1. Introduction
Tropical cyclones (TC) are one of the most devastating natural disasters in South Korea, incurring approximately KRW 193 billion (approximately 0.2 billion U.S. dollars) in costs annually in the 2011–20 period, according to the annual disaster report released by the South Korean government. In addition to this direct effect, the recovery cost for TC-induced destruction was approximately KRW 509 billion (approximately 0.5 billion U.S. dollars) annually throughout the same period. To relieve the enormous expense caused by TC, various long-term government-level efforts have been made, such as numerical weather prediction, the introduction of supercomputers for weather forecasting, and the establishment of a central disaster relief center (which seems thus far effective) (Park et al. 2015). The current global climate change concern requires initiative. To achieve success in future preventative measures, it will be important to understand the long-term variation of TC activity, assisting in predicting TC activity more precisely.
TC activity in South Korea, in terms of both frequency and intensity, has interdecadal variations that are associated with the Pacific decadal oscillation (PDO) (Choi and Kim 2019; Choi et al. 2016, 2019; Choi et al. 2010). Choi et al. (2010) suggested that the analysis period, approximately five decades, can be classified into three subperiods based on the frequency in TC affecting South Korea. They also found that the TC intensity at landfall was stronger during the latest high-frequency period than during the other two periods. According to previous studies (Choi and Kim 2019; Choi et al. 2019), the number of TCs affecting South Korea is significantly correlated with PDO; a high frequency is observed in the negative phase of PDO and a low frequency is observed in the positive phase of PDO. In addition, TC intensity at landfall in the positive phase was slightly weaker than that in the negative phase. Min et al. (2021) argued that there is no discernable anthropogenic effect in the record-breaking high TC frequency in South Korea in 2019. These results imply that natural interdecadal variation is still predominant compared to the global warming signal for TC activity in South Korea.
A contradiction has been found, particularly in mechanisms between the studies, although many studies have consistently suggested that TC activity in South Korea is related to PDO in terms of both frequency and intensity. According to previous studies (Choi and Kim 2019; Choi et al. 2019), during the positive phase, favorable conditions for TC formation prevail in the northwestern part of the western North Pacific (WNP), resulting in a northwestward shift in TC genesis. In addition, large-scale environments do not favor the strengthening of TCs in the midlatitudes, which may be the main cause for the weaker TC intensity in the positive phase. However, this is not a general feature of the positive phase reported in other studies. During the positive phase in the tropics, the sea surface temperature (SST) cools in the western Pacific, while it warms in the central and eastern Pacific (Newman et al. 2016). This allows thermodynamic conditions to be advantageous for TC development in the southeastern area of the WNP but not in the northwestern area. In addition to the thermodynamic environment, the Walker circulation slows down during the positive phase (Garcia and Kayano 2008), and consequently, weaker vertical wind shear in the southeastern part of the WNP acts as a favorable condition for TC development (Liu and Chan 2013). Similarly, other studies have explained the recent reduction in TC genesis frequency, particularly in the southeastern WNP, based on La Niña–like SST warming (resembling the negative PDO), although they did not directly link their findings with PDO (Chang et al. 2021; Hsu et al. 2014). La Niña–like SST warming cannot be considered separately from a recent negative phase of the PDO (Sohn et al. 2013; Trenberth and Fasullo 2013; Zhao and Allen 2019). The conclusions of these studies match that of Liu and Chan (2013), which showed that although there is a significant positive correlation of PDO with TC frequency in the southeastern area of the WNP, there is no considerable correlation in the northwestern area of the WNP. Other studies also consistently showed a southeastward shift in the TC genesis location during the positive phase compared to the genesis location in the negative phase of PDO (Kim et al. 2020; Lee et al. 2021). These results suggest that a positive PDO enhances TC activity in the southeastern WNP but not in the northwestern WNP.
Hence, we reexamined the TC activity associated with PDO in South Korea in terms of both frequency and intensity. Related large-scale environments were also assessed to suggest a possible mechanism for our results.
