Interannual Variability of the Basinwide Translation Speed ...

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Interannual Variability of the Basinwide Translation Speed of Tropical

Cyclones in the Western North Pacific

Article in Journal of Climate · July 2020

DOI: 10.1175/JCLI-D-19-0995.1

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1

Interannual variability of the basin-wide translation speed of tropical cyclones in 1

the western North Pacific 2

3

Chao Wang1, Liguang Wu2, Jun Lu1, Qingyuan Liu3, Haikun Zhao1, Wei Tian1 and 4

Jian Cao1 5

6

1Key Laboratory of Meteorological Disaster of Ministry of Education, Joint 7

International Research Laboratory of Climate and Environment Change and 8

Collaborative Innovation Center on Forecast and Evaluation of Meteorological 9

Disasters, 10

Nanjing University of Information Science and Technology, Nanjing, China 11

2Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric 12

Sciences, Fudan University, Shanghai, China 13

3Jiangsu Institute of Meteorological Sciences, Nanjing, China 14

15

16

July 18, 2020 17

revised for Journal of Climate 18

19

20

Corresponding author: 21

Dr Chao Wang, E-mail: [email protected] 22

Early Online Release: This preliminary version has been accepted for publication in Journal of Climate, may be fully cited, and has been assigned DOI he final typeset copyedited article will replace the EOR at the above DOI when it is published. © 20 ological Society

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20 American Meteor

10.1175/JCLI-D-19-0995.1.

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Abstract 23

Understanding variations in tropical cyclone (TC) translation speed (TCS) is of 24

great importance for islands and coastal regions since it is an important factor in 25

determining the TC-induced local damages. While investigating the long-term change 26

in TCS was usually subjected to substantial limitations in the quality of historical TC 27

records, here we investigated the interannual variability in TCS over the western North 28

Pacific (WNP) by using the reliable satellite TC records. It was found that both the 29

temporal changes in large-scale steering flow and TC track greatly contributed to 30

interannual variability in the WNP TCS. In the peak season (July-September), TCS 31

changes were closely related to temporal variations in large-scale steering flow, which 32

was linked to the intensity of western North Pacific subtropical high. However, for the 33

late season (October-December), changes in TC track played a vital role in interannual 34

variability in TCS, while the impacts of temporal variations in large-scale steering were 35

weak. The changes in TC track were mainly contributed by the El Niño-Southern 36

Oscillation induced zonal migrations in TC genesis locations, which makes more/less 37

TCs move to the subtropical WNP and thus leads to notable changes in the basin-wide 38

TCS due to the much greater large-scale steering in the subtropical WNP. The increased 39

influences of TC track change on TCS in the late season was linked to the greater 40

contrast between the subtropical and the tropical large-scale steering in the late season. 41

These results have important implications for understanding the current and future 42

variations in TCS. 43

44

Accepted for publication in Journal of Climate. DOI 10.1175/JCLI-D-19-0995.1.

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1. Introduction 45

Tropical cyclones (TCs) in the western North Pacific (WNP) usually bring 46

enormous disasters including heavy precipitation to Pacific islands and coastal regions 47

(Zhang et al. 2009; Peduzzi et al. 2012). Understanding its variability is thus of great 48

importance for disaster mitigation (King et al. 2010). TC activities in the western North 49

Pacific (WNP) experience notable interannual variability, which was found to be 50

closely linked to El Niño-Southern Oscillation (ENSO) (Chan 1985; Lander 1994; Chen 51

et al. 1998; Wang and Chan 2002; Chia and Ropelewski 2002; Camargo and Sobel 2005; 52

Camargo et al. 2007a,b; Chen and Tam 2010; Zhang et al. 2012). Particularly, the warm 53

phase of ENSO (i.e., El Niño) leads to an southeastward shift in TC formation locations 54

through a modification of large-scale circulation patterns and the associated 55

environmental parameters (Wang and Chan 2002; Wu et al. 2012; Wang and Wu 2016; 56

