• 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, doi:10.1175/1520-0442(2000)013<2960:TCAOTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chan, J. C. L., 2007: Interannual variations of intense typhoon activity. Tellus, 59A, 455460, doi:10.1111/j.1600-0870.2007.00241.x.

  • Chan, J. C. L., , and K. S. Liu, 2004: Global warming and western North Pacific typhoon activity from an observational perspective. J. Climate, 17, 45904602, doi:10.1175/3240.1.

    • Search Google Scholar
    • Export Citation
  • Chen, J.-M., , H.-S. Chen, , and J.-S. Liu, 2013: Coherent interdecadal variability of tropical cyclone rainfall and seasonal rainfall in Taiwan during October. J. Climate, 26, 308321, doi:10.1175/JCLI-D-11-00697.1.

    • Search Google Scholar
    • Export Citation
  • Chen, W. Y., 1982: Fluctuation in Northern Hemisphere 700-mb height field associated with the Southern Oscillation. Mon. Wea. Rev., 110, 808823, doi:10.1175/1520-0493(1982)110<0808:FINHMH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Davis, R. E., 1976: Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. J. Phys. Oceanogr., 6, 249266, doi:10.1175/1520-0485(1976)006<0249:POSSTA>2.0.CO;2.

    • 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, doi:10.1175/2010JCLI3890.1.

    • Search Google Scholar
    • Export Citation
  • Goh, A. Z.-C., , and J. C. L. Chan, 2010: Interannual and interdecadal variations of tropical cyclone activity in the South China Sea. Int. J. Climatol., 30, 827843, doi:10.1002/joc.1943.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669700, doi:10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155–218.

  • Hirakawa, K., 1974: The comparison of powers of distribution-free two-sample tests. TRU Math., 10, 6582.

  • Hsu, P.-C., , T. Li, , and C.-H. Tsou, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part I: Energetics diagnosis. J. Climate, 24, 927941, doi:10.1175/2010JCLI3833.1.

    • Search Google Scholar
    • Export Citation
  • Kajikawa, Y., , and B. Wang, 2012: Interdecadal change of the South China Sea summer monsoon onset. J. Climate, 25, 32073218, doi:10.1175/JCLI-D-11-00207.1.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., , C.-H. Ho, , and P.-S. Chu, 2010: Dipolar redistribution of summertime tropical cyclone genesis between the Philippine Sea and the northern South China Sea and its possible mechanisms. J. Geophys. Res., 115, D06104, doi:10.1029/2009JD012196.

    • Search Google Scholar
    • Export Citation
  • Leung, Y. K., , M. C. Wu, , and W. L. Chang, 2006: Variations of tropical cyclone activity in the South China Sea. ESCAP/WMO Typhoon Committee Annual Review 2005. Hong Kong Observatory Reprint 675, 12 pp. [Available online at http://www.hko.gov.hk/publica/reprint/r675.pdf.]

  • Li, R. C. Y., , and W. Zhou, 2012: Changes in western Pacific tropical cyclones associated with the El Niño–Southern Oscillation cycle. J. Climate, 25, 58645878, doi:10.1175/JCLI-D-11-00430.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , and W. Zhou, 2013a: Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: Genesis and intensity. J. Climate, 26, 29042918, doi:10.1175/JCLI-D-12-00210.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , and W. Zhou, 2013b: Modulation of western North Pacific tropical cyclone activities by the ISO. Part II: Tracks and landfalls. J. Climate, 26, 29192930, doi:10.1175/JCLI-D-12-00211.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , W. Zhou, , J. C. L. Chan, , and P. Huang, 2012: Asymmetric modulation of western North Pacific cyclogenesis by the Madden–Julian oscillation under ENSO conditions. J. Climate, 25, 53745385, doi:10.1175/JCLI-D-11-00337.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , W. Zhou, , and T. Li, 2014: Influences of the Pacific–Japan teleconnection pattern on synoptic-scale variability in the western North Pacific. J. Climate, 27, 140154, doi:10.1175/JCLI-D-13-00183.1.

    • 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.

    • 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, doi:10.1175//2554.1.

    • Search Google Scholar
    • Export Citation
  • Liu, K. S., , and J. C. L. Chan, 2013: Inactive period of western North Pacific tropical cyclone activity in 1998–2011. J. Climate, 26, 26142630, doi:10.1175/JCLI-D-12-00053.1.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708, doi:10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: Observations. J. Atmos. Sci., 58, 25452558, doi:10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mann, H. B., , and D. R. Whitney, 1947: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 5060, doi:10.1214/aoms/1177730491.

    • Search Google Scholar
    • Export Citation
  • Rodionov, S. N., 2004: A sequential algorithm for testing climate regime shifts. Geophys. Res. Lett.,31, L09204, doi:10.1029/2004GL019448.

  • Smith, T. M., , and R. W. Reynolds, 2004: Improved extended reconstruction of SST (1854–1997). J. Climate, 17, 24662477, doi:10.1175/1520-0442(2004)017<2466:IEROS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sooraj, K. P., , D. Kim, , J.-S. Kug, , S.-W. Yeh, , F.-F. Jin, , and I.-S. Kang, 2009: Effects of low-frequency zonal wind variation on the high frequency atmospheric variability over the tropics. Climate Dyn., 33, 495507, doi:10.1007/s00382-008-0483-6.

    • 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, doi:10.1175/1520-0442(2002)015<1643:HSEEAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , Y. Yang, , Q.-H. Ding, , H. Murakami, , and F. Huang, 2010: Climate controls of the global tropical storm days (1965–2008). Geophys. Res. Lett., 37, L07704, doi:10.1029/2010GL042487.

    • Search Google Scholar
    • Export Citation
  • Wang, G., , J. Su, , Y. Ding, , and D. Chen, 2007: Tropical cyclone genesis over the South China Sea. J. Mar. Syst., 68, 318326, doi:10.1016/j.jmarsys.2006.12.002.

