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Variability of tropical cyclone occurrence date in the South China Sea and its relationship with SST warming
Dynamics of Atmospheres and Oceans 55–56 (2012) 45–59
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Variability of tropical cyclone occurrence date in the South China Sea and its relationship with SST warming Youfang Yan a,∗, Yiquan Qi a, Wen Zhou b a State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China b Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
a r t i c l e
i n f o
Article history: Received 1 August 2011 Received in revised form 15 April 2012 Accepted 2 May 2012 Available online 10 May 2012 Keywords: Tropical cyclone Occurrence date SST ENSO South China Sea
a b s t r a c t Analyses of tropical cyclone (TC) occurrence dates in the South China Sea (SCS) for the past 60 yrs indicate a trend toward an earlier occurrence of the first annual TC in the SCS. On the other hand, a significant increasing trend in sea surface temperature (SST) in early summer (May–June) has been observed in the SCS. The negative correlation between the first annual TC occurrence date and SST in early summer during the period 1945–2009 suggests that the earlier occurrence of the first annual TC is related not only to the increasing of SST in the SCS, but also to the variability of SST in the ˜ Nino3.4 region. Quantitative analysis of the SCS TC occurrence date and SST by quantile regression also reveals such a relationship and confirms that the SCS early-season TCs tend to occur earlier when ˜ SSTs in the SCS and Nino3.4 region are increasing. Since the SCS ˜ SST anomalies are influenced by the El Nino-Southern Oscillation (ENSO), the relationship between the first annual TC occurrence date and ENSO-related large-scale atmospheric circulation including 850-hPa relative vorticity (RV), vertical wind shear (VWS), and moist static energy (MSE) in early summer are also investigated. It is found that variations of VWS and MSE have influences on first annual SCS TC occurrence dates, although there is not a statistically significant relationship between 850-hPa RV and first annual SCS TC occurrence date. These results suggest that the earlier occurrence of the first annual TC in the SCS is influenced not only by local SST, but also by ENSO through the alternation of early summer VWS and MSE in the SCS. © 2012 Elsevier B.V. All rights reserved.
∗ Corresponding author. E-mail address: [email protected] (Y. Yan). 0377-0265/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.dynatmoce.2012.05.001
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1. Introduction A topic of both societal and scientific importance in the study of global climate change is whether there is a causal connection between global warming and tropical cyclone (TC) activity (e.g. Landsea, 2000; Trenberth, 2005; Webster et al., 2005; Chan, 2006; Kossin et al., 2007; Vecchi et al., 2008; Knutson et al., 2010). Concerns about the possible impacts of global warming on TC activity have triggered a number of theoretical, modeling, and empirical studies. For instance, based on the theoretical studies of potential intensity, Emanuel (1987) and Holland (1997) found that the potential intensity of TCs was increasing substantially with anthropogenic global warming, leading to a prediction that actual TC intensity should increase with time in a greenhouse gas-warmed climate by directly forcing a higher rate of energy transfer from the tropical ocean. This prediction has also been discussed based on the more sophisticated global climate model (e.g. Emanuel, 1987; Broccoli and Manabe, 1990; Haarsma et al., 1993; Krishnamurti et al., 1998; Sugi et al., 2002, 2009; Yoshimura and Matsumura, 2005; Gualdi et al., 2008; Zhao et al., 2009; Yamada et al., 2010) and regional climate models (e.g. Knutson and Kurihara, 1998; Knutson and Tuleya, 1999, 2004; Shen et al., 2000; Bender et al., 2010). The variability of tropical sea surface temperature (SST), which is generally believed to be related to ongoing global warming, to increase TC activity is also examined based on observations (e.g. Emanuel, 2005; Trenberth, 2005; Webster et al., 2005). Within the past few decades of observations, Trenberth (2005) noticed that Atlantic hurricane activity has increased significantly on the accumulated cyclone energy (ACE) index since 1995 under a warming climate. By defining the power dissipation index (PDI) of TCs, Emanuel (2005) reported that the annual accumulated PDI has increased markedly since the mid-1970s, and this index is highly correlated with tropical SST in the main development region over the North Atlantic and Western North Pacific. By examining the number as well as the intensity of TCs over the past 35 yrs for all tropical basins, Webster et al. (2005) showed that the increase in the number and proportion of hurricanes reaching categories 4 and 5 is closely related to the increasing SSTs in the main TC development regions. All these results seem to open the door to the possibility that human-induced warming climate might have a great impact on TC activity. However, there is not yet a strong consensus regarding the impacts of warming climate on the TC activity because many studies have failed to identify such relationships. For example, using SSTs averaged monthly from 1967 to 1986, Evans (1993) found no statistically significant relationship between SST and the average intensities of TCs over five ocean basins, including the North Atlantic, Western North Pacific, South Pacific-Australia, north Indian, and southwest Indian Oceans. Based on observations, Wang and Chan (2002) revealed that TC activity over the Western North Pacific (WNP) ˜ Oscillation (ENSO) events, although the total is strongly related to changes in El Nino-Southern number of TCs formed in the entire WNP does not vary significantly. Wang et al. (2010) showed that global tropical storm days show a large amplitude fluctuation driven by ENSO and the Pacific Decadal Oscillation (PDO), although the SSTs related to global warming in the tropics are increasing. Similar results have also been reported by Chan and Liu (2004), McDonald et al. (2005) and Chan (2006). In previous studies, the focus was primarily on the number and intensity of TCs. Here, we choose TC occurrence date as an alternative way to measure TC activity, because TC occurrence dates, which reflect the length of the TC season, should also be related to some extent to the PDI (Emanuel, 2005). Thus, knowledge of the variability in TC occurrence dates may provide a basis for further understanding the variability in TC activity. Thus far, however, the variability in TC occurrence date over the past decades and its relationship with global climate change has not yet been fully revealed in the tropical ocean, especially in coastal areas where human population is increasing. In this study, we shift attention to TC occurrence dates, particularly to the variability in TC occurrence date and its relationship with SST increasing in the South China Sea (SCS). Because the focus of this study is the SCS, a brief background of TCs in the SCS is first provided in Section 2. Datasets and analysis methods are then described in Section 3. Section 4 describes the variations in TC occurrence date in the SCS and discusses which properties of TCs are related to SST variability and ENSO events, as well as the extent of this relationship. Section 5 presents a discussion and summary.
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2. Background The South China Sea, located at the westernmost edge of the Western North Pacific (WNP), is one of the largest semienclosed marginal seas, connecting to the Pacific Ocean through a number of passages and straits (see inset in the left panel of Fig. 1). The SCS is also located at the junction of the Indian monsoon, the WNP monsoon, and the East Asian subtropical monsoon (Wang and Wu, 1997) and is often subject to tropical cyclones (TCs). TCs, which affect the SCS, are formed in either the SCS or the WNP. In this study, any TCs entering the SCS from the WNP were omitted; only TCs formed in the SCS were considered. Earlier researchers often regarded SCS TCs as WNP TCs (Chia and Ropelewski, 2002). However, SCS TCs seem quite different from those in the WNP due to their unique monsoonal characteristics, as shown in Wang et al. (2007). For instance, there are almost no TCs formed in the SCS from January to March (Fig. 1, left panel), but the number of TCs formed in the WNP during this period is 76, accounting for 5.0% of the total annual number of TCs formed in the WNP. The statistical difference between SCS TCs and WNP TCs is also significant based on the analysis of 10,000 trials using the Monte Carlo simulation (Fig. 1, right panel). The results suggest that SCS TCs exhibit different behavior from those of the WNP, and thus they should be regarded as a separate entity rather than a subsample of the WNP in the study of TC variability.
3. Datasets and methods 3.1. Datasets Best-track data of TC records at 6-h intervals for the period 1945–2009 are obtained at the Joint Typhoon Warning Center (JTWC; http://www.usno.navy.mil/NOOC/nmfcph/RSS/jtwc/best tracks). This data can also be found on the home page of the Kerry Emanuel website (http://wind.mit.edu/∼emanuel/home.html). To verify the reliability of TC records, TC data from the Regional Specialized Meteorological Center (RSMC) for the period 1950–2009 is also adopted. The TC genesis date in this study is defined as the first time each individual TC originates in the SCS, while the first (last) annual TC occurrence date is defined as the initial date of the first (last) annual TC in the SCS. Although the inhomogeneity in the methods used to determine TCs over time
Fig. 1. Left panel: the monthly number of tropical cyclones formed in the WNP (shaded gray) and in the SCS (shaded dark) during the period 1945–2009. Right panel: Box-whisker plot for the actual and simulated TC number of 10,000 experiments of SCS TCs (N = 291) from the large sample of WNP plus SCS TCs using Monte Carlo simulations. The * indicates the actual observed TCs in the SCS. The blue box indicates the interquartile range, which is the difference between the upper quartile (75th percentile) and the lower quartile (25th percentile), and the line inside the box marks the median (50th percentile) of the 10,000 experiments. It can be seen that the actual SCS TCs are not situated totally within the interquartile range, suggesting that SCS TCs are not a subsample of TCs in the WNP. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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is recognized (e.g. Neumann et al., 1993; Landsea et al., 1999; Landsea, 2007), the variability in TCs in the SCS over the period of 1945–2009 is examined here. The monthly mean extended reconstructed sea surface temperature (ERSST) from the National Oceanic and Atmospheric Administration (NOAA) spanning from 1945 to 2009 (Smith et al., 2008), are used. The upper ocean temperature dataset produced by Ishii et al. (2006) based on in situ observations ˜ for the period of 1945–2006 is also adopted. In this study, we use the Nino3.4 SST, which is the areaaveraged SST over the eastern equatorial Pacific (120◦ W–170◦ W, 5◦ S–5◦ N), to represent the strength of ENSO (Barnston et al., 1997). In addition, the monthly atmospheric dataset from the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis over the period 1948–2009 (Kalnay et al., 1996) are used to study the variability in largescale parameters including low-level relative vorticity, vertical shear of zonal wind, and moist static energy and their relationship with TC occurrence dates in the SCS. 3.2. Methodology Correlation analysis was used to qualitatively examine the relationship between the variability in SCS TC occurrence dates and SST. The Student’s t-test and Mann–Kendall trend test (e.g. Mann, 1945; Kendall, 1975) were used to assess the significance of the correlation and trend. To address if and how SCS SSTs affect SCS TCs independently from ENSO, regression analysis of SST at each grid point was performed in order to remove the ENSO signal from SST, following Clark et al. (2000). Quantile regression (e.g. Koenker and Bassett, 1978; Koenker, 2005) was also used to quantitatively examine the relationship between SCS TC occurrence dates and SST, and an empirical orthogonal function (EOF) analysis was performed on 850-hPa relative vorticity, vertical wind shear, and moist static energy to extract the principal EOF modes of large-scale atmosphere parameters. 4. Results 4.1. Earlier occurrence of first annual TCs in the SCS The temporal evolution of TC occurrence dates over the period 1945–2009 for all TCs, the first annual TC, and the last annual TC in the SCS are shown in Fig. 2. The corresponding trends are also
Fig. 2. (a) Variability of occurrence dates for all TCs in the SCS during the period 1945–2009. (b) Same as (a) but for the first annual (black line) and last annual (blue line) TCs based on data from the Joint Typhoon Warning Center (JTWC). The corresponding trends estimated by least-squares regression are shown by the red lines. The Mann–Kendall test of trend shows a decreasing trend (p-value = 0.075) in first annual TC occurrence dates, while no significant trend is observed for all TCs (p-value = 0.922) and the last annual TC (p-value = 0.119) at the 90% confidence level. (c) and (d) Same as (a) and (b) for data from the Regional Specialized Meteorological Center (RSMC). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3. Variability of ERSST and Ishii SST in the SCS. (a) Linear coefficient of ERSST in the SCS from 1945 to 2009. The unit is ◦ C per century. (b) Time series of ERSST anomalies in early summer (May–June), which is spatially averaged over the whole SCS. (c) and (d) Same as (a) and (b) but for the dataset produced by Ishii et al. (2006) over the period 1945–2006.
