Abstract

This study investigated the impacts of the interannual variability in the boreal spring regional Hadley circulation over the Indo-Pacific warm pool (IPWP) on the tropical cyclone (TC) activity over the western North Pacific (WNP). The principal modes of the interannual variability in the IPWP Hadley circulation were calculated using empirical orthogonal function (EOF) analysis. The leading mode (EOF-1) features cross-equatorial southerly wind anomalies over the Indian Ocean and Maritime Continent and has an evident impact on WNP TC activity during summer. In the summer following a positive phase of the EOF-1, a cyclonic circulation anomaly, with upward motion, positive relative vorticity anomalies, and weak sea level pressure, dominates the WNP, and this favors increased TC genesis. However, large positive vertical wind shear anomalies over the South China Sea and Philippine Sea inhibit the TC intensification. A positive wind–sea surface temperature (SST)–precipitation feedback was found to facilitate the ability of the signal of the EOF-1 to persist until the summer. The westerly wind anomalies converge around 10°N over the WNP, thus increasing precipitation, and this increased precipitation enhances the westerly wind anomalies via a Gill-type response. The strengthened westerly wind anomalies increase total wind speeds, which in turn cool the SST in the Bay of Bengal and the South China Sea, and warm the SST in the eastern WNP, increasing the zonal SST gradient. Consequently, this increased zonal SST gradient further enhances the westerly wind anomalies, strengthens the monsoon trough, and increases the WNP precipitation further. Therefore, the WNP precipitation anomalies are sustained into the summer.

1. Introduction

The Hadley circulation (HC) is one of the most important atmospheric circulations in the tropics and consists of two cells on the flanks of the equator, with a common ascending branch over the equatorial belt and two descending branches in the subtropics (e.g., Glickman 2000). Many pioneering studies have led to a better understanding of the spatial and temporal variability of the HC (e.g., Oort and Rasmusson 1970; Lindzen and Hou 1988; Oort and Yienger 1996; Dima and Wallace 2003). A feature of the HC is that it is not always symmetric about the equator. Oort and Rasmusson (1970) suggested that only during the transition months of April, May, October, and November does the HC show some degree of equatorial symmetry. During all other months, the winter hemisphere cell extends into the summer hemisphere and dominates the overall circulation. Furthermore, both the annual progression (Dima and Wallace 2003) and year-to-year variability of the seasonal mean and annual mean HC (Ma and Li 2008; Feng et al. 2013; Guo et al. 2016a,b) are dominated by two components: an equatorial asymmetric component and an equatorial symmetric component. The asymmetric component features strong cross-equatorial wind anomalies in the upper and lower troposphere, with upward and downward motions on the flanks of the equator, whereas the symmetric component features an upward motion over the equatorial belt and downward motion on the flanks of the equator. Given these different spatial structures, the HC plays an important role in the variability of the global climate, including tropical cyclone (TC) activity (Zhou and Cui 2008; Zhang and Wang 2013, 2015) and subtropical droughts and precipitation (Lindzen 1994; Chang 1995; Hou 1998; Feng et al. 2013).

Of these climate variations, the TC activity has been extensively studied because of its serious impacts. The western North Pacific (WNP) has the most frequent TC formation around the globe, with the WNP accounting for nearly one-third of the total global TCs each year. Large-scale environment factors, such as the weak vertical wind shear (VWS), cyclonic circulation at low levels, large-scale upward motion, and warm sea surface temperature (SST), are among the favorable conditions for WNP TC development (Gray 1968; Wang and Chan 2002; Kaplan and DeMaria 2003; Swanson 2008; Neelin et al. 2009; Gu et al. 2015). And tropical synoptic-scale wave activity, especially westward-propagating waves, plays an important role in modulating the TC activity over the WNP (Dickinson and Molinari 2002; Frank and Roundy 2006; Zhou and Wang 2007; Schreck et al. 2012; Wu et al. 2015a,b). It is reported that the regional HC has important impacts on TC activity through affecting both the large-scale environment factors and the synoptic-scale wave activity (Zhang and Wang 2013, 2015).

Recently, Zhang and Wang (2013) suggested that the leading mode of the Atlantic HC, which is similar to the spatial structure of the asymmetric component of the global mean HC, has a significant influence on the TC activity over the tropical Atlantic by connecting the multiple climate factors and modulating the tropical synoptic-scale wave activity, such as the tropical easterly waves. Further, Zhang and Wang (2015) revealed that the leading mode of the eastern Pacific HC also plays a similar role, but affecting the eastern North Pacific TC activity. They also suggested that the leading mode of the Atlantic HC has an influence on eastern North Pacific TC activity, highlighting the role of the Atlantic HC in connecting the eastern North Pacific and the Atlantic. Early studies paid close attention to the WNP, because it exhibits the most active TC formation when compared with other ocean basins, and noted that there are three anomalous cross-equatorial flows locating near 105°, 125°, and 150°E, which all have important impacts on the TC genesis frequency (TCGF) over the WNP (Wang and Leftwich 1984; Love 1985; Wang and Zhou 2008). Wang and Leftwich (1984) further pointed out that these three cross-equatorial flows have HC features in the meridional plane, meaning that these cross-equatorial flows are possibly the lower branches of the regional mean HC. Additionally, these three cross-equatorial flows occur over the Indo-Pacific warm pool (IPWP), suggesting that the IPWP regional mean HC (IPWP_HC) may have some connections to variations in WNP TC activity.