2. Data and methods
Over the WNP, all TCs occurring from June to October of 1979–2020 were analyzed using the Joint Typhoon Warning Center (JTWC) best track data. The analysis period was chosen with the consideration of data reliability, as the geostationary meteorological satellite has operated in the basin since the late 1970s (Kossin et al. 2007). In this study, TC genesis refers to the time when a tropical disturbance first attained tropical storm strength; in other words, the point at which the maximum sustained wind speed was over 17 m s−1. As in previous studies (Choi and Kim 2019; Choi et al. 2019), in the latter part of the TC lifetime, TCs weakened to the stage of tropical depression (TD), and extratropical transitioned cyclone (ET) were included in the analysis. A TC was defined as a KOR TC at the point at which the TC center entered an area 300 km away from the coastline of South Korea, which is the same as in previous studies (Nam et al. 2018; Park et al. 2016, 2021). Note that the KOR TC definition used in this study corresponds well with the characteristics of the influential TCs officially acknowledged in the Typhoon White Book (National Typhoon Center 2011); the correlation coefficient between interannual variations of KOR TCs defined in this study and TCs reported in the Typhoon White Book is approximately 0.77 over the analysis period. As the TCs in the Typhoon White Book are indicated by forecasters that consider their tracks as well as their actual societal effect on the country, they seem to be the most reliable analog from both scientific and socioeconomic viewpoints. The KOR TC frequency was calculated by obtaining the number of KOR TCs that were present from June to October of each year, whereas the KOR TC intensity was obtained by averaging the maximum sustained wind speeds when the TCs first entered the area defined above. Following Park et al. (2013), the genesis and track frequency densities were calculated by counting the number of TCs in a 5° square window, moving at 1° latitude and longitude intervals at genesis time and during the lifetime, respectively. When the center of a TC appeared multiple times in a 5° square window, it was counted.
The PDO index was obtained directly from the National Centers for Environmental Information. The monthly values from June to October were averaged to calculate the correlations with the annual KOR TC activity. Positive and negative PDO phases were selected for years in which the average PDO index was greater or less than 0.5 standard deviations, respectively, from the climatological mean for 1979–2019. The years selected for the positive and negative PDO phases are listed in Table 1. The difference in the mean SST between the positive and negative PDO phases (negative minus positive) shows significant positive SST anomalies in the central and western North Pacific (above 20°N) and negative anomalies in the central and eastern tropical Pacific, which is a typical SST pattern of the negative PDO phase (Fig. S1 in the online supplemental material).
List of years classified by the positive and negative phases of PDO. The years have a PDO index beyond ±0.5 standard deviations from the climatological mean from June to October of 1979–2020.
Although the pattern resembles the ENSO-related SST pattern, the effect of ENSO on KOR TC activity can be disregarded. This is because preceding studies have already indicated that both the frequency and intensity of KOR TC are not statistically correlated with ENSO (Choi et al. 2011; Ho and Kim 2011). We confirmed this fact by calculating the correlation coefficients between the KOR TC activity and 10-yr low-pass filtered Niño-3.4 index (r = 0.01 for frequency and r = 0.09 for intensity, both of which are not statistically significant at the 90% confidence levels).
To confirm the long-term variability, Lanczos filtering was applied to filter the raw data with a 10-yr low pass (Duchon 1979). Note that in some years, no TC entered Korea, resulting in missing values for the KOR TC intensity. Hence, the missing values in KOR TC intensity were filled with the climatological average (57.95 kt; 1 kt ≈ 0.51 m s−1) before filtering. For correlation, the Pearson correlation was used. It is noted that the effective degrees of freedom were considered (Livezey and Chen 1983) when testing the statistical significance of the low-pass filtered data.
