W. Zhang , G. A. Vecchi , H. Murakami , G. Villarini , L

Home , Homogenization (climate), Pacific Meridional Mode

1 The Pacific Meridional Mode and the Occurrence of Tropical 2 Cyclones in the Western North Pacific 3 4 1,2,* 1,2 1,2 3 5 W. Zhang , G. A. Vecchi , H. Murakami , G. Villarini ,

1,2 6 L. Jia

7 8 1National Oceanic and Atmospheric Administration/Geophysical Fluid 9 Dynamics Laboratory, Princeton, NJ, USA 10 2Atmospheric and Oceanic Sciences Program, Princeton University, 11 Princeton, NJ, USA 12 3 IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, 13 Iowa 14 15 16 17 18 19 *Corresponding author: 20 Wei Zhang 21 22 National Oceanic and Atmospheric Administration/Geophysical Fluid 23 Dynamics Laboratory, Princeton, NJ, USA, 08540 24 Phone: 609-452-5817 25 Email: [email protected] 26 27 28 29

30 Abstract

31 This study investigates the association between the Pacific Meridional Mode (PMM)

32 and tropical cyclone (TC) activity in the western North Pacific (WNP). It is found that

33 the positive PMM phase favors the occurrence of TCs in the WNP while the negative

34 PMM phase inhibits the occurrence of TCs there. Observed relationships are

35 consistent with those from a long-term preindustrial control experiment (1000 years)

36 of a high-resolution TC-resolving Geophysical Fluid Dynamics Laboratory (GFDL)

37 Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model. The

38 diagnostic relationship between the PMM and TCs in observations and the model is

39 further supported by sensitivity experiments with FLOR. The modulation of TC

40 genesis by the PMM is primarily through the anomalous zonal vertical wind shear

41 (ZVWS) changes in the WNP, especially in the southeastern WNP. The anomalous

42 ZVWS can be attributed to the responses of the atmosphere to the anomalous

43 warming in the northwestern part of the PMM in the positive PMM phase, which

44 resembles a classic Matsuno-Gill pattern. Such influences on TC genesis are

45 strengthened by a cyclonic flow over the WNP. The significant relationship between

46 TCs and the PMM identified here may provide a useful reference for seasonal

47 forecasting of TCs and interpreting changes in TC activity in the WNP.

51 2

52 1. Introduction

53 Tropical cyclones (TC) are among the most destructive and costly natural disasters on

54 the earth (e.g., Rappaport 2000; Pielke Jr et al., 2008; Zhang et al., 2009). The

55 occurrence of TCs has long been the subject of scientific inquiry(e.g., Mitchell, 1932;

56 Gray, 1968; Henderson-Sellers et al., 1998; Simpson et al., 1998; Vitart and Stockdale,

57 2001; Liu and Chan, 2003; Klotzback, 2007; Chan, 2008; Sippel and Zhang, 2008;

58 Lin et al., 2014; Vecchi et al., 2014). Large-scale climate variations in various basins

59 affect the occurrence of TCs in the WNP by both local and remote forcing. In general,

60 variations in TC occurrence in the WNP are closely associated with the sea surface

61 temperature (SST) patterns such as the El Niño Southern Oscillation (ENSO) (Chan,

62 1985; Wu and Lau, 1992; Chan, 2000; Wang and Chan, 2002; Camargo and Sobel,

63 2005; Zhang et al., 2012), the Indian ocean warming (Du et al., 2010; Zhan et al.,

64 2010; Zhan et al., 2014), the North Pacific Gyre Oscillation (Zhang et al., 2013) and

65 the Pacific Decadal Oscillation (PDO; Lee et al., 2012; Liu and Chan, 2012;

66 Girishkumar et al., 2014). Recently, it was suggested that the SST in the North

67 Atlantic also modulates TC activity in the WNP (Li et al., 2013; Huo et al., 2015).

68 The Pacific Meridional Mode (PMM) is here defined as the first Maximum

69 Covariance Analysis (MCA) Mode of SST and 10-m wind vectors in the Pacific, and

70 describes meridional variations in SST, winds and convection in the tropical Pacific

71 Ocean (Chiang and Vimont, 2004). The PMM bears marked resemblance to the

72 Atlantic Meridional Mode (AMM) in terms of its principal mechanisms, impacts and

73 interactions with extra-tropical climate systems (Chiang and Vimont, 2004). For

74 example, the North Pacific Oscillation (NPO) has been found to force the PMM,

75 while the North Atlantic Oscillation (NAO) acts to force the AMM (Vimont et al.,

76 2003).

