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Intensified Impact of East Indian Ocean SST Anomaly on Tropical Cyclone Genesis Frequency over the Western North Pacific RUIFEN ZHAN Shanghai Typhoon Institute of China Meteorological Administration, Shanghai, China
YUQING WANG International Pacific Research Center, and Department of Meteorology, School of Ocean and Earth Science and Technology, University of Hawai‘i at Manoa, Honolulu, Hawaii
LI TAO Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China (Manuscript received 10 February 2014, in final form 23 June 2014) ABSTRACT A recent finding is the significant impact of the sea surface temperature anomaly (SSTA) over the east Indian Ocean (EIO) on the genesis frequency of tropical cyclones (TCs) over the western North Pacific (WNP). In this study it is shown that such an impact is significant only after the late 1970s. The results based on both data analysis and numerical model experiments demonstrate that prior to the late 1970s the EIO SSTA is positively correlated with the equatorial central Pacific SSTA and the latter produces an opposite atmospheric circulation response over the WNP to the former. As a result, the impact of the EIO SSTA on the TC genesis over the WNP is largely suppressed by the latter. After the late 1970s, the area coverage of the EIO SSTA is expanding. This considerably enhances the large-scale circulation response over the WNP to the EIO SSTA and significantly intensifies the impact of the EIO SSTA on TC genesis frequency over the WNP. The results from this study have great implications for seasonal prediction of TC activity over the WNP.
1. Introduction Sea surface temperature (SST) in the tropical Indian Ocean (IO) is known to have profound impacts on weather and climate over East Asia and the western North Pacific (WNP) (Yoo et al. 2006; Yang et al. 2010; Luo et al. 2012). Such impacts, as a discharging capacitor, can persist through the summer after an El Niño event has dissipated (Klein et al. 1999; Xie et al. 2009). Recent studies using data since the early 1980s have shown that variations of SST in the east Indian Ocean (EIO) significantly affect the interannual variability of tropical cyclone (TC) genesis frequency over the WNP (Zhan et al.
Corresponding author address: Dr. Yuqing Wang, IPRC/SOEST, University of Hawai‘i at Manoa, POST 409G, 1680 East-West Road, Honolulu, HI 96822. E-mail:
[email protected] DOI: 10.1175/JCLI-D-14-00119.1 Ó 2014 American Meteorological Society
2011a,b). When the EIO is anomalously warm, a warm equatorial atmospheric Kelvin wave would be excited to the east with low-level convergence toward the equator and divergence and anticyclonic circulation anomalies off the equator in the tropical WNP. These would also weaken the WNP summer monsoon trough and thus suppress TC genesis over the WNP, and vice versa when the EIO is anomalously cold. As a result, considerably more TCs form in years when the EIO is unusually cold than in years when the EIO is unusually warm (Du et al. 2011; Zhan et al. 2011a,b). This relationship has potential implications for the predictability of TC activity over the WNP on the seasonal time scale (Zhan et al. 2012). It is generally believed that seasonal TC activity is potentially predictable because it is largely controlled by the slowly evolving large-scale atmospheric circulation patterns and the oceanic thermodynamic conditions (Gray 1984a,b; Nicholls 1985; Camargo et al. 2007; Zhan
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et al. 2012). However, if the source of the predictability changes abruptly or experiences any significant climatic shift, the prediction skill would be subject to change accordingly or originally skillful prediction scheme might become less skillful. This could be the case for the seasonal prediction of TC activity over the WNP as recently documented by Zhan et al. (2012). They showed that the prediction skill for TC genesis frequency over the WNP has degraded since 2008, even though our prediction tools and models have been improved steadily. Such degraded prediction skills might be related to changes in the statistical relationships between seasonal TC activity and slowly evolving processes or changes in external impacts on large-scale environmental conditions associated with TC activity over the WNP. This is particularly true for statistical prediction schemes that were constructed based on data in a limited time period that did not reflect the climate shift/change. Recent studies have revealed that in the late 1970s a climate shift took place in the Indo-Pacific region, which has also altered the relationship between the tropical IO SST anomaly (SSTA) and the WNP climate (Ding et al. 2010; Huang et al. 2010; Xie et al. 2010). Other studies have shown that the correlation between climate variability and seasonal/interannual WNP TC activity might experience drastic changes over time. For example, the statistically significant relationship between the WNP TC activity and the stratospheric quasi-biennial oscillation (QBO) from the 1950s to the 1980s was found to be no longer present in later years (Camargo and Sobel 2010). Another example is the interdecadal change in the relationship between the annual intense TC frequency over the WNP and the El Niño–South Oscillation (ENSO) index recently reported by Tao et al. (2012). They showed that the positive correlation between the annual intense TC count over the WNP and the ENSO index on the interannual time scale exhibits a significant interdecadal variation. The relationship between the EIO SSTA and TC genesis frequency over the WNP might have experienced a similar interdecadal change. In particular, a significant warming signal has been detected in the tropical IO since the 1950s (Alory et al. 2007; Ihara et al. 2008). The warming trend may enhance the convective activity and large-scale atmospheric circulation response over the WNP to the interannual variability of tropical IO SSTA (Xie et al. 2010). In this study, we found that the impact of EIO SSTA on TC genesis frequency over the WNP has undergone a dramatic enhancement since the late 1970s. We will show that the correlation between the TC genesis frequency over the WNP and the EIO SSTA is quite significant since the late 1970s whereas it is insignificant prior to the late 1970s. A better understanding of changes in the relationship between TC activity and climate
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variability on seasonal/interannual time scales is fundamental to skillful seasonal prediction of TC activity. The objectives of this paper are to document the interdecadal change in the relationship between the EIO SSTA and the TC genesis frequency over the WNP and to understand the mechanisms responsible for such a change based on both data analysis and numerical experiments using both regional and global models. The rest of the paper is organized as follows. Section 2 describes the data, analysis methods, and numerical models used in the study. Results from data analysis and numerical simulations are discussed in sections 3 and 4, respectively. Main conclusions and discussions are given in the last section.
