Article

Future Changes in Western North Pacific Tropical
Cyclone Genesis Environment in High-Resolution
Large-Ensemble Simulations
Hironori Fudeyasu 1, * , Kohei Yoshida 2
1
2
3
4
5

*

and Ryuji Yoshida 3,4,5

College of Education, Yokohama National University, Yokohama 240-8501, Japan
Meteorological Research Institute, Tsukuba 305-0052, Japan; kyoshida@mri-jma.go.jp
CIRES, University of Colorado Boulder, NOAA Chemical Sciences Laboratory, Boulder, CO 80305, USA;ryuji.yoshida@noaa.gov
RIKEN Center for Computational Science, Kobe 650-0047, Japan
Research Center for Urban Safety and Security, Kobe University, Kobe 657-8501, Japan
Correspondence: fude@ynu.ac.jp; Tel.: +81-45-339-3346

Received: 14 October 2020; Accepted: 10 December 2020; Published: 18 December 2020


Abstract: This study applied the database for Policy Decision making for Future climate change(d4PDF) and tropical cyclone (TC) genesis (TCG) environment factors to project future changes in
the frequency and characteristics of TCs over the western North Pacific. We examined current andfuture TCG environmental conditions in terms of the contribution of five factors: shear line (SL),confluence region (CR), monsoon gyre, easterly wave (EW), and Rossby wave energy dispersion froma preexisting TC (PTC). Among summer and autumn TCs, the contributions of SL and EW to future
TCG increased by about 4% and 1%, respectively, whereas those of CR and PTC decreased by thesame amounts. In future climate projections, the average lifetime maximum intensity (LMI) of TCs
associated with EW (EW-TCs) was significantly higher than those of TCs associated with other factors
except PTC. At higher sea surface temperatures and wetter conditions, higher lower-troposphericrelative vorticity was related to increases in the development rate of EW-TCs. Findings of this study
suggest that increases in the average LMI of all future TCs were caused by large contributions fromthe average LMI of future EW-TCs.
Keywords: tropical cyclone genesis environment; tropical cyclone projection; western North Pacifictropical cyclone

1. Introduction
Many studies have reported predictions of tropical cyclone (TC) activity, including the frequency
of TC genesis (TCG) and lifetime maximum intensity (LMI), under future global warming using
high-resolution global models. One such study, Yoshida et al. [1], applied a large-scale ensemble
dataset based on a high-resolution global atmospheric model the database for Policy Decision making
for Future climate change (d4PDF) to assess the uncertainty of atmospheric internal variations andsea surface temperature (SST) increases; the frequency of global TCG was projected to decreasesignificantly and the proportion of intense TCs to increase by the end of the 21st century, based on the
Representative Concentration Pathway 8.5 (RCP8.5) scenario. Over the western North Pacific (WNP),
the TCG frequency was projected to decrease by about 40%, and the average LMI of TCs to increase by
about 10%.
Several other TC projection studies have shown variant results. A review of Knutson et al. [2]that have projected future TC activity found a medium to high confidence for projected LMI increases,
but less consensus for future TCG frequency changes. For example, Roberts et al. [3] projected a
Oceans 2020, 1, 355–368; doi:10.3390/oceans1040024