shown, from which it can be seen that the occurrence dates of all TCs over the period are characterized by strong interannual and interdecadal variation. This variation is also found in the first and last annual TC occurrence dates. In addition, a statistically significant downward trend for first annual TC occurrence dates, tested by the Mann–Kendall method at the 90% confidence level, is observed over the study period, although trends for all TCs and last annual TC occurrence dates are not significant. The downward trend for first annual TC occurrence dates suggests that there has been a tendency for first annual TCs to occur earlier in the SCS in recent decades. Similar results (Fig. 2d) are also revealed by TC records from the RSMC, which used a different criterion for TC identification than that used by the JTWC, and this further suggests that first annual TCs are tending to occur earlier in the SCS. 4.2. First annual TC occurrence date-early summer SST relationship What causes the earlier occurrence of first annual TCs in the SCS? A previous study showed that the earlier occurrence of early-season TCs in the North Atlantic may be linked to the increasing of tropical Atlantic SSTs (Kossin, 2008). In this section, we first examine the variability in SCS SST, particularly for early summer (May–June). Fig. 3a shows the linear coefficient of ERSST at each grid point, which is fitted by a linear-least square. It appears that over the period 1945–2009, SCS SST is dominated by a positive trend, with SST increasing by approximately 0.5–1.0 ◦ C per century. This increasing rate is relatively slower than that in the North Atlantic, where the temperature in the main TC occurrence region is increasing by approximately 0.5–1.5 ◦ C per century (e.g. Agudelo and Curry, 2004; Webster et al., 2005; Kossin, 2008). The area-averaged SST anomalies over the SCS during May–June show a noticeable warming trend in early summer in the past decades (Fig. 3b). The increasing trend in early summer SSTs in the SCS is also found from the upper-ocean temperature dataset produced by Ishii et al. (2006) (Fig. 3c and d).
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Fig. 4. (a) Relationship between the first annual TC occurrence date and early-summer (May–June) SST. Solid lines denote positive relationships, while dashed lines denote negative relationships. Shaded areas indicate that the correlations are statistically significant at the 95% confidence level. (b) Same as (a) but for the correlation after removing the ENSO effect.
To determine the relationship between early-summer SST and first annual TC occurrence date in the SCS, we correlate SST anomalies during May–June at each grid point with the SCS first annual TC occurrence dates over the period 1945–2009. The first annual TC occurrence dates in the SCS are found to have significant negative correlations with SCS SSTs (Fig. 4a). Such negative correlations are also ˜ found in the Nino3.4 region. The results suggest that the variation of first annual TC occurrence date ˜ in the SCS is related not only to the variability of SST in the SCS, but also to SST in the Nino3.4 region. In other words, earlier occurrence of first annual TCs in the SCS is influenced both by variability of SCS SST and ENSO event. It is widely recognized that ENSO is the largest source of global climate variability associated with the phenomenon of air-sea coupling. Although ENSO involving maximum SST anomalies occurs in the equatorial central and eastern Pacific, it can also significantly impact SST variability in distant regions through atmospheric bridges (e.g. Tourre and White, 1995; Wallace, 1998; Chiang and Sobel, 2002; Su and Neelin, 2002; Chiang and Lintner, 2005). Much of the existing literature has shown that SSTs in remote ocean basins such as the tropical Atlantic, the Indian, and the SCS are usually above normal when SSTs in the central and eastern Pacific are above normal (e.g. Pan and Oort, 1983; Yulaeva and Wallace, 1994; Tourre and White, 1995; Enfield and Mayer, 1997; Wang et al., 2006; Zhou and Chan, 2007). The changed SSTs associated with ENSO exert a strong influence on TC activity over these basins (e.g. Chan and Liu, 2004; Wang and Lee, 2008). Since the main focus of this study is to examine TC
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˜ Fig. 5. (a) Correlations between SST anomalies in the North Pacific and Nino3.4 SST anomalies in the period September– December. Solid lines denote positive relationships, while dashed lines denote negative relationships. The shaded area indicates ˜ region. (b) Same as statistically significant correlations at the 95% confidence level. The rectangular box indicates the Nino3.4 (a) but for 1970–2009.
occurrence dates and their relationship with SSTs in the SCS, the influence of ENSO on SCS SSTs is investigated here. Following several precedents (e.g. Klein et al., 1999; Chan and Liu, 2004; Wang et al., 2006), the ˜ correlation coefficients between monthly Nino3.4 SST anomalies during September–December and simultaneous SST anomalies over the North Pacific are displayed (Fig. 5). In agreement with Klein et al. (1999) and Wang et al. (2006), significant ENSO-related SST anomalies are observed over many regions, such as the tropical eastern WNP, the extratropical North Pacific, and the SCS. Positive correlations, with a maximum coefficient of 0.5, are found in the SCS (Fig. 5a). Such correlations are more prominent for the period 1970–2009, during which the majority of ENSO events occurred (Fig. 5b). This strong relationship may have originated from a stronger anomalous Walker–Hadley circulation after the late 1970s. A strong Walker–Hadley circulation has been shown to lead to more persistent ENSO-induced SST warming in the SCS and Indian Ocean (e.g. Trenberth and Hurrell, 1994; Zhang et al., 1997; Yun ˜ et al., 2010). The interannual variations in SST, which are averaged over the SCS and the Nino3.4 region from 1945–2009, are also computed (Fig. 6). The variability in SCS SST is closely related to ENSO events, ˜ with the Nino3.4 SST anomalies leading the SCS SST by about 5 months, confirming previous results
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˜ Fig. 6. Comparisons of Nino3.4 (120◦ W–170◦ W, 5◦ S–5◦ N) SST anomalies (shaded curve) with SST anomalies (black curve) in the SCS for the period of 1945–2009.
(e.g. Klein et al., 1999; Wang et al., 2000, 2006). Therefore, in the following examination of the impact of local SST on SCS TC occurrence dates, the influence of ENSO on the SCS SST should be removed. Closely following Clark et al. (2000), we remove the ENSO signal from the SCS SST, and the remaining (non-ENSO-related) SST – that is, the local SST – is then correlated with first annual TC occurrence dates over the SCS. As shown in Fig. 4b, although the magnitude and areal extent of the correlations are both reduced, the negative correlations previously observed over the SCS are still significant after the removal of the ENSO effect (cf. Fig. 4a and b), which demonstrates the significant effect of local SST on SCS TC occurrence dates. To further quantitatively determine how the strength of early-summer SST and the SCS first annual TC occurrence date correlation varies with and without the ENSO effect, quantile regression analysis is also used (Koenker, 2005). Note that in this analysis all TC occurrence dates during the 65-yr period and their corresponding monthly averaged SSTs are considered. As shown in Fig. 7a, the trends ˇ(), ranging from −90 to −100 days/◦ C for early-season (<20th percentile) TCs, suggest that SCS TCs tend to occur earlier when SSTs are increasing. The result is quite different when the same regression analysis is performed for the non-ENSO-related SSTs. The negative correlations previously observed
Fig. 7. (a) Coefficients (trends) of quantile regression using all TC occurrence dates over the period 1945–2009 as the response variable and the corresponding monthly mean SST as the covariate in the SCS. The shading shows the 95% point-wise confidence band about these trend estimates. The dates (day/month) associated with the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles are shown on the top axis based on the full sample over the study period. (b) Same as (a) but for the coefficients (trends) after the removal of the ENSO effect.