Zhao et al. 2019). The eastward shift in TC genesis locations tends to prolong TC tracks, 57

which increase the time for TCs staying on the warm ocean and thus lead to the stronger 58

TC intensity in El Niño years (Wang and Chan 2002; Camargo and Sobel 2005; Zhao 59

et al. 2018). Moreover, El Niño induced TC genesis and steering flow anomalies make 60

more TCs influence east Asia in the peak season (Zhao et al. 2010). The shifted tracks 61

and intensified TCs further lead to the stronger TC precipitation in the east Asia in El 62

Niño years compared to that in La Niña years(Ren et al. 2006; Zhang et al. 2018). 63

Meanwhile, TC size is also an important factor that controls the duration of strong wind 64

and heavy precipitation and thus the TC-induced damages(Zhai and Jiang 2014; 65

Holland et al. 2019). 66


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These studies mainly focused on the TC characteristics such as genesis, track, 67

intensity, size and precipitation. However, from the view of a specific location, TC-68

induced disasters are not only determined by TC characteristic itself, but also the TC 69

translation speed (TCS), which is an important factor determining TC-induced local 70

damages. For example, the slow translation speed of Typhoon Morakot (2009) 71

produced the record-breaking amount of rainfall and caused catastrophic disasters to 72

Taiwan (Wu 2013). However, relatively few studies focused on the interannual 73

variations in TCS over the WNP. Moreover, since TCS is predominantly controlled by 74

large-scale steering flow (Chan and Gray 1982; Holland 1983), changes in TCS were 75

usually interpreted by temporal variations in steering flows (Kossin 2018). However, 76

for TCs in the globe or a specific TC basin, changes in TC track can also lead to notable 77

variations in the basin-wide TCS due to the non-uniform spatially distributed large-78

scale steering flow (Chu et al. 2012). Considering the notable interannual variations in 79

TC track over the WNP (Wang and Chan 2002; Camargo et al. 2007b), it is convincible 80

that such TC track changes can contribute to variations in TCS in the WNP. Up to now, 81

to what extent can TC track contribute to TCS variations remains unclear. For the 82

aforementioned reasons, our objectives in this study are to investigate the interannual 83

variability in TCS over the WNP, and to explore roles of temporal variations in steering 84

flow and TC track in interannual variability of TCS. The results gained from this study 85

will enrich the understanding of the current and future changes in TCS, which is a hot 86

topic in the community of TC climate study (Kossin 2018; Lanzante 2019; Moon et al. 87

2019; Kossin 2019; Chan 2019). 88


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The remainder of this paper is organized as follows. Section 2 describes the 89

data and methods used in this study, and section 3 illustrates the interannual variability 90

of TCS over the WNP. Section 4 interprets the mechanism linked to TCS variations in 91

the peak season and the late season, and the season-dependent influence of TC track 92

change on TCS is discussed in section 5. A summary and a discussion are presented in 93

section 6. 94

95

2. Data and method 96

TCS was derived from the Joint Typhoon Warming Center (JTWC, Chu et al. 97

2002), which provides six-hourly records of TC center positions (latitudes and 98

longitudes) and maximum sustain wind speeds. The annual TCS was calculated by 99

using the all 6-hourly TC track records for a specific year, great circle arc was used to 100

calculate the distance between neighboring positions along each TC track. In this study, 101

only the records whose maximum wind speed reach tropical storm intensity or exceed 102

17.2 m s-1 were included to calculate TCS. The operational polar-satellite monitor began 103

available since 1966 and TCs would be unlikely to be missed since then (JTWC 1966; 104

McAdie et al. 2009). Song et al (2010) found TC location differences among three best 105

track datasets over the WNP are small during 1951-2007, and there is no abrupt change 106

in the late 1970s when geostationary satellite monitoring was introduced. These results 107

indicate TC locations since the polar-satellite era is sufficient to be used to derive 108

variability of TCS. Therefore, the analysis period in this study was 1966–2018 (Moon 109

et al. 2019; Lanzante 2019). 110


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Monthly wind data from the National Centers for Environmental Prediction 111

