JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D12101, doi: /2012jd017565, 2012

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi: /2012jd017565, 2012 Low-frequency variability of tropical cyclone-induced heavy rainfall over East Asia associated with tropical and North Pacific sea surface temperatures Min-Hee Lee, 1 Chang-Hoi Ho, 1 Joo-Hong Kim, 2 and Hyo-Jong Song 3 Received 3 February 2012; revised 2 May 2012; accepted 2 May 2012; published 16 June [1] This study investigates the relationship between tropical cyclone (TC) induced heavy rainfall over East Asia (EA) and large-scale climate variability during June October for the period of An empirical orthogonal function analysis is applied to the seasonal-total TC-induced heavy rainfall obtained in meteorological stations over EA. The first leading mode shows a dipole pattern between South China (SC) and Northeast Asia (NEA; i.e., Southeast-East China, Taiwan, and Japan). This dipole pattern is found to be associated with the two modes of sea surface temperature (SST) variations over the Pacific: one in the tropical Pacific, and the other spanning from EA to the North Pacific Ocean. The former is located in the NINO4 region, while the latter is characterized by the North Pacific center of the Pacific Decadal Oscillation (PDO). The dipole mode is generally well explained by the combined NINO4 and PDO impacts on TC tracks. During positive NINO4, cyclonic steering flows appear over inshore Southeast China, which increases recurving TCs. Meanwhile, the midlatitude North Pacific SST warming during negative PDO is overlaid by the barotropic anticyclone. The anomalous steering easterlies along N related to the anticyclone increase TC occurrence toward Southeast-East China and Taiwan. Furthermore, the precipitable water greatly increases in the midlatitude ocean during negative PDO years, which may help to enhance the rainfall amount while TCs approach Japan. To sum up, in a climatological sense, the first mode of TC-induced heavy rainfall over EA can be interpreted by the combined variations of negative (positive) PDO with positive (negative) NINO4. Citation: Lee, M.-H., C.-H. Ho, J.-H. Kim, and H.-J. Song (2012), Low-frequency variability of tropical cyclone-induced heavy rainfall over East Asia associated with tropical and North Pacific sea surface temperatures, J. Geophys. Res., 117,, doi: /2012jd Introduction [2] Tropical cyclone (TC) is one of the most disastrous natural phenomena and can have a large influence on human beings. Damage from TCs is mainly caused by strong winds and heavy rainfall. In particular, torrential rain accompanying landfalling TCs pours down for a short period over relatively broad regions. The East Asian continent is adjacent to the western North Pacific (WNP) basin, where TCs frequently 1 School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea. 2 Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan. 3 Korea Institute of Atmospheric Prediction Systems, Seoul, South Korea. Corresponding author: J.-H. Kim, Department of Atmospheric Sciences, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 106, Taiwan. (jhkim@as.ntu.edu.tw) American Geophysical Union. All Rights Reserved. form; this area experiences one third of the global number of TCs, approximately 26 per year. A large portion of socioeconomic losses caused by natural disasters over East Asia can be attributed to heavy rainfall induced by TCs [e.g., Zhang et al., 2010; Park et al., 2011; Xiao et al., 2011]. While numerous studies tackle various cases of TC-induced heavy rainfall over East Asia to understand the effect of orography, TC size and speed, and synoptic-scale environments [e.g., Park and Lee, 2007; Chien and Kuo, 2011; Ge et al., 2010], low-frequency climate variations of TC-induced rainfall (or heavy rainfall) are relatively less studied [e.g., Kim et al., 2006; Ren et al., 2006; Wu et al., 2007; Lyon and Camargo, 2009; Lee et al., 2010]. [3] To understand low-frequency climate variations in seasonal TC activity and induced rainfall, the role of seasonalmean large-scale environments may emerge to be primary compared to those of local topography or transient synopticscale environments. In this study, we regard this conjecture to be reasonable because the topographic and transient effects vary by cases so that their importance seems to become less if various cases in a season are considered together. Among the various large-scale environments, the warm sea surface 1of11

