Correlation Analysis between Korean Spring Drought and Large-scale Teleconnection Patterns for Drought Forecasting

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1 KSCE Journal of Civil Engineering (2017) 21(1): Copyright c2017 Korean Society of Civil Engineers DOI /s TECHNICAL NOTE Water Engineering pissn , eissn Correlation Analysis between Korean Spring Drought and Large-scale Teleconnection Patterns for Drought Forecasting Jong-Suk Kim*, Gil-Su Seo**, Ho-Won Jang***, and Joo-Heon Lee**** Received July 31, 2015/Revised December 30, 2015/Accepted February 15, 2016/Published Online April 29, 2016 Abstract This study aims to examine the causes and predictability of spring droughts in the Korean Peninsula. To achieve this goal, we conducted a correlation analysis of global atmospheric circulation patterns and space-time changes in droughts in Korea. The analysis of the contemporaneous correlation between precipitation and atmospheric teleconnection patterns showed a higher correlation between precipitation in the Global Precipitation Climatology Project (GPCP) and the West Pacific (WP) pattern in spring within East China, Kyushu Island, and South Korea. However, since this correlation was identified for the same season, it cannot be used as a predicting factor for spring droughts. The North Atlantic Oscillation (NAO) pattern displayed a lagged correlation with precipitation (with a time lag of three months) and was identified as a possible parameter for improving the ability to predict droughts in Korea. When comparing the average precipitation in spring with the levels of precipitation during years with NAO outliers, droughts occurred when the precipitation in years of NAO outliers was lower than the average precipitation. Therefore, rather than the WP pattern, the NAO may be an appropriate parameter for predicting droughts in the Korean Peninsula. We expect that these findings will support the establishment of practical adaptation strategies for spring droughts in East Asia. Keywords: drought, GPCP, atmospheric teleconnection patterns, NAO pattern 1. Introduction Due to global warming, the climatic characteristics of East Asia have recently changed. Among extreme climatic events, droughts are a significant issue in society, leading to shortages in natural precipitation and increased demands for water. Extreme droughts are the most challenging natural disasters (Mishar and Desai, 2006; Kim and Valdés, 2003). Compared to other climatic disasters, droughts may cause serious damage over time to large areas. Therefore, mid/long-term prediction is required to minimize the damage caused by droughts. In addition, an accurate understanding of the causes of droughts is essential for this prediction. Recent research into drought prediction relies on statistical methods (Ganguli and Reddy, 2014; Lee et al., 2013; Fundel et al., 2012; Lee et al., 2012; Sadri and Burn, 2012; Mishra et al., 2007; Morid et al., 2007; Kim et al., 2004). These methods include statistical analysis of climatic factors and other dynamic characteristics, such as precipitation and drought indices, dynamic initial force, and monthly predictions of droughts as they progress. However, these methods are applicable to certain areas, and they are useful for short-term prediction, they are limited for predicting droughts in the entire Korean Peninsula or for droughts lasting longer than three months. To overcome the limitations of statistical model-based methods, many researchers attempt to clarify the causes of droughts to improve prediction, specifically by analyzing the effects of atmospheric circulation on local precipitation over large regions and by examining correlation with atmospheric teleconnections. To understand the physical causes of droughts, Namias (1991) suggests that it is crucial to examine not only the characteristics of the regional pressure system but also atmospheric teleconnections on a large scale. Kim et al. (2005) also argue that droughts in the Korean Peninsula are