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  • View in gallery

    SST analyzed by (a) NCEP FNL and (b) SST anomaly present on 1 Mar 2015 from climatology derived from ERA-Interim (1980–2009). The black cross shows the location of Pam’s genesis and the black square shows the location of Pam’s minimum pressure. The closed circle shows the location of Bavi’s genesis. Tracks of Pam and Bavi are also shown with black lines. The box bounded by dotted black lines highlights the region where an SST anomaly remained in the CPSSTA experiments.

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    Monthly SSTAs in relation to the 30-yr (1981–2010) mean derived from HadISST for (a) September 2014, (b) October 2014, (c) November 2014, (d) December 2014, (e) January 2015, (f) February 2015, (g) March 2015, (h) April 2015, (i) May 2015, and (j) June 2015. Monthly Niño-3 (top number) and Niño-4 (bottom number) indices obtained from http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices based on OISST V2 (Reynolds et al. 2002) are also shown in each panel.

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    (a) RMM phase diagram for February 2015 (red line) and March 2015 (blue line), and (b) Hovmöller diagram of U850 anomalies from the climatological seasonal cycle (shading) averaged in 15°N–15°S and reconstructed from the RMM (contours). The green and pink curves show the trajectories of Pam and Bavi, respectively. The green cross shows Pam’s genesis longitude and time.

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    RMM phase diagram of analyzed (black lines) and simulated in OBSSST (red lines) and in CLMSST (blue lines) ensemble means of experiments initialized (a) 21–25 Feb, (b) 26 Feb–2 Mar, and (c) 3–8 Mar. The RMMs simulated in CPSSTA (green line) ensemble means of experiments initialized 26 Feb–2 Mar are also shown in (b). The triangle shows values recorded for (a) 25 Feb, (b) 2 Mar, and (c) 8 Mar, respectively. Crosses in (a)–(c) are shown every 5 days. Closed circles in (a)–(c) on each curve show the RMMs on 9 Mar.

  • View in gallery

    (a) Anomalies for a 96-day low-pass-filtered U850 (color) and OLR (contours at intervals of 10 W m−2) derived from JRA-55 and NOAA OLR from the climatological seasonal cycle at 1 Mar, and (b) the differences in the ensemble mean of U850 (shading) and OLR (contours at interval of 10 W m−2) between the OBSSST and CLMSST experiments during 26 Feb–2 Mar initialized 21–25 Feb. The contour for zero is omitted.

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    (a) Simulated genesis locations (numbers) and timings (color) for Pam in OBSSST experiments (top) initialized 21–25 Feb, (middle) initialized between 26 Feb and 2 Mar, and (bottom) initialized 3–8 Mar. (b) The same data as in (a), but for the CLMSST experiments. Numbers indicate the unit digit of the initialization day of month (e.g., 5 in the top panel represents a run initialized on 25 Feb and that in the bottom panel is for a run initialized on 5 Mar). The blue, green, and red colors indicate hit, early genesis, and late genesis, respectively. Character “O” indicates the observed genesis location at 0600 UTC 9 Mar, and the dashed circle marks a radial distance of 10° from O.

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    Time series of horizontal wind (vector) and cyclonic relative vorticity (contour) at 850 hPa and vertical wind shear between 850 and 200 hPa (shaded) at 0000 UTC from 7 to 11 Mar in (a) JRA-55 and ensemble mean of (b) OBSSST and (c) CLMSST experiments initialized 26 Feb–2 Mar. The locations of pre-Pam (blue) and Pam (red) are shown by crosses in (a) or numbers that indicate the unit digit of the initialization day of month in (b) and (c). The contours are drawn for −2 × 10−5 to −14 × 10−5 s−1 with an interval of 2 × 10−5 s−1. Observed Pam was formed at 0600 UTC 9 Mar.

  • View in gallery

    Time series of horizontal wind (vector), cyclonic relative vorticity (contour) at 850 hPa, and precipitable water (shaded) at 0000 UTC from 7 to 11 Mar in (a) JRA-55 and ensemble mean of (b) OBSSST and (c) CLMSST experiments initialized 26 Feb–2 Mar. The locations of pre-Pam and Pam are shown by black crosses (see also Fig. 8). The contours are drawn for −2 × 10−5 to −14 × 10−5 s−1 with an interval of 2 × 10−5 s−1. Observed Pam was formed at 0600 UTC 9 Mar.

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    Time series of ensemble mean of (a) vertical wind shear between 850 and 200 hPa and (b) precipitable water (shaded) at 0000 UTC from 7 to 11 Mar simulated in CPSSTA experiments initialized 26 Feb–2 Mar. Horizontal wind (vector) and cyclonic relative vorticity (contour) are also shown. The locations of pre-Pam (blue) and Pam (red) are shown by numbers that indicate the unit digit of the initialization day of month. The contours are drawn for −2 × 10−5 to −14 × 10−5 s−1 with an interval of 2 × 10−5 s−1. Observed Pam was formed at 0600 UTC 9 Mar.

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Genesis of Super Cyclone Pam (2015): Modulation of Low-Frequency Large-Scale Circulations and the Madden–Julian Oscillation by Sea Surface Temperature Anomalies

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  • 1 Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
  • | 2 Faculty of Science, Hokkaido University, Sapporo, and Japan Agency for Marine-Earth Science and Technology, Yokosuka, Japan
  • | 3 Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan
  • | 4 Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
  • | 5 Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba, Japan
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Abstract

Super Cyclone Pam (2015) formed in the central tropical Pacific under conditions that included El Niño Modoki and the passage of a convectively enhanced phase of the Madden–Julian oscillation (MJO) in the western Pacific. This study examines the influence that sea surface temperature anomalies (SSTAs) have on the MJO and low-frequency large-scale circulation, and establishes how they modulated the genesis of Pam. Two series of numerical experiments were conducted by using a nonhydrostatic global atmospheric model with observed (OBSSST) and climatological (CLMSST) SSTs. The results suggested that low-frequency westerly winds at 850 hPa (U850) were intensified in the central tropical Pacific due to the observed SSTA. The amplitude of the MJO simulated in OBSSST was larger than in CLMSST. In addition, the experiments initialized 26 February–2 March exhibited that the phase of the MJO in OBSSST was ahead of that in CLMSST, and that the genesis location in OBSSST was ~10° to the east of that in CLMSST. An analysis of large-scale fields indicated that a positive U850 maintained by SSTAs and intensification of U850 by the MJO modified distribution of large-scale cyclonic vorticity and precipitable water. These changes in large-scale fields modified the location and timing of intensification of the disturbance that become Pam and resulted in Pam’s genesis location being 10° farther east with slight impact on its genesis probability. Additional experiments showed that SSTAs in the central tropical Pacific were the dominant cause of modifications to large-scale fields, the MJO, and Pam’s genesis location.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Masuo Nakano, masuo@jamstec.go.jp