77 The PMM is closely associated with ENSO (Chang et al., 2007; Zhang et al., 2009;

78 Larson and Kirtman, 2013; Lin et al., 2014), with the PMM peaking around April and

79 May whereas ENSO usually reaches its peak in boreal winter (Chang et al., 2007;

80 Zhang et al., 2009; Larson and Kirtman, 2013; Lin et al., 2014). Previous studies have

81 indicated that the PMM may set the stage for the seasonal peak of ENSO (Larson and

82 Kirtman, 2014). However, the PMM can also exist independently of ENSO, with the

83 ENSO effect routinely removed through linear regression onto the cold tongue index

84 (CTI) when calculating the PMM index (Chiang and Vimont, 2004; Zhang et al., 2009;

85 Larson and Kirtman, 2014).

86 Previous research has found that the seasonal occurrence and accumulated energy of

87 hurricanes in the North Atlantic are strongly influenced by the AMM (Vimont and

88 Kossin, 2007; Smirnov and Vimont, 2010). More recently, Patricola et al., (2014)

89 examined the combined influence of the PMM and ENSO on hurricane activity in the

90 North Atlantic. Basically, the PMM consists of two parts: a northwestern part and a

91 southeastern part in the Eastern Pacific (Chiang and Vimont, 2004). During the

92 positive PMM phase, positive SST anomalies prevail in the northwestern part while

93 negative SST anomalies prevail in the southeastern part (Chiang and Vimont, 2004).

94 Such SST anomalies can be hypothesized to affect TC activity in the WNP by

95 modulating the large-scale circulation, much like the PMM has been found to

96 modulate weather and climate in East Asia (Li et al., 2010; Li and Ma, 2011).

97 Therefore, it is of great interest to examine whether the PMM modulates the

98 occurrence of TCs in the WNP.

99 The objectives of this study are thus: i) to assess whether and to what extent the PMM

100 modulates TC activity in the WNP, and ii) to dissect the mechanisms underpinning the

101 modulation based on both observations and model simulation. This study is expected

102 to enhance the understanding of TC activity in the WNP and to provide valuable

103 insights for seasonal forecasting of the occurrence of TCs over that region. Moreover,

104 since the PMM is linked to extra-tropical climate systems such as NPO (Rogers, 1981)

105 and NPGO (Di Lorenzo et al., 2010) which is the second principal mode of SST

106 anomalies in the North Pacific (Vimont et al., 2001), this study can play a role in

107 bridging the association of extra-tropical and tropical interactions.

108 The remainder of this paper is organized as follows. Section 2 presents the data and

109 methodology, while Section 3 discusses the results based on observations and

110 simulations. Section 4 includes the discussion and summarizes the main conclusions.

111 2. Data and Methodology 112 2.1 Data

113 The TC data are obtained from International Best Track Archive for Climate

114 Stewardship (IBTrACS; Knapp et al., 2010). The study period is 1961 - 2013 because

115 TCs after the 1960s are more reliable (Chan, 2008). The key meteorological variables

116 such as zonal and meridional wind fields, geopotential height, vorticity and relative

117 humidity are from the Japan Meteorology Agency (JMA) 55 reanalysis data (JRA-55).

118 To substantiate and reinforce the results, the National Centers for Environmental

119 Prediction and the National Center for Atmospheric Research (NCEP/NCAR; Kalnay

120 et al,. 1996) reanalysis data and the Modern Era Retrospective-analysis for Research

121 and Applications (MERRA) reanalysis are also employed. The JRA-55 is available

122 since 1958 on a global basis with a spatial resolution of 1.25° × 1.25°. It is based on a

123 new data assimilation system that reduces many of the problems reported in the first

124 JMA reanalysis (Ebita et al., 2011; Kobayashi et al., 2015). MERRA is produced by

125 the National Aeronautics and Space Administration (NASA) for the satellite era, using

126 a major new version of the Goddard Earth Observing System Data Assimilation

127 System Version 5 (GEOS-5) (Rienecker et al., 2011). MERRA data is only available

128 from 1979. The zonal vertical wind shear (|du/dz| or |uz|) is defined as the magnitude

129 of the differences in zonal wind between 200 hPa and 850 hPa level. The years used

130 in the above three reanalysis data are consistent with TC data.

131 Monthly estimates of SST are taken from the Met Office Hadley Centre

132 HadSST3.1.1.0 (Kennedy et al., 2011). The PMM index is calculated following the

133 method described in Chiang and Vimont (2004). In calculating it, the seasonal cycle

134 and the linear trend are first removed by applying a three-month running mean to the

135 data, and then subtracting the linear fit to the cold tongue index (CTI, Deser and

136 Wallace, 1987) from every single spatial point to remove correlations with El Niño

137 (Chiang and Vimont, 2004). The CTI is defined as the average SST anomaly over

138 6 °N - 6 °S, 180 - 90 °W minus the global mean SST (Deser and Wallace, 1987).