related to abnormal developments in high continental pressure in the Eurasian continent and to weakening of the subtropical anticyclone in the Pacific Northwest. Bothe et al. (2012), who analyze the correlation between teleconnections and precipitation, indicate that precipitation is affected by regional changes in the climate that affect teleconnection patterns. Precipitation may also be affected by teleconnection patterns that cause climate changes. Lee et al. (2003) compare patterns between the regional atmospheric pressure system and wet year pressures, concluding that spring droughts affect the atmosphere in the North Atlantic and that marine patterns affect spring precipitation. *Research Professor, Dept. of Civil Engineering, University of Seoul, Seoul 02504, Korea ( jongsuk@uos.ac.kr) **M.S. Student, Dept. of Civil Engineering, Joongbu University, Goyang 10279, Korea ( tjrlftn02@nate.com) ***Ph.D. Student, Dept. of Civil Engineering, Joongbu University, Goyang 10279, Korea ( hs980216@hotmail.com) ****Member, Professor, Dept. of Civil Engineering, Joongbu University, Goyang 10279, Korea (Corresponding Author, leejh@joongbu.ac.kr) 458

2 Correlation Analysis between Korean Spring Drought and Large-scale Teleconnection Patterns for Drought Forecasting To understand and predict spring droughts in Korea, Kim et al. (2004) relate atmospheric circulation over the northern hemisphere to long-term fluctuations in spring precipitation. By investigating the space-time characteristics and differences in broad-based droughts using atmospheric pattern teleconnection, Jun et al. (2008) conclude that summer droughts in the Korean Peninsula are lag correlated with droughts in East Asia. Brahmananda et al. (2006) reveal that the increase in precipitation in Northeastern Brazil is negatively correlated with droughts in the southern portions of the Sahara Desert. Mishra (2003) finds that the Sea Surface Temperature Anomaly (SSTA) in the Western Indian Ocean and the Outgoing Longwave Radiation (OLR) anomaly of the West Pacific correlate positively, demonstrating that large-scale atmospheric patterns are correlated with regional precipitation and the presence of droughts. In addition, studies on the change in the regional or local extreme events according to the change pattern of El Niño and atmospheric teleconnection patterns were carried out (Kim et al., 2012a, b; Rye et al., 2010). However, research comparing the regional characteristics of droughts in the Korean Peninsula to atmospheric teleconnections and long-term fluctuations is limited. Therefore, to identify the causes and to improve the prediction of spring droughts in Korea, this study uses correlation and composite analysis of global air circulation patterns. The study also analyzes the characteristics of droughts in the Korean Peninsula temporally and spatially based on teleconnection and lag-correlation patterns to provide timely and reliable seasonal forecasting. 2. Materials and Methods 2.1 Precipitation and Standardized Precipitation Index (SPI) Index The precipitation data, which was used to analyze the correlation between precipitation and teleconnections in East Asia, was provided by the GPCP that covers East Asia ( E, N) and contains monthly precipitation data on a grid dataset. The GPCP combines station-based precipitation records and grid-based global precipitation estimations using satellite imagery. Spring (March to May) data in South Korea were obtained from 59 observatories of the Korea Meteorological Administration (Fig. 1). These weather stations are evenly distributed in South Korea, with continuous data records for a period of 38-yearS ( ) continuous data record. The data used to analyze atmospheric air circulation were NCEP/NCAR reanalyzed data, included geopotential heights, Outgoing Longwave Radiation (OLR), and zonal wind for each altitude from 1979 to Among the various indices used to define droughts, the SPI is less restrictive on building data when calculating a drought and is Fig. 1. Spatial Distribution of 59 Meteorological Observatories used in this Study. Major Observatories Were Selected From Northern, Central, and Southern Parts of South Korea (Seoul, Daejeon, Gwangju, and Milyang) Vol. 21, No. 1 / January