Apart from the simultaneous relationship between the regional mean HC and TCGF, Zhou and Cui (2008) found that the strength of the global mean HC in boreal spring [March–May (MAM)] is highly correlated with the WNP TCGF in the following summer. This suggests that the strength of the global mean HC may act as a precursory signal for TC genesis over the WNP. They also suggested that downward motion of the boreal spring HC simultaneously excites positive SST anomalies (SSTAs) over the IPWP region, which can persist until the following summer, leading to atmospheric anomalies that affect TC genesis in the WNP. However, it is unclear how these SSTAs sustain the HC signals until the following summer. It also remains unclear as to whether the HC has an impact on other aspects of the TC activity, such as the genesis location and intensity. Given that the aforementioned cross-equatorial flows, which can affect the WNP TC activity, locate over the IPWP region and the air–sea interaction processes relevant to the HC signal sustainment occur in the IPWP region as well (Zhou and Cui 2008), it is reasonable to seek to identify the dominant modes of the boreal spring IPWP_HC interannual variability and examine how they are related to the WNP TC activity.

In this study, we investigated the principal modes of the interannual variability in the boreal spring IPWP_HC, as well as their possible impacts on the boreal summer WNP TC activity. We also examined the underlying mechanism that allows the signals of the boreal spring IPWP_HC to persist until the following summer and so affect the WNP TC activity. The remainder of this paper is arranged as follows. Section 2 introduces the datasets and methods used in this study. Section 3 investigates the principal modes of interannual variability in the boreal spring IPWP_HC, and section 4 analyzes the possible impacts of the principal modes on WNP TC activity. Section 5 explores the underlying mechanisms by which the principal modes persist and regulate WNP TC activity in summer. Section 6 provides our conclusions and a discussion of the results.

2. Datasets and methodology

a. Datasets

The best-track TC datasets used in this study were from the Regional Specialized Meteorological Center of the Japan Meteorological Agency (JMA) and the Joint Typhoon Warning Center (JTWC; Chu et al. 2002) for the period 1979–2014. Both datasets include the 6-hourly geographical positions of the TC centers and the maximum sustained 10-m wind speeds. We only considered the TCs that reached tropical storm intensity (with the maximum sustained wind speed ≥17 m s−1) in this study. The boreal summer TC season is defined as June through October (JJASO), during which about 80% of the WNP TCs occur (Zhan et al. 2013). The meridional winds used to calculate the mass streamfunction (MSF) between 1979 and 2014 were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI; Dee et al. 2011) and the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis data (NCEP-1; Kalnay et al. 1996). Other atmospheric variables, such as zonal wind, vertical velocity, and the sea level pressure (SLP), were obtained from ERAI unless stated otherwise. ERAI has a horizontal resolution of approximately 0.75° × 0.75° (T255 spectral) on 60 vertical levels, and the NCEP-1 dataset has a horizontal resolution of 2.5° × 2.5° (longitude × latitude) with 17 vertical levels. A monthly mean precipitation dataset was obtained from the Global Precipitation Climatology Project (GPCP) run by the National Oceanic and Atmospheric Administration (NOAA) (http://www.esrl.noaa.gov/psd/), with a horizontal resolution of 2.5° × 2.5° (longitude × latitude; Huffman et al. 2009). We also used the monthly mean SST dataset from the Extended Reconstructed SST, version 3b (ERSST.v3b), reanalysis from NOAA, which has a horizontal resolution of 2.0° × 2.0° (longitude × latitude; Smith et al. 2008).

b. Methodology

The meridional MSF is commonly used to characterize the HC (e.g., Oort and Rasmusson 1970). Over the global domain, the meridional circulation is assumed to be nondivergent; that is, ∂υ/∂ϕ + ∂w/∂p = 0. In such cases, there is no zonal net mass flux. The MSF can be calculated by vertically integrating the zonal mean meridional wind as follows:

 
formula

where υ is the meridional wind, R is the radius of Earth, ϕ is the latitude, g is the acceleration due to gravity, and p is atmospheric pressure. The overbar and square brackets indicate the temporal and zonal mean, respectively.

To depict the regional mean HC, the MSF can be derived by using the irrotational component of the meridional wind averaged in a regional domain (e.g., Zhang and Wang 2013, 2015), which is expressed as follows:

 
formula

where υχ is the irrotational component of the meridional wind. The other variables are the same as those in Eq. (1). Similarly, the overbar and brackets indicate the temporal and zonal mean, respectively. The interval of the integral is from 10 hPa to the surface in this study. We used the empirical orthogonal function (EOF) analysis to calculate the principal modes of the IPWP_HC interannual variability. To represent the spring IPWP_HC, the irrotational meridional wind was averaged over the longitude band of 80°–150°E, which includes most of the IPWP region. Varying this longitude band from 80°–150° to 80°–130°E does not qualitatively affect the robustness of the dominant modes of the IPWP_HC (figure not shown). El Niño–Southern Oscillation (ENSO) has a substantial impact on circulation patterns and precipitation anomalies over the WNP in the boreal summer (e.g., Wang et al. 2000; Xie et al. 2009; Wu et al. 2010; Du et al. 2011). To reduce the possible impacts of a simultaneous ENSO signal on the TCGF, the JJASO ENSO signals were subtracted from the TCGF and large-scale environment factors by linear regression with respect to the simultaneous Niño-3.4 index (area mean SST anomalies over the region of 5°S–5°N, 170°–120°W) in JJASO. Note that the spring ENSO signal was retained as it was the main source of the HC interannual variability. As the present study focuses on the interannual time scales, all the variables were subjected to a 9-yr high-pass Lanczos filter (Duchon 1979) and their linear trend was also removed.

3. Interannual variability in the boreal spring IPWP_HC

a. Principal modes

To obtain the principal modes of the interannual variability in the boreal spring IPWP_HC, Fig. 1 shows the climatology and the first two EOF modes of the boreal spring MSF of IPWP_HC over the period 1979–2014. Both the ERAI and NCEP-1 datasets were used to validate the reliability of dominant modes of spring regional HC interannual variability. The climatological IPWP_HC based on the two reanalysis datasets both have a similar structure, with two comparable cells on the flanks of the equator. They also resemble the spatial structure of the global mean HC, with ascending motion over the equatorial belt and descending motions over the subtropics (Dima and Wallace 2003).