139 The peak season for TCs is defined as July-October. The results are consistent if we

140 slightly change the definition of the TC peak season from June to November or from

141 July to November (not shown).

142 2.2 Model

143 This study employs a newly developed high-resolution coupled climate model for

144 experiments, the Geophysical Fluid Dynamics Laboratory (GFDL) Forecast-oriented

145 Low Ocean Resolution Version of CM2.5 or FLOR (e.g., Vecchi et al., 2014; Jia et al.,

146 2015;Yang et al., 2015). The aim in the development of FLOR was to represent

147 regional scales and extreme weather and climate events (e.g., TCs) keeping

148 computational costs relatively low. For these reasons, this model is characterized by

149 high-resolution land and atmosphere components but relatively low-resolution ocean

150 component (Vecchi et al., 2014). The atmosphere and land are the same as those of the

151 GFDL Climate Model version (CM) 2.5 (CM2.5; Delworth et al., 2012) with a spatial

152 resolution of 50 km × 50 km. The ocean and sea ice components of FLOR are similar

153 to those in the CM2.1 (Delworth et al., 2006) with a spatial resolution of 1×1 degree.

154 The relatively low spatial resolution of the ocean and sea ice components in FLOR

155 enables large ensembles and better efficiency for seasonal forecasting. This study

156 utilizes the flux-adjusted version of FLOR (FLOR-FA) for sensitivity experiments in

157 which climatological adjustments are applied to the model's momentum, enthalpy and

158 freshwater flux from the atmosphere to the ocean to give the model's SST and surface 7

159 wind stress a closer climatology to the observations over 1979 - 2012 (Vecchi et al.,

160 2014). For more details about the FLOR model, please refer to Vecchi et al. (2014),

161 Jia et al. (2015) and references therein.

162 2.3 Control simulation

163 A 2500-year long control simulation was performed with the FLOR-FA by prescribing

164 radiative forcing and land use representative of 1860. These experiments are referred

165 to as "Pre-industrial" experiments with FLOR-FA. The control experiments are run for

166 a total of 2500 years, but here we focus on the first 1000 years for the analysis of

167 PMM and TCs for the sake of computing efficiency. The 500-year control simulation

168 by prescribing radiative forcing and land use representative of 1990 with FLOR-FA is

169 also analyzed on PMM-TC association. Because the PMM-TC association based on

170 FLOR-FA 1990 is consistent with that on FLOR-FA 1860, this study only shows

171 results based on FLOR-FA 1860.

172 2.4 Tracking algorithm

173 TCs in FLOR are tracked from 6-hourly model output by using the tracker developed

174 by Harris et al. (in prep) as implemented in Murakami et al. (2015, submitted to

175 Journal of Climate). The temperature anomaly averaged vertically over 300 and 500

176 hPa (ta) and sea level pressure (SLP) are key factors of this tracker. The tracking

177 procedures are as follows.

178 (1) Local minima of the smoothed SLP field are found. The location of the center is

179 properly adjusted by fitting a biquadratic to the SLP and locating the center at the

180 minimum.

181 (2) Closed contours is in an interval of 2 hPa (dp) around every single SLP low center.

182 The Nth contour is marked as the contiguous region surrounding a low central pressure

183 P with pressures lower than dp × N + P, as detected by a “flood fill” algorithm. It is

184 noted that the contours are not required to be circular and a maximum radius of 3,000

185 km is searched from each candidate low center.

186 (3) If the distances of contours are close enough, the low is counted as a TC center. In

187 this way, the tracker attempts to find all closed contours within a certain distance of

188 the low center and without entering contours belonging to another low. The maximum

189 10-m wind inside the set of closed contours is taken as the maximum wind speed at

190 that time for the storm.

191 (4) Warm cores are detected via similar processes: closed 1K contours for FLOR are

192 found surrounding the maximum ta within a TC's identified contours, no more than 1

193 degree from the low center. This contour must have a radius less than 3 degrees in

194 distance. If such a core is not found, it should not be marked as a warm-core low

195 center and the center is rejected.

196 (5) TC centers are combined into a track by taking a low center at time T – dt,

197 extrapolating its motion forward dt, and then seeking for storms within 750 km. It is

198 noted that a deeper low center has higher priority of tracking.

199 (6) The following criteria are required to pick up the final TCs.

200 a. A lifespan of at least 72 hours.

201 b. At least 48 cumulative (not necessarily consecutive) hours with a warm core.

202 c. At least 36 consecutive hours of a warm core with winds greater than 17.5

203 m s–1.

204 TC genesis is confined equatorward of 40 °N. TC density in the WNP is binned into

205 5° × 5° grid boxes at a 6-hour interval.

206 3. Results

207 The analysis results of the PMM-TC association are derived from observations, a

208 1000 year control experiment, and sensitivity experiments based on FLOR-FA.

