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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/343703528
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
T
20 American Meteor
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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
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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
611
Accepted for publication in Journal of Climate. DOI 10.1175/JCLI-D-19-0995.1.
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