Fig. 1.

Climatology (shading; 1.0 × 1010 kg s−1) and the EOF-1 (contours; 1.0 × 1010 kg s−1) of the MAM MSF during 1979–2014 over IPWP based on (a) ERAI and (c) NCEP-1. (b),(d) As in (a),(c), but for EOF-2 (contours). The contour intervals are 5.0 × 109 kg s−1. The values at the top right of the panels represent the explained variances.

Fig. 1.

Climatology (shading; 1.0 × 1010 kg s−1) and the EOF-1 (contours; 1.0 × 1010 kg s−1) of the MAM MSF during 1979–2014 over IPWP based on (a) ERAI and (c) NCEP-1. (b),(d) As in (a),(c), but for EOF-2 (contours). The contour intervals are 5.0 × 109 kg s−1. The values at the top right of the panels represent the explained variances.

The leading mode (EOF-1) explains 66.3% (60.1%) of the total variance for ERAI (NCEP-1) and displays a pair of equivalent cells on the flanks of the equator. The Southern Hemisphere cell extends into the Northern Hemisphere by about 10° latitude (Figs. 1a,c), which corresponds to the summertime intertropical convergence zone (ITCZ) position (Schneider et al. 2014). The second mode (EOF-2) explains 12.8% (14.5%) of the total variance for ERAI (NCEP-1). It shows a positive cell across the equator extending from 10°S to 20°N, with another negative cell accompanied in the Southern Hemisphere (Figs. 1b,d). It is suggested that ERAI and NCEP-1 show biases in the spatial structures of the higher order of EOF modes for the global mean HC (Guo et al. 2016a). But these two reanalysis datasets show consistent spatial structures in the first two EOF modes for the spring IPWP_HC, indicating relatively lower data uncertainties of these modes in this study.

The time series [principal component (PC)] of the first two EOF modes in the ERAI and NECP-1 datasets are shown in Fig. 2. The PCs based on the two reanalysis datasets are highly correlated, with correlation coefficients above 0.9 (significant at the 99.9% confidence level). Both the EOF modes and PCs between the two reanalysis datasets are in good agreement, highlighting that the first two EOF modes of the boreal spring IPWP_HC can be reliably identified. Moreover, Fig. 3 further shows the fractional variances as well as the sampling errors of the first four EOF modes. Based on the rule given by North et al. (1982), the first two EOF modes can be statistically distinguished from each other and from the remaining two eigenvectors as indicated by the sampling error bars (Fig. 3). These results further enhance the reliability of the first two EOF modes.

Fig. 2.

Normalized time series for (a) PC-1 and (b) PC-2 of the boreal spring IPWP_HC during 1979–2014 based on NCEP-1 (red lines) and ERAI (blue lines).

Fig. 2.

Normalized time series for (a) PC-1 and (b) PC-2 of the boreal spring IPWP_HC during 1979–2014 based on NCEP-1 (red lines) and ERAI (blue lines).

Fig. 3.

Explained variances by the first four EOF modes of the boreal spring IPWP_HC and their unit standard deviation of the sampling errors based on (a) ERAI and (b) NCEP-1 for the period of 1979–2014.

Fig. 3.

Explained variances by the first four EOF modes of the boreal spring IPWP_HC and their unit standard deviation of the sampling errors based on (a) ERAI and (b) NCEP-1 for the period of 1979–2014.

The two EOF modes together account for 70%–80% of the total variance, which is slightly larger than that of the global mean HC (Feng et al. 2013). Importantly, these two modes are both separable from the remaining eigenvectors using the rule of North et al. (1982), but Feng et al. (2013) found that the EOF-2 of the global mean HC was not. This implies that the global mean HC may consist of more signals from different regions. The first two modes of the IPWP_HC may also be related to the global mean HC strength because the latter includes the contribution from the IPWP region. However, when analyzing the relationship between HC and the WNP TC activity, it seems more appropriate to focus on the regional HC (IPWP_HC) because the global mean HC includes signals from other tropical regions. Overall, given that the two reanalysis datasets are consistent in representing the first two modes, the following sections will be based only on the ERAI unless stated otherwise.

b. Atmospheric circulation, SST, and precipitation anomalies associated with the principal modes

Figure 4 shows the boreal spring 850-hPa wind anomalies, SSTA, and precipitation anomalies regressed onto the standardized first and second PCs (PC-1 and PC-2) of the boreal spring IPWP_HC over the period 1979–2014, respectively. The SSTA associated with the EOF-1 shows a La Niña–like pattern with negative SSTA over the central and eastern Pacific, and the WNP exhibits positive SSTA, while the tropical Indian Ocean exhibits negative SSTA with the southern Indian Ocean cooler than the northern Indian Ocean (Fig. 4a). The SSTA distribution shows a zonal SST gradient over the equatorial Pacific and a cross-equatorial SST gradient over the Indian Ocean. The zonal SST gradient leads to strong equatorial easterly wind anomalies over the Pacific (Fig. 4a). The EOF-1 is characterized by a strong cross-equatorial flow in the lower troposphere and upward motion around 10°N (Figs. 1a,c). The precipitation anomalies in response to the SSTA (Fig. 4c) indicate off-equatorial heating and the cross-equatorial winds converge at the precipitation heating center (Fig. 4a). As the off-equatorial heating is crucial to the equatorial asymmetric HC anomalies (Lindzen and Hou 1988), the positive precipitation anomalies over the South China Sea (SCS) and Philippine Sea may play a role in inducing the cross-equatorial wind. Additionally, the southern Indian Ocean shows cooling of the SST (Fig. 4a), indicating a cross-equatorial SST gradient and possibly an enlarged sea–land thermal contrast with the Indian subcontinent and Indo-China (Li and Yanai 1996; Zhou and Zou 2010). These conditions may also affect the cross-equatorial wind shown in Fig. 4a. The Australian summer monsoon shows a strong cross-equatorial wind over the Maritime Continent and has not yet completely retreated by March (Li et al. 2012). As the cross-equatorial winds exist between March and May, and extend from the western boundary of the tropical Indian Ocean to the Maritime Continent, the interannual variability in the Australian summer monsoon may contribute to the cross-equatorial winds.

