1. Introduction
As the most energetic short-term climate variability on Earth, El Niño–Southern Oscillation (ENSO) significantly influences weather and climate across the globe (Philander 1990). Classic ENSO theory attributes ENSO formation to low-frequency atmosphere–ocean coupling within the tropical Pacific (Suarez and Schopf 1988; Jin 1997), whereas multiplicative atmospheric processes are also proposed to play a role (Penland and Sardeshmukh 1995; Moore and Kleeman 1999; Chen et al. 2015; Lian and Chen 2021). The tropical cyclones (TCs) in the western North Pacific (WNP) are potential candidates. The breakthrough work conducted by Camargo and Sobel (2005) and Sobel and Camargo (2005) pointed out that WNP TCs increase the sea surface temperature (SST) in the eastern equatorial Pacific by inducing strong anomalous equatorial surface westerlies, and the intensity of WNP TCs significantly leads ENSO by ∼6 months. Lian et al. (2019) further indicated that near-equatorial WNP TCs (NE-WNP TCs) are more likely to trigger strong equatorial westerlies and confirmed their impacts on ENSO dynamics in a numerical model for the first time.
The genesis of NE-WNP TCs thus deserves further consideration. Many authors have discussed the formation of WNP TCs in the TC peak season, namely, from June to November (Chan et al. 1998, 2001; Ying and Wan 2011; Li et al. 2013; Kim et al. 2013). For example, Wang and Chan (2002) indicated that the interannual activity of WNP TCs is closely related to the large-scale circulation anomalies associated with ENSO. P. Huang et al. (2011) and Li and Zhou (2013) showed that the Madden–Julian oscillation (MJO) significantly contributes to the genesis of WNP TCs through the influence on midlevel relative humidity. However, less attention has been given to the cause of springtime WNP TCs. Ramsay (2017) noted WNP TCs forming fairly often even during off-season months. Although WNP TCs in spring are less frequent than those in the peak season, they are located closer to the equator (Wang and Chan 2002; Lian et al. 2018; Song et al. 2021) and thus matter more for El Niño genesis (Lengaigne et al. 2002).
It has been well documented that the interannual activity of WNP TCs is primarily modulated by ENSO (Kim et al. 2011; Li and Zhou 2012; Chen et al. 2017; Zhao and Wang 2019). Regarding the impact on WNP TCs in the peak season, Zhan et al. (2011) indicated that in the mature phase of El Niño, more TCs occur over the southeast quadrant of the WNP. As the location of TC genesis is in the far warm ocean, TCs in El Niño years exhibit longer durations and stronger intensities than those in La Niña and neutral years (Wang et al. 2013). Zhao and Wang (2019) noted that El Niño with a maximum SST anomaly in the equatorial central Pacific, known as the central Pacific El Niño type (Fu et al. 1986), is more likely to weaken the vertical shear of tropical zonal wind over the WNP. Because such an El Niño type has occurred more frequently in the last two decades, the relationship between ENSO and WNP TCs has been closer since 1998. Regarding the impact of ENSO on springtime WNP TCs, Wang and Chan (2002) showed that the frequency of springtime WNP TCs after the El Niño peak phase was less than normal, mainly because the anticyclone circulation in the WNP induced by El Niño suppresses TC genesis. Note that these works focus on the influence of ENSO on WNP TCs. The impact on the springtime NE-WNP TCs has not been explored explicitly. In the El Niño onset phase, as the El Niño intensity is weak at that time, the impact of ENSO on WNP TCs and further on NE-WNP TCs is expected to be weak.
Some studies have indicated that the Pacific meridional mode (PMM) is another source that modulates WNP TC activity at the interannual time scale, especially in ENSO-neutral years (Huang et al. 2009; Y. Huang et al. 2011; Zhang et al. 2016; Wu et al. 2021). The positive PMM is characterized by anomalous surface southwesterlies and warm SSTs in the subtropical northeastern Pacific in boreal winter (Chiang and Vimont 2004), and the warm SST anomaly in general gradually extends to the central equatorial Pacific in the following summer (Vimont et al. 2009; Alexander et al. 2010). Once the warm signal reaches the equatorial region, the positive Bjerknes feedback kicks in (Anderson et al. 2013), and an El Niño event is expected to occur in winter (Chang et al. 2007). Zhang et al. (2016) argued that the warm SST anomaly associated with the positive PMM weakens the vertical shear of tropical zonal wind, thus significantly increasing the frequency and intensity of WNP TCs in summer.
