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A Multimodel Intercomparison of an Intense Typhoon in Future,
Warmer Climates by Four 5-km-Mesh Models
SACHIE KANADA,a TETSUYA TAKEMI,b MASAYA KATO,a SHOTA YAMASAKI,c
HIRONORI FUDEYASU,c KAZUHISA TSUBOKI,a OSAMU ARAKAWA,d AND IZURU TAKAYABUeaInstitute for Space-Earth Environmental Research, Nagoya University, Nagoya, Aichi, Japanb
Disaster Prevention Research Institute, Kyoto University, Uji, Kyoto, Japanc
Yokohama National University, Yokohama, Kanagawa, Japand
University of Tsukuba, Tsukuba, Ibaraki, Japane
Meteorological Research Institute, JMA, Tsukuba, Ibaraki, Japan(Manuscript received 2 October 2016, in final form 13 February 2017)
ABSTRACT

Intense tropical cyclones (TCs) sometimes cause huge disasters, so it is imperative to explore the impacts ofclimate change on such TCs. Therefore, the authors conducted numerical simulations of the most destructive
historical TC in Japanese history, Typhoon Vera (1959), in the current climate and a global warming climate. The
authors used four nonhydrostatic models with a horizontal resolution of 5 km: the cloud-resolving storm simulator,
the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model,
the Japan Meteorological Agency (JMA) operational nonhydrostatic mesoscale model, and the Weather Research and Forecasting Model. Initial and boundary conditions for the control simulation were provided by theJapanese 55-year Reanalysis dataset. Changes between the periods of 1979–2003 and 2075–99 were estimatedfrom climate runs of a 20-km-mesh atmospheric general circulation model, and these changes were added to theinitial and boundary conditions of the control simulation to produce the future climate conditions.
Although the representation of inner-core structures varies largely between the models, all models project
an increase in the maximum intensity of future typhoons. It is found that structural changes only appeared
around the storm center with sudden changes in precipitation and near-surface wind speeds as the radius ofmaximum wind speed (RMW) contracted. In the future climate, the water vapor mixing ratio in the lower
troposphere increased by 3–4 g kg21. The increased water vapor allowed the eyewall updrafts to form continuously inside the RMW and contributed to rapid condensation in the taller and more intense updrafts.

1. Introduction
An increase in sea surface temperature (SST) is aknown consequence of anthropogenic greenhousewarming (e.g., Collins et al. 2013; Mizuta et al. 2014).
The SST increase projected by several state-of-the-art
models implies a potential increase in tropical cyclone(TC) intensity in the future, warmer climate because
the maximum intensity of a TC generally increases
as SST increases (e.g., DeMaria and Kaplan 1994;
Emanuel 1986, 1988; Holland 1997). Intense TCs such as
Hurricane Katrina (2005) (Knabb et al. 2005), Typhoon

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

Corresponding author: Sachie Kanada, skanada@nagoya-u.jp

Haiyan (2013) (Lin et al. 2013; Mori et al. 2014), andCyclone Pam (2015) (Needham et al. 2015) have caused
hugely destructive disasters in countries with exposed
coasts. Thus, to prepare for future disasters, it is essential to understand how such intense TCs will change
under future, warmer environmental conditions.
General circulation models (GCMs) with horizontal
resolutions of several tens of kilometers have playeda crucial role in projecting changes in the frequency,
track, and location of occurrence of future TCs (e.g.,
Christensen et al. 2013; Stowasser et al. 2007; Murakami
et al. 2012), but simulated TC intensity depends onmodel resolution, and it becomes more realistic when
the resolution is increased (e.g., Bengtsson et al. 2007;
Murakami and Sugi 2010; Roberts et al. 2015). TC intensity is closely related to TC inner-core structures.
Sensitivity experiments of model resolutions on a simulated intense TC suggest that to simulate the maximum

DOI: 10.1175/JCLI-D-16-0715.1
Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

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intensity and inner-core structures of an intense TC, thehorizontal resolution of the model should not exceed
5 km (e.g., Fierro et al. 2009; Kanada and Wada 2016).
In addition to resolution, projections of future TC intensity are affected by the model physics, such as the
cumulus parameterization (e.g., Murakami et al. 2012)
and the microphysics parameterization (e.g., McFarquhar
et al. 2012; Zhu et al. 2015) schemes. The diffusion processes that determine the properties of the surface layer
and the planetary boundary layer (PBL) are also crucial(Rotunno and Bryan 2012) because heat and moisture
fluxes from the sea surface are major energy sources
for TC development (e.g., Emanuel 1986; Rotunno and
Emanuel 1987). Thus, the two major considerations inprojections of future changes of an intense TC are thehorizontal resolution and physics of the model.
One approach to increasing model resolution to study
future changes in TC activity is to conduct dynamical
downscaling experiments with regional models (e.g.,
Knutson et al. 2013, 2015). Knutson and Tuleya (1999)
adopted this approach with a regional model with ahorizontal resolution of 18 km; in simulations of 51
present-day cases and 51 future cases by GCMs, they
showed that in the northwest Pacific, TC intensity andnear-storm precipitation would significantly increase.
Recently, 2-km-mesh nonhydrostatic models have beenused for dynamical downscaling experiments to project
the maximum intensity of intense TCs in the future climate (e.g., Kanada et al. 2013; Tsuboki et al. 2015).
However, a limitation of the dynamical downscaling
approach is that the boundary conditions of both thepresent-day and future cases are provided by the climate
runs with GCMs. Therefore, it is important to discuss
how actual intense TCs in the current climate will bechanged in the future climate.
Another downscaling approach for examining the effect of future environments on TC intensity is toperform a conditional ensemble experiment. In thisapproach, a TC vortex is embedded in the initial andboundary conditions, and profiles of an environmental
parameter, such as SST, moisture, or temperature, arevaried (e.g., Knutson and Tuleya 2004; Knutson et al.
2001; Shen et al. 2000). Such conditional ensemble experiments can elicit the pure response of a TC vortex to agiven environmental condition over a large parameter
space. Hill and Lackmann (2011) performed 78 idealized
simulations with a 6-km-mesh model and an additional
six simulations with a 2-km-mesh model; in these simulations, they varied the future boundary conditions byusing the output of 13 individual atmosphere–ocean
GCM runs under different emission scenarios (A1B,
B1, or A2), as well as the model physics, such as thesurface layer–PBL scheme and the cloud microphysics

VOLUME 30

parameterization scheme, and then evaluated the effectof various changes in the thermodynamic environment on
maximum TC intensity.
Most theoretical and numerical studies have suggested that in the future, TCs will most likely have increased maximum intensity and increased inner-core
precipitation. These findings led us to pose the following
question: How would a TC that was extremely intense in
the current climate, such as Hurricane Katrina or
Typhoon Haiyan, be changed in the future climate? Toanswer this question, the pseudo-global-warming (PW)
method has been proposed (e.g., Kimura and Kitoh
2007). Although most conditional ensemble experiments are conducted as idealized experiments, the PW
method involves dynamical downscaling experiments inwhich the boundary conditions are set by adding climatechange differences between future and current climates
simulated by GCMs to the real synoptic environment
such as reanalysis datasets. This method has been widely
applied in studies of the impacts of climate change on
weather events, such as the rainy season in East Asia
(i.e., the baiu; Kawase et al. 2009), extreme tornadic
storm events (Trapp and Hoogewind 2016), and winterstorm (Rasmussen et al. 2011) and flood events in North
America (Lackmann 2013).
The PW method has also been used to study futurechanges in current intense TCs, including Hurricane
Katrina (2005) (Lynn et al. 2009), Hurricane Sandy(2012) (Lackmann 2015), Typhoon Songda (2004) (Itoet al. 2016), and Typhoon Haiyan (2013) (Nakamura
et al. 2016; Takayabu et al. 2015). However, most ofthese studies have focused on changes in maximum intensity and tracks and resultant natural disasters such as
storm surges. The inner-core structures of a TC dependon its intensity and vary as the TC intensifies (e.g.,
Rogers et al. 2013). In particular, it is known that intense
TCs intensify rapidly (Kaplan and DeMaria 2003). How
will climate change affect the intensification process andcause a future TC to increase its maximum intensity?
To answer this question, we conducted PW experiments for Typhoon Vera (1959), the most destructive
historical TC known, in both the current climate and theglobal warming climate. Typhoon Vera was an extraordinarily intense TC in which minimum centralpressure (CP) reached 895 hPa. The typhoon struck Japan on 26 September 1959 and caused 5098 fatalities,
making it the deadliest typhoon on record to makelandfall in Japan. Because there are large uncertainties
in TC representation by models and in model physics,
we used four 5-km-mesh nonhydrostatic models withdifferent physics schemes, the cloud-resolving storm
simulator (CReSS), the Japan Meteorological Agency(JMA) operational nonhydrostatic mesoscale model1 AUGUST 2017

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TABLE 1. Specifications of the models.