(NCEP)-National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al. 112

1996) were used to derive large-scale steering flow, which was defined as the seasonal 113

mean pressure-weighted wind averaged between 850 and 300 hPa (Wu and Wang 2004; 114

Wu et al. 2005). Correlation and composite analyses were used and statistical 115

significance was assessed using the two-tailed Student’s t-test (Wilks 2006). 116

The trajectory model proposed by Wang and Wu (2004) was used to investigate 117

the relative contribution of TC genesis and large-scale steering flow to TC track 118

frequency anomalies. TC track frequency was defined as the number of TCs entering a 119

specific grid box of 2.5° latitudes by 2.5° longitudes. In this model, TCs are treated as 120

point vortexes which start at observed genesis locations and move along with translation 121

vectors which are taken as the sum of large-scale steering and the mean beta drift. Wang 122

and Wu (2015) added random synoptic-scale perturbations to translation vectors to 123

simulate the possible influence of synoptic-scale weather systems on TC motions. The 124

variance and spatial distribution of the random perturbations were calculated from 125

synoptic-scale (3-8 days) winds in the six-hourly NCEP Final (FNL) Operational 126

Global Analysis data. TC tracks simulation terminates once TCs move out of the WNP 127

basin (0°-50°N, 100°E-180°). 128

129

3. Interannual variabilities of TCS in the peak and late seasons 130

Figure 1a shows the time series of annual TCS over the WNP during 1966-2018, 131

which shows an insignificant increasing trend of 0.01 m s-1 decade-1. However, when 132


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TC records for the pre-satellite era (1949-1965) were included, a significant decreasing 133

trend in TCS over the WNP can be identified over 1949-2018 (Fig. 1b, Kossin 2018). 134

The decreasing trend in TCS during 1949-2018 was supposed to be caused by the 135

systematic biases in TC records during the pre-satellite era (Moon et al. 2019; Lanzante 136

2019). Therefore, here we focused on the post-satellite era from 1966 to 2018 (Moon et 137

al. 2019). The insignificant trend in TCS for 1966-2018 was removed to focus on its 138

interannual variability. Considering the notable seasonality in background circulation 139

pattern and TC activity(Lander 1994; Wang 2006), variations in TCS were examined 140

season by season. By comparing TCSs in the early season (April-June), the peak season 141

(July-September) and the late season (October-December) (Wang and Chan 2002), it 142

was found that interannual variability of the WNP TCS is mainly produced by TCs in 143

the peak season and the late season (figure not shown). To investigate the mechanism 144

controlling the variation in the basin-wide TCS, years with anomalous translation speed 145

exceeding one standard deviation were selected to construct the composite. For the 146

peak season, the selected high-speed years were 1969, 1975, 1993, 1995, 1997, 2003 147

and 2016, and the selected low-speed years were 1977, 1978, 1982, 1986, 2000, 2001 148

and 2011. For the late season, the selected high-speed years were 1969, 1975, 1976, 149

1997, 2002, 2011 and 2013, and the selected low-speed years were 1970, 1973, 1983, 150

1985, 2000, 2005 and 2007. 151

For the peak season, the composite large-scale steering flows featured 152

anomalous easterlies in the tropical WNP but westerly anomalies in the subtropical 153

WNP, making up an anomalous anti-cyclonic steering over the WNP (Fig. 2a). The anti-154


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cyclonic steering superposed on the climatological tropical eastward and subtropical 155

westward steering, speeding up the large-scale steering and thus accelerating TC motion. 156