2 temperature (SST) and the weak vertical wind shear are critical thermodynamic and dynamic factors, respectively, that develop and sustain TCs [Gray, 1979]. TCs are directly influenced by the local SST and ambient atmospheric conditions where they traverse, but the local atmospheric state depends on remote as well as local SSTs. Thus, TCs are also indirectly influenced by the remote SST through its modulation of the local atmosphere [e.g., Chan, 2000; Yeh et al., 2010]. [4] For WNP TC activity, the effect of large-scale SST variations has been primarily investigated through El Niño- Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), or both [e.g., Wang and Chan, 2002; Camargo and Sobel, 2005; Ho et al., 2005; Chen et al., 2006; Camargo et al., 2007b; Chan, 2000, 2008]. ENSO significantly influences WNP TC activity on an interannual time scale: in years of El Niño, more TCs form over the southeastern part of the WNP basin, and their intensity tends to be stronger due to longer lifetime [Wang and Chan, 2002; Camargo and Sobel, 2005]. On the other hand, some studies demonstrated the PDO impact on WNP TC activity [e.g., Chan, 2008; Liu and Chan, 2008], wherein the PDO is considered to be the decadal component of ENSO variability. Because ENSO and the PDO are, respectively, representative of interannual and interdecadal variability in the Pacific Ocean, they should be treated together to interpret low-frequency variability of TC activity [e.g., Chan, 2008; Goh and Chan, 2010; Kubota and Chan, 2009]. For example, in the Philippines, landfalling TC frequencies are significantly higher in La Niña years than in El Niño years during the low PDO phase, while indistinguishable between El Niño and La Niña during the high PDO phase [Kubota and Chan, 2009]. Goh and Chan [2010] investigated the PDO influence on TC activity over the South China Sea as a factor independent of ENSO. [5] As mentioned at the beginning, relatively fewer studies have tackled the low-frequency variability of seasonal TC activity at landfall and associated heavy rainfall. Moreover, all of them focus on limit regions, not on the entire East Asian area (including China, Taiwan, Korea, and Japan). Therefore, it is worthwhile to study the dominant climate mode of TC-induced heavy rainfall covering entire East Asia and responsible large-scale mechanism. In fact, the low-frequency variability of TC-induced heavy rainfall can be speculated from the studies about WNP TC activity associated with ENSO or PDO. For example, more straightmoving TCs toward the South China Sea during La Niña autumn [Wu and Wang, 2004] may result in more seasonal TC-induced heavy rainfall over South China compared to its northeastern regions. In this conjecture, more TC approaches at a region are the governing factor for the increase in seasonal TC-induced heavy rainfall therein. This is indeed oversimplified by discarding several critical factors (such as orography, TC size, intensity and speed of TC, large-scale moisture flux, etc.) that have been frequently invoked in many case studies tackling TC precipitation pattern and intensity at landfall. Nevertheless, we find that the simplified thinking is not invalid in a climate research which tries to research the low-frequency climate mode over season. This is an empirical basis that ensures the validity of our approach in this work. [6] This paper is organized as follows. Section 2 describes the data used and the definition of TC-induced heavy rainfall. The dominant modes of seasonal TC-induced heavy rainfall over East Asia are demonstrated in section 3. Next, section 4 discusses the Pacific SST pattern relevant to the dominant mode of seasonal TC heavy rainfall over East Asia. Finally, summary and concluding remarks are given in section Data and Methods 2.1. Data [7] This study examines the TC best track data for the WNP basin taken from the Regional Specialized Meteorological Center Tokyo - Typhoon Center. The data set contains sixhourly (particularly three-hourly within a warning sphere of Japan) locations of TC center, minimum central pressure, and 10-min-averaged maximum sustained wind speed (v max ). All storm stages (including tropical depression and extratropical cyclone) are analyzed because a large amount of rainfall is often induced in spite of weakening of TC intensity after landfall. As WNP and East Asian TC activities are most vigorous from June through October, this period is typically defined as a typhoon season. For this reason, all analyses are confined to the typhoon season. [8] Daily rainfall data were obtained from stations operated by the governmental meteorological administration in China, Japan, Korea, and Taiwan. From numerous stations across the four countries, 196 stations in China, 118 stations in Japan, 14 stations in Korea, and 5 stations in Taiwan are selected based on the data quality and the non-negligible TC influence. Depending on the shortest available period in Japanese stations, the analysis period is confined to [9] The SST, tropospheric layer mean flow (TLMF; i.e., pressure-weighted vertically averaged winds between 300 and 850 hpa), horizontal winds and temperature at multiple levels (between 1000 and 100 hpa), and precipitable water are investigated in order to examine the relevant large-scale environments. The daily data set of the Extended Reconstruction SST (ERSST) version 3 [Smith et al., 2008] was obtained from the National Climatic Data Center, National Oceanic and Atmospheric Administration ( noaa.gov). The atmospheric variables were taken from the National Centers for the Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis data [Kalnay et al., 1996]. The horizontal resolutions are 2 2 and for ERSST and NCEP-NCAR reanalysis data, respectively. In addition, the PDO index was obtained from the Website of the University of Washington ( Methods [10] Before showing our analysis results, two primary definitions should be provided. First, TC-induced rainfall needs to be defined. In fact, it is more accurately measured by considering the asymmetric structure of TC s direct and indirect rainbands and checking station rainfall one-by-one within the cloud shield from satellite imagery. However, previous climatological studies adopted a simplified definition that uses an effective radius under the assumption of symmetric structure with a 5 TC radius [e.g., Kim et al., 2006; Lee et al., 2010]. 2of11

3 station to produce the station-based TC-induced heavy rainfall data. Figure 1. (a) Eigenvector and (b) normalized PC time series of the 1st EOF mode of TC-induced heavy rainfall over East Asia. The units are mm 5-month 1 (June through October) for the eigenvector. The time series is represented by an arbitrary unit divided by its standard deviation. Following this guideline, TC-induced rainfall is defined as station rainfall within a 5 radius from TC center. [11] The second definition concerns heavy rainfall. There are two representative methods commonly used to identify this term. One is based on a fixed threshold (e.g., 30 mm day 1 ) and the other uses a percentile threshold (e.g., 95th percentiles of daily rainfall records for each station). We follow the latter; that is, the accumulated rainfall over the 95th percentile of daily rainfall records for each station, because it is more objective to define heavy rainfall over wide regions without worrying about the region-dependent amount of seasonal total rainfall [Chu et al., 2009]. It is found that the daily rainfall amounts corresponding to the 95th percentile threshold vary from 24 mm day 1 to 138 mm day 1 among the 333 stations used (figure not shown). As a combination of two definitions, TC-induced heavy rainfall is yielded by the accumulated rainfall only for the TC-induced rainy events that exceed the threshold (i.e., the 95th percentile) during the typhoon season. This definition is separately applied for each 3. Results 3.1. Dominant Variation of TC-Induced Heavy Rainfall [12] To examine the dominant variation of seasonal-total TC-induced heavy rainfall over East Asia, an empirical orthogonal function (EOF) analysis is performed with the station-based TC-induced heavy rainfall data obtained by the procedure described in section 2. Figure 1 displays the eigenvector and normalized principal component (PC) time series of the first leading EOF mode with an explained variance of 21.6%. Since an explained variance of the second leading EOF mode drops to 9.5%, the first leading mode can be considered to be statistically independent from higher modes by rule of thumb [North et al., 1982]. Interestingly, a simple dipole pattern characterizes the eigenvector, which implies the importance of large-scale controls rather than the smaller-scale factors (e.g., orography). The eastern regions of Northeast Asia (e.g., Southeast-East China, Taiwan, and Japan) show positive values, while South China (more specifically, Guangdong Province in China) shows negative values. This mode explains that Southeast-East China, Taiwan, and Japan experience more (less) TC heavy rainfall when South China suffers less (more). Hereafter, we refer to south China as SC (blue circles) and Northeast Asian regions as NEA (red circles) for convenience. [13] Figure 2 depicts TC tracks that induced heavy rainfall at any stations over East Asia in the years when the amplitude of the normalized PC time series exceeds one standard deviation (s). The seven and six years of large positive (i.e., high PC 1s) and negative (i.e., low PC 1s) PC values are respectively selected from Figure 1b (Table 1). Although not shown in the figure, the plot of all TC tracks is quite similar to Figure 2, indicating that the majority of TCs induced heavy rainfall over East Asia. To compare the number of affecting TCs between NEA and SC, we count the entry