3 Jong-Suk Kim, Gil-Su Seo, Ho-Won Jang, and Joo-Heon Lee able to evaluate short-term and long-term droughts simultaneously by selecting different timescales (Mckee et al., 1993; Kim et al., 2014). Therefore, in this study, we used the SPI index to analyze the occurrence of regional droughts in Korea. The drought (SPI -1) frequency was calculated for spring (March to May), summer (June to August), autumn (September to November), and winter (December to February). Table 1. Major Atmospheric Teleconnection Patterns in Different Regions of the Northern Hemisphere Locations Prominent Patterns North Atlantic Oscillation (NAO) North Atlantic East Atlantic (EA) East Atlantic/Western Russia (EATL/WRUS) Eurasia Scandinavia (SCA) Polar/Eurasia North Pacific/North America West Pacific (WP) East Pacific - North Pacific (EP-NP) Pacific/North American (PNA) Tropical/Northern Hemisphere (TNH) Pacific Transition (PT) 2.2 Atmospheric Teleconnection Patterns Teleconnection patterns that result from atmospheric circulation in the Northern Hemisphere vary spatially (Table 1). The NOAA provided Northern Hemisphere teleconnection patterns as indices. These indices were created based on the Rotated Principal Component Analysis (RPCA), as suggested by Barnston and Livezey (1987). These major teleconnection patterns include the North Atlantic Oscillation (NAO), the East Atlantic (EA), the East Atlantic/Western Russia (EA-WR), the Scandinavia (SCA), the Polar/Eurasia (POL), the West Pacific (WP), the East Pacific- North Pacific (EP-NP), the Pacific/North American (PNA), the Tropical/Northern Hemisphere (TNH), and the Pacific Transition (PT). Among these atmospheric teleconnection patterns, eight indices (NAO, EA, EA-WR, SCA, POL, WP, EP-NP, and PNA) were selected for this study, and their correlations with droughts in East Asia and the Korean peninsula were analyzed. To investigate variations in spring precipitation in East Asia, a lag-correlation analysis was conducted, and the primary air circulation pattern was identified. In addition, to complete composite analysis, and with the use of the air circulation modes from , the positive years with standardized anomalies higher than a Standard Deviation (SD) of +1.0 were separated Fig. 2. Space-time Distribution of Seasonal Droughts in South Korea: (a) Spring (MAM), (b) Summ (JJA), (c) Autumn (SON), (d) Winter (DJF) Fig. 3. Space-time Distribution of Monthly Droughts in Spring: (a) March, (b) April, (c) May 460 KSCE Journal of Civil Engineering

4 Correlation Analysis between Korean Spring Drought and Large-scale Teleconnection Patterns for Drought Forecasting from the negative years with anomalies lower than -1.0 SD. This composite analysis was used to analyze the relation between air circulation and spatiotemporal changes in drought frequency in South Korea, and variations in spring precipitation in East Asia. 3. Results 3.1 Characteristics of Spring Drought The spatial and temporal drought distribution is illustrated in Fig. 2. The Drought frequency in Korea was higher during spring (March-May) (Fig. 3). The most significant alteration in the drought frequency occurred in the central and southern areas of Korea (Fig. 3(b)). Precipitation outliers were analyzed for the entire period (January to December) and for the spring season (March to May) (Tables 2 and 3). P Outlier spr P spr entire spr = σ spr entire In this equation, Outlier spr represents the outlier in the mean spring precipitation ( P spr ), P spr entire is the mean spring precipitation for the entire period, and σ spr entire represents the SD of spring precipitation for the entire period. When the SD of an outlier was > +1.0σ or < -1.0σ, the outlier was classified as a wet or dry year, respectively. Table 2 shows the wet and dry years