209 3.1 Observations

210 The regressions of SST and 10-m wind fields onto the PMM index in observations are

211 shown in Figure 1a. There is a characteristic pattern to the PMM in the central and

212 eastern Pacific, with warming in the northwestern part (subtropical region) and

213 cooling in the southeastern part (tropical) (Figure 1a). The warming in the central

214 Pacific is in the vicinity of the western North Pacific and may affect TC activity there.

215 The regressions of TC track density onto the PMM index for 1961-2013 are largely

216 positive in the WNP in observations, indicating that TC activity is enhanced

217 (suppressed) in the WNP in the positive (negative) PMM phase (Figure 1b). In

218 addition, we calculate the correlation between the time series of the PMM index and

219 the TC frequency in the WNP for the period of 1961 - 2013. For each year, the PMM

220 index is calculated by averaging the monthly PMM index over the peak TC season

221 (July-October). The correlation coefficient between the two indices is 0.46, significant

222 at the 0.01 level (Figure 2). Although previous studies have argued that the occurrence

223 of the PMM is independent of ENSO (Zhang et al., 2009; Larson and Kirtman, 2014),

224 and the linear ENSO effect has been removed when calculating the PMM index,

225 partial correlation is calculated by controlling the Niño 3 index to further exclude the

226 linear influence of ENSO on the PMM-TC association. The partial correlation

227 coefficient is slightly more significant (0.48) after controlling for the ENSO effect

228 (not shown).

229 To analyze the observed relationship between WNP TC genesis and PMM, the WNP

230 is divided into five sub-regions following the definition of sub-regions in the WNP in

231 Wang et al. (2013): the South China Sea (SCS), northwestern part (NW), northeastern

232 part (NE), southwestern part (SW) and southeastern part (SE) (Figure 3). The SE part

233 has a relatively high density of TC genesis (Figure 3). The PMM index has a

234 significant positive correlation with TC frequency in the SE, NE and NW parts

235 (Figure 3).

236 To further assess which large-scale atmospheric conditions may be acting to modulate

237 the TC occurrence, a suite of variables associated with TC genesis (Chan, 2000; Wang

238 and Chan, 2002) based on JRA-55 are regressed onto the PMM index (Figure 4).

239 Chan and Liu (2004) found that the increase in local SST has no significant influence

240 on TC activity in the WNP. Indeed, the modulation of local large-scale circulation by

241 remote SST anomalies (e.g., ENSO) largely leads to changes in TC activity there

242 (Chan, 2000; Wang and Chan, 2002; Chan and Liu, 2004; Camargo and Sobel, 2005).

243 Therefore, instead of analyzing SST and maximum potential intensity, we analyze

244 600-hPa relative humidity, 850-hPa relative vorticity and zonal vertical wind shear

245 (ZVWS) for TC genesis. In general, the regressions of the PMM index on JRA-55,

246 NCEP/NCAR, and MERRA reanalysis data are consistent. Thus, we only show the

247 results from JRA-55 reanalysis data. The regression of ZVWS is characterized by

248 strong negative anomalies in a large portion of the WNP (0 - 20 °N, 120 °E - 180 °E),

249 which would act to enhance TC genesis (Figure 4). Further analysis indicates that the

250 changes in ZVWS arise from anomalous westerly (easterly) in 850 hPa (200 hPa)

251 level (Figure 4) although the anomalous westerly in 850 hPa is mainly located in the

252 western part of the WNP. Such anomalous winds in the lower and upper levels can

253 weaken the prevalent westerlies in the upper level (200 hPa) and the prevalent

254 easterlies (850 hPa) in the lower level over the WNP. We hypothesize that the large-

255 scale circulation responses to the SST patterns of the PMM alter the Walker

256 Circulation, which is responsible for the changes in the lower and upper level wind

257 flow. The mechanisms underlying how the vertical wind shear is modulated by the

258 large-scale circulation are discussed in detail in Subsection 3.3. The anomalous

259 positive relative humidity in the SE part also supports higher TC genesis in the WNP

260 in MERRA. Although the MERRA reanalysis is only available from 1979, the

261 satellite-based data may be more reliable (not shown). The AMM pattern (the Atlantic

262 counterpart of PMM) also has a significant positive correlation with hurricane

263 frequency in the North Atlantic because of increased subtropical SST, convergence

264 and low sea level pressure and decreased vertical wind shear (Vimont and Kossin,

265 2007; Smirnov and Vimont, 2010).

266 267 3.2 Results from the long-term control run

268 The 1000-year control simulation of FLOR-FA with 1860 radiative forcing is

269 analyzed to further understand the PMM-TC association. The main purpose is to test

270 whether such PMM-TC association also holds for the long-term control simulation of

271 FLOR-FA, which would provide a dynamical framework in which to understand the

272 causes of this connection. The PMM index in the control experiment is also calculated

273 based on the method proposed in Chiang and Vimont (2004). The PMM in FLOR-FA

274 control simulation (Figure 5a) captures the fundamental features of the observed

275 PMM (Figure 1a), indicating that the FLOR-FA model is capable of simulating the

276 basic structure of the PMM in a fully-coupled numerical climate system. Despite

277 these encouraging comparison, the control experiment of FLOR-FA produces a

278 weaker cooling and 10-m wind fields in the southeastern part of PMM, and stronger

279 cooling northwest of the positive anomalies compared to observation. In addition, the

280 squared covariance fraction of the PMM in observation is 53% (Chiang and Vimont,

281 2004) whereas it is 30% in the control experiment, suggesting a weaker role of PMM

282 in the control experiment.