In accordance with the composite results, time series of TCS and large-scale steering 157

over the WNP (weight by climatological TC track frequency) were significantly 158

correlated with a correlation coefficient of 0.41(p<0.05) over 1966-2018 (Fig. 3a). It 159

should be note that large-scale steering speed was less than the actual TCS in the peak 160

season due to the effect of beta drift. These results indicate that temporal variations in 161

large-scale steering indeed play an important role in change of the basin-wide TCS over 162

the WNP in the peak season. 163

For the late season, significant acceleration in large-scale steering was only 164

occurred in the northern and southern boundary of TC activity region in the western 165

WNP, while deceleration in large-scale steering was found in eastern WNP and the 166

active TC activity region (10°N-20°N) (Fig. 2b). This non-uniform distributed 167

anomalies in large-scale steering actually indicate that temporal variations of large-168

scale steering in the late season may play a secondary role in change in the basin-wide 169

TCS over the WNP. To verify this argument, time series of the basin-wide large-scale 170

steering, which was weight by climatological TC track frequency, were compared to 171

that of TCS (Fig. 4a). As expected, their correlation coefficient was only -0.05. This 172

insignificant relationship further confirmed the weak impact of temporal variations in 173

large-scale steering on the basin-wide TCS in the late season. So, what exactly caused 174

the variability in TCS in the late season? Previous studies suggested that TCS is 175

predominantly determined by its embedded environmental steering flows (Holland 176


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1983). Because large-scale steering flow is not spatially uniform, TC track changes can 177

also lead to the change of the basin-wide TCS. Figure 4b shows time series of ambient 178

large-scale steering along individual TC tracks and TCS in the late season over 1966-179

2018. The large-scale steering along TC paths was defined as the magnitude of seasonal 180

mean steering vectors in grid boxes that TCs pass across. It was found that they are 181

significantly correlated with a correlation coefficient of 0.59 (p<0.05), which is in 182

distinct contrast with that between the basin-wide large-scale steering and TCS. The 183

changes in the ambient steering along individual TC tracks can result both from the 184

temporal change of large-scale steering and TC track. To isolate the temporal variability 185

in ambient steering flow, climatological steering flow over 1966-2018 was used to 186

calculate the steering flow along individual TC tracks. In this case, steering flow field 187

was identical in each year, but TC tracks varied year by year. Therefore, the derived 188

change in large-scale steering was solely due to variations in TC track. Interestingly, 189

the climatological steering along individual TC tracks was significantly correlated 190

(r=0.83) with steering calculated from the yearly-varying wind fields over 1966-2018. 191

Meanwhile, time series of the derived large-scale steering with the climatological 192

steering flow and TCS were significantly correlated (r=0.59, p<0.05) as well (Fig. 4c). 193

Thus, we can conclude that changes in TC track played a dominant role in variations of 194

TCS over the WNP in the late season. 195

196

4. Physical interpretation for variations in TCS over the WNP 197

Variations in large-scale steering are tightly linked to changes in the surrounding 198


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large-scale circulations. The composite large-scale steering flow between the high-199

speed years and low-speed years in the peak season featured anomalous westerly 200

steering in the subtropical WNP but easterly steering anomalies in the tropical WNP 201

(Fig. 2a), which indicates a possible linkage between the western North Pacific 202

subtropical high (WNPSH) and TCS in the peak season. Figure 5a further shows the 203

composite 850 hPa wind and geopotential height anomalies between the high-speed 204

years and the low-speed years in the peak season, which features an anti-cyclonic 205

circulation surrounding the zonally elongated positive geopotential height anomaly. 206

This pattern indicates an intensification in the WNPSH in the high-speed years. The 207

intensified WNPSH tends to enhance the mean large-scale steering flow and thus 208

accelerate TC motions. As a result, time series of the WNPSH intensity index (defined 209

as the difference of the zonal wind at 850 hPa between 25°N–35°N, 120°E–150°E and 210