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10–20% decrease in TCG frequency using a high-resolution atmospheric model Met. Office Unified
Model Global Atmosphere 3 configuration for RCP8.5 by the end of the 21st century. Knutson et al. [4]simulated a 35% decrease in TCG frequency using the high-resolution atmospheric model (HiRAM) and
the Geophysical Fluid Dynamics Laboratory (GFDL) hurricane model for Representative Concentration
Pathway 4.5 (RCP4.5). In contrast, the atmosphere–ocean-coupled high-resolution forecast-oriented
low ocean resolution (HiFLOR) model [5] showed an increase in TCG frequency of 5.8%. A recent study
by Zhang et al. [6] used a TC downscaling model and found that the Pacific TCs are going to become
more frequent and more intense. Thus, future TC activity remains highly uncertain, and it is difficult
to design measures such as social infrastructure to adapt to anticipated increases in TC variability.
To reduce the uncertainty of TC activity projections, it is necessary to develop a comprehensive
understanding of the detailed mechanisms of TCs in the models.
To assess modeled TCG, it is important to understand the environmental factors that influence TCG
over the WNP. Ritchie and Holland [7] examined five large-scale flow patterns as factors contributing
to TCG: shear line (SL), confluence region (CR), monsoon gyre (GY), easterly wave (EW), and Rossby
wave energy dispersion from a preexisting TC (PTC). The SL is enhanced by horizontal cyclonic shear
over the WNP. The CR is a confluence zone situated between the easterly trade winds and westerly
monsoon winds at the eastern extremity of the Asian monsoon. The GY is a synoptic-scale gyreembedded within a developed Asian monsoon trough [8,9]. Thus, these factors are closely related to
Asian monsoons over the WNP. The EW is related to the synoptic-scale trade easterly wind system;
the trough of easterly trade winds provides an environment favorable for TCG [10,11]. A mature TC
disperses its energy as a Rossby wave in a southeastward direction; low-pressure areas of the wave
train sometimes develop into TCs [12–14].
Yoshida and Ishikawa [15] developed a TCG score detection method (TGS) as an objective index to
identify the main environmental factors contributing to TCG. Using the TGS and data collected during
the Japanese 25-year Reanalysis Project [16], Fudeyasu and Yoshida [17] determined the statisticalcharacteristics of TCs over the WNP, stratified by five TCG environmental factors in the summer andautumn during 1979–2013. TCs that formed in large-scale flows mainly associated with a GY (hereafter,
GY-TCs, and similar terms indicate a TC that formed in a large-scale flow mainly associated with an
GY or similar factor) tended to develop slowly. The average LMI of PTC-TCs is larger than those ofother factors, due to their longer development stage. On average, TC size of GY-TCs is larger at the
TCG time than other TCG factors, whereas those of EW-TCs and PTC-TCs are smaller. Fudeyasu andYoshida [17] summarized differences in the TCG factors that lead to characterization of TCs.
Previous studies [18,19] have projected the environmental factors on TCG over the WNP under
future climate change in terms of genesis potential index or similar indices. None has exploredfuture changes in the five TCG environment factors identified by Ritchie and Holland [7]. Assumingthat five TCG factors are also the main contributors to TCG over the WNP, future changes in TC
characteristics may be projected in terms of TCG frequency or the average LMI of future TCs. To enhance
our understanding of TC activity under future climate change, the present study used the d4PDF
large-scale ensemble dataset and TGS to investigate the TCG environment statistically under futureclimate simulations and stratified changes in TC characteristics by the TCG factors.
2. Methodology
In this study, we used the d4PDF large-scale ensemble simulation dataset for current and futureclimates. The d4PDF dataset was produced by the Meteorological Research Institute–Atmospheric
General Circulation Model Version 3.2 (MRI–AGCM3.2H), which is based on a numerical weather
prediction model of the Japan Meteorological Agency (JMA), with a horizontal resolution of 60 km [20].
The model has 64 vertical layers, and the model top is set at 0.01 hPa. Current (future) climate simulationsof the d4PDF dataset were achieved using 100 (90) ensemble members based on initial atmosphericperturbations and short-term monthly SST perturbations. Six SST warming patterns used in futureclimate simulations are projected by six coupled models: the Community Climate System Model,

