Clim Dyn (2015) 45:3243–3255
DOI 10.1007/s00382-015-2536-y
Interdecadal change in typhoon genesis condition over the western North Pacific
Yumi Choi · Kyung‑Ja Ha · Chang‑Hoi Ho ·
Chul Eddy Chung Received: 28 May 2014 / Accepted: 20 February 2015 / Published online: 3 March 2015
© Springer-Verlag Berlin Heidelberg 2015
Abstract The interdecadal changes in typhoon (categories 1–3) frequency and its genesis condition over the
western North Pacific during the period of 1979–2011 are
investigated with consideration for discrepancies among
best track datasets. To tide over data uncertainty, a detection-produced dataset is utilized as a homogeneous dataset
with five available best track datasets. Typhoon experienced interdecadal changes around the mid-1990s and the
mid-2000s in their genesis conditions. Even under the oceanic warm state, typhoon frequency has decreased since the
mid-1990s, showing a northwestward movement of its genesis location over the main formation region. The eastward
gradient of vertical wind shear is the most significant factor
for the change in typhoon genesis condition in recent decades. The vertical wind shear behavior is strongly linked
with zonal asymmetry of local SST. We demonstrate that a
westward gradient of local SST is the most important modulator of the recent typhoon behavior through the movement of favorable genesis location. The present results indicate that the horizontal distribution, not magnitude, of local
SST can be a key factor for prediction of future typhoon
activity, thus contributing to natural disaster mitigation and
climate change adaptation strategies.
Y. Choi · K.‑J. Ha (*) Division of Earth Environmental System, Pusan National
University, Busan 609‑735, Republic of Korea
e-mail: kjha@pusan.ac.kr
C.‑H. Ho Climate Physics Laboratory, School of Earth and Environmental
Sciences, Seoul National University, Seoul, Republic of Korea
C. E. Chung Division of Atmospheric Sciences, Desert Research Institute,
Reno, USA
Keywords Typhoon frequency · Typhoon activity ·
Genesis condition · Vertical wind shear · Interdecadal
change · SST gradient
1 Introduction
The western North Pacific (WNP) is a hot spot for studying changes in tropical cyclone (TC) activity because of
its massive social impact on Earth’s most populous region
and because it is the most active region of TC formation.
Furthermore, TC activity under warm climate has attracted
much attention (Bengtsson et al. 2007; Chan 2009). A controversy was triggered by the statement in Webster et al.
(2005) that the number of intense TCs can increase as the
ocean surface gets warmer. Despite the significance of
understanding WNP TC activity associated with climate
change, uncertainty still exists regarding whether anthropogenic warming could have caused the recent changes in TC
activity over and above natural variability (Knutson et al.
2010; Lee et al. 2012). The uncertainty results from both
data quality issues (Landsea et al. 2006; Wu et al. 2006;
Kossin et al. 2007; Song et al. 2010; Knapp and Kruk 2010;
Knutson et al. 2010; Barcikowska et al. 2012) and its own
distinctive characteristics with respect to atmospheric and
oceanic conditions (Chan 2008).
Landsea et al. (2006) raised a question whether the
global TC datasets are reliable enough to determine long
term trends in TC intensity or not. Agencies commonly
use the Dvorak Technique (Velden et al. 2006) to estimate
TC intensity. This technique is an indirect measurement
of maximum sustained wind (MSW) by the use of satellite images. It may result in the discrepancies in TC intensity estimation not only because it is a subjective method,