    • Search Google Scholar
    • Export Citation
  • Wang, L., , R. Huang, , and R. Wu, 2013: Interdecadal variability of tropical cyclone frequency over the South China Sea and its association with the Indian Ocean sea surface temperature. Geophys. Res. Lett., 40, 768771, doi:10.1002/grl.50171.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , W. Zhou, , C. Li, , and D. Wang, 2012: Effects of the East Asian summer monsoon on tropical cyclone genesis over the South China Sea on an interdecadal time scale. Adv. Atmos. Sci., 29, 249262, doi:10.1007/s00376-011-1080-x.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , W. Zhou, , C. Li, , and D. Wang, 2014: Comparison of the impact of two types of El Niño on tropical cyclone genesis over the South China Sea. Int. J. Climatol., doi: 10.1002/joc.3865, in press.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences.Academic Press, 464 pp.

  • Wu, M. C., , W. L. Chang, , and W. M. Leung, 2004: Impacts of El Niño–Southern Oscillation events on tropical cyclone landfalling activity in the western North Pacific. J. Climate, 17, 14191428, doi:10.1175/1520-0442(2004)017<1419:IOENOE>2.0.CO;2.

    • 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, doi:10.1175/2008JCLI2544.1.

    • Search Google Scholar
    • Export Citation
  • Zhan, R., , Y. Wang, , and X. Lei, 2011: Contributions of ENSO and east Indian Ocean SSTA to the interannual variability of northwest Pacific tropical cyclone frequency. J. Climate, 24, 509521, doi:10.1175/2010JCLI3808.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., , Y. Leung, , and J. Min, 2013: North Pacific Gyre Oscillation and the occurrence of western North Pacific tropical cyclones. Geophys. Res. Lett., 40, 52055211, doi:10.1002/grl.50955.

    • Search Google Scholar
    • Export Citation
  • Zhao, X., , and P.-S. Chu, 2010: Bayesian changepoint analysis for extreme events (typhoons, heavy rainfall, and heat waves): An RJMCMC approach. J. Climate, 23, 10341046, doi:10.1175/2009JCLI2597.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, W., , and J. C. L. Chan, 2005: Intraseasonal oscillations and the South China Sea summer monsoon onset. Int. J. Climatol., 25, 15851609, doi:10.1002/joc.1209.

    • Search Google Scholar
    • Export Citation
  • Zhou, W., , and J. C. L. Chan, 2007: ENSO and South China Sea summer monsoon onset. Int. J. Climatol., 27, 157167, doi:10.1002/joc.1380.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    (a) Time series from the Joint Typhoon Warning Center of summer SCS TC frequency during 1979–2010, (b) the corresponding power spectrum, (c) the posterior probability of the number of changepoints associated with the SCS TC frequency, and the posterior probability mass function for the (d) first and (e) second changepoint. The green dashed line in (a) denotes the 32-yr average of the TC frequency, while the red solid line denotes the mean TC number in the respective active and inactive periods. The green dashed line in (b) represents the Markov red noise spectrum, while the red and blue dashed lines represent the 95% and 5% confidence levels, respectively.

  • View in gallery

    As in Fig. 1, but using the China Meteorological Administration TC datasets.

  • View in gallery

    Spatial distribution of SCS TCs (green circles) and the associated anomalies (shading) for (a) period 1 (1979–93), (b) period 2 (1994–2002), and (c) period 3 (2003–10).

  • View in gallery

    Correlation between JJA TC frequency and the SST. Solid contours represent correlations significant at 90% confidence.

  • View in gallery

    Standardized time series of the original (bar chart) and 10-yr low-pass filtered (dashed lines) summertime (a) SCS TC frequency, (b) NIO SST, (c) WNP SST, and (d) ZSG between NIO and WNP during 1979–2010.

  • View in gallery

    Composite differences of (a),(b) total vertical wind shear (m s−1), (c),(d) 500-hPa vertical pressure velocity (10−2 Pa s−1), and (e),(f) 600-hPa relative humidity (%) between periods 1 and 2 and between periods 3 and 2. Shading represents regions where the difference in the mean between the two periods is statistically significant at the 10% level based on the Mann–Whitney U statistics.

  • View in gallery

    Composites of MJO activity (W m−2), represented by the standard deviation of 30–60-day filtered OLR anomalies, during periods (a) 1, (b) 2, and (c) 3. (d),(e) The corresponding differences between periods 1 and 2 and between periods 3 and 2, respectively.

  • View in gallery

    Composites of the evolution of 30–60-day filtered OLR (contours, W m−2; values over 90% confidence are shaded) and 850-hPa wind anomalies (vectors, m s−1; only values over 90% confidence are drawn) during periods (a) 1, (b) 2, and (c) 3. (d)–(f) The associated Hovmöller plot of 30–60-day filtered OLR anomalies (contours, W m−2; values over 90% confidence are shaded) averaged over 10°–25°N during periods 1, 2, and 3, respectively; day 0 refers to the day when the filtered convection attains its maximum value over the SCS, while n (−n) refers to n days after (before) day 0.

  • View in gallery

    Regression of (a) OLR (W m−2) and 850-hPa wind (m s−1), (b) total vertical wind shear (m s−1), (c) 500-hPa vertical pressure velocity (10−2 Pa s−1), and (d) 600-hPa relative humidity (%) anomalies against the ZSG index. Only values exceeding 90% confidence are shown.

  • View in gallery

    Schematic diagram illustrating the differences in atmospheric circulations associated with (a) positive and (b) negative zonal SST gradient.

  • View in gallery

    As in Fig. 5, except (b) and (c) are the time series of the PDO and the East Asian jet stream intensity, respectively. The East Asian jet stream intensity is the first principal component (PC1) derived based on empirical orthogonal function analysis of 200-hPa zonal wind, as in Wang et al. (2012).

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 66 66 13
PDF Downloads 42 42 14

Interdecadal Change in South China Sea Tropical Cyclone Frequency in Association with Zonal Sea Surface Temperature Gradient

View More View Less
  • 1 Guy Carpenter Asia-Pacific Climate Impact Center, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
© Get Permissions
Full access

Abstract

This study investigates the interdecadal changes in summertime tropical cyclone (TC) frequency over the South China Sea (SCS) during 1979–2010. Based on changepoint detection algorithms and spectral analysis, two inactive TC periods (period 1: 1979–93 and period 3: 2003–10) and one active TC period (period 2: 1994–2002) have been identified, with a dominant spectral peak of approximately 9–10 yr. Correlation analysis further reveals a significant negative relationship between TC frequency and the zonal sea surface temperature gradient (ZSG) between the northern Indian Ocean (NIO) and the western North Pacific (WNP) at both interannual and interdecadal time scales. That is, a positive ZSG between the NIO and the WNP tends to suppress cyclogenesis over the SCS, whereas a negative ZSG is generally favorable for SCS TC formation.