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version 4 (CCSM4), the Geophysical Fluid Dynamics Laboratory Climate Model, version 3 (GFDL-CM3),
the Hadley Centre Global Environment Model, version 2—Atmosphere and Ocean (HadGEM2-AO),
the Model for Interdisciplinary Research on Climate, version 5 (MIROC5), the Max Planck Institute
Earth System Model, medium resolution (MPI-ESM-MR), and the Meteorological Research InstituteCoupled Atmosphere–Ocean General Circulation Model, version 3 (MRI-CGCM3). For the RCP8.5
scenario, SST warming patterns are calculated as monthly differences in SSTs between 1991–2010
and 2080–2099. Climatological differences are scaled by a multiplier, such that MRI–AGCM3.2H
reproduces surface air warming of 4 K above pre-industrial levels (+4 K). Current climate simulations
were integrated for 60 years (1951–2010) using observed SSTs and sea ice concentrations obtained
from the Centennial in situ Observation-Based Estimates (COBE) SST2 product [21]. Future climate
simulations were performed for a 60-year period with constant warming roughly corresponding to
the levels projected for 2090 under the RCP8.5 scenario adopted in Phase 5 of the Coupled Model
Intercomparison Project (CMIP5). For each of the six warming SST patterns, 15-member ensemble
experiments were conducted with adding small SST perturbations, giving a total of 90 members(see Mizuta et al. [22] in detail). Thus, the ensemble datasets for present and +4 K future climate
scenarios are summarized as follows: 1951–2010 × 100 members (a 6000-year dataset) for the current
climate, and 2051–2110 × 90 members (a 5400-year dataset) for the future climate. Further details of
the experimental design were provided in Mizuta et al. [22].
In this study, we analyzed 180,753 (87,746) TCs in current (future) climate simulations of thed4PDF dataset. TC track data for the d4PDF dataset were derived from Yoshida et al. [1], which applied
the same TC tracking method and threshold values as Murakami et al. [23], which reported that
MRI–AGCM3.2H reproduced realistic TC activity in all aspects except for intensity. The TC tracking
method and threshold values are provided in Yoshida et al. [1] and Murakami et al. [23].
This study applied the TGS developed by Yoshida and Ishikawa [15] to estimate the contributionsof each TCG factor [7] for wind speeds at 850 hPa and sea level pressure, derived from d4PDF.
The location and time of the first TC track data entry were used as the TCG location and time.
The maximum contribution score was determined as the major flow pattern that mainly affected theTCG, among the SL, CR, GY, EW, and PTC. When a contributing factor could not be detected by the
TGS algorithm, the flow pattern was categorized as unclassified flow (UCF). Details of the objective
TGS algorithm are provided in previous studies [15,17,24].
This study performed statistical analysis of the following environmental conditions related to
TCG, as derived from the d4PDF dataset: SST, magnitude of vertical shear (SHR), middle-tropospherichumidity (HMD), and lower tropospheric relative vorticity. SHR was defined as the difference inhorizontal wind speed between heights of 200 and 850 hPa. HMD was defined as the specifichumidity at a height of 500 hPa. The 850-hPa relative vorticity (VOR850) was calculated to represent
lower-tropospheric relative vorticity. These physical parameters were averaged over a 5◦ × 5◦ box
relative to the TC center.
3. Results
3.1. Characteristics of Current TCs Stratified by TCG Environmental Factors
The annual average TCG over the WNP in the current d4PDF climate simulations was 30.1,
which was slightly larger than the observed average of 26.2, obtained from the best-track archives of
the Regional Specialized Meteorological Centers Tokyo–Typhoon Center. Figure 1a shows the seasonalchanges in TCs stratified by the five TCG environmental factors and UCF. SL-TCs occurred throughout
the year. The number of CR-TCs and GY-TCs tended to increase in summer. PTC-TCs tended to occur
in autumn, when many TCs formed at lower latitudes. The seasonality of TCs, stratified by TCG
environmental factors, was similar to that reported in Yoshida and Ishikawa [15], which considered
TCG factors for all TCs, including tropical disturbances.

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Figure 1. Monthly annual number of tropical cyclones (TCs), stratified by five flow patterns andunclassified flow (UCF) for (a) current and (b) future climate simulations performed using the d4PDF
dataset. SL, shear line; CR, confluence region; GY, monsoon gyre; EW, easterly wave; PTC, Rossby
wave energy dispersion from a preexisting TC.

To compare the characteristics of TCs accurately among TCG factors, we considered TCs in summer
and autumn from June to November; all of the examined environmental factors occurred during thisperiod. Among the 143,734 TCs that occurred in summer and autumn (Figure 2a), the most frequent
were SL-TCs (72,673 TCs, 50.6% of the total), followed by CR-TCs (27,912 TCs, 19.4%), and EW-TCs(16,113 TCs, 11.2%). PTC-TCs (11,257 TCs) and GY-TCs (10,970 TCs) accounted for 7.8% and 7.6% of allTCs, respectively. The least frequent TCs were those for which the major contributing factors could not
be determined, UCF-TCs (4809 TCs, 3.3%).

Figure 2. Distribution of frequency of TC genesis (TCG) environments stratified by five TCG factors and
UCF from June to November over the analysis period for (a) current and (b) future climate simulations
performed using the d4PDF.

Using TC data derived from the International Best Track Archive for Climate Stewardship(IBTrACS) [25] and Japanese 25-year Reanalysis Project [16], Fudeyasu and Yoshida [17] analyzed
TCG frequency over the WNP, stratified by TCG factors. Among the 677 TCs that occurred in June toNovember during the analysis period (1979–2013), the most frequent were SL-TCs (300 TCs, 44.3% of
the total), followed by CR-TCs (130 TCs, 19.2%), EW-TCs (92 TCs, 13.6%), GY-TCs (61 TCs, 9.0%),Oceans 2020, 1