Fig. 4.

Anomalies of the boreal spring (a) 850-hPa wind (vectors; m s−1) and SST (shading; K) and (c) precipitation (mm day−1) regressed upon the standardized PC-1 of the boreal spring IPWP_HC during 1979–2014. (b),(d) As in (a),(c), but for PC-2. The stippled areas indicate the values are significant at the 95% confidence level. The vectors are only shown when the wind anomalies are significant at the 95% confidence level.

Fig. 4.

Anomalies of the boreal spring (a) 850-hPa wind (vectors; m s−1) and SST (shading; K) and (c) precipitation (mm day−1) regressed upon the standardized PC-1 of the boreal spring IPWP_HC during 1979–2014. (b),(d) As in (a),(c), but for PC-2. The stippled areas indicate the values are significant at the 95% confidence level. The vectors are only shown when the wind anomalies are significant at the 95% confidence level.

The SSTA and precipitation anomalies associated with the EOF-2 display a meridionally reversed distribution when compared with those associated with the EOF-1. The northern tropical Pacific shows a negative SSTA, whereas the southern Pacific (around the northern coast of the Australian mainland) shows a positive SSTA. In response to such SSTA patterns, precipitation is seen to decrease over the central Pacific in the Northern Hemisphere and increases over northern Australia and the Maritime Continent in the Southern Hemisphere. This anomalous precipitation pattern, together with the SSTA, drives an anomalous northerly wind across the equator corresponding to the spatial structure of the EOF-2 (Figs. 1b,d).

As the spring HC has an important impact on the summertime WNP TCGF (Zhou and Cui 2008), it remains to be determined whether the dominant modes of interannual variability in the spring IPWP_HC are related to the WNP TC activity in summer.

4. Impacts of the principal modes on WNP TC activity

Figure 5 shows the standardized PC-1 and PC-2 of the boreal spring IPWP_HC and the JJASO TCGF over the WNP during 1979–2014. The TCGF was obtained from both the JMA and JTWC in order to cross-validate the relationship between TCGF the IPWP_HC principal modes. The WNP TCs considered in this study are those that formed within the longitude range from 105°E to the date line in the Northern Hemisphere, including the SCS. The PC-1 and the TCGF shows good in-phase relationship (Fig. 5a), with a correlation coefficient of 0.51 (exceeding the 99.0% confidence level). Note that the linear trends of the TCGF and PC-1 were both removed, and consequently, their correlation does not incorporate influence from long-term trends. To further validate the close relationship between the PC-1 and TCGF, their correlation coefficients were also calculated based on NCEP-1 and JMA, as well as ERAI and JTWC (Table 1). The resulting correlation coefficients are 0.51 between NCEP-1 and JMA and 0.48 between ERAI and JTWC. Both of the above correlation coefficients are statistically significant at the 99.0% confidence level. These results indicate that the relationship between the PC-1 and TCGF is robust. In contrast, PC-2 and TCGF shows no relationship with correlation coefficients less than 0.1.

Fig. 5.

Normalized time series of the JJASO TCGF and (a) PC-1 and (b) PC-2 of the boreal spring IPWP_HC during 1979–2014. The blue lines represent the TCGF. The red lines in (a) and (b) denote the PC-1 and PC-2, respectively.

Fig. 5.

Normalized time series of the JJASO TCGF and (a) PC-1 and (b) PC-2 of the boreal spring IPWP_HC during 1979–2014. The blue lines represent the TCGF. The red lines in (a) and (b) denote the PC-1 and PC-2, respectively.

Table 1.

Correlation coefficients between PC-1 and the TCGF among the different reanalysis datasets and best track datasets. The asterisk indicates that a value is significant at the 95% confidence level.

Correlation coefficients between PC-1 and the TCGF among the different reanalysis datasets and best track datasets. The asterisk indicates that a value is significant at the 95% confidence level.
Correlation coefficients between PC-1 and the TCGF among the different reanalysis datasets and best track datasets. The asterisk indicates that a value is significant at the 95% confidence level.

To further determine the possible impacts of the EOF-1 on TC activity, Fig. 6 shows the composite storm tracks and intensity during the 12 positive years (1982, 1984, 1985, 1986, 1988, 1989, 1994, 1996, 1999, 2000, 2008, and 2011) and the 9 negative years (1983, 1987, 1992, 1997, 1998, 2002, 2007, 2010, and 2014) of the EOF-1. The positive (negative) years are chosen with a threshold of PC-1 being larger (smaller) than 0.5 (−0.5) standard deviation. The mean number of TCs formed during the positive years is 19.7, while the mean number of TCs during negative years is 16.1. This is consistent with the positive correlation between TCGF and the PC-1 discussed above. Additionally, during positive years on average 2.2 TCs (~11.2%) reached Saffir–Simpson category 3 or above (with maximum wind speed greater than 48 m s–1), and during negative years on average 3.6 TCs (~22.4%) reached the same intensity. The mean difference of the intense TC ratios between positive and negative years is statistically significant with p value of 0.052. Figure 6c also shows the composite difference of the mean TCGF in each 5.0° × 5.0° (longitude × latitude) grid box between positive and negative years. It turns out that the positive years lead to larger TCGF over the middle WNP. These results suggest that the positive phase of the EOF-1 is associated with increased genesis of less intense TCs, whereas the negative phase of the EOF-1 is associated with reduced TCs genesis, but with more intense TCs. But previous studies suggested that the TCs formed over the open ocean spend a longer time moving over the warm ocean and are easier to develop into major TCs (Vimont and Kossin 2007; Kossin and Vimont 2007; Zhang and Wang 2013), which seems contrary to the results here. We will explore this in the following section.