ModelsVertical levels(top height)
Time step
Cumulus convection
Cloud microphysics
Radiation
Turbulence/PBL
schemeSurface boundary
layer
Nudging
Ocean

CReSS
80 (24 km)

JMANHM55 (27 km)

4s
None
Simple two-moment
three-ice bulk scheme
Tsuboki and
Sakakibara (2002)
Deardorff

15 sKain–Fritsch
Simple two-moment
three-ice bulk scheme
Yabu et al. (2005);
Kitagawa (2000)
Level 3 Mellor–Yamada–
Nakanishi–Niino
Louis et al. (1982)
Louis et al. (1982)
with Kondo (1975)
with Kondo (1975)
Tsuboki and Sakakibara (2002) Nakano et al. (2012)
1D slabNone(JMANHM), the fifth-generation Pennsylvania State
University–National Center for Atmospheric Research
Mesoscale Model (MM5), and the Weather Researchand Forecasting (WRF) Model, and conducted PW
experiments in which we paid special attention to
structural changes of the inner core during the intensification period. The goal of this study was to examine
how the inner-core structures of a simulated intense
TC change in response to warmer climate conditions
and how those changes lead to a maximum intensity
increase.
The rest of the paper is organized as follows. Section 2
describes the models and the methodology. The environmental conditions used in the simulations of thecurrent and future climates are summarized in section 3,
and the results of the multimodel ensemble experiments
are presented in section 4. The results are discussed in
section 5, and section 6 is a summary.

2. Models and methodology
a. Model descriptions
We used four nonhydrostatic models, each with ahorizontal resolution of 5 km: CReSS (Tsuboki and
Sakakibara 2002); JMANHM (Saito et al. 2007); MM5(Dudhia 1993; Grell et al. 1994); and WRF, version 3.3.1(Skamarock et al. 2008) (Table 1). JMANHM, MM5, andWRF all use the Kain–Fritsch cumulus parameterization
scheme (Kain and Fritsch 1993). Other model physics
schemes that affect the simulated TC intensity are thoserelated to the PBL and surface enthalpy fluxes.
JMANHM applies a level 3 Mellor–Yamada–Nakanishi–
Niino (MYNN) closure PBL scheme (Nakanishi andNiino 2004) and the Louis surface boundary layer scheme(Louis et al. 1982) with a surface-roughness-length

MM5

WRF

55 (27 km)

56 (26 km)

4sKain–Fritsch
Goddard microphysics
with hail–graupel
New Rapid Radiative
Transfer Model
Eta–Mellor–Yamada–Janjić20 sKain–Fritsch
WRF single-moment 6-classmicrophysics scheme
New Rapid Radiative
Transfer Model
Yonsei University nonlocal

Eta–Mellor–Yamada–Janjić Zhang and Anthes (1982)
Stauffer and Seaman (1990) Wang et al. (2010)
None
None

formulation based on Kondo (1975). WRF uses theYonsei University nonlocal PBL scheme (Hong et al.
2006) with a surface boundary layer scheme based on
Zhang and Anthes (1982). MM5 uses the Eta–Mellor–
Yamada scheme (MYJ) developed by Janjić (1994), andCReSS uses the Louis surface boundary layer scheme(Louis et al. 1982) with a surface-roughness-length formulation based on Kondo (1975), but does not apply any
parameterization for the PBL. Cloud–radiation interaction is not considered in CReSS, while long- andshortwave radiation processes at the surface are considered (Tsuboki and Sakakibara 2002). JMANHM(Nakano et al. 2012) and WRF (Wang et al. 2010) apply
spectral nudging schemes developed for downscaling
experiments, whereas MM5 and CReSS use an analysis
nudging scheme (Stauffer and Seaman 1990). To makethe initial spinup, the scheme in CReSS (MM5) was used
for 72 h (36 h) from initial time. In addition, WRF can also
use a tropical cyclone bogus scheme (TC bogus scheme;
Wang et al. 2010). We conducted sensitivity experimentswith the WRF Model that included the TC bogus scheme(WRFB). Among the four models, only CReSS considers
the ocean effect. A simple thermal diffusion model(1D-slab model) is used to express temperature changes
due to ocean mixing. The effect of ocean upwelling,
however, is not included in the 1D-slab model. Hourly
and 3-hourly outputs of all models were used for thesurface and atmospheric analyses.

b. Experimental designFirst, control simulations of Typhoon Vera in the current climate (CNTL) were performed with the four
nonhydrostatic models using a horizontal resolution of
5 km (CNTL simulations). Initial and lateral atmosphericboundary conditions and SST were provided every 6 h fromthe Japanese 55-year Reanalysis (JRA-55; Kobayashi

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FIG. 1. The JMANHM simulation domain showing the track of Typhoon Vera (1959) at 12-hintervals (large dots, 0000 UTC; small dots, 1200 UTC). Large dots are labeled with the day in
September 1959 and the central pressure at that location.

et al. 2015) dataset with a spatial resolution of 1.258 3
1.258. All simulations started at 0000 UTC 22 September
1959 and were integrated over at least 5 days. The computational domain of JMANHM was 4500 km 3 4200 km(Fig. 1), and the domains of other models were set tocover approximately the same region (not shown).
To obtain the initial and boundary conditions for the
global warming climate, we used the results of climate runsby the JMA atmospheric GCM (AGCM) with a horizontal
resolution of 20 km (AGCM20; Mizuta et al. 2012;
Murakami et al. 2012). A present-day climate run for theperiod 1979–2003 and four future climate runs for the period 2075–99 were conducted with AGCM20. The futureclimate runs were driven by four different SST change
patterns projected by models from phase 5 of the Coupled
Model Intercomparison Project (CMIP5) under the
RCP8.5 scenario (Mizuta et al. 2014): the CMIP5 multimodel ensemble mean (PWMM) and three SST distribution clusters (PWC1, PWC2, and PWC3). Because
Typhoon Vera occurred in September, future changes in
the September monthly mean for SST, atmospherictemperature, and water vapor were added to the initialand boundary conditions of the CNTL simulation. All

increments include horizontal variability. The change in
relative humidity was not considered because recent
studies have indicated little change in tropospheric relative
humidity between the present-day and future climates(e.g., Knutson and Tuleya 2004; Takemi et al. 2012). Following Ito et al. (2016), the future climate conditions arereferred to as PW conditions. Thus, we used four sets of
PW conditions for four global warming climate simulations. Several sensitivity experiments were conducted with
JMANHM: the CNTL simulations without the spectral
nudging scheme (noSN experiment) and that started from0000 UTC 21 September 1959 (2100 experiment). We also
conducted the PWMM simulation by JMANHM withwater vapor increments (QV experiment) and wind increments (UV experiment) to study the impacts of thechange in relative humidity and vertical wind shear (e.g.,Gray 1968; Reasor et al. 2013).

3. Environmental conditions for the current
climate and the future, warmer climate
The initial conditions used for the current andfuture climate simulations are shown in Fig. 2. At

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FIG. 2. Horizontal distributions of sea surface temperature in the (a) CNTL, (b) PWMM, (c) PWC1, (d) PWC2, and (e) PWC3 simulations. Mean profiles of changes in (f) temperature and (g) the water vapor mixing ratio in 108–308N, 1308–1608E in the PWMM (red),
PWC1 (green), PWC2 (orange), and PWC3 (purple) simulations, compared with the CNTL simulation values. A red dashed line in(g) indicates the change for the QV-increment simulation.

0000 UTC 22 September 1959, Typhoon Vera was ataround 16.58N, 148.58E and traveling northwestward,
according to the JMA Regional Specialized Meteorological Center (RSMC) best-track dataset (Fig. 1).
In that region, SST was higher than 308C at that time,
indicating that sea conditions around the typhoon
were favorable for intense TC development (e.g.,
DeMaria and Kaplan 1994). In the future climate, SST
increases 28–38 to the south of the Japanese islands,
and regions with SST higher than 308C extend to
western Japan. Meanwhile, mean profiles of air temperature changes in 108–308N, 1308–1608E show
warming throughout the troposphere that is maximized in the upper troposphere (Fig. 2f). This increaseof the temperature change with height indicates astabilization of atmospheric conditions. Therefore,
the future changes in environmental conditions could
have both positive and negative effects on TC development, consistent with the findings of previous
studies (e.g., Hill and Lackmann 2011; Knutson and
Tuleya 2004; Shen et al. 2000). Water vapor also

increases in response to warming. In the future climate, the temperature increase in the lower troposphere is approximately 3 K, and the water vapor
mixing ratio increase in the lower troposphere is approximately 3.5 g kg21.
To estimate the maximum TC intensity changes dueto the changes in the atmospheric environment and SST,
we used the Emanuel (2006) model to calculate maximum potential intensity (E-MPI) from the atmosphericconditions (pressure, temperature, and water vapor
mixing ratio) and SST in the vicinity of the typhoon at
0000 UTC 22 September 1959. E-MPI in the current
climate simulation was 862 hPa in terms of minimum
central pressure and 84 m s21 in terms of the maximum
10-m wind speed (Table 2). In the future climate, E-MPI
in terms of minimum central pressure (maximum 10-m
wind speed) decreased (increased) by 17–18 hPa (5–6 m s21).
Therefore, despite the stabilization of atmospheric conditions, all environmental changes in the future climate leadto an increase in the maximum intensity of the
future TC.