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Fig. 8. The spatial pattern of the first mode (EOF1) for the vertical shear of zonal wind (VWS) in early summer (May–June). Solid lines denote positive values, while dashed lines denote negative values.
in the early-season show an obvious weakening after the removal of the ENSO effect (cf. Fig. 7a and b), suggesting that the early-season SCS TC occurrence date is closely related to ENSO, and confirming the above results. 4.3. Impact of ENSO on first annual TC occurrence date To further determine how ENSO affects the first annual SCS TC occurrence date, we extend our analysis by examining the variations of large-scale atmosphere parameters – including vertical wind shear, 850-hPa relative vorticity, and moist static energy in early summer – as well as their connection to the first annual TC occurrence date in the SCS. Vertical wind shear (VWS), defined by the zonal wind difference between the upper (200 hPa) and lower (850 hPa) atmosphere, is averaged over May–June, and an EOF analysis is performed on its anomalous field. The mode that is significantly related to the SCS first annual TC occurrence date will be discussed. Examination of the relationship between the first principal component (PC1) of VWS and the SCS first annual TC occurrence date shows that the latter is positively correlated with the first mode of VWS. The spatial pattern of the first mode (EOF1), which explains 46% of the total variance, shows a south–north dipole over the WNP with a positive (negative) sign north (south) of 12◦ N (Fig. 8). This pattern somewhat resembles that of Chan and Liu (2004), although their analysis is for May to November during 1960–2003. An area of positive sign is also found in the SCS, which suggests that the increasing of VWS does not favor the earlier occurrence of TC in the SCS. On the other hand, the temporal evolution of PC1 is positively correlated with early summer SST anomalies ˜ region, but negatively correlated in the WNP and SCS. Negative correlation means that in the Nino3.4 an increase in SST is correlated with a decrease in VWS, which is likely to be associated with stronger ˜ region and subsidence. After the removal of the ENSO effect, the high correlations over the Nino3.4 the SCS are both reduced and become insignificant, although the correlations are still significant near the Philippine Sea (Fig. 9b). This result suggests that the variation in early-summer VWS that is forced by ENSO events may provide a favorable condition for the earlier occurrence of first annual TCs in the SCS, further supporting the above results. Since the boundary layer Ekman convergence is proportional to 850-hPa relative vorticity, the increase in background low-level vorticity would help spin up TCs by increasing moisture convergence and entraining potential vorticity into them (Gray, 1979). As a result, 850-hPa relative vorticity is a meaningful indication for TC activity. Fig. 10 shows the first mode (EOF1) of 850-hPa relative vorticity, which accounts for ∼39% of the total variance and is related to the SCS first annual TC occurrence date (r = 0.27). A striking tripole pattern with a negative sign located in the region ranging from 9◦ N to 27◦ N and a positive sign at lower (south of 9◦ N) and higher (north of 27◦ N) latitudes is found. The temporal ˜ evolution of PC1 of 850-hPa relative vorticity is correlated with the SST in the Nino3.4 region, but the
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Fig. 9. (a) Correlations between the time series of EOF1 for the vertical shear of zonal wind and SST in early summer (May–June). Solid lines denote positive relationships and dashed lines denote negative relationships. Shaded areas indicate statistically significant correlations at the 95% confidence level. (b) Same as (a) but for the correlations after the removal of the ENSO effect.
Fig. 10. Same as Fig. 8 but for 850-hPa relative vorticity (RV).
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Fig. 11. Same as Fig. 9 but for 850-hPa relative vorticity (RV).
statistically significant correlation is not found in the SCS (Fig. 11a). After the removal of the ENSO signal, the correlation coefficient shows little change in the SCS (Fig. 11b). The results suggest that variability in relative vorticity that is related to ENSO in early summer has little impact on the SCS first annual TC occurrence date. Since the first mode (EOF1) of moist static energy (MSE), which is integrated between layers of 1000–500 hPa from May to June, is not significantly correlated to the SCS first annual TC occurrence date, the second mode (EOF2), accounting for ∼34.6% of the total covariance and significantly correlated with the SCS first annual TC occurrence date (r = −0.24), is discussed here. The negative correlation suggests that a higher MSE favors an earlier occurrence of the first annual TC. The second EOF shows an ˜ east–west and south–north dipole over the tropical Pacific with a positive sign in the Nino3.4 region but a negative sign in the WNP and SCS (Fig. 12). On the other hand, positive correlations between ˜ region, while negative correlations are PC2 and early-summer SST anomalies are found in the Nino3.4 found in the WNP and SCS (Fig. 13a). This means that in most parts of WNP and SCS, an increase in SST actually correlates with an increase in MSE. Since MSE is not independent from ENSO, the correlation between the local SST and PC2 after the removal of the ENSO effect is also examined. It is found that the negative correlation between the local SST and PC2 become larger in the SCS after the removal of the ENSO signal but the magnitude is quite small (Fig. 13b). A higher MSE is likely associated with non-subsidence of dry midtroposphere. In other words, the subsidence of dry midtroposphere which is forced by ENSO tends to decrease MSE, and thereby provides an unfavorable condition for the earlier occurrence of first annual TC over the SCS, but the uncertainty in assessing this relationship is high.
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Fig. 12. Same as Fig. 8 but for EOF2 of moist static energy (MSE).
Fig. 13. Same as Fig. 9 but for EOF2 of moist static energy (MSE).
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5. Discussion and summary Long-term change in tropical cyclone (TC) intensity and frequency in a warming environment has attracted much attention recently (e.g. Emanuel, 2005; Webster et al., 2005; Chan, 2006; Kossin, 2008; Wang et al., 2010). With the observed warming of the tropics in past decades, examining the observed change in TC activity may shed light on the impact of global warming on TC activity. In this study, we shifted the focus to the variability of TC occurrence dates in the South China Sea (SCS) and its relationship with variability of SST based on observations. Our results show that the first annual TCs in the SCS have tended to occur earlier during the past decades. On the other hand, an obvious warming trend in early-summer SST has been observed in the SCS. Result of qualitative analysis show that the SCS first annual TC occurrences date is related not only to the variability of SST in the SCS, ˜ region. Significant correlations between the SCS first but also to the variability of SST in the Nino3.4 TC occurrence date and the SCS SST are still found when the ENSO influence is removed, although the magnitude and areal extent of the correlations are reduced. These results suggest that the earlier occurrence of first annual TCs in the SCS is influenced not only by local SST, but also by ENSO events. Quantitative analyses by quantile regression of TC occurrence dates and the basin warming of SCS SST with and without the ENSO effect also confirm this result. The trend value of about −100 days/◦ C for early-season (<20th percentile) TCs matches well with the negative correlation of early-summer SCS SST with first annual TC occurrence date, suggesting that the first annual TCs in the SCS tend to occur earlier when basin-scale SSTs are increasing. The high value of the trend in the early season is greatly reduced by the removal of the ENSO signal, further suggesting that early-season TC occurrence dates in the SCS are also constrained by ENSO. The influences of ENSO on the SCS first annual TC occurrence date through the alternation of large-scale circulation including 850-hPa relative vorticity, VWS, and MSE in early summer are also investigated. It is found that early-summer variations in VWS and MSE induced by ENSO have statistically relationships with the SCS first annual TC occurrence date, although the uncertainty in the last two is high. In other words, in addition to local SST, weaker VWS which is induced by ENSO in early summer is also responsible for the earlier occurrence of first annual TCs in the SCS. This result is consistent with that of Tu et al. (2011), suggesting that changes in large-scale variables including SST, upper-ocean heat content, VWS, and tropospheric water vapor might be responsible for the abrupt increase in early-summer intense typhoons over the WNP. To summarize, the first annual TC occurrence dates in the SCS are found to correlate not only with local SST, but also with ENSO events. An increase in early-summer (May–June) SST could lead to a higher rate of energy transfer from the tropical ocean and thus provide a favorable thermodynamic condition for the earlier occurrence of the first annual TCs in the SCS. In addition, the above-normal ˜ SST over the Nino3.4 region might also affect the SCS first annual TC occurrence date by remotely modifying the VWS and MSE over the SCS in early summer. This observational evidence supports the notion that increased TC activity will occur with higher SSTs. These results also confirm earlier findings suggesting that TC activity in the SCS is also influenced by ENSO events. However, it should also be noted that although the effect of basin-scale SST anomalies may be significant, it is not the only oceanic parameter that constrains TC activity (e.g. Swanson, 2008; Vecchi and Knutson, 2008; Knutson et al., 2010). In addition, as shown above, the correlation between local SST and the first annual TCs occurrence date is high, which suggests an important impact of local SST on the earlier occurrence of first annual TCs in the SCS. However, why the local SST becomes favorable for the earlier occurrence of first annual TCs in the SCS, an in-depth analysis of local SST inducing changes of vertical motion, water vapor and relative humidity are worth further investigation. Acknowledgments We thank the US Navy’s Joint Typhoon Warning Center (JTWC) for providing the tropical cyclone datasets. Thanks are also given to quantile regression codes from http://www.econ.uiuc. edu/∼roger/research/rq/rq.html. The right panel of Fig. 1 is kindly provided by Professor James P. Kossin, and we extend our sincere thanks for his support. This work is partly supported by the National Basic Research Program of China (2010CB950401; 2011CB403504), the R&D Special Fund for Public