The negative connection between cyclogenesis and ZSG may be explained by the influences of the ZSG on atmospheric circulations as well as Madden–Julian oscillation (MJO) activity over the SCS, which reveal prominent contrasts during the study periods. A positive ZSG between the tropical Pacific and the Indian Ocean induces an anomalous Walker-like circulation, which results in an anomalous subsidence and boundary layer divergence over the northern SCS. This also suppresses the moisture as well as MJO activity over the SCS, leading to a significant reduction in TC frequency during inactive periods 1 and 3. In contrast, a negative ZSG induces surface westerlies and favorable environmental conditions for TCs, thereby greatly enhancing SCS cyclogenesis during period 2.

Corresponding author address: Dr. Wen Zhou, Guy Carpenter Asia-Pacific Climate Impact Center, School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China. E-mail: wenzhou@cityu.edu.hk

Abstract

This study investigates the interdecadal changes in summertime tropical cyclone (TC) frequency over the South China Sea (SCS) during 1979–2010. Based on changepoint detection algorithms and spectral analysis, two inactive TC periods (period 1: 1979–93 and period 3: 2003–10) and one active TC period (period 2: 1994–2002) have been identified, with a dominant spectral peak of approximately 9–10 yr. Correlation analysis further reveals a significant negative relationship between TC frequency and the zonal sea surface temperature gradient (ZSG) between the northern Indian Ocean (NIO) and the western North Pacific (WNP) at both interannual and interdecadal time scales. That is, a positive ZSG between the NIO and the WNP tends to suppress cyclogenesis over the SCS, whereas a negative ZSG is generally favorable for SCS TC formation.

The negative connection between cyclogenesis and ZSG may be explained by the influences of the ZSG on atmospheric circulations as well as Madden–Julian oscillation (MJO) activity over the SCS, which reveal prominent contrasts during the study periods. A positive ZSG between the tropical Pacific and the Indian Ocean induces an anomalous Walker-like circulation, which results in an anomalous subsidence and boundary layer divergence over the northern SCS. This also suppresses the moisture as well as MJO activity over the SCS, leading to a significant reduction in TC frequency during inactive periods 1 and 3. In contrast, a negative ZSG induces surface westerlies and favorable environmental conditions for TCs, thereby greatly enhancing SCS cyclogenesis during period 2.

Corresponding author address: Dr. Wen Zhou, Guy Carpenter Asia-Pacific Climate Impact Center, School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong SAR, China. E-mail: wenzhou@cityu.edu.hk

1. Introduction

As a sea located on the western margin of the western North Pacific (WNP), the South China Sea (SCS) is a region with considerable cyclogenesis. About one-third of the tropical cyclones (TCs) affecting China originate within this region (Wang et al. 2007). Understanding and accurately predicting the behavior of TCs in the SCS is of particular importance owing to their close proximity to coastal regions and their potentially catastrophic impacts.

Previous studies concerning TC activity in the SCS have focused mainly on the variability at interannual time scales. Similar to TC activity in the WNP (Chan 2000; Wang and Chan 2002; Li and Zhou 2012), interannual TC variability in the SCS may be related to El Niño–Southern Oscillation (ENSO) to a certain extent. For example, Liu and Chan (2003) and Wu et al. (2004) demonstrated that a strong El Niño (La Niña) event reduces (enhances) the frequency of landfalling TCs along the south China coast. Goh and Chan (2010) further pointed out that the total TC frequency over the SCS tends to be higher (lower) in La Niña (El Niño) years during boreal winter, whereas such a relationship breaks down in boreal summer. Wang et al. (2014) recently discovered that the impact of warm pool El Niño on SCS cyclogenesis is indeed stronger than that of canonical cold tongue El Niño during September–November. The remote sea surface temperature (SST) forcing associated with ENSO can induce significant changes in large-scale atmospheric circulations, which subsequently modulate TC activity over the SCS (Liu and Chan 2003; Zhou and Chan 2007; Goh and Chan 2010).

On the other hand, there are far fewer studies on the interdecadal variability of SCS TCs, thus motivating further investigation. At interdecadal scales, Leung et al. (2006) and Goh and Chan (2010) suggested that the variation in TC frequency might be related to the Pacific decadal oscillation (PDO). They proposed that the positive (negative) phase of the PDO generally favors fewer (more) TCs in the SCS, although no physical explanation was given. Kim et al. (2010) found a dipole oscillatory TC pattern between the Philippine Sea and the northern SCS that varies at interdecadal time scales, although the exact causes are currently unknown. Wang et al. (2012) identified two high-frequency periods (1965–74 and 1995–2004) and one low-frequency period (1979–93) during 1965–2004 and attributed the interdecadal TC variations to changes in the intensity of the East Asian jet stream. More recently, using data from 1958 to 2001, Wang et al. (2013) tried to relate the significant reduction in SCS TC frequency after the mid-1970s to an increase in SST over the tropical Indian Ocean. However, although the ENSO–TC theory is widely accepted, consensus has not been reached regarding interdecadal TC variability, and the associated modulation mechanism remains unclear. Because of the ambiguities and uncertainties involved, it is thus essential to further examine interdecadal changes in TC frequency in the SCS.

The remainder of this paper is organized as follows. Section 2 introduces the data and methodology used in this study. Following that, evidence of interdecadal changes in SCS TC frequency are presented in section 3, while the close relationship with the zonal SST gradient is discussed in section 4. Section 5 further illustrates the possible underlying mechanisms by examining different TC-related parameters. Finally, a discussion and summary are given in section 6.