Abstract

Super Cyclone Pam (2015) formed in the central tropical Pacific under conditions that included El Niño Modoki and the passage of a convectively enhanced phase of the Madden–Julian oscillation (MJO) in the western Pacific. This study examines the influence that sea surface temperature anomalies (SSTAs) have on the MJO and low-frequency large-scale circulation, and establishes how they modulated the genesis of Pam. Two series of numerical experiments were conducted by using a nonhydrostatic global atmospheric model with observed (OBSSST) and climatological (CLMSST) SSTs. The results suggested that low-frequency westerly winds at 850 hPa (U850) were intensified in the central tropical Pacific due to the observed SSTA. The amplitude of the MJO simulated in OBSSST was larger than in CLMSST. In addition, the experiments initialized 26 February–2 March exhibited that the phase of the MJO in OBSSST was ahead of that in CLMSST, and that the genesis location in OBSSST was ~10° to the east of that in CLMSST. An analysis of large-scale fields indicated that a positive U850 maintained by SSTAs and intensification of U850 by the MJO modified distribution of large-scale cyclonic vorticity and precipitable water. These changes in large-scale fields modified the location and timing of intensification of the disturbance that become Pam and resulted in Pam’s genesis location being 10° farther east with slight impact on its genesis probability. Additional experiments showed that SSTAs in the central tropical Pacific were the dominant cause of modifications to large-scale fields, the MJO, and Pam’s genesis location.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Masuo Nakano, masuo@jamstec.go.jp

1. Introduction

Tropical cyclone (TC) genesis requires favorable large-scale dynamic and thermodynamic conditions. Gray (1975, 1979) found that TC genesis occurs in areas that meet the following conditions: (i) the Coriolis parameter has some nonzero magnitude, (ii) low-level cyclonic vorticity exists, (iii) tropospheric vertical wind shear is small, (iv) sea surface temperatures (SSTs) are >26°C, (v) convective instability exists, and (vi) midtropospheric relative humidity is high. Gray (1979) used the abovementioned constraints to develop the daily genesis parameter, and Emanuel and Nolan (2004) subsequently incorporated those principles into the genesis potential index (GPI). Large-scale conditions conducive to TC genesis are modulated by internal variability of the atmosphere–ocean coupled system, such as El Niño–Southern Oscillation (ENSO; Rasmusson and Carpenter 1982), the Madden–Julian oscillation (MJO; Madden and Julian 1971, 1972), and the boreal summer intraseasonal oscillation (BSISO; Wang and Rui 1990; Wang and Xie 1997). As reviewed later, numerous studies have investigated relationships between SST anomalies (SSTAs) associated with ENSO and TC genesis, along with those for SSTAs and the MJO, and the MJO and TC genesis. However, relatively few studies have examined the impact of both ENSO and the MJO in combination on TC genesis (e.g., Chand and Walsh 2009). Because Super Cyclone Pam (2015) formed under the existence of the El Niño Modoki (Ashok et al. 2007) and the passage of a convectively enhanced phase of the MJO simultaneously, this case is selected to examine in detail.

ENSO drastically changes the SST distribution in the tropics at seasonal to annual time scales, thereby affecting large-scale environmental circulations such as the Walker circulation. Many authors have found that ENSO has a large impact on TC activity [e.g., in the western North Pacific (Chan 1985), in the eastern Pacific (Irwin and Davis 1999), in the North Atlantic (Gray 1984), in the southern Indian Ocean (Ho et al. 2006), in the Australian region (Solow and Nicholls 1990), and in the vicinity of Fiji (Chand and Walsh 2009)]. Camargo et al. (2007) used the GPI to diagnose which large-scale modifications associated with ENSO have large impacts on TC activity, and found that different factors contribute to modifications of TC activity in different basins. In the central Pacific, for example, low-level vorticity is enhanced (reduced) in El Niño (La Niña) years, and TC activity was also enhanced (reduced).

The MJO changes large-scale atmospheric circulation and moisture in the tropics on a subseasonal time scale and modulates TC activity [e.g., in the western Pacific (Nakazawa 1986, 2006) and also Indian Ocean (Liebmann et al. 1994), eastern Pacific (Molinari et al. 1997), in the Gulf of Mexico (Maloney and Hartmann 2000), in the southern Indian Ocean (Bessafi and Wheeler 2006), in the Australian region (Hall et al. 2001), and in the vicinity of Fiji (Chand and Walsh 2010)]. Using the GPI, Camargo et al. (2009) found that midlevel humidity and low-level vorticity can contribute to the modulation of TC activity. Recently, Klotzbach (2014) used unified metrics to review and summarize the impact of the MJO in all basins worldwide and clarified that the large-scale parameters that impact TC activity are different in each basin. For example, the variation of thermodynamical parameters is extensive in the Eastern Hemisphere (north Indian Ocean and northwest Pacific), and that of dynamical parameters is large in the Western Hemisphere (e.g., North Atlantic). In the South Pacific, dynamically favorable large-scale conditions (MJO phases 7–8) lag behind thermodynamically favorable conditions (MJO phases 6–7). Chand and Walsh (2010) showed that TC genesis in the Fiji region is 5 times more likely during the active phase of the MJO compared to the inactive phase, and that El Niño enhances this modulation.

Because the time scale of ENSO is longer than that of the MJO, the impacts of SSTAs associated with ENSO on the MJO may exist. However, as indicated by previous studies, the influence of SSTAs on the MJO remains controversial. Slingo et al. (1999) and Hendon et al. (1999) pointed out that there is no relationship between ENSO and the MJO, whereas Liu et al. (2016) showed that an SSTA in the central tropical Pacific impacted on amplification of the MJO in that region. Recently, ENSO has been shown to have a diverse character (Capotondi et al. 2015), and some studies examined the different impacts that canonical El Niño, also known as eastern Pacific El Niño, and El Niño Modoki (Ashok et al. 2007), also known as central Pacific El Niño, have on MJO characteristics. For example, Gushchina and Dewitte (2012) showed that MJO activity is enhanced during boreal spring and summer prior to canonical El Niño and that MJO activity is also enhanced during mature and decaying phases of El Niño Modoki.

