Modulation of the Influence of ENSO on Northward-Moving Tropical Cyclones in the Western North Pacific by the North Atlantic Tripole SST Anomaly Pattern

Shuang Li aState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
bTianjin Key Laboratory for Oceanic Meteorology, Tianjin, China
cTianjin Institute of Meteorological Science, Tianjin, China
dUniversity of Chinese Academy of Sciences, Beijing, China

Search for other papers by Shuang Li in
Current site
Google Scholar
PubMed
Close
,
Ziniu Xiao aState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

Search for other papers by Ziniu Xiao in
Current site
Google Scholar
PubMed
Close
, and
Yuchun Zhao eXiamen Key Laboratory of Straits Meteorology, Xiamen, China

Search for other papers by Yuchun Zhao in
Current site
Google Scholar
PubMed
Close
Free access

Abstract

The frequency characteristics of northward-moving tropical cyclones (NTCs) in the western North Pacific (WNP) are analyzed, and the possible combined effect of El Niño–Southern Oscillation (ENSO) and the North Atlantic tripole (NAT) sea surface temperature anomaly (SSTA) is investigated. Results show that the NTC frequency in summer shows obvious interannual and decadal variations. The SSTA in the eastern tropical Pacific has an effect on the NTC frequency, but this relationship is modulated by the NAT on the decadal time scale. During positive NAT phases, the effect of ENSO on NTCs is clear. There are fewer NTCs in El Niño–following years, whereas in La Niña–following years the NTC frequency is higher. However, during negative NAT phases, only El Niño has an effect on the NTC frequency, whereas there is no obvious feature found for La Niña, which may be related to the asymmetry of ENSO. The combined effect of La Niña and positive NAT phases presents an anomalous meridional dipole circulation at the low latitudes and mid–high latitudes near East Asia, which leads to TCs moving northward. The cold SSTA response in the tropical Indian Ocean may contribute to an anomalous cyclone in the WNP. The negative–positive–negative NAT SSTA mode can persist into the ensuing summer and favor wave pattern propagating eastward along the high-level jet waveguide so that there exists an anomalous anticyclone in Northeast Asia, which helps TCs move farther north. The influence of El Niño modulated by negative NAT phases is roughly opposite.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ziniu Xiao, xiaozn@lasg.iap.ac.cn

Abstract

The frequency characteristics of northward-moving tropical cyclones (NTCs) in the western North Pacific (WNP) are analyzed, and the possible combined effect of El Niño–Southern Oscillation (ENSO) and the North Atlantic tripole (NAT) sea surface temperature anomaly (SSTA) is investigated. Results show that the NTC frequency in summer shows obvious interannual and decadal variations. The SSTA in the eastern tropical Pacific has an effect on the NTC frequency, but this relationship is modulated by the NAT on the decadal time scale. During positive NAT phases, the effect of ENSO on NTCs is clear. There are fewer NTCs in El Niño–following years, whereas in La Niña–following years the NTC frequency is higher. However, during negative NAT phases, only El Niño has an effect on the NTC frequency, whereas there is no obvious feature found for La Niña, which may be related to the asymmetry of ENSO. The combined effect of La Niña and positive NAT phases presents an anomalous meridional dipole circulation at the low latitudes and mid–high latitudes near East Asia, which leads to TCs moving northward. The cold SSTA response in the tropical Indian Ocean may contribute to an anomalous cyclone in the WNP. The negative–positive–negative NAT SSTA mode can persist into the ensuing summer and favor wave pattern propagating eastward along the high-level jet waveguide so that there exists an anomalous anticyclone in Northeast Asia, which helps TCs move farther north. The influence of El Niño modulated by negative NAT phases is roughly opposite.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Ziniu Xiao, xiaozn@lasg.iap.ac.cn

1. Introduction

Tropical cyclones (TCs) are an extremely serious type of natural disaster because of the strong gales, heavy rainfall, and storm surges that they usually bring (Avila and Rappaport 1996; Zhang et al. 2009; Park et al. 2011; Corporal-Lodangco et al. 2016; Zhou and Lu 2019). TC track changes have the potential to alter their areas of influence and the TC intensity (Zhang et al. 2018), and so the study of TC tracks is highly important. Based on data from 1965 to 2003, Wu et al. (2005) were the first to put forward that the impact of TCs on subtropical East Asia has increased and the influence over the South China Sea has declined considerably. Yang et al. (2018) also found that more TCs made landfall along the eastern China coast from 1998 to 2010. In recent years, some studies have pointed out that the TC tracks in the western North Pacific (WNP) exhibit an obvious variation: the westward movement is decreasing, and the northward movement is increasing (Zhao and Wu 2014; Daloz and Camargo 2018; Zhang et al. 2018; Sun et al. 2019; Tu et al. 2020). Indeed, in the past three years, there have been more northward-moving TCs (NTCs) in the WNP. In 2020, three TCs attacked Northeast China in half a month, which is unprecedented. Additionally, multimodel projections suggest that TC tracks in the WNP will continue to shift poleward in the next several decades (Wang et al. 2011; Nakamura et al. 2017; Tamarin-Brodsky and Kaspi 2017). Since the defensive ability with respect to TC disasters in northern China is relatively weak, NTCs tend to result in severe adverse impacts. Consequently, the study of NTCs carries an urgent and crucial significance.

It has been verified in previous studies that the western North Pacific subtropical high (WNPSH) plays an important role in NTCs (Ren et al. 2007; Huang and Wang 2010; Sun et al. 2015, 2017; Gao et al. 2017; Liu et al. 2018, 2019; Wu et al. 2020). For instance, Gao et al. (2017) found that the occurrence of an NTC is associated with the leading position, shape, and intensity of the WNPSH. Specifically, steering flow at the western edge of the WNPSH causes the NTC to move directly northward or to recurve. Liu et al. (2019) pointed out that, due to the WNPSH, most TCs near the east coast of China move northward in August, whereas more TCs move northeastwards in September. As the most significant signal of interannual variability in the climate system, El Niño–Southern Oscillation (ENSO) has an important effect on the WNPSH and TC activities in the WNP. Most studies indicate that, during the TC season following El Niño, the anomalous atmospheric circulation suppresses TC activities in the WNP, whereas TC activities are enhanced following La Niña (Chan 2000; Wang and Chan 2002; Xie et al. 2009; Chen and Wang 2018). Liu and Chan (2003) suggested that in early summer following El Niño (La Niña), fewer (more) TCs make landfall in South China. Furthermore, the TC tracks in the WNP as impacted by different patterns of El Niño decaying have also been investigated in detail (Ha et al. 2013; Kim et al. 2016). The results revealed that when eastern-Pacific-type El Niño events decay into La Niña, more TCs move westward and make landfall along the southern coast of East Asia. In contrast, if an eastern-Pacific-type El Niño decays into a neutral phase, the majority of TCs recurve northeastward and fewer move northwestward to make landfall along the East Asian coast. In the decaying years of a central-Pacific-type El Niño, TC activity is enhanced in the western WNP (Ha et al. 2013). Kim et al. (2016) also pointed out that TCs are more frequent over East Asia with relatively strong intensity when the decaying of a central-Pacific-type El Niño is prolonged or symmetric.