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PTC-TCs (66 TCs, 9.7%), and UCF-TCs (28 TCs, 4.1%). Although the proportion of SL-TCs (EW-TCs
and PTC-TCs) in the d4PDF dataset was slightly higher (lower), the TCG frequency stratified by
five TCG factors and UCF in the present study was almost the same as those reported in Fudeyasu
and Yoshida [17].
This study performed statistical analysis of TCs stratified by TCG factors in current d4PDF climate
simulations. For each factor that influences TCG, the relationship between the average contribution ofeach factor and those of all other factors was assessed using Student’s t-test at a significance level ofp < 0.01. For example, average characteristics of SL-TCs were compared to those of CR-TCs, GY-TCs,
EW-TCs, PTC-TCs, and UCF-TCs.
Table 1 show the average locations of TCG for all TCs (Figure 3) and TCG factors (Figure 4)
from June to November. There were significant differences in average TCG location among all factors.
On average, SL-TCs and GY-TCs occurred significantly farther to the west than the other TCG factors(p < 0.01). On average, CR-TCs, EW-TCs, and UCF-TCs (GY-TCs, and PTC-TCs) occurred farther to thenorth (south) than other TCG factors.
Table 1. Statistical summary of the tropical cyclone (TC) characteristics associated with TCG environmental
factors from June to November over a 6000-year analysis period in current climate simulations using thed4PDF dataset. Significant differences are indicated in bold and italic fonts (Student’s t-test; p < 0.01).
ALL, averaged for all TCs; SL, shear line; CR, confluence region; GY, monsoon gyre; EW, easterly wave;
PTC, Rossby wave energy dispersion from a preexisting TC; UCF, unclassified flow.

Longitude at TCG time (◦ N)
Latitude at TCG time (◦ E)
TC size at TCG time (km)Development rate (m s−1 6 h−1 )Movement speed (km h−1 )Duration of development (day)LMI (m s−1 )

ALL

SL

CR

GY

EW

PTC

UCF

137.4
16.6
333.5
0.61
16.0
3.1
25.9

135.2
16.6
343.2
0.63
15.4
3.2
26.4

141.0
17.2
321.7
0.56
16.6
3.3
25.4

132.9
16.3
349.2
0.55
14.7
2.9
23.4

140.6
17.7
317.0
0.60
17.5
3.0
25.8

141.4
13.5
262.1
0.69
17.6
3.3
27.8

138.8
17.6
358.6
0.55
15.6
2.8
22.4

Figure 3. Distribution rate of TCG location, expressed as percentage values of the total number of TCG
for (a) current all TCs and (b) future all TCs into a 2.5◦ × 2.5◦ latitude-longitude grid box with respect
to the total number of TCG for (a) current all TCs and (b) future all TCs formed in the WMP from
June to November over the analysis period in current and future climate simulations performed using
the d4PDF.

Oceans 2020, 1

Figure 4. Same as Figure 3, except distribution rate (percent) of TCG location for (a) current SL-TCs,(b) future SL-TCs, (c) current CR-TCs, (d) future CR-TCs, (e) current GY-TCs, (f) future GY-TCs,(g) current EW-TCs, (h) future EW-TCs, (i) current PTC-TCs, and (j) future PTC-TCs.

360

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Fudeyasu and Yoshida [17] clarified the characteristics of observed TCs in summer and autumn
from 1979 to 2013, stratified by five TCG factors and UCF. The average TCG locations differedsignificantly among all factors (p < 0.05). The average location of EW-TCs (PTC-TCs) was farther eastand north (south), whereas that of SL-TCs was farther west than those of all other TCG factors, which isconsistent with our results (Table 1); we observed significant differences in average location for CR-TCs,
GY-TCs, and UCF-TCs for current d4PDF climate simulations, due to very large sample sizes.
We defined TC size as the horizontal extent of TCs at the time of TCG; TC size was calculated as the
maximum radius of the 1000-hPa contour from the TC center (Table 1). On average, GY-TCs were larger
than TCs of all other factors, whereas EW-TCs and PTC-TCs were smaller (Table 1), which was consistent
with the observations of Fudeyasu and Yoshida [17]. In the current d4PDF climate simulations, SL-TCs
and UCF-TCs (CR-TCs) were larger (smaller) than TCs of the other factors.
The development rate at TCG, defined as the increase in maximum wind speed 6 h after the timeof TCG, differed significantly among factors. GY-TCs had lower development rates than all other TCG
factors, which was consistent with the observations reported in Fudeyasu and Yoshida [17]. In thecurrent d4PDF climate simulations, the average development rates of SL-TCs and PTC-TCs (CR-TCs
and UCF-TCs) were higher (lower) than those of the other TCG factors.
Table 1 also shows the average TC movement speed during the development stage, which isthe duration from the time of TCG to maturity. The mature time is defined as the time at which
maximum wind speed occurred. The movement speed was calculated in terms of the hourly horizontal
displacement of the TC. The average movement speeds of CR-TCs, EW-TCs, and PTC-TCs were higher
than those of the other factors in the development stage. The average duration of the development
stage was longer for PTC-TCs than for the other TCG factors, which was consistent with Fudeyasu andYoshida [17]. In the current d4PDF climate simulations, the average development stage of CR-TCs was
longer than those of the other factors.
Highly significant differences in LMI among TCG factors were observed (Table 1 and Figure 5).
PTC-TCs (UCF-TCs) tended to have higher (lower) LMI, which was consistent with Fudeyasu andYoshida [17]. In the current d4PDF climate simulations, the average LMI of SL-TCs (CR-TCs and
GY-TCs) was significantly higher (lower) than those of the other TCG factors (p < 0.01).