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TABLE 2. Minimum central pressures in JRA-55, the RSMC best-track data, CNTL simulations, and the ensemble mean of the PW
simulations (PW_Av); dCP is the difference between the CNTL and the PW ensemble mean values for each model.

CNTL
PW_Av
dCP

JRA-55BT

CReSS

JMANHM

MM5

WRF

WRFB

MPI

922
—
—

895
—
—

916
894
22

907
884
23

905
868
37

928
909
19

901
892
9

862
844
18

4. Results of the multimodel ensemble simulations
a. Intensity changes of simulated typhoons
The multimodel ensemble experiment results, including those in the sensitivity experiments, all showedrobustly that the maximum intensity of the typhoon isincreased under future PW conditions (Table 2 andFig. 3). All future typhoons decrease the minimum
central pressures, regardless of the model physics andthe boundary conditions. The ensemble-mean decreases
in the minimum central pressure in each model, except
WRFB, ranged between 19 and 37 hPa. The simulated
minimum central pressure decreases are slightly larger
than the E-MPI of 18 hPa. Most models except CReSS
simulated almost the same tracks as the RSMC besttrack data (not shown).
Figure 4 compared the temporal evolution of centralpressure, maximum azimuthal mean tangential windspeed at an altitude of 10 m Vtmax and the radius of
Vtmax RMW10m between the CNTL and PW simulations. According to the RSMC best-track data, Typhoon
Vera intensified rapidly and attained a minimum centralpressure of 895 hPa at 0600 UTC 23 September 1959.
Subsequently, the typhoon’s intensity weakened once

to a central pressure of 905 hPa but then intensified
again to a central pressure of 900 hPa at 0000 UTC
25 September. JRA-55 did not capture the evolutionof Typhoon Vera from 1200 UTC 23 September to1200 UTC 24 September, and the CNTL storms in allmodels followed JRA-55 because the initial vortex
was provided by JRA-55. The impact of the initialvortex on the TC simulation is discussed in section 5by the results of the WRFB simulations.
Although all models underestimated the first intensity
peak of Typhoon Vera at the beginning of the integration time, JMANHM and MM5 successfully simulated CNTL storms that developed into intense
typhoons with the minimum central pressures lower
than 910 hPa. Both models indicated that not only the
maximum intensity but also the intensification rate of
the typhoon in the future climate would increase while
RMW10m decreased rapidly (Fig. 4). For example, the
CNTL and PW storms simulated by JMANHM developed at similar rates from the beginning of the integration time until 1200 UTC 24 September 1959.
Then, all PW storms started to intensify at high rates,
a decrease in central pressure more than 210.5 hPa in
a 6-h period, meeting the rapid intensification (RI)

FIG. 3. Minimum central pressures in the CNTL (cyan) and PW (red) simulations by CReSS,JMANHM, MM5, WRF, and WRFB. Red dots indicate the ensemble mean of the PW simulations. Results of the sensitivity experiments by JMANHM are shown by blue (CNTL) andpurple (PW) crosses, respectively.

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FIG. 4. Temporal evolution of (a) central pressure (CP), (b) maximum azimuthally averaged tangential wind at 10-m altitude (Vtmax),and (c) radius of Vtmax (RMW) in simulations by CReSS. The black dots and black line in (a) indicate the central pressure in the RSMC
best-track data and JRA-55, respectively. Cyan and red lines indicate values for the CNTL and the four PW simulations, respectively.
(d)–(f) As in (a)–(c), but for simulations by JMANHM. (g)–(i) As in (a)–(c), but for simulations by MM5. (j)–(l) As in (a)–(c), but for
simulations by WRF. Green (noSN), blue (2100), orange (QV), and purple (UV) lines in (d)–(f) indicate the results of sensitivity
experiments.

criterion, which is defined as a decrease in centralpressure of at least 42 hPa in a 24-h period (Holliday andThompson 1979). The PW storms simulated by MM5also intensified rapidly after 2100 UTC 24 September

1959 while RMW10m rapidly decreased. As a result,
RMW10m was smaller in all storms in the PW simulations than it was in the CNTL simulations, regardless ofthe model physics.

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b. Structural changes of the inner core during themature phase
We first focus on the inner-core structures of thesimulated typhoons during the mature phase, defined by
the 6 h prior to the time that the minimum centralpressure was reached. The major structural changes in
the inner core during the mature phase appeared
within a radius of 100 km according to the 6-h mean
radial profiles of sea level pressure, hourly precipitation,and tangential wind speed at an altitude of 10 m (Vt10m)(Fig. 5). The peak values and radial changes of hourly
precipitation and Vt10m within RMW10m, which werevery large in the PW simulations, indicate that suddenchanges occur in precipitation and winds around thestorm center. Inward shifts of the peaks indicate contraction of the eye and of RMW10m.
The inner-core structure is closely related to the eyewall updrafts. Figure 6 shows eyewall updrafts, defined by
the azimuthally averaged vertical velocity, during themature phase. There were, indeed, several significant
differences in intensity, radial location, and the shape ofthe eyewall updraft among the models. However, it isevident that all future PW storms exhibited taller andmore intense eyewall updrafts, compared with the CNTL
storms. The regions with eyewall updrafts stronger than
0.2 m s21 exceeded at an altitude of 17 km in all the future
simulations. In addition, the radial locations of eyewall
updrafts were smaller in all the future simulations than in
the current simulations, which were consistent with contractions of the eye and RMW10m.

c. Structural changes of the inner core during theperiod with a high intensification rate
To understand the processes through which a PW
storm intensifies rapidly as contracting the RMW, we
explored the inner-core structures during the period of
rapid intensification. We selected the PWMM simulation by JMANHM as a typical PW case because the
CNTL storm simulated by JMANHM developed into an
intense typhoon with a minimum central pressure lower
than 910 hPa and best reproduced the maximum intensity location of Typhoon Vera (not shown).
The PWMM storm exhibited typical changes compared with the CNTL storm (Fig. 7), namely, a higher
intensification rate during the RI phase and a smaller
RMW10m during the mature phase. The maximum intensification rate of the PWMM storm was more thandoubled that of the CNTL storm (Fig. 7b). Although the
PWMM storm intensified at a higher rate and its maximum intensity was increased compared with that of the
CNTL storm, changes in the near-surface structuresof both storms appeared within a radius of 100 km

VOLUME 30(Figs. 5d–f). Indeed, there was not much difference inthe location of the 980-hPa contour between the CNTL
and PWMM storms (Figs. 7d,e), although the eye of the
PWMM storm was much smaller than that of the
CNTL storm.
On the basis of the intensification rate, the intensification period was divided into two phases: a gradualintensification phase (phase I) and an RI phase (phase
II). Phase I was defined as the period before phase II
with a decrease in central pressure less than 210.5 hPa
in a 6-h period, and phase II was defined as the periodwith a decrease in central pressure more than 210.5 hPa.
In addition, the period during which the 6-h mean central pressure of the PWMM storm was equal to that of
the mature CNTL storm was defined as period A. Phase
I, phase II, and period A of the PWMM storm are indicated in Fig. 7a.
Figure 8 compares mean radial–height cross sectionsof vertical wind velocity, radial wind velocity Vr, and thewater vapor mixing ratio in the PWMM storm during
period A with those in the CNTL storm during themature phase. The mean minimum central pressure and
RMW10m of the CNTL storm during the mature phaseand those of the PWMM storm during period A wereequivalent (Fig. 7). Following Rogers et al. (2013), theradial axis in Fig. 8 is normalized by the radius of maximum wind speed at an altitude of 2 km RMW2km.
Although there were no significant differences in storm
intensity, the eyewall updraft of the PWMM storm during
period A was already considerably taller and stronger thanthat of the mature CNTL storm (Figs. 8a,b). The regionswith a vertical velocity increase more than 1 m s21 show
increases in the updraft in the mid-to-upper tropospherearound RMW2km and in the lower-to-midtroposphere inside RMW2km in the PWMM simulation (Fig. 8c).
The upper-tropospheric outflows in the PWMM storm
were stronger and appeared at higher altitudes than thosein the CNTL storm (Figs. 8d–f). Below the uppertropospheric outflow between altitudes of 5 and 10 km,
regions with inflow smaller than 21 m s21 appeared outside RMW2km in the PWMM storm. The inward transportof absolute angular momentum (AAM) is associated with a
midtropospheric inflow below the upper-troposphericoutflows (not shown). According to Fudeyasu and Wang(2011), midtropospheric inflow is induced by diabatic
heating in upper-tropospheric anvil clouds and transports
AAM inward, causing the circulation outside the eyewall
to spin up. There was little difference, however, in the nearsurface inflow between the mature CNTL storm and the
PWMM storm during period A.
Despite the lack of a large difference between thenear-surface inflows, the eyewall updraft of the PWMM
storm could obtain a large amount of water vapor