The tripole mode is one of the most important sea surface temperature anomaly (SSTA) modes in the North Atlantic. In recent years, numerous studies have revealed that the North Atlantic tripole (NAT) SSTA in winter and spring could affect the East Asian atmospheric circulation in summer by means of a teleconnection wave train (Wu et al. 2009, 2011, 2012; Zuo et al. 2012; Zheng et al. 2016; Li et al. 2018, 2019; Chen et al. 2020b). The NAT SSTA is a great seasonal predictor. Wu et al. (2009, 2012) showed that a positive–negative–positive NAT SSTA mode in spring can persist into the ensuing summer and trigger a downstream Rossby wave train to prevail over northern Eurasia, which strengthens the blocking highs over the Ural Mountains and Okhotsk Sea, meaning that subsequently the East Asian summer monsoon is strong. The effect of the negative–positive–negative NAT SSTA mode is opposite. Chen et al. (2020b) suggested that the positive phase of the North Atlantic Oscillation (NAO) can force the NAT SSTA pattern in winter, which switches to a dipole mode in summer through the air–sea interaction. This summer dipole SSTA induces a downstream atmospheric wave train, resulting in a positive geopotential height anomaly over northeastern Eurasia. However, the above is influenced by the interannual variation of the NAT, and the NAT also has apparent decadal variation (Wu and Liu 2005; Álvarez-García et al. 2011; Krishnamurthy and Krishnamurthy 2014, 2016). On the decadal time scale, a winter positive–negative–positive tripole SSTA mode in the North Atlantic can lead to more mei-yu precipitation (Gu et al. 2009).

ENSO and the NAT are two very important factors influencing the atmospheric circulation in East Asia in summer. Wu et al. (2009) investigated the joint interannual effect of a winter ENSO and spring NAO on the summer monsoon, but the combined effect of a winter ENSO and the decadal NAT signal on the summer atmospheric circulation is an open question. Additionally, in previous studies, the TC tracks in the WNP have usually been categorized into westward-moving, northwestward-moving, and northeastward-recurving (Wu et al. 2005; Liu and Chan 2008; Tian et al. 2010; Zhao et al. 2010, 2012; Park et al. 2014; Zhang et al. 2018) and are often studied from the perspective of either the interannual or decadal time scale. Meanwhile, there is lack of studies on NTCs, and it is necessary to combine the interannual with the decadal time scale in operational forecasts. To comprehensively understand the physical mechanism of NTC tracks and provide accurate forecasts of NTCs, the combined effect of a winter ENSO and the decadal NAT signal on NTCs is investigated in this study.

2. Data and methods

The TC best-track data used in this study are from the Shanghai Typhoon Institute of the China Meteorological Administration (CMA), and they include the TC location (latitude and longitude) and maximum sustained wind speed at 6-h intervals for the period 1965–2019 (Ying et al. 2014; Lu et al. 2021). (Note that the information was recorded at 3-h intervals within 24 h before landfall since 2017.) Several researchers have pointed out that the CMA’s TC dataset is particularly advantageous for TC activities affecting China owing to its more detailed and accurate information there (Liang et al. 2010; Ren et al. 2011). In terms of its definition, a TC here is a system generated in the WNP west of 180° with the maximum sustained wind speed reaching tropical storm intensity (17.2 m s−1).

Monthly and daily atmospheric variables including geopotential height, wind, and temperature are derived from the National Centers for Environmental Prediction and National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset with a horizontal resolution of 2.5° × 2.5° (Kalnay et al. 1996). The monthly SST data are from the Extended Reconstructed SST dataset of National Oceanic and Atmospheric Administration (NOAA), and our analysis uses version 5 (ERSST.v5) with a horizontal resolution of 2° × 2° (Huang et al. 2017). All of these datasets cover the period 1965–2019. The monthly outgoing longwave radiation data are from NOAA with a horizontal resolution of 2.5° × 2.5° for 1974–2019 (Liebmann and Smith 1996).

The Niño-3 index (5°S–5°N, 150°–90°W) is from the Climate Prediction Center of NOAA. The WNPSH intensity and westward-extending ridge indexes are provided by the National Climate Center of China. The WNPSH intensity index is defined as the accumulated value of the multiplication between the geopotential height subtracting 5870 gpm and the area on some grids. The grid mentioned above is where the 500-hPa geopotential height is larger than or equal to 5880 gpm in the domain (10°–60°N, 110°E–180°). The WNPSH westward-extending ridge index refers to the longitude of the westernmost 5880 gpm in the domain 10°–60°N, 110°E–180°.

To focus on the interannual and decadal variations, we applied the fast Fourier filtering to obtain the interannual component ≤ 7 years and decadal component > 7 years. Based on ±0.6 standard deviations of the filtered December–February-averaged Niño-3 index, ENSO is categorized into El Niño and La Niña. Here, we considered the interannual variation of ENSO and decadal variation of the NAT.

The strength of the synoptic-scale eddy activity is measured by the square of bandpass-filtered (2–8 days) meridional wind (Lee et al. 2012; Li et al. 2022). The Eady growth rate (EGR) is used to express the atmospheric baroclinicity, which is calculated as follows (Hoskins and Valdes 1990):
EGR=0.31fN|dudz|,
where f and N represent the Coriolis parameter and Brunt–Väisälä frequency, respectively; u is the zonal wind and z denotes the vertical height. The positive (negative) EGR anomaly shows the increment (reduction) of atmospheric baroclinicity.
Taking into account of the impact on northern China, an NTC is defined as a TC that enters the areas to the west of 130°E and to the north of 32°N (Fig. 1). Other statistical methods used in this work are power spectrum, Pearson correlation, composite, linear regression, and empirical orthogonal function (EOF) analysis. In addition, the statistical significance is evaluated using the Student’s t test, and the effective degree of freedom is calculated according to Yan et al. (2004) and Yu et al. (2019). To analyze the propagation of Rossby waves, the wave activity flux (WAF) from Plumb (1985) is calculated. The horizontal WAF formula is as follows:
Fλ=p2a2cosφp0[(ψλ)2ψ2ψλ2],
Fφ=p2a2p0(ψλψφψ2ψλφ),
where φ, λ, and ψ represent latitude, longitude, and streamfunction, respectively; a is Earth’s radius, variables marked with a prime denote zonal deviation, and p0 = 1000 hPa. Moreover, the preceding winter is defined as December–February—for example, winter 1965 would be the winter season of 1964/65. Likewise, spring is defined as March–May and summer as July and August. The period of analysis in this study is 1965–2019, and all data were detrended prior to our analysis to eliminate the possible effects of global warming.
Fig. 1.
Fig. 1.

The tracks of NTCs during 1965–2019. The red box represents the area of influence to the west of 130°E and to the north of 32°N.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

3. Variational characteristics of NTCs

The monthly distribution of the NTC frequency from 1965 to 2019 is shown in Fig. 2. As we can see, NTCs primarily occurred in the period from June to September, accounting for 97%. The earliest NTC appeared in April 2003, while the latest was in November 1972. A total of 126 NTCs occurred in the peak season of July and August, which is consistent with Zou et al. (1997) and Wang and Liang (2006). The NTC occurrence in July and August accounts for about 70% of the annual total. Therefore, the variational characteristics and factors influencing NTCs in July and August are the focus of our following analysis. The average NTC frequency in summer is 2.3 per year. As seen in Fig. 3a, the NTC frequency exhibits obvious interannual and decadal variation. Many more NTCs occurred in 1994 and 2018 than other years, with approximately 7 TCs moving northward. Also, the power spectrum of NTC frequency shows significant peaks at cycles of about 3 and 9 years, which indicates periodic variation on interannual and decadal time scales (Fig. 3b). The NTCs are generally strong, with 95% of them reaching strong tropical storm intensity (24.5 m s−1), in which the super typhoon grade (51 m s−1) accounts for 28%. In addition, the NTC frequency has a significant positive correlation with the TC genesis frequency in the WNP, with a correlation coefficient of 0.42 at the 99% confidence level. The proportion of the average NTC frequency to the average TC genesis frequency in the WNP is 24%. The highest NTC frequency was about 7 in 2018 when the TC genesis frequency was 14. This seems to indicate that there are more NTCs in the years when more TCs are generated. However, the data from 1965 to 2019 show that there are 25 years with high TC genesis frequency in the 55 years, in which more NTCs occur for 14 years, accounting for 56%. On the other hand, there is no obvious correlation found between the NTC frequency and the latitude of TC genesis in the WNP; their correlation coefficient is only 0.16. Therefore, a farther north location of TC genesis does not necessarily guarantee the northward moving of TCs.