Because an SSTA pattern resembling the El Niño Modoki pattern and the convectively active phase of the MJO coexisted at the time of Pam’s genesis, the influences of the SSTA on both the MJO and low-frequency variations of large-scale environmental circulation could have affected Pam’s genesis. In this study, we attempt to clarify how low-frequency large-scale environmental flow and the MJO, which also modulate large-scale environmental flow, were modulated by the SSTA and how those modifications modulated the genesis of Pam. This is accomplished with numerical experiments that employ a nonhydrostatic global atmospheric model, which is able to explicitly represent the convective systems associated with the MJO and related TC genesis cases with statistically high performance levels (Miyakawa et al. 2014; Nakano et al. 2015).

Section 2 gives an overview of Pam’s genesis. The design of numerical experiments and analysis methods are described in section 3, and results are presented in section 4. Section 5 presents the discussion, and conclusions are given in section 6.

2. Case overview

The World Meteorological Organization’s International Best Track Archive for Climate Stewardship (IBTrACS-WMO, version v03r09; Knapp et al. 2010) shows that Super Cyclone Pam (2015) was the first cyclone with a minimum pressure below 900 hPa during March in the South Pacific since at least 1980. The cyclone caused tremendous damage in Vanuatu. The IBTrACS indicates that a tropical depression (hereafter referred to as pre-Pam) reached tropical storm intensity (10-min maximum wind speed = 17.5 m s−1 estimated by satellite observations) at 0600 UTC 9 March 2015 at 8.4°S, 169.8°E (black cross in Fig. 1). The storm was then named Pam (hereafter we refer to this as genesis.). It then, moved poleward and strengthened to a category 5 storm on the Saffir–Simpson hurricane wind scale at 1200 UTC 12 March. At 0600 UTC 13 March, Pam’s minimum central pressure fell below 900 hPa, and its lowest minimum central pressure of 896 hPa occurred at 20.4°S, 169.2°E (black square in Fig. 1) at 0000 UTC 14 March 2015, as determined by satellite estimates. Meanwhile, in the Northern Hemisphere, Tropical Cyclone Bavi formed at 6.8°N, 170.0°E (closed circle in Fig. 1) at 0600 UTC 11 March, and moved westward. At the time of Pam’s genesis, the central tropical Pacific was characterized by warm SSTAs and the presence of an amplified MJO event.

Fig. 1.
Fig. 1.

SST analyzed by (a) NCEP FNL and (b) SST anomaly present on 1 Mar 2015 from climatology derived from ERA-Interim (1980–2009). The black cross shows the location of Pam’s genesis and the black square shows the location of Pam’s minimum pressure. The closed circle shows the location of Bavi’s genesis. Tracks of Pam and Bavi are also shown with black lines. The box bounded by dotted black lines highlights the region where an SST anomaly remained in the CPSSTA experiments.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

The National Centers for Environmental Prediction Final operational global analysis (NCEP FNL) data show that the SST in the central tropical Pacific on 1 March 2015 was higher than 30°C, which resulted in a warm SSTA of 1°–2°C (Fig. 1). Pam formed at the southern edge of the SSTA. Figure 2 shows SSTAs from September 2014 to June 2015 differenced from climatological SSTs (1981–2010) derived based on monthly SST analysis from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST; Rayner et al. 2003). Although data sources and averaging time windows (daily or monthly) are different between Figs. 1b and 2g, they show a generally similar SSTA pattern. In the equatorial Pacific, a positive SSTA existed from the central Pacific to the eastern Pacific in November 2014 (Fig. 2c). This positive anomaly retreated to the west in December 2014 and January 2015 (Figs. 2d,e) and mainly covered the central Pacific in February and March (Figs. 2f,g), which resembles the El Niño Modoki pattern (Ashok et al. 2007). This anomaly rapidly extended eastward again in April and May 2015 (Figs. 2h,i), with a more canonical El Niño pattern observed by June 2015 (Fig. 2j). Yeh et al. (2009) defined El Niño Modoki (CP–El Niño) and canonical El Niño (EP–El Niño) using Niño-3 (SSTA in 5°N–5°S, 150°–90°W from climatological seasonal mean SST) and Niño-4 (SSTA in 5°N–5°S, 160°E–150°W from climatological seasonal mean SST) indices during the boreal winter months of December–January–February (DJF). According to that definition, the 2014/15 DJF was classified as El Niño Modoki because the DJF mean Niño-4 index (0.93) was greater than the Niño-3 index (0.45) and greater than a threshold value of 0.5. By contrast, the 2015/16 DJF was classified as canonical El Niño (S. Chen et al. 2016). Therefore, the period is considered to be the mature stage of the El Niño Modoki event, with the predevelopment stage of the canonical El Niño event in March 2015.

Fig. 2.
Fig. 2.

Monthly SSTAs in relation to the 30-yr (1981–2010) mean derived from HadISST for (a) September 2014, (b) October 2014, (c) November 2014, (d) December 2014, (e) January 2015, (f) February 2015, (g) March 2015, (h) April 2015, (i) May 2015, and (j) June 2015. Monthly Niño-3 (top number) and Niño-4 (bottom number) indices obtained from http://www.cpc.ncep.noaa.gov/data/indices/sstoi.indices based on OISST V2 (Reynolds et al. 2002) are also shown in each panel.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

The phase diagram of the Real-time Multivariate MJO index (RMM; Wheeler and Hendon 2004, hereafter WH04) (Fig. 3a) indicates that the activity of the MJO was fairly weak (amplitude <1) in February 2015, except for 2–8 February. The MJO initiated (amplitude >1) over the Maritime Continent on 3 March and was traveling eastward across the western Pacific at the time of Pam’s genesis on 9 March. Shortly afterward, the MJO reached a record-breaking (since 1974) amplitude (>4) on 16 March 2015 (Marshall et al. 2016). Figure 3b shows a Hovmöller diagram of the anomaly of zonal winds at 850 hPa (U850) in the tropics (15°S–15°N) derived from the Japanese 55-year Reanalysis (JRA-55) dataset (Kobayashi et al. 2015) and its MJO component reconstructed from the RMM. Although the magnitude of the MJO was very weak in February (Fig. 3a), a stationary positive U850 anomaly existed at 140°E–180°, which represented a large-scale response to the SSTA. Moreover, another positive anomaly initiated at around 60°E in the beginning of March and propagated eastward while amplifying. This signal corresponded to an MJO signal. The maximum of the anomaly was observed in the vicinity of and at around the time of Pam’s genesis (cross in Fig. 3b). Although the MJO signal was propagating eastward, this local maximum in U850 propagated westward during 11–17 March. This westward propagation was associated with the translation of Tropical Storm Bavi (pink curve in Fig. 3b).