2. Data, analysis methods, and numerical models a. Data and analysis methods Three TC best-track datasets from the Shanghai Typhoon Institute of the China Meteorological Administration (CMA), the Joint Typhoon Warning Center (JTWC), and the Regional Specialized Meteorological Center of the Japan Meteorological Agency (JMA) are used as independent TC datasets to confirm our results. The best-track TC data include 6-hourly longitude and latitude of the TC center and maximum sustained 10-m wind speed. Only TCs that reached tropical storm intensity (with the maximum sustained 10-m wind speed being 17.2 m s21 or larger) in the period 1951–2012 are considered in our analysis. It should be mentioned that the best-track TC data prior to the 1960s are likely to have some uncertainties. Here the period 1951–2012 is chosen for three reasons. First, we repeated the analysis on the relationship between TC genesis frequency and EIO SSTA using the data in the 1965–2012 period and got similar results, suggesting that our conclusions are not affected by the TC data quality. Second, since this study mainly examines the difference between two periods, a relatively longer record is better for examining the significance. Finally, the TC data are not directly used to discuss the involved mechanisms. The SST data from 1951 to 2012 are the improved extended reconstructed sea surface temperature dataset version 3 (ERSST.v3) from the National Oceanic and Atmospheric Administration (NOAA) (Smith and Reynolds 2004). The large-scale atmospheric features are examined using the reanalysis data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) during 1958–2012 (Kalnay et al. 1996) and the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) data from 1958
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to 2001 (Simmons and Gibson 2000). In addition, the observed sea level pressure (SLP) dataset from the Kaplan analysis of the Comprehensive Ocean–Atmosphere Data Set (COADS) is used, which consists of monthly mean SLP on 48 3 48 grids over the global marine regions from 1958 to 2000 (Kaplan et al. 2000). Following Zhan et al. (2011a), we defined the EIO SST index as the SSTA averaged in the region 108S–22.58N, 758–1008E. We also found that the SSTAs in the equatorial central Pacific (CP; 1608E–1808, 108S–2.58N) and the eastern Pacific (EP; 808–1108W, 08–108N) show potential impacts on the large-scale circulation over the WNP. To assess the relative importance of the EIO SSTA in modulating the large-scale circulation and TC genesis over the WNP, the CP or EP SSTA signal is removed from the effect of the EIO SSTA time series following Ashok et al. (2003): ^ I~ , IEIO 5 IAEIO 1 r(IEIO , IY )O Y
(1)
where IY indicates the CP or EP SSTA index, I~Y is the ^ is the standard deviation of the EIO SSTA normalized IY, O index IEIO, r(IEIO, IY) denotes the correlation between the IEIO and I~Y , and IAEIO is the remainder of the IEIO with the variable Y signal removed.
b. Numerical models The International Pacific Research Center (IPRC) Regional Atmospheric Model (iRAM) developed in the IPRC at the University of Hawai‘i (Wang et al. 2003) was used in this study to perform a series of sensitivity experiments. The model has been applied to the studies of dynamically downscaled TC activity (Stowasser et al. 2007; Zhan et al. 2011a; Wu et al. 2012) and other regional climate modeling studies (e.g., Wang et al. 2007). The model uses hydrostatic, primitive equations in spherical coordinates with sigma (pressure normalized by surface pressure) as the vertical coordinate. The model equations are solved with a fourth-order conservative finite differencing scheme on an unstaggered longitude–latitude grid system. The time integration is performed using a leapfrog scheme with intermittent application of an Euler backward scheme. The model physics include the cloud microphysics scheme of Wang (2001); a mass flux scheme for subgrid shallow convection, midlevel convection, and deep convection developed by Tiedtke (1989) with some modifications outlined in Wang et al. (2003, 2004, 2007); the radiation package developed by Edwards and Slingo (1996) and further improved by Sun and Rikus (1999); the Biosphere–Atmosphere–Transfer Scheme (BATS) developed by Dickinson et al. (1993) for land surface processes; a modified Monin–Obukhov similarity scheme for flux calculations at the ocean surface; and a nonlocal E–«
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turbulence closure scheme for subgrid-scale vertical mixing (Langland and Liou 1996), which was modified to include the effect of cloud buoyancy production of turbulence kinetic energy (Wang 1999). A one-way nesting is used to update the model time integration in a buffer zone, 58 in extent, near the lateral boundaries within which the model prognostic variables are nudged to reanalysis data with an exponential nudging coefficient proposed by Giorgi et al. (1993) and later modified by Liang et al. (2001). The model has 28 vertical levels with relatively higher resolution in the planetary boundary layer. The lowest model level is roughly 35 m above the surface. More details of the model can be found in Wang et al. (2003, 2004, 2007). The initial and lateral boundary conditions for iRAM were constructed using the NCEP–NCAR reanalysis, available at 2.58 3 2.58 horizontal resolution with 17 vertical pressure levels at 6-h intervals. SSTs were obtained from the Reynolds weekly SST data at 18 3 18 horizontal resolution (Reynolds et al. 2002). All data were interpolated onto the model grids by cubic spline interpolation in the horizontal and linear interpolation in both the vertical and time. The overall model settings and configuration are the same as those used in Zhan et al. (2011a), Wu et al. (2012), and Zhan et al. (2013). The method for detecting and tracking the model TCs is based on Zhan et al. (2011a). We identified a TC in the iRAM simulations with 6-hourly model outputs using the following criteria: 1) there must be a local maximum in relative vorticity at 850 hPa exceeding 5 3 1025 s21; 2) there must be a local minimum in SLP within a distance of 48 latitude from the vorticity maximum (the location of this minimum pressure is defined as the center of the model storm); 3) the azimuthal mean tangential wind speed at 850 hPa must be higher than that at 300 hPa; 4) the closest local maximum in temperature averaged between 500 and 200 hPa is distinguishable and is defined as the center of the warm core (the distance between the center of the warm core and the center of the storm must not exceed 2.58 latitudes—from the center of the warm core the temperature must decrease by at least 0.58C in all directions within a distance of 7.58 latitude); 5) the storm must form south of 358N and east of 1008E; and 6) the storm must last at least 1 day and have a maximum wind speed over 17 m s21 at the lowest model level. The atmospheric general circulation model used is the European Centre Hamburg Model version 4.8 (ECHAM) developed at the Max Planck Institute for Meteorology (Roeckner et al. 1996). The model uses a 19-level hybrid sigma–pressure coordinate system with a spectral T42 horizontal resolution. The model physics include the turbulent surface fluxes calculated from Monin–Obukhov