numbers to NEA and SC. If a TC passes through both regions, it is considered to enter one of the regions where more stations experienced TC-induced heavy rainfall. In the high PC years, a total of 102 TCs formed in the WNP, and most of them (74) passed through NEA. More TCs formed in the southeastern Philippine Sea during the high PC years (Figure 2a). Those TCs reached stronger intensities during their lifetime because they spend more time over the warm open ocean where abundant thermodynamic energy is supplied [Kim et al., 2011]. In contrast, during the low PC years, relatively more TCs (45 out of 83) moved toward SC, wherein they induced heavy rainfall (Figure 2b). The rest recurved to NEA but scarcely passed through Southeast-East China and Taiwan. The difference in TC tracks between the high and low PC years is expressed in terms of gridded TC occurrence frequency (Figure 2c). This figure is strikingly consistent with the 1st EOF loading pattern (Figure 1a). This implies that the TC occurrence frequency near the landmass is the critical factor forming the 1st leading mode of TCinduced heavy rainfall over East Asia. [14] Although the order and details of the modes differ depending upon the analysis period and method, there has been a consensus among studies that the dipole pattern 3of11

4 experiencing TC-induced heavy rainfall. In the following section, we try to investigate the contributing large-scale environmental factors for the dipole pattern observed in areas that experienced TC-induced heavy rainfall, whereby the tracks and intensity of TCs approaching East Asia are modulated Influence of Pacific SST Variations [15] The potential influence of SSTs on the first mode of TC-induced heavy rainfall is investigated through composite differences of SSTs between the high and low PC years (Figure 3a). Light and dark shaded areas are statistically significant at the 95% and 99% confidence levels, respectively. In comparison to the low PC years (i.e., more TCinduced heavy rainfall in SC), anomalous warming prevails over the tropical central Pacific and midlatitude North Pacific Ocean in the high PC years (i.e., more TC-induced heavy rainfall in NEA). Interestingly, we notice that this anomalous SST pattern consists of the representative SST anomaly patterns related to ENSO and PDO [Zhang et al., 1997]. That is, we can get a similar pattern to Figure 3a if we add the SST anomalies during the typhoon season of El Niño and negative PDO (Figure 3d). Accordingly, we demonstrate the composite differences based on NINO and PDO indices, respectively (Figures 3b and 3c). Among various NINO indices representing ENSO, the NINO4 (5 S 5 N, 160 E 150 W) index is selected here due to large variability along the tropical central Pacific during the typhoon season. In the composite difference based on the NINO4 index, the positive SST anomalies dominate over the tropical central and eastern Pacific and the Gulf of Alaska, while the negative anomalies exist over the Maritime Continent and the midlatitude North Pacific Ocean along 40 N (Figure 3b). It is noted that the SST anomaly over the midlatitude North Pacific Ocean is very weak, which is distinct from that associated with the 1st EOF mode. [16] The zonally elongated strong SST anomaly over the North Pacific Ocean is clearly presented in the composite difference based on the PDO index (Figure 3c). Note that the PDO index is multiplied by 1 to show its negative phase as a representative pattern. If we simply add the two (Figures 3b and 3c), the 1st EOF mode-related pattern is almost reconstructed (figure not shown), which indicates the 1st EOF mode of TC-induced heavy rainfall over East Asia is explained by the combination of the two dominant climate variability in the Pacific Ocean, i.e., ENSO and PDO. The normalized time series of the 1st EOF PC, NINO4, PDO, and NINO4 PDO are displayed in Figure 3e. The correlation of the 1st EOF PC time series with NINO4 PDO is 0.48, which is higher than the correlation with NINO4 (0.36) or PDO Figure 2. TC tracks of the (a) seven high years and (b) six low years of EOF 1st PC time series, which brought heavy rainfall into East Asia. (c) Its difference between high and low years is also represented by gridded values with the 95% (light gray) and 99% (dark gray) confidence levels. between SC and NEA is one of the dominant variations of WNP TC tracks [e.g., Kim et al., 2005; Liu and Chan, 2008]. Based on these studies, one could infer that there would be a similar dipole pattern between SC and NEA in areas Table 1. List of High and Low EOF 1st PC, NINO4, and PDO Years for Typhoon Seasons (June to October) During PC Value Years EOF 1 PC High 1962, 1963, 1966, 1990, 2001, 2004, 2005 Low 1964, 1976, 1978, 1983, 1993, 1995 NINO4 High 1982, 1987, 1991, 1994, 1997, 2002, 2004 Low 1964, 1971, 1973, 1974, 1975, 1988, 1998, 1999 PDO High 1983, 1987, 1992, 1993, 1995, 1997 Low 1961, 1962, 1967, 1970, 1975, 1999, 2000, of11