Unlike ENSO, the intensity of PMM in the preceding winter and spring is comparable to that in summer (Vimont et al. 2009). Thus, it is possible that the PMM could influence springtime NE-WNP TCs. As will be shown later, we indeed found such a linkage. A statistical model with respect to the impact of PMM on springtime NE-WNP TCs and further on ENSO intensity was also constructed. The rest of this paper is organized as follows. The data, model, and method are described in section 2. In section 3, we investigate the genesis of springtime WNP TCs that are closely related to ENSO. In section 4, we validate the proposed mechanism using AGCM and CGCM experiments. The statistical model is introduced in section 5, followed by conclusions and a discussion in section 6.
2. Data, method, and model
a. Data and method
The TC dataset consists of the TC best track data from the International Best Track Archive for Climate Stewardship (IBTrACS) version 4 (Knapp et al. 2010), including TC center and intensity at a 3-h interval. Because we focused on the impact of TCs on ENSO by changing the momentum flux at the sea surface, we used the accumulated cyclonic energy (ACE) index, a metric that considers the duration and intensity of TCs, to measure TC intensity (Bell et al. 2000). The ACE is estimated as the sum of the square of the maximum sustained wind speed at 10 m. Monthly SST was provided by the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed SST v5 (Huang et al. 2017), with a resolution of 2.0° longitude × 2.0° latitude. Monthly wind data, surface relative humidity, and sea level pressure were obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research Reanalysis 1 (NCEP–NCAR R1; Kalnay et al. 1996), with a resolution of 2.5° longitude × 2.5° latitude. Additionally, daily versions of zonal wind at 200, 850, and 1000 hPa were obtained from NCEP–NCAR R1. The vertical shear of zonal wind is estimated as the wind difference between 850 and 200 hPa (Zhan and Wang 2016). The period of all datasets is 1979–2020. We denote the current year as year 0 and the year before the current year as year −1. For example, DJF(0) represents the period from December in the preceding year to February in the current year, and DJF(1) represents the period from December in the current year to February in the next year.
The PMM is identified as the first singular vector of SST and 10 m wind in the tropical eastern Pacific (Chiang and Vimont 2004), and the PMM index can be found at https://psl.noaa.gov/data/timeseries/monthly/PMM/. Following Zhang et al. (2016), a positive PMM year is one with a PMM index exceeding 0.5 from March to May (MAM), and a negative PMM year is one with a PMM index smaller than −0.5. The Niño-3.4 index, which is used to scale ENSO intensity, consists of SST anomalies averaged over 5°S–5°N, 170°–120°W and was obtained from https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/. In this study, the WNP refers to the area of 0°–30°N, 105°E–180°, and the NE-WNP refers to the area of 5°–15°N, 135°–170°E. The equatorial western Pacific refers to the area at the equatorial flank of the NE-WNP, that is, the area of 5°S–5°N, 135°–170°E.
b. Model and model experiment
In this study, we used an AGCM to verify that the PMM benefits the genesis of NE-WNP TCs and a CGCM to show that PMM-induced NE-WNP TCs are favorable for El Niño development.
The AGCM used here is the Community Atmospheric Model version 5 (CAM5) developed by NCAR (Vertenstein et al. 2012). It has 30 vertical levels from the surface (approximately 992.5 hPa) to the top of the stratosphere (approximately 3.5 hPa) and a horizontal resolution of 0.47° longitude × 0.63° latitude. It has been validated that CAM5 with such a resolution simulates the statistics of WNP TCs to a reasonable extent, including the annual frequency, intensity, and spatial distributions (Li and Sriver 2016). We conducted two ensemble AGCM experiments to understand the impact of PMM on springtime NE-WNP TCs. The first was the AGCM control (Ctrl-AGCM) run in which the SST boundary was set as the observed climatology, and the second was the forced run in which SST within the PMM region (10°S–10°N, 150°E–60°W and 10°–30°N, 160°–110°W, respectively) was specified as the mean SST in the positive PMM years, termed the PMM run. Each experiment was integrated for 6 months from January to June. The PMM run contained 15 ensemble numbers with different initial atmospheric conditions every 2 days in January.