Fig. 3.
Fig. 3.

(a) RMM phase diagram for February 2015 (red line) and March 2015 (blue line), and (b) Hovmöller diagram of U850 anomalies from the climatological seasonal cycle (shading) averaged in 15°N–15°S and reconstructed from the RMM (contours). The green and pink curves show the trajectories of Pam and Bavi, respectively. The green cross shows Pam’s genesis longitude and time.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

3. Numerical experiments and experimental design

a. Model

The Nonhydrostatic Icosahedral Atmospheric Model (Satoh et al. 2014), version 2014 (NICAM.14), was used with a horizontal grid interval of approximately 14 km. This model has been successfully used for studies of the MJO (Miura et al. 2007; Nasuno 2013; Miyakawa et al. 2014), TC genesis associated with boreal winter MJO events (Fudeyasu et al. 2008, 2010a, b), and the BSISO (Oouchi et al. 2009; Taniguchi et al. 2010; Yanase et al. 2010; Nakano et al. 2015). The model employed a terrain-following vertical coordinate system with 38 layers, with the bottom layer located 80 m above the surface and the top layer at an altitude of 36.7 km. The NICAM single-moment six-hydrometeor-species cloud microphysics scheme (NSW6; Tomita 2008) is used without any convective parameterization. Longwave and shortwave radiation processes were calculated by the MstranX broadband radiative transfer code (Sekiguchi and Nakajima 2008). The Mellor–Yamada–Nakanishi–Niino Level-2 scheme (Nakanishi and Niino 2004; Noda et al. 2010) was used for the planetary boundary layer. The model was coupled with a slab ocean model, which has been used in global climate models (e.g., McFarlane et al. 1992, see their section 3) to simply represent the variability of SST due to atmosphere–ocean interaction. The variability of SST is caused by surface heat budget (e.g., sensible/latent heat flux and radiation) and internal oceanic heat flux (e.g., three-dimensional advection). The former is calculated by assuming a specific heat capacity, which depends on slab depth. Because the latter is simply represented by a nudging term of SST, a reference SST is needed during the simulation. Details regarding setting of the reference SST are described in section 3b. The SST was nudged toward a time-varying reference SST with an e-folding time of 7 days. The slab ocean depth was set to 15 m.

The model configurations adopted in this study were the same as those used by Miyakawa et al. (2014), who showed that the MJO can be predicted four weeks in advance, and by Nakano et al. (2015), who demonstrated that the genesis of TCs in the active period of the BSISO (August 2004) can be predicted two weeks in advance. Therefore, the model was a suitable tool for the present study.

b. Experimental design

Experimental design to examine the influence of SSTAs is described in detail. Initial atmospheric condition data were generated using linear interpolation from the Japan Meteorological Agency operational global objective analysis (available at http://database.rish.kyoto-u.ac.jp/arch/jmadata/data/gpv/original/), which has a horizontal resolution of 0.5° × 0.5° and 17 vertical layers between 1000 and 10 hPa. In this study, we assumed that SSTAs induced anomalous low-frequency large-scale environmental circulations (e.g., Chand and Walsh 2009) and also modulated the MJO (e.g., Gushchina and Dewitte 2012). Therefore, three series of experiments (OBSSST, CLMSST, and CPSSTA) were conducted to investigate their influence. In OBSSST experiments, the observed SST was used as the initial SST condition. This initial SST can be decomposed into daily climatological SST and SSTA at the initial time. By adding this SSTA at the initial time to daily varying climatological SST, the reference SST used by the slab ocean model was created. In CLMSST experiments, the climatological daily SST was used as both the initial and reference SST. CPSSTA experiments were similar to CLMSST experiments, but SSTAs in the central Pacific Ocean (10°S–10°N, 155°E–155°W, the box bounded by dotted lines shown in Fig. 1b) were added back to the initial and reference SST. The NCEP FNL was used for the observed SST. Daily climatological SST data were derived from 30 yr (1980–2009) of European Centre for Medium-Range Weather Forecasts interim reanalysis (Dee et al. 2011). The model was initialized at 0000 UTC of each day between 21 February and 8 March 2015 for OBSSST and CLMSST (between 26 February and 2 March 2015 for CPSSTA), and integrated for 30 days. Anomalous large-scale environmental circulations induced by SSTAs were contained in the initial atmospheric data for both CLMSST and OBSSST. However, as would be expected, the anomalous large-scale environmental circulation decayed rapidly in CLMSST.

c. Analysis methods

The amplitude and phase of the MJO was evaluated using the RMM (WH04). First, combined empirical orthogonal functions (EOFs) were defined using OLR and zonal wind anomalies at 850- and 200-hPa time series in the deep tropics (15°S–15°N) during 1979–2001. The anomalies were calculated by subtracting their climatological seasonal cycle and the interannual variability associated with ENSO. WH04 demonstrated that the first and second mode of EOFs (EOF1 and EOF2, respectively) together sufficiently represent variations associated with the MJO. Second, RMM (RMM1 and RMM2) indices were obtained by projecting daily anomaly fields onto the aforementioned pair of EOFs. The climatological seasonal cycle was defined by the annual mean and the first three harmonics of intra-annual variability. The interannual variability mainly associated with ENSO can be removed subtracting the latest 120-day mean anomaly from the climatological seasonal cycle. This is a widely used procedure among the MJO research community (Gottschalck et al. 2010), since data from any future analyses are unnecessary to complete the calculation of RMMs. Therefore, it is suitable for real-time monitoring of the MJO. The RMM1–RMM2 phase space (e.g., Fig. 3a) was divided into eight phases. When the amplitude ≥1, the MJO was active (the MJO was inactive at <1). WH04 showed that the preferred location of the enhanced convective signal depends on the phase of the MJO. Enhanced convection exists in the Indian Ocean at phases 2–3, the Maritime Continent at phases 4–5, the western Pacific at phases 6–7, and the Western Hemisphere and Africa at phases 8–1, respectively. Following WH04, the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 1996), and National Oceanic and Atmospheric Administration (NOAA) daily-interpolated OLR (Liebmann and Smith 1996) were used to calculate the EOFs. To calculate RMMs for the present study case, we used daily anomaly fields derived from JRA-55 and NOAA daily noninterpolated OLR data (available online at http://www.esrl.noaa.gov/psd/data/gridded/data.uninterp_OLR.html). Missing values in the daily noninterpolated OLR data were filled using the spatiotemporal interpolation method documented by Liebmann and Smith (1996). In this study, we derived the ensemble mean fields using the lagged averaged forecast method (Hoffman and Kalnay 1983). In this method, five or six model simulations initialized in consecutive days (e.g., 21–25 February, 26 February–2 March, and 3–8 March) were averaged for each valid date. The ensemble mean fields were derived before calculating the RMM.