based on the data collected from four observatories (Seoul, Daejeon, Gwangju, and Milyang). When analyzing the average annual precipitation, the worst droughts in Korean history occurred in 1988 and 1994, while severe spring droughts were present on a national scale in 1978 and (1) 3.2 Correlation Analysis between Spring Precipitation and Northern Hemisphere Teleconnections Figure 4 shows the correlation between precipitation in the major areas of South Korea and teleconnections in the Northern Hemisphere. For correlation between PNA and EA-WR patterns was relatively low, which differed from the other atmospheric teleconnections (e.g., WP, NAO, and SCA). Among these patterns, the correlation between the WP pattern and spring precipitation showed statistically significant for the southern areas of Korea. Fig. 5 shows the seasonal correlation between the GPCP precipitation and the WP pattern in East Asia. In spring, the correlation was especially high in the central portion of East China (including Nanjing and Shanghai), in Kyushu in Japan, and in South Korea. In contrast, the correlation was not significant in the other seasons. In terms of the lag correlation between East Asian GPCP precipitation and the teleconnections, each monthly teleconnection index lagged between 0 and -3 to analyze the correlation with monthly precipitation. Eight teleconnection patterns were analyzed and a lag of -1 in the NAO showed a significant negative correlation with the Korean Peninsula and the East Sea, indicating that spring precipitation in the Korean Peninsula was affected by the winter NAO approximately three months earlier. This study identified the NAO pattern as the major atmospheric teleconnections affecting local precipitation in Korea. The following sections show the results of composite anomalies in association with outliers in atmospheric teleconnection pattern. 3.3 Changes in Spring Precipitation due to Outliers in Atmospheric Teleconnection Patterns To analyze the changes in spring precipitation due to telecon- Table 2. Dry Years ( 1σ) and Wet Years ( +1σ) Based Annual Precipitation at Each Observatory Station Year Seoul Wet years ( +1σ): 1990, 1998, 2003, 2010, 2011 Dry years ( 1σ): 1982, 1988, 1994 Daejeon Wet years ( +1σ): 1985, 1987, 1997, 1998, 2000, 2003, 2007, 2011 Dry years ( 1σ): 1976, 1982, 1988, 1992, 1994, 2001, 2008 Gwangju Wet years ( +1σ): 1980, 1985, 1998, 1998, 2003, 2004 Dry years ( 1σ): 1977, 1982, 1983, 1988, 1994, 1995, 2008 Miryang Wet years ( +1σ): 1980, 1985, 1989, 1997, 1998, 1999, 2003 Dry years ( 1σ): 1977, 1988, 1994, 1995, 1996, 2008 Drought Year Dry years ( 1σ): 1976, 1977, 1982, 1983, 1988, 1992, 1994, 1995, 1996, 2001, 2008 Common Drought Year Dry years ( 1σ): 1988, 1994 Table 3. Dry Years ( 1σ) and Wet Years ( +1σ) Based on Spring (MAM) Precipitation at Each Observatory Station Year Seoul Wet years ( +1σ): 1977, 1979, 1980, 1985, 1990, 1997, 2007 Dry years ( 1σ): 1976, 1978, 1984, 1986, 1988, 2000, 2001 Daejeon Wet years ( +1σ): 1977, 1980, 1998, 2002, 2003, 2010 Dry years ( 1σ): 1976, 1978, 1981, 2001, 2008 Gwangju Wet years ( +1σ): 1977, 1980, 1985, 1998, 2003 Dry years ( 1σ): 1978, 1981, 1989, 2000, 2001 Miryang Wet years ( +1σ): 1977, 1980, 1985, 2003 Dry years ( 1σ): 1978, 1981, 1989, 2000, 2001 Drought Year Dry years ( 1σ): 1976, 1978, 1981, 1984, 1986, 1988, 2000, 2001, 2008 Common Drought Year Dry years ( 1σ): 1978, 2001 Vol. 21, No. 1 / January

5 Jong-Suk Kim, Gil-Su Seo, Ho-Won Jang, and Joo-Heon Lee Fig. 4. Correlation Analysis between Droughts and Atmospheric Teleconnection Patterns Over the Northern Hemisphere in Reference to Spring Precipitation South Korea. The Hatched Polygons Indicate Statistically Significant at the 10% Confidence Level: (a) NAO, (b) EA, (c) WP, (d) POL, (e) EP-NP, (f) PNA, (g) EA-WR, (h) SCA Fig. 5. Analysis of Seasonal Contemporaneous Correlation between GPCP Precipitation and WP Pattern. The Colored Shading Shows Statistically Significant at the 10% Confidence Level: (a) Spring (MAM), (b) Summer (JJA), (c) Autumn (SON), (d) Winter (DJF) 462 KSCE Journal of Civil Engineering