Fig. 2.
Fig. 2.

Monthly distribution of the NTC frequency during 1965–2019. The number denotes the total NTC frequency in this month during 1965–2019.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

Fig. 3.
Fig. 3.

(a) NTC frequency variation in summer during 1965–2019. The red line represents the average. (b) Power spectrum analysis of NTC frequency. The blue line indicates statistical significance at the 90% confidence level, and the green line is the red noise spectrum.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

It was found, by means of filtering, that the interannual variation explains 75% of the total variance and the decadal variance contribution is 25%. The interannual and decadal variance contributions are both relatively large. Therefore, the NTC frequency may be influenced not only by the interannual variation but also by the decadal background.

4. Association between ENSO, the NAT, and NTCs

Previous studies show that TC tracks in the WNP are closely related to SSTA in the preceding winter (Chan 2000; Liu and Chan 2003; Ha et al. 2013; Kim et al. 2016; Li et al. 2018; Xie et al. 2018). As shown in Fig. 4a, the most significant negative correlation coefficient between the NTC frequency and SSTA is located in the eastern tropical Pacific on the interannual time scale, with a value of −0.4 at the 95% confidence level. Therefore, a colder SSTA in the eastern tropical Pacific in the preceding winter is conducive to more NTCs in the following summer. On the decadal time scale, the effective degree of freedom for the statistical significance of correlation is 15, and it is same in the following analysis. The distribution of correlation coefficients between the NTC frequency in summer and SSTA in the preceding winter on the decadal time scale is shown in Fig. 4b. As can be seen, the distribution of correlation coefficients presents a negative–positive–negative tripole pattern in the North Atlantic, which suggests that the NTC frequency is closely related to the preceding winter NAT (Fig. 4b). When the distribution of SSTA in the North Atlantic exhibits a negative–positive–negative pattern, it favors TCs moving northward in the following summer. Consequently, modulated by the decadal NAT signal, the interannual Niño-3 signal may be a key factor influencing the occurrence of NTCs.

Fig. 4.
Fig. 4.

Distribution of correlation coefficients between the NTC frequency in summer and SSTA in the preceding winter on the (a) interannual and (b) decadal time scales. Dotted areas denote statistical significance at the 95% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

In this study, the Niño-3 index represents the interannual eastern tropical Pacific SSTA, and the NAT index is represented by the normalized time series [first principal component (PC1)] associated with the first EOF mode of the decadal SSTA in the North Atlantic (0°–60°N, 90°W–0°). It can be seen in Fig. 5 that the first EOF mode consists of the tripole SSTA pattern and explains 44% of the total variance, which is similar to the correlation distribution in Fig. 4b. PC1 has apparent variation with period beyond 9 years, and a positive PC1 corresponds to negative–positive–negative tripole SSTA pattern in the North Atlantic. So PC1 is regarded as the NAT index to investigate the decadal regulation of the occurrence of NTCs. Figure 6 shows the variation in the normalized NTC frequency in summer along with the Niño-3 and NAT indexes in the preceding winter. The interannual components of the NTC frequency and Niño-3 index match well and their correlation coefficient is −0.42 at the 99% confidence level, which indicates that the Niño-3 index plays an important role in the NTC frequency in the following years. The decadal variation of NTC frequency is by and large in phase with that of the NAT, and their correlation coefficient is 0.66 at the 99% confidence level. Hence, it is favorable for TCs moving northward when the North Atlantic SSTA presents a negative–positive–negative mode. In the 55-yr study period, the opposite phases between the interannual and decadal components of NTC frequency appear for 31 years, in which Niño-3 index controlling the actual NTC frequency only occurs for 15 years. So the influence of Niño-3 index on the actual NTC frequency is weakened when the interannual and decadal components of NTC frequency are out of phase. For practical forecasting, it is crucial to take the decadal background into consideration. Considering not only the Niño-3 index but also the NAT index, the NTC frequency can be better confirmed. As a result, the actual NTC frequency is both affected by the Niño-3 index and modulated by the decadal background of the NAT.

Fig. 5.
Fig. 5.

(a) Spatial pattern of the first EOF mode of the low-pass-filtered North Atlantic SSTA in the preceding winter (°C). (b) Normalized PC1 associated with the first EOF mode.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

Fig. 6.
Fig. 6.

Variation of the normalized NTC frequency (bars) in summer along with the (a) Niño-3 index and (b) NAT index in the preceding winter.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

Then what are the features of NTCs in the following summer under the influence of Niño-3 index modulated by the NAT index? ENSO is identified based on the threshold of ±0.6 standard deviations. Table 1 shows anomalous NTC frequency in La Niña–following and El Niño–following years during different NAT phases, and it presents the more or fewer standard deviations of NTC frequency than the climate mean in parentheses. When the NAT is positive, generally there are obviously more NTCs in La Niña–following years and the average NTC is +1.2 standard deviations (+1.9 TCs). During this period, the probability of a more-NTC year is 86%, and the largest anomaly of the more-NTC years is +3.1 standard deviations (+5 TCs), which occurred in 2018. On the contrary, NTCs are fewer in the majority of El Niño–following years during positive NAT phases, with a mean of −0.5 standard deviations. Although the influence of a positive NAT phase is opposite to that of El Niño, compared with the relatively strong interannual signal of El Niño, the positive NAT phase is the relatively weak decadal background. So the NTCs seem to be mainly affected by El Niño during positive NAT phases. When the NAT is negative, El Niño events are distinctly associated with fewer NTCs, and the mean NTC is −0.9 standard deviations (−1.4 TCs). There is an 83% probability of a fewer-NTC year occurring, and the smallest anomaly of the fewer-NTC year is −1.6 standard deviations (−2.6 TCs). In addition, the probability of fewer NTCs is 67% in La Niña–following years during negative NAT phases. The mean NTC is −0.2 standard deviations, and the characteristic is not apparent. Consequently, negative NAT phases and La Niña may have an antagonistic effect and offset each other. Figure 7 further demonstrates this feature, in which the absolute value of the linear fitting slope is maximal during La Niña in the positive NAT phases and El Niño in the negative NAT phases, and it is the second largest in the positive NAT phases. The two linear fitting slopes mentioned above are significant at the 99% confidence level. Nevertheless, the linear fitting is not significant in the negative NAT phases. In addition, during ENSO in the positive NAT phases, the correlation coefficient between the NTC frequency and Niño-3 index is −0.7 at the 99% confidence level. During ENSO in the negative NAT phases, the insignificant correlation coefficient is −0.37. During La Niña in the positive NAT phases and El Niño in the negative NAT phases, the correlation coefficient is −0.73 at the 99% confidence level. Thus, with respect to the NTCs affected by ENSO, the NAT is an important modulation in the decadal background. In La Niña–following years, NTCs are apparently more frequent during positive NAT phases, which may be synergistic. However, there is no obvious feature during negative NAT phases, and the antagonistic effect probably comes into being. Similarly, in El Niño–following years, NTCs are apparently fewer in number during negative NAT phases and also tend to be fewer in number during positive NAT phases. The difference is that the extent to which they are fewer in number is smaller during positive NAT phases. Below, the individual and combined mechanisms of influence of ENSO and the NAT are studied in detail.

Fig. 7.
Fig. 7.

Scatterplot of the normalized NTC frequency and Niño-3 index. The asterisks represent ENSO, in which the blue (red) asterisks denote positive (negative) NAT phases. The blue (red) line indicates linear fitting of all years during positive (negative) NAT phases, and the green line is the linear fitting of the asterisks following La Niña during positive NAT phases and El Niño during negative NAT phases. The shaded region denotes the neutral-following years.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

Table 1

The anomalous summer NTC frequency in La Niña–following and El Niño–following years during different NAT phases from 1965 to 2019. The values in parentheses indicate more (+) or fewer (−) standard deviations of NTC frequency than the climate mean.