3. Results
Figure 1 presents the time series of the average PDO index as well as the KOR TC frequency and intensity. The average PDO index shows a dominant decadal variation (gray bars in Fig. 1). Overall, the PDO index is almost out of phase with the KOR TC frequency (blue dashed line in Fig. 1), and it is almost in phase with the KOR TC intensity (red solid line in Fig. 1). The 10-yr low-pass filtered PDO is significantly correlated with the 10-yr low-pass filtered KOR TC frequency (r = −0.34) and KOR TC intensity (r = +0.51) at the 90% confidence levels, respectively. Note that the raw PDO is also significantly correlated with the raw KOR TC frequency (r = −0.27) and KOR TC intensity (r = +0.34) at the 90% and 95% confidence levels, respectively.
Time series of raw and 10-yr low-pass filtered PDO index (gray bars and thick gray solid line), KOR TC frequency (blue dashed and thick blue dashed lines), and KOR TC intensity (red solid and thick red solid lines) during 1979–2020.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0302.1
On average, the KOR TC frequency increased by 1.8 in the negative PDO phase over that of the positive PDO phase, which was significant at the 95% confidence level (Table 2). As such, all calculations herein were based on a comparison of the negative and positive phases. The increased KOR TC frequency coincides with a previous study that showed that more TCs go toward East Asia during the negative phase of PDO (Basconcillo and Moon 2022). Conversely, the KOR TC intensity decreased by 10.1 kt on average, which was significant at the 90% confidence level (Table 2). The KOR TC frequency and ratio for each intensity category in the positive and negative PDO phases were also examined (Fig. 2). In the negative PDO phase, the frequency of weak TCs (i.e., TD + ET) and moderate TCs [tropical storms plus Saffir–Simpson category 1 typhoons (TS + CAT1)] significantly increased, whereas that of intense TCs [Saffir–Simpson category 2 and 3 typhoons (CAT23)] showed no significant change (Fig. 2a). For the ratio of each category to the total number of KOR TCs, the ratio of TDs in the negative PDO phase greatly increased from 7.4% to 25.0%, whereas that of CAT23 considerably decreased from 25.9% to 17.5% (Fig. 2b). South Korea has not experienced Saffir–Simpson category 4 and 5 typhoons for the periods in which the best track data are available. These results imply that, during the negative PDO phase, TCs tend to influence South Korea more frequently but with weaker intensity. This is inconsistent with previous studies in terms of the KOR TC intensity (Choi and Kim 2019; Choi et al. 2019), which is discussed in detail in the discussion.
Annual KOR TC mean (a) frequency and (b) ratio by each intensity category. The white, gray, and black bars indicate tropical depressions and extratropical transitioned cyclones (TD + ET), tropical storms plus Saffir–Simpson category 1 typhoons (TS + CAT1), and Saffir–Simpson category 2 and 3 typhoons (CAT23), respectively. The parentheses denote the value of each group.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0302.1
Average KOR TC frequencies (number) and intensities (kt) in the negative and positive PDO phases and the differences between the negative and positive PDO phases. Single and double asterisks indicate that the differences are statistically significant at the 90% and 95% confidence levels by the Mann–Whitney U test, respectively.
To understand the relationship between KOR TC activity and PDO, other statistics for KOR TC activity were examined. Figure 3 shows the difference in the genesis frequency density between the two PDO phases. In the negative PDO phase, the mean TC genesis location moved northwest within the main formation region of the KOR TCs (Fig. 3). The main formation region is plotted as a black box in Fig. 3, in which each side is set away from the climatological mean latitude and longitude of the KOR TC genesis location by one standard deviation in latitude and longitude (Fig. S2a). The difference in the mean TC genesis location between the two PDO phases (negative minus positive phase) was +4.2° in latitude and −8.1° in longitude, which was significant at the 95% confidence level. The advance of the TC genesis location toward the Korean Peninsula in the negative phase can lead to a greater number of TCs approaching South Korea.