The methodology used to detect Pam in the model was the same as that used in Nakano et al. (2015). First, low pressure systems with a sea level pressure anomaly lower than 0.5 hPa below the average in 7° × 7° box centered on the minimum pressure grid point were detected for each snapshot of the SLP field. Second, the nearest system in the next time step was regarded as the same system among two consecutive time steps, and then those SLP minima are connected as a track. After repeating this procedure, tracks of storms are constructed. From those tracked data, we selected a candidate system for Pam as follows. First, we selected a system existing within 10° of the observed genesis location (8.4°S, 169.8°E; black cross in Fig. 1) within 1 day of the observed genesis time (0600 UTC 9 March 2015) as a candidate. Second, we examined whether the candidate vortex met the TC criteria within 5 days of the observed genesis time. A vortex that passed these tests was considered representative of Pam in the model. If the simulated Pam met the TC criteria within 1 day of the observed genesis time, the genesis forecast was evaluated as a “hit”; otherwise, the forecast was evaluated as an “early” or a “late” genesis. The TC criteria consisted of (i) a 10-m maximum wind >17.5 m s−1; (ii) a wind speed averaged in the surrounding 10° box at 300 hPa less than that at 850 hPa; (iii) summation of areal averages of temperature anomaly in 7° × 7° box centered on the vortex center from average on the edge of the boxes at 700, 500, and 300 hPa >2°C; and (iv) lifetime >36 h.

As shown later, the genesis positions of Pam simulated in OBSSST and CLMSST were systematically different, which we interpret to have been a result of the different intensifications of the pre-Pam disturbances. Tory et al. (2006, 2007) examined the differences between developing and nondeveloping systems near Australia using model simulations, and showed that large vertical wind shear and insufficient large-scale vorticity prohibit intensification of nondeveloping systems and that mass redistribution forced by diabatic heating results in intensification of developing disturbances. Smith and Montgomery (2012) observed via dropsondes that the troposphere became drier in a nondeveloping case. Peng et al. (2012) and Fu et al. (2012) identified that vertical wind shear, vorticity in the lower troposphere, vertically integrated water vapor (precipitable water), and rain rate are key predictors for the development of tropical disturbances. In this study, we examined vertical wind shear between 850 and 200 hPa, relative vorticity, and precipitable water to understand the difference in genesis position simulated by OBSSST and CLMSST.

We employed a time-filtering technique to analyze low-frequency large-scale fields modified by SSTA. Gao and Li (2011) regarded the 25–70-day component and longer than 90-day component as the MJO and low frequency, respectively. Wheeler and Kiladis (1999) defined the eastward-moving 30–96-day component as the MJO. In this study, we used a 96-day Lanczos low-pass filter (Duchon 1979) centered on the analysis day. Because the filtered fields contained seasonal cycles, we subtracted a climatological seasonal cycle defined by the annual mean and the first three harmonics of intra-annual variability in order to clearly distinguish anomaly fields impacted by SSTA.

4. Results

a. Changes in MJO

Figure 4 shows the RMM phase diagrams derived from ensemble mean fields. The MJO simulated in OBSSST had a larger amplitude over the Maritime Continent and western Pacific than the amplitude simulated in CLMSST. Moreover, the phase of the MJO in OBSSST was ahead of the phase in the CLMSST experiments initialized before 3 March. The CLMSST experiments initialized 21–25 February (Fig. 4a) initiate the MJO over the Maritime Continent, but the phase was late and the amplitude decayed quickly. Conversely, OBSSST successfully reproduced the initiation of the MJO, as well as its eastward migration. The OBSSST experiments simulated amplitudes >1, but they were smaller than those actually observed. In the experiments initialized 26 February–2 March (Fig. 4b), CLMSST simulated an MJO with a late phase and a small amplitude, whereas OBSSST simulated the RMM well in terms of both amplitude and phase in phases 5–6. In the experiments initialized 3–8 March (Fig. 4c), the simulated MJO phase in CLMSST agreed with the MJO phase in OBSSST, albeit with a smaller simulated amplitude.

Fig. 4.
Fig. 4.

RMM phase diagram of analyzed (black lines) and simulated in OBSSST (red lines) and in CLMSST (blue lines) ensemble means of experiments initialized (a) 21–25 Feb, (b) 26 Feb–2 Mar, and (c) 3–8 Mar. The RMMs simulated in CPSSTA (green line) ensemble means of experiments initialized 26 Feb–2 Mar are also shown in (b). The triangle shows values recorded for (a) 25 Feb, (b) 2 Mar, and (c) 8 Mar, respectively. Crosses in (a)–(c) are shown every 5 days. Closed circles in (a)–(c) on each curve show the RMMs on 9 Mar.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

b. Changes in large-scale environmental circulations

Figure 5a shows analyzed U850 and OLR anomalies by subtracting the climatological seasonal cycle from 96-day low-pass-filtered fields centered on 1 March. These distributions show that both a positive U850 anomaly and a negative OLR anomaly existed at 140°E–180° along the equator, as also illustrated in a Hovmöller diagram (Fig. 3b). In experiments initialized before 25 February, there is a 5-day lead time before initiation of the MJO in the model (Fig. 4a). Therefore, if the large-scale environmental circulation associated with the SSTA, which is contained in the initial atmospheric conditions even in CLMSST, decays rapidly in the CLMSST experiments only, the difference between OBSSST and CLMSST experiments may represent some part of the model response to the SSTA.