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FIG. 5. Storm-centered composite radial profiles of 6-h mean azimuth-averaged (a) sea level pressure (SLP), (b) hourly precipitation(RAIN), and (c) tangential wind speed at 10-m altitude (Vt) during the mature phase in the CNTL (cyan) and PW (red) simulations by
CReSS. (d)–(f) As in (a)–(c), but for JMANHM. (g)–(i) As in (a)–(c), but for MM5. (j)–(l) As in (a)–(c), but for WRF. Green (noSN), blue
(2100), orange (QV), and purple (UV) lines in (d)–(f) indicate the results of sensitivity experiments.

because the amount of water vapor in the lower troposphere was substantially increased compared with the
CNTL storm (Figs. 8g–i); in fact, the water vapor mixing
ratio in the inflow boundary layer (IBL) of the PWMM
storm was greater than that in the IBL of the CNTL
storm by more than 4 g kg21. In addition, the presence ofregions with a water vapor increase larger than 3 g kg21

inside the eyewall updraft suggests that the upward
transport of water vapor by the intense eyewall updrafts
was increased in the PWMM storm. Warming by thelatent heat (LH) release of condensation would enhance
upward buoyancy in the updrafts. Thus, the secondary
circulation above the IBL would be intensified in the
future climate simulation.

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FIG. 6. Six-hour azimuth-averaged radial–vertical cross sections of vertical velocity during the mature phase in the (a) CNTL,(b) PWMM, (c) PWC1, (d) PWC2, and (e) PWC3 simulations by CReSS. (f)–(j) As in (a)–(e), but for JMANHM. (k)–(o) As in (a)–(e),but for MM5. (p),(q) As in (a)–(e), but for WRF. Thick black lines indicate azimuth-averaged tangential wind velocity (Vt) of 50 m s–1, andthick green lines indicate the Vt axis defined by the radius of maximum Vt at each altitude.

The PWMM storm underwent extreme RI from1200 UTC 24 September to 0300 UTC 25 September
1959. Mean changes in the inner-core structure during
those 18 h (phase II) in the PWMM simulation by
JMANHM are shown in Fig. 9. The vertical velocitychange indicates that the eyewall updraft intensified inside the RMW2km during phase II (Fig. 9a), whereas theregions showing a negative vertical velocity change outside RMW2km imply contraction of the eyewall updraft
region. The region of increased updrafts inside RMW2km
corresponds well to the region of increased water vapor(Fig. 9b), which indicates that the upward water vapor
flux increased in the intensifying updraft inside RMW2km.
In addition, the total solid water (graupel, snow, and ice)
mixing ratio also increased in the moist and intense eyewall updraft, suggesting that a large amount of latent heat
was released inside the RMW2km (Fig. 9c).

According to previous modeling studies, the efficiency
with which diabatic heating drives increases in the maximum
winds of the vortex depends on the radial location of theheating relative to the RMW; heating inside the RMW ismore efficient than heating outside the RMW (e.g., Hackand Schubert 1986; Pendergrass and Willoughby 2009; Vighand Schubert 2009). In addition, abundant transport of water
vapor moistened the deep layers in the vicinity of the eyewall
updraft, and this deep-layer moistening is an essential condition for a high intensification rate (e.g., Nolan 2007).
Similar results to those shown in Fig. 9 were found in the PW
simulations by the other models during phase II (not shown).

d. Evolution and near-surface water vapor of thesimulated typhoons
The results presented in section 4c indicate that anincrease in the amount of lower-tropospheric water1 AUGUST 2017

KANADA ET AL.

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FIG. 7. Six-hour moving average temporal evolution of (a) CP, (b) 6-h changes in central pressure (dCP6h), and(c) radius of Vtmax (RMW10m) in the CNTL (cyan) and PWMM (red) simulations by JMANHM. Horizontal
distributions of 6-h mean precipitation for (d) the CNTL storm and (e) the PWMM storm during the mature phase.
The thick black contour in (d) and (e) indicates a mean sea level pressure of 980 hPa.

vapor plays a key role in increasing the intensification
rate during RI. Therefore, we examined the temporal
evolution of lower-tropospheric water vapor in theeyewall region in the CReSS, JMANHM, and MM5
simulations (Fig. 10). We defined lower-troposphericwater vapor in the eyewall region as the mean water
vapor mixing ratio between normalized radii of 0.7 and1.3 averaged over the lowest 500 m (see Fig. 8).
During phase I, a simulated typhoon gradually intensified as the RMW10m decreased (Fig. 4). After the
RMW10m had contracted to less than 150 km, the lowertropospheric water vapor in the eyewall region increased gradually, and then the storm initiated RI. It isnotable that the mean water vapor mixing ratio in the
PW storms was quite large in all models, increasing overthat in the CNTL storm by 3–4 g kg21.
The increases in lower-tropospheric water vapor
should be closely related to the sensible and latent heat

fluxes beneath the inner core. Despite future changes in
the mean sensible heat (SH) flux within a radius of
200 km being relatively small in all models (Figs. 10d–f),
the mean latent heat flux significantly increased from the
CNTL to the PW simulations (Figs. 10g–i). Enthalpy
fluxes in the vicinity of eyewall updrafts are key factors
in TC development (Xu and Wang 2010). According toMiyamoto and Takemi (2013), RI initiates when lowlevel parcels obtain high enthalpy from the underlying
ocean. In the present study, most of the PW storms
simulated by JMANHM and MM5 initiated RI when thelower-tropospheric water vapor beneath the eyewall
updrafts was maximized. This finding suggests that theenhancement of intensification rates during RI in the
future climate is attributable to an abundant supply ofwater vapor to the lower troposphere from the warmer
sea. In fact, the models that simulated intense typhoons(JMANHM and MM5) showed a relatively large latent

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FIG. 8. Six-hour azimuth-averaged radial–vertical cross sections of vertical velocity w in the (a) CNTL simulation during the maturephase and (b) PWMM simulation during period A by JMANHM; (c) PWMM minus CNTL. (d)–(f) As in (a)–(c), but for Vr. (g)–(i) As in(d)–(f), but for the water vapor mixing ratio (QV). Black contours in (a) and (b) indicate Vt (60 and 70 m s21), and those in (c)–(i) indicate
vertical velocity (1 and 2 m s–1). The x-axis quantity r* indicates radius normalized by the RMW at an altitude of 2 km.

heat flux, whereas the intensity of typhoons simulated by
the model showing a relatively small latent heat flux
(CReSS) was relatively weak.
Thus, the remaining question to be solved is why the
future storms could not initiate RI earlier, while thelower-tropospheric water vapor was much larger thanthe values when the CNTL storms initiated RI. The
previous studies indicate that inertial stability (IS) inside
the RMW is closely related to the RI because an increase of inertial stability prevents air parcels from being displaced outward in the radial direction and allows
for a more efficient dynamic response to imposed

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6029

FIG. 9. Mean azimuth-averaged radial–vertical cross sections of changes in (a) vertical velocity (i.e., w; m s21 h21);(b) the water vapor mixing ratio (QV; g kg21 h21); (c) the sum of the graupel, snow, and ice mixing ratios(QG 1 QS 1 QI; g kg21 h21); and (d) Vr (m s21 h21) during phase II in the PWMM simulation by JMANHM. Black
contours show the mean updraft (1 and 2 m s21); the r* is as in Fig. 8.

sources of convective heating inside the RMW (Hackand Schubert 1986; Pendergrass and Willoughby 2009;
Vigh and Schubert 2009). The temporal evolutions ofmean inertial stability below an altitude of 10 km inside
RMW2km are shown in Figs. 10j–l. The initial vortex
given by JRA-55 was weak so that the inertial stability
was low at the beginning of integration time. As theRMW contracted, the inertial stability around the storm
center increased gradually through vortex axisymmetrization. There were no large differences in the inertial
stability values between the CNTL and PW storms
during the period. However, around the time when thevalues increased by 0.5 3 1026 s22, the PW storms
started to intensify at higher rates than the CNTL storm.
After the onset of RI, the simulated storms with larger
amounts of the lower-tropospheric water vapor intensify

at higher rates (Fig. 11). The 12-h changes in centralpressure and mean lower-tropospheric water vapor in all
the simulations show a high correlation coefficient valueof 20.81.