The CGCM used here is the Community Earth System Model (CESM1.2.0; Vertenstein et al. 2012). The atmospheric module is CAM5, whereas the oceanic module is POP2 (Smith et al. 1992), with resolutions of 0.47° longitude × 0.63° latitude and approximately 1.0° longitude × 0.5° latitude, respectively. Four experiments were conducted. The first was the CGCM control (Ctrl-CGCM) run. The second run was a forced run in which a cyclonic wind stress structure representing the NE-WNP TCs was added to the tropical northwestern Pacific from March to May during coupling (TC-Full run). The TC-Full run contained 15 different members. For each ensemble member, the timing and length of the modeled NE-WNP TCs were purely random, but the mean of the intensity, frequency, duration, and location are set to be the ones in observation. The difference between the TC-Full and control runs thus manifested the impact of NE-WNP TCs on ENSO. The third run was PMM-TC run in which the PMM-like forcing is added in model in DJF(0) via changing the heat flux into the ocean. The fourth run was the same as the PMM-TC run but removed the TC in MAM through the spatial smoothing approach (PMM-TC-D run). In details, we use the regional average of wind stress within a 20° × 20° box centered at each grid point in the WNP to force the ocean in MAM in the coupled model experiment. By doing so, the TC-related wind stress over the northwestern tropical Pacific can be largely removed. The PMM-TC run and PMM-TC-D run contained 15 different members.
The NE-WNP TCs were added to the CGCM as follows. First, we selected all TCs in the NE-WNP listed in the IBTrACS dataset. Then, we calculated the composite of the wind stress on these TC days using the NCEP–NCAR R1. Here, the wind stress was estimated using the wind stress bulk formula and wind at 1000 hPa. The drag coefficient used in the bulk formula was 2.20 × 10−3. The center of the modeled TCs was set to 10°N, 152.5°E, the mean location of NE-WNP TCs in the observations. Following Li et al. (2022), the composite wind stresses within the 20° × 20° area around the TC center were regarded as the TC-induced wind stresses and were used as the modeled TCs in the TC-Full run. We also conducted two additional ensemble runs in which wind stresses within the 30° × 30° and 40° × 40° areas around the TC center were regarded as the TC-induced wind stresses. Notably, reanalysis products generally underestimate surface wind speeds associated with TCs, and the drag coefficient under extreme wind conditions is expected to be much stronger than 2.20 × 10−3 (e.g., Hsu et al. 2017). As a result, the intensity of TC-related wind stresses may have been underestimated in the TC-Full run.
Following Lian et al. (2018), TCs in model experiments were identified using daily surface pressure data. In this technique, the selected TCs should satisfy two conditions. First, the starting latitude is within 20° of latitude from the equator, thus excluding extratropical cyclones. Second, the magnitude of the negative daily surface pressure anomaly is larger than 3 times the standard deviation. Lian et al. (2018) compared the TCs identified using their method and those from the IBTrACS dataset and found that TCs identified using their method generally matched those from the IBTRACS data, and the size of the identified TCs was comparable with that of the observed TCs.
3. Impact of PMM on springtime NE-WNP TCs
We first show the spatial distribution of the climatology springtime (MAM) ACE in the WNP. The strongest ACE was within the northeastern Philippines (Fig. 1a). Figure 1b shows the regression of the ACE in MAM on the Niño-3.4 index in DJF(1). Although the overall regression pattern resembled the ACE climatology, only ACE in the NE-WNP passed the significance test, suggesting that TCs in the NE-WNP are more likely to be associated with ENSO onset. The result echoes that presented by Lian et al. (2018), who showed that TCs close to the equator are more likely to induce strong westerly anomalies. Shown in Fig. 1c are the percentages of ACE in MAM in the NE-WNP to the entire WNP. The percentages were in general larger in El Niño years than in the neutral and La Niña years.