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Welfare Industry (GYHY201106036) and the Strategy Research Project of City University of Hong Kong (7002717). References Agudelo, P.A., Curry, J.A., 2004. Analysis of spatial distribution in tropospheric temperature trends. Geophys. Res. Lett. 31, L22207, http://dx.doi.org/10.1029/2004GL02818. Barnston, A.G., Chelliah, M., Goldenberg, S.B., 1997. Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmosphere-Ocean 35, 367–383. Bender, M.A., Knutson, T.R., Tuleya, R.E., Sirutis, J.J., Vecchi, G.A., Garner, S.T., Held, I.M., 2010. Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science 327, 454–458. Broccoli, A.J., Manabe, S., 1990. Can existing climate models be used to study anthropogenic changes in tropical cyclone climate? Geophys. Res. Lett. 17, 1917–1920. Chan, J.C.L., 2006. Comment on changes in tropical cyclone number, duration, and intensity in a warming environment. Science 311 (5768), 1713. Chan, J.C.L., Liu, K.S., 2004. Global warming and Western North Pacific typhoon activity from an observational perspective. J. Climate 17, 4590–4602. Chia, H.H., Ropelewski, C.F., 2002. The interannual variability in the genesis location of tropical cyclones in the Northwest Pacific. J. Climate 15, 2934–2944. ˜ J. Climate 18, 4130–4149. Chiang, J.C.H., Lintner, B.R., 2005. Mechanisms of remote tropical surface warming during El Nino. Chiang, J.C.H., Sobel, A.H., 2002. Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate 15, 2616–2631. Clark, C.O., Cole, J.E., Webster, P.J., 2000. Indian Ocean SST and Indian summer rainfall: predictive relationships and their decadal variability. J. Climate 13, 2503–2519. Emanuel, K.A., 1987. The dependence of hurricane intensity on climate. Nature 326, 483–485. Emanuel, K.A., 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436, 686–688. ˜ Enfield, D.B., Mayer, D.A., 1997. Tropical Atlantic sea surface temperature variability and its relation to El Nino-Southern oscillation. J. Geophys. Res. 102, 929–945. Evans, J.L., 1993. Sensitivity of tropical cyclone intensity to sea surface temperature. J. Climate 6, 1133–1140. Gray, W.M., 1979. Hurricanes: their formation, structure and likely role in the tropical circulation. Meteorology over the tropical oceans. In: Shaw, D.B. (Ed.), Roy. Meteor. Soc., 155–218. Gualdi, S., Scoccimarro, E., Navarra, A., 2008. Changes in tropical cyclone activity due to global warming: results from a highresolution coupled general circulation model. J. Climate 21, 5204–5228. Haarsma, R.J., Mitchell, J.F.B., Senior, C.A., 1993. Tropical disturbances in a GCM. Climate Dyn. 8, 247–257. Holland, G.J., 1997. The maximum potential intensity of tropical cyclones. J. Atmos. Sci. 54, 2519–2541. Ishii, M., Kimoto, M., Sakamoto, K., Iwasaki, S.I., 2006. Steric sea level changes estimated from historical ocean subsurface temperature and salinity analyses. J. Oceanogr. 62 (2), 155–170. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., Joseph, D., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteor. Soc. 77, 437–470. Kendall, M.G., 1975. Rank Correlation Methods. Charles Griffin, London. Klein, S.A., Soden, B.J., Lau, N.C., 1999. Remote sea surface temperature variations during ENSO: evidence for a tropical atmospheric bridge. J. Climate 12, 917–932. Knutson, T.R., Kurihara, Y., 1998. Simulated increase of hurricane intensities in a CO2 -warmed world. Science 279, 1018–1020. Knutson, T.R., Mcbride, J., Chan, J.C.L., Emanuel, K.A., Holland, G., Landsea, C., Held, I.M., Kossin, J.P., Srivastava, A.K., Sugi, M., 2010. Tropical cyclones and climate change. Nat. Geosci. 3, 157–163. Knutson, T.R., Tuleya, R.E., 1999. Increased hurricane intensities with CO2 -induced global warming as simulated using the GFDL hurricane prediction system. Climate Dyn. 15, 503–519. Knutson, T.R., Tuleya, R.E., 2004. Impact of CO2 -induced warming on simulated hurricane intensity and precipitation: sensitivity to the choice of climate model and convective parameterization. J. Climate 17, 3477–3495. Koenker, R.W., 2005. Quantile Regression. Cambridge University Press, New York. Koenker, R.W., Bassett, G.J., 1978. Regression quantiles. Econometrica – Econ. Soc. 46 (1), 33–50. Kossin, J.P., 2008. Is the North Atlantic hurricane season getting longer? Geophys. Res. Lett. 35, L23705, http://dx.doi.org/10.1029/2008GL036012. Kossin, J.P., Knapp, K.R., Vimont, D.J., Murnane, R.J., Harper, B.A., 2007. A globally consistent reanalysis of hurricane variability and trends. Geophys. Res. Lett. 34, L04815, http://dx.doi.org/10.1029/2006GL028836. Krishnamurti, T.N., Torres, R.C., Latif, M., Daughenbaugh, G., 1998. The impact of current and possibly future sea surface temperature anomalies on the frequency of Atlantic hurricanes. Tellus 50, 186–210. ˜ oscillation and the seasonal variability of tropical cyclones. In: Diaz, H.F., Markgraf, V. Landsea, C.W., 2000. El Nino-Southern ˜ Impact of Multiscale Variability on Natural Ecosystems and Society. Cambridge University Press, pp. 149–181. (Eds.), El Nino: Landsea, C.W., 2007. Counting Atlantic tropical cyclones back to 1900. Eos Trans. AGU 88 (18), http://dx.doi.org/10.1029/2007EO180001. ˜ R.A.P., Knaff, A.M.J.A., 1999. Atlantic basin hurricanes: indices of climatic changes. Climatic Landsea Jr., C.W., Mestas-Nunez, Change 42, 89–129. Mann, H.B., 1945. Non-parametric tests against trend. Econometrica 13, 245–259. McDonald, R.E., Bleaken, D.G., Cresswell, D.R., Pope, V.D., Senior, C.A., 2005. Tropical storms: representation and diagnosis in climate models and the impacts of climate change. Climate Dyn. 25, 19–36. Neumann, C.J., Jarvinen, B.R., McAdie, C.J., Elms, J.D., 1993. Tropical Cyclones of the North Atlantic Ocean. Historical Climatology Series 6-2. National Climatic Data Center, 193 pp.
Y. Yan et al. / Dynamics of Atmospheres and Oceans 55–56 (2012) 45–59
59
Pan, Y.H., Oort, A.H., 1983. Global climate variations connected with sea surface temperature anomalies in the eastern equatorial Pacific Ocean for the 1958–73 periods. Mon. Weather Rev. 111, 1244–1258. Shen, W., Tuleya, R.E., Ginis, I., 2000. A sensitivity study of the thermodynamic environment on GFDL model hurricane intensity: implications for global warming. J. Climate 13, 109–121. Smith, T.M., Reynolds, R.W., Peterson, T.C., Lawrimore, J., 2008. Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J. Climate 21, 2283–2296. ˜ J. Atmos. Sci. 59, Su, H., Neelin, J.D., 2002. Teleconnection mechanisms for tropical Pacfic desent anomalies during El Nino. 2694–2712. Sugi, M., Murakami, H., Yoshimura, J., 2009. A reduction in global tropical cyclone frequency due to global warming. SOLA 5, 164–167. Sugi, M., Noda, A., Sato, N., 2002. Influence of the global warming on tropical cyclone climatology: an experiment with the JMA global model. J. Meteor. Soc. Jpn. 80, 249–272. Swanson, K.L., 2008. Nonlocality of Atlantic tropical cyclone intensities. Geochem. Geophys. Geosyst. 9, Q04V01, http://dx.doi.org/10.1029/2007GC001844. Tourre, Y.M., White, W.B., 1995. ENSO signals in global upper ocean temperature. J. Phys. Oceanogr. 25, 1317–1332. Trenberth, K., 2005. Uncertainty in hurricanes and global warming. Science 308, 1753–1754. Trenberth, K.E., Hurrell, J.W., 1994. Decadal atmosphere-ocean variations in the Pacific. Climate Dyn. 9, 303–319. Tu, J.Y., Chou, C., Huang, P., Huang, R.H., 2011. An abrupt increase of intense typhoons over the western North Pacific in early summer. Environ. Res. Lett. (6), 034013. Vecchi, G.A., Knutson, T.R., 2008. On estimates of historical North Atlantic tropical cyclone activity. J. Climate 21, 3580–3600. Vecchi, G.A., Swanson, K.L., Soden, B.J., 2008. Climate change. Whither hurricane activity? Science 322 (5902), 687–689. Wallace, J.M., 1998. On the structure and evolution of ENSO-related climate variability in the tropical Pacific: lessons from TOGA. J. Geophys. Res. 103, 14241–14259. Wang, B., Chan, J.C.L., 2002. How strong ENSO events affect tropical storm activity over the western North Pacific. J. Climate 15, 1643–1658. Wang, B., Wu, R., 1997. Peculiar temporal structure of the South China Sea summer monsoon. Adv. Atmos. Sci. 14, 177–194. Wang, B., Yang, Y., Ding, Q.-H., Murakami, H., Huang, F., 2010. Climate control of the global tropical storm days (1965–2008). Geophys. Res. Lett. 37, L07704, http://dx.doi.org/10.1029/2010GL042487. Wang, C., Lee, S.K., 2008. Global warming and United States landfalling hurricanes. Geophys. Res. Lett. 35 (2), L02708, http://dx.doi.org/10.1029/2007GL032396. ˜ J. Geophys. Wang, C., Wang, W., Wang, D., Wang, Q., 2006. Interannual variability of the South China Sea associated with El Nino. Res. 111, C03023, http://dx.doi.org/10.1029/2005JC003333. Wang, G., Su, J., Ding, Y., Chen, D., 2007. Tropical cyclone genesis over the south China sea. J. Mar. Syst. 68 (3-4), 318–326. Wang, W., Wang, D., Qi, Y., 2000. Large-scale characteristics of interannual variability of sea surface temperature in the South China Sea. Acta Oceanol. Sin. 22, 8–16. Webster, P.J., Holland, G.J., Curry, J.A., Chang, H.R., 2005. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309, 1844–1846. Yamada, Y., Oouchi, K., Satoh, M., Tomita, H., Yanase, W., 2010. Projection of changes in tropical cyclone activity and cloud height due to greenhouse warming: global-cloud system-resolving approach. Geophys. Res. Lett. 37, L07709, http://dx.doi.org/10.1029/2010GL042518. Yoshimura, H., Matsumura, T., 2005. A two-time-level vertically conservative semi-Lagrangian semi implicit double Fourier series AGCM. In: CAS/JSC WGNE Research Activities in Atmospheric and Ocean Modeling, vol. 35, no. 1, pp. 27–28. Yulaeva, E., Wallace, J.M., 1994. The signature of ENSO in global temperature and precipitation fields derived from the microwave sounding unit. J. Climate 7, 1719–1736. Yun, K.S., Seo, K.H., Ha, K.J., 2010. Interdecadal change in the relationship between ENSO and the intraseasonal oscillation in East Asia. J. Climate 23 (13), 3599–3612. Zhang, Y., Wallace, J.M., Battisti, D.S., 1997. ENSO-like interdecadal variability 1900–93. J. Climate 10, 1004–1020. Zhao, M., Held, I.M., Lin, S.J., Vecchi, G.A., 2009. Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate 22, 6653–6678. Zhou, W., Chan, J.C.L., 2007. ENSO and South China Sea summer monsoon onset. Int. J. Climatol. 27, 157–167.