5. Discussiona. How does climate change affect the TC
intensification process?
All future typhoons simulated by the four different
models showed increased maximum intensity comparedwith that in the current climate. Furthermore, all models
indicated that not only the maximum intensity but also
the intensification rate of the typhoon in the future climate would increase while RMW10m decreased rapidly(Fig. 4).

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FIG. 10. Temporal evolution of mean lower-tropospheric water vapor (QV; g kg21) in the eyewall region in the CNTL (cyan) and PW(red) simulations by (a) CReSS, (b) JMANHM, and (c) MM5. The eyewall region was defined as 0.7 # r * # 1.3. (d)–(f) As in (a)–(c), but
for mean SH (W m22) within a radius of 200 km. (g)–(i) As in (d)–(f), but for LH (W m22). (j)–(l) As in (d)–(f), but for mean squared
IS (1026 s22) below an altitude of 10 km inside the radius of maximum wind speed at an altitude of 2 km. Black dashed vertical lines
indicate the beginning and end of phase II in the PWMM simulations by each model.

A schematic diagram of the intensification processof a future intense TC is shown in Fig. 12. At the beginning of the integration time, the simulated TC intensifies gradually as the RMW contracts (phase I in
Fig. 7). The results of all four models indicated that
there was little difference in the temporal evolution ofsimulated typhoons between the current and futureclimates during phase I (Fig. 4). The impact of future,

warmer climate conditions appeared during the RI
phase when the intensification rate increased greatly.
In the future climate, SST to the south of the Japaneseislands is projected to increase by 38C (Fig. 2), and awarmer sea provides more water vapor to the lower
troposphere (Fig. 10). The abundant water vapor was
supplied to tall and intense eyewall updrafts, which
formed continuously inside the RMW (Figs. 8 and 9),

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FIG. 11. Scatter diagram of mean lower-tropospheric water vapor (QV; g kg21) in the eyewall region
at the beginning of phase II and the 12-h changes in central pressure (dCP12h) from the beginning ofphase II in the CNTL (open circles) and PW (filled circles) simulations by CReSS (blue), JMANHM(red), MM5 (green), and WRF (black). Large (small) circles indicate that the mean squared inertial
stability (IS in Fig. 10) at the beginning of phase II was higher (lower) than 1.0 3 1026 s22.

and contributed to rapid formation of condensed waterin the updrafts (Fig. 9).
A study of intensification processes before and after
the onset of RI has revealed that a clear difference indiabatic heating associated with the eyewall updraft

appears in the inner core from before to after the onset(Miyamoto and Takemi 2015): a high diabatic heating
region occurs around the RMW after the TC initiates
RI. On the basis of their results, Miyamoto and Takemi(2015) suggested a positive feedback process operated

FIG. 12. Schematic diagrams of the intensification process during phases I and II.

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during RI between the secondary circulation and diabatic heating in the eyewall (e.g., Charney and Eliassen
1964; Ooyama 1969). In fact, our study showed a large
increase in the total solid water mixing ratio around andinside the RMW during phase II. Furthermore, the increase in near-surface inflow only around the eyewall
region (Fig. 9d), a pattern similar to that in Vr inducedby heating shown by Stern et al. (2015), also suggests theenhancements of heating-induced near-surface inflow
and hence horizontal wind speed in the vicinity of theeyewall in the future climate. Thus, the RI process in the
future simulations is enhanced by the release of latent
heat associated with a rapid increase in the solid water
mixing ratio caused by a large increase in near-surface
water vapor derived from the future, warmer sea.
Model surface parameters have large impacts on theenthalpy fluxes from the sea surface, which are crucial for
TC development (e.g., Emanuel 1986; Rotunno and
Emanuel 1987). Indeed, CReSS, which considered theocean cooling effect by the 1D-slab ocean model, simulated relatively weak storms. The IBL structures determined by the PBL scheme control the water vapor
supply to the eyewall updraft in simulations (e.g., Kanada
et al. 2013; Rotunno and Bryan 2012). In addition, microphysics processes affect the amount and location of
diabatic heating (e.g., McFarquhar et al. 2012; Zhu et al.
2015). Nonetheless, the most important findings in thepresent study are that all four models, despite their having
different schemes, project the same results: an intense TC
will intensify more rapidly and become more intense under future, warmer climate conditions. This result does notdepend on whether the model considers the ocean effect.
Thus, the results of multimodel ensemble experiments
strongly support the robustness of the changes in intense
TCs in the future climate shown in the present study.

b. Impact of the initial TC vortex
All four 5-km-mesh models underestimated the rate ofintensification from 1200 UTC 23 September to 1200 UTC
24 September 1959 for Typhoon Vera (Fig. 4). This underestimation was due to the initial vortex structure provided by JRA-55. The three-dimensional structure of thesimulated TCs depends considerably on the model resolution (e.g., Bengtsson et al. 2007; Gentry and Lackmann
2010; Roberts et al. 2015; Kanada and Wada 2016), and thehorizontal resolution of the initial atmospheric data of
JRA-55, 1.258 3 1.258, is too coarse to represent the threedimensional typhoon vortex structure. To simulate theevolution of an intense TC by a nonhydrostatic model, theinitial vortex is crucial because an axisymmetric inner-core
structure is essential to produce a high intensification rate
(e.g., Alvey et al. 2015; Kieper and Jiang 2012; Rogers et al.
2013). The inertial stability increases through vortex

VOLUME 30

axisymmetrization. In fact, mean inertial stability inside the
RMW was considerably low in the beginning of integration
time. The values increased gradually for 2–3 days and thenthe storms initiated the RI (Fig. 10).
Various initialization methods have been developed to
improve initial conditions in TC simulations, including
vortex relocation (e.g., Hsiao et al. 2010) and dynamical
initialization (e.g., Cha and Wang 2013) schemes. The
vortex relocation scheme, which is a TC bogus scheme,
inserts a synthetic Rankin vortex in the initial conditions(e.g., Leslie and Holland 1995; Ueno 1989).
To study the impact of the initial vortex on simulationsof Typhoon Vera, we conducted sensitivity experiments
for the CNTL and PW simulations by using WRF bothwith (WRFB) and without (WRF) the TC bogus scheme(Fig. 13). The initial bogus vortex was set to have themaximum wind speed of 50 m s21 and the radius of the
maximum wind of 50 km, with the same wind speed being
set in the depth from the surface to the 600-hPa level, but
the wind speed exponentially decreased above the 600-hPa
level to the 100-hPa level. The same initial vortex was
imposed both for the CNTL and PW simulations. The
choice of this intensity was intended to reproduce the rapid
intensification and the maximum intensity of Vera in the
September 1959 condition. The discussion on the use of the
TC bogus scheme can be found in Takemi et al. (2016).
The CNTL storm simulated by WRFB apparently captured the temporal evolution of central pressure (Fig. 13a)but not the evolutions of Vtmax and RMW10m. In general,the maximum wind speed of a TC increases and its centralpressure decreases as the RMW contracts (e.g., Miyamoto
and Takemi 2013). In all WRFB simulations, however, theinitial RMW10m increased with time as Vtmax decreased.
The reason why the response to the warming in thesimulations with the TC bogus scheme is different fromthat in the simulations without the TC bogus is due to thedifferent temporal evolution of the size of the TC core asshown in terms of the RMW. Without the TC bogus
scheme the contraction of the TC core can be seen (seeFig. 4l), while with the TC bogus such a contraction of thesize of the TC core is not seen (Fig. 13c). In addition, thedifference in the RMW between the CNTL and PW
simulations is much smaller in the simulations with the
TC bogus scheme than without the TC bogus. Similar TC
core sizes between the CNTL and PW simulations with
the TC bogus scheme led to the smaller difference in thewarming impact on Vera’s intensity in the simulations
with the TC bogus than without the TC bogus.
The results of the sensitivity experiments showed that
the TC evolution in a simulation is highly sensitive to theinitial vortex. Indeed, a number of studies have shownthat a TC bogus scheme can reasonably reproduce TC
features with improved track and intensity (e.g., Knutson

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6033

FIG. 13. As in Fig. 4, but for WRFB.