but also because the spatial resolution of satellites has
13
3244
Y. Choi et al.
Table 1 Summary of available tropical cyclone best track datasets in the western North Pacific
Agency
JTWC
RSMC
HKO
CMA
IBTrACS
Period
Averaging time interval of MSW
Unit of MSW
1945—present
1 min
knot
1951/1977 (MSW)—present
10 min
knot
1961—present
10 min
knot
1949—present
2 min (assumed 10 min)
m s−1
1945—present
10 min
knot
Multiplicative factor
0.88
–
–
–
–
The discrepancies among the datasets caused by the different averaging time interval of maximum sustained wind (MSW) are reduced by multiplying the linear multiplicative factor to unify the averaging time interval as 10 min
increased (Landsea et al. 2006). Furthermore, the operational methods for estimating TC intensity have developed
by various agencies in their own ways (Landsea et al. 2006;
Barcikowska et al. 2012). Wu et al. (2006) argued that the
different averaging period of MSW among the agencies
results in the discrepancies in TC intensity estimation,
particular in strong typhoon. Moreover, nonlinear relationships in TC intensities among the agencies exist according
to categories (Song et al. 2010; Barcikowska et al. 2012).
The influence of climate change on WNP TC activity is
much more uncertain (Knutson et al. 2010), compared to
the Atlantic Ocean, where great efforts are being made to
establish a homogeneous best track dataset (Kossin et al.
2007; Vecchi and Knutson 2011). Thus, the discrepancies
among the best track datasets should be considered to study
on a long term change in the WNP TC activity (Wu et al.
2006; Song et al. 2010; Barcikowska et al. 2012; Lee et al.
2012).
A trend-like behavior of WNP TC frequency is not only
in disagreement across various datasets (Lee et al. 2012),
but also could be a part of multi-decadal variability whose
cause remains uncertain (Chan 2006, 2008; Knutson et al.
2010; Lee et al. 2012). The long-term changes in WNP
TC frequency have been studied with various perspectives (Chan 2008; Yeh et al. 2010; Liu and Chan 2013;
Hsu et al. 2014). Chan (2008) emphasized that intense TCs
(categories 4–5) have a 16–32 years long-term variation
contributed by the El Niño–Southern Oscillation (ENSO)
and Pacific Decadal Oscillation (PDO) on the similar time
scales. They demonstrated that both thermodynamic and
dynamic conditions over the southeastern part of WNP
play a significant role in intense TC activity. Liu and Chan
(2013) investigated the recent low TC frequency, showing
a significant interdecadal variation of WNP TC activity:
two active (1960–1974 and 1989–1997) and two inactive
(1975–1988 and 1998–2011) periods. They focused on
the recent decrease (1998–2011) in TC frequency over the
southeastern part of WNP, which are related to both strong
vertical wind shear and strong subtropical high. Hsu et al.
(2014) recently showed a significant interdecadal change in
late season (October–December) typhoon frequency mostly
caused by low-level vorticity anomaly. They showed that
13
the abrupt shift of typhoon frequency could be related to
the similar change in late season tropical SST.
Although previous studies made efforts to understand
interdecadal changes in WNP TC frequency, it can be still
meaningful to reduce its uncertainty which comes from
both TC data quality and different characteristics of TC
activity according to the categories. Frank and Young
(2007) and Zhan et al. (2011) hinted that TC behavior can be different according to its intensity. Therefore,
our objective in this study is to investigate interdecadal
changes in WNP TC genesis condition according to the
categories and its causal linkage with environmental
changes with consideration for data discrepancy. We
examine how changes in oceanic and atmospheric conditions contribute to TC genesis condition in recent three
decades and its possible cause.
The next section describes datasets and analysis methods
used in this study. Interdecadal changes in TC activity and
contributions of surrounding environments to the change
are explored in Sect. 3. The dominant modes of principal
environmental factor and possible causes for the change are
addressed in Sect. 4. The last contains our major findings
and discussion.
2 Data and analysis methods
2.1 Best track datasets
To obtain reliable information on changes that have
occurred in TC activity, all five available WNP best track
datasets were analyzed (Table 1). The best track datasets
were obtained from the Joint Typhoon Warning Center
(JTWC), the Regional Specialized Meteorological Center
(RSMC) Tokyo—Typhoon Center, Hong Kong Observatory (HKO), and the China Meteorological Administration
(CMA). The International Best Track Archive for Climate
Stewardship (IBTrACS) best track dataset derived from the
combination with the datasets from the four agencies and
other collections was also used. Details in the IBTrACS
can be found in Knapp et al. (2010) and Knapp and Kruk
(2010).