Fig. 6.

Composites of the storm track and intensity for (a) positive and (b) negative phases of the EOF-1 of the spring IPWP_HC. The TC intensity is represented by the surface maximum sustained wind [knot (kt); 1 kt ≈ 0.51 m s−1], indicated by the colors on the TC tracks. The composites are based on 12 positive and 9 negative years (see text for specific years) with respect to standardized PC-1 of the spring IPWP_HC. (c) The composite difference of the mean TCGF in each 5.0° × 5.0° (longitude × latitude) grid box between positive and negative years. The stippled areas indicate the values are statistically significant at the 90% confidence level.

Fig. 6.

Composites of the storm track and intensity for (a) positive and (b) negative phases of the EOF-1 of the spring IPWP_HC. The TC intensity is represented by the surface maximum sustained wind [knot (kt); 1 kt ≈ 0.51 m s−1], indicated by the colors on the TC tracks. The composites are based on 12 positive and 9 negative years (see text for specific years) with respect to standardized PC-1 of the spring IPWP_HC. (c) The composite difference of the mean TCGF in each 5.0° × 5.0° (longitude × latitude) grid box between positive and negative years. The stippled areas indicate the values are statistically significant at the 90% confidence level.

5. Physical mechanism

a. Changes in large-scale dynamic fields during JJASO

The large-scale environment factors, such as the VWS, the vertical velocity, and the low-level vorticity, can exert important effects on the TC activity (e.g., Gray 1968; Molinari and Vollaro 2013). For instance, the strong VWS can weaken a TC’s intensification as well as its genesis. On the other hand, large-scale upward motion and cyclonic anomaly is favorable for disturbances to grow into TCs. To investigate the possible mechanism that might allow the EOF-1 to influence the WNP TC activity, Fig. 7 presents the regressed anomalies of horizontal winds at 850 hPa, SLP, SSTA, vertical velocity at 500 hPa, relative vorticity at 850 hPa, and the VWS between 200 and 850 hPa in JJASO with respect to the PC-1 of the boreal spring IPWP_HC during 1979–2014. It is evident that the WNP region is dominated by a strong cyclonic circulation anomaly and negative SLP anomalies. The anomalous cyclonic circulation anomaly over the WNP features a strong westerly wind anomaly to the south (Fig. 7a), which in turn induces upward motion at 500 hPa (Fig. 7b) and positive relative vorticity anomalies at 850 hPa (Fig. 7c) over the WNP. The westerly wind anomalies on the southern side of the cyclonic circulation anomaly strengthens the monsoon westerly wind further and increases the VWS over the Maritime Continent (Fig. 7d). The enhanced monsoon westerly winds will lead to cooling of the SST over the SCS, increasing the zonal SST gradient (Fig. 7b). In addition, the anomalous upward motion centers are situated on the maximum zonal SST gradient, indicating a positive feedback of the zonal SST gradient on the convection, which is in agreement with Lindzen and Nigam (1987). These large-scale dynamic factor changes in response to the positive phase of the EOF-1 are favorable for WNP TC genesis (e.g., Gray 1968; Molinari and Vollaro 2013; Wu and Duan 2015; Gu et al. 2015).

Fig. 7.

Anomalies of JJASO (a) 850-hPa wind (vectors; m s−1) and SLP (shading; Pa), (b) SST (shading; K) and 500-hPa vertical velocity (contours; 10−2 Pa s−1), (c) 850-hPa relative vorticity (10−6 s−1), and (d) the vertical shear of horizontal wind between 200 and 850 hPa (m s−1) regressed upon PC-1 of the boreal spring IPWP_HC during 1979–2014. The stippled areas indicate the values exceed the 95% confidence level. The anomalies of 850-hPa wind and 500-hPa vertical velocity in (a) and (b) only indicate those significant at the 95% confidence level.

Fig. 7.

Anomalies of JJASO (a) 850-hPa wind (vectors; m s−1) and SLP (shading; Pa), (b) SST (shading; K) and 500-hPa vertical velocity (contours; 10−2 Pa s−1), (c) 850-hPa relative vorticity (10−6 s−1), and (d) the vertical shear of horizontal wind between 200 and 850 hPa (m s−1) regressed upon PC-1 of the boreal spring IPWP_HC during 1979–2014. The stippled areas indicate the values exceed the 95% confidence level. The anomalies of 850-hPa wind and 500-hPa vertical velocity in (a) and (b) only indicate those significant at the 95% confidence level.

The strengthened monsoon westerly winds can further enhance the monsoon trough and force the eastern edge of the trough to extend farther eastward (Fig. 8). PC-1 is also closely related to the Southeast Asian monsoon index, which is defined as the mean dynamical normalized seasonality index over 2.5°–20°N, 70°–110°E (Li and Zeng 2002) with a correlation coefficient of 0.48 (significant at 95% confidence level). This suggests that a positive phase of the EOF-1 can induce a strengthened Southeast Asian monsoon circulation, which in turn enhances the monsoon trough. As the monsoon trough provides a favorable environment for TC genesis (Gray 1968; Chen and Huang 2008; Wu et al. 2012; Molinari and Vollaro 2013), a strengthened monsoon trough during a positive phase of the EOF-1 will enhance the TC genesis over the WNP and the eastward-extending monsoon trough will drive increased TC genesis over the open ocean.