10°N–20°N, 130°E–150°E, He and Zhou 2015) were significantly correlated with the 211

basin-wide averaged large-scale steering (r=0.63, p<0.05) and TCS (r=0.43, p<0.05) 212

during 1966-2018 (Fig. 5b). Previous studies have found variations in the WNPSH are 213

tightly linked to SST anomalies across the Indo-Pacific (Wang et al. 2013; Xie et al. 214

2016; Wu et al. 2018; Wang and Wang 2019; Wu et al. 2020). Particularly, Wang et al 215

(2013) decomposed the interannual variability of the WNPSH into the air-sea coupled 216

mode and the ENSO-forced mode. Here we found that the coupled mode of the WNPSH, 217

which is maintained by the local atmosphere-ocean interaction, is more relevant to 218

variations in the peak season TCS over the WNP, while the ENSO-forced mode may 219

play a secondary role (figure not shown). The results indicate that temporal variations 220


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in large-scale steering flow were tightly linked to the intensity of the WNPSH in the 221

peak season. 222

In the late season, interannual changes in TC track played a dominant role in 223

variations of TCS over the WNP (section 3). Figure 6a shows the composite difference 224

in TC track frequency between the high-speed years and the low-speed years, which 225

features negative anomalies in the western tropical WNP but positive anomalies in the 226

eastern WNP. Due to the much greater large-scale steering in the subtropical WNP 227

compared to the tropical WNP, the increased subtropical TC track frequency eventually 228

acted to increase the basin-wide TCS. Changes in TC track frequency can result from 229

both changes in TC genesis and large-scale steering flow. A trajectory model was used 230

to investigate their relative role in the anomalous TC track frequency. The model can 231

reasonably reproduce the observed TC track frequency anomalies between the high-232

speed years and the low-speed years (Fig. 6b). Contributions of changes in TC genesis 233

and large-scale steering flow to the anomalous TC track frequency were further 234

examined by fixing the large-scale steering and TC genesis to the climatological mean, 235

respectively. It can be found that the anomalous TC track frequency was mainly 236

contributed by TC genesis anomalies, while contribution from large-scale steering was 237

relatively weak (Fig. 6c-d). Particularly, a large portion of TCs formed in the eastern 238

(western) WNP, made more TC enter subtropical (tropical) WNP and thus lead to the 239

acceleration (deceleration) the basin-wide TCS (figure not shown). In the late season, 240

zonal migration in TC formation was found to be associated with the El Niño-Southern 241

Oscillation (Wang and Chan 2002). Figure 7 shows the composite SST and 850 hPa 242


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wind anomalies between the high-speed years and the low-speed years, which features 243

a prominent SST warming in the equatorial central-eastern Pacific. As wave responses 244

to the anomalous convection heating in the equatorial central-eastern Pacific and the 245

Maritime Continent, an anomalous cyclonic circulation occurred in the eastern WNP 246

and an anomalous anti-cyclonic circulation occurred in the western WNP and the North 247

Indian ocean(Wang et al. 2000). The cyclonic circulation over the eastern WNP and the 248

anti-cyclonic circulation over the western WNP were in accordance with the enhanced 249

TC genesis in the eastern WNP but the suppressed TC genesis in the western WNP 250

(Gray 1968, 1979) during the high-speed years. In summary, variations of TCS in the 251

late season were closely linked to the El Niño-Southern Oscillation induced zonal 252

migration in TC genesis locations, which makes more/fewer TCs move to the 253

subtropical WNP and thus leads to notable changes in the basin-wide TCS. 254

We further compared temporal evolutions of the simulated and the observed 255

TCS in the peak season and the late season. It was found the trajectory model can only 256

moderately reproduce inter-annual variability of TCS in the peak season, while it shows 257

a poor skill in simulating the temporal evaluation of TCS in the late season (figure not 258

shown). The relatively poor skill in simulating the late season TCS indicates that inter-259

annual variability of TCS is more sensitive to changes in TC track in the late season 260

than that in the peak season, which is in accordance with the above results. With the 261

observed TC track, large-scale steering surrounding individual TC tracks are 262

significantly correlated with TCS in the late season (Fig. 5b). The results suggest that 263

the TC track model is insufficient to directly describe the interannual variability in TCS 264