and Tuleya 1999). However, our results also imply that
great care is needed when choosing the characteristics ofthe bogus vortex. We can evaluate the results of CNTL
simulations by using best-track datasets. However, in PW
simulations, considerable uncertainties remain in theinitial vortex because we have no references for the initialvortex in the future climate. Use of advanced initialization methods such as a dynamical initialization scheme(e.g., Cha and Wang 2013) and piecewise potential vorticity inversion (Davis 1992) will help to produce better
simulations and forecasts of TC evolution.

c. Other future intense TCs
In the present study, all CNTL and PW simulations
used the same initial vortex. Therefore, the results
shown represent the changes that can be expected inintensity and inner-core structure of a Typhoon Vera–like vortex in the future climate. This raises the questionof whether the future changes found in this study arerobust for other intense TCs.
Recently, Kanada et al. (2013) used a 2-km-mesh nonhydrostatic model to examine the effects of global warming on the structure of intense TCs. They chose 6 of the top
10 TCs in global warming projections calculated by
AGCM20 (Murakami et al. 2012) for the present-day andfuture climates and conducted dynamical downscaling
experiments. In fact, their comparison between presentday and future intense TCs showed structural changes of
the inner core in the future climate similar to those shownin the present study (Fig. 5): relatively tall and intense
eyewall updrafts and decreases in the eye diameter and
RMW were accompanied by increases in precipitation andnear-surface wind speeds within a radius of 100 km.
The cause of high intensification rates in future TCs is
an increase in lower-tropospheric water vapor due towarmer SSTs, a known consequence of anthropogenic
greenhouse warming (e.g., Collins et al. 2013; Mizuta
et al. 2014). In fact, the results in the present study

indicate a close linkage between the intensification rateduring the RI and the water vapor amount around theRMW (Fig. 11). Therefore, changes in an intense typhoon and the associated intensification process shownin the present study are most likely applicable to otherintense TCs under warmer conditions.

6. Summary
To explore the impacts of future climate changes on theintensity and structures of an intense TC, we conductednumerical simulations of the most destructive historical TC
to strike Japan, Typhoon Vera (1959), for the current climate and the future global warming climate with four
5-km-mesh nonhydrostatic models. Initial and boundary
conditions for the control simulations of Typhoon Vera
were provided by the JMA 55-year Reanalysis dataset.
Future changes from the present-day period, 1979–2003,
and the future period, 2075–99, were estimated by performing climate runs with a 20-km-mesh atmosphericgeneral circulation model and then adding the results to theinitial and boundary conditions of the control simulations.
The multimodel ensemble experiments produced robust
signals showing that the maximum intensity of the typhoon
increased in all warmer climate simulations (Table 2 andFig. 3). The height and intensity of eyewall updrafts increased in all future simulations (Fig. 6). Similar changes
have been suggested by idealized simulations (Shen et al.
2000; Hill and Lackmann 2011). However, our simulations
were conducted by using the real synoptic environment of ahistorical intense TC, Typhoon Vera (1959). Furthermore,
the results provided the additional information about
structural changes that in a future TC the major changes
appear within a radius of 100 km from the storm center(Fig. 5). These structural changes are accompanied by thecontinuous formation of intense eyewall updrafts inside
the RMW (Fig. 9) and by contraction of the RMW during
the rapid intensification phase. During that phase, a large

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increase in the total solid water mixing ratio, which suggests
latent heat release, occurs in the tall and intense eyewall
updrafts, which formed continuously inside the RMW. The
increase in near-surface inflow only around the eyewall region (Fig. 9d) suggests that enhancements of near-surface
inflows and, hence, of the horizontal wind speed, are induced
by strong diabatic heating in the vicinity of the eyewall.
There were, of course, some differences in the simulated intensities before the onset of rapid intensification.
However, it is on the intensification process during the
rapid intensification phase that future environmentchanges will have large impacts.
It should be noted that the PW approach cannot be used
to project changes in the frequency or track of future TCs.
Nonetheless, all four 5-km-mesh models projected that a
future typhoon would intensify more rapidly as RMWcontracted. Rapid contraction and the resultant smaller
RMW imply rapid increases in the maximum wind speed
in the vicinity of the RMW (Montgomery and Smith 2014).
Thus, the present study results also show that people living
in coastal regions in the future climate will need to takeprecautions against sudden increases in high winds andprecipitation associated with the passage of intense TCs.
Acknowledgments. The authors are grateful to threeanonymous reviewers and Dr. S. L. Sessions for instructive comments. This study was supported by the
Ministry of Education, Culture, Sports, Science andTechnology of Japan under the framework of the Sousei
Program and by the Japan Society for the Promotion of
Science, JSPS KAKENHI Grant 26400466. Numericalsimulations were performed with the Earth Simulator.

REFERENCES
Alvey, G. R., III, J. Zawislak, and E. Zipser, 2015: Precipitation
properties observed during tropical cyclone intensity change.
Mon. Wea. Rev., 143, 4476–4492, doi:10.1175/MWR-D-15-0065.1.
Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh,
J.-J. Luo, and T. Yamagata, 2007: How may tropical cyclones
change in a warmer climate? Tellus, 59A, 539–561, doi:10.1111/
j.1600-0870.2007.00251.x.
Cha, D. H., and Y. Q. Wang, 2013: A dynamical initialization scheme
for real-time forecasts of tropical cyclones using the WRF Model.
Mon. Wea. Rev., 141, 964–986, doi:10.1175/MWR-D-12-00077.1.
Charney, J. G., and A. Eliassen, 1964: On the growth of the hurricane depression. J. Atmos. Sci., 21, 68–75, doi:10.1175/
1520-0469(1964)021,0068:OTGOTH.2.0.CO;2.
Christensen, J. H., and Coauthors, 2013: Climate phenomena andtheir relevance for future regional climate change. Climate
Change 2013: The Physical Science Basis, T. F. Stocker et al.,
Eds., Cambridge University Press, 1217–1308.
Collins, M., and Coauthors, 2013: Long-term climate change:
Projections, commitments and irreversibility. Climate Change
2013: The Physical Science Basis, T. F. Stocker et al., Eds.,
Cambridge University Press, 1029–1136.

VOLUME 30

Davis, C. A., 1992: Piecewise potential vorticity inversion. J. Atmos.
Sci., 49, 1397–1411, doi:10.1175/1520-0469(1992)049,1397:
PPVI.2.0.CO;2.
DeMaria, M., and J. Kaplan, 1994: Sea surface temperature andthe maximum intensity of Atlantic tropical cyclones.
J. Climate, 7, 1324–1334, doi:10.1175/1520-0442(1994)007,1324:
SSTATM.2.0.CO;2.
Dudhia, J., 1993: A nonhydrostatic version of the Penn State–NCAR
Mesoscale Model: Validation tests and simulation of an Atlantic
cyclone and cold front. Mon. Wea. Rev., 121, 1493–1513, doi:10.1175/
1520-0493(1993)121,1493:ANVOTP.2.0.CO;2.
Emanuel, K. A., 1986: An air–sea interaction theory for tropical cyclones. Part I: Steady-state maintenance. J. Atmos. Sci., 43, 585–605,
doi:10.1175/1520-0469(1986)043,0585:AASITF.2.0.CO;2.
——, 1988: The maximum intensity of hurricanes. J. Atmos. Sci., 45, 1143–
1155, doi:10.1175/1520-0469(1988)045,1143:TMIOH.2.0.CO;2.
——, 2006: Subroutine to calculate the maximum wind speed andminimum central pressure achievable in tropical cyclone, given asounding and a sea surface temperature. [Available online at ftp://
texmex.mit.edu/pub/emanuel/TCMAX/pcmin_revised.f.]
Fierro, A. O., R. F. Rogers, F. D. Marks, and D. S. Nolan, 2009: The
impact of horizontal grid spacing on the microphysical andkinematic structures of strong tropical cyclones simulated with
the WRF-ARW Model. Mon. Wea. Rev., 137, 3717–3743,
doi:10.1175/2009MWR2946.1.
Fudeyasu, H., and Y. Wang, 2011: Balanced contribution to theintensification of a tropical cyclone simulated in TCM4: Outer-corespinup process. J. Atmos. Sci., 68, 430–449, doi:10.1175/2010JAS3523.1.
Gentry, M. S., and G. M. Lackmann, 2010: Sensitivity of simulated
tropical cyclone structure and intensity to horizontal resolution.
Mon. Wea. Rev., 138, 688–704, doi:10.1175/2009MWR2976.1.
Gray, W. M., 1968: Global view of the origin of tropical disturbances and storms. Mon. Wea. Rev., 96, 669–700, doi:10.1175/
1520-0493(1968)096,0669:GVOTOO.2.0.CO;2.
Grell, G. A., J. Dudhia, and D. Stauffer, 1994: A description of thefifth-generation Penn State/NCAR Mesoscale Model (MM5).
NCAR Tech. Note NCAR/TN-3981STR, 128 pp., doi:10.5065/
D60Z716B.
Hack, J. J., and W. H. Schubert, 1986: Nonlinear response of atmosphericvortices to heating by organized cumulus convection. J. Atmos.
Sci., 43, 1559–1573, doi:10.1175/1520-0469(1986)043,1559:
NROAVT.2.0.CO;2.
Hill, K. A., and G. M. Lackmann, 2011: The impact of future climatechange on TC intensity and structure: A downscaling approach.
J. Climate, 24, 4644–4661, doi:10.1175/2011JCLI3761.1.
Holland, G. J., 1997: The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54, 2519–2541, doi:10.1175/
1520-0469(1997)054,2519:TMPIOT.2.0.CO;2.
Holliday, C. R., and A. H. Thompson, 1979: Climatological characteristics of rapidly intensifying typhoons. Mon. Wea. Rev., 107, 1022–
1034, doi:10.1175/1520-0493(1979)107,1022:CCORIT.2.0.CO;2.
Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion
package with an explicit treatment of entrainment processes.
Mon. Wea. Rev., 134, 2318–2341, doi:10.1175/MWR3199.1.
Hsiao, L. F., C. S. Liou, T. C. Yeh, Y. R. Guo, D. S. Chen, K. N.
Huang, C. T. Terng, and J. H. Chen, 2010: A vortex relocation scheme for tropical cyclone initialization in Advanced
Research WRF. Mon. Wea. Rev., 138, 3298–3315, doi:10.1175/
2010MWR3275.1.
Ito, R., T. Takemi, and O. Arakawa, 2016: A possible reduction in
the severity of typhoon wind in the northern part of Japan
under global warming: A case study. SOLA, 12, 100–105,
doi:10.2151/sola.2016-023.