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similarity theory, the horizontal diffusion in a hyperLaplacian form, the ECMWF radiation scheme, and the parameterization of cumulus convection (shallow, midlevel, and deep) based on the bulk mass flux scheme developed by Tiedtke (1989) and later modified by Nordeng (1994).
3. Results from data analysis To examine the interdecadal change in the relationship between the EIO SST and the genesis frequency of TCs over the WNP, we first calculated the 20-yr running correlations between them during 1951–2012 (Fig. 1). We restrict our analysis of WNP TCs to the typhoon season (1 June–31 October) during which about 80% of WNP TCs form. Prior to the late 1970s, the negative correlation between EIO SST and TC genesis frequency over the WNP is not statistically significant. Around 1978/79, the 20-yr running correlations become significant over a 95% confidence level and remain significant afterward, suggesting an intensified impact of the EIO SSTA on TC genesis over the WNP. Three best-track TC data show quite similar results. To further understand the above interdecadal change, we take two periods, 1958–77 and 1980–2012, in the following analyses. Note that both 1978 and 1979 were considered to be transitional years and thus were simply excluded in forming the two periods. Note also that the two periods have different lengths, which is based on the following two reasons. First, the ERA-40 data have a shorter record (1958–2001) than the NCEP–NCAR reanalysis data (1948–present). To examine the data dependency, we intentionally chose the first period from 1958 to 1977 since both datasets are available. Second, the period 1980–2012 is selected because a relatively longer record is better for examining the significance. In fact, we also did the analyses based on the NCEP– NCAR reanalysis data using the same length (1949–77 and 1980–2008) for the prior and the recent periods and got very similar results to those discussed herein. TC activity is closely related to environmental conditions (Emanuel 2007). We first examine the difference in the large-scale atmospheric circulation responses over the WNP to the summer EIO SSTA before and after the late 1970s. Figure 2 shows the regression fields of seasonal mean 850-hPa winds, SLP, 850-hPa relative vorticity, and 500-hPa vertical pressure velocity in the typhoon season with respect to the summer EIO SSTA for the periods 1958–77 and 1980–2012, respectively, based on NCEP– NCAR reanalysis data. These large-scale variables, which are believed to affect TC genesis, show remarkably different responses to the EIO SSTA between the two periods. During the recent period, the anomalous easterly
FIG. 1. The 20-yr running correlations of the EIO SSTA with the WNP TC frequency. TC frequency in the typhoon season (June– October) each year is derived from the CMA, JTWC, and JMA best-track data. SST data are averaged in summer (June–August). Both TC data and SST data are taken from 1951 to 2012. The horizontal gray line shows the 95% significance level.
(Fig. 2b) and low SLP (Fig. 2d) over the EIO extend eastward to the equatorial western Pacific, indicating an equatorial Kelvin wave response to convective activity over the EIO. In contrast, during the prior period the equatorial Kelvin wave response is quite weak. The largescale atmospheric circulation response over the WNP to the EIO SSTA is more significant and thus imposes a much stronger impact on TC genesis over the WNP during the recent period than during the prior period. It includes zonally elongated anomalous anticyclonic circulation, increased SLP, reduced low-level vorticity, and increased subsidence in response to the positive EIO SSTA, as reported in previous studies (Zhan et al. 2011a,b; Tao et al. 2012). To verify the above results being independent of the reanalysis data, we redid the linear regressions using the ERA-40 data. Since the ERA-40 data have a shorter record than the NCEP–NCAR reanalysis data, we selected the period 1958–2001 when the two reanalysis datasets coexist. Figures 3a–d show the regression patterns of seasonal mean 850-hPa winds in the typhoon season with respect to the summer EIO SSTA based on the two reanalysis datasets. As we expected, the results from the two datasets display high consistency. Both show a very weak low-level circulation response to the EIO SSTA in the prior period and a quite significant response to the EIO SSTA with easterly anomalies over the equatorial western Pacific and a zonally elongated anomalous anticyclonic circulation over the WNP in the recent period. To check the independence of the above results from the particular data used, we further examined the SLP using the independent observed SLP data shown in Figs. 2c and 2d. As we can see from Figs. 3e–h, the regressed SLP fields over the WNP in response to the EIO SSTA based on the NCEP–NCAR reanalysis data
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FIG. 2. Regression fields with respect to the summer EIO SSTA for the seasonal mean (a),(b) 850-hPa winds (m s21), (c),(d) SLP (hPa), (e),(f) 850-hPa vorticity (1025 s21), and (g),(h) 500-hPa vertical pressure velocity (Pa s21) in the typhoon season based on the NCEP reanalysis for (left) 1958–77 and (right) 1980–2012. The positive (negative) differences statistically significant at the 95% confidence level based on the F test are shaded in dark (light) gray.