Table 1

5. Combined effect of ENSO and the NAT on NTCs

Owing to the more (fewer) NTCs in the years following La Niña (El Niño) during positive (negative) NAT phases, the composite difference between La Niña during positive NAT phases and El Niño during negative NAT phases is used to highlight the feature. TC tracks are mainly affected by the large-scale environmental flow. In general, integrating the wind fields between the low and high atmospheric layers is a good way to represent the steering flow. Here, following Yang et al. (2018), the wind fields between 850 and 200 hPa are integrated. First, in order to analyze the circulation pattern, we select the more-NTC (7 years) and fewer-NTC years (6 years) based on the threshold of ±1.0 standard deviations, and then we composite their difference in the anomalous steering flow and geopotential height at 500 hPa (Fig. 8a). As we can see, compared to the fewer-NTC years, in more-NTC years the significant anomalous cyclone is in the WNP, corresponding to the negative geopotential height anomaly at 500 hPa. This leads to the more eastward-shrinking and weaker WNPSH. The atmospheric circulation guides TCs to move northeastward, which is unfavorable for TC landfall along the coast of Southeast China. Meanwhile, a significant anomalous anticyclone appears in Northeast Asia, which could guide TCs to move northwestward so as to move farther north. Therefore, the cyclonic atmospheric circulation anomaly at the low latitudes and anticyclonic atmospheric circulation anomaly at the mid–high latitudes is conducive to TC moving to Northeast China, and the atmospheric circulation pattern is consistent with the previous result reported by Choi et al. (2015).

Fig. 8.
Fig. 8.

Composite anomalous steering flow [vectors; m s−1; the black (gray) vectors denote statistical significance at the 95% (90%) confidence level] and geopotential height at 500 hPa [shaded; gpm; the white (black) dots denote statistical significance at the 95% (90%) confidence level] in summer, showing the (a) difference between the more-NTC and fewer-NTC years, (b) difference between La Niña and El Niño, (c) difference between La Niña during positive NAT phases and El Niño during negative NAT phases, and (d) difference between positive and negative NAT phases. (e) Composite 5880 gpm at 500 hPa, in which the red (blue) contour denotes La Niña (El Niño) during positive (negative) NAT phases, and the black line denotes the climatology.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

To analyze the individual and combined influences of ENSO and the NAT on the NTC, the atmospheric circulations are composited in Figs. 8b–e. As suggested by previous studies, in La Niña–following (El Niño–following) years, an anomalous cyclone (anticyclone) exists in the WNP (Du et al. 2009, 2011; Xie et al. 2009, 2016; Tao et al. 2012; Chen et al. 2017; Tao et al. 2017). It can also be seen that there is significant anomalous cyclonic steering flow in the WNP, along with negative geopotential height anomaly at 500 hPa, in the individual La Niña–following years (Fig. 8b). The anomalous cyclonic steering flow at the low latitudes could lead to TCs moving northward and entering the Shandong Peninsula. However, it only permits TCs to the south of 38°N and is not conducive to farther northward movement. It is roughly opposite in the individual El Niño–following years. In spite of the weak anomalous steering flow affected by the NAT alone, a significant anomalous anticyclone exists at the mid–high latitudes during positive NAT phases (Fig. 8d), which could guide TCs to move farther north. Additionally, the NAT also has a weak impact on the anomalous circulation at the low latitudes. The effect of negative NAT phases is opposite by and large. With regard to La Niña, the significant anomalous anticyclonic circulation near Northeast China only appears at low levels and does not exist at mid–high levels (figure omitted). Therefore, the steering effect at the mid–high latitudes is not obvious in La Niña–following years.

Therefore, what is the circulation pattern in La Niña–following (El Niño–following) years during negative (positive) NAT phases? As shown in Fig. 8c, the circulation pattern is roughly consistent with that of the difference between the more-NTC and fewer-NTC years mentioned above (Fig. 8a). The combined effect of ENSO and the NAT also presents anomalous meridional dipole circulation at the low latitudes and mid–high latitudes near East Asia, so ENSO is a key factor influencing NTCs under the modulation of the NAT. The combination of ENSO and the NAT can better explain the circulation pattern of NTCs. When La Niña (El Niño) and positive (negative) NAT phases occur at the same time, the anomalous anticyclonic (cyclonic) steering flow in Northeast Asia becomes significant. The anomalous southeasterly flow of the anticyclone steers TCs to Northeast China, which is already located in the northern Philippines Sea under the effect of cyclonic steering flow in the WNP.

With regard to the TC movement in the WNP, the WNPSH is generally recognized as the major source of steering flow (Ho et al. 2004; Chu et al. 2012; Wang and Wang 2013; Wang and Chen 2018; Tu et al. 2020). Calculating the correlation of the WNPSH index, it is found that the correlation coefficient between Niño-3 index and the WNPSH intensity index is 0.67, while that between Niño-3 index and the WNPSH westward-extending ridge index is −0.69, both at the 99% confidence level. In the decadal background, the NAT index and the WNPSH intensity index are basically irrelevant, with a correlation coefficient of −0.08. Meanwhile, the correlation coefficient between the NAT index and the WNPSH westward-extending ridge index is 0.33, but it is not statistically significant. It can be seen in Fig. 8e that in La Niña–following years during positive NAT phases, the WNPSH is more eastward-shrinking and weaker, which is favorable for the northward moving of TCs.

The vertical wind shear and relative vorticity on the low level are two other important atmospheric-environment factors related to TC activity (Gao et al. 2018; Liu and Chan 2018; Li et al. 2021). The individual and combined influences of ENSO and the NAT on the vertical wind shear and relative vorticity are studied next. As shown in the low-level relative vorticity anomaly affected by La Niña alone (Fig. 9a), the largest significant positive anomaly is located in the central Philippines Sea. During positive NAT phases, it is weak and located farther northwest compared with La Niña (Fig. 9c). When La Niña is modulated by positive NAT phases, the significant positive vorticity anomaly in the central Philippines Sea is enhanced and stretches to the northwest, which favors TC moving northward (Fig. 9e). As for the vertical wind shear (Figs. 9b,d,f), the significant negative anomaly affected by La Niña alone is located in the central Philippine Sea, while the significant negative anomaly caused by positive NAT phases is located from the north of the Indochina Peninsula to the central Philippine Sea. When it is combined, the significant negative anomaly from South China to the central Philippine Sea is so strong that the NTC would be strengthened. The weak vertical wind shear also exists in the south of the Northeast Asia, although it is not significant, which favors TC moving northward. The situations of El Niño and negative NAT phases are roughly opposite to that of La Niña and positive NAT phases.

Fig. 9.
Fig. 9.

Composite anomalous relative vorticity at 850 hPa (10−6 s−1) and vertical wind shear (U200–U850; m s−1) in summer, showing the (a),(b) difference between La Niña and El Niño, (c),(d) difference between positive and negative NAT phases, and (e),(f) difference between La Niña during positive NAT phases and El Niño during negative NAT phases. Dots denote statistical significance at the 95% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

6. Possible physical mechanism for the NAT to modulate the ENSO influence on NTCs

It can be inferred from section 5 that the key atmospheric circulation systems in summer are the anomalous cyclonic (anticyclonic) circulation at the low latitudes and the anomalous anticyclonic (cyclonic) flow at the mid–high latitudes. To reveal how the anomalous circulation forms and explore the possible physical mechanism by which the NAT modulates the influence of ENSO on NTCs, the difference in SSTA and wind anomaly at 850 hPa from the preceding winter to summer is composited (Fig. 10). In the preceding winter (Fig. 10a), due to the dominating influence of La Niña (El Niño), the SSTA distribution from the tropical Indian Ocean to the tropical Pacific Ocean exhibits a negative–positive–negative (positive–negative–positive) pattern and there is anomalous cyclonic (anticyclonic) circulation in the WNP. Primarily influenced by positive (negative) NAT phases, the North Atlantic SSTA exhibits a negative–positive–negative (positive–negative–positive) meridional tripole pattern, with an NAO-like atmospheric circulation pattern.