5 Figure 3. Composite differences of SSTs (unit: C) between high and low years of time series for (a) EOF 1st PC, (b) NINO4, (c) PDO, and (d) NINO4 PDO, and (3) normalized time series of the indices (solid line: EOF 1st PC, white bar: NINO4, gray bar: PDO, dashed line: NINO4 PDO). Light and dark shaded areas are statistically significant at the 95% and 99% confidence levels, respectively. ( 0.15). This again supports that the dominant variation of TC-induced heavy rainfall over East Asia is the combined mode of ENSO and PDO. [17] What are the respective roles of ENSO and PDO in TC-induced heavy rainfall? How are they combined to yield the 1st EOF loading pattern? To deal with these questions, first of all, TC tracks that brought heavy rainfall are separately presented for the high and low years of NINO4 and PDO (Figures 4a, 4b, 4d, and 4e). Also presented are the composite differences of gridded TC occurrence frequency between the high and low years of NINO4 and PDO (Figures 4c and 4f). [18] In the high NINO4 years (Figure 4a), recurving tracks to NEA are more frequent than the straight-moving tracks to SC (9.9 yr 1 versus 3.3 yr 1 ). The number of TCs affecting SC (6.8 yr 1 ) increases substantially in the low NINO4 years (Figure 4b), though still slightly less than those moving to NEA (7.4 yr 1 ). The difference of gridded TC occurrence frequency between the high and low years of NINO4 clearly presents the eastward shift of TC tracks (Figure 4c). More frequent movement toward Japan during the SST warming over the central Pacific is consistent with Camargo et al. s [2007a, 2007b] finding using the cluster analysis of numerous WNP TC tracks. Next, TC tracks for the different phases of PDO (Figures 4d and 4e) and their difference of gridded TC occurrence frequency (Figure 4f ) are found to be distinctive from those based on NINO4. Compared to the high PDO years, more frequent TC impact 5of11

6 Figure 4. TC tracks of the (a) seven high years and (b) eight low years of NINO4, and those of the (d) eight low years and (e) six high years of PDO, which brought heavy rainfall into East Asia. Difference of tracks between high and low years of (c) NINO4 and (f) PDO is also represented by gridded values with the 95% (light gray) and 99% (dark gray) confidence levels. is observed in Southeast-East China and Taiwan during the low PDO years, while slightly less impact at the southeastern tip of the main island of Japan. Based on these track analyses, it is concluded that neither ENSO nor PDO solely explains the dominant variation of TC-induced heavy rainfall. Only the combined effect of the two leads to the anomalous pattern of gridded TC occurrence frequency (Figure 2c) and, in turn, the dominant variation of TCinduced heavy rainfall (Figure 1a) Atmospheric Large-Scale Environments [19] To understand the atmospheric processes that connect SSTs and TC-induced heavy rainfall, Figure 5 displays largescale environments such as the TLMF, vertical shear of zonal winds between 200 and 850 hpa, and 200-hPa zonal wind. Figure 5a shows the composite difference of TLMF based on the high and low years of the EOF 1st PC time series. From a climatological point of view, the TLMF is generally considered to be the primary factor steering TCs. During the high PC years, the decisive circulation pattern that causes more recurving TC tracks is the anomalous cyclonic steering flow centered over the eastern sea of Taiwan. This tropospheric layer-mean anomalous cyclone seems the ENSOrelated response as it is also found in the composite differences based on NINO4 (Figure 5b). The El Niño-induced anomalous cyclonic circulation over East Asia reflects an enhanced East Asian trough, which is known to deepen due to the cold land surface temperature over East Asia during the warming phase of ENSO [Wang and Zhang, 2002]. This cyclonic anomaly, which indicates the retreat of the subtropical high, weakens steering easterlies and, as a result, the number of recurving TCs toward Japan increases (Figure 4c), inducing an increase in heavy rainfall over NEA. [20] The role of PDO in variability of TC tracks is different from that of NINO4 (Figure 4c versus Figure 4f). The negative PDO phase is characterized by a gigantic anomalous 6of11