283 The regressions of TC track density on the PMM index are generally positive in the

284 WNP in control experiment (1000-year, Figure 5b), consistent with observations

285 (Figure 1b). However, the positive regressions are largely confined in the southeastern

286 WNP (Figure 5b). The correlation coefficient between the simulated PMM and WNP

287 TC frequency is 0.37 for the 1000-year simulation, significant at 0.01 level of

288 significance (Figure 6a). Figure 6b shows the histogram of the correlation coefficients

289 in moving 50-year chunks and all are positive. The correlation coefficient from the

290 observations is within the model spread. This suggests that the observed TC-PMM

291 association may be robust, as it is significant even in a long-term climate control

292 simulation.

293 The model shows negative anomalies in ZVWS in the southeastern part (0 – 20 °N,

294 150 °E–180 °E) of the WNP (Figure 7), similar to those based on observations

295 (Figures 4). There are positive anomalies of ZVWS westward of 150°E in the control

296 experiment, suggesting unfavorable condition for TC genesis there. This is

297 responsible for different spatial patterns for TC genesis between observation and

298 control simulation (Figure 3). Observations show that TC genesis is promoted in

299 almost all the sub-regions in observations (Figure 3). In contrast, the control

300 simulation shows that TC genesis is enhanced in the southeastern part while

301 suppressed in the southwestern sub-region of the WNP (Figure 3). It is noted that we

302 have also analyzed the 500-year FLOR-FA control with 1990 radiative forcing and the

303 results derived from both control runs are consistent in terms of the PMM pattern and

304 TC-PMM association. A possible reason responsible for discrepancy in TC genesis

305 between the control experiment and observations is the pre-industrial radiative forcing

306 in the control experiment.

307 In summary, the 1000-year control experiment produces quantitatively consistent

308 results with observation, with positive phases of the PMM conducive to WNP TC

309 genesis, and anomalous reductions of ZVWS during the positive PMM phases.

310 However, TC genesis exhibits different regional relationships between observations

311 and the control experiment, which may be explained by different anomalous ZVWS

312 patterns in model and observations. Interestingly, the regressions of relative humidity

313 in this control experiment is consistent with those in MERRA reanalysis (figure not

314 shown) in which positive anomalies are observed eastward of 140°E. In order to

315 further investigate the mechanisms underpinning how PMM modulates TC genesis in

316 the WNP, a set of sensitivity experiments have been performed using FLOR-FA and

317 will be discussed in next subsection.

318 3.3 Results from sensitivity experiments

319 To isolate the role of the PMM, a set of perturbation experiments were performed

320 with FLOR-FA using 1860 radiative forcing. In the first experiment, SST is restored

321 to a repeating annual cycle in the PMM region with a 5-day restoring timescale,

322 which is taken as the control run (CTRL) (Figure 8). The second experiment is set up

323 by prescribing the sum of the annual cycle of SST and the temporally constant SST

324 anomalies associated with positive PMM patterns (denoted as PPMM) in the PMM

325 region (Figure 8). After an initial 100-year spinup, both experiments are integrated for

326 100 years. The mask of the restoring SST is shown in Figure 8, with the coefficient

327 varying from 1 to 0 when moving from the center to the boundary of the masking

328 region. By subtracting the CTRL experiment from the PPMM experiment, one can

329 assess the net influence of the positive PMM phase on the climate system.

330 The WNP mean TC frequency during the peak TC season (JASO, July to October) in

331 the 100-year PPMM experiment (19.4) is significantly higher than the control

332 experiment (17.5) at 0.01 significance level (Table 1). The difference in WNP TC

333 frequency between the PPMM experiment (26.7) and CTRL (24.7) is also significant

334 for June-November (JJASON) (Table 1). To better represent the differences between

335 the two experiments, TC density is computed by binning TC centers onto each 5°×5°

336 grid box in the WNP. Positive anomalies of TC density are observed in the eastern

337 part of the WNP (Figure 9). However, the positive anomalies have higher magnitude

338 than the negative ones. These positive anomalies arise from an enhanced TC genesis

339 in the PPMM experiment (Figure 10). The occurrence of TC genesis in the PPMM

340 experiment is higher than that in the control run in the eastern part of the WNP

341 (Figure 10). These experiments corroborate that the positive PMM phases promote

342 TC genesis in the WNP, especially in the eastern portion of the WNP. A number of

343 studies have proposed mechanisms on how remote SST patterns affect TC genesis in

344 the WNP, including the Matsuno-Gill pattern (Matsuno, 1966; Gill, 1980), Pacific-

345 East Asia teleconnections (Wang et al., 2000; Wang and Chan, 2002), the Philippine

346 anticyclone induced by Indian ocean warming (Xie et al., 2009; Ha et al., 2014) and

347 SST gradients (Zhan et al., 2013; Li and Zhou, 2014). These studies have emphasized

348 the importance of the Philippines anticyclone and monsoon trough in modulating TC

349 genesis and how SST anomalies modulate the large-scale circulation in remote areas.