(a) The pattern of climatological ACE (unit: m2 s−2) in MAM and (b) the regression of ACE in MAM on the Niño-3.4 index in DJF(1). (c) The percentage of ACE in NE-WNP to that in WNP in MAM. The red box in (a) denotes the WNP. The green box in (b) denotes the NE-WNP. Black dots in (b) denote a regression significant at the 95% confidence level. The red line in (c) denotes climatology.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
The closer relationship between the springtime NE-WNP TCs and ENSO onset is presumably because these TCs generally co-occur with strong equatorial westerlies. To this end, Fig. 2a shows the regression of 850-hPa wind on the NE-WNP ACE in MAM. Significant westerly anomalies exceeding 2.0 m s−1 are seen in the equatorial western Pacific. The result closely matches that presented by Sobel and Camargo (2005), who indicated that strong equatorial surface westerlies are at the equatorward flank of the NE-WNP TCs. Lian et al. (2019) further indicated that more than half of NE-WNP TCs have the potential to increase equatorial westerly anomalies at the equator, suggesting that NE-WNP TCs lead to rather than co-occur with strong westerly anomalies over the western–central equatorial Pacific. Figure 2b shows the regression of the SST anomaly from June to August (JJA) on the springtime NE-WNP ACE, which clearly demonstrates that the NE-WNP TCs may induce significant SST warming in the cold tongue and cooling in the warm pool. Note that the NE-WNP ACE only accounts for ∼28% of the total ACE in the entire WNP in MAM (Fig. 1c) but seems to be a good predictor of ENSO intensity in the following summer.
Regression of (a) anomalous 850-hPa wind in MAM (unit: m s−1) and (b) SST in JJA (unit: °C) on the NE-WNP ACE in MAM. (c) Composite of NE-WNP TC-related wind stresses (unit: N m−2) based on the TCs in the NE-WNP listed in the IBTrACS dataset in period 1979–2020. The center of the composite is set to 10°N, 152.5°E. Shading in (a) and black dots in (b) denote regressions significant at the 95% confidence level. The green box in (a) denotes the NE-WNP. The green boxes in (c) denote three areas—20° × 20°, 30° × 30°, and 40° × 40°—around the composite TC center.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
The composite of wind stress associated with the NE-WNP TCs is shown in Fig. 2c. The average wind stress at the equatorial flank of the NE-WNP TCs was ∼0.12 N m−2. The monthly average day lengths of the NE-WNP TCs in March, April, and May were 1.9, 3.1, and 2.6 days, respectively. In the El Niño developing year, the average day lengths increased to 6.1, 4.4, and 7.4 days, respectively. Note that these numbers were obtained from the record of IBTrACS, which contains TCs ranging from tropical depressions to super typhoons. If we only use TCs ranging from typhoons to super typhoons, the number and life span of the NE-WNP TCs will definitely decrease. However, the magnitude of the composite wind stress would be severely enhanced accordingly.
Since the summer SST regressed with the MAM NE-WNP TCs exhibited an El Niño–like pattern, it was necessary to determine the cause of the NE-WNP TCs in spring. The interannual activity of WNP TCs is widely accepted to be determined by ENSO of the first order (Wang and Chan 2002). However, as shown in Fig. 3a, springtime NE-WNP TCs were almost independent of ENSO intensity. The simultaneous correlation coefficient was only 0.13. To decipher this poor correlation, Figs. 3b and 3c present the composite of ACE and 850-hPa wind anomalies in MAM in the decay years of El Niño and La Niña, respectively. The cause of the insensitivity of NE-WNP TCs to ENSO is threefold. First, as seen in Fig. 3b, there was a strong anticyclone over the WNP after the El Niño peak (Wang et al. 2000; Wang and Chan 2002). The anticyclone prevents NE-WNP TC genesis; thus, the number of WNP TCs and NE-WNP TCs is small. Second, although the number of WNP TCs was above average in spring after the La Niña peak (Wang and Chan 2002), the large-scale cyclone induced by La Niña was confined to the northwestern quadrant of the WNP. The NE-WNP was dominated by a weak anticyclone (Fig. 3c). As a result, the number of NE-WNP TCs was still small (Song et al. 2021). Last, in spring, but not after ENSO years as ENSO intensity is weak, the simultaneous correlation must be low.