and the Kaplan SLP data for 1958–77 and 1980–2000 show very similar patterns, with a zonally elongated anomalous positive SLP belt centered in the north for 1958–77 and in the south for 1980–2000, respectively. Especially, the Kaplan dataset shows that the SLP response to the EIO SSTA is insignificant in the prior period but quite significant in the recent period, consistent with those
from the NCEP–NCAR reanalysis. The above comparison further suggests that our results based on the NCEP– NCAR reanalysis data are robust and independent of the dataset used. A fundamental question arises as to what causes the intensified atmospheric circulation response over the WNP to the EIO SSTA, thus affecting TC genesis frequency in
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FIG. 3. Regression fields with respect to the summer EIO SSTA for the seasonal mean 850-hPa winds (m s21) in the typhoon season based on the (a),(b) NCEP–NCAR and (c),(d) ERA-40 data for (left) 1958–77 and (right) 1980–2001, and for the SLP (hPa) based on the (e),(f) NCEP–NCAR reanalysis and (g),(h) Kaplan analysis data for (left) 1958– 77 and (right) 1980–2000. Areas where the difference is statistically significant at the 95% confidence level based on the F test are shaded.
the recent period. We found that the change in the area coverage of SSTA over the Indian Ocean and the SSTA forcing in other regions are two key factors. To elaborate this possibility, we first examine the correlations between the EIO SSTA and global SSTAs in summer. Figures 4a and 4b present the correlations between them in the entire period 1958–2012 from the original data and from the data
with the linear trend removed, respectively. In the original data (Fig. 4a), the EIO SSTA exhibits significant positive correlations with the SSTAs over most of the Indian Ocean, western Pacific and southeast Pacific. With the linear trend removed from the SSTA time series (Fig. 4b), the correlations over the tropical western Pacific with the EIO SSTA become insignificant. Meanwhile, those over
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FIG. 4. The interannual correlation between the EIO SSTA and global SSTAs. Correlations of the EIO SSTA and global SSTA in summer during (a),(b) 1958–2012, (c),(d) 1958–77, and (e),(f) 1980–2012 based on (left) the original data and (right) the data with the line trend removed. The shades in dark (light) gray indicate areas where the negative and positive differences are statistically significant at the 99% (95%) confidence level by the Student’s t test, respectively. In (b), the solid box indicates the EIO, and in (d) and (f), the solid (dashed) boxes indicate pattern 1 (CP) and pattern 2 (EP), respectively. These refer to regions with imposed SSTA in numerical experiments that will be discussed in section 4.
the southeast Pacific become weaker. Furthermore, similar correlations between the EIO SSTA and global SSTAs in the two periods are calculated, respectively (Figs. 4c–f). In general, the correlation patterns in the recent period are very similar to those in the entire period, but significantly different from those in the prior period. We found two interesting features: 1) the highly correlated region in the EIO remarkably expands toward the South China Sea and west tropical IO in the recent period, referred to as pattern 2 (508–1208E, 108S–22.58N), and correspondingly the region 608–1008E, 08–22.58N for the prior period is termed pattern 1; and 2) the EIO SSTA is positively correlated with the SSTA in the equatorial central Pacific (1608E– 1808, 108S–2.58N) during the prior period, and with that in the eastern Pacific (808–1108W, 08–108N) during the recent period. Previous studies have suggested that the SSTAs in these two regions also affect TC activity over the WNP (Lander 1994; Chen and Tam 2010; Tao et al. 2012).
In the following, we examine the time series of the CP and EP SSTAs, and the large-scale atmospheric circulation response to either of them based on composite analysis, which is often used in climate research. The normalized time series of the EIO, CP and EP SSTAs in summer during 1958–2012 are shown in Fig. 5. As indicated above, prior to the late 1970s the EIO SSTA is positively correlated with the CP SSTA with the correlation coefficient of 0.51 (0.50) based on the raw (detrended) SSTA time series, well above 95% confidence level. In this period, the correlation between the EIO SSTA and the EP SSTA is extremely weak, with the correlation coefficient of 0.08 for both the raw and detrended data. During 1980–2012 the correlation with the CP SSTA is not significant, while that with the EP SSTA becomes significant with the correlation coefficient reaching 0.45 in the raw data and 0.46 in the detrended data, respectively, both at the 95% confidence level. To
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resulting in the insignificant correlation between the WNP TC genesis frequency and the EIO SSTA. In sharp contrast, the positive EP SSTA produces an anomalous lowlevel anticyclonic circulation over the WNP (Fig. 6b), which enhances the anomalous anticyclonic circulation response to the positive EIO SSTA to some degree. The anomalous anticyclonic circulation response over the WNP to the positive EIO SSTA is still strong even after the effect of the EP SSTA is removed during the recent period (Fig. 6d), suggesting that the EP SSTA contributes marginally to the intensified impact of the EIO SSTA on the TC genesis frequency over the WNP in the recent period.