350 To explain why TC genesis in the PPMM experiment is higher in the eastern part of

351 the WNP, the large-scale atmospheric factors related to TC genesis are analyzed. The

352 differences between the PPMM and control run resemble the regression of those

353 variables onto the PMM index in both control experiment and observations (Figure

354 11). This suggests that the SST pattern in the positive PMM phase and within that

355 particular box drives the modeled and observed TC genesis in the WNP, especially in

356 its eastern part. Specifically, negative anomalies of ZVWS are induced by the PPMM,

357 arising from westerly anomalies at the850 hPa level and easterly anomalies at the 200

358 hPa (Figure 11). These changes can be described as a change in the Walker

359 Circulation. The question is then related to the mechanisms that lead to the changes in

360 the Walker Circulation. It is known that the positive PMM pattern features a warming

361 in the northwestern part and a cooling in the southeastern part in the tropical eastern

362 Pacific (Figures 1, 5, 12). A cyclonic 850 hPa flow is forced by the anomalous

363 warming in the tropical central Pacific through the Matsuno-Gill response (Matsuno,

364 1966; Gill, 1980). The responses to anomalous positive PMM warming are quite

365 similar to the Matsuno-Gill pattern under asymmetric heating (Gill, 1980), showing

366 that a cyclonic pattern is located northwestward of the heating (Figure 12a). In

367 addition, the diabatic heating center can also be identified by negative outgoing

368 longwave radiation (OLR) anomalies (Figure 12a). The anomalous cyclonic flow

369 pattern in the WNP is more likely to cause a weaker Philippine anticyclone, a weaker

370 subtropical high and elongated monsoon trough, which are favorable conditions for

371 TC genesis in the WNP (Wang et al., 2000; Wang and Chan, 2002). Intensified low-

372 level westerly and easterly flows move towards the PPMM heating center from east

373 and west, respectively (Figure. 12a). Such patterns are also pronounced in the profile

374 of zonal and vertical winds (Figure 12b). An updraft of the Walker circulation shifts to

375 the eastern part of the WNP (around 180°). Notice that the zonal wind is averaged

376 over 0° to 20°N when calculating vertical profile for the zonal and vertical wind

377 (Figure 12b). Anomalous easterlies (westerlies) in upper (lower) levels is elongated

378 from 140°E to 180°E (Figure 12b). Such tropospheric flow patterns weaken the

379 westerlies in the upper levels (e.g., 200 hPa) and easterlies in the lower levels (e.g.,

380 850 hPa). The combined effect of changes in the lower and upper level winds is

381 responsible for the weakened zonal vertical wind shear in the eastern part of the WNP

382 (Figure 11), which leads to increased TC genesis there. Therefore, enhanced TC

383 genesis in the WNP can be attributed to weaker ZVWS, which may arise from the

384 responses of the atmosphere to the anomalous warming which is part of the positive

385 PMM pattern by the Matsuno-Gill mechanisms.

386 387 4. Discussion and conclusion 388 The Pacific Meridional Mode (PMM) has been the subject of heightened interest by

389 the scientific community because of its role in bridging across the extra-tropical and

390 tropical climate systems. It is thus of high scientific relevance whether the PMM is

391 linked to TCs in the WNP given that previous studies claimed that its counterpart

392 AMM is intimately associated with hurricane activity in the North Atlantic (Vimont

393 and Kossin, 2007; Smirnov and Vimont, 2010; Patricola et al., 2014).

394 This study has analyzed the observed and modeled association between the PMM and

395 TC activity in the WNP. Our results show that the positive PMM phase promotes the

396 occurrence of TCs in the WNP. There is a general consistency in the diagnostic TC-

397 PMM relationship in observations and in a long-term control experiment (1000 years)

398 of FLOR-FA. Sensitivity experiments using the model show causality in the PMM/TC

399 relation. The modulation TC genesis by the PMM originates mainly from the

400 abnormal ZVWS in the WNP, especially in its southeastern portion as shown in the

401 control experiment and sensitivity experiments. The anomalous ZVWS can be

402 attributed to the responses to the atmosphere to the anomalous warming in the

403 positive PMM pattern through Matsuno-Gill mechanisms. Such results are supported

404 by the cyclonic flow in the Philippine Sea and relevant flow patterns over the Pacific

405 in the sensitivity experiments.

406 The PMM is linked to the climate modes in the North Pacific such as NPO and NPGO

407 (Chiang and Vimont, 2004). For example, NPO, which is the atmospheric component

408 of NPGO, tends to force the PMM (Chiang and Vimont, 2004). Therefore, PMM

409 bridges the extra-tropical and tropical climate systems. As shown in Zhang et al.