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Dynamics of Atmospheres and Oceans journal homepage: www.elsevier.com/locate/dynatmoce
Variability of tropical cyclone occurrence date in the South China Sea and its relationship with SST warming Youfang Yan a,∗, Yiquan Qi a, Wen Zhou b a State Key Laboratory of Tropical Oceanography (South China Sea Institute of Oceanology, Chinese Academy of Sciences), Guangzhou 510301, China b Guy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, China
a r t i c l e
i n f o
Article history: Received 1 August 2011 Received in revised form 15 April 2012 Accepted 2 May 2012 Available online 10 May 2012 Keywords: Tropical cyclone Occurrence date SST ENSO South China Sea
a b s t r a c t Analyses of tropical cyclone (TC) occurrence dates in the South China Sea (SCS) for the past 60 yrs indicate a trend toward an earlier occurrence of the first annual TC in the SCS. On the other hand, a significant increasing trend in sea surface temperature (SST) in early summer (May–June) has been observed in the SCS. The negative correlation between the first annual TC occurrence date and SST in early summer during the period 1945–2009 suggests that the earlier occurrence of the first annual TC is related not only to the increasing of SST in the SCS, but also to the variability of SST in the ˜ Nino3.4 region. Quantitative analysis of the SCS TC occurrence date and SST by quantile regression also reveals such a relationship and confirms that the SCS early-season TCs tend to occur earlier when ˜ SSTs in the SCS and Nino3.4 region are increasing. Since the SCS ˜ SST anomalies are influenced by the El Nino-Southern Oscillation (ENSO), the relationship between the first annual TC occurrence date and ENSO-related large-scale atmospheric circulation including 850-hPa relative vorticity (RV), vertical wind shear (VWS), and moist static energy (MSE) in early summer are also investigated. It is found that variations of VWS and MSE have influences on first annual SCS TC occurrence dates, although there is not a statistically significant relationship between 850-hPa RV and first annual SCS TC occurrence date. These results suggest that the earlier occurrence of the first annual TC in the SCS is influenced not only by local SST, but also by ENSO through the alternation of early summer VWS and MSE in the SCS. © 2012 Elsevier B.V. All rights reserved.
∗ Corresponding author. E-mail address: [email protected] (Y. Yan). 0377-0265/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.dynatmoce.2012.05.001
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1. Introduction A topic of both societal and scientific importance in the study of global climate change is whether there is a causal connection between global warming and tropical cyclone (TC) activity (e.g. Landsea, 2000; Trenberth, 2005; Webster et al., 2005; Chan, 2006; Kossin et al., 2007; Vecchi et al., 2008; Knutson et al., 2010). Concerns about the possible impacts of global warming on TC activity have triggered a number of theoretical, modeling, and empirical studies. For instance, based on the theoretical studies of potential intensity, Emanuel (1987) and Holland (1997) found that the potential intensity of TCs was increasing substantially with anthropogenic global warming, leading to a prediction that actual TC intensity should increase with time in a greenhouse gas-warmed climate by directly forcing a higher rate of energy transfer from the tropical ocean. This prediction has also been discussed based on the more sophisticated global climate model (e.g. Emanuel, 1987; Broccoli and Manabe, 1990; Haarsma et al., 1993; Krishnamurti et al., 1998; Sugi et al., 2002, 2009; Yoshimura and Matsumura, 2005; Gualdi et al., 2008; Zhao et al., 2009; Yamada et al., 2010) and regional climate models (e.g. Knutson and Kurihara, 1998; Knutson and Tuleya, 1999, 2004; Shen et al., 2000; Bender et al., 2010). The variability of tropical sea surface temperature (SST), which is generally believed to be related to ongoing global warming, to increase TC activity is also examined based on observations (e.g. Emanuel, 2005; Trenberth, 2005; Webster et al., 2005). Within the past few decades of observations, Trenberth (2005) noticed that Atlantic hurricane activity has increased significantly on the accumulated cyclone energy (ACE) index since 1995 under a warming climate. By defining the power dissipation index (PDI) of TCs, Emanuel (2005) reported that the annual accumulated PDI has increased markedly since the mid-1970s, and this index is highly correlated with tropical SST in the main development region over the North Atlantic and Western North Pacific. By examining the number as well as the intensity of TCs over the past 35 yrs for all tropical basins, Webster et al. (2005) showed that the increase in the number and proportion of hurricanes reaching categories 4 and 5 is closely related to the increasing SSTs in the main TC development regions. All these results seem to open the door to the possibility that human-induced warming climate might have a great impact on TC activity. However, there is not yet a strong consensus regarding the impacts of warming climate on the TC activity because many studies have failed to identify such relationships. For example, using SSTs averaged monthly from 1967 to 1986, Evans (1993) found no statistically significant relationship between SST and the average intensities of TCs over five ocean basins, including the North Atlantic, Western North Pacific, South Pacific-Australia, north Indian, and southwest Indian Oceans. Based on observations, Wang and Chan (2002) revealed that TC activity over the Western North Pacific (WNP) ˜ Oscillation (ENSO) events, although the total is strongly related to changes in El Nino-Southern number of TCs formed in the entire WNP does not vary significantly. Wang et al. (2010) showed that global tropical storm days show a large amplitude fluctuation driven by ENSO and the Pacific Decadal Oscillation (PDO), although the SSTs related to global warming in the tropics are increasing. Similar results have also been reported by Chan and Liu (2004), McDonald et al. (2005) and Chan (2006). In previous studies, the focus was primarily on the number and intensity of TCs. Here, we choose TC occurrence date as an alternative way to measure TC activity, because TC occurrence dates, which reflect the length of the TC season, should also be related to some extent to the PDI (Emanuel, 2005). Thus, knowledge of the variability in TC occurrence dates may provide a basis for further understanding the variability in TC activity. Thus far, however, the variability in TC occurrence date over the past decades and its relationship with global climate change has not yet been fully revealed in the tropical ocean, especially in coastal areas where human population is increasing. In this study, we shift attention to TC occurrence dates, particularly to the variability in TC occurrence date and its relationship with SST increasing in the South China Sea (SCS). Because the focus of this study is the SCS, a brief background of TCs in the SCS is first provided in Section 2. Datasets and analysis methods are then described in Section 3. Section 4 describes the variations in TC occurrence date in the SCS and discusses which properties of TCs are related to SST variability and ENSO events, as well as the extent of this relationship. Section 5 presents a discussion and summary.
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2. Background The South China Sea, located at the westernmost edge of the Western North Pacific (WNP), is one of the largest semienclosed marginal seas, connecting to the Pacific Ocean through a number of passages and straits (see inset in the left panel of Fig. 1). The SCS is also located at the junction of the Indian monsoon, the WNP monsoon, and the East Asian subtropical monsoon (Wang and Wu, 1997) and is often subject to tropical cyclones (TCs). TCs, which affect the SCS, are formed in either the SCS or the WNP. In this study, any TCs entering the SCS from the WNP were omitted; only TCs formed in the SCS were considered. Earlier researchers often regarded SCS TCs as WNP TCs (Chia and Ropelewski, 2002). However, SCS TCs seem quite different from those in the WNP due to their unique monsoonal characteristics, as shown in Wang et al. (2007). For instance, there are almost no TCs formed in the SCS from January to March (Fig. 1, left panel), but the number of TCs formed in the WNP during this period is 76, accounting for 5.0% of the total annual number of TCs formed in the WNP. The statistical difference between SCS TCs and WNP TCs is also significant based on the analysis of 10,000 trials using the Monte Carlo simulation (Fig. 1, right panel). The results suggest that SCS TCs exhibit different behavior from those of the WNP, and thus they should be regarded as a separate entity rather than a subsample of the WNP in the study of TC variability.
3. Datasets and methods 3.1. Datasets Best-track data of TC records at 6-h intervals for the period 1945–2009 are obtained at the Joint Typhoon Warning Center (JTWC; http://www.usno.navy.mil/NOOC/nmfcph/RSS/jtwc/best tracks). This data can also be found on the home page of the Kerry Emanuel website (http://wind.mit.edu/∼emanuel/home.html). To verify the reliability of TC records, TC data from the Regional Specialized Meteorological Center (RSMC) for the period 1950–2009 is also adopted. The TC genesis date in this study is defined as the first time each individual TC originates in the SCS, while the first (last) annual TC occurrence date is defined as the initial date of the first (last) annual TC in the SCS. Although the inhomogeneity in the methods used to determine TCs over time
Fig. 1. Left panel: the monthly number of tropical cyclones formed in the WNP (shaded gray) and in the SCS (shaded dark) during the period 1945–2009. Right panel: Box-whisker plot for the actual and simulated TC number of 10,000 experiments of SCS TCs (N = 291) from the large sample of WNP plus SCS TCs using Monte Carlo simulations. The * indicates the actual observed TCs in the SCS. The blue box indicates the interquartile range, which is the difference between the upper quartile (75th percentile) and the lower quartile (25th percentile), and the line inside the box marks the median (50th percentile) of the 10,000 experiments. It can be seen that the actual SCS TCs are not situated totally within the interquartile range, suggesting that SCS TCs are not a subsample of TCs in the WNP. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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is recognized (e.g. Neumann et al., 1993; Landsea et al., 1999; Landsea, 2007), the variability in TCs in the SCS over the period of 1945–2009 is examined here. The monthly mean extended reconstructed sea surface temperature (ERSST) from the National Oceanic and Atmospheric Administration (NOAA) spanning from 1945 to 2009 (Smith et al., 2008), are used. The upper ocean temperature dataset produced by Ishii et al. (2006) based on in situ observations ˜ for the period of 1945–2006 is also adopted. In this study, we use the Nino3.4 SST, which is the areaaveraged SST over the eastern equatorial Pacific (120◦ W–170◦ W, 5◦ S–5◦ N), to represent the strength of ENSO (Barnston et al., 1997). In addition, the monthly atmospheric dataset from the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis over the period 1948–2009 (Kalnay et al., 1996) are used to study the variability in largescale parameters including low-level relative vorticity, vertical shear of zonal wind, and moist static energy and their relationship with TC occurrence dates in the SCS. 3.2. Methodology Correlation analysis was used to qualitatively examine the relationship between the variability in SCS TC occurrence dates and SST. The Student’s t-test and Mann–Kendall trend test (e.g. Mann, 1945; Kendall, 1975) were used to assess the significance of the correlation and trend. To address if and how SCS SSTs affect SCS TCs independently from ENSO, regression analysis of SST at each grid point was performed in order to remove the ENSO signal from SST, following Clark et al. (2000). Quantile regression (e.g. Koenker and Bassett, 1978; Koenker, 2005) was also used to quantitatively examine the relationship between SCS TC occurrence dates and SST, and an empirical orthogonal function (EOF) analysis was performed on 850-hPa relative vorticity, vertical wind shear, and moist static energy to extract the principal EOF modes of large-scale atmosphere parameters. 4. Results 4.1. Earlier occurrence of first annual TCs in the SCS The temporal evolution of TC occurrence dates over the period 1945–2009 for all TCs, the first annual TC, and the last annual TC in the SCS are shown in Fig. 2. The corresponding trends are also
Fig. 2. (a) Variability of occurrence dates for all TCs in the SCS during the period 1945–2009. (b) Same as (a) but for the first annual (black line) and last annual (blue line) TCs based on data from the Joint Typhoon Warning Center (JTWC). The corresponding trends estimated by least-squares regression are shown by the red lines. The Mann–Kendall test of trend shows a decreasing trend (p-value = 0.075) in first annual TC occurrence dates, while no significant trend is observed for all TCs (p-value = 0.922) and the last annual TC (p-value = 0.119) at the 90% confidence level. (c) and (d) Same as (a) and (b) for data from the Regional Specialized Meteorological Center (RSMC). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
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Fig. 3. Variability of ERSST and Ishii SST in the SCS. (a) Linear coefficient of ERSST in the SCS from 1945 to 2009. The unit is ◦ C per century. (b) Time series of ERSST anomalies in early summer (May–June), which is spatially averaged over the whole SCS. (c) and (d) Same as (a) and (b) but for the dataset produced by Ishii et al. (2006) over the period 1945–2006.