Fig. 5.
Fig. 5.

(a) Anomalies for a 96-day low-pass-filtered U850 (color) and OLR (contours at intervals of 10 W m−2) derived from JRA-55 and NOAA OLR from the climatological seasonal cycle at 1 Mar, and (b) the differences in the ensemble mean of U850 (shading) and OLR (contours at interval of 10 W m−2) between the OBSSST and CLMSST experiments during 26 Feb–2 Mar initialized 21–25 Feb. The contour for zero is omitted.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

Indeed, the signal became obvious about 5 days after the initial date. Figure 5b shows the differences in U850 and OLR between OBSSST and CLMSST averaged during 26 February–2 March in experiments initialized 21–25 February. In the central tropical Pacific, the U850 in OBSSST was larger than the U850 in CLMSST, whereas the OLR anomaly was more negative, as found in the analysis results (Fig. 5a). These signals are quantitatively weak because the CLMSST experiments might still be in a transition period to climatological SST. The qualitative similarity between model response to SSTA and analyzed low-frequency anomaly suggests that maintenance of large-scale environmental circulation anomalies was dependent on the SSTA.

c. Changes in the TC genesis and probability

Figure 6 shows the simulated genesis positions and timings of Pam in experiments initialized 21–25 February 2015 (top panels), 26 February–2 March (middle panels), and 3–8 March (bottom panels), respectively. The model began to simulate Pam’s genesis 12 days before the observed genesis date (9 March), whereas the experiments initialized before 26 February in both OBSSST and CLMSST hardly reproduce a TC corresponding to Pam (top panels of Fig. 6). In the experiments initialized 26 February–2 March (middle panels of Fig. 6), OBSSST generated Pam in four out of five experiments, which included two hits. All of the predicted genesis positions were distributed within 4° east of the observed longitude of genesis and about 5°–7° south of the observed latitude of genesis. CLMSST generated Pam in four out of five experiments, with one hit, thus reflecting a comparable probability of genesis to the OBSSST experiments. However, with the exception of the experiment initialized 1 March, all of the simulated genesis positions were located 8°–12° west of the observed longitude of genesis. Because the MJO (Fig. 4) and low-frequency large-scale circulations (Fig. 5b) were modified by SSTA, the response to the SSTAs led to the eastward shift in Pam’s genesis location. The CLMSST and OBSSST experiments initialized after 3 March 2015 (bottom panels of Fig. 6) generated TCs corresponding to Pam that were better in terms of their genesis times (all simulated TCs were categorized as hits), and no systematic differences can be found. These results also indicated that Pam’s genesis location depended on the existence of an SSTA especially in the experiments initialized 26 February–2 March. The cause of significant differences in Pam’s genesis location in the experiments initialized 26 February–2 March are explored below.

Fig. 6.
Fig. 6.

(a) Simulated genesis locations (numbers) and timings (color) for Pam in OBSSST experiments (top) initialized 21–25 Feb, (middle) initialized between 26 Feb and 2 Mar, and (bottom) initialized 3–8 Mar. (b) The same data as in (a), but for the CLMSST experiments. Numbers indicate the unit digit of the initialization day of month (e.g., 5 in the top panel represents a run initialized on 25 Feb and that in the bottom panel is for a run initialized on 5 Mar). The blue, green, and red colors indicate hit, early genesis, and late genesis, respectively. Character “O” indicates the observed genesis location at 0600 UTC 9 Mar, and the dashed circle marks a radial distance of 10° from O.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

d. Cause of differences in TC genesis

The dynamical and thermodynamical aspects of the impacts of SSTA on the TC genesis are examined in this subsection. Figure 7 shows horizontal winds and cyclonic vorticity at 850 hPa and magnitude of vertical wind shear between 850 and 200 hPa from 7 to 11 March in JRA-55 and in the simulations. JRA-55 (Fig. 7a) shows that strong westerly and easterly winds existed, especially in 2.5°–7.5°S, 160°E–180° and 12.5°–17.5°S, 160°E–180° on 7 March, respectively. Between the westerly winds in the north and easterly winds in the south, cyclonic vorticity was enhanced with a vertical shear of <8 m s−1. The strong lower-tropospheric westerly winds and the vertical shear were typical of the MJO (e.g., Kiladis et al. 2005), which are accompanied with an enhanced lower-tropospheric cyclonic vorticity on both flanks of the westerly peak near the equator. Pre-Pam formed in the area with cyclonic vorticity and small vertical shear on 8 March and the storm continuously intensified its cyclonic vorticity as it moved southward (Figs. 7a).

Fig. 7.
Fig. 7.

Time series of horizontal wind (vector) and cyclonic relative vorticity (contour) at 850 hPa and vertical wind shear between 850 and 200 hPa (shaded) at 0000 UTC from 7 to 11 Mar in (a) JRA-55 and ensemble mean of (b) OBSSST and (c) CLMSST experiments initialized 26 Feb–2 Mar. The locations of pre-Pam (blue) and Pam (red) are shown by crosses in (a) or numbers that indicate the unit digit of the initialization day of month in (b) and (c). The contours are drawn for −2 × 10−5 to −14 × 10−5 s−1 with an interval of 2 × 10−5 s−1. Observed Pam was formed at 0600 UTC 9 Mar.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

The ensemble mean fields simulated in OBSSST and CLMSST experiments initialized 26 February–2 March are examined for the simulations. Whereas storm-scale features are diluted by averaging, large-scale features causing systematic difference in Pam’s genesis position can be captured. In OBSSST experiments (Fig. 7b), westerly winds were intensifying in 0°–10°S, 140°E–180°, and easterly winds existed dominantly in 17.5°–25°S, 140°E–180°. Between the westerly and easterly winds (10°–17.5°S, 140°E–180°) cyclonic vorticity intensified with a small vertical wind shear. In CLMSST experiments (Fig. 7c), westerly winds were intensifying in 140°–170°E, 0°–10°S and easterly winds existed in 17.5°–25°S, 140°E–180°. Cyclonic vorticity formed between the westerly and easterly winds, but the zonal scale of the cyclonic vorticity area was smaller than that in OBSSST because the eastward extension of westerly winds is smaller. Large-scale model response to SSTA (Fig. 5b) and changes in the low-level strong westerly wind associated with the MJO (westerly wind burst) in CLMSST experiments weakened over time in 0°–10°S, 170°E–180° (Fig. 7c).