Fig. 10.
Fig. 10.

Composite difference in the SSTA (shaded; °C; dots denote statistical significance at the 95% confidence level) and wind anomaly at 850 hPa [vectors; m s−1; black (gray) vectors are equal to or greater than the 95% (90%) confidence level] between La Niña during positive NAT phases and El Niño during negative NAT phases in (a) the preceding winter, (b) spring, and (c) summer.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

In spring (Fig. 10b), the distributional features of SSTA in the tropical Pacific Ocean and Indian Ocean change very little, and the strength is weakened in the tropical Pacific. The anomalous cyclone (anticyclone) still sustains in the WNP. The NAT SSTA also exists, and the tropical, midlatitude, and subpolar SSTA poles all become stronger, with a clear anomalous anticyclone (cyclone) at the mid–high latitudes. As suggested in previous studies, ENSO in the preceding winter can induce the interannual NAT SSTA signal in spring (Li et al. 2019; Yu et al. 2021). Figure 11a shows the regression of spring interannual SSTA on the Niño-3 index in the preceding winter, which is multiplied by a factor of −1. As we can see, the North Atlantic SSTA essentially appears a negative–positive–negative tripole pattern following La Niña, which is in accordance with Li et al. (2019) and Yu et al. (2021). Furthermore, the tropical Indian Ocean SSTA is still cold on the interannual time scale, corresponding to La Niña. In Fig. 11c, the decadal NAT signal in the preceding winter persists. In spring, the overlapping of the interannual and decadal signals possibly enhances the tripole SSTA pattern in the North Atlantic.

Fig. 11.
Fig. 11.

Regressed interannual SSTA (°C) in (a) spring and (b) summer on the Niño-3 index multiplied by ‒1, and decadal SSTA in (c) spring and (d) summer on the NAT index. Dots denote statistical significance at the 95% confidence level.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

In summer (Fig. 10c), in the combined effect of La Niña and positive NAT phases, there is still a cold SSTA in the tropical Indian Ocean, which sustains from the preceding winter to summer. Combined with the simultaneous outgoing longwave radiation (figure omitted), it is found that the cold SSTA in the tropical Indian Ocean corresponds to a positive outgoing longwave radiation anomaly, which indicates the convection anomaly is suppressed. The combined effect of El Niño and the negative NAT phases is roughly opposite. According to the Indian Ocean capacitor effect, this source of cold (warm) temperatures could stimulate cold (warm) Kelvin waves to penetrate the western Pacific, which would induce an anomalous cyclone (anticyclone) (Du et al. 2009, 2011; Xie et al. 2009, 2016; Tao et al. 2012; Yu et al. 2016; Chen et al. 2017). Figure 11b shows the individual effect of ENSO on the interannual SSTA in summer. The tropical Indian Ocean SSTA has a significant cold response to La Niña, and the tropical and subpolar North Atlantic SSTA is obviously cold. In addition, the decadal positive NAT phases not only influence North Atlantic, but also bring the weak cold SSTA in the north Indian Ocean (Fig. 11d). Therefore, on the one hand, positive NAT phases enhance the effect of La Niña on the tropical Indian Ocean SSTA in summer, which may contribute to the Indian Ocean discharge effect. On the other hand, La Niña strengthens the negative–positive–negative SSTA in the North Atlantic. Now attention is paid to the North Atlantic SSTA in the combined effect of ENSO and NAT (Fig. 10c). The NAT SSTA in the preceding winter persists to summer, in spite of the weak pattern in summer, and an anomalous anticyclone (cyclone) also exists constantly at the mid–high latitudes, which is weakened and moves northwestward in summer.

What is the nature of the relationship between the NAT SSTA and anomalous anticyclone in Northeast Asia? This is analyzed in detail next. Figure 12a shows the composite difference in the meridional wind anomaly and WAF at 300 hPa in summer. It can be seen that the anomalous anticyclone associated with the tripole SSTA is located in the western North Atlantic at the mid–high latitudes, which is a part of the wave pattern. The wave pattern tends to propagate eastward along the high-level jet waveguide and induce the anomalous anticyclone in Northeast Asia. Previous studies indicated that the synoptic-scale eddy forcing is crucial to the atmospheric wave pattern at the mid–high latitudes (Lau 1988; Zuo et al. 2013; Hu et al. 2018; Chen et al. 2020a; An et al. 2021). The development of synoptic-scale eddies at the mid–high latitudes is closely related to the atmospheric baroclinicity, which is represented as the EGR. Influenced by the negative–positive–negative NAT SSTA mode, the positive EGR anomaly can be seen between 50° and 70°N (Fig. 13a), which indicates that the atmospheric baroclinicity is strengthened. The strengthened atmospheric baroclinicity provides a favorable environment for the growth of synoptic-scale eddies (Fig. 13b), which induces the anomalous westerly wind and the anomalous anticyclone to its south (Lau 1988; Gong et al. 2011; Chen et al. 2014, 2020a). This explains the formation of the anomalous anticyclone in the western North Atlantic of the mid–high latitudes in association with the positive NAT phases. In addition, the anomalous westerly wind strengthens the high-level jet, along with the northward shift of the high-level jet, which contributes to the wave pattern propagating eastward (An et al. 2021), eventually resulting in the anomalous anticyclone in Northeast Asia.

Fig. 12.
Fig. 12.

The summer meridional wind anomaly (shaded; m s−1; dots denote statistical significance at the 90% confidence level) and WAF (vectors; m2 s−2) at 300 hPa (a) composited difference between La Niña during positive NAT phases and El Niño during negative NAT phases and (b) regressed on the summer NAT index. The purple contours represent the climatological zonal wind.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

Fig. 13.
Fig. 13.

Composite anomalous (a) EGR at 850 hPa (shaded; day−1) and (b) synoptic-scale eddies at 300 hPa (shaded; m2 s−2) in summer. Dots denote statistical significance at the 90% confidence level. The purple contours in (b) represent the climatological zonal wind.

Citation: Journal of Climate 36, 2; 10.1175/JCLI-D-21-0704.1

To further confirm the influence of actual summer NAT SSTA on the wave pattern, the summer NAT index is defined as the differences between the area-averaged SSTA of 38°–46°N, 75°–45°W and the area-averaged SSTA of 0°–30°N, 90°–30°W and 50°–70°N, 70°–15°W according to the composite map (Fig. 10c). As can be seen in Fig. 12b, the wave pattern regressed on the actual summer NAT index is roughly similar to the composite result in Fig. 12a, which verifies the importance of the summer NAT SSTA in the combined effect of the preceding winter ENSO and the NAT. To sum up, in the combined effect of La Niña and positive NAT phases, the negative–positive–negative NAT SSTA mode can sustain from the preceding winter to summer, which is conducive to the anomalous anticyclone at the mid–high latitudes. In summer, the wave pattern propagates eastward along the high-level jet waveguide and induces the anomalous anticyclone in Northeast Asia, which is favorable for TCs moving farther north. The combined effect of El Niño and negative NAT phases is roughly opposite.

7. Summary and discussion

Previous studies have suggested that ENSO in the preceding winter has an important influence on the TC tracks in the WNP (Chan 2000; Liu and Chan 2003; Ha et al. 2013; Kim et al. 2016; Li et al. 2018; Xie et al. 2018). In this study, the combined impact of ENSO and the NAT on NTCs has been further investigated. Results show that, for 1965–2019, the NTC occurrence peaks in the summer season. The summer NTC frequency shows prominent interannual and decadal variations. Furthermore, NTCs are generally strong, with the overwhelming majority reaching strong tropical storm intensity. There is no correlation found between NTC frequency and the latitudinal position of TC genesis in the WNP.