shown, from which it can be seen that the occurrence dates of all TCs over the period are characterized by strong interannual and interdecadal variation. This variation is also found in the first and last annual TC occurrence dates. In addition, a statistically significant downward trend for first annual TC occurrence dates, tested by the Mann–Kendall method at the 90% confidence level, is observed over the study period, although trends for all TCs and last annual TC occurrence dates are not significant. The downward trend for first annual TC occurrence dates suggests that there has been a tendency for first annual TCs to occur earlier in the SCS in recent decades. Similar results (Fig. 2d) are also revealed by TC records from the RSMC, which used a different criterion for TC identification than that used by the JTWC, and this further suggests that first annual TCs are tending to occur earlier in the SCS. 4.2. First annual TC occurrence date-early summer SST relationship What causes the earlier occurrence of first annual TCs in the SCS? A previous study showed that the earlier occurrence of early-season TCs in the North Atlantic may be linked to the increasing of tropical Atlantic SSTs (Kossin, 2008). In this section, we first examine the variability in SCS SST, particularly for early summer (May–June). Fig. 3a shows the linear coefficient of ERSST at each grid point, which is fitted by a linear-least square. It appears that over the period 1945–2009, SCS SST is dominated by a positive trend, with SST increasing by approximately 0.5–1.0 ◦ C per century. This increasing rate is relatively slower than that in the North Atlantic, where the temperature in the main TC occurrence region is increasing by approximately 0.5–1.5 ◦ C per century (e.g. Agudelo and Curry, 2004; Webster et al., 2005; Kossin, 2008). The area-averaged SST anomalies over the SCS during May–June show a noticeable warming trend in early summer in the past decades (Fig. 3b). The increasing trend in early summer SSTs in the SCS is also found from the upper-ocean temperature dataset produced by Ishii et al. (2006) (Fig. 3c and d).
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Fig. 4. (a) Relationship between the first annual TC occurrence date and early-summer (May–June) SST. Solid lines denote positive relationships, while dashed lines denote negative relationships. Shaded areas indicate that the correlations are statistically significant at the 95% confidence level. (b) Same as (a) but for the correlation after removing the ENSO effect.
To determine the relationship between early-summer SST and first annual TC occurrence date in the SCS, we correlate SST anomalies during May–June at each grid point with the SCS first annual TC occurrence dates over the period 1945–2009. The first annual TC occurrence dates in the SCS are found to have significant negative correlations with SCS SSTs (Fig. 4a). Such negative correlations are also ˜ found in the Nino3.4 region. The results suggest that the variation of first annual TC occurrence date ˜ in the SCS is related not only to the variability of SST in the SCS, but also to SST in the Nino3.4 region. In other words, earlier occurrence of first annual TCs in the SCS is influenced both by variability of SCS SST and ENSO event. It is widely recognized that ENSO is the largest source of global climate variability associated with the phenomenon of air-sea coupling. Although ENSO involving maximum SST anomalies occurs in the equatorial central and eastern Pacific, it can also significantly impact SST variability in distant regions through atmospheric bridges (e.g. Tourre and White, 1995; Wallace, 1998; Chiang and Sobel, 2002; Su and Neelin, 2002; Chiang and Lintner, 2005). Much of the existing literature has shown that SSTs in remote ocean basins such as the tropical Atlantic, the Indian, and the SCS are usually above normal when SSTs in the central and eastern Pacific are above normal (e.g. Pan and Oort, 1983; Yulaeva and Wallace, 1994; Tourre and White, 1995; Enfield and Mayer, 1997; Wang et al., 2006; Zhou and Chan, 2007). The changed SSTs associated with ENSO exert a strong influence on TC activity over these basins (e.g. Chan and Liu, 2004; Wang and Lee, 2008). Since the main focus of this study is to examine TC
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˜ Fig. 5. (a) Correlations between SST anomalies in the North Pacific and Nino3.4 SST anomalies in the period September– December. Solid lines denote positive relationships, while dashed lines denote negative relationships. The shaded area indicates ˜ region. (b) Same as statistically significant correlations at the 95% confidence level. The rectangular box indicates the Nino3.4 (a) but for 1970–2009.
occurrence dates and their relationship with SSTs in the SCS, the influence of ENSO on SCS SSTs is investigated here. Following several precedents (e.g. Klein et al., 1999; Chan and Liu, 2004; Wang et al., 2006), the ˜ correlation coefficients between monthly Nino3.4 SST anomalies during September–December and simultaneous SST anomalies over the North Pacific are displayed (Fig. 5). In agreement with Klein et al. (1999) and Wang et al. (2006), significant ENSO-related SST anomalies are observed over many regions, such as the tropical eastern WNP, the extratropical North Pacific, and the SCS. Positive correlations, with a maximum coefficient of 0.5, are found in the SCS (Fig. 5a). Such correlations are more prominent for the period 1970–2009, during which the majority of ENSO events occurred (Fig. 5b). This strong relationship may have originated from a stronger anomalous Walker–Hadley circulation after the late 1970s. A strong Walker–Hadley circulation has been shown to lead to more persistent ENSO-induced SST warming in the SCS and Indian Ocean (e.g. Trenberth and Hurrell, 1994; Zhang et al., 1997; Yun ˜ et al., 2010). The interannual variations in SST, which are averaged over the SCS and the Nino3.4 region from 1945–2009, are also computed (Fig. 6). The variability in SCS SST is closely related to ENSO events, ˜ with the Nino3.4 SST anomalies leading the SCS SST by about 5 months, confirming previous results
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˜ Fig. 6. Comparisons of Nino3.4 (120◦ W–170◦ W, 5◦ S–5◦ N) SST anomalies (shaded curve) with SST anomalies (black curve) in the SCS for the period of 1945–2009.
(e.g. Klein et al., 1999; Wang et al., 2000, 2006). Therefore, in the following examination of the impact of local SST on SCS TC occurrence dates, the influence of ENSO on the SCS SST should be removed. Closely following Clark et al. (2000), we remove the ENSO signal from the SCS SST, and the remaining (non-ENSO-related) SST – that is, the local SST – is then correlated with first annual TC occurrence dates over the SCS. As shown in Fig. 4b, although the magnitude and areal extent of the correlations are both reduced, the negative correlations previously observed over the SCS are still significant after the removal of the ENSO effect (cf. Fig. 4a and b), which demonstrates the significant effect of local SST on SCS TC occurrence dates. To further quantitatively determine how the strength of early-summer SST and the SCS first annual TC occurrence date correlation varies with and without the ENSO effect, quantile regression analysis is also used (Koenker, 2005). Note that in this analysis all TC occurrence dates during the 65-yr period and their corresponding monthly averaged SSTs are considered. As shown in Fig. 7a, the trends ˇ(), ranging from −90 to −100 days/◦ C for early-season (<20th percentile) TCs, suggest that SCS TCs tend to occur earlier when SSTs are increasing. The result is quite different when the same regression analysis is performed for the non-ENSO-related SSTs. The negative correlations previously observed
Fig. 7. (a) Coefficients (trends) of quantile regression using all TC occurrence dates over the period 1945–2009 as the response variable and the corresponding monthly mean SST as the covariate in the SCS. The shading shows the 95% point-wise confidence band about these trend estimates. The dates (day/month) associated with the 0.1, 0.3, 0.5, 0.7, and 0.9 quantiles are shown on the top axis based on the full sample over the study period. (b) Same as (a) but for the coefficients (trends) after the removal of the ENSO effect.