(a) Evolution of the normalized NE-WNP ACE and Niño-3.4 index in MAM. (b),(c) Composite of ACE (unit: m2 s−2) and 850-hPa wind (unit: m s−1) in MAM in the decay years of El Niño (1983, 1987, 1992, 1995, 1998, 2003, 2010, and 2016) and La Niña (1985, 1989, 1996, 1999, 2000, 2008, 2011, 2012, and 2018), respectively. The red and green boxes in (b) and (c) denote the WNP and NE-WNP, respectively.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
The PMM was significantly associated with NE-WNP TCs in spring. As shown in Figs. 4a and 4b, the regression patterns of anomalous SST in the DJF(0) and MAM seasons on the NE-WNP ACE in MAM strongly resembled the PMM structure, as manifested by the anomalous warm SST strip from the subtropical northeastern Pacific to the equatorial central Pacific (Chiang and Vimont 2004). Note that most of the regression coefficients in this strip were significant, and the regions with significant coefficients gradually moved to the equatorial central Pacific as the seasonal footprint (Chiang and Vimont 2004).
The regression of the anomalous SST in (a) DJF(0) and (b) MAM on the NE-WNP ACE in MAM (unit: °C). Black dots denote regressions significant at the 95% confidence level. The green box denotes the NE-WNP.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
Figure 5a presents the regression of the ACE of the WNP TCs in MAM on the PMM index at the same time. The springtime WNP TCs that were significantly correlated with the PMM fall right in the NE-WNP. Figure 5b further shows the monthly correlation coefficients between the PMM index from December(−1) to May and the NE-WNP ACE in MAM. The correlation coefficients were generally higher than 0.6. The maximum correlations occurred in February and March, indicating that PMM preconditions the NE-WNP TCs rather than co-occur with them.
(a) The regression of ACE (unit: m2 s−2) on the PMM index in MAM. (b) Monthly correlation between the PMM index from December(−1) to May and the NE-WNP ACE in MAM. The green box in (a) denotes the NE-WNP.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
We therefore analyzed the large-scale environments in spring that were associated with the PMM. The warm SST anomaly associated with the positive PMM induced anomalous southwesterlies in the off-equatorial northern Pacific (Chiang and Vimont 2004). As a result, vertical wind shear decreased in the NE-WNP (Fig. 6a), which was favorable for TC genesis and intensification in that area (Chia and Ropelewski 2002). The westerly anomaly also led to an increase in relative humidity in the NE-WNP by weakening the climatological trade wind (Fig. 6c) and favored deep convection over the off-equatorial central Pacific (Figs. 6b,d). According to the preconditions of TCs proposed by Gray (1979), all of these large-scale environmental changes in spring of positive PMM years favor NE-WNP TC genesis.
The regression of (a) wind shear (unit: m s−1), (b) 500-hPa vertical velocity (unit: 10−3 Pa s−1), (c) 600-hPa relative humidity (unit: %), and (d) sea level pressure (unit: hPa) anomalies in MAM on the PMM index in MAM. Black dots denote regressions significant at the 95% confidence level. The green box denotes the NE-WNP.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
Chang et al. (2007) showed that a positive PMM leads to large-scale low-frequency surface westerly anomalies over the equatorial western Pacific. To clarify whether the positive PMM also induces small-scale high-frequency equatorial westerly anomalies by triggering more NE-WNP TCs, Fig. 7 exhibits the difference in 850-hPa zonal wind between TC days and non-TC days in positive PMM years, which exclude the decay years of El Niño and La Niña. The zonal winds over the equatorial western Pacific were much stronger on TC days than on non-TC days.
The difference in the 850-hPa zonal wind (unit: m s−1) between the composites on TC days and non-TC days in positive PMM years (1980, 1981, 1982, 1986, 1988, 1990, 1991, 1994, 1997, 2004, 2014, 2015, and 2019). Black dots denote differences significant from zero at the 95% confidence level based on a one-sample Student’s t test. Solid green and dashed black boxes denote the NE-WNP and the equatorial western Pacific, respectively.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
4. Model experiments
The observation analysis clearly indicates that the PMM tends to induce NE-WNP TCs in spring by providing favorable TC preconditions. To confirm these dynamics, we compared model simulations from the control and forced runs using the AGCM. Figure 8 presents the changes in the large-scale environment from the Ctrl-AGCM run to the PMM run in MAM. Compared with the observation results (Fig. 6), the CAM5 model exhibited excellent performance in reproducing the response of the wind shear, 500-hPa vertical velocity, 600-hPa relative humidity, and surface pressure to the positive PMM forcing.