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due to its poor skill in simulating the interannual variation in TC track. 265

266

5. Season-dependent influence of TC track change on TCS 267

Why does TC track play a vital role in the basin-wide TCS in the late season, 268

while its impact is relatively weak in the peak season? Because TCS is predominantly 269

determined by the environmental steering flows along individual TC tracks, temporal 270

variations in both TC track and large-scale steering can cause variations in the basin-271

wide TCS. Therefore, we first compared ratios of inter-annual standard deviation in 272

large-scale steering to its long-term mean in the peak season and the late season. It was 273

found that interannual standard deviation in large-scale steering can lead to changes in 274

steering speed by 18% of its climatological mean in the peak season, while the ratio 275

decreased to 9% for the late season. It means that temporal variability in large-scale 276

steering acted as a more effective factor in influencing TCS in the peak season than that 277

in the late season. 278

A remaining issue is why track shifts played a vital role in TCS changes in the 279

late season over the WNP. Actually, the increased sensitivity of the basin-wide TCS to 280

TC track shift in the late season was associated with the spatial distribution of 281

climatological TC track frequency and large-scale steering (Fig. 8). During the late 282

season, the climatological TC tracks are confined to the south of 30°N, where large-283

scale steering is much weaker than that to the north of 30°N (Fig. 8b). Once TCs move 284

into the subtropical westerly jet, it may greatly increase the basin-wide TCS. Because 285

the subtropical large-scale steering is much greater than that in the peak season (Fig.8), 286


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anomalous TC track frequency in subtropical WNP can lead to more notable TCS 287

changes in the late season than those in the peak season. For comparison, TCS showed 288

a lower sensitivity to TC track shift in the peak season due to the weaker contrast in the 289

tropical-subtropical large-scale steering (Fig. 8a). Even so, if we only the large-scale 290

steering along individual TC tracks, correlation coefficient between large-scale steering 291

and TCS in the peak season increased from 0.41 to 0.62 over 1966-2018 (Fig. 3b), 292

indicating the secondary but important role of track shift in TCS during the peak season. 293

Sensitivity experiments with the trajectory model suggested both the anomalous TC 294

genesis and large-scale steering flow contribute to TC track frequency anomalies in the 295

peak season, and the anomalous steering flow plays a dominant role. Moreover, if we 296

considered TCs occurred in July-December as a whole, inter-annual TC track shift also 297

plays a more important role than temporal variability of large-scaling steering in the 298

basin-wide TCS. These results introduce the important role of TC track shift in variation 299

of the basin-wide TCS over the WNP. 300

301

6. Summary and discussion 302

Investigating the long-term change in TCS was usually subjected to substantial 303

limitations in the quality of historical TC records, here we investigated the interannual 304

variability in TCS over the WNP by using the reliable satellite TC records from 1966 305

to 2018. It was found that both changes in large-scale steering and TC track can greatly 306

contribute to inter-annual variability in TCS over the WNP. In the peak season (July-307

September), changes in TCS were closely related to temporal variations in large-scale 308


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steering, while TC track change played a secondary role. The variability in large-scale 309

steering in the peak season was tightly linked to intensity of the WNPSH. Particularly, 310

an intensified (weakened) WNPSH tended to speed up large-scale steering and thus 311

accelerate (decelerate) TC motion. However, for the late season (October-December), 312

changes in TC track played a vital role in the interannual variability in TCS, while 313

impacts of temporal variations in large-scale steering were relatively weak. The 314

interannual changes in TC track were mainly controlled by the El Niño-Southern 315

Oscillation induced zonal migration in TC genesis locations, which makes more/less 316

TCs move to the subtropical WNP and thus leads to notable changes in the basin-wide 317