4. Model results FIG. 5. Standardized time series of the summer CP SSTA and the summer EP SSTA during 1958–2012. The standardization is calculated based on the periods (a) 1958–77 and (b) 1980–2012. The horizontal solid lines show the value of 60.8 of the standard deviation.
facilitate composite analysis, we first defined the warm/ cold CP and warm/cold EP years, and then chose those years with the normalized SSTA greater (smaller) than 0.8 (20.8) of its standard deviation as the anomalous years, simply the warm (cold) years in the following discussion. In the prior period, the warm CP years include 1966, 1969, 1970, 1972, and 1977, and the cold CP years include 1960, 1961, 1964, 1971, and 1975. In the recent period, the warm EP years include 1983, 1987, 1997, 1998, and 2009, and the cold EP years include 1981, 1984, 1985, 1988, 1996, and 2007. To reveal the individual roles of the above two SSTAs in modulating the impact of the EIO SSTA on the WNP TC genesis, we examine the differences in the composite 850-hPa winds between the warm and cold years of the CP (Fig. 6a) and the EP (Fig. 6b), respectively, and the correlations between the summer EIO SSTA and the seasonal mean 850-hPa winds after the effect of, respectively, the CP SSTA during the prior period (Fig. 6c) and the EP SSTA during the recent period (Fig. 6d) is removed using Eq. (1). Consistent with previous studies, the positive CP SSTA induces a low-level anomalous cyclonic circulation over the WNP as a Gill-type Rossby wave response (Gill 1980), which is opposite to the atmospheric anticyclonic circulation response over the WNP to the positive EIO SSTA. With the impact of the CP SSTA removed, a prominent anomalous anticyclonic circulation appears over the WNP (Fig. 6c). This suggests that during the prior period the low-level anomalous anticyclonic circulation response over the WNP to the positive EIO SSTA is partially offset by the impact of the positive CP SSTA,
The results discussed in section 3 are based on reanalysis data. In this section we will further substantiate those results by numerical experiments using both a regional atmospheric model (iRAM) and a global general circulation model (ECHAM) as briefly introduced in section 2b.
a. Regional model results The regional model simulations started from 0000 UTC 27 May through the end of October, 2004, a year in which the TC frequency in the typhoon season is close to the climatological mean. Two groups of experiments are conducted (Table 1). Each group consists of a control (CTRL) simulation with the observed SST and four sensitivity experiments with different imposed SSTAs in the EIO and/or in the CP/EP for 2004. The differences between the sensitivity and the CTRL runs can imply the relative importance of individual SSTA forcings. The model domain in the CTRL_1_run extends from 158S to 56.58N and from 708E to 1608W with a grid spacing of 0.58 in both zonal and meridional directions, while that in the CTRL_2_run extends from 158S to 56.58N and from 708E to 758W with the same grid spacing. The observed SST was used to perform simulations in both control runs. The EIO, CP, and EP runs were identical to the corresponding control runs except that 18C was added to SSTs in the EIO (758–1008E, 108S–22.58N) and the CP (1608E–1808, 108S–2.58N), and 28C to the EP (808–1108W, 08–108N), respectively. The EIO1.0&CP1.0_run and EIO0.5&CP1.0_run were identical to the CTRL_1_run, but in the EIO1.0&CP1.0_run 18C was added to SSTs in the EIO and the CP, while in the EIO0.5&CP1.0_run 0.58C was added to SSTs in the EIO and 18C to the CP. In the EIO1.0&EP2.0_run, 1.08C was added to SSTs in the EIO and 2.08C to SSTs in the EP, while SSTs elsewhere were identical to that in the CTRL_2_run.
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FIG. 6. The impacts of the EIO SSTA, the CP SSTA, and the EP SSTA on the atmospheric anomalies over the WNP. Composite differences in 850-hPa winds between warm and cold (a) CP years during 1958–77 and (b) EP years during 1980–2012. Differences statistically significant at the 95% confidence level by the Student’s t test are shaded. Also shown are correlations of the summer EIO SSTA and the seasonal mean 850-hPa winds (m s21) in the typhoon season after removing (c) the CP SSTA signal during 1958–77 and (d) the EP SSTA signal during 1980–2012. Values significant at the 95% significance level by the Student’s t test are shaded.
The model results show that the positive EIO SSTA leads to significant anomalous easterlies over the equatorial western Pacific and an anomalous anticyclonic circulation over the WNP main TC genesis region (Fig. 7a). These suggest the strong equatorial Kelvin wave and weak WNP monsoon in response to the positive EIO SSTA, unfavorable for TC genesis over the WNP. On the contrary, the positive CP SSTA produces anomalous westerlies
over the equatorial western Pacific and an anomalous cyclonic circulation to the north, favorable for TC genesis over the WNP (Fig. 7b). When positive SSTAs are imposed onto the EIO and CP simultaneously, the atmospheric circulation response over the WNP depends on the amplitude of the SSTA. Once the CP SSTA reaches twice as large as the EIO SSTA, the atmospheric circulation response is similar to that to the CP SSTA alone, favoring TC
TABLE 1. Experimental design of the regional model (iRAM) simulations. The regions of the EIO, CP, and EP are also referred to Fig. 4. Name CTRL_1_run EIO_run
Domain
SST
158S–56.58N, 708E–1608W
Observed SST. Identical to the CTRL_1 run except that 18C was added to SSTs in the EIO (758–1008E, 108S–22.58N). Identical to the CTRL_1 run except that 18C was added to SSTs in the CP (1608E–1808, 108S–2.58N). Identical to the CTRL_1 run except that 0.58C was added to SSTs in the EIO and 18C to the CP. Identical to the CTRL_1 run except that 1.08C was added to SSTs in the EIO and the CP, respectively. Observed SST. Identical to the CTRL_2 run except that 28C was added to SSTs in the EP (808–1108W, 08–108N). Identical to the CTRL_2 run except that 18C was added to SSTs in the EIO and 28C to the EP.