Changes in the ensemble-mean (a) wind shear (unit: m s−1), (b) 500-hPa vertical velocity (unit: 10–3 Pa s−1), (c) 600-hPa relative humidity (unit: %), and (d) sea level pressure (unit: hPa) in MAM from the Ctrl-AGCM run to the PMM run. The green boxes denote the NE-WNP.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
Figure 9a shows a comparison of the length of day when TCs were detected in the NE-WNP in MAM in the Ctrl-AGCM and PMM runs. The average day length of NE-WNP TCs in MAM in the Ctrl-AGCM run was approximately 2.7 days but sharply increased to approximately 26.1 days in the PMM run. Figure 9b shows a comparison of the difference in the regionally averaged 850-hPa zonal wind in the equatorial western Pacific between the TC days and non-TC days in the two experiments. In the PMM run, the average difference was approximately 2.8 m s−1 and was significantly greater than the difference in the Ctrl-AGCM run.
(a) The day length of the NE-WNP TCs in MAM and (b) the difference in the regionally averaged 850-hPa zonal wind in the equatorial western Pacific between TC days and non-TC days (unit: m s−1) in the Ctrl-AGCM and PMM runs. (c),(d) The difference in 850-hPa zonal wind (unit: m s−1) between the TC days and non-TC days in the Ctrl-AGCM and PMM runs, respectively. The error bars in (a) and (b) denote one standard deviation. The solid green and dashed black boxes in (c) and (d) denote the NE-WNP and the equatorial western Pacific, respectively.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
It should be noted that the increase in the equatorial westerly anomaly on TC days within the equatorial western Pacific was not due to the increase in NE-WNP TC intensity in the PMM run. Rather, it was mainly caused by the eastward extension of NE-WNP TCs. To this end, Figs. 9c and 9d present the difference in 850-hPa zonal wind between the TC days and non-TC days in the Ctrl-AGCM and PMM runs. The eastward extension of TC-related westerly anomalies was clearly evidenced.
To estimate the impact of MAM NE-WNP TCs on ENSO, we compared the results from the Ctrl-CGCM and TC-Full runs using the CESM coupled model. When using the composite of NE-WNP TC wind stresses within a 20° × 20° area around the TC center as modeled TCs, an El Niño–like response was observed in SST (Fig. 10a). The Niño-3.4 index increased by ∼0.31°C in DJF(1) in this case (Fig. 10b). When the added wind stress area extended to 30° × 30° and 40° × 40°, the Niño-3.4 index in DJF(1) further increased by ∼0.47°C and ∼0.44°C, respectively (Figs. 10c,d). The intensity of the TC-induced warming in the Niño-3.4 area is comparable with that induced by the summer deep convection mechanism (Amaya et al. 2019). The results clearly confirm that the NE-WNP TCs significantly contribute to the intensity of El Niño. Notably, in the 20° × 20° case, the TC-related wind stresses did not cross the equator, yet they can still lead to an El Niño–like response in the following winter.
(a) Change in SST (unit: °C) from the Ctrl-CGCM run to the TC-Full run in DJF(1) using the composite wind stresses within the 20° × 20° box around the TC center. (b) Evolution of the change in the Niño-3.4 index (unit: °C) from the Ctrl-CGCM run to the TC-Full run using wind stresses within the 20° × 20° box. (c),(d) Change in the Niño-3.4 index using wind stresses within the 30° × 30° and 40° × 40° boxes, respectively. The black box in (a) denotes the Niño-3.4 region. The red lines in (b)–(d) denote the ensemble mean.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
The change of the Niño-3.4 index and SST anomalies from the Ctrl-CGCM run to the PMM-TC run and the PMM-TC-D run are given in Fig. 11. Adding the PMM-like forcing sharply increases the Niño-3.4 index by about 0.69°C in DJF(1), as shown in Fig. 11a. However, when the impact of the TCs is removed in the PMM-TC-D run, the Niño-3.4 index only increases by about 0.30°C (Fig. 11b), suggesting that the PMM-induced NE-WNP TCs contribute to El Niño growth by about 0.39°C. The results shown here strongly suggest that the positive PMM is benefit for the generation of NE-WNP TCs, and these NE-WNP TC contribute considerably to El Niño growth.