TCS due to the much greater large-scale steering in the subtropical WNP. The increased 318

influences of TC track change on TCS in the late season was attributed to the greater 319

contrast between the subtropical and tropical large-scale steering in the late season. 320

These results introduce the important role of TC track change in TCS variations, which 321

has important implications for understanding the current and future variations in TCS 322

over the WNP. 323

While future changes in the WNPSH are still quite uncertain (He and Zhou 324

2015), most model studies projected a poleward shift in the WNP TC tracks due to the 325

expansion of tropics in warming scenarios (Murakami et al. 2012; Lucas et al. 2014; 326

Bell et al. 2019; Wang and Wu 2018; Nakamura et al. 2017). The poleward shift in TC 327

tracks indicates an accelerating in the basin-wide TCS in warming scenarios due to the 328

much greater subtropical large-scale steering. In comparing to the uncertain change in 329

large-scale steering associated with the WNPSH, the projected poleward extension of 330


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the TC track indicates its possible more important role in TCS in warming scenarios. 331

Meanwhile, the projected expansion of tropics tends to weaken the contrast in large-332

scale steering between the tropical and subtropical WNP, and thus offset the 333

accelerating effects of track shift on TCS. However, the relative role of the poleward 334

shift in TC track and the weakened subtropical-tropical large-scale steering contrast in 335

TCS in warming scenarios is still unclear. Additionally, TC-induced damages should 336

be more relevant to the variations in TCS of landfalling TCs, whether the identified 337

mechanisms controlling the basin-wide TCS also hold for landfalling TCs is an 338

interesting topic for future study. 339

340

Acknowledgements: This research was jointly supported by the Natural Science 341

Foundation of Jiangsu Province (BK20170941) and the National Natural Science 342

Foundation of China (Grant No. 41705060 and 41730961). The NCEP reanalysis data 343

are available at https://www.esrl.noaa.gov/psd/data/reanalysis/reanalysis.shtml. The 344

best track data from the JTWC are available at http://www.usno.navy.mil/NOOC/ 345

nmfcph/RSS/jtwc/best_tracks. 346

347

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509

510

Figure Captions: 511

Figure 1 Time series of annual mean TCS (red line) for (a) 1966-2018 and (b) 1949-512

2018. Linear trends (blue dash lines) are shown with their 95% two-sided confidence 513

intervals (blue shading). 514

Figure 2 Composite difference in large-scale steering speed (shadings, m s-1) and 515

steering flow (vectors, m s-1) between high-speed years and low-speed years for the (a) 516

peak season and (b) the late season. Dots denote regions where the differences are 517

significant at 95% confidence level. The black contours in (a) and (b) show the mean 518

TC track frequency (number year-1) in the peak season and late season, respectively. 519

Figure 3 Detrended time series of TCS (red line) and the basin-wide averaged large-520

scale steering speed (blue line) in the peak season during 1966-2018, (b) detrended time 521

series of TCS (red line) and large-scale steering speed along individual TC paths (blue 522

line) in the peak season during 1966-2018. 523

Figure 4 Detrended time series of TCS (red line) and basin-wide averaged large-scale 524

steering speed (blue line) in the late season during 1966-2018, (b) detrended time series 525

of TCS (red line) and large-scale steering speed along individual TC paths (blue line) 526

in the late season during 1966-2018, (c) TCS (red line) and climatological large-scale 527

steering speed along individual TC paths (blue line) in the late season during 1966-528


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2018. Linear correlations (R) are shown in the top left of panels. 529

Figure 5 (a) Composite 850 hPa wind (vectors, m s-1) and geopotential height (shadings, 530

gpm) between the high-speed years and the low-speed years in the peak season. (b) 531

Normalized and detrended time series of the basin-wide averaged large-scale steering 532