Figure 5. Boxplots of TC lifetime maximum intensity (LMI) stratified by TCG factors and UCF from
June to November over the analysis period in current and future climate simulations performed using
the d4PDF dataset. For each factor, boxes on the left and right are derived from current and futureclimate simulations, respectively. Vertical bar indicates the full range of the distributions; box indicatesthe interquartile range of 25–75% of all cases; bold horizontal line indicates the median.

Oceans 2020, 1362

3.2. Characteristics of Future TCs Stratified by TCG Environmental Factors
The annual average of TCG over the WNP in the future climate simulations was 16.2, while thatin the current climate was 30.1. It was almost a half of the number in the current climate simulations,and this significant decrease was consistent with a future trend of TCG frequency reported in previous
studies [26,27]. Figure 1b shows the seasonal changes in TCs stratified the five TCG factors and UCF;
the seasonal changes due to each TCG factor were very similar to those shown in the current climate
simulations. Among the 70,920 TCs projected to occur in summer and autumn in future climate
simulations, the most frequent were SL-TCs (38,584 TCs, 54.4% of the total; Figure 2b). The percentage
of SL-TCs was about 3.8% higher than that of the current climate simulations. The percentage of CR-TCs(10,666 TCs) was 15.0%, about 4.4% lower than that of the current climate simulations. The percentages
of EW-TCs (8420 TCs, 11.9%) and PTC-TCs (5200 TCs, 7.3%) were about 1% higher and lower thanthose of the current climate, respectively. The percentages of GY-TCs (5404 TCs, 7.6%) and UCF-TCs(2646 TCs, 3.7%) were almost same as those of the current climate simulations.
The averages characteristics of all TCs for the current and future climate simulations are shownin the leftmost columns of Tables 1 and 2, respectively. A comparison of these averages for all TCs
showed many significant differences (p < 0.01). The average TCG locations were farther to the eastand north for the future climate simulations (Figure 3b) than for the current climate simulations(Figure 3a). The average sizes and development rates of future TCs were larger than those of current
TCs. The increase in average TC sizes at the TCG time was consistent with Khairoutdinov andEmanuel [28], who showed that the storm size of the initial TC vortex increased under the warming
experiments. The future increase in TC size was also analyzed in the projection simulations by previous
studies [4,29–31] which were reviewed in Knutson et al. [2].
Table 2. Statistical summary of TC characteristics associated with TCG environmental factors from
June to November over a 5600-year analysis period in future climate simulations using the d4PDF
dataset. Significant differences are indicated in bold and italic fonts (Student’s t-test; p < 0.01); changes
are shown below each parameter.

Longitude at TCG time (◦ N)
Latitude at TCG time (◦ E)
TC size at TCG time (km)Development rate (m s−1 6 h−1 )Movement speed (km h−1 )Duration of development (day)LMI (m s−1 )

ALL

SL

CR

GY

EW

PTC

UCF

138.5
+1.1
18.1
+1.5
352.2
+18.7
0.91
+0.30
16.1
+0.1
2.6
−0.5
29.1
+3.2

137.2
+2.0
18.0
+1.4
364.8
+21.6
0.94
+0.31
15.7
+0.3
2.7
−0.5
29.8
+3.4

141.8
+0.8
18.9
+1.7
332.7
+11.0
0.82
+0.26
16.7
+0.1
2.7
−0.6
27.6
+2.2

133.8
+0.9
17.9
+1.6
367.2
+18.0
0.80
+0.25
15.2
+0.5
2.4
−0.5
26.3
+2.9

140.0
−0.6
19.2
+1.5
330.7
+13.7
0.93
+0.33
17.3
−0.2
2.5
−0.5
30.1
+4.3

141.1
−0.3
15.3
+1.8
285.6
+23.5
0.97
+0.28
16.7
−0.9
2.8
−0.5
30.6
+2.8

144.0
+5.2
19.3
+1.7
371.8
+13.2
0.78
+0.23
16.3
+0.7
2.3
−0.5
24.6
+2.2

The average movement speed of TCs during the development stage was slightly higher for the
future climate simulations than for the current climate simulations. The average duration of thedevelopment stage was shorter for future TCs than for current TCs. The average LMI of future TCs(Figure 5) was higher than those of current TCs, which is consistent with the findings of previous studies.
Figure 4 and Table 2 show differences in average TC characteristics stratified by TCG factors
between current and future climate simulations. The stratified average characteristics were similar to
those of all TCs, except for average TCG location and movement speed during the development stage
for EW-TCs and PTC-TCs. Although the average locations of SL-TCs, CR-TCs, GY-TCs, and UCF-TCs
were farther east under future climate change, those of EW-TCs and PTC-TCs were farther west.