2. Data and methodology

a. Data

The TC dataset used in this study was acquired from the Joint Typhoon Warning Center (http://jtwccdn.appspot.com/NOOC/nmfc-ph/RSS/jtwc/best_tracks/) at 6-h intervals. Another best track dataset from the China Meteorological Administration (http://www.typhoon.gov.cn) was also employed to validate our results. The present study focuses on boreal summer [June–August (JJA)], when cyclogenesis is the most active over the SCS (Wang et al. 2007). Only TCs achieving at least tropical storm intensity (maximum sustained wind speed greater than 34 kt; 1 kt ≈ 0.51 m s−1) and forming locally within the SCS (0°–25°N, 105°–120°E) were counted. Based on the availability of routine satellite observations, daily averaged 2.5° × 2.5° outgoing longwave radiation (OLR), which is used to diagnose the activity of the Madden–Julian oscillation (MJO), was obtained from National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites for the period 1979–2010 (Liebmann and Smith 1996). Monthly atmospheric data including wind, omega, and relative humidity for the same period were archived from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996), while monthly 2° × 2° extended reconstructed SST data (Smith and Reynolds 2004) were obtained from NOAA. The monthly PDO index was obtained from the Joint Institute for the Study of the Atmosphere and Ocean of the University of Washington (http://jisao.washington.edu/pdo/). A 10-yr low-pass Lanczos filter was used to extract the interdecadal signals. This frequency band was chosen based on spectral analysis, which will be discussed further in section 3.

b. Changepoint detection

To identify significant interdecadal shifts in the TC time series, both the regime shift detection algorithm developed by Rodionov (2004) and the Bayesian changepoint method developed by Zhao and Chu (2010) have been employed. Based on the Student’s t test, the regime shift detection method of Rodionov (2004) detects a significant change in the sequential running means of the time series, where a regime shift is identified when the differences between the two means exceed a certain confidence level. Further details of this algorithm can be found in Rodionov (2004). Recently, Liu and Chan (2013) adopted this algorithm to investigate the interdecadal change in TC frequency over the WNP. Similar to their study, a cutoff length of 10 yr is used and 90% confidence is chosen for detecting the changes in TC frequency over the SCS.

On the other hand, the Bayesian changepoint method of Zhao and Chu (2010) is specifically designed for identifying multiple changepoints in an extreme event time series, which is modeled as a Poisson process with a gamma distributed rate. This method has been previously demonstrated to be successful in determining the associated changepoints of some extreme events, including the annual supertyphoon counts in the WNP, and the annual extreme heavy rainfall counts in Honolulu (Zhao and Chu 2010). Readers can refer to Zhao and Chu (2010) for further details of this method.

c. Significance test for differences in large-scale environmental variables

The classical nonparametric Mann–Whitney U test (Mann and Whitney 1947; Wilks 1995) is used in this study to examine the significance of the difference in the large-scale environmental variables in the three identified periods. This test is particularly useful when the sample distribution is unknown or when the sample size is small. The test procedure starts with pooling and ranking the observations from two batches of data. The Mann–Whitney U statistics can then be calculated by
eq1
eq2
where R1 and R2 are the sum of the ranks of sample 1 and sample 2, respectively, and n1 and n2 are their sample sizes. Finally, the U statistic at every grid point is compared with the associated critical value to determine whether the null hypothesis that the two samples come from the same distribution should be rejected.

d. Estimating the significance of correlation between filtered time series

To account for the reduction in the degrees of freedom of the filtered time series, the effective degrees of freedom, which was used previously by Davis (1976), Chen (1982), and Chen et al. (2013), is adopted in this study to estimate the significance of the correlation between the filtered variables. The effective degrees of freedom is calculated as n/T, where n is the number of sample observations and T = between the two fields. Here, and are the autocorrelation coefficients of the two variables with a time lag of t, and the maximum of integer K corresponds to n/2.

3. Interdecadal variations in cyclogenesis in the SCS

Figure 1a shows the time series of the summertime SCS TC frequency based on the Joint Typhoon Warning Center datasets. The time series depicts pronounced interdecadal variations, with two inactive periods (period 1: 1979–93 and period 3: 2003–10) and one active period (period 2: 1994–2002) being identified based on the regime shift detection algorithm (Rodionov 2004). The first inactive period spans from 1979 to 1993 and has a mean genesis frequency of 0.67 (Table 1). Of these 13 years, 11 recorded fewer TCs than normal. The inactive period is followed by an active period, when the average TC number over the SCS increases to 2.67 during 1994–2002. After that, the second inactive period begins in 2003. Seven out of the next eight years have lower-than-average TC activity, and the mean genesis number falls back to 0.75. The same changepoints can also be identified based on the Bayesian changepoint method (Zhao and Chu 2010). As shown in Fig. 1c, the probability that the time series contains two changepoints overwhelms that of the other hypothesis, and the two changepoints are likely to occur around 1994 and 2003, respectively (Figs. 1d and 1e). Such a significant interdecadal variation can also be captured by spectral analysis. Consistently, the power spectrum of JJA TC frequency reveals a dominant peak of approximately 9–10 yr at decadal time scales (Fig. 1b), suggesting that the interdecadal signal associated with SCS cyclogenesis is robust. Compared to the prominent interdecadal variations, the interannual variability of TC frequency is apparently weaker and less significant (Fig. 1b). This agrees with Goh and Chan (2010) and Wang et al. (2014), who indicated that the impact of ENSO on cyclogenesis is much weaker during boreal summer than it is in the fall. Notice that the result here has been further confirmed by using a different TC dataset from the China Meteorological Administration (Fig. 2), which reveals a similar interdecadal change in SCS TC frequency. Also shown in Fig. 3 is the spatial distribution of cyclogenesis during these three periods. It is worth noting that the major changes in TC activity occur in the northern SCS (15°–25°N, 110°–120°E), where the majority of TCs form and develop. The results here are basically consistent with those of Wang et al. (2012), who identified a similar interdecadal variation based on the Lepage test (Hirakawa 1974), although they failed to recognize the second inactive period because they considered only a limited period up to 2004.

Fig. 1.
Fig. 1.

(a) Time series from the Joint Typhoon Warning Center of summer SCS TC frequency during 1979–2010, (b) the corresponding power spectrum, (c) the posterior probability of the number of changepoints associated with the SCS TC frequency, and the posterior probability mass function for the (d) first and (e) second changepoint. The green dashed line in (a) denotes the 32-yr average of the TC frequency, while the red solid line denotes the mean TC number in the respective active and inactive periods. The green dashed line in (b) represents the Markov red noise spectrum, while the red and blue dashed lines represent the 95% and 5% confidence levels, respectively.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

Table 1.

Summary of SCS TC statistics during the active period 2 and inactive periods 1 and 3.