1 AUGUST 2017

KANADA ET AL.

Janjić, Z. I., 1994: A step-mountain eta coordinate model: Further
developments of the convection, viscous sublayer, and turbulence
closure schemes. Mon. Wea. Rev., 122, 927–945, doi:10.1175/
1520-0493(1994)122,0927:TSMECM.2.0.CO;2.
Kain, J., and J. Fritsch, 1993: Convective parameterization formesoscale models: The Kain–Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteor.
Monogr., No. 24, Amer. Meteor. Soc., 165–170.
Kanada, S., and A. Wada, 2016: Sensitivity to horizontal resolutionof the simulated intensifying rate and inner-core structure of
typhoon Ida, an extremely intense typhoon. J. Meteor. Soc.
Japan, 94A, 181–190, doi:10.2151/jmsj.2015-037.
——, ——, and M. Sugi, 2013: Future changes in structures ofextremely intense tropical cyclones using a 2-km mesh nonhydrostatic model. J. Climate, 26, 9986–10 005, doi:10.1175/
JCLI-D-12-00477.1.
Kaplan, J., and M. DeMaria, 2003: Large-scale characteristics of rapidly
intensifying tropical cyclones in the North Atlantic basin. Wea.
Forecasting, 18, 1093–1108, doi:10.1175/1520-0434(2003)018,1093:
LCORIT.2.0.CO;2.
Kawase, H., T. Yoshikane, M. Hara, F. Kimura, T. Yasunari,
B. Ailikun, H. Ueda, and T. Inoue, 2009: Intermodel variability of future changes in the baiu rainband estimated by thepseudo global warming downscaling method. J. Geophys. Res.,
114, D24110, doi:10.1029/2009JD011803.
Kieper, M., and H. Jiang, 2012: Predicting tropical cyclone rapid
intensification using the 37 GHz ring pattern identified frompassive microwave measurements. Geophys. Res. Lett., 39,
L13804, doi:10.1029/2012GL052115.
Kimura, F., and A. Kitoh, 2007: Downscaling by pseudo global
warning method. Research project on the impact of climatechanges on agricultural production system in arid areas, Research Institute for Humanity and Nature Final Rep., 43–46.
Kitagawa, H., 2000: Radiation processes (in Japanese). JMA Numerical Prediction Division Tech. Rep. 46, 16–31.
Knabb, R. D., D. P. Brown, and J. R. Rhome, 2005: Tropical cyclone report: Hurricane Katrina, 23–30 August 2005. NOAA/
NWS/NHC Rep., 43 pp. [Available online at http://www.nhc.
noaa.gov/data/tcr/AL122005_Katrina.pdf.]
Knutson, T. R., and R. E. Tuleya, 1999: Increased hurricane intensities with CO2-induced warming as simulated using the
GFDL hurricane prediction system. Climate Dyn., 15, 503–
519, doi:10.1007/s003820050296.
——, and ——, 2004: Impact of CO2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the
choice of climate model and convective parameterization.
J. Climate, 17, 3477–3495, doi:10.1175/1520-0442(2004)017,3477:
IOCWOS.2.0.CO;2.
——, ——, W. Shen, and I. Ginis, 2001: Impact of CO2-induced
warming on hurricane intensities as simulated in a hurricane
model with ocean coupling. J. Climate, 14, 2458–2468, doi:10.1175/
1520-0442(2001)014,2458:IOCIWO.2.0.CO;2.
——, and Coauthors, 2013: Dynamical downscaling projections oftwenty-first-century Atlantic hurricane activity: CMIP3 andCMIP5 model-based scenario. J. Climate, 26, 6591–6617,
doi:10.1175/JCLI-D-12-00539.1.
——, and Coauthors, 2015: Global projections of intense tropical
cyclone activity for the late twenty-first century from dynamical
downscaling of CMIP5/RCP4.5 scenarios. J. Climate, 28, 7203–
7224, doi:10.1175/JCLI-D-15-0129.1.
Kobayashi, S., and Coauthors, 2015: The JRA-55 reanalysis:
General specifications and basic characteristics. J. Meteor.
Soc. Japan, 93, 5–48, doi:10.2151/jmsj.2015-001.

6035

Kondo, J., 1975: Air–sea bulk transfer coefficients in diabatic
conditions. Bound.-Layer Meteor., 9, 91–112, doi:10.1007/
BF00232256.
Lackmann, G. M., 2013: The south-central U.S. flood of May 2010:
Present and future. J. Climate, 26, 4688–4709, doi:10.1175/
JCLI-D-12-00392.1.
——, 2015: Hurricane Sandy before 1900 and after 2100. Bull. Amer.
Meteor. Soc., 96, 547–560, doi:10.1175/BAMS-D-14-00123.1.
Leslie, L. M., and G. J. Holland, 1995: On the bogussing of
tropical cyclones in numerical models: A comparison of vortex
profiles. Meteor. Atmos. Phys., 56, 101–110, doi:10.1007/
BF01022523.
Lin, I.-I., and Coauthors, 2013: An ocean coupling potential intensity index for tropical cyclones. Geophys. Res. Lett., 40,
1878–1882, doi:10.1002/grl.50091.
Louis, J. F., M. Tiedtke, and J. F. Geleyn, 1982: A short history of
the operational PBL parameterization at ECMWF. Proc.
Workshop on Planetary Boundary Layer Parameterization,
Reading, United Kingdom, ECMWF, 59–79.
Lynn, B. H., R. Healy, and L. M. Druyan, 2009: Investigation of
Hurricane Katrina characteristics for future warmer climates.
Climate Res., 39, 75–86, doi:10.3354/cr00801.
McFarquhar, G. M., B. F. Jewett, M. S. Gilmore, S. W. Nesbitt, and
T.- L. Hsieh, 2012: Vertical velocity and microphysical distributions
related to rapid intensification in a simulation of Hurricane Dennis(2005). J. Atmos. Sci., 69, 3515–3534, doi:10.1175/JAS-D-12-016.1.
Miyamoto, Y., and T. Takemi, 2013: A transition mechanism for the
spontaneous axisymmetric intensification of tropical cyclones.
J. Atmos. Sci., 70, 112–129, doi:10.1175/JAS-D-11-0285.1.
——, and ——, 2015: A triggering mechanism for rapid intensification of tropical cyclones. J. Atmos. Sci., 72, 2666–2681,
doi:10.1175/JAS-D-14-0193.1.
Mizuta, R., and Coauthors, 2012: Climate simulations using MRIAGCM with 20-km grid. J. Meteor. Soc. Japan, 90A, 233–260,
doi:10.2151/jmsj.2012-A12.
——, O. Arakawa, T. Ose, S. Kusunoki, H. Endo, and A. Kitoh,
2014: Classification of CMIP5 future climate responses by thetropical sea surface temperature changes. SOLA, 10, 167–171,
doi:10.2151/sola.2014-035.
Montgomery, M. T., and R. K. Smith, 2014: Paradigms for tropical
cyclone intensification. Aust. Meteor. Oceanogr. J., 64, 37–66.
Mori, N., M. Kato, S. Kim, H. Mase, Y. Shibutani, T. Takemi,
K. Tsuboki, and T. Yasuda, 2014: Local amplification of storm
surge by Super Typhoon Haiyan in Leyte Gulf. Geophys. Res.
Lett., 41, 5106–5113, doi:10.1002/2014GL060689.
Murakami, H., and M. Sugi, 2010: Effect of model resolution on
tropical cyclone climate projections. SOLA, 6, 73–76.
——, and Coauthors, 2012: Future changes in tropical cyclone activity projected by the new high-resolution MRI-AGCM.
J. Climate, 25, 3237–3260, doi:10.1175/JCLI-D-11-00415.1.
Nakamura, R., T. Shibayama, M. Esteban, and T. Iwamoto, 2016:
Future typhoon and storm surges under different global
warming scenarios: Case study of typhoon Haiyan (2013). Nat.
Hazards, 82, 1645–1681, doi:10.1007/s11069-016-2259-3.
Nakanishi, M., and H. Niino, 2004: An improved Mellor–Yamadalevel 3 model with condensation physics: Its design and verification. Bound.-Layer Meteor., 112, 1–31, doi:10.1023/
B:BOUN.0000020164.04146.98.
Nakano, M., T. Kato, S. Hayashi, S. Kanada, Y. Yamada, andK. Kurihara, 2012: Development of a 5-km-mesh cloudsystem-resolving regional climate model at the Meteorological Research Institute. J. Meteor. Soc. Japan, 90A, 339–350,
doi:10.2151/jmsj.2012-A19.