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Therefore, the TCG classified with monsoon related factors tend to shift eastward, and that witheasterly related factors tends to shift westward.
Two modes of distribution change in EW-TCs location can be found in the future climate
simulations, expanding to the north-eastward broadly and increasing near the northern part ofPhilippine sea and South China Sea locally. The former trend is similar to monsoon related factors.
But the contribution of the TCG increase around Philippine island results in the statistical significancefor the westward shift of the EW-TCs genesis location in the future climate simulations.
Average movement speeds during the development stage of EW-TCs and PTC-TCs were lower
in the future climate simulations than those of the current climate simulations. Yamaguchi et al. [32]found that the annual average of TC movement speed under future climate change simulated using
d4PDF data had variation among latitudes. Since the dependency of the TCG location to the TCG
factors was found in this study, the TC movement speed may have been dependent on TCG factors.
Most TC characteristics among the different TCG factors in the future climate simulations (Table 2)
showed the similar for current climate simulations (Table 1), with some exceptions. Interestingly,
the average LMI (Figure 5) and TC development rate were significantly higher for EW-TCs thanfor other TCG factors except PTC in the future climate simulations, but not in the current climate
simulations. The average TC development rates for all factors were higher in the future climate
simulations; however, the largest increase in the average development rates for EW-TCs resulted in thelargest increase in LMI for EW-TC (Table 2).
3.3. Future Changes in Environmental Physical Parameters
This study further analyzed the changes in SST and atmospheric parameters associated with TCs
from June to November between current and future climate simulations. The average TC characteristics
at the both time of TCG and maturity under current and future simulated climate simulations areshown in Tables 3 and 4, respectively. Future SST averages for all TCs and each TCG factor were about
3.0 K higher than those in the current climate simulations at both the time of TCG and maturity.
Table 3. Statistical summary of sea surface temperatures (SSTs) and atmospheric parameters near TCs
associated with TCG environmental factors from June to November over the 6000-year analysis period
in current climate simulations performed using the d4PDF dataset. Significant differences are indicatedin bold and italic fonts (Student’s t-test; p < 0.01). SHR, difference in horizontal wind speed between
200 and 850 hPa; HMD, specific humidity at 500 hPa; VOR850, relative vorticity at 850 hPa.

SST at TCG time (K)SHR at TCG time (m s−1 )HMD at TCG time (kg kg−1 )VOR850 at TCG time (×10−5 s−1 )SST at mature time (K)SHR at mature time (m s−1 )HMD at mature time (kg kg−1 )VOR850 at mature time (×10−5 s−1 )

ALL

SL

CR

GY

EW

PTC

UCF

301.9
13.5
4.14
2.71
301.0
17.9
3.73
3.34

301.9
13.7
4.18
2.83
301.0
18.1
3.78
3.43

302.1
13.5
4.25
2.56
301.0
17.9
3.83
3.40

301.8
13.5
4.17
2.60
301.1
17.2
3.85
3.14

301.8
13.3
3.86
2.49
300.8
17.6
3.44
2.99

301.7
13.5
4.04
2.86
300.7
18.0
3.44
3.47

301.8
13.6
4.09
2.45
301.0
17.1
3.72
3.05

Table 4. Statistical summary of SSTs and atmospheric parameters near TCs associated with TCG
environmental factors from June to November over the 5600-year analysis period in future climate
simulations performed using the d4PDF dataset. Significant differences are indicated in bold and italic
fonts (Student’s t-test; p < 0.01); changes are shown below each parameter.

SST at TCG time (K)

ALL

SL

CR

GY

EW

PTC

UCF

304.9
+3.0

304.9
+3.0

305.0
+2.9

304.8
+3.0

304.7
+2.9

304.8
+3.0

304.8
+3.0

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Table 4. Cont.