Interdecadal change in typhoon genesis condition
We investigated the changes in typhoon activity from
period of 1979 to 2011, which is commonly accepted as
the higher quality era because of operationally used satellite data (Webster et al. 2005; Emanuel 2007) and overlaps
with that of the Modern-Era Retrospective analysis for
Research and Applications (MERRA) reanalysis data (Rienecker et al. 2011) used to analyze atmospheric conditions.
To reduce discrepancies caused by a different definition
of MSW which is commonly used to represent TC intensity, MSW averaging intervals were unified to 10 min by
multiplying linear factor (Atkinson 1974). We adopted an
assumption which it is reasonable to define MSW averaged
over 10 min rather than the 2 min for the CMA dataset,
considering relatively reduced discrepancies with both the
RSMC and HKO datasets, which officially use the 10-min
definition (Knapp and Kruk 2010; Barcikowska et al.
2012). To investigate changes in TC activity, normalized
time series of TC frequency and averaged genesis location
were analyzed. The average of all the best track datasets
was used to detect a significant change-point and to calculate correlation coefficients with environmental factors.
TCs were classified into three categories based on the
Saffir-Simpson scale (Song et al. 2010) (Fig. 1): strong
typhoon (typhoon categories 4–5; more than 58.2 m s−1),
typhoon (typhoon categories 1–3; 32.6–58.1 m s−1), and
tropical storm (17.2–32.5 m s−1). Our TCs analysis was
limited to the domain (0°–50°N, 100°E–180°).
2.2 Tropical cyclone detection method
To tide over the uncertainty caused by discrepancies among
the best track datasets, a detection-produced dataset was
utilized as a homogeneous dataset. TCs are detected under
the condition that dynamic and thermodynamic variables
satisfy specific thresholds (Camargo and Zebiak 2002).
We followed the TC detection method of Lee et al. (2011),
which evaluated the best track data produced using a modified version of a tracking and detection method (Camargo
and Zebiak 2002) utilizing the MERRA reanalysis dataset
during the period 1998–2009. The MERRA dataset was
verified to capture climatological-mean features of the
tropical cyclone over the WNP (Lee et al. 2011; Murakami
2014).
In brief, the following are required for the tropical
cyclone detection and tracking algorithm: sea level pressure; relative vorticity at 850-hPa; temperature at 200, 500,
700, and 850-hPa; and atmospheric winds at 200, 850, 925hPa, and 10 m above the surface. These datasets were interpolated into 1° by 1° horizontal resolution for convenience.
A period from April to November was selected for the TC
detection season, considering its active period in the WNP.
We selected three thresholds, 2.96 × 10−5 s−1, 3.34 m s−1,
and 0.47 K, for two standard deviations of relative vorticity
3245
Fig. 1 Time series of the number of annual tropical cyclone in the
western North Pacific (0°–50°N, 100°–180°E) for the period 1979–
2011. All available best track datasets, including JTWC (orange solid
line), RSMC (pink solid line), HKO (red solid line), CMA (cyan solid
line), and IBTrACS (green dotted line), were used. The number of
annual tropical cyclones is classified into a strong typhoon (typhoon
categories 4–5), b typhoon (typhoon categories 1–3), and c tropical
storm based on the Saffir-Simpson Scale
at 850-hPa, a sum of global average and one standard deviation of wind speed at 10 m above the surface, and one
standard deviation of vertically integrated temperature at
the tropical cyclone center, respectively. When a point is
considered as the TC location based on the algorithm, the
TC is simultaneously tracked forward and backward to produce a complete TC track. This work ensures that a storm
is not counted more than once. Also, a TC has to last at
least 1.5 days.