Fig. 8.

Composite streamlines at 850 hPa in JJASO for (a) positive and (b) negative phases of the EOF-1 of the spring IPWP_HC based on the four strongest positive years (1984, 1989, 1994, and 1999) and the four strongest negative years (1983, 1987, 1998, and 2010) with respect to the standardized PC-1 of the spring IPWP_HC. The dashed lines indicate the monsoon troughs.

Fig. 8.

Composite streamlines at 850 hPa in JJASO for (a) positive and (b) negative phases of the EOF-1 of the spring IPWP_HC based on the four strongest positive years (1984, 1989, 1994, and 1999) and the four strongest negative years (1983, 1987, 1998, and 2010) with respect to the standardized PC-1 of the spring IPWP_HC. The dashed lines indicate the monsoon troughs.

Note that strong positive VWS anomalies were observed over the South China Sea and Philippine Sea in the positive phase of the EOF-1 (Fig. 7d). Given that the VWS increase can inhibit the TC intensification, it could be expected that the TCs moving across the Philippine Sea region during positive phases of EOF-1 would be less intense than those in negative phases. From Figs. 6a,b, it can be clearly seen that there are more intense TC tracks across the Philippine Sea in negative phases than those in positive phases. The aforementioned mean ratio of the intense TCs is higher in negative phases (~22.4%) than those in positive phases (~11.2%), and the difference between these two ratios is statistically significant with a p value of 0.052. Both of the above pieces of evidence indicate that the VWS distributions in positive and negative phases of EOF-1 have impacts on the TC intensity when the TCs are crossing the Philippine Sea region. Although the TCs formed over the open ocean are easier to develop into major TCs (Vimont and Kossin 2007; Kossin and Vimont 2007; Zhang and Wang 2013), the results here further suggested that the environments, such as the VWS, on the tracks of these TCs are also important to the intensity change.

b. Evolution of the atmospheric circulation, SST, and precipitation anomalies

To further explain how the signals of the spring IPWP_HC persist to summer and affect the large-scale environments over the WNP, Fig. 9 displays the evolutions of the 3-month running mean anomalies of the 850-hPa horizontal winds and SST from MAM to August–October (ASO) regressed upon the standardized PC-1 during 1979–2014. Since the latent heating is the dominant component of the diabatic heating associated with the HC (not shown), the precipitation anomalies can be used to represent the vertically integrated diabatic heating anomaly (Yu and Zwiers 2007; Chen et al. 2014). Figure 10 shows the evolution of the precipitation anomalies and the 850-hPa horizontal wind anomalies, similar to those in Fig. 9. It is evident that in boreal spring, the southern Indian Ocean is cooler than the northern Indian Ocean, and this results in a cross-equatorial SST gradient over the tropical Indian Ocean and Maritime Continent (Fig. 9a). This cross-equatorial SST gradient leads to cross-equatorial southerly wind anomalies locating over the Indian Ocean and Maritime Continent (Fig. 9a). The anomalous southerly winds are redirected into westerly wind anomalies (redirected eastward) after crossing the equator by the Coriolis force and converge around 10°N, which induces off-equatorial convections over the Indo-Pacific region (60°–170°E), as indicated by the regressed precipitation anomalies (Fig. 10a). The positive precipitation anomalies over the WNP excite a Rossby wave with ascending motion over the WNP (Figs. 9a and 10a) due to the Gill-type response (Gill 1980). The Rossby wave (indicated by a cyclonic circulation anomaly) persists to the early autumn, with the strongest response occurring in summer (Figs. 9e,f). The anomalous cyclonic circulation and precipitation over the WNP are crucial to the large-scale environmental change in association with the EOF-1 (Fig. 7). It is worth noting that the precipitation anomalies actually show a dipole pattern with a negative pole over the southwest of Sumatra and positive pole over the WNP. This is more complex than the single positive heating condition as suggested in Gill (1980). In a recent study, Feng et al. (2016) conducted a numerical experiment with Gill’s model, in which the HC shows equatorial asymmetric response to a dipole heating source similar to the anomalous precipitation dipole described in this study (Fig. 10a). Their results indicate that the response of circulation to a dipole heating force resembles the responses to monsoon-like forcing in Gill (1980), but has stronger magnitude.

Fig. 9.

Regressed anomalies of 850-hPa horizontal wind (vectors; m s−1) and SST (shading; K) in (a) MAM, (b) AMJ, (c) MJJ, (d) June–August (JJA), (e) July–September (JAS), and (f) ASO regressed upon the standardized PC-1 of the boreal spring IPWP_HC during 1979–2014. The vectors are only shown when the values are significant at the 95% confidence level. The stippled areas in the plots indicate the region where the regressed SST anomalies are significant at the 95% confidence level.

Fig. 9.

Regressed anomalies of 850-hPa horizontal wind (vectors; m s−1) and SST (shading; K) in (a) MAM, (b) AMJ, (c) MJJ, (d) June–August (JJA), (e) July–September (JAS), and (f) ASO regressed upon the standardized PC-1 of the boreal spring IPWP_HC during 1979–2014. The vectors are only shown when the values are significant at the 95% confidence level. The stippled areas in the plots indicate the region where the regressed SST anomalies are significant at the 95% confidence level.

Fig. 10.

As in Fig. 9, but for precipitation (shading; mm day−1) anomalies and 850-hPa horizontal wind (vectors; m s−1). The vectors are only shown when the values are significant at the 95% confidence level. The stippled areas in the plots indicate the region where the regressed SST anomalies are significant at the 95% confidence level.

Fig. 10.

As in Fig. 9, but for precipitation (shading; mm day−1) anomalies and 850-hPa horizontal wind (vectors; m s−1). The vectors are only shown when the values are significant at the 95% confidence level. The stippled areas in the plots indicate the region where the regressed SST anomalies are significant at the 95% confidence level.