SHR at TCG time (m s−1 )HMD at TCG time (kg kg−1 )VOR850 at TCG time (×10−5 s−1 )SST at mature time(K)SHR at mature time (m s−1 )HMD at mature time (kg kg−1 )VOR850 at mature time (×10−5 s−1 )

ALL

SL

CR

GY

EW

PTC

UCF

14.1
+0.6
5.81
+1.66
3.10
+0.39
304.0
+3.1
18.1
+0.3
5.27
+1.54
3.76
+0.42

14.2
+0.5
5.87
+1.69
3.23
+0.40
304.1
+3.1
18.3
+0.2
5.36
+1.57
3.89
+0.46

13.7
+0.5
5.95
+1.70
2.90
+0.34
304.0
+3.0
17.9
+0.0
5.45
+1.61
3.73
+0.33

14.2
+0.8
5.78
+1.62
2.92
+0.32
304.2
+3.1
17.7
+0.5
5.34
+1.49
3.48
+0.34

13.9
+0.5
5.47
+1.61
2.96
+0.47
303.9
+3.1
18.2
+0.6
4.86
+1.42
3.47
+0.48

14.4
+0.5
5.69
+1.66
3.21
+0.35
304.0
+3.2
18.4
+0.2
4.89
+1.45
3.84
+0.37

14.4
+0.8
5.65
+1.56
2.78
+0.33
304.1
+3.0
17.3
+0.2
5.31
+1.50
3.28
+0.23

The atmospheric parameters SHR, HMD, and VOR850 were also significantly higher for all TCs
in the future climate simulations than in the current climate simulations (p < 0.01). Figure 6 shows
the differences between current and future climate simulations for the 850-hPa horizontal winds at
the time of TCG in summer and autumn. A trend of anomalous westerlies appeared in an area from
10–20 ◦ N of the central Pacific, which enhances cyclonic circulation anomalies over the northeasternpart of the WNP in the future climate simulation. The cyclonic circulation anomalies were favorable
for TCG in this region, resulting in the northeast shift of the average TCG locations in the futureclimate simulation.

Figure 6. Future changes in horizontal winds (m s−1 arrows, scale at bottom) and relative vorticity(×10−6 s−1 color) at 850 hPa at the time of TCG for TCs from June to November over the analysis period
in current and future climate simulations performed using the d4PDF.

The average SSTs near CR-TCs (GY-TCs, EW-TCs, PTC-TCs, and UCF-TCs) at the TCG time werehigher (lower) than those near TCs associated with other factors in the current climate simulations.
At mature time, the average SSTs near EW-TCs and PTC-TCs were lower than those near TCs associatedwith the other factors. Characteristics of SSTs among the TCG factors in the future climate simulations
were similar to those in the current climate simulations.

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In the current climate simulations, the average SHR near SL-TCs (EW-TCs) was higher (lower) at
the TCG time, whereas the SHR near SL-TCs (GY-TCs, EW-TCs, and UCF-TCs) was higher (lower) at
the mature time. In the future climate simulations, the average SHR around PTC-TCs and UCF-TCs(CR-TCs and EW-TCs) was higher (lower) at the TCG time, whereas that around SL-TCs and PTC-TCs(CR-TCs, GY-TCs, and UCF-TCs) was higher (lower) at mature time.
At both time of TCG and maturity in the current climate simulations, average HMD was higher
around SL-TCs, CR-TCs, and GY-TCs, due to the Asian monsoon. In contrast, drier conditions wereobserved around EW-TCs, PTC-TCs, and UCF-TCs. Characteristics of the average HMD among the
TCG factors in the future climate simulations were similar to those in the current climate simulations.
At both time of TCG and maturity in the current climate simulations, the average VOR850
was higher near SL-TCs and PTC-TCs, and lower near GY-TCs, EW-TCs, and UCF-TCs (p < 0.01).
Characteristics of the VOR850 among the TCG factors in the future climate simulations were similar
to those in the current climate simulations. Differences in the averages of physical environmental
parameters for the different TCG factors between current and future climate simulations are shownin Table 4. Interestingly, changes in VOR850 of each factor were similar to changes in development
rate at the TCG time (Table 2). Figure 7 shows the relationship between future changes in VOR850
and development rate stratified by TCG factors. The increases in the average development rate
were correlated with the increases in VOR850 (correlation coefficient = 0.91). Thus, higher averagedevelopment rate of future EW-TCs than those of other factors was also caused by this highest
increase in VOR850 around EW-TCs, supported by higher SSTs and wetter conditions under the futureclimate simulations.

Figure 7. Scatterplot of the relationship between changes in VOR850 and development rate between
current and future climate simulations stratified by TCG factors.