7 Figure 5. Composite differences of (a c) tropospheric layer mean flows (TLMF), (d f) vertical shear of zonal winds, and (g i) 200-hPa zonal winds between high and low years of the time series for EOF 1st PC (Figures 5a, 5d, and 5g), NINO4 (Figures 5b, 5e, and 5h), and PDO (Figures 5c, 5f, and 5i). All units are ms 1. Gray shaded grids in Figures 5a 5c are statistically significant at the 95% confidence level. Light and dark shaded areas in Figures 5d 5i are statistically significant at the 95% and 99% confidence levels, respectively. Thick solid lines in Figures 5g 5i are the climatological isotach of 25 m s 1. The rectangle in Figure 5f presents the area where pressure change rates were analyzed in Figure 7. high over Korea, north Japan, and the North Pacific Ocean, a weakened subtropical ridge along 20 N, and a shrunken monsoon trough along 10 N (Figure 5c). The TC movement toward Southeast-East China and Taiwan becomes frequent in the negative PDO phase possibly due to the impact of strong anomalous steering easterlies between 20 N and 40 N. This implies that the PDO, through its modulation in seasonal TC pathways, partly contributes to the dipole pattern especially concentrated on Southeast-East China and Taiwan. It should be noted that the TLMF related to the EOF 1st mode are also congruent with the combination of TLMFs related with NINO4 and PDO. [21] The difference in TC occurrence frequency between the high and low PC years (Figure 2c) is not as significant as that in TC-induced heavy rainfall in Japan (Figure 1a). This indicates that there may be additional factors other than their modulation of TC occurrence frequency near Japan. Among others, we choose the rate of intensity change of TCs toward Japan and the large-scale moisture amount (e.g., precipitable water) as the other plausible factors in addition to TC occurrence frequency due to their possible roles in the precipitation intensity. [22] As the vertical wind shear is one of representative variables that are critical for the TC intensification [Gray, 1979], its composite difference patterns based on the EOF 1st PC, NINO4 and PDO are, respectively, demonstrated in Figures 5d, 5e, and 5f. In addition, the upper-tropospheric zonal wind is also shown because of its dominant contribution to the vertical wind shear in the subtropics and midlatitudes and its representation of the East Asian jet (Figures 5g 5i). By comparing among the three composite differences, it can be seen that the EOF 1st mode-related anomalies of the two fields are also largely congruent with the combination of NINO4 and PDO like in the TLMF. In the high PC years when TCinduced heavy rainfall increased over NEA, the negative anomalies of vertical wind shear prevailed between 30 N and 40 N (Figure 5d). Based on the anomaly patterns, the role of the vertical wind shear looks different between ENSO and PDO. While the contribution of ENSO is more prevalent in the EOF 1st mode-related pattern south of 30 N, the contribution of PDO dominates over NEA. [23] The pattern of the vertical wind shear largely comes from the upper-troposphere (middle panels versus bottom panels in Figure 5). In the high PC or low PDO years, the 7of11

8 Figure 6. (a) Meridional vertical cross sections of climatology and composite differences of temperature (shaded) and zonal wind (contour) averaged between the longitudes 120 and 150 E between high and low years of the time series for (b) EOF 1st PC, (c) NINO4, and (d) PDO. Open rectangle and closed circle in (b c) are statistically significant at the 95% confidence level for temperature and zonal wind, respectively. East Asian jet is significantly decelerated (Figures 5g and 5i) and, as a result, the vertical wind shear is reduced over NEA (Figures 5d and 5f). It is well known that the change in the upper-level zonal winds is dynamically connected to that of the meridional temperature gradient through the thermal wind relationship [Holton, 1992]. Thus, we expect a large weakening of meridional temperature gradient over NEA in the high PC or low PDO years. Figure 6 shows the meridional vertical cross sections of temperature and zonal wind averaged between 120 E and 150 E. The climatological pattern is given in Figure 6a. In the high PC years, tropospheric temperature over the midlatitudes (i.e., N) significantly increases with its maximum in the upper-troposphere, which reduces the meridional temperature gradient and, in turn, the westerly jet is weakened (Figure 6b). These structures of temperature and zonal winds over the midlatitude region resemble those for the low phase of PDO (Figure 6d), while those over the low-latitude are more or less similar with the high phase of NINO4 (Figure 6c). Therefore, the weakened upper-level zonal winds are expected during low PDO years when the SST anomalies over the midlatitude North Pacific are positive, resulting in the weakened vertical wind shear over NEA. [24] The weaker vertical wind shear can delay the dissipation of TCs approaching Japan, which, in turn, may be thought to partly contribute to increases in TC-induced heavy rainfall