the rural station temperatures and in the adjusted temperatures of the urban stations are smaller. In both the original and adjusted temperature series, maximum positive trends were found during winter with minimum trends during summer. Before adjusting (a) Spring (b) Summer 7 6 (c) Fall - (d) Winter (e) Annual Adj. larger urban sta. Adj. smaller urban sta. Rural sta. Figure 8. Annual and seasonal mean temperature for the group of large urban stations, the group of smaller urban stations and the group of rural stations after the correction, Table VII. Estimates of -year trends ( C) in annual and seasonal mean temperature time series after (adjusted) and before (original) the correction, Spring Summer Fall Winter Annual Original temperature 4 large urban stations smaller urban stations rural stations Adjusted temperature 4 large urban stations smaller urban stations

12 588 Y. CHOI ET AL. for the urban bias, there were relatively large differences between urban stations and rural stations, and these differences have become larger since the late 98s. However, differences between them are partly eliminated after estimates of urban bias are removed. Therefore, it may be concluded that urbanization effects played an important role in the warming trends in urban areas of South Korea. It should be mentioned that temperature differences might also occur because of differences in station location or as a result of biases associated with local environmental factors, such as proximity to the coast or nearby mountains. Correlation coefficients were calculated to investigate possible linear relationships between temperature and geographical positions for stations (Table VIII). The results show that consistently significant relationships exist for the temperature with longitude and latitude in Korea. However, the stations used for this study have been randomly distributed, as shown in Figure 9, and the effect of geographical location would be quite small. Figure shows the annual and seasonal mean temperature series for South Korea before and after the correction of urban bias. Warming trends still remain after the urban bias has been removed. Annual mean Table VIII. Correlation coefficients between the annual and seasonal mean temperatures and three location variables (longitude, latitude and height) Spring Summer Fall Winter Annual Longitude Latitude Height :significant at.; :significant at.5. Longitude (a) Ulsan Kangnung Pohang Pusan Taegu CPN Chongju Yosu Suwon Chonju Inchon Seoul Kunsan Sosan Mokpo 5. Chunchon Latitude (b) Chunchon Inchon Seoul Kangnung 7. Suwon Sosan 6.5 Chongju 6. CPN Kunsan Ulsan 5.5 Taegu Chonju Pohang 5. Pusan Mokpo Yosu Figure 9. The scatterplot between location variables and annual mean temperature: (a) longitude; (b) latitude

13 URBAN TEMPERATURE BIAS ADJUSTMENT (a) Spring (c) Fall 7 6 (b) Summer (d) Winter - 6 (e) Annual 9 Original Temp. Adjusted Temp. Figure. Annual and seasonal mean temperature of South Korea after (adjusted) and before (original) the correction, temperature has increased by.7 C without adjusting the urban bias, but this trend is reduced to.76 C after the adjustment (Table IX). This rate of warming is slightly higher than in previous studies. Kim et al. (999) showed that for the past 4 years (954 9) the warming rate in Korea was.6 C, but the urban warming solely contributed.4 C. Lee and Kang (997) also showed a. C temperature increase due to urban effects for a period of years (97 9). The difference of rate might result from the different study period and the addition of data from the period of Jones et al. (99) calculated linear trends of surface temperature in European parts of the former Soviet Union, the contiguous USA, eastern Australia and eastern China using large-scale hemispheric data and found only significant urban warming effects over the contiguous USA. They concluded that the urban bias is an order of magnitude smaller than the.5 C hemispheric warming seen over the last years, considering that effects were significantly less in western Europe and assuming effects were less in many other regions of Table IX. Estimates of -year trends ( C) in annual and seasonal mean temperature series of South Korea after (adjusted) and before (original) the correction, Spring Summer Fall Winter Annual Original temperature Adjusted temperature

14 59 Y. CHOI ET AL. the world. Also, they suggested that the urban effect might be halved in hemispheric and global temperature estimates when the marine data are incorporated. However, Portman (99) examined urban bias in China s northern plains and demonstrated that the mean trend in the annual temperature is at least twice as large as any trend found in the adjusted annual temperatures. These divergent results imply that, despite efforts to remove them, urban heat island biases not only still remain in some of the most widely used land surface data sets but also may actually be much more serious than was previously expected. 8. SUMMARY A detailed analysis of urban bias in regional surface temperature series for South Korea has been performed using systematic statistical procedures. The application of a median rank-score procedure showed that the annual surface temperature series for all 6 stations were found to exhibit no large potential inhomogeneities. A systematic sampling procedure was used to estimate urban bias in annual and seasonal mean temperatures for three groups of urban stations relative to a single group of six rural stations, and urban and rural stations were distinguished based on population density in. The results of this study showed that temperatures of large urban stations exhibit higher urban bias than those of smaller urban stations and that the magnitude of urban bias has increased since the late 98s. The estimated magnitudes of urban bias were found to be greater during the fall than for to the other seasons, but trends were more distinct during winter. The results of this study can only be applied to correct possible urban bias not in individual station temperature series but in regional temperature series due to the large variation of urban bias magnitude. For this study, temperatures of each group of urban stations were compared with those of the rural stations, so that urban and rural stations have somewhat different geographical distributions. However, the approach has eliminated the need to make an ad hoc decision to match each urban station with a single rural station (Portman, 99). None of the rural stations used for this study can represent a true non-urbanized environment. Therefore, estimates of urban bias magnitude and trend from this study might be considered as measures of relative bias between heavily urbanized industrialized centres and less-urbanized agricultural centres, as with other studies (Balling and Idso, 989; Karl and Quayle, 989). The results showed that the urban growth biases are very serious in South Korea and must be taken into account when assessing the reliability of temperature trends. ACKNOWLEDGEMENTS This research was performed for the Greenhouse Gas Research Center, one of the Critical Technology Programs, funded by the Ministry of Science and Technology of Korea. REFERENCES Balling Jr BC, Idso SB Historical temperature trends in the United States and the effect of urban population growth. Journal of Geophysical Research 94: Boo K-O, Oh S-N.. Characteristics of spatial and temporal distribution of air temperature in Seoul, 999 Journal of Korean Meteorological Society 6: (in Korean with English abstract). Cayan DR, Douglas AV Urban influences on surface temperatures in the southwestern United States during recent decades. Journal of Climate and Applied Meteorology :. Conrad V, Pollak LD. 96. Methods in Climatology. Harvard University Press. Downton MW, Katz RW. 99. A test for inhomogeneous variance in time-averaged temperature data. Journal of Climate 6: Houghton JT, Ding Y, Griggs DT, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds).. Climate Change : The Scientific Basis. Cambridge University Press. Jones PD, Raper SCB, Bradley RS, Diaz HF, Kelly PM, Wigley TML Northern hemisphere variations: Journal of Climate and Applied Meteorology : Jones PD, Kelly PM, Goodess CM, Karl T The effect of urban warming on the Northern Hemisphere temperature average. Journal of Climate : Jones PD, Groisman YP, Coughlan M, Plummer N, Wang WC, Karl TR. 99. Assessment of urbanization effects in time series of surface air temperature over land. Nature 47: Karl TR, Quayle RG Climate change in fact and in theory: are we collecting the facts? Climatic Change : 5 7. Karl TR, Quayle RG Urban bias in area-averaged surface air temperature trends. Bulletin of the American Meteorological Society 7: 65 7.

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