Difference in genesis frequency density between the negative and positive PDO phases (negative minus positive phase). The black box denotes the main formation region of KOR TCs. The linear hatching and crosshatching indicate differences significant at the 90% and 95% confidence level by the Mann–Whitney U test, respectively.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0302.1
However, the northwestward shift in the mean TC genesis location is not sufficient to explain the increased KOR TC frequency during the negative phase, as TC translation is notably affected by the large-scale tropospheric background flow (Chan 2005; George and Gray 1976). Figure 4a shows the differences in TC track density between the two PDO phases. Here, the TC track density was plotted by considering only the TCs forming in the main formation region. This makes it possible to successfully disregard track changes in TCs that hardly affect South Korea. For example, if we include TCs forming east of 160°E, which generally cannot reach South Korea owing to strong westerlies in the midlatitudes (Figs. S3 and S4), their track changes could contaminate the track change signals, which are more important for understanding KOR TC activity. Note that the TC track density changes resemble TC genesis frequency density changes, i.e., northwest-positive and southeast-negative patterns (cf. Figs. 3 and 4a). The magnitudes of the track density differences are substantially large considering the magnitude of the track density climatology (Fig. S2b). This implies that the TC track is largely affected by the location of the TC genesis. However, positive anomalies extending from southern China to South Korea were found, whereas there were weak negative anomalies over Japan. This means that the northwest shift in the TC genesis location does not guarantee that the larger number of TCs affect the midlatitude countries, including Korea and Japan.
Difference in (a) track frequency density and (b) geopotential height at the 500 hPa level (Z500) with steering flow (arrows) between the negative and positive PDO phases (negative minus positive phase). The steering flow is defined as the pressure-weighted mean wind vector from the 850 to 200 hPa levels. The black boxes denote the main formation region of the KOR TCs. The linear hatching and crosshatching (yellow and green arrows) indicate the differences significant at the 90% and 95% confidence levels by the Mann–Whitney U test, respectively.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0302.1
The track density changes in the midlatitudes; that is, more and fewer TCs in Korea and Japan, respectively, were closely related to changes in the large-scale circulation (Fig. 4b). The arrows in Fig. 4b present the steering flow, which is the pressure-weighted mean wind from the 850 to 200 hPa levels (Chu 2002) and the 500 hPa geopotential height. In the negative PDO phase, anomalous anticyclonic circulation appears north of the WNP, which is a typical pattern related to PDO (Lee et al. 2021; Liu and Chan 2008). The southeasterly anomalies of steering flow related to this anticyclonic circulation provide favorable conditions for TCs to reach the Korean Peninsula. In other words, TCs are more frequently generated in the northwestern region of the WNP and are transported to the Korean Peninsula by the southeasterly steering flow in the negative PDO phase. Meanwhile, the easterly anomalies of steering flow in Japan may push TCs toward Korea while hindering TCs from approaching Japan. Thus, the large-scale tropospheric flow is responsible for the increased KOR TC frequency in the negative PDO phase along with the shifts in genesis location.
In terms of KOR TC intensity, however, the northwestward shift in the genesis location was a key factor for the weakened intensity during the negative phase. For a TC to grow in strength, it must stay in a warm ocean, that is, an energy source, for a longer time. As TCs form closer to the East Asian continent in the negative phase, the time TCs spend over the ocean is reduced; the duration from genesis to lifetime maximum intensity (LMI) significantly decreases by 1.6 days at the 95% confidence level (Table 3). This causes early termination of energy supply from the ocean; consequently, the average LMI is significantly reduced by 18.1 kt at the 95% confidence level (Table 3). Then again, the differences in intensification rates from genesis to LMI are negligible (Table 3), which suggests that the reduced LMI in the negative phase is mostly attributed to the shortened duration from genesis to LMI, that is, a closer genesis location to the East Asian continent. In addition, both the duration and decay rates from LMI to landfall were not significantly different (Table 3), indicating that the decreased LMI caused a decrease in the KOR TC intensity. Therefore, the mean genesis location changes mainly determined the KOR TC intensity.
Averages of LMI (kt), duration from genesis to LMI (days), duration from LMI to landfall (days), intensification rate from genesis to LMI (kt day−1), and decaying rate from LMI to landfall (kt day−1) of KOR TCs in the negative and positive PDO phases and their differences between the negative and positive PDO phases. Double asterisks indicate that the differences are statistically significant at the 95% confidence level by the Mann–Whitney U test, respectively.