410 (2013), NPGO/NPO modulates the occurrence of TCs in the WNP and it plays an

411 even stronger role than ENSO and PDO based on observations. In addition, Chen et al.

412 (2015) reported that the Spring NPO can modulate TC activity in the WNP in the

413 following peak TC season. However, detailed mechanisms are yet to be unraveled.

414 This study may provide some hints to explain the impacts of NPGO or NPO on TCs

415 in the WNP. It is known that the PMM pattern is obtained by removing the effect of

416 ENSO via linearly removing of the cold tongue index (Deser and Wallace, 1990). TCs

417 in the WNP are affected by various climate modes such as ENSO, the Indian ocean

418 warming, NPGO, PDO, and the SST in the North Atlantic Ocean. This study has also

419 found that the PMM significantly modulates the occurrence of TCs in the WNP. An

420 on-going comprehensive study involving all of these SST patterns is analyzing their

421 relative roles in influencing TC genesis in the WNP. Given the important role of the

422 PMM in modulating the climate, the linkage between PMM and TC activity in other

423 ocean basins will be analyzed in our future study. Moreover, the results of this work

424 indicate that the predictability of the PMM is potentially of great value for the

425 prediction of TC activity; future studies will examine the predictability of the PMM

426 and TCs, by leveraging seasonal forecasting experiments with FLOR-FA.

427

428 Acknowledgments

429 The authors thank Xiaosong Yang and Andrew Wittenberg for their valuable

430 comments on an earlier version of this paper. WZ benefits from discussion with

431 Lakshmi Krishnamurthy on this study. The authors thank Seth Underwood and

432 Fanrong Zeng for their helpful assistance in experiments.

433

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640 List of Tables

641 Table 1. TC frequencies in JASO and JJASON for PPMM and control experiment

642 respectively. Boldface numbers represent the differences that are statistically

643 significant and * represents 0.01 level of significance.

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661 List of Figures

662 Figure 1. The regressions of (a) SST (unit: °C) and 10 m wind fields (unit: ms-1) and

663 (b) tropical cyclone track density onto the standardized PMM index. The spatial

664 domain in (a) is consistent with that in Chiang and Vimont (2004).

665 Figure 2. (a) The standardized time series of the yearly PMM index and WNP TC

666 frequency for the period 1961-2013 and (b) scatterplot of PMM vs WNP TC

667 frequency (dot) and fitted line (black line). The yearly PMM index and WNP TC

668 frequency are calculated by averaging over July-October .

669 Figure 3. The five sub-regions of TC genesis in the WNP. Blue dots represent TC

670 genesis locations in the period of 1961 - 2013. The text "obs" denotes the correlation

671 between PMM and TC frequency in observations whereas "ctrl" denotes that in

672 control run. The numbers following "obs" or "ctrl" denote correlation coefficients in

673 each subregion. "WNP(obs)" represents the correlation coefficient between PMM and

674 TC frequency in the WNP in observations while "WNP(ctrl)" denotes that in control

675 run (1000 years). *,**, and *** represent the significance level 0.1, 0.05 and 0.01,

676 respectively.

677 Figure 4. The regressions of 600 hPa relative humidity, 850 hPa relative vorticity

678 (×10-6), zonal vertical wind shear (ms-1), u850 (ms-1) and u200 (ms-1) onto the

679 PMM index based on JRA-55 reanalysis. The thick red lines are used to divide the

680 five subregions in the WNP.

681

682 Figure 5. Regression of (a) SST anomalies (°C) and 10 m wind fields (ms-1) and (b)

683 TC density onto the standardized PMM index in a 1000-yr control simulation of the

684 FLOR-FA.

685 Figure 6. (a) The scatterplot of PMM vs WNP TC frequency (blue dots) and fitted

686 line (black) in the control experiment, and (b) the histogram of correlation between

687 WNP TC frequency and PMM for every 50 years (the correlation between WNP TC

688 frequency and PMM based on observations is shown in the thin red bar).

689 Figure 7. The regressions of 600 hPa relative humidity, 850 hPa vorticity (×10-6),

690 zonal vertical wind shear (ms-1), u850 (ms-1) and u200 (ms-1) onto the PMM index

691 in the control experiment of FLOR-FA. The thick red lines are used to divide the five

692 subregions in the WNP.

693 Figure 8. The mask for the PMM region and the SST anomalies (°C) in positive PMM

694 phase added to the seasonal cycle in the experiment.

695 Figure 9. TC track density in the PPMM experiment (top panel), control experiment

696 (CTRL; middle panel) and the difference PPMM - CTRL (bottom panel).

697 Figure 10. As in Fig.9, but for TC genesis.

698 Figure 11. The differences in 600 hPa relative humidity, 850 hPa relative vorticity

699 (×10-6), ZVWS (200 - 850 hPa, unit: ms-1), u850 (ms-1) and u200 (ms-1) between

700 PPMM and control experiments. The thick red lines are used to divide the five

701 subregions in the WNP.