Table 1.
Fig. 2.
Fig. 2.

As in Fig. 1, but using the China Meteorological Administration TC datasets.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

Fig. 3.
Fig. 3.

Spatial distribution of SCS TCs (green circles) and the associated anomalies (shading) for (a) period 1 (1979–93), (b) period 2 (1994–2002), and (c) period 3 (2003–10).

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

4. Relationship between interdecadal TC variability and zonal SST gradient between the NIO and WNP

Given an evident interdecadal change in SCS TC frequency, the next question we need to answer is what causes such variation. Previous studies have suggested that anomalous SST over the Indian Ocean can exert a significant influence on interannual TC variability over the WNP (Du et al. 2011; Zhan et al. 2011) through the excitation of a warm Kelvin wave (Xie et al. 2009). A recent study by Wang et al. (2013) also attempted to attribute the reduction in May–November SCS TC frequency after the mid-1970s to the increase in SST over the Indian Ocean. Motivated by this work, we first use correlation analysis to investigate the relationship between SCS TCs and SST. As shown in Fig. 4, two key regions revealing significant correlations can be identified: one in the northern Indian Ocean (NIO; 5°–20°N, 60°–100°E), with a significant negative correlation, and another over the WNP (5°–20°N, 125°E–180°), showing a prominent positive relationship. In contrast, the correlation between TC frequency and the local SST over the SCS is weak. Chan and Liu (2004) and Chan (2007) pointed out that changes in TC frequency in the WNP are independent of the local SST but are controlled by changes in atmospheric circulations induced by remote SST. Similarly, our results here suggest that the SCS TC frequency is associated mainly with remote SST forcing over the NIO and WNP rather than the local SST.

Fig. 4.
Fig. 4.

Correlation between JJA TC frequency and the SST. Solid contours represent correlations significant at 90% confidence.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

From correlations illustrated in Fig. 4, there is an apparent close relationship between SCS TC frequency and the SST over the NIO and WNP. To further elucidate their mutual relationship, Fig. 5 compares the variations in these various time series at interannual as well as interdecadal time scales. Here, the interdecadal signals are extracted using a 10-yr low-pass Lanczos filter, and correlations are computed based on both the original and the filtered time series. First, we consider the NIO and the WNP separately. Interannually, the SST time series over these two regions correlates reasonably well with JJA TC frequency (Table 2), with a correlation coefficient of −0.27 for the NIO and 0.31 for the WNP. On the other hand, the SST time series over both the NIO and the WNP are dominated by an obvious increasing trend at interdecadal time scales (Figs. 5b,c), which does not match well with the interdecadal change in the TC time series (Fig. 5a). The associated correlation coefficients for the NIO and WNP are −0.073 and 0.44, respectively. The larger correlation coefficient for the WNP compared to that of the NIO indicates its nonnegligible role in interdecadal TC modulation. Still, simply considering the NIO or WNP alone seems to be insufficient to explain the corresponding TC variability. In view of this, we also examine the combined effect of these two regions to see whether the predictability of SCS TC frequency can be improved. Taking into account the SST conditions over both the NIO and the WNP, a zonal SST gradient (ZSG) index is constructed based on the SST difference between the NIO and the WNP. A positive (negative) ZSG index is thus associated with warmer (cooler) SST over the NIO and cooler (warmer) SST in the WNP. As shown in Fig. 5 and Table 2, it turns out that such an index serves as a much better predictor for SCS TC frequency at both interannual and interdecadal time scales. A significant negative interannual correlation of −0.52 and interdecadal correlation of −0.83 is noted, which coincides well with the TC variability over the SCS. During periods 1 and 3, when there is below-normal TC activity, positive ZSG tends to occur. In contrast, negative ZSG during period 2 matches well with the above-normal TC frequency. That is, a positive ZSG between the NIO and the WNP tends to suppress cyclogenesis over the SCS, whereas a negative ZSG is generally favorable for SCS TC formation. In contrast to the previous study of Wang et al. (2013), who focused solely on the impact of the NIO, our results here suggest that it is the ZSG between the NIO and the WNP that plays a more substantial role in shaping the interdecadal TC variability over the SCS and that it can act as an even better potential TC predictor at both interannual and interdecadal time scales.

Fig. 5.
Fig. 5.

Standardized time series of the original (bar chart) and 10-yr low-pass filtered (dashed lines) summertime (a) SCS TC frequency, (b) NIO SST, (c) WNP SST, and (d) ZSG between NIO and WNP during 1979–2010.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

Table 2.

Correlations between summertime SCS TC frequency and SST over different regions. Interannual correlations are computed based on the original time series, while interdecadal correlations are computed based on the 10-yr low-pass filtered time series. Boldface values indicate significant correlation at 90% confidence. The effective degrees of freedom are used when estimating the significance of the interdecadal correlations.

Table 2.

5. Possible mechanisms

The previous section has identified an evident negative association between SCS TC frequency and the SST gradient between the NIO and the WNP. Thus we postulate that the ZSG might be a key factor responsible for interdecadal changes in TC frequency over the SCS through modulating the atmospheric circulations. To disentangle the mechanisms underlying this association, different TC-related dynamic and thermodynamic parameters (Gray 1968, 1979) are evaluated in this section.

a. Differences in dynamic and thermodynamic factors

Figure 6 shows the composite differences in various TC-related dynamic and thermodynamic parameters between the inactive and active TC periods. Compared to the active period, both the inactive periods are characterized by significantly enhanced vertical wind shear, suppressed vertical motion, and reduced moisture supply over the northern SCS, coinciding with the region where significant interdecadal changes in SCS TC frequency occur (Fig. 3). The unfavorable environmental conditions result in a significant reduction in SCS TC formation during the inactive periods. The results here suggest that different dynamic and thermodynamic backgrounds have been subjected to similar interdecadal variations in accordance with changes in TC frequency as well as in ZSG. The possible process of these influences will be discussed in further detail in section 5c.

Fig. 6.
Fig. 6.