Evolution of the difference in Niño-3.4 index simulated in the (a) PMM-TC run and (b) PMM-TC-D run relative to the Ctrl-CGCM run. (c) SST difference between the ensemble mean of PMM-TC run and the control run in DJF(1). (d) SST difference between the ensemble mean of PMM-TC-D run and the control run in DJF(1). The red line and shading in (a) and (b) denote the ensemble average and the one standard deviation, respectively. The black box in (c) and (d) denotes the Niño-3.4 region.
Citation: Journal of Climate 36, 21; 10.1175/JCLI-D-22-0957.1
5. A statistical PMM–TC–ENSO forecast model
The results from the observation and model experiment indicated that the positive PMM significantly increased the NE-WNP TCs, and the associated equatorial westerly anomaly tended to move eastward. Previous studies have shown that the equatorial westerly anomaly near the date line is more likely to contribute to SST warming in the cold tongue and thus is more effective in triggering and maintaining El Niño (Harrison and Vecchi 1997; Hayashi and Watanabe 2019). A recent study from Xuan et al. (2022) showed that the burst-like equatorial westerly anomaly in spring can accurately forecast ENSO intensity in the mature phase in a statistical model. These findings, along with the impact of PMM on NE-NWP TCs unveiled here, prompted us to establish a statistical model to explore the possible role of PMM in ENSO forecasting with the effect of WNP TCs taken into account.
Of particular interest is the validity of this statistical model. The correlation coefficients between the predicted values on the left-hand side of Eqs. (1)–(4) and the indexes in the observation were 0.69, 0.66, 0.82, and 0.83, respectively, all passing the 95% confidence level. The large correlation coefficients strongly indicate that the PMM indeed modulated the springtime NE-WNP TCs and further the ENSO intensity in DJF(1). The explained variances of the predicted ACEMAM, Niño-3.4JJA, ACEJJASON, and Niño-3.4DJF(1) were 47.8%, 44.2%, 67.2%, and 68.2%, respectively. Note that although the explained variance in each forecast step was not small, the total explained variance of the Niño-3.4DJF(1) index predicted by PMMDFJ(0), as estimated by the product of explained variances in the four forecast steps, was only ∼9.8%.
For the statistical model built here, the change of Niño-3.4 in JJA and DJF(1) was attributed only to the TCs in MAM and JJASON seasons, respectively [Eqs. (2) and (4)]. The assumption apparently excludes the change of Niño-3.4 due to the recharged ocean heat content in earlier seasons, and to the Bjerknes feedback associated with the equatorial westerly anomalies that are not associated with TCs. The oversimplified assumption used here leads to the difference between the numerical and statistical models. However, the statistical model can clearly show how the PMM influences ENSO step by step though the newly proposed mechanism.
6. Conclusions and discussion
Previous researchers have noted that tropical cyclones in the near-equatorial western North Pacific (NE-WNP TCs) in boreal spring are associated with the onset of El Niño–Southern Oscillation (ENSO). In this study, we investigated the cause of NE-WNP TCs from March to May (MAM). Observational analysis showed that although the number of NE-WNP TCs only accounted for a small portion of the total TCs in the WNP, they were significantly correlated with ENSO intensity in summer, presumably because they are more likely to trigger strong equatorial westerly anomalies that are important for El Niño genesis and development. The interannual activity of springtime NE-WNP TCs is insensitive to ENSO strength, mostly because the large-scale environments in spring after both El Niño and La Niña are unfavorable for NE-WNP TC genesis. In spring after a neutral year, as ENSO intensity is weak, the impact from ENSO on NE-WNP TCs is unimportant. On the other hand, we found that the genesis of NE-WNP TCs is closely related to the Pacific meridional mode (PMM). The lead–lag correlation between PMM and NE-WNP TCs is large from January to April, and the evolution of the SST anomaly associated with the NE-WNP TCs strongly manifests the seasonal footprint of the PMM. The positive PMM favors NE-WNP TC genesis by establishing favorable large-scale environments for TCs, including a reduced vertical shear of zonal wind and sea level pressure and an enhanced vertical velocity in the middle atmosphere and surface humidity in the WNP. Numerical experiments using the CAM5 model confirm this impact. In addition, by conducting CESM coupled experiments, we showed that adding PMM-induced NE-WNP TCs leads to an El Niño–like response in winter, with the Niño-3.4 index increasing by ∼0.31°C in the DJF(1) season. This increase in ENSO intensity is comparable to that due to the summer deep convection mechanism (Amaya et al. 2019). We also constructed a statistical regression model with respect to the impact of PMM on NE-WNP TCs and further on ENSO intensity. The results imply that modulating the NE-WNP TCs accounts for ∼38.7% of the total explained variance in the ENSO intensity associated with the PMM.