702 Figure 12. (a) The differences in 850 hPa wind fields (vector, unit: ms-1) and OLR

703 (contour, unit: W·m-2) between PPMM and CTRL experiments with FLOR-FA, and

704 the SST pattern of the positive PMM pattern (shading, unit: °C) and (b) the vertical

705 profile of zonal wind (ms-1) from 1000 hPa to 100 hPa level (the zonal wind is

706 averaged over 0° and 20°N) between PPMM and CTRL experiments. The vertical

707 velocity is multiplied by -100 in (b).

708

709 710

711 Table 1. TC frequencies in JASO and JJASON for PPMM and control experiment 712 (CTRL) respectively. Boldface numbers represent the differences that are statistically 713 significant, with *** representing results significant at the 0.01 level based on t-test. Frequency JJASON JASO PPMM 26.7 19.4 CTRL 24.7 17.5 PPMM - CTRL 2.0*** 1.9*** 714

715 Figures

716 Figure 1. The regressions of (a) SST (unit: °C) and 10 m wind fields (unit: ms-1) and

717 (b) tropical cyclone track density onto the standardized PMM index. The spatial

718 domain in (a) is consistent with that in Chiang and Vimont (2004).

719

720

721 722 Figure 2. (a) The standardized time series of the yearly PMM index and WNP TC 723 frequency for the period 1961-2013 and (b) scatterplot of PMM vs WNP TC

724 frequency (dot) and fitted line (black line). The yearly PMM index and WNP TC 725 frequency are calculated by averaging over July-October . 726

727

728 Figure 3. The five sub-regions of TC genesis in the WNP. Blue dots represent TC

729 genesis locations in the period of 1961 - 2013. The text "obs" denotes the correlation

730 between PMM and TC frequency in observations whereas "ctrl" denotes that in

731 control run. The numbers following "obs" or "ctrl" denote correlation coefficients in

732 each subregion. "WNP(obs)" represents the correlation coefficient between PMM and

733 TC frequency in the WNP in observations while "WNP(ctrl)" denotes that in control

734 run (1000 years). *,**, and *** represent the significance level 0.1, 0.05 and 0.01,

735 respectively.

736

737 738 Figure 4. The regressions of 600 hPa relative humidity, 850 hPa relative vorticity 739 (×10-6), zonal vertical wind shear (ms-1), u850 (ms-1) and u200 (ms-1) onto the PMM 740 index based on JRA-55 reanalysis. The thick red lines are used to divide the five 741 subregions in the WNP.

742

743 744 Figure 5. Regression of (a) SST anomalies (°C) and 10 m wind fields (ms-1) and (b) 745 TC density onto the standardized PMM index in a 1000-yr control simulation of the 746 FLOR-FA. 747 748 749 750

751 752 Figure 6. (a) The scatterplot of PMM vs WNP TC frequency (blue dots) and fitted line 753 (black), and (b) the histogram of correlation between WNP TC frequency and PMM 754 for every 50 years (the correlation between WNP TC frequency and PMM based on 755 observations is shown in the thin red bar) in the control experiment of FLOR-FA. 756

757 758 Figure 7. The regressions of 600 hPa relative humidity, 850 hPa vorticity (×10-6), 759 zonal vertical wind shear (ms-1), u850 (ms-1) and u200 (ms-1) onto the PMM index in 760 the control experiment of FLOR-FA. The thick red lines are used to divide the five 761 subregions in the WNP. 762

763 764 Figure 8. The mask for the PMM region and the SST anomalies (°C) in positive PMM 765 phase added to the seasonal cycle in the experiment. 766 767 768

769 770 Figure 9. TC track density in the PPMM experiment (top panel), control experiment 771 (CTRL; middle panel) and the difference PPMM - CTRL (bottom panel). 772 773

774 775 Figure 10. As in Fig.9, but for TC genesis. 776

777 778 Figure 11. The differences in 600 hPa relative humidity, 850 hPa relative vorticity 779 (×10-6), ZVWS (200 - 850 hPa, unit: ms-1), u850 (ms-1) and u200 (ms-1) between 780 PPMM and control experiments. The thick red lines are used to divide the five 781 subregions in the WNP. 782

783 784 Figure 12. (a) The differences in 850 hPa wind fields (vector, unit: ms-1) and OLR 785 (contour, unit: W·m-2) between PPMM and CTRL experiments with FLOR-FA, and 786 the SST pattern of the positive PMM pattern (shading, unit: °C) and (b) the vertical 787 profile of zonal wind (ms-1) from 1000 hPa to 100 hPa level (the zonal wind is 788 averaged over 0° and 20°N) between PPMM and CTRL experiments. The vertical 789 velocity is multiplied by -100 in (b). 790 791