The SSTA in the eastern tropical Pacific has an effect on NTC frequency, but this relationship is modulated by the NAT on the decadal time scale. Considering not only the Niño-3 index but also the NAT index, the NTC frequency can be better confirmed. During positive NAT phases, the effect of ENSO on NTCs is clear. In El Niño–following years, NTCs are less frequent, whereas in La Niña–following years their frequency is higher. However, during negative NAT phases, only El Niño has an effect on the NTC frequency, with no obvious feature found in La Niña–following years. For El Niño during positive and negative NAT phases, in both cases there are fewer NTCs, but the difference is that the level of the fewer is greater during negative NAT phases.

In La Niña–following years during positive NAT phases, there is a significant anomalous cyclone in the WNP, corresponding to a more eastward-shrinking and weaker WNPSH. The atmospheric circulation can guide TCs to move northeastward and become unfavorable for TCs making landfall along the coast of Southeast China. Meanwhile, a significant anomalous anticyclone appears in Northeast Asia, which can guide TCs to move northwestward so as to move farther north. Consequently, the anomalous meridional dipole circulation at the low latitudes and mid–high latitudes near East Asia is the key atmospheric circulation system. In El Niño–following years during negative NAT phases, it is opposite by and large.

For La Niña modulated by positive NAT phases, the anomalous cyclone in the WNP in the following summer is associated with the cold SSTA in the tropical Indian Ocean responding to La Niña, which may stimulate Kelvin waves to penetrate into the western Pacific. In addition, the negative–positive–negative NAT SSTA mode can sustain from the preceding winter to summer, which is conducive to the anomalous anticyclone at the mid–high latitudes. In summer, the wave pattern propagates eastward along the high-level jet waveguide and induces the anomalous anticyclone in Northeast Asia. The anomalous meridional dipole circulation in the WNP and in Northeast Asia is favorable for TCs moving northward. The influence of El Niño modulated by negative NAT phases is roughly opposite.

This study concentrates mainly on the possible combined effect of La Niña (El Niño) and positive (negative) NAT phases on the occurrence of NTCs. However, in El Niño–following years during positive NAT phases, NTCs tend to be fewer, whereas there is no obvious feature in La Niña–following years during negative NAT phases. Why is the nonsynergistic effect of ENSO and the NAT not perfectly symmetrical? On the one hand, it is possible that ENSO has the most important influence on NTCs and that the NAT merely has a regulatory effect. On the other hand, it could be associated with the asymmetric climatic effects of ENSO. El Niño has larger amplitude compared with La Niña, so the climatic effect of El Niño is more remarkable. As a result, in El Niño–following years during positive NAT phases, the impact of the NAT would be smaller in comparison with El Niño, meaning it mainly reflects the influence of El Niño. As for La Niña and negative NAT phases, the extent of impact is roughly similar, meaning that the two factors offset each other. These aspects require further specific analysis in the future.

The NTC frequency possibly depends on the TC genesis frequency and TC tracks in the WNP. In this study, we have mainly analyzed the modulation of TC tracks on the NTC frequency. However, what is the relationship between the NTC frequency and TC genesis frequency in the combined influence of ENSO and the NAT? Table 2 shows the anomalous TC genesis frequency in the WNP in La Niña–following and El Niño–following years during different NAT phases. As can be seen, during negative NAT phases, the feature of TC genesis frequency is not apparent in La Niña–following years, whereas the TC genesis frequency is lower in El Niño–following years. It corresponds to the similar characteristic of NTC frequency in El Niño–following years during negative NAT phases, when the NTC frequency seems to be associated with the simultaneous TC genesis frequency. During positive NAT phases, whether in La Niña–following years or El Niño–following years, there is no apparent feature of TC genesis frequency, which is different from that of the NTC frequency. Consequently, TC genesis frequency in the WNP does not absolutely correspond to NTC frequency in La Niña–following and El Niño–following years during different NAT phases.

Table 2

The anomalous summer TC genesis frequency in the WNP in La Niña–following and El Niño–following years during different NAT phases from 1965 to 2019. The values in parentheses indicate more (+) or fewer (−) standard deviations of TC genesis frequency than the climate mean.

Table 2

Acknowledgments.

This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA20060501) and the Open Project of the Laboratory of Straits Meteorology (2020KX02). There are no conflicts of interest.

Data availability statement.

The TC best-track dataset from the CMA can be downloaded from http://tcdata.typhoon.org.cn/dlrdqx_sm.html. The ERSST.v5 sea surface temperature, outgoing longwave radiation, and NCEP reanalysis datasets are available at https://www.psl.noaa.gov/data/gridded/. The Niño-3 index can be accessed via https://www.cpc.ncep.noaa.gov/data/indices/ersst5.nino.mth.81-10.ascii. The WNPSH intensity and westward-extending ridge indices can be downloaded from http://cmdp.ncc-cma.net/Monitoring/cn_stp_wpshp.php.

REFERENCES

  • Álvarez-García, F. J., M. J. Ortiz-Bevia, and W. D. Cabos-Narvaez, 2011: On the structure and teleconnections of North Atlantic decadal variability. J. Climate, 24, 22092223, https://doi.org/10.1175/2011JCLI3478.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • An, X., L. Sheng, and J. Li, 2021: Synergistic effect of SST anomalies in the North Pacific and North Atlantic on summer surface air temperature over the Mongolian Plateau. Climate Dyn., 56, 14491465, https://doi.org/10.1007/s00382-020-05541-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Avila, L. A., and E. N. Rappaport, 1996: Atlantic hurricane season of 1994. Mon. Wea. Rev., 124, 15581578, https://doi.org/10.1175/1520-0493(1996)124<1558:AHSO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, J. C. L., 2000: Tropical cyclone activity over the western North Pacific associated with El Niño and La Niña events. J. Climate, 13, 29602972, https://doi.org/10.1175/1520-0442(2000)013<2960:TCAOTW>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., and K. Wang, 2018: Why is the tropical cyclone activity over the western North Pacific so distinct in 2016 and 1998 following super El Niño events? J. Meteor. Soc. Japan, 96, 97110, https://doi.org/10.2151/jmsj.2018-013.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., B. Yu, and W. Chen, 2014: An analysis on the physical process of the influence of AO on ENSO. Climate Dyn., 42, 973989, https://doi.org/10.1007/s00382-012-1654-z.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., R. Wu, W. Chen, K. Hu, and B. Yu, 2020a: Structure and dynamics of a springtime atmospheric wave train over the North Atlantic and Eurasia. Climate Dyn., 54, 51115126, https://doi.org/10.1007/s00382-020-05274-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, S., R. Wu, W. Chen, and K. Li, 2020b: Why does a colder (warmer) winter tend to be followed by a warmer (cooler) summer over northeast Eurasia? J. Climate, 33, 72557274, https://doi.org/10.1175/JCLI-D-20-0036.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, Z., Z. Wen, R. Wu, and Y. Du, 2017: Roles of tropical SST anomalies in modulating the western North Pacific anomalous cyclone during strong La Niña decaying years. Climate Dyn., 49, 633647, https://doi.org/10.1007/s00382-016-3364-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choi, K.-S., S. Park, K.-H. Chang, and J.-H. Lee, 2015: A possible relationship between East Indian Ocean SST and tropical cyclone affecting Korea. Nat. Hazards, 76, 283301, https://doi.org/10.1007/s11069-014-1489-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P.-S., J.-H. Kim, and Y. Ruan Chen, 2012: Have steering flows in the western North Pacific and the South China Sea changed over the last 50 years? Geophys. Res. Lett., 39, L10704, https://doi.org/10.1029/2012GL051709.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Corporal-Lodangco, I. L., L. M. Leslie, and P. J. Lamb, 2016: Impacts of ENSO on Philippine tropical cyclone activity. J. Climate, 29, 18771897, https://doi.org/10.1175/JCLI-D-14-00723.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daloz, A. S., and S. J. Camargo, 2018: Is the poleward migration of tropical cyclone maximum intensity associated with a poleward migration of tropical cyclone genesis? Climate Dyn., 50, 705715, https://doi.org/10.1007/s00382-017-3636-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, Y., S.-P. Xie, G. Huang, and K. Hu, 2009: Role of air–sea interaction in the long persistence of El Niño–induced north Indian Ocean warming. J. Climate, 22, 20232038, https://doi.org/10.1175/2008JCLI2590.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Du, Y., L. Yang, and S.-P. Xie, 2011: Tropical Indian Ocean influence on northwest Pacific tropical cyclones in summer following strong El Niño. J. Climate, 24, 315322, https://doi.org/10.1175/2010JCLI3890.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gao, S., T. Zhao, L. Song, Z. Meng, J. Luo, L. Xu, and B. He, 2017: Study of northward moving tropical cyclones in 1949–2015 (in Chinese). Mater. Sci. Technol., 45, 313323, https://doi.org/10.19517/j.1671-6345.20160229.