Composite differences of (a),(b) total vertical wind shear (m s−1), (c),(d) 500-hPa vertical pressure velocity (10−2 Pa s−1), and (e),(f) 600-hPa relative humidity (%) between periods 1 and 2 and between periods 3 and 2. Shading represents regions where the difference in the mean between the two periods is statistically significant at the 10% level based on the Mann–Whitney U statistics.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

b. Differences in MJO activity

Apart from the mean flow differences, interdecadal changes can also be found in the MJO activity over the SCS. MJO is characterized by planetary-scale, northeastward-propagating convective anomalies in boreal summer, with a dominant period of about 30–60 days (Madden and Julian 1971). It originates from the Indian Ocean and propagates eastward across the Maritime Continent to the WNP, significantly modulating synoptic-scale activity as well as TC activity there (Zhou and Chan 2005; Li et al. 2012; Li and Zhou 2013a,b; Li et al. 2014). Figure 7 shows the composites of the MJO activity (represented by the standard deviation of 30–60-day filtered OLR anomalies) and their associated differences for the active and inactive TC periods. Obviously, the MJO activity during the active period is much stronger than it is in the inactive periods. Examination of the spatial and temporal evolution of the MJO also shows more pronounced and better-organized northeastward-propagating MJO signals over the NIO and the SCS during period 2 (Fig. 8). At day −10, a clear convective center develops in the NIO during period 2, whereas that in periods 1 and 3 is weak (Figs. 8a–c). This is followed by a prominent northeastward propagation of the convection from the NIO to the Maritime Continent and the SCS from day −10 to day 0 (Figs. 8b,e). In contrast, the eastward-propagating convection associated with the MJO is greatly weakened and less significant during period 1 (Figs. 8a,d) and period 3 (Figs. 8c,f). Physically, such strengthened and better-organized MJO convections during period 2 can promote further enhancement in synoptic-scale activities (including TC activity) over the SCS by providing a favorable environmental background (Li et al. 2012; Li and Zhou 2013a,b; Li et al. 2014) or through barotropic energy conversions (Maloney and Hartmann 2001; Hsu et al. 2011). Thus, both the atmospheric circulations and strengthened MJO activity provide favorable environmental conditions for SCS TC genesis during period 2, whereas the unfavorable background and weakened MJO activity tend to inhibit TC formation during periods 1 and 3.

Fig. 7.
Fig. 7.

Composites of MJO activity (W m−2), represented by the standard deviation of 30–60-day filtered OLR anomalies, during periods (a) 1, (b) 2, and (c) 3. (d),(e) The corresponding differences between periods 1 and 2 and between periods 3 and 2, respectively.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

Fig. 8.
Fig. 8.

Composites of the evolution of 30–60-day filtered OLR (contours, W m−2; values over 90% confidence are shaded) and 850-hPa wind anomalies (vectors, m s−1; only values over 90% confidence are drawn) during periods (a) 1, (b) 2, and (c) 3. (d)–(f) The associated Hovmöller plot of 30–60-day filtered OLR anomalies (contours, W m−2; values over 90% confidence are shaded) averaged over 10°–25°N during periods 1, 2, and 3, respectively; day 0 refers to the day when the filtered convection attains its maximum value over the SCS, while n (−n) refers to n days after (before) day 0.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

c. Possible process of influences of ZSG on interdecadal changes in SCS TC frequency

Given significant differences in atmospheric circulations and MJO activity during the three periods, the next question is how these changes are possibly related to the ZSG between the NIO and the WNP. Figure 9 shows the regression of various TC-related parameters against the ZSG index. Consistent with the negative ZSG–TC relationship we found in section 4, it can be seen that a positive ZSG is actually associated with suppressed convection over the northern SCS together with anomalous easterlies dominating south of 20°N. These changes are also accompanied by an increase in total vertical wind shear, a stronger descending motion, and a reduction in moisture over the northern SCS, which is the region where significant interdecadal changes in SCS TC frequency occur (Fig. 3). The regression patterns here are consistent and generally resemble the composite differences shown in Fig. 6. This further confirms that the changes in atmospheric circulations are closely linked to variations in ZSG between the NIO and the WNP. How does a positive ZSG induce inhibiting effects on the atmospheric background and cyclogenesis over the SCS? On one hand, a positive SST gradient between the NIO and the WNP (warmer in the NIO and cooler in the WNP) can force anomalous low-level easterlies between the tropical Pacific and the Indian Ocean by setting up an anomalous pressure gradient. This in turn can stimulate an anomalous Walker-like circulation with anomalous subsidence and boundary layer divergence over the SCS as a result of conservation of momentum, which further reduces moisture and leads to suppressed convection over the SCS. On the other hand, prevailing easterlies south of 20°N will also inhibit the eastward propagation of the MJO (Sooraj et al. 2009; Li et al. 2012), while the suppressed moisture and convection over the subsidence branch tend to weaken the MJO activity, further suppressing TC genesis over the SCS. The situation associated with a negative SST gradient is just the reverse. A schematic view of the possible mechanisms associated with positive and negative ZSG is given in Fig. 10. A recent study by Kajikawa and Wang (2012) has similarly highlighted the importance of intraseasonal variability in association with SST over the western Pacific in modulating the interdecadal summer monsoon onset over the SCS. Overall, our results here suggest that the interdecadal variation in SCS TC frequency is probably rooted in zonal changes in the SST gradient between the NIO and the WNP, which lead to subsequent changes in atmospheric circulations as well as MJO activity over the SCS.

Fig. 9.
Fig. 9.

Regression of (a) OLR (W m−2) and 850-hPa wind (m s−1), (b) total vertical wind shear (m s−1), (c) 500-hPa vertical pressure velocity (10−2 Pa s−1), and (d) 600-hPa relative humidity (%) anomalies against the ZSG index. Only values exceeding 90% confidence are shown.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

Fig. 10.
Fig. 10.

Schematic diagram illustrating the differences in atmospheric circulations associated with (a) positive and (b) negative zonal SST gradient.