To explain how the precipitation anomalies over the western Pacific are sustained to the early autumn, we proposed a positive feedback mechanism among the precipitation over the WNP, the westerly wind anomalies over the Indo-Pacific region, and the local SST. The westerly wind anomalies converge around 10°N inducing anomalous precipitation over the western Pacific in boreal spring (Fig. 10a). The precipitation anomalies can in turn reinforce the westerly wind anomalies by a Gill-type response. In April–June (AMJ), the anomalous westerly winds can enhance the total wind speed over the Bay of Bengal (BOB) (Figs. 10b and 11a), which further cools the local SST by increasing the evaporation (Fig. 9b). With the annual progression, the climatological westerly winds extend to the SCS in May–July (MJJ) (Fig. 11b) as a result of the development of the summer monsoon. The total wind speeds over BOB–SCS (eastern WNP; 145°–175°E) are further strengthened (weakened) by the westerly anomalies (Fig. 12a), which increase (decrease) the evaporation (Fig. 12b) and cool (warm) the local SSTA (Fig. 12c). The BOB–SCS cooling and eastern WNP warming in MJJ can increase the zonal SST gradient over the Philippine Sea (Fig. 9c), which in turn can sustain the westerly wind anomalies (Wang et al. 2013; Hu et al. 2016). Consequently, the sustained westerly wind anomalies in May–August strengthen the Southeast Asian summer monsoon (Fig. 8a), which enhances the precipitation anomalies over the WNP (Fig. 10d). The increased zonal SST gradient can also contribute to the convection over the WNP (Lindzen and Nigam 1987; Ohba and Ueda 2006). Overall, as a result of this positive wind–SST–precipitation feedback, the precipitation anomalies over the WNP persist to the summer.

Fig. 11.

Climatological wind field at 850 hPa (vectors; m s−1) in (a) AMJ and (b) MJJ based on the period of 1979–2014. The red and blue boxes denote the eastern WNP region (10°–20°N, 145°–175°E) and BOB–SCS region (5°–20°N, 90°–120°E), respectively.

Fig. 11.

Climatological wind field at 850 hPa (vectors; m s−1) in (a) AMJ and (b) MJJ based on the period of 1979–2014. The red and blue boxes denote the eastern WNP region (10°–20°N, 145°–175°E) and BOB–SCS region (5°–20°N, 90°–120°E), respectively.

Fig. 12.

Correlation coefficients between PC-1 of the boreal spring IPWP_HC and (a) 1000-hPa wind speed anomalies, (b) surface latent heat flux anomalies based on NCEP-1, and (c) SSTA in MJJ during 1979–2014. The stippled areas indicate the values exceed the 95% confidence level.

Fig. 12.

Correlation coefficients between PC-1 of the boreal spring IPWP_HC and (a) 1000-hPa wind speed anomalies, (b) surface latent heat flux anomalies based on NCEP-1, and (c) SSTA in MJJ during 1979–2014. The stippled areas indicate the values exceed the 95% confidence level.

6. Conclusions and discussion

This study investigated the interannual variability of the boreal spring IPWP_HC and its impacts on the WNP TC activity. Two principal modes were reliably identified and dominate the interannual variability of the boreal spring IPWP_HC. The EOF-1 and EOF-2 explain over 60% and 12% of the total variances, respectively. The EOF-1 consists of a pair of equivalent cells locating on the flanks of the equator, with the Southern Hemisphere cell extending into the Northern Hemisphere by about 10° latitude. The EOF-2 consists of a single cell across the equator, which is accompanied by another cell in the Southern Hemisphere. The EOF-1 shows substantial impacts on the summertime WNP TC activity. In the positive phase of the EOF-1, more TCs formed over the middle WNP than those in the negative phase. The positive phase of EOF-1 leads to more TCs genesis but less intense TCs than those in negative phase. This is because the positive phase of the EOF-1 is related to favorable conditions over the WNP around 20°N in JJASO (Fig. 7) but is also related to positive VWS anomalies over the Philippine Sea, which is unfavorable for TC intensification.

The physical mechanism through which the EOF-1 signal persists to the following summer to affect the WNP large-scale environment involves a positive feedback among the precipitation, surface winds, and local SST. This mechanism is schematically shown in Fig. 13. The boreal spring EOF-1 of IPWP_HC features a cross-equatorial southerly wind over the Indian Ocean and Maritime Continent. The cross-equatorial southerly wind anomalies are redirected eastward by the Coriolis force in the Northern Hemisphere and turn into westerly wind anomalies, which converge around 10°N and induce positive precipitation anomalies over the WNP. The increased precipitation anomalies can further strengthen the westerly wind anomalies by Gill-type response. By enhancing (weakening) the total wind speeds, the westerly wind anomalies lead to the BOB–SCS cooling and the eastern WNP warming, which enlarge the zonal SST gradient over the Philippine Sea. The enlarged zonal SST gradient further enhances the westerly wind anomalies. In this process, the Southeast Asian monsoon and the monsoon trough are strengthened, which sustains the WNP precipitation anomalies to the ensuing summer (Figs. 10d,e). These results imply that the EOF-1 of the spring IPWP_HC may have significant climatic impacts on the WNP and could be a potential predictor of the WNP summer climate.

Fig. 13.