CP_run EIO0.5&CP1.0_run EIO1.0&CP1.0_run CTRL_2_run EP_run EIO1.0&EP2.0_run
158S–56.58N, 708E–758W
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FIG. 7. The simulated differences in 850-hPa winds (m s21) between (a) the EIO_run, (b) the CP_run, (c) the EIO0.5&CP1.0_run, (d) the EP_run, and (e) the EIO1.0&EP2.0_run, and the CTRL run from the iRAM. Shading shows the wind speed in meters per second.
genesis over the WNP (Fig. 7c). In fact, the variance of the CP SSTA is larger than that of the EIO SSTA in the prior period, thus the impact of the EIO SSTA on WNP TC genesis in this period is largely offset by the impact of the CP SSTA. The positive EP SSTA produces a quite weak low-level atmospheric circulation response over the WNP (Fig. 7d). The low-level atmospheric circulation response to both the EIO and EP SSTAs is similar to that to the positive EIO SSTA alone (Fig. 7e), suggesting that the impact of the EP SSTA is insignificant, consistent with the results based on the reanalysis data discussed in section 3. The above results can be further quantified by counting TCs in the iRAM simulations (Table 2). The observation shows 22 TCs in the typhoon season of
2004 and our CTRL simulation produces 21 TCs, very close to the observed. It is clear that the positive EIO SSTA produces less TCs than that in the CTRL run (15 versus 21), while the positive CP SSTA leads to more TCs than that in the CTRL run (26 versus 21). When the positive CP SSTA is twice as large as the positive EIO SSTA, more TCs form in the WNP (35). Therefore, the TABLE 2. Total TCs over the WNP in observation and various model simulations in the typhoon season of 2004. EIO1.0& EIO0.5& Obs CTRL EIO_run CP_run CP1.0_run CP1.0_run Total TCs
22
21
15
26
17
35
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TABLE 3. Experimental design of the global model (ECHAM) simulations. The regions of the EIO, CP, pattern 1 (P1), and pattern 2 (P2) are also referred to in Fig. 4. Name
Period
SST
GCM_CTRL_1_run GCM_EIO_run
The prior period
Observed climatological monthly SST averaged in the prior period. Identical to the GCM_CTRL_1 run except that 18C was added to SSTs in the EIO (758–1008E, 108S–22.58N). Identical to the GCM_CTRL_1 run except that 18C was added to SSTs in the CP (1608E–1808, 108S–2.58N). Identical to the GCM_CTRL_1 run except that 18C was added to SSTs in the P1 region (608–1008E, 08–22.58N). Identical to the GCM_CTRL_1 run except that 18C was added to SSTs in the region P2 (508–1208E, 108S–22.58N). Observed climatological monthly SST averaged in the recent period. Identical to the GCM_CTRL_2 run except that 18C was added to SSTs in the P1 region (608–1008E, 08–22.58N). Identical to the GCM_CTRL_2 run except that 18C was added to SSTs in the P2 region (508–1208E, 108S–22.58N).
GCM_CP_run GCM_P1_1_run GCM_P2_1_run GCM_CTRL_2_run GCM_P1_2_run
The recent period
GCM_P2_2_run
EIO warming and CP warming have opposite impacts on TC genesis over the WNP. In the warm EP SSTA run, TC count is close to that in the CTRL run, further demonstrating that the EP SSTA has an insignificant impact on WNP TC genesis frequency, consistent with previous studies (Zhan et al. 2011b).