6 Correlation Analysis between Korean Spring Drought and Large-scale Teleconnection Patterns for Drought Forecasting nections, the outliers of spring precipitation were calculated using Eq. (1) (Tables 4). Table 5 shows the analysis between the years of drought in South Korea and the NAO outliers: case 1 ( +1σ), case 2 (+0.5 σ anomaly +1 σ). A clear relationship was identified between the years of drought in South Korea and the years in which NAO outliers were present. Though no severe droughts were identified in the precipitation outliers, the NAO outliers were found concomitantly in 1989, 1999, 2005, and 2012, Table 4. Outliers (1976~2013) in NAO Winter Seasons (December to February) Year Positive years ( +1σ): 1984, 1989, 1994, 1995, 2000, 2012 Positive years ( +0.5σ): 1981, 1983, 1988, 1991, 1993, 1999, 2005, NAO 2008 Negative years ( 1σ): 1977, 1978, 1979, 1985, 1996, 2010, 2011 with droughts mainly occurring in spring or summer of the following year. Despite the presence of the NAO outliers, droughts did not occur in South Korea in 1991 and Droughts occurred in 1976, 1977, 1982, and 2001, though NAO outliers were not identified in these years. Therefore, the NAO outliers accurately predicted droughts in South Korea, especially when the NAO outliers were greater than +1σ (e.g., 1984, 1989, 1995, and 2000). In terms of the composite analysis results, when the NAO outliers were less than -1 σ, spring rainfall was higher than the precipitation that occurred during the normal 30-year ( ). It was common in all of East Asia. In contrast, when it was greater than 1 σ, the opposite tendency occurred, and the southern portion of South Korea clearly showed a pattern of precipitation reduction. The GPCP precipitation in the Korean Peninsula was mm lower than precipitation of the normal years (Fig. 8). The correlation analysis between the drought Table 5. Analysis of Years of Drought in South Korea and in the years with NAO Outliers Year Drought Years in Connection with Precipitation Dry years ( 1σ): 1976, 1977, 1982, 1983, 1988, 1992, 1994, 1995, 1996, 2001, 2008 During the Entire Period Spring Drought Years in Connection with Dry years ( 1σ): 1976, 1978, 1981, 1984, 1986, 1988, 2000, 2001, 2008 Precipitation during the Spring Positive years ( +1σ): 1984, 1989, 1994, 1995, 2000, 2012 Years of NAO Outliers in Winter Positive years (+0.5 Anomaly +1.0): 1981, 1983, 1988, 1991, 1993, 1999, 2005, 2008 Fig. 6. Lag-correlation between GPCP Spring Precipitation & NAO Pattern. The Colored Shading Shows Statistically Significant at the 10% Confidence Level: (a) NAO-GPCP (Lag0), (b) NAO-GPCP (Lag-1), (c) NAO-GPCP (Lag-2), (d) NAO-GPCP (Lag-3) Vol. 21, No. 1 / January

7 Jong-Suk Kim, Gil-Su Seo, Ho-Won Jang, and Joo-Heon Lee Table 6. The Difference between the Average Spring Precipitation (1981~2010) at Each Observatory and the Average Spring Precipitation in the Years with NAO Outliers (units: mm) Seoul Wonju Daejeon Daegu Pusan Gwangju Jeonju Chengju Spring Precipitation in the Years of NAO + Outliers (B) Normal Spring Precipitation in Years of 1981~2010 (A) (A) - (B) Fig. 7. Outliers of NAO Average Values in Winter (December to February) years and the teleconnection outliers revealed that the years with droughts and the years with winter NAO outliers presented similar patterns, though some differences characterized major drought events. Table 4 shows the average spring precipitation from 59 observatories between 1981 and 2010 and the precipitation for years with NAO outliers (+1σ) for each major region. In South Korea, the precipitation in the years with NAO outliers (+1σ) was mm lower than the average recorded spring precipitation. Fig. 9 shows the difference between spring precipitation and the precipitation for years in which NAO outliers were present (+1σ) in South Korea. For most regions, the volume of precipitation decreased, especially for the Yeongsang and Seomjin River basins, the southern area of Jeju, and Tongyeong. The decrease in the precipitation volume may have occurred because the North Pacific anticyclone in the positive NAO years failed to reach the mid-latitude region of East Asia, preventing a Fig. 9. Difference between Average Spring Precipitation (1981~2010) and that in the Years of NAO Outliers Fig. 8. Outliers (mm) of Spring Precipitation Depending on NAO Outliers: (a) Spring Precipitation in the Years of NAO ( +1σ), (b) Spring Precipitaion in the Years of NAO ( 1σ) 464 KSCE Journal of Civil Engineering