    • Search Google Scholar
    • Export Citation
  • Gao, S., Z. Chen, and W. Zhang, 2018: Impacts of tropical North Atlantic SST on western North Pacific landfalling tropical cyclones. J. Climate, 31, 853862, https://doi.org/10.1175/JCLI-D-17-0325.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gong, D.-Y., J. Yang, S.-J. Kim, Y. Gao, D. Guo, T. Zhou, and M. Hu, 2011: Spring Arctic Oscillation–East Asian summer monsoon connection through circulation changes over the western North Pacific. Climate Dyn., 37, 21992216, https://doi.org/10.1007/s00382-011-1041-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gu, W., C. Li, X. Wang, W. Zhou, and W. Li, 2009: Linkage between mei-yu precipitation and North Atlantic SST on the decadal timescale. Adv. Atmos. Sci., 26, 101108, https://doi.org/10.1007/s00376-009-0101-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ha, Y., Z. Zhong, X. Yang, and Y. Sun, 2013: Different Pacific Ocean warming decaying types and northwest Pacific tropical cyclone activity. J. Climate, 26, 89798994, https://doi.org/10.1175/JCLI-D-13-00097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ho, C.-H., J.-J. Baik, J.-H. Kim, D.-Y. Gong, and C.-H. Sui, 2004: Interdecadal changes in summertime typhoon tracks. J. Climate, 17, 17671776, https://doi.org/10.1175/1520-0442(2004)017<1767:ICISTT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoskins, B. J., and P. J. Valdes, 1990: On the existence of storm-tracks. J. Atmos. Sci., 47, 18541864, https://doi.org/10.1175/1520-0469(1990)047<1854:OTEOST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hu, K., G. Huang, R. Wu, and L. Wang, 2018: Structure and dynamics of a wave train along the wintertime Asian jet and its impact on East Asian climate. Climate Dyn., 51, 41234137, https://doi.org/10.1007/s00382-017-3674-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 81798205, https://doi.org/10.1175/JCLI-D-16-0836.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, R., and L. Wang, 2010: Interannual variation of the landfalling locations of typhoons in China and its association with the summer East Asia/Pacific pattern teleconnection (in Chinese). Chin. J. Atmos. Sci., 34, 853864, https://doi.org/10.3878/j.issn.1006-9895.2010.05.01.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J.-S., S. T. Kim, L. Wang, X. Wang, and Y.-Il Moon, 2016: Tropical cyclone activity in the northwestern Pacific associated with decaying central Pacific El Niños. Stochastic Environ. Res. Risk Assess., 30, 13351345, https://doi.org/10.1007/s00477-016-1256-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, L., and V. Krishnamurthy, 2014: Decadal scale oscillations and trend in the Indian monsoon rainfall. Climate Dyn., 43, 319331, https://doi.org/10.1007/s00382-013-1870-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krishnamurthy, L., and V. Krishnamurthy, 2016: Teleconnections of Indian monsoon rainfall with AMO and Atlantic tripole. Climate Dyn., 46, 22692285, https://doi.org/10.1007/s00382-015-2701-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lau, N.-C., 1988: Variability of the observed midlatitude storm tracks in relation to low-frequency changes in the circulation pattern. J. Atmos. Sci., 45, 27182743, https://doi.org/10.1175/1520-0469(1988)045<2718:VOTOMS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S.-S., J.-Y. Lee, B. Wang, K.-J. Ha, K.-Y. Heo, F.-F. Jin, D. M. Straus, and J. Shukla, 2012: Interdecadal changes in the storm track activity over the North Pacific and North Atlantic. Climate Dyn., 39, 313327, https://doi.org/10.1007/s00382-011-1188-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, H., K. Fan, H. Li, and Z. Xu, 2022: Impacts of central tropical Pacific SST on the reversal of December and January surface air temperature anomalies over Central Asia. Front. Earth Sci., 10, 873040, https://doi.org/10.3389/feart.2022.873040.

    • Search Google Scholar
    • Export Citation
  • Li, J., F. Zheng, C. Sun, J. Feng, and J. Wang, 2019: Pathways of influence of the Northern Hemisphere mid-high latitudes on East Asian climate: A review. Adv. Atmos. Sci., 36, 902921, https://doi.org/10.1007/s00376-019-8236-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, S., Z. Xiao, and Y. Zhao, 2021: Combined effect of the PDO and ENSO on the date of the first tropical cyclone landfall in continental East Asia. J. Geophys. Res. Atmos., 126, e2020JD034059, https://doi.org/10.1029/2020JD034059.

    • Search Google Scholar
    • Export Citation
  • Li, W., H.-C. Ren, J. Zuo, and H.-L. Ren, 2018: Early summer southern China rainfall variability and its oceanic drivers. Climate Dyn., 50, 46914705, https://doi.org/10.1007/s00382-017-3898-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liang, J., F. Ren, and X. Yang, 2010: Study on the differences between CMA and JTWC tropical cyclone datasets for northwest Pacific (in Chinese). Acta Oceanol. Sin., 32, 1022.

    • Search Google Scholar
    • Export Citation
  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, https://doi.org/10.1175/1520-0477-77.6.1274.

    • Search Google Scholar
    • Export Citation
  • Liu, K. S., and J. C. L. Chan, 2003: Climatological characteristics and seasonal forecasting of tropical cyclones making landfall along the South China coast. Mon. Wea. Rev., 131, 16501662, https://doi.org/10.1175//2554.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, K. S., and J. C. L. Chan, 2008: Interdecadal variability of western North Pacific tropical cyclone tracks. J. Climate, 21, 44644476, https://doi.org/10.1175/2008JCLI2207.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, K. S., and J. C. L. Chan, 2018: Changing relationship between La Niña and tropical cyclone landfalling activity in South China (La Niña and TC landfalling activity in South China). Int. J. Climatol., 38, 12701284, https://doi.org/10.1002/joc.5242.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Q., T. Li, and W. Zhou, 2018: Impact of 10–60-day low-frequency steering flows on straight northward-moving typhoon tracks over the western North Pacific. J. Meteor. Res., 32, 394409, https://doi.org/10.1007/s13351-018-7035-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, Q., W. Zhou, M. Peng, and T. Li, 2019: Factors controlling northward and north-eastward moving tropical cyclones near the coast of East Asia. Front. Earth Sci., 13, 778790, https://doi.org/10.1007/s11707-019-0797-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, X., H. Yu, M. Ying, B. Zhao, S. Zhang, L. Lin, L. Bai, and R. Wan, 2021: Western North Pacific tropical cyclone database created by the China Meteorological Administration. Adv. Atmos. Sci., 38, 690699, https://doi.org/10.1007/s00376-020-0211-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, J., and Coauthors, 2017: Western North Pacific tropical cyclone model tracks in present and future climates. J. Geophys. Res. Atmos., 122, 97219744, https://doi.org/10.1002/2017JD027007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Park, D.-S. R., C.-H. Ho, J.-H. Kim, and H.-S. Kim, 2011: Strong landfall typhoons in Korea and Japan in a recent decade. J. Geophys. Res., 116, D07105, https://doi.org/10.1029/2010JD014801.