To determine the large-scale circulations responsible for the mean TC genesis location changes, GPI and its components (i.e., relative humidity at 600 hPa, potential intensity, absolute vorticity at 850 hPa, and vertical wind shear) were examined (Fig. 5). The GPI difference spatial pattern corresponded to that of the TC genesis location (Figs. 3 and 5a), implying that GPI sufficiently explains the TC genesis location changes in response to PDO. To estimate the local changes in GPI in more detail, the main formation region is divided into two subregions (i.e., northwestern and southeastern regions; R1 and R2, respectively). Figures 5b–e display the difference of each GPI component between the two PDO phases. Relative humidity and potential intensity significantly increased over the entire KOR TC main formation region (Figs. 5b,c). Conversely, absolute vorticity significantly decreased in the southeastern region (where latitude < 20°N and longitude > 130°E) and slightly increased in the northwestern region (Fig. 5d). The vertical wind shear also significantly increased in the southeastern region (Fig. 5e).
Differences in (a) GPI, (b) relative humidity at 600 hPa, (c) potential intensity, (d) absolute vorticity at 850 hPa, and (e) vertical wind shear between the negative and positive PDO phases (negative minus positive phase). The black boxes denote the two subregions (i.e., northwestern and southeastern regions; R1 and R2, respectively) in the main formation region of KOR TCs. The linear hatching and crosshatching indicate the differences significant at the 90% and 95% confidence level by Mann–Whitney U test, respectively.
Citation: Journal of Climate 36, 9; 10.1175/JCLI-D-22-0302.1
The relative contribution of each component to the total GPI change estimated by taking the logarithm of GPI (Table 4) suggests that changes in thermodynamic conditions and vertical wind shear according to the PDO phases have key roles in the changes in TC genesis location. Table 4 indicates the total change in the logarithm of GPI and partial change induced by each component averaged over R1 and R2. The total change values averaged over R1 and R2 are 0.194 and −0.042, respectively. The thermodynamic environmental conditions (i.e., relative humidity and potential intensity) significantly contribute to the increase in GPI in both R1 and R2. The mean values of partial change related to the relative humidity are 0.206 and 0.145 in R1 and R2, respectively, and those related to the potential intensity are 0.096 and 0.107, respectively. The summation of the partial change related to these two components is considerably larger than the total change in both regions. The component that counteracts the effect of thermodynamic conditions is vertical wind shear. The vertical wind shear contributes to the reduction in GPI in both R1 and R2 regions. The mean values of partial change induced by vertical wind shear in R1 and R2 are −0.105 and −0.263, respectively. The negative effect of vertical wind shear overwhelms the positive effect of thermodynamic conditions, especially in R2. Therefore, the GPI can increase in the northwestern region but decrease in the southeastern region. However, the contribution of vorticity seems to be negligible.
Total change in the logarithm of GPI and partial change induced by relative humidity at 600 hPa (RH600), potential intensity (Vpot), absolute vorticity at 850 hPa (η850), and vertical wind shear (VWS) averaged over R1 and R2.
4. Discussion
Before concluding, it is necessary to examine the TC data reliability problem. According to Fig. 2, we can easily assume that the significant correlation between PDO and KOR TC frequency becomes weak when only TCs stronger than or equal to 17 m s−1 are considered in the analyses, as reported by a recent study by Chang et al. (2022). This is because the considerable change in TD + ET, as shown in Fig. 2, was disregarded in the calculation. This selection sensitivity according to TC intensity categories raises a data reliability issue, particularly considering that TD and ET are in the calculation. Generally, weak TCs are less reliable because they are difficult to accurately observe in terms of their positions and intensities (Kossin et al. 2013; Torn and Snyder 2012; Velden et al. 1998). The centers of weak TCs, whose eyes are usually covered by cirrus, have not been well identified (Torn and Snyder 2012). The Dvorak technique to estimate TC intensity is generally accurate for developed TCs whose eyes and eyewall structures are well organized (Kossin et al. 2013; Velden et al. 1998); thus, TDs and ETs could have less reliable intensity estimates.