in Japan by sustaining strong TC intensity. Figure 7 shows the dissipation rates of daily minimum central pressure calculated for the box area ( E, N, shown as a rectangle in Figure 5f) where the high probability of TC influence on Japan is expected. The dissipate rate is defined as the difference in the daily change rates of TC central pressure between before and after TC enters the box area. In line with our expectation, the dissipation rates are shown to be generally lower for the negative PDO years compared to the positive PDO years, as represented by the contracted box of the 25th 75th percentile range as well as the lower 10th and 90th percentile ticks of whiskers. The similar feature is obtained for the NINO4 index. During the positive NINO4 years, the vertical wind shear anomaly becomes positive along the latitudinal belt of N (Figure 5e), which may lead TCs to dissipate with higher rates as shown in Figure 7. However, the difference based on the EOF 1st PC is not as evident as that based on PDO or ENSO. Because the opposite phase of PDO and ENSO is involved in forming the EOF 1st mode, their respective influences in TC intensity seem to be canceled out. Similar to the dissipation rate, the actual TC intensity at landfall does not show any notable difference between the opposite phases of the EOF 1st PC either (not shown). These 8of11

9 Figure 7. Box and whisker diagram for pressure change rates of TCs that enter the rectangle ( E, N) as presented in Figure 5f. results imply that the dominant variation of TC-induced heavy rainfall in Japan is not necessarily explained by differences in TC intensity, except for the predominant PDO years. [25] Next, the precipitable water is examined to understand whether the background environmental moisture plays a possible role (Figure 8). More precipitable water is observed in the midlatitudes, including Japan, during the high PC years, compared to the low PC years (Figure 8a). This feature manifests the negative PDO phase (Figure 8c) even though the positive NINO4 phase plays an opposite role in the precipitable water near Japan (Figure 8b). Based on this feature, we speculate that the TC landfall in Japan with more abundant background precipitable water may produce heavier rainfall in Japan during the high EOF 1st PC years. Accordingly, the role of a humid background state can be nominated as another possible factor to build up the large EOF loading of TC-induced heavy rainfall in Japan Verification of the Predictability Using PDO and ENSO [26] The results presented so far suggest that the dominant spatiotemporal variation of seasonal TC-induced heavy rainfall over East Asia can be explained by the modulation of TC track and precipitable water by PDO and ENSO. Now it is necessary to check how well the phases of ENSO and/or PDO indices correspond to the actual anomalies of TCinduced heavy rainfall over the two regions. Figure 9 shows the time series of the actual precipitation anomalies averaged over SC (white bar, the stations where longitude 116 E, latitude 28 N) and NEA (gray bar, the rest stations) for the period of Black circles are marked above/below bars if more/less TC-induced heavy rainfall occurred over NEA/SC (SC/NEA) when the index of NINO4 minus PDO was positive (negative). Approximately 60% (27/45) and 56% (25/45) of the precipitation anomalies match the expected sign of the NINO4 minus PDO index over SC and NEA, respectively. In particular, the index accounts for 71% (10/14) and 80% (8/10) of the anomalies exceeding 1s (dashed line) over SC and NEA, respectively, demonstrating that the NINO4 minus PDO index is an effective indicator for predicting the anomalies of TC-induced heavy rainfall over the two regions. [27] Figure 10 presents the regional precipitation anomalies averaged over the years marked by the black circles in Figure 9. All the precipitation anomalies are shown as expected and are statistically significant at the 99% confidence level. During the years when NINO4 minus PDO is less than 0, significant positive and negative anomalies were observed over SC and NEA, respectively. The opposite is true during the years when NINO4 minus PDO was greater than 0. All of these results suggest that the combination of ENSO and PDO effectively contribute to the dipole pattern of TC-induced heavy rainfall between SC and NEA. 4. Summary and Concluding Remarks [28] This study examined the influence of SST changes in the tropical central and midlatitude North Pacific basins on seasonal-total TC-induced heavy rainfall over East Asia. Figure 8. Composite differences of precipitable water between high and low years of the time series for (a) EOF 1st PC, (b) NINO4, and (c) PDO. All units are kg m 2. Gray dots are statistically significant at the 95% confidence level. 9of11