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Fig. 8. The spatial pattern of the first mode (EOF1) for the vertical shear of zonal wind (VWS) in early summer (May–June). Solid lines denote positive values, while dashed lines denote negative values.
in the early-season show an obvious weakening after the removal of the ENSO effect (cf. Fig. 7a and b), suggesting that the early-season SCS TC occurrence date is closely related to ENSO, and confirming the above results. 4.3. Impact of ENSO on first annual TC occurrence date To further determine how ENSO affects the first annual SCS TC occurrence date, we extend our analysis by examining the variations of large-scale atmosphere parameters – including vertical wind shear, 850-hPa relative vorticity, and moist static energy in early summer – as well as their connection to the first annual TC occurrence date in the SCS. Vertical wind shear (VWS), defined by the zonal wind difference between the upper (200 hPa) and lower (850 hPa) atmosphere, is averaged over May–June, and an EOF analysis is performed on its anomalous field. The mode that is significantly related to the SCS first annual TC occurrence date will be discussed. Examination of the relationship between the first principal component (PC1) of VWS and the SCS first annual TC occurrence date shows that the latter is positively correlated with the first mode of VWS. The spatial pattern of the first mode (EOF1), which explains 46% of the total variance, shows a south–north dipole over the WNP with a positive (negative) sign north (south) of 12◦ N (Fig. 8). This pattern somewhat resembles that of Chan and Liu (2004), although their analysis is for May to November during 1960–2003. An area of positive sign is also found in the SCS, which suggests that the increasing of VWS does not favor the earlier occurrence of TC in the SCS. On the other hand, the temporal evolution of PC1 is positively correlated with early summer SST anomalies ˜ region, but negatively correlated in the WNP and SCS. Negative correlation means that in the Nino3.4 an increase in SST is correlated with a decrease in VWS, which is likely to be associated with stronger ˜ region and subsidence. After the removal of the ENSO effect, the high correlations over the Nino3.4 the SCS are both reduced and become insignificant, although the correlations are still significant near the Philippine Sea (Fig. 9b). This result suggests that the variation in early-summer VWS that is forced by ENSO events may provide a favorable condition for the earlier occurrence of first annual TCs in the SCS, further supporting the above results. Since the boundary layer Ekman convergence is proportional to 850-hPa relative vorticity, the increase in background low-level vorticity would help spin up TCs by increasing moisture convergence and entraining potential vorticity into them (Gray, 1979). As a result, 850-hPa relative vorticity is a meaningful indication for TC activity. Fig. 10 shows the first mode (EOF1) of 850-hPa relative vorticity, which accounts for ∼39% of the total variance and is related to the SCS first annual TC occurrence date (r = 0.27). A striking tripole pattern with a negative sign located in the region ranging from 9◦ N to 27◦ N and a positive sign at lower (south of 9◦ N) and higher (north of 27◦ N) latitudes is found. The temporal ˜ evolution of PC1 of 850-hPa relative vorticity is correlated with the SST in the Nino3.4 region, but the
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Fig. 9. (a) Correlations between the time series of EOF1 for the vertical shear of zonal wind and SST in early summer (May–June). Solid lines denote positive relationships and dashed lines denote negative relationships. Shaded areas indicate statistically significant correlations at the 95% confidence level. (b) Same as (a) but for the correlations after the removal of the ENSO effect.
Fig. 10. Same as Fig. 8 but for 850-hPa relative vorticity (RV).
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Fig. 11. Same as Fig. 9 but for 850-hPa relative vorticity (RV).
statistically significant correlation is not found in the SCS (Fig. 11a). After the removal of the ENSO signal, the correlation coefficient shows little change in the SCS (Fig. 11b). The results suggest that variability in relative vorticity that is related to ENSO in early summer has little impact on the SCS first annual TC occurrence date. Since the first mode (EOF1) of moist static energy (MSE), which is integrated between layers of 1000–500 hPa from May to June, is not significantly correlated to the SCS first annual TC occurrence date, the second mode (EOF2), accounting for ∼34.6% of the total covariance and significantly correlated with the SCS first annual TC occurrence date (r = −0.24), is discussed here. The negative correlation suggests that a higher MSE favors an earlier occurrence of the first annual TC. The second EOF shows an ˜ east–west and south–north dipole over the tropical Pacific with a positive sign in the Nino3.4 region but a negative sign in the WNP and SCS (Fig. 12). On the other hand, positive correlations between ˜ region, while negative correlations are PC2 and early-summer SST anomalies are found in the Nino3.4 found in the WNP and SCS (Fig. 13a). This means that in most parts of WNP and SCS, an increase in SST actually correlates with an increase in MSE. Since MSE is not independent from ENSO, the correlation between the local SST and PC2 after the removal of the ENSO effect is also examined. It is found that the negative correlation between the local SST and PC2 become larger in the SCS after the removal of the ENSO signal but the magnitude is quite small (Fig. 13b). A higher MSE is likely associated with non-subsidence of dry midtroposphere. In other words, the subsidence of dry midtroposphere which is forced by ENSO tends to decrease MSE, and thereby provides an unfavorable condition for the earlier occurrence of first annual TC over the SCS, but the uncertainty in assessing this relationship is high.
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Fig. 12. Same as Fig. 8 but for EOF2 of moist static energy (MSE).
Fig. 13. Same as Fig. 9 but for EOF2 of moist static energy (MSE).
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5. Discussion and summary Long-term change in tropical cyclone (TC) intensity and frequency in a warming environment has attracted much attention recently (e.g. Emanuel, 2005; Webster et al., 2005; Chan, 2006; Kossin, 2008; Wang et al., 2010). With the observed warming of the tropics in past decades, examining the observed change in TC activity may shed light on the impact of global warming on TC activity. In this study, we shifted the focus to the variability of TC occurrence dates in the South China Sea (SCS) and its relationship with variability of SST based on observations. Our results show that the first annual TCs in the SCS have tended to occur earlier during the past decades. On the other hand, an obvious warming trend in early-summer SST has been observed in the SCS. Result of qualitative analysis show that the SCS first annual TC occurrences date is related not only to the variability of SST in the SCS, ˜ region. Significant correlations between the SCS first but also to the variability of SST in the Nino3.4 TC occurrence date and the SCS SST are still found when the ENSO influence is removed, although the magnitude and areal extent of the correlations are reduced. These results suggest that the earlier occurrence of first annual TCs in the SCS is influenced not only by local SST, but also by ENSO events. Quantitative analyses by quantile regression of TC occurrence dates and the basin warming of SCS SST with and without the ENSO effect also confirm this result. The trend value of about −100 days/◦ C for early-season (<20th percentile) TCs matches well with the negative correlation of early-summer SCS SST with first annual TC occurrence date, suggesting that the first annual TCs in the SCS tend to occur earlier when basin-scale SSTs are increasing. The high value of the trend in the early season is greatly reduced by the removal of the ENSO signal, further suggesting that early-season TC occurrence dates in the SCS are also constrained by ENSO. The influences of ENSO on the SCS first annual TC occurrence date through the alternation of large-scale circulation including 850-hPa relative vorticity, VWS, and MSE in early summer are also investigated. It is found that early-summer variations in VWS and MSE induced by ENSO have statistically relationships with the SCS first annual TC occurrence date, although the uncertainty in the last two is high. In other words, in addition to local SST, weaker VWS which is induced by ENSO in early summer is also responsible for the earlier occurrence of first annual TCs in the SCS. This result is consistent with that of Tu et al. (2011), suggesting that changes in large-scale variables including SST, upper-ocean heat content, VWS, and tropospheric water vapor might be responsible for the abrupt increase in early-summer intense typhoons over the WNP. To summarize, the first annual TC occurrence dates in the SCS are found to correlate not only with local SST, but also with ENSO events. An increase in early-summer (May–June) SST could lead to a higher rate of energy transfer from the tropical ocean and thus provide a favorable thermodynamic condition for the earlier occurrence of the first annual TCs in the SCS. In addition, the above-normal ˜ SST over the Nino3.4 region might also affect the SCS first annual TC occurrence date by remotely modifying the VWS and MSE over the SCS in early summer. This observational evidence supports the notion that increased TC activity will occur with higher SSTs. These results also confirm earlier findings suggesting that TC activity in the SCS is also influenced by ENSO events. However, it should also be noted that although the effect of basin-scale SST anomalies may be significant, it is not the only oceanic parameter that constrains TC activity (e.g. Swanson, 2008; Vecchi and Knutson, 2008; Knutson et al., 2010). In addition, as shown above, the correlation between local SST and the first annual TCs occurrence date is high, which suggests an important impact of local SST on the earlier occurrence of first annual TCs in the SCS. However, why the local SST becomes favorable for the earlier occurrence of first annual TCs in the SCS, an in-depth analysis of local SST inducing changes of vertical motion, water vapor and relative humidity are worth further investigation. Acknowledgments We thank the US Navy’s Joint Typhoon Warning Center (JTWC) for providing the tropical cyclone datasets. Thanks are also given to quantile regression codes from http://www.econ.uiuc. edu/∼roger/research/rq/rq.html. The right panel of Fig. 1 is kindly provided by Professor James P. Kossin, and we extend our sincere thanks for his support. This work is partly supported by the National Basic Research Program of China (2010CB950401; 2011CB403504), the R&D Special Fund for Public