b. Global model results To confirm the iRAM results above, we also conducted similar numerical experiments using the general circulation model ECHAM as outlined in section 2b. To access the individual impacts of EIO and CP SSTAs from other effects, three experiments are performed with different SST conditions. As shown in Table 3, the control run (GCM_CTRL_1_run) was performed with the observed climatological monthly mean SST in the prior period. The other two experiments were denoted as GCM_EIO_run with 18C EIO SSTA added to the climatological SST and as GCM_CP_run with 18C CP SSTA added to the climatological SST, respectively. Figure 8 shows the simulated differences in lowlevel winds between the GCM_EIO_run and the GCM_CTRL_1_run, and the GCM_CP_run and the GCM_CTRL_1_run during the prior period in ECHAM. Consistent with the results from iRAM, the simulations by ECHAM also show that the positive EIO SSTA produces significant easterly anomalies over the equatorial western Pacific as a warm Kelvin wave response and an anomalous anticyclonic circulation over the TC main genesis region of the WNP, while the positive CP SSTA generates an anomalous cyclonic circulation over the WNP. Although the latter is somewhat south of that in the iRAM run, it still weakens the impact of the warm EIO on the atmospheric anomalies and thus suppresses TC genesis over the WNP. The
ECHAM results are thus generally consistent with those from the iRAM. Finally, we discuss the possible impact of the increased area coverage of the EIO SSTA, namely two SSTA patterns, on the large-scale circulation and thus TC genesis over the WNP. To avoid lateral boundary effects from the large SSTA pattern in the regional model, the experiments are carried out using the ECHAM with positive SSTAs of patterns 1 and 2, respectively. Two groups of experiments were carried out with different climatological SST fields based on the two different periods (Table 3). Each group consists of three experiments, including the control run with the observed climatological monthly mean SST in the prior period (GCM_CTRL_1_run) or in the recent period (GCM_ CTRL_2_run); the pattern-1 run with 18C SSTA over the region 608–1008E, 08–22.58N added to the GCM_ CTRL_1_run (GCM_P1_1_run) or GCM_CTRL_2_run (GCM_P1_2); and the pattern-2 run with 18C SSTA over the region 508–1208E, 108S–22.58N added to the GCM_ CTRL_1_run (GCM_P2_1_run) or GCM_CTRL_2_run (GCM_P2_2_run), respectively. The model results show that the atmospheric circulation response to pattern 2 becomes much stronger than that to pattern 1 (Fig. 9a), suggesting that the increased area coverage of the EIO SSTA also contributed to the intensified impact of the EIO SSTA on TC genesis frequency over the WNP in the recent period. The atmospheric circulation response over the WNP to the increased area coverage of the positive EIO SSTA based on the climatological SST in the prior period (Fig. 9b) is similar to the response to the positive EIO SSTA in the recent period. These results further confirm that the increased area coverage of the EIO SSTA in the recent period also contributed to the intensified
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FIG. 8. The simulated differences in 850-hPa winds (m s21) (a) between the GCM_EIO_run and the GCM_CTRL_1 run and (b) between the GCM_CP_run and the GCM_CTRL_1 run during the prior period from the ECHAM. Shading shows the wind speed in meters per second.
impact of the EIO SSTA on TC genesis frequency over the WNP.
5. Conclusions and discussion a. Conclusions This study documents the intensified impact of the EIO SSTA on TC genesis frequency over the WNP since the late 1970s. Results from both data analysis and numerical experiments using regional and global atmospheric models demonstrate that such an enhancement can be attributed to the increase in the area coverage of the EIO SSTA since the late 1970s and the suppressed impact of the EIO SSTA is due to the offset of an opposite large-scale circulation response over the WNP to the CP SSTA prior to the late 1970s. Consistent with previous studies, in general the warm SSTA over the EIO can excite a warm equatorial atmospheric Kelvin wave to the east over the WNP with
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FIG. 9. The simulated differences in 850-hPa winds (m s21) between pattern-2 and pattern-1 EIO SSTAs from the ECHAM (a) in the prior period and (b) in the recent period. Shading shows the wind speed in meters per second.
low-level convergence toward the equator and divergence and anticyclonic circulation anomaly off the equator in the main TC genesis region over the WNP. The warm SSTA over the EIO also leads to the weakened summer monsoon trough over the WNP. Both effects suppress TC genesis over the WNP and vice versa for the cold EIO SSTA. Prior to the late 1970s the EIO SSTA is positively correlated with the CP SSTA in the typhoon season (Fig. 10a). The latter produces an opposite atmospheric circulation response over the WNP to the EIO SSTA, namely, the impact of EIO SSTA on TC genesis frequency over the WNP was substantially suppressed by the effect of the CP SSTA. As a result, the relationship between the EIO SSTA and TC genesis frequency over the WNP shows insignificance prior to the later 1970s. After the late 1970s, the area coverage of SSTA over the IO is shown to play a dominant role in intensifying the impact of the EIO SSTA over TC genesis frequency over the WNP (Fig. 10b). This increased area coverage imposes a stronger response in the large-scale circulation
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FIG. 11. Time series of the summer EIO SST (8C) during 1951– 2012. The two horizontal gray lines show the mean SST averaged during 1951–77 and averaged during 1980–2012.
FIG. 10. Schematic diagrams showing (a) the suppressed impact of the EIO SSTA on the WNP TC genesis in the prior period and (b) the intensified impact in the recent period. See text for details.
anomaly to the SSTA over the EIO, giving stronger control of TC genesis frequency over the WNP by the EIO SSTA. In addition, in this period the EIO SSTA is positively correlated with the equatorial eastern Pacific SSTA. Although the equatorial eastern Pacific SSTA also strengthens the WNP atmospheric circulation response to the EIO SSTA and thus intensifies the impact of the EIO SSTA on TC genesis frequency over the WNP, its effect is shown to be marginal and insignificant.
b. Discussion Several issues need to be discussed further. First, although in this study the regional model simulations were only done for 2004 because of limited computational resources, the simulated results provide a good reference. The iRAM has shown its good capability of reproducing the interannual variability of TC frequency and large-scale circulation over the WNP (Zhan et al. 2011b; Wu et al. 2012). Moreover, the model can reproduce the observed relationship between the EIO SSTA and the WNP TC genesis frequency as well as the involved physical mechanisms reasonably well (Zhan et al. 2011a). To confirm the results from the single simulation for 2004 discussed in section 4, two additional simulations were also performed. One used the GCM as was discussed in section 4b and the other used the iRAM but for 2000 (not shown). The results from these additional simulations are generally consistent with those from the iRAM simulation for 2004.