Besides PDO, other possible factors influencing long-term variability on KOR TC activity were investigated, i.e., Atlantic multidecadal oscillation (AMO) and global warming, which are known factors affecting TC activity in the WNP (Chan and Liu 2022; Murakami et al. 2020; Sun et al. 2020; Zhang et al. 2018; Zhao et al. 2018, 2020). Table S2 shows that it is hard to detect any influence of AMO or global warming signals in KOR TC activity. As our main analysis period, 1979–2020, is not long enough to distinguish a global warming signal from the decadal mode, our results were tested by applying various extended analysis periods (1960–2020, 1970–2020, and 1979–2020). The correlation coefficients of AMO with KOR TC activity were found to be negligible. Note that for AMO, the time series were 10-yr low-pass filtered before the correlation coefficients were calculated. The linear trends of KOR TC activity were also very weak and not significant. However, the relationship between PDO and KOR TC activity seems much more robust than the other factors. Only the correlation coefficient of TC frequency with PDO over 1960–2020 was not statistically significant, differing from the other analysis periods.
5. Summary and conclusions
This study investigated variations in the KOR TC frequency and intensity according to the PDO phases. According to our results, the KOR TC frequency significantly increased with weaker intensity in the negative PDO phase compared to the KOR TC activity in the positive PDO phase. The changes in KOR TC activity are attributable to large-scale environmental changes. The larger KOR TC frequency in the negative phase is due to the northwestward shift in the mean TC genesis location and southeasterly anomalies over South Korea. Both facilitate the arrival of TCs on the Korean Peninsula. Research based on GPI and its components suggests that the northwestward shift in the TC genesis location can be explained by thermodynamic conditions and vertical wind shear. The southeasterly over the Korean Peninsula is related to anomalous anticyclonic circulation over the North Pacific in the negative phase, which is known as a typical pattern. In contrast, because of the northwestward shift in the TC genesis location, the time spent over the warm ocean was significantly reduced, such that LMI became weaker in the negative phase. As there are no discernible differences in duration and decay rate after the onset of LMI, the KOR TC intensity consequently decreases. Therefore, the northwestward TC genesis location is a key factor for the weak KOR TC intensity in the negative phase.
Our results are inconsistent with those of previous studies, particularly in terms of KOR TC intensity (Choi and Kim 2019; Choi et al. 2019), though various sensitivity tests performed in this study more support our conclusion. The reason for the discrepancy with previous studies (Choi and Kim 2019; Choi et al. 2019) could be the different best track data used, analysis period, KOR TC definition, and TC season defined (see Table S3). However, we obtained similar results with the present study using different data and methods, although some of the statistical significances differed (Tables S1, S2, and S4–S6). In Choi et al. (2019), the KOR TC was defined when the center of the TC entered north of 28°N and west of 132°E, whereas, in Choi and Kim (2019), it was defined when the center of the TC encountered the coastline of the Korean Peninsula. The analysis periods were 1951–2013 and 1981–2015 for Choi et al. (2019) and Choi and Kim (2019), respectively. Both studies mainly utilized the RSMC best track data and defined the TC season as July to September.
Acknowledgments.
This study was supported by the National Research Foundation of the Korean Government (NRF-2019R1I1A3A01058100, NRF-2020R1A4A3079510, and NRF-2021R1A6A3A14044418).
Data availability statement.
The JTWC best track data are available at https://www.metoc.navy.mil/jtwc/jtwc.html?western-pacific. The PDO index was obtained from https://www.ncdc.noaa.gov/teleconnections/pdo/. The ERA-5 data can be accessed at https://cds.climate.copernicus.eu/#!/search?text=ERA5&type=dataset. HadISST data can be downloaded from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. The data produced by this study (e.g., genesis and track frequency densities and GPI) are available from the corresponding author upon reasonable request.
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