The PMM has been widely accepted as a potential trigger of ENSO. The primary dynamics are believed to be associated with the positive feedback among the anomalous large-scale surface wind, evaporation, and SST, known as the WES feedback (Xie and Philander 1994), from the subtropical northeastern Pacific to the tropical central Pacific from winter to summer (Liu and Xie 1994; Alexander et al. 2010; Vimont et al. 2009). Once the SST anomalies reach the central equatorial Pacific in summer, the positive Bjerknes feedback comes into play, and an El Niño is expected in winter. Along with the WES feedback, two other dynamics were also proposed to explain the impact of PMM on ENSO. The trade wind charging mechanism proposed by Anderson et al. (2013) and Anderson and Perez (2015) suggests that the off-equatorial wind stress curl anomalies associated with the PMM in winter and spring can charge the equatorial upper ocean heat content through meridional mass transport, making the PMM lead ENSO by almost 1 year. The summer deep convection proposed by Amaya et al. (2019) emphasized the role of the intertropical convergent zone (ITCZ) in late summer. Within this framework, the warm SST anomaly in the subtropical northeastern Pacific in summer associated with the positive PMM induces an anomalous meridional shift of the ITCZ. Convective heating anomalies in the midtroposphere then drive a Gill-like atmospheric circulation (Gill 1980), which further induces oceanic downwelling Kelvin waves to increase El Niño intensity in the fall.
Unlike the abovementioned mechanisms that link the PMM to ENSO through low-frequency large-scale circulation, we showed that the PMM also influences ENSO by triggering high-frequency small-scale NE-WNP TCs. This new mechanism, which is named the PMM-induced near-equatorial TC (PINET), not only supplements the existing tropical–subtropical processes associated with the PMM and ENSO but also implies a multiscale interaction among the PMM, TCs, and ENSO. Note that the current study focuses only on the modulation of PMM on springtime NE-WNP TCs. As indicated by previous work (Huang et al. 2009; Y. Huang et al. 2011; Zhang et al. 2016; Wu et al. 2021), PMM also impacts WNP TCs in the peak season. It is thus possible to build a framework that includes the total impacts of PMM on WNP-TCs and further on ENSO. It is also necessary to estimate the relative contributions of PINET, WES, trade wind charging, and summer deep convection to ENSO growth. We leave this topic for future work.
The main pathway for TCs to influence ENSO is through the change in the intensity of the anomalous equatorial westerly wind bursts in the western Pacific (Sobel and Camargo 2005; Menkes et al. 2014). Burst-like westerly anomalies are associated with TCs (Keen 1982; Hartten 1996; Lian et al. 2018), the East Asian cold surge (Yu et al. 2003; Feng et al. 2022), and the MJO (Puy et al. 2015; Feng and Lian 2018). Among them, WNP TCs account for the majority of westerly wind bursts. We showed here that in the northern Pacific, the region where TCs are mostly associated with ENSO is within the NE-WNP, and the NE-WNP TCs are primarily caused by the PMM. Nevertheless, TCs in the southern Pacific are also linked to a large proportion of westerly wind bursts, especially in boreal spring and winter (Lian et al. 2018). It is natural to ask which region in the southern Pacific is most related to ENSO development and whether there are any preconditions for these TCs.
Acknowledgments.
This study was supported by the National Natural Science Foundation of China (42130610, 42022043, 41975077, 42205027, and 41905053).
Data availability statement.
All data analyzed here are openly available. The IBTrACS V4 dataset is available at https://www.ncdc.noaa.gov/ibtracs/. The NCEP reanalysis datasets were obtained from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html, and the ERSST5 was obtained from https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html.
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