The wind speed at 925-hPa is a substitute for the MSW
definition based on the wind speed at 10 m above the surface in observation datasets. However, the TC intensity of
the MERRA detection, which is produced by use of the
MERRA reanalysis dataset, is still underestimated compared with that of other observation datasets (Fig. 2). None
of the strong typhoon was detected, because of the coarse
resolution and limitation of dynamic cores of the reanalysis
data (Knutson et al. 2010; Lee et al. 2011). Thus, we reclassified the dataset under the consideration of the underestimation of TC intensity by reducing the criteria for the TC
categorization as follows.
Firstly, MSWs are sorted in descending order. Secondly, percentages of TC frequency in the three categories
13
3246
Y. Choi et al.
Fig. 2 Percentage of the number of tropical cyclones classified into
strong typhoon, typhoon, and tropical storm in the detection season
(April–November) for the period 1979–2011. In consideration of
underestimation of tropical cyclone intensity because of the limita-
tions of the model resolution and dynamic core, MERRA detection,
which is produced by use of MERRA reanalysis data, is reclassified
as MERRA, modifying the tropical cyclone intensity criteria to have
the same percentage as that of the observation mean
are computed with regard to five other datasets (Fig. 2).
Thirdly, criteria to categorize TCs having same percentage of each category as observation mean are calculated. Finally, the MERRA detection is reclassified into
strong typhoon (more than 39.3 m s−1), typhoon (25.5–
39.3 m s−1), and tropical storm (17.2–25.5 m s−1) based
on the observation mean: strong typhoon (7.6 %), typhoon
(50.5 %), and tropical storm (41.9 %) (Fig. 2). The reclassified best track dataset, which is called MERRA, reveals
reliable interannual and seasonal variations with various
categories in the detection season (April–November) for
the period 1979–2011, compared with the observational
results.
respectively. The MERRA has been based on a new version of the Goddard Earth Observing System Data Assimilation System (GEOS-5) to synthesize various observation
data including satellite data (Rienecker et al. 2011). The
MERRA has a horizontal resolution of 1.25° by 1.25° with
72 levels and is provided a period from 1979 to present.
The monthly Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) with the horizontal resolution
of 1° by 1° was obtained from the Met Office Hadley Centre (Rayner et al. 2003).
2.3 Genesis potential index and environmental datasets
3.1 Typhoon frequency
A modified version of the Emanuel–Nolan (2004) Genesis Potential Index (GPI) by Murakami and Wang (2010)
was used in determining the influences of factors that are
known to be the significant contributors for TC formation.
The modified GPI (Murakami and Wang 2010) is defined
as follows.
A long term change in TC frequency should be investigated with caution because of the uncertainty caused by
discrepancies among the best track datasets (Wu et al.
2006; Song et al. 2010; Barcikowska et al. 2012; Lee et al.
2012). Despite the unification of different MSW averaging
intervals to 10 min, relative discrepancies still remain in
the number of annual TCs among the datasets, particularly
within the strong typhoon category, and between the JTWC
and the others (Fig. 1). Even if nonlinear relationships of
TC intensity among the agencies according to categories
are considered, uncertainty remains (Song et al. 2010; Barcikowska et al. 2012). This uncertainty results from inhomogeneous input datasets for Dvorak Technique and different operational procedures to estimate the TC intensity
among the agencies (Landsea et al. 2006; Wu et al. 2006;
Song et al. 2010; Barcikowska et al. 2012).
Typhoon category not only has more than half the total
number of TCs (Fig. 2), but can also be considered the
most reliable category among the datasets (Fig. 1). In addition, the TCs show different behaviors between typhoon
and tropical storm categories on the interdecadal time
scale (Fig. 3b, c). Frank and Young (2007) and Zhan et al.
3 3 Vpot 3
5 2 RH
−2 −ω + 0.1
(1 + 0.1 Vs )
10 η
50
70
0.1
where η is the absolute vorticity (s−1) at 850-hPa, RH is
the relative humidity (%) at 700-hPa, Vpot is the potential
intensity (PI) (m s−1) based on Emanuel (1995), and Vs is
the magnitude of the vertical wind shear (m s−1) defined as
difference between the 200- and 850-hPa horizontal winds
(Murakami and Wang 2010). ω which is the vertical wind
velocity (Pa s−1) at 500-hPa was added in the original version of the GPI. This modification prevents from revealing
the downward motion over the positive GPI region, and
vice versa.