Figure 7 also displays the positions of pre-Pam and Pam in IBTrACS and in experiments initialized 26 February–2 March. Simulated differences in Pam’s genesis position were caused by differences in pre-Pam’s location (for simulations initialized on 26 and 28 February) and the difference in the timing of pre-Pam’s intensification to Pam (initialized 1–2 March). No pre-Pam formed before 7 March (not shown). The storm position simulated in the experiments initialized on 26 February shows that pre-Pam in OBSSST formed at 13°S, 170°E on 10 March, which is 8° east of where pre-Pam formed in CLMSST on 9 March. The storm position in experiments initialized on 28 February shows that pre-Pam in OBSSST formed at 10°S, 175°E on 7 March, that is 3° south and 14° east of where pre-Pam formed in CLMSST on 8 March. In the experiments initialized on 1 March, pre-Pam existed at almost same position in both simulations on 7 March. Although pre-Pam in CLMSST intensified to Pam on 8 March (1 day earlier than in the observations), pre-Pam in OBSSST intensified to Pam on 11 March (2 days later than in the observations). Consequently, Pam in OBSSST formed 5° south and 3° west of its position in CLMSST. In the experiments initialized on 2 March, pre-Pam existed at almost same location in CLMSST as Pam did in OBSSST on 9 March. But the storm in CLMSST intensified to Pam on 12 March (not shown). Consequently, Pam in OBSSST formed 9° east of where it formed in CLMSST (Fig. 6b). These results indicate that large-scale circulation differences (e.g., vertical wind shear and low-level cyclonic vorticity) caused differences in pre-Pam formation and timing of intensification to Pam, which resulted in differences in Pam’s genesis location.

The difference in large-scale circulation also changes the horizontal distribution of water vapor, which can alter TC genesis location (e.g., Gray 1979). Figure 8 shows the time evolution of the horizontal distribution of column-integrated water vapor amount (precipitable water). JRA-55 (Fig. 8a) shows that the region with precipitable water >60 mm existed in the cyclonic vorticity region on 7 March and that this region increased in area and its maximum value over time.

Fig. 8.
Fig. 8.

Time series of horizontal wind (vector), cyclonic relative vorticity (contour) at 850 hPa, and precipitable water (shaded) at 0000 UTC from 7 to 11 Mar in (a) JRA-55 and ensemble mean of (b) OBSSST and (c) CLMSST experiments initialized 26 Feb–2 Mar. The locations of pre-Pam and Pam are shown by black crosses (see also Fig. 8). The contours are drawn for −2 × 10−5 to −14 × 10−5 s−1 with an interval of 2 × 10−5 s−1. Observed Pam was formed at 0600 UTC 9 Mar.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

The horizontal distribution of precipitable water simulated in OBSSST and CLMSST experiments are systematically different, with the model tending to overestimate precipitable water. The ensemble mean of OBSSST experiments initialized 26 February–2 March (Fig. 8b) showed precipitable water higher than 60 mm in 0°–7.5°S, 145°E–180°, where strong westerly winds existed on 7 March. This wet area slowly moved southward, with a peak value existing in 170°E–180°. Pre-Pam formed and intensified to Pam near the peak. CLMSST experiments (Fig. 8c) simulate high precipitable water in 0°–10°S, 145°–175°E, on 9 March, with peak values existing in 155°–165°E. All simulated pre-Pam storms formed and intensified to Pam near the peak except in the experiment initialized on 1 March. The horizontal distribution of vertically integrated water vapor flux convergence (not shown) indicated a similar pattern to that of precipitable water (Fig. 8) and water vapor flux from the surface (not shown) is much smaller than vertically integrated water vapor flux. These results indicate that differences in water vapor flux caused by modulated large-scale circulation contribute to differences in the horizontal distribution of precipitable water.

e. Can SSTAs in the central tropical Pacific be responsible for these differences?

The influence of SSTA in the central tropical Pacific (box bounded by dotted lines shown in Fig. 1b) was examined by setting SSTA values elsewhere to zero (i.e., CPSSTA). An RMM phase diagram simulated by these experiments initialized 26 February–2 March has been shown in Fig. 4b by green line. Despite SST being the same as in the CLMSST outside of the central tropical Pacific, the simulated RMM was very close to that produced in the OBSSST experiments, albeit with a slight phase delay.

Figure 9a shows the ensemble mean horizontal wind and vorticity at 850 hPa and magnitude of vertical wind shear between 850 and 200 hPa simulated in CPSSTA experiments initialized 26 February–2 March. Strong westerly winds existed on 8–9 March at 0°–10°S, 140°–175°E and cyclonic vorticity formed to the south of the westerlies. The horizontal distribution of precipitable water (Fig. 9b) shows that precipitable water became high at 5°–15°S, 165°–175°E, where cyclonic vorticity is large on 8–9 March. In total, four of five experiments simulated Pam’s formation on 9–10 March near this large cyclonic vorticity and high precipitable water region (1, 2, 6, and 7 in Fig. 9). Three out of five simulated genesis positions are distributed between 168° and 172°E. This distribution is similar to that in in OBSSST rather than CLMSST (middle panels of Fig. 6). These results indicate that the SSTA in the central tropical Pacific had a strong impact on the MJO and large-scale environment at the time of Pam’s genesis.

Fig. 9.
Fig. 9.

Time series of ensemble mean of (a) vertical wind shear between 850 and 200 hPa and (b) precipitable water (shaded) at 0000 UTC from 7 to 11 Mar simulated in CPSSTA experiments initialized 26 Feb–2 Mar. Horizontal wind (vector) and cyclonic relative vorticity (contour) are also shown. The locations of pre-Pam (blue) and Pam (red) are shown by numbers that indicate the unit digit of the initialization day of month. The contours are drawn for −2 × 10−5 to −14 × 10−5 s−1 with an interval of 2 × 10−5 s−1. Observed Pam was formed at 0600 UTC 9 Mar.