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Welfare Industry (GYHY201106036) and the Strategy Research Project of City University of Hong Kong (7002717). References Agudelo, P.A., Curry, J.A., 2004. Analysis of spatial distribution in tropospheric temperature trends. Geophys. Res. Lett. 31, L22207, http://dx.doi.org/10.1029/2004GL02818. Barnston, A.G., Chelliah, M., Goldenberg, S.B., 1997. Documentation of a highly ENSO-related SST region in the equatorial Pacific. Atmosphere-Ocean 35, 367–383. Bender, M.A., Knutson, T.R., Tuleya, R.E., Sirutis, J.J., Vecchi, G.A., Garner, S.T., Held, I.M., 2010. Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science 327, 454–458. Broccoli, A.J., Manabe, S., 1990. Can existing climate models be used to study anthropogenic changes in tropical cyclone climate? Geophys. Res. Lett. 17, 1917–1920. Chan, J.C.L., 2006. Comment on changes in tropical cyclone number, duration, and intensity in a warming environment. Science 311 (5768), 1713. Chan, J.C.L., Liu, K.S., 2004. Global warming and Western North Pacific typhoon activity from an observational perspective. J. Climate 17, 4590–4602. Chia, H.H., Ropelewski, C.F., 2002. The interannual variability in the genesis location of tropical cyclones in the Northwest Pacific. J. Climate 15, 2934–2944. ˜ J. Climate 18, 4130–4149. Chiang, J.C.H., Lintner, B.R., 2005. Mechanisms of remote tropical surface warming during El Nino. Chiang, J.C.H., Sobel, A.H., 2002. Tropical tropospheric temperature variations caused by ENSO and their influence on the remote tropical climate. J. Climate 15, 2616–2631. Clark, C.O., Cole, J.E., Webster, P.J., 2000. Indian Ocean SST and Indian summer rainfall: predictive relationships and their decadal variability. J. Climate 13, 2503–2519. Emanuel, K.A., 1987. The dependence of hurricane intensity on climate. Nature 326, 483–485. Emanuel, K.A., 2005. Increasing destructiveness of tropical cyclones over the past 30 years. Nature 436, 686–688. ˜ Enfield, D.B., Mayer, D.A., 1997. Tropical Atlantic sea surface temperature variability and its relation to El Nino-Southern oscillation. J. Geophys. Res. 102, 929–945. Evans, J.L., 1993. Sensitivity of tropical cyclone intensity to sea surface temperature. J. Climate 6, 1133–1140. Gray, W.M., 1979. Hurricanes: their formation, structure and likely role in the tropical circulation. Meteorology over the tropical oceans. In: Shaw, D.B. (Ed.), Roy. Meteor. Soc., 155–218. Gualdi, S., Scoccimarro, E., Navarra, A., 2008. Changes in tropical cyclone activity due to global warming: results from a highresolution coupled general circulation model. J. Climate 21, 5204–5228. Haarsma, R.J., Mitchell, J.F.B., Senior, C.A., 1993. Tropical disturbances in a GCM. Climate Dyn. 8, 247–257. Holland, G.J., 1997. The maximum potential intensity of tropical cyclones. J. Atmos. Sci. 54, 2519–2541. Ishii, M., Kimoto, M., Sakamoto, K., Iwasaki, S.I., 2006. Steric sea level changes estimated from historical ocean subsurface temperature and salinity analyses. J. Oceanogr. 62 (2), 155–170. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., Joseph, D., 1996. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteor. Soc. 77, 437–470. Kendall, M.G., 1975. Rank Correlation Methods. Charles Griffin, London. Klein, S.A., Soden, B.J., Lau, N.C., 1999. Remote sea surface temperature variations during ENSO: evidence for a tropical atmospheric bridge. J. Climate 12, 917–932. Knutson, T.R., Kurihara, Y., 1998. Simulated increase of hurricane intensities in a CO2 -warmed world. Science 279, 1018–1020. Knutson, T.R., Mcbride, J., Chan, J.C.L., Emanuel, K.A., Holland, G., Landsea, C., Held, I.M., Kossin, J.P., Srivastava, A.K., Sugi, M., 2010. Tropical cyclones and climate change. Nat. Geosci. 3, 157–163. Knutson, T.R., Tuleya, R.E., 1999. Increased hurricane intensities with CO2 -induced global warming as simulated using the GFDL hurricane prediction system. Climate Dyn. 15, 503–519. Knutson, T.R., Tuleya, R.E., 2004. Impact of CO2 -induced warming on simulated hurricane intensity and precipitation: sensitivity to the choice of climate model and convective parameterization. J. Climate 17, 3477–3495. Koenker, R.W., 2005. Quantile Regression. Cambridge University Press, New York. Koenker, R.W., Bassett, G.J., 1978. Regression quantiles. Econometrica – Econ. Soc. 46 (1), 33–50. Kossin, J.P., 2008. Is the North Atlantic hurricane season getting longer? Geophys. Res. Lett. 35, L23705, http://dx.doi.org/10.1029/2008GL036012. Kossin, J.P., Knapp, K.R., Vimont, D.J., Murnane, R.J., Harper, B.A., 2007. A globally consistent reanalysis of hurricane variability and trends. Geophys. Res. Lett. 34, L04815, http://dx.doi.org/10.1029/2006GL028836. Krishnamurti, T.N., Torres, R.C., Latif, M., Daughenbaugh, G., 1998. The impact of current and possibly future sea surface temperature anomalies on the frequency of Atlantic hurricanes. Tellus 50, 186–210. ˜ oscillation and the seasonal variability of tropical cyclones. In: Diaz, H.F., Markgraf, V. Landsea, C.W., 2000. El Nino-Southern ˜ Impact of Multiscale Variability on Natural Ecosystems and Society. Cambridge University Press, pp. 149–181. (Eds.), El Nino: Landsea, C.W., 2007. Counting Atlantic tropical cyclones back to 1900. Eos Trans. AGU 88 (18), http://dx.doi.org/10.1029/2007EO180001. ˜ R.A.P., Knaff, A.M.J.A., 1999. Atlantic basin hurricanes: indices of climatic changes. Climatic Landsea Jr., C.W., Mestas-Nunez, Change 42, 89–129. Mann, H.B., 1945. Non-parametric tests against trend. Econometrica 13, 245–259. McDonald, R.E., Bleaken, D.G., Cresswell, D.R., Pope, V.D., Senior, C.A., 2005. Tropical storms: representation and diagnosis in climate models and the impacts of climate change. Climate Dyn. 25, 19–36. Neumann, C.J., Jarvinen, B.R., McAdie, C.J., Elms, J.D., 1993. Tropical Cyclones of the North Atlantic Ocean. Historical Climatology Series 6-2. National Climatic Data Center, 193 pp.
Y. Yan et al. / Dynamics of Atmospheres and Oceans 55–56 (2012) 45–59
59
Pan, Y.H., Oort, A.H., 1983. Global climate variations connected with sea surface temperature anomalies in the eastern equatorial Pacific Ocean for the 1958–73 periods. Mon. Weather Rev. 111, 1244–1258. Shen, W., Tuleya, R.E., Ginis, I., 2000. A sensitivity study of the thermodynamic environment on GFDL model hurricane intensity: implications for global warming. J. Climate 13, 109–121. Smith, T.M., Reynolds, R.W., Peterson, T.C., Lawrimore, J., 2008. Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J. Climate 21, 2283–2296. ˜ J. Atmos. Sci. 59, Su, H., Neelin, J.D., 2002. Teleconnection mechanisms for tropical Pacfic desent anomalies during El Nino. 2694–2712. Sugi, M., Murakami, H., Yoshimura, J., 2009. A reduction in global tropical cyclone frequency due to global warming. SOLA 5, 164–167. Sugi, M., Noda, A., Sato, N., 2002. Influence of the global warming on tropical cyclone climatology: an experiment with the JMA global model. J. Meteor. Soc. Jpn. 80, 249–272. Swanson, K.L., 2008. Nonlocality of Atlantic tropical cyclone intensities. Geochem. Geophys. Geosyst. 9, Q04V01, http://dx.doi.org/10.1029/2007GC001844. Tourre, Y.M., White, W.B., 1995. ENSO signals in global upper ocean temperature. J. Phys. Oceanogr. 25, 1317–1332. Trenberth, K., 2005. Uncertainty in hurricanes and global warming. Science 308, 1753–1754. Trenberth, K.E., Hurrell, J.W., 1994. Decadal atmosphere-ocean variations in the Pacific. Climate Dyn. 9, 303–319. Tu, J.Y., Chou, C., Huang, P., Huang, R.H., 2011. An abrupt increase of intense typhoons over the western North Pacific in early summer. Environ. Res. Lett. (6), 034013. Vecchi, G.A., Knutson, T.R., 2008. On estimates of historical North Atlantic tropical cyclone activity. J. Climate 21, 3580–3600. Vecchi, G.A., Swanson, K.L., Soden, B.J., 2008. Climate change. Whither hurricane activity? Science 322 (5902), 687–689. Wallace, J.M., 1998. On the structure and evolution of ENSO-related climate variability in the tropical Pacific: lessons from TOGA. J. Geophys. Res. 103, 14241–14259. Wang, B., Chan, J.C.L., 2002. How strong ENSO events affect tropical storm activity over the western North Pacific. J. Climate 15, 1643–1658. Wang, B., Wu, R., 1997. Peculiar temporal structure of the South China Sea summer monsoon. Adv. Atmos. Sci. 14, 177–194. Wang, B., Yang, Y., Ding, Q.-H., Murakami, H., Huang, F., 2010. Climate control of the global tropical storm days (1965–2008). Geophys. Res. Lett. 37, L07704, http://dx.doi.org/10.1029/2010GL042487. Wang, C., Lee, S.K., 2008. Global warming and United States landfalling hurricanes. Geophys. Res. Lett. 35 (2), L02708, http://dx.doi.org/10.1029/2007GL032396. ˜ J. Geophys. Wang, C., Wang, W., Wang, D., Wang, Q., 2006. Interannual variability of the South China Sea associated with El Nino. Res. 111, C03023, http://dx.doi.org/10.1029/2005JC003333. Wang, G., Su, J., Ding, Y., Chen, D., 2007. Tropical cyclone genesis over the south China sea. J. Mar. Syst. 68 (3-4), 318–326. Wang, W., Wang, D., Qi, Y., 2000. Large-scale characteristics of interannual variability of sea surface temperature in the South China Sea. Acta Oceanol. Sin. 22, 8–16. Webster, P.J., Holland, G.J., Curry, J.A., Chang, H.R., 2005. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science 309, 1844–1846. Yamada, Y., Oouchi, K., Satoh, M., Tomita, H., Yanase, W., 2010. Projection of changes in tropical cyclone activity and cloud height due to greenhouse warming: global-cloud system-resolving approach. Geophys. Res. Lett. 37, L07709, http://dx.doi.org/10.1029/2010GL042518. Yoshimura, H., Matsumura, T., 2005. A two-time-level vertically conservative semi-Lagrangian semi implicit double Fourier series AGCM. In: CAS/JSC WGNE Research Activities in Atmospheric and Ocean Modeling, vol. 35, no. 1, pp. 27–28. Yulaeva, E., Wallace, J.M., 1994. The signature of ENSO in global temperature and precipitation fields derived from the microwave sounding unit. J. Climate 7, 1719–1736. Yun, K.S., Seo, K.H., Ha, K.J., 2010. Interdecadal change in the relationship between ENSO and the intraseasonal oscillation in East Asia. J. Climate 23 (13), 3599–3612. Zhang, Y., Wallace, J.M., Battisti, D.S., 1997. ENSO-like interdecadal variability 1900–93. J. Climate 10, 1004–1020. Zhao, M., Held, I.M., Lin, S.J., Vecchi, G.A., 2009. Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Climate 22, 6653–6678. Zhou, W., Chan, J.C.L., 2007. ENSO and South China Sea summer monsoon onset. Int. J. Climatol. 27, 157–167.