4. Discussion
This study evaluated the differences in average TC characteristics over the WNP between current
and future climate simulations. The average TCG locations were farther to the east and to the northin the future climate simulations than in the current climate simulations. Murakami et al. [33] andOceans 2020, 1

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Yokoi et al. [34] showed an eastward shift in the positions of northward-recurving TC tracks and TCG
location over the WNP under future climate. They discussed that the eastward shift was caused bychanges in dynamical environmental conditions such as lower-tropospheric relative vorticity, directly
resulted from the eastward extension of the monsoon trough. By using simulations from CMIP5,
Wang and Wu [35] showed the monsoon trough over the WNP was intensify and extend eastward in
the warming scenarios. We have extended their work to examine the contributions of an eastward shift
in the TCG location stratified by the environmental factors that influenced TCG. In the future climate
simulations, the average TCG locations of SL-TCs, CR-TCs, and GY-TCs, which are related to Asianmonsoon, were farther east than those of other factors. Yokoi and Takayabu [26] examined the global
warming impact on TCG over the WNP projected by five models that participate in Phase 3 of the
Coupled Model Intercomparison Project (CMIP3). All of the five models projected an increasing trendof the TCG frequency over the central North Pacific. They concluded that the increasing trend over thecentral North Pacific resulted from changes of SST and large-scale circulation field over the central
North Pacific that exhibit an El Niño-like pattern. The El Niño-like pattern was characterized by the
relative vorticity in the lower troposphere over the central North Pacific that became more favorable
for TCG. Their finding was consistent with the results of this study. We found that the average LMI(Figure 5) and TC development rate (Table 2) in the future climate simulations were significantly
higher for EW-TCs, mainly occurred over the central North Pacific. In the future climate, the higher
development rate of EW-TCs than those of other factors was caused by SST and enhanced cyclonic
circulation anomalies (Figure 6) around TCG region of EW-TCs.
The large-scale flow pattern over the WNP changed in the future climate simulations (Figure 6),
consistent with previous studies [36,37]. However, the contributions of TCG stratified by environmental
factors did not so much change in the future climate simulations. Since the contributions of TCG
environment associated with Asian monsoon for the TCG frequency over the WNP, namely SL andCR, were large (Figure 1), the future change of TCG frequency depends on that of monsoon activity.
On the other hand, the increases in average LMI projected for all future TCs over the WNP, consistent
with several previous studies, were caused by large contributions from the average LMI of future
EW-TCs. Our results suggest that the frequency of EW-TCs in the warming scenarios is important for
TC projection research.
5. Conclusions
This study investigated future changes in the frequency of TCG and TC characteristics in summer
and autumn over the WNP, stratified by TCG environmental factors classified by Ritchie and Holland [7],using the d4PDF large-scale ensemble dataset and the TGS method. The TCG factors explained theenvironmental conditions of TCG under the future climate simulations, as well as under current
climate simulations. Among summer and autumn TCs, SL-TCs (54.4% of all TCs) were about 3.8%
more frequent in the future climate simulations than in the current climate simulations, whereas
CR-TCs (15.0%) were about 4.4% less frequent and the frequency of EW-TCs (11.9%) and PTC-TCs(7.3%) changed by <1%, respectively. The frequencies of GY-TCs (7.6%) and UCF-TCs (3.7%) were
unchanged between the climate simulations.
The statistical characteristics of TCs stratified by TCG factors were similar between the current
and future climate simulations, except that average LMI of EW-TCs was significantly higher than thoseof other TCG factors in the future climate simulations, due to the higher development rate, higher SSTs,
higher VOR850, and wetter conditions associated with EW in the future climate simulations. Together,
these results suggest that the high increases in average LMI projected for all future TCs over the WNP
in several previous studies are due to the relatively high contribution of EW-TCs. Our elucidation of
future trends in EW-TCs will be important for disaster prevention under climate change.
Author Contributions: Conceptualization, H.F.; methodology, H.F., K.Y., and R.Y.; formal analysis, H.F.; writing—original draft preparation, H.F.; writing—review and editing, H.F., K.Y., and R.Y. All authors have read and agreed
to the published version of the manuscript.

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Funding: This research was funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT),
Japan, KAKENHI Grants JP19H05696, and partly supported by KAKENHI Grants JP19H00705 and the Integrated
Research Program for Advancing Climate Models (TOUGOU) Grant JPMXD0717935498 from MEXT, Japan.
Acknowledgments: The authors are grateful to the editor and anonymous reviewers for their useful and criticalcomments. This study used d4PDF produced with the Earth Simulator jointly by science programs (SOUSEI,TOUGOU, SI-CAT, DIAS) of the Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan.
The d4PDF data set is available from the DIAS website (https://www.diasjp.net/en/). This work is supported by
MEXT KAKENHI Grants 17H02956, 17K14398, JP19H05696 and TOUGOU program.
Conflicts of Interest: The authors declare no conflict of interest.

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