Citation: Monthly Weather Review 145, 8; 10.1175/MWR-D-16-0208.1

5. Discussion on the relationship between MJO, ENSO, and TC genesis

The frequency of TC genesis depends strongly on the MJO phase, as the MJO modulates large-scale dynamic and thermodynamic fields. Pam’s genesis occurred in MJO phase 5, which is not normally a favorable phase for TC genesis in the southern Pacific (Klotzbach 2014). On the other hand, a positive SSTA associated with the El Niño Modoki event of 2014/15, also existed at the time of Pam’s genesis. Considering that the Niño-4 index peaked in April 2015 (Fig. 2), Pam formed during a mature phase of the El Niño Modoki. The MJO amplitude in the central Pacific tends to be large during such mature phases (Gushchina and Dewitte 2012; Feng et al. 2015; Liu et al. 2016; X. Chen et al. 2016). For example, Feng et al. (2015) showed that the MJO tends to have a large amplitude in phases 3–5. Because canonical El Niño occurred in the winter of 2015/16 (S. Chen et al. 2016), March 2015 can be interpreted as its predevelopment phase. Recent studies have shown that the MJO tends to amplify in the boreal spring prior to canonical El Niño (Gushchina and Dewitte 2012; X. Chen et al. 2016). As shown in Fig. 7, Pam’s genesis occurred in the region with U850 > 10 m s−1, which extended from the west. Given that this region was associated with propagation of the MJO, the larger amplitude in the Maritime Continent and the western Pacific simulated in the OBSSST experiments than in the CLMSST experiments would provide more favorable conditions for TC genesis.

The MJO phase simulated in the CLMSST experiments lagged behind that in OBSSST. This phase delay in CLMSST is attributed to a slower propagation during the early period of time integration (Fig. 4), which suggests that the MJO was adjusting to a given SST under CLMSST conditions. Moreover, the equatorial westerly region identified in CLMSST continuously retreated, which suggests that the anomalous large-scale circulation associated with a SSTA rapidly decayed in the CLMSST experiments. Both adjustments of MJO and large-scale circulation in CLMSST caused Pam’s genesis location to trend westward. Considering that the SSTA enlarged the zonal SST gradient in the western Pacific, this may result in faster propagation speeds for the MJO in the western Pacific and slower propagation in the eastern Pacific (Maloney et al. 2010; Takasuka et al. 2015). However, X. Chen et al. (2016) showed that the phase speed of the MJO under El Niño Modoki conditions is slightly slower than that under neutral ENSO and canonical El Niño conditions. Further study into this phase speed problem is needed to form a complete understanding.

6. Conclusions

We conducted two series of experiments using a global nonhydrostatic atmospheric model, NICAM, with observed conditions (OBSSST) and with no sea surface temperature anomalies (SSTAs) (CLMSST) in order to investigate the influence of SSTAs associated with El Niño Modoki on the MJO and low-frequency large-scale environmental circulation. These data were additionally used to establish how such modifications influenced the genesis of Super Cyclone Pam in March 2015. Both an atmospheric reanalysis dataset and experiment results suggested that the SSTA intensified westerly winds at 850 hPa (U850) and convective activity in the tropical central Pacific. The amplitude of the MJO simulated in OBSSST was larger than the amplitude simulated in CLMSST. The experiments initialized 26 February–2 March 2015 were analyzed in detail as the MJO in OBSSST agreed with observations and the location of Pam’s genesis differed between OBSSST and CLMSST. The MJO phase in OBSSST was ahead of the phase in CLMSST and the genesis location in OBSSST was 10° to the east of the genesis location in CLMSST. Analysis of the large-scale fields indicated that (i) a positive U850 anomaly maintained by SSTAs and (ii) intensification of U850, a so-called westerly wind burst, accompanied the MJO modified horizontal distribution of large-scale cyclonic vorticity and precipitable water. These changes in large-scale fields altered (i) the location of pre-Pam and (ii) the timing of intensification of pre-Pam to Pam and resulted in the location of Pam’s genesis being10° farther east with only a slight impact on its genesis probability. Additional experiments with SSTAs in the tropical Pacific only (CPSSTA) were performed, and these results were very similar to those of OBSSST. Therefore, the SSTA in the central tropical Pacific is interpreted to have been the dominant cause of the modification of the large-scale fields and Pam’s genesis location.

As discussed in section 5, many studies explored how SSTAs influence the MJO and TC genesis using observed data. However, it seems that those studies suffer from a limitation in the number of cases. In this study, we investigated the influence of SSTAs in March 2015. Given that recent high-resolution global models (e.g., a nonhydrostatic global model) can reasonably simulate the MJO and TCs, the use of large number of climate simulations would deepen our understanding of the relationship between SSTAs and the MJO and TC genesis. It should be noted that one example is presented by Marshall et al. (2016), who examined the relationship between SSTA and the MJO in the present case (March 2015) and concluded that amplification of the convective anomaly caused by an SSTA in the tropical central Pacific enhanced the MJO amplitude.

In this study, we do not explore how SSTAs impacted Pam’s intensity and track after genesis. Generally, track forecasts beyond one week suffer from model biases in the large-scale field. Model resolution has an impact on intensity forecasts (Magnusson et al. 2014; Nakano et al. 2017), although the horizontal resolution used in this study is not high enough to simulate TC inner-core structures. Recently predictability of TCs at subseasonal to seasonal time scale (2 weeks–1 year) has been promoted worldwide (Vitart et al. 2012, 2017). Improvement of global models in TC forecasts based on understanding of the model biases is an important topic for future studies.

Acknowledgments

We thank Harry Hendon and Kazuhisa Tsuboki for their suggestions and fruitful discussions. We also appreciate constructive and valuable comments by Philip J. Klotzbach and two anonymous reviewers. All numerical simulations were conducted on the Earth Simulator (NEC SX-ACE) at JAMSTEC. This study was undertaken as part of the FLAGSHIP 2020 Project and Program for Risk Information on Climate Change of MEXT. MN was supported by the HPCI Strategic Programs for Innovative Research Field 3 and FLAGSHIP 2020 Project of MEXT. TN was supported by JSPS KAKENHI Grant 26400475. The Japan Meteorological Agency operational global analysis data were obtained from the data server of the Research Institute for Sustainable Humanosphere, Kyoto University (http://database.rish.kyoto-u.ac.jp/index-e.html). Uninterpolated and interpolated OLR data were provided by the NOAA/OAR/ESRL PSD http://www.esrl.noaa.gov/psd/. The GFD-DENNOU library (http://www.gfd-dennou.org/library/dcl/) was used for drawing the figures.

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