(blue line), WNPSH intensity index (black line) and TCS (red line) in the peak season 533

during 1966-2018. Dots in (a) denote regions where the composite geopotential 534

anomalies are significant at 95% confidence level. 535

Figure 6 (a) Observed and (b) simulated difference in TC track frequencies (shadings, 536

number year-1) between the high-speed years and low-speed years in the late season. 537

TC track frequency anomalies (number year-1) induced by anomalous (c) TC genesis 538

and (d) large-scale steering between the high-speed years and low-speed years. 539

Figure 7 Composite SST (shadings, °C) and 850 hPa wind anomalies (vectors, m s-1) 540

between the high-speed years and the low-speed years in the late season. Dots denote 541

regions where the composite SST anomalies are significant at 95% confidence level. 542

Figure 8 Climatological large-scale steering speed (shadings, m s-1) and TC track 543

frequency (contours, number year-1) in (a) the peak season and (b) the late season during 544

1966-2018. 545

546

547

548

549

550


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Figures: 551

552

553

554

Figure 1 Time series of annual mean TCS (red line) for (a) 1966-2018 and (b) 1949-555

2018. Linear trends (blue dash lines) are shown with their 95% two-sided confidence 556

intervals (blue shading). 557


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558

Figure 2 Composite difference in large-scale steering speed (shadings, m s-1) and 559

steering flow (vectors, m s-1) between high-speed years and low-speed years for the (a) 560

peak season and (b) the late season. Dots denote regions where the differences are 561

significant at 95% confidence level. The black contours in (a) and (b) show the mean 562

TC track frequency (number year-1) in the peak season and late season, respectively. 563

564


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565

Figure 3 Detrended time series of TCS (red line) and the basin-wide averaged large-566

scale steering speed (blue line) in the peak season during 1966-2018, (b) detrended time 567

series of TCS (red line) and large-scale steering speed along individual TC paths (blue 568

line) in the peak season during 1966-2018. Linear correlations (R) are shown in the top 569

left of panels. 570

571

572


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573

Figure 4 Detrended time series of TCS (red line) and basin-wide averaged large-scale 574

steering speed (blue line) in the late season during 1966-2018, (b) detrended time series 575

of TCS (red line) and large-scale steering speed along individual TC paths (blue line) 576

in the late season during 1966-2018, (c) TCS (red line) and climatological large-scale 577

steering speed along individual TC paths (blue line) in the late season during 1966-578

2018. Linear correlations (R) are shown in the top left of panels. 579


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580

Figure 5 (a) Composite 850 hPa wind (vectors, m s-1) and geopotential height (shadings, 581

gpm) between the high-speed years and the low-speed years in the peak season. (b) 582

Normalized and detrended time series of the basin-wide averaged large-scale steering 583

(blue line), WNPSH intensity index (black line) and TCS (red line) in the peak season 584

during 1966-2018. Dots in (a) denote regions where the composite geopotential 585

anomalies are significant at 95% confidence level. 586


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587

Figure 6 (a) Observed and (b) simulated difference in TC track frequencies (shadings, 588

number year-1) between the high-speed years and low-speed years in the late season. 589

TC track frequency anomalies (number year-1) induced by anomalous (c) TC genesis 590

and (d) large-scale steering between the high-speed years and low-speed years. Dots in 591

(a) denote regions where TC track frequency anomalies are significant at 95% 592

confidence level. The black contours in (a) and (b) denote the observed and simulated 593

climatological TC track frequency (number year-1) in the late season. 594

595

596

597

598

599


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600

Figure 7 Composite SST (shadings, °C) and 850 hPa wind anomalies (vectors, m s-1) 601

between the high-speed years and the low-speed years in the late season. Dots denote 602

regions where the composite SST anomalies are significant at 95% confidence level. 603

604

605

606


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607

Figure 8 Climatological large-scale steering speed (shadings, m s-1) and TC track 608

frequency (contours, number year-1) in (a) the peak season and (b) the late season during 609

1966-2018. 610

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