8 Correlation Analysis between Korean Spring Drought and Large-scale Teleconnection Patterns for Drought Forecasting Fig. 10. Composite Anomalies of Large-scale Atmospheric Components of: (a) 850 hpa Geopotential Height, (b) Outgong Longwave Radiation (OLR, W/m 2 ), (c) Zonal Wind (m/s) for the Positive NAO Years (up) and Negative NAO Years (down). The Composite Mean Anomalies Depart from the Normal Anomalies in the Years Ranging from 1981 to 2010 powerful convention current from occurring in the area over 30 N, including South Korea. During the negative NAO years, however, the opposite spatial pattern was present in Korea where convention currents were active. In terms of the zonal wind analysis results, which were gathered from the latitude gained by averaging out 120 and 135 E (the longitude range of Korea), a descending air current was present in South Korea at 30 N. In the positive NAO years, there was a relatively straight current in the Korean Peninsula. Therefore, strong anomaly of downdraft caused severe droughts in the Korean Peninsula (Fig. 10). 4. Conclusions This study analyzed the correlation between precipitation and atmospheric teleconnection patterns in the Northern Hemisphere to examine the causes of spring droughts and to improve the ability to predict droughts in the area. In addition, this study analyzed the relationship between atmospheric teleconnections, droughts in Korea, and spatial and temporal drought changes based on contemporaneous correlation and lag-correlation patterns. Based on precipitation data from , the SPI3 was estimated for observatories in Seoul, Daejeon, Gwangju, and Milyang. Based on the SPI3, drought reports, and news released by mass media, several significant droughts ( , , 1988, , , and ) were identified. Spring (March May) droughts were more frequent during April and in central region of South Korea and Gyeongsangbuk-do. The contemporaneous correlational analysis of precipitation and atmospheric teleconnections revealed a higher correlation between the GPCP precipitation and the WP pattern in spring in East China, Kyushu Island, and South Korea. However, because the correlation was identified for the same season, it cannot be used as a predicting factor for spring droughts. The GPCP precipitation and the NAO did not show a contemporaneous correlation, but a negative correlation was present following the lag-correlation analysis, when the lag increased. The most significant correlation was a lag of -3. The analysis between drought and winter NAO outlier years showed a positive correlation, especially for major droughts. When comparing the average spring precipitation to the precipitation observed in years in which NAO outliers appeared, droughts occurred when the precipitation in years with NAO outliers was lower than the average. Therefore, the NAO pattern, as opposed to the WP pattern, may be an appropriate parameter for drought prediction in South Korea. This study provides information that allows for the diagnosis of droughts in East Asia, while clarifying the techniques for predicting droughts and practical strategies to cope with spring droughts. Acknowledgements It is noted that parts of this study s results were updated from the co-author, Gil-Su Seo s master s thesis (Seo, 2015). This research was also supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2013R1A1A ). Vol. 21, No. 1 / January

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