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

6. Discussion and summary

In this study, we have found that a significant interdecadal change in SCS TC frequency is closely associated with zonal changes in the SST gradient between the NIO and the WNP. Based on the changepoint detection algorithms and spectral analysis, two inactive TC periods (period 1: 1979–93, and period 3: 2003–10) and one active TC period (period 2: 1994–2002) have been identified, with a dominant spectral peak of approximately 9–10 yr. Correlation analysis further reveals a significant negative relationship between ZSG and SCS TC frequency, with increased TC predictability at both interannual and interdecadal time scales compared with individual SST time series. The negative connection between cyclogenesis and ZSG may be explained by the influences of the ZSG on the atmospheric circulations as well as on MJO activity over the SCS, which show significant contrasts during these periods. Compared to the active period, both the inactive periods are characterized by significantly enhanced vertical wind shear, suppressed vertical motion, and reduced moisture supply, as well as weakened MJO activity over the northern SCS, which is the region where significant differences in cyclogenesis occur. Associated with a positive ZSG, anomalous easterlies between the tropical Pacific and the Indian Ocean induce an anomalous Walker-like circulation, which in turn leads to an anomalous subsidence and boundary layer divergence over the SCS, further suppressing the moisture as well as MJO activity over the SCS during the inactive periods. In contrast, a negative ZSG induces surface westerlies and favorable environmental conditions for TCs, thereby greatly enhancing SCS cyclogenesis during period 2.

Previous studies have proposed several other factors, such as the PDO (Goh and Chan 2010; Wang et al. 2010) and the intensity of the East Asian jet stream (Wang et al. 2012), that might also be related to the interdecadal changes in SCS TC frequency. As an extension, we further examine their relationship with TCs over the SCS. For the PDO, the time series experiences a clear shift from a positive to a negative phase around 1998 (Fig. 11), with interannual and interdecadal correlations of −0.063 and −0.068, respectively, with the SCS TC frequency. The weak correlations between the two suggest that the PDO exerts only a marginal impact on SCS TC frequency during boreal summer. As mentioned by Goh and Chan (2010), the reason may be that the effects of the PDO on TCs tend to be stronger in the late season, when the PDO matures. The results here are consistent with those of Zhang et al. (2013), who found similarly that the PDO correlates only weakly with the WNP TC frequency at both interannual and interdecadal scales. As for the intensity of the East Asian jet stream, it reveals a much stronger correlation of −0.54 and −0.51 with the TC frequency at both interannual and interdecadal time scales. This result is comparable to that of Wang et al. (2012), who found that a weakening of the jet stream can induce anomalous divergence of wave activity fluxes at the upper level over the SCS, which is favorable for TC genesis. Yet it is worth mentioning that the significant negative relationship starts to break down in the early 2000s during period 3 (in fact, a positive relationship is revealed), implying that the aforementioned mechanism proposed by Wang et al. (2012) could have been altered, which deserves further investigation. Compared with the PDO and the jet stream, the ZSG found in this study shows the most coherent relationship with SCS cyclogenesis and turns out to be a much better predictor for the interdecadal changes in TC frequency over the SCS.

Fig. 11.
Fig. 11.

As in Fig. 5, except (b) and (c) are the time series of the PDO and the East Asian jet stream intensity, respectively. The East Asian jet stream intensity is the first principal component (PC1) derived based on empirical orthogonal function analysis of 200-hPa zonal wind, as in Wang et al. (2012).

Citation: Journal of Climate 27, 14; 10.1175/JCLI-D-13-00744.1

To recapitulate, we have examined the interdecadal variations in SCS TC frequency, which are found to be significantly related to changes in ZSG between the NIO and the WNP. Nevertheless, several questions remain, such as whether this close TC–ZSG relationship can be captured by climate models. Therefore, future modeling studies will be crucial to further clarify the relationship between TC frequency and ZSG and to build up a decadal prediction scheme for TC frequency over the SCS.

Acknowledgments

This research is supported by Nature Science Foundation of China Grants 41175079 and 41375096, and CityU Strategic Research Grant 7004004.

REFERENCES

  • 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, doi:10.1175/1520-0442(2000)013<2960:TCAOTW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Chan, J. C. L., 2007: Interannual variations of intense typhoon activity. Tellus, 59A, 455460, doi:10.1111/j.1600-0870.2007.00241.x.

  • Chan, J. C. L., , and K. S. Liu, 2004: Global warming and western North Pacific typhoon activity from an observational perspective. J. Climate, 17, 45904602, doi:10.1175/3240.1.

    • Search Google Scholar
    • Export Citation
  • Chen, J.-M., , H.-S. Chen, , and J.-S. Liu, 2013: Coherent interdecadal variability of tropical cyclone rainfall and seasonal rainfall in Taiwan during October. J. Climate, 26, 308321, doi:10.1175/JCLI-D-11-00697.1.

    • Search Google Scholar
    • Export Citation
  • Chen, W. Y., 1982: Fluctuation in Northern Hemisphere 700-mb height field associated with the Southern Oscillation. Mon. Wea. Rev., 110, 808823, doi:10.1175/1520-0493(1982)110<0808:FINHMH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Davis, R. E., 1976: Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. J. Phys. Oceanogr., 6, 249266, doi:10.1175/1520-0485(1976)006<0249:POSSTA>2.0.CO;2.

    • 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, doi:10.1175/2010JCLI3890.1.

    • Search Google Scholar
    • Export Citation
  • Goh, A. Z.-C., , and J. C. L. Chan, 2010: Interannual and interdecadal variations of tropical cyclone activity in the South China Sea. Int. J. Climatol., 30, 827843, doi:10.1002/joc.1943.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669700, doi:10.1175/1520-0493(1968)096<0669:GVOTOO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the tropical circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155–218.

  • Hirakawa, K., 1974: The comparison of powers of distribution-free two-sample tests. TRU Math., 10, 6582.

  • Hsu, P.-C., , T. Li, , and C.-H. Tsou, 2011: Interactions between boreal summer intraseasonal oscillations and synoptic-scale disturbances over the western North Pacific. Part I: Energetics diagnosis. J. Climate, 24, 927941, doi:10.1175/2010JCLI3833.1.

    • Search Google Scholar
    • Export Citation
  • Kajikawa, Y., , and B. Wang, 2012: Interdecadal change of the South China Sea summer monsoon onset. J. Climate, 25, 32073218, doi:10.1175/JCLI-D-11-00207.1.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., , C.-H. Ho, , and P.-S. Chu, 2010: Dipolar redistribution of summertime tropical cyclone genesis between the Philippine Sea and the northern South China Sea and its possible mechanisms. J. Geophys. Res., 115, D06104, doi:10.1029/2009JD012196.