    • Search Google Scholar
    • Export Citation
  • Park, D.-S. R., C.-H. Ho, and J.-H. Kim, 2014: Growing threat of intense tropical cyclones to East Asia over the period 1977–2010. Environ. Res. Lett., 9, 014008, https://doi.org/10.1088/1748-9326/9/1/014008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Plumb, R. A., 1985: On the three-dimensional propagation of stationary waves. J. Atmos. Sci., 42, 217229, https://doi.org/10.1175/1520-0469(1985)042<0217:OTTDPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, F., J. Liang, G. Wu, W. Dong, and X. Yang, 2011: Reliability analysis of climate change of tropical cyclone activity over the western North Pacific. J. Climate, 24, 58875898, https://doi.org/10.1175/2011JCLI3996.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ren, S. L., Y. M. Liu, and G. X. Wu, 2007: Interactions between typhoon and subtropical anticyclone over western Pacific revealed by numerical experiments (in Chinese). Acta Meteor. Sin., 65, 329340, https://doi.org/10.11676/qxxb2007.032.

    • Search Google Scholar
    • Export Citation
  • Sun, J., D. Wang, X. Hu, Z. Ling, and L. Wang, 2019: Ongoing poleward migration of tropical cyclone occurrence over the western North Pacific Ocean. Geophys. Res. Lett., 46, 91109117, https://doi.org/10.1029/2019GL084260.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y., Z. Zhong, L. Yi, T. Li, M. Chen, H. Wan, Y. Wang, and K. Zhong, 2015: Dependence of the relationship between the tropical cyclone track and western Pacific subtropical high intensity on initial storm size: A numerical investigation. J. Geophys. Res. Atmos., 120, 11 45111 467, https://doi.org/10.1002/2015JD023716.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, Y., and Coauthors, 2017: Impact of ocean warming on tropical cyclone track over the western North Pacific: A numerical investigation based on two case studies. J. Geophys. Res. Atmos., 122, 86178630, https://doi.org/10.1002/2017JD026959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tamarin-Brodsky, T., and Y. Kaspi, 2017: Enhanced poleward propagation of storms under climate change. Nat. Geosci., 10, 908913, https://doi.org/10.1038/s41561-017-0001-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, L., L. Wu, Y. Wang, and J. Yang, 2012: Influence of tropical Indian Ocean warming and ENSO on tropical cyclone activity over the western North Pacific. J. Meteor. Soc. Japan, 90, 127144, https://doi.org/10.2151/jmsj.2012-107.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tao, W., G. Huang, R. Wu, K. Hu, P. Wang, and D. Chen, 2017: Asymmetry in summertime atmospheric circulation anomalies over the northwest Pacific during decaying phase of El Niño and La Niña. Climate Dyn., 49, 20072023, https://doi.org/10.1007/s00382-016-3432-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tian, H., C. Li, and H. Yang, 2010: Modulation of typhoon tracks over the western North Pacific by the intraseasonal oscillation (in Chinese). Chin. J. Atmos. Sci., 34, 559579, https://doi.org/10.3878/j.issn.1006-9895.2010.03.09.

    • Search Google Scholar
    • Export Citation
  • Tu, J.-Y., J.-M. Chen, L. Wu, and C.-Z. Chi, 2020: Inter-decadal and inter-annual variability of meridional tropical cyclone activity during September–October in the northwestern North Pacific after 1998. Int. J. Climatol., 40, 16861702, https://doi.org/10.1002/joc.6295.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and J. C. L. Chan, 2002: How strong ENSO events affect tropical storm activity over the western North Pacific. J. Climate, 15, 16431658, https://doi.org/10.1175/1520-0442(2002)015<1643:HSEEAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C., and X. Wang, 2013: Classifying El Niño Modoki I and II by different impacts on rainfall in southern China and typhoon tracks. J. Climate, 26, 13221338, https://doi.org/10.1175/JCLI-D-12-00107.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, L., and G. Chen, 2018: Relationship between South China Sea summer monsoon onset and landfalling tropical cyclone frequency in China. Int. J. Climatol., 38, 32093214, https://doi.org/10.1002/joc.5485.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, R., L. Wu, and C. Wang, 2011: Typhoon track changes associated with global warming. J. Climate, 24, 37483752, https://doi.org/10.1175/JCLI-D-11-00074.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., and J. Liang, 2006: Some climatic features of tropical cyclones influencing Northern China for recent 52 years (in Chinese). Meteor. Mon., 32, 7680, https://doi.org/10.3969/j.issn.1000-0526.2006.10.012.

    • Search Google Scholar
    • Export Citation
  • Wu, L., and Z. Liu, 2005: North Atlantic decadal variability: Air–sea coupling, oceanic memory, and potential Northern Hemisphere resonance. J. Climate, 18, 331349, https://doi.org/10.1175/JCLI-3264.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, L., B. Wang, and S. Geng, 2005: Growing typhoon influence on East Asia. Geophys. Res. Lett., 32, L18703, https://doi.org/10.1029/2005GL022937.

  • Wu, Q., X. Wang, and L. Tao, 2020: Interannual and interdecadal impact of western North Pacific subtropical high on tropical cyclone activity. Climate Dyn., 54, 22372248, https://doi.org/10.1007/s00382-019-05110-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, R., S. Yang, S. Liu, L. Sun, Y. Lian, and Z. Gao, 2011: Northeast China summer temperature and North Atlantic SST. J. Geophys. Res., 116, D16116, https://doi.org/10.1029/2011JD015779.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., B. Wang, J. Li, and F.-F. Jin, 2009: An empirical seasonal prediction model of the East Asian summer monsoon using ENSO and NAO. J. Geophys. Res., 114, D18120, https://doi.org/10.1029/2009JD011733.

    • Search Google Scholar
    • Export Citation
  • Wu, Z., J. Li, Z. Jiang, J. He, and X. Zhu, 2012: Possible effects of the North Atlantic Oscillation on the strengthening relationship between the East Asian summer monsoon and ENSO. Int. J. Climatol., 32, 794800, https://doi.org/10.1002/joc.2309.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, P., L. Tao, J. Li, and D. Huang, 2018: Variation of tropical cyclone track in the western North Pacific during ENSO developing and decaying years (in Chinese). Chin. J. Atmos. Sci., 42, 987999, https://doi.org/10.3878/j.issn.1006-9895.1708.17118.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., K. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T. Sampe, 2009: Indian Ocean capacitor effect on Indo-western Pacific climate during the summer following El Niño. J. Climate, 22, 730747, https://doi.org/10.1175/2008JCLI2544.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., Y. Kosaka, Y. Du, K. Hu, J. S. Chowdary, and G. Huang, 2016: Indo-western Pacific Ocean capacitor and coherent climate anomalies in post-ENSO summer: A review. Adv. Atmos. Sci., 33, 411432, https://doi.org/10.1007/s00376-015-5192-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yan, H., M. Zhong, and Y. Zhu, 2004: Determination of the degree of freedom of digital filtered time series with an application to the correlation analysis between the length of day and the Southern Oscillation index. Chin. Astron. Astrophys., 28, 120126, https://doi.org/10.1016/S0275-1062(04)90014-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, L., S. Chen, C. Wang, D. Wang, and X. Wang, 2018: Potential impact of the Pacific decadal oscillation and sea surface temperature in the tropical Indian Ocean–western Pacific on the variability of typhoon landfall on the China coast. Climate Dyn., 51