6036

JOURNAL OF CLIMATE

Needham, H. F., B. D. Keim, and D. Sathiaraj, 2015: A review of tropical
cyclone–generated storm surges: Global data sources, observations,and impacts. Rev. Geophys., 53, 545–591, doi:10.1002/2014RG000477.
Nolan, D. S., 2007: What is the trigger for tropical cyclogenesis?
Aust. Meteor. Mag., 56, 241–266.
Ooyama, K., 1969: Numerical simulation of the life cycle oftropical cyclones. J. Atmos. Sci., 26, 3–40, doi:10.1175/
1520-0469(1969)026,0003:NSOTLC.2.0.CO;2.
Pendergrass, A. G., and H. E. Willoughby, 2009: Diabatically induced
secondary flows in tropical cyclones. Part I: Quasi-steady forcing.
Mon. Wea. Rev., 137, 805–821, doi:10.1175/2008MWR2657.1.
Rasmussen, R., and Coauthors, 2011: High-resolution coupledclimate runoff simulations of seasonal snowfall over Colorado:
A process study of current and warmer climate. J. Climate, 24,3015–3048, doi:10.1175/2010JCLI3985.1.
Reasor, P. D., R. Rogers, and S. Lorsolo, 2013: Environmental flow
impacts on tropical cyclone structure diagnosed from airborne
Doppler radar composites. Mon. Wea. Rev., 141, 2949–2969,
doi:10.1175/MWR-D-12-00334.1.
Roberts, M. J., and Coauthors, 2015: Tropical cyclones in the
UPSCALE ensemble of high-resolution global climate models.
J. Climate, 28, 574–596, doi:10.1175/JCLI-D-14-00131.1.
Rogers, R. F., P. Reasor, and S. Lorsolo, 2013: Airborne Doppler
observations of the inner-core structural differences between
intensifying and steady-state tropical cyclones. Mon. Wea.
Rev., 141, 2970–2991, doi:10.1175/MWR-D-12-00357.1.
Rotunno, R., and K. A. Emanuel, 1987: An air–sea interaction theory
for tropical cyclones. Part II: Evolutionary study using a nonhydrostatic axisymmetric numerical model. J. Atmos. Sci., 44, 542–
561, doi:10.1175/1520-0469(1987)044,0542:AAITFT.2.0.CO;2.
——, and G. H. Bryan, 2012: Effects of parameterized diffusion on
simulated hurricanes. J. Atmos. Sci., 69, 2284–2299, doi:10.1175/
JAS-D-11-0204.1.
Saito, K., J. Ishida, K. Aranami, T. Hara, T. Segawa, M. Narita, andY. Honda, 2007: Nonhydrostatic atmospheric models andoperational development at JMA. J. Meteor. Soc. Japan, 85B,
271–304, doi:10.2151/jmsj.85B.271.
Shen, W., R. E. Tuleya, and I. Ginis, 2000: A sensitivity study of thethermodynamic environment on GFDL model hurricane intensity: Implications for global warming. J. Climate, 13, 109–121,
doi:10.1175/1520-0442(2000)013,0109:ASSOTT.2.0.CO;2.
Skamarock, W. C., and Coauthors, 2008: A description of theAdvanced Research WRF version 3. NCAR Tech. Note
NCAR/TN-4751STR, 113 pp., doi:10.5065/D68S4MVH.
Stauffer, D. R., and N. L. Seaman, 1990: Use of four-dimensional data
assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev., 118, 1250–1277,
doi:10.1175/1520-0493(1990)118,1250:UOFDDA.2.0.CO;2.
Stern, D. P., J. L. Vigh, D. S. Nolan, and F. Zhang, 2015: Revisiting therelationship between eyewall contraction and intensification.
J. Atmos. Sci., 72, 1283–1306, doi:10.1175/JAS-D-14-0261.1.

VOLUME 30

Stowasser, M., Y. Wang, and K. Hamilton, 2007: Tropical cyclonechanges in the western North Pacific in a global warming
scenario. J. Climate, 20, 2378–2396, doi:10.1175/JCLI4126.1.
Takayabu, I., K. Hibino, H. Sasaki, H. Shiogama, N. Mori,
Y. Shibutani, and T. Takemi, 2015: Climate change effects on
the worst-case storm surge: A case study of Typhoon Haiyan.
Environ. Res. Lett., 10, 064011, doi:10.1088/1748-9326/10/6/
064011.
Takemi, T., S. Nomura, Y. Oku, and H. Ishikawa, 2012: A regionalscale evaluation of changes in environmental stability forsummertime afternoon precipitation under global warmingfrom super-high-resolution GCM simulations: A study for thecase in the Kanto Plain. J. Meteor. Soc. Japan, 90A, 189–212,
doi:10.2151/jmsj.2012-A10.
——, R. Ito, and O. Arakawa, 2016: Robustness and uncertainty
of projected changes in the impacts of Typhoon Vera (1959)
under global warming. Hydrol. Res. Lett., 10, 88–94,
doi:10.3178/hrl.10.88.
Trapp, R. J., and K. Hoogewind, 2016: The realization of extreme
tornadic storm events under future anthropogenic climate change.
J. Climate, 29, 5251–5265, doi:10.1175/JCLI-D-15-0623.1.
Tsuboki, K., and A. Sakakibara, 2002: Large-scale parallel computing of cloud resolving storm simulator. High Performance
Computing, H. P. Zima et al., Eds., Springer, 243–259.
——, M. K. Yoshioka, T. Shinoda, M. Kato, S. Kanada, andA. Kitoh, 2015: Future increase of supertyphoon intensity
associated with climate change. Geophys. Res. Lett., 42, 646–
652, doi:10.1002/2014GL061793.
Ueno, M., 1989: Operational bogussing and numerical predictionof typhoon in JMA. JMA Numerical Prediction Division
Tech. Rep. 28, 48 pp.
Vigh, J. L., and W. H. Schubert, 2009: Rapid development of thetropical cyclone warm core. J. Atmos. Sci., 66, 3335–3350,
doi:10.1175/2009JAS3092.1.
Wang, W., and Coauthors, 2010: ARW version 3 modeling systemuser’s guide. NCAR Mesoscale and Microscale Meteorology
Division Rep., 312 pp.
Xu, J., and Y. Wang, 2010: Sensitivity of tropical cyclone inner-coresize and intensity to the radial distribution of surface entropy
flux. J. Atmos. Sci., 67, 1831–1852, doi:10.1175/2010JAS3387.1.
Yabu, S., S. Murai, and H. Kitagawa, 2005: Clear sky radiation
scheme (in Japanese). JMA Numerical Prediction DivisionRep. 51, 53–64.
Zhang, D.-L., and R. A. Anthes, 1982: A high-resolution model ofthe planetary boundary layer—Sensitivity tests and comparisonswith SESAME-79 data. J. Appl. Meteor., 21, 1594–1609,
doi:10.1175/1520-0450(1982)021,1594:AHRMOT.2.0.CO;2.
Zhu, P., and Coauthors, 2015: Impact of subgrid-scale processes on
eyewall replacement cycle of tropical cyclones in HWRF
system. Geophys. Res. Lett., 42, 10 027–10 036, doi:10.1002/
2015GL066436.