    • Search Google Scholar
    • Export Citation
  • Leung, Y. K., , M. C. Wu, , and W. L. Chang, 2006: Variations of tropical cyclone activity in the South China Sea. ESCAP/WMO Typhoon Committee Annual Review 2005. Hong Kong Observatory Reprint 675, 12 pp. [Available online at http://www.hko.gov.hk/publica/reprint/r675.pdf.]

  • Li, R. C. Y., , and W. Zhou, 2012: Changes in western Pacific tropical cyclones associated with the El Niño–Southern Oscillation cycle. J. Climate, 25, 58645878, doi:10.1175/JCLI-D-11-00430.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , and W. Zhou, 2013a: Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: Genesis and intensity. J. Climate, 26, 29042918, doi:10.1175/JCLI-D-12-00210.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , and W. Zhou, 2013b: Modulation of western North Pacific tropical cyclone activities by the ISO. Part II: Tracks and landfalls. J. Climate, 26, 29192930, doi:10.1175/JCLI-D-12-00211.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , W. Zhou, , J. C. L. Chan, , and P. Huang, 2012: Asymmetric modulation of western North Pacific cyclogenesis by the Madden–Julian oscillation under ENSO conditions. J. Climate, 25, 53745385, doi:10.1175/JCLI-D-11-00337.1.

    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., , W. Zhou, , and T. Li, 2014: Influences of the Pacific–Japan teleconnection pattern on synoptic-scale variability in the western North Pacific. J. Climate, 27, 140154, doi:10.1175/JCLI-D-13-00183.1.

    • 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.

    • 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, doi:10.1175//2554.1.

    • Search Google Scholar
    • Export Citation
  • Liu, K. S., , and J. C. L. Chan, 2013: Inactive period of western North Pacific tropical cyclone activity in 1998–2011. J. Climate, 26, 26142630, doi:10.1175/JCLI-D-12-00053.1.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., , and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708, doi:10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., , and D. L. Hartmann, 2001: The Madden–Julian oscillation, barotropic dynamics, and North Pacific tropical cyclone formation. Part I: Observations. J. Atmos. Sci., 58, 25452558, doi:10.1175/1520-0469(2001)058<2545:TMJOBD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Mann, H. B., , and D. R. Whitney, 1947: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 5060, doi:10.1214/aoms/1177730491.

    • Search Google Scholar
    • Export Citation
  • Rodionov, S. N., 2004: A sequential algorithm for testing climate regime shifts. Geophys. Res. Lett.,31, L09204, doi:10.1029/2004GL019448.

  • Smith, T. M., , and R. W. Reynolds, 2004: Improved extended reconstruction of SST (1854–1997). J. Climate, 17, 24662477, doi:10.1175/1520-0442(2004)017<2466:IEROS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sooraj, K. P., , D. Kim, , J.-S. Kug, , S.-W. Yeh, , F.-F. Jin, , and I.-S. Kang, 2009: Effects of low-frequency zonal wind variation on the high frequency atmospheric variability over the tropics. Climate Dyn., 33, 495507, doi:10.1007/s00382-008-0483-6.

    • 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, doi:10.1175/1520-0442(2002)015<1643:HSEEAT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, B., , Y. Yang, , Q.-H. Ding, , H. Murakami, , and F. Huang, 2010: Climate controls of the global tropical storm days (1965–2008). Geophys. Res. Lett., 37, L07704, doi:10.1029/2010GL042487.

    • Search Google Scholar
    • Export Citation
  • Wang, G., , J. Su, , Y. Ding, , and D. Chen, 2007: Tropical cyclone genesis over the South China Sea. J. Mar. Syst., 68, 318326, doi:10.1016/j.jmarsys.2006.12.002.

    • Search Google Scholar
    • Export Citation
  • Wang, L., , R. Huang, , and R. Wu, 2013: Interdecadal variability of tropical cyclone frequency over the South China Sea and its association with the Indian Ocean sea surface temperature. Geophys. Res. Lett., 40, 768771, doi:10.1002/grl.50171.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , W. Zhou, , C. Li, , and D. Wang, 2012: Effects of the East Asian summer monsoon on tropical cyclone genesis over the South China Sea on an interdecadal time scale. Adv. Atmos. Sci., 29, 249262, doi:10.1007/s00376-011-1080-x.

    • Search Google Scholar
    • Export Citation
  • Wang, X., , W. Zhou, , C. Li, , and D. Wang, 2014: Comparison of the impact of two types of El Niño on tropical cyclone genesis over the South China Sea. Int. J. Climatol., doi: 10.1002/joc.3865, in press.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences.Academic Press, 464 pp.

  • Wu, M. C., , W. L. Chang, , and W. M. Leung, 2004: Impacts of El Niño–Southern Oscillation events on tropical cyclone landfalling activity in the western North Pacific. J. Climate, 17, 14191428, doi:10.1175/1520-0442(2004)017<1419:IOENOE>2.0.CO;2.

    • 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, doi:10.1175/2008JCLI2544.1.

    • Search Google Scholar
    • Export Citation
  • Zhan, R., , Y. Wang, , and X. Lei, 2011: Contributions of ENSO and east Indian Ocean SSTA to the interannual variability of northwest Pacific tropical cyclone frequency. J. Climate, 24, 509521, doi:10.1175/2010JCLI3808.1.

    • Search Google Scholar
    • Export Citation
  • Zhang, W., , Y. Leung, , and J. Min, 2013: North Pacific Gyre Oscillation and the occurrence of western North Pacific tropical cyclones. Geophys. Res. Lett., 40, 52055211, doi:10.1002/grl.50955.

    • Search Google Scholar
    • Export Citation
  • Zhao, X., , and P.-S. Chu, 2010: Bayesian changepoint analysis for extreme events (typhoons, heavy rainfall, and heat waves): An RJMCMC approach. J. Climate, 23, 10341046, doi:10.1175/2009JCLI2597.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, W., , and J. C. L. Chan, 2005: Intraseasonal oscillations and the South China Sea summer monsoon onset. Int. J. Climatol., 25, 15851609, doi:10.1002/joc.1209.

    • Search Google Scholar
    • Export Citation
  • Zhou, W., , and J. C. L. Chan, 2007: ENSO and South China Sea summer monsoon onset. Int. J. Climatol., 27, 157167, doi:10.1002/joc.1380.

    • Search Google Scholar
    • Export Citation
Save