Second, although our results have demonstrated that the increase in the area coverage of SSTA over the IO is a dominant factor that intensifies the control of TC genesis frequency over the WNP by the EIO SSTA in the recent period, we also noticed that since the 1950s the mean tropical IO SST has been increasing steadily at a rate of 0.18C decade21 (Alory et al. 2007; Ihara et al. 2008). After removing the longterm linear trend, the tropical IO SST displays a pronounced interdecadal change around the mid-1970s (Li et al. 2012). In the EIO subregion the mean SST is also significantly increasing since the 1950s and is about 0.58C warmer during the recent period than during the prior period (Fig. 11). With the increased mean SST and likely enhanced convective activity over the EIO, a stronger equatorial Kelvin wave can be excited over the equatorial western Pacific (Fig. 2). In this sense, the increase in the mean SST over the EIO during the recent period might also have contributed to the intensified impact of the EIO SSTA on TC genesis over the WNP. This possibility is yet to be examined in our future work. The results from this study have implications for seasonal prediction of TC activity over the WNP. On the one hand, the statistical relationships between various climate forcings and TC activity are subject to abrupt change. Since any statistical seasonal prediction scheme was constructed based on some statistical relationships identified using historical data in a given time period, the skill of the prediction scheme could be degraded or lost once some of the relationships are no longer statistically significant. On the other hand, it is also possible that some important factors might not be identified and included in a statistical prediction scheme by the time the scheme was developed. In both cases, large forecast errors would be expected in the prediction. The decreased prediction skills for TC genesis frequency over the WNP in recent years documented in Zhan et al. (2012) might be associated with changes in the statistical relationships of TC genesis frequency over the WNP with the selected
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FIG. 12. Lead–lag correlation of the monthly EIO SSTA with the monthly CP SSTA during 1958–77 and the monthly EP SSTA during 1980–2012. Positive lags correspond to EIO SSTA leading. The dashed horizontal lines indicate the positive correlations significant at 99% confidence level.
predictors previously built based on earlier historical data. Therefore, efforts toward understanding these changes or climate shift would help improve seasonal prediction for TC activity over the WNP. It is recommended that statistical relationships used in building a statistical prediction scheme should be frequently reevaluated and possible new factors may be introduced in order to maintain and improve the skill of the seasonal prediction for TC activity. A question relevant to the seasonal prediction of TC activity over the WNP is whether the variability of SSTA over the CP and the EP is independent of the EIO warming. To address this issue, we calculated the lead–lag correlation of the monthly EIO SSTA with the monthly CP SSTA during 1958–77 and with the monthly EP SSTA during 1980–2012, respectively. Here the monthly anomaly is defined as the difference between the monthly mean and the long-term climatological mean of the month. The results are given in Fig. 12, which clearly shows that the variability of SSTAs over the CP and the EP leads the variability over the EIO by 2 and 4 months, respectively. The aforementioned results are consistent with those of previous studies, which have suggested that the variability of SSTs over the central-eastern Pacific is important for the Indian Ocean SST variability [see a review by Schott et al. (2009)]. It is well accepted that when the CP/EP warms, atmospheric convection over
the EIO is suppressed, and the resultant increase in solar radiation contributes to the EIO warming. The EIO warming is then maintained by local air–sea interactions and ocean dynamics (Du et al. 2009) after the CP/EP SSTA has decayed. In addition, it has also been argued that the Indian Ocean SST variability can in turn influence the ENSO cycle through both the atmosphere and the ocean processes (Yu et al. 2002; Annamalai et al. 2005; Luo et al. 2012), which could be implied by the weak 1–3-month lagged correlation shown in Fig. 12. On one hand, the Indian Ocean warming causes strong Pacific trade winds via the atmospheric circulation response and hence contributes to the development of SSTA over the CP/EP. On the other hand, the Indonesian Throughflow associated with the Indian Ocean warming could modulate the warm pool in the western Pacific and thus affect the SST variability over the CP/EP. The above analyses suggest that the SSTAs over both the CP and the EP could interact with the EIO warming rather than act as independent predictors. Furthermore, we also used the stepwise regression method to check their relationship and roles in a statistical seasonal prediction model of TC genesis frequency over the WNP. The results show that the CP and EP SSTAs are not good predictors in comparison with the EIO SSTA in predicting seasonal TC genesis frequency over the WNP even various lead times are considered (not shown). This suggests that even though the SSTA over the EIO is mainly a response to the SSTA variability over the EP/CP it can maintain itself through the local air–sea interaction and impose its own impact on the large-scale circulation and thus TC genesis over the WNP. As a result, the EIO SSTA would be a better predictor than either the EP or CP SSTA. Acknowledgments. The authors are grateful to Prof. Shang-Ping Xie for his helpful comments on an earlier version of the manuscript. This study has been supported by the National Basic Research Program ‘‘973’’ (Grant 2012CB956003), the China NSFC Grants 41375093 and 41375098, and GYHY201006008. Yuqing Wang acknowledges the financial support by USGS Grant G12AC20501 to the University of Hawai‘i. REFERENCES Alory, G., S. Wijffels, and G. Meyers, 2007: Observed temperature trends in the Indian Ocean over 1960–1999 and associated mechanisms. Geophys. Res. Lett., 34, L02606, doi:10.1029/ 2006GL028044. Annamalai, H., S. P. Xie, J. P. McCreary, and R. Murtugudde, 2005: Impact of Indian Ocean sea surface temperature on developing El Niño. J. Climate, 18, 302–319, doi:10.1175/JCLI-3268.1.
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