Schematic diagram showing the air–sea feedback between the westerly wind anomalies (solid arrows), the underlying SST over the BOB and the SCS, and the precipitation anomalies (denoted by the cloud and rain symbol) over the WNP. In boreal spring, the precipitation anomalies excite a Rossby wave with ascending motions over the WNP. The overlap of the anomalous winds (denoted by the horizontal solid arrows) with the mean winds (denoted by the double empty arrows) over the BOB–SCS and the eastern WNP results in the enhanced total wind speeds over the BOB–SCS and a weakened total wind speeds over the eastern WNP, which cools the BOB–SCS and warms the eastern WNP. The BOB–SCS cooling (denoted by the green-shaded ellipse) and the eastern WNP warming (denoted by the red ellipse) increase the zonal SST gradient over the Philippine Sea, which in turn enhances the anomalous westerly winds (denoted by the three parallel arrows over the Philippines). The enhanced westerly wind further strengthens the monsoon trough (denoted by the solid purple curve) and sustains the WNP precipitation anomalies to the ensuing summer. The cyclonic circulation anomaly over the WNP (denoted by a black ellipse with arrows) excited by the precipitation anomalies over the WNP also sustains and leads to a favorable environment for the WNP TC genesis.

Fig. 13.

Schematic diagram showing the air–sea feedback between the westerly wind anomalies (solid arrows), the underlying SST over the BOB and the SCS, and the precipitation anomalies (denoted by the cloud and rain symbol) over the WNP. In boreal spring, the precipitation anomalies excite a Rossby wave with ascending motions over the WNP. The overlap of the anomalous winds (denoted by the horizontal solid arrows) with the mean winds (denoted by the double empty arrows) over the BOB–SCS and the eastern WNP results in the enhanced total wind speeds over the BOB–SCS and a weakened total wind speeds over the eastern WNP, which cools the BOB–SCS and warms the eastern WNP. The BOB–SCS cooling (denoted by the green-shaded ellipse) and the eastern WNP warming (denoted by the red ellipse) increase the zonal SST gradient over the Philippine Sea, which in turn enhances the anomalous westerly winds (denoted by the three parallel arrows over the Philippines). The enhanced westerly wind further strengthens the monsoon trough (denoted by the solid purple curve) and sustains the WNP precipitation anomalies to the ensuing summer. The cyclonic circulation anomaly over the WNP (denoted by a black ellipse with arrows) excited by the precipitation anomalies over the WNP also sustains and leads to a favorable environment for the WNP TC genesis.

This study addressed the important issue of how the spring large-scale circulation (or HC) signals persist to summer and affect the climate (or TC activity) over the WNP. Zhou and Cui (2008) originally suggested that there is a close relationship between the spring HC strength and WNP TCGF. The persistence of the HC signals can be attributed to the memory of the warm SSTA over the Indian Ocean and SCS region that are induced by downward motion of the HC. However, the HC is not necessarily always associated with downward motion over these regions owing to different spatial structures of the dominant modes of HC variability (Guo et al. 2016a,b). Therefore, this study analyzed the dominant modes of the regional HC. Based on the dominant modes of the IPWP_HC and their relationship with WNP TCGF, a more detailed physical mechanism is proposed, in which the SSTA over the BOB–SCS region is due to a positive wind–evaporation feedback. Our results also suggest more details of the WNP TC activities in response to the EOF-1 of the spring IPWP_HC. For instance, the positive EOF-1 corresponds to more TC genesis with weaker intensity, while the negative EOF-1 corresponds to less TC genesis with stronger intensity.

The positive feedback mechanism proposed here can also be well supported by other studies. Kosaka et al. (2013) proposed an air–sea coupled mode, which suggested that the Pacific–Japan pattern associated precipitation anomalies over the WNP can excite a cold Rossby wave. The corresponding easterly wind anomalies (opposite to the westerly in this study) can warm the Indian Ocean and SCS region by decreasing the total wind speed and reducing the local evaporation. The warmed SST over the Indian Ocean and SCS can in turn suppress the WNP precipitation by exciting eastward-propagating Kelvin wave. This precipitation–wind–SST feedback is similar to that proposed in this study but with some differences. In the present study, the feedback of BOB–SCS SST cooling to WNP precipitation anomalies is driven by an increase of zonal SST gradient (Fig. 7b), which can both enhance the monsoon trough (Fig. 8a) and affect the local convection (Lindzen and Nigam 1987; Ohba and Ueda 2006). This feedback is probably not the only one that maintains the WNP precipitation; the gradually weakening character of the Indian Ocean Basin mode is also an important factor that can maintain the WNP precipitation (Xie et al. 2009; Wu et al. 2010).

Overall, this study demonstrates that the EOF-1 of spring IPWP_HC has a possible impact on the WNP TC activity in summer, suggesting that the EOF-1 could be used as a potential predictor of the WNP TCGF. Climatic factors, such as ENSO, the strength of the Australian monsoon, and intensity of the subtropical high in the South Pacific, are important predictors for most statistical models of the WNP TC activity (e.g., Chan et al. 2001; Zhan et al. 2012). Therefore, given the impacts of the EOF-1 on WNP TC activity, incorporating the mode as a predictor in statistical models may improve their predictability. Most of the current general circulation models (GCMs) simulate the interannual variability in the HC (Guo et al. 2016b) and the TC activity (e.g., Kim et al. 2014; Murakami et al. 2015) reasonably well. However, Zhao et al. (2009) stated that the atmospheric GCM they used had lower skill in simulating the interannual variability in the TCGF over the WNP than that over the North Atlantic basin. Our results imply that a better simulation of the spring IPWP_HC variability may improve TC simulation over the WNP.

Acknowledgments

We thank the editor and the three anonymous referees, whose comments improved the paper. We acknowledge the ECMWF, NCEP–NCAR, NOAA, the JMA, and the JTWC for making their research datasets publicly available. This work is jointly sponsored by the National Natural Science Foundation of China through Grants 41461164008 and 41705057, the National Key Project for Basic Research (973 Project) under Grant 2015CB425803, the Natural Science Foundation of Jiangsu Province (BK20170637), and the Foundation Research Funds for the Central Universities (020714380026).

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Footnotes

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