10 Figure 11. Schematic diagram demonstrating the linkages among anomalous positive TC-induced heavy rainfall over NEA, more TCs toward NEA, and tropical central (+NINO4) and North Pacific ( PDO) SST warming. The black solid and dashed line with H and L denotes 10 and 5 hpa isotach of 500-hPa geopotential height anomalies, respectively (i.e., high and low pressure system). The gray line and blue dot area indicate the weakened areas vertical shear of zonal winds and more precipitable water, respectively. The dominant tracks of TC under this condition are also displayed by black arrow with TC mark. Figure 9. Time series of precipitation anomalies averaged over (a) SC and (b) NEA during The years when the anomalies are explained by the expected sign of the combined index of NINO4 minus PDO (e.g., the positive value of the index for negative SC and positive NEA anomalies) are marked by black circles. Figure 10. Area-averaged precipitation anomalies over SC (white bar) and NEA (gray bar) that are explained by the expected sign of the combined index of NINO4 minus PDO (marked by black circles in Figure 9). The vertical lines and dots indicate the significant limit with 95% and 99% confidence levels, respectively. Figure 11 shows a schematic diagram for the linkages during the high EOF 1st PC years among anomalous positive TCinduced heavy rainfall over NEA (i.e., Southeast-East China, Taiwan, and Japan), with more TCs found toward NEA, more precipitable water and weaker vertical wind shear over NEA, and tropical central (+NINO4) and North Pacific (-PDO) SST warming. The ENSO-related cyclonic flow anomaly centered over Southeast China impinging upon the PDO-related broad anticyclonic flow anomaly to its northeast was found to steer TCs toward NEA during the high phase of EOF 1st mode. Therefore, more frequent TC-induced heavy rainfall occurs over NEA where the TCs strike directly. In addition, the negative phase of PDO plays a distinct role in forming a reservoir of more precipitable water in the midlatitude NEA, which may play another possible role in inducing more TC-induced heavy rainfall over NEA (particularly in Japan). The role of vertical wind shear in sustaining TC intensity toward Japan is not as clear as that of precipitable water. The possible role of vertical wind shear may emerge as important as the other factors for the years when the PDO is predominant over ENSO. The response to the opposite phase is nearly the mirror-like image of Figure 11. [29] The dipole pattern between SC and NEA of TCinduced heavy rainfall explained 21.6% of the total variance. In addition, the correlation coefficient of its PC time series with the combined index of NINO4 minus PDO was These results are somewhat surprising in regards to the complex nature of TC-induced heavy rainfall. The respective time series of TC-induced heavy rainfall over SC and NEA were inversely related and explained fairly well by the sign of the combined index of NINO4 minus PDO (Figures 9 and 10). This again supports the idea that the combined impact of NINO4 and PDO on TC-induced heavy rainfall forms the dipole mode between SC and NEA. The finding of this study 10 of 11

11 should be considered as an extension of Lee et al. [2010], who showed that the dipole pattern between Guangdong and its neighbors to the northeast is the dominant mode in TCinduced rainfall over South China. Because the stations over the midlatitude East Asian regions (e.g., east and north China, Korea, and Japan) were not included in Lee et al. [2010], the variability of seasonal TC tracks was found to dominate the dipole mode over South China and Taiwan, which was also the case in this study (Figure 2). [30] This work revealed that SST variations in the tropical central and midlatitude North Pacific basins play a substantial role in modulating the variation of TC-induced heavy rainfall over East Asia. Those variations in SSTs can improve the predictability of heavy rainfall induced by TC through a statistical downscaling using the relationship between the predictor (e.g., the SST variability in the tropical central and midlatitude North Pacific basins) and the predictand (e.g., TC-induced heavy rainfall). There has been difficulty simulating TCs and heavy rainfall within the global climate model owing to computational power. Thus, the statistical downscaling based on the results of this study provides useful information for disaster mitigation, which will be our next step. [31] Acknowledgments. This research was supported by Basic Sciences Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( ) and the Korean Meteorological Administration Research and Development Program under grant CATER J.-H. 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