The MERRA reanalysis 3-hourly and monthly data
were used for the TC detection and atmospheric data,
13
3 Interdecadal change in typhoon activity
3247
Interdecadal change in typhoon genesis condition
Fig. 3 The normalized time series of sea surface temperature (SST)
averaged over the main formation region (5°–20°N, 120°–180°E) and
tropical cyclone frequency according to the intensity in the typhoon
season July–November (JASON) for the period 1979–2011: The
9-year moving averages of a SST (red line), b typhoon frequency,
and c tropical storm frequency obtained from JTWC (orange line),
RSMC (pink line), HKO (red line), CMA (cyan line), IBTrACS
(green line), and MERRA (blue line). Black contours and error bars
denote average and standard deviation of all the datasets, respectively
(2011) showed that an important factor in TC frequency
can be different according to its intensity on the interannual time scale. Zhan et al. (2011) determined that the East
Indian Ocean SST anomaly can be considered as modulator for interannual variability of both the total and weak
TC frequency, whereas ENSO shows significant correlation with that of the intense TC. Thus, typhoon category
has been mainly explored in relation to their surrounding
environments. Furthermore, the peak TC season varies with
each category (not shown). The main typhoon season can
be considered period from July to November (JASON)
because approximately 78 % of the annual typhoon occurs
during the season. Thus, JASON seasonal mean will be
analyzed for the rest of the current study.
Underlying SST can directly influence typhoon formation thermodynamically. Figure 3a shows that the SST
averaged over the main TC formation region (5°–20°N,
120°E–180°) sharply increased until the end of 1990s and
then has remained steady until the early of 2010s, showing an oceanic warm state on the decadal time scale. It is
noted that typhoon frequency has decreased since the mid1990s (Fig. 3b). The increase of the underlying SST supports the increase in typhoon frequency only until the early
Fig. 4 The normalized time series of JASON typhoon (categories
1–3) genesis location for the period 1979–2011. The 9-year moving
averages of averaged genesis location where typhoons occurred in the
western North Pacific (0°–50°N, 100°–180°E) in a meridional and b
zonal directions. JTWC (orange line), RSMC (pink line), HKO (red
line), CMA (cyan line), IBTrACS (green line), and MERRA (blue
line) were used. Black contours and error bars denote average and
standard deviation of all the datasets, respectively
of 1990s. Even under the warm climate, typhoon frequency
has decreased on the decadal time scale, which is consistent with results in Liu and Chan (2013) and Hsu et al.
(2014). It was argued that thermodynamic factors must
cooperate with dynamic factors for TC development (Chan
2008, 2009). Based on the period 1979–2011, climatological JASON SST averaged over the main formation region
was 29.12 °C, that is, higher than the temperature necessary for TC formation (26.5 °C) (Gray 1979). From these
changes in oceanic condition and typhoon frequency, it is
expected that atmospheric condition could be unfavorable
for typhoon formation since the mid-1990s. Chan (2009)
also pointed out that oceanic warming does not necessarily indicate intense TCs more frequently occur because
oceanic condition for the main typhoon season in WNP
is warm enough to develop intense TCs. The influence of
environmental factors for TC formation on the change in
TC frequency will be analyzed in Sect. 3.3.
3.2 Typhoon genesis location
The decrease of typhoon frequency can be explained with
the movement of favorable genesis location. The averaged typhoon genesis location (14.1°N, 144.7°E during
the period 1979–2011) has moved northwestward since
the mid-1990s (Fig. 4). This movement indicates that TCs
generated in recent decades do not easily reach typhoon
intensity. A particular category of TCs, representing their
intensity, can be determined based on how they experience
13
3248
Y. Choi et al.
Fig. 5 Spatial patterns of
JASON mean genesis potential
index (GPI) difference arisen
from changes in environmental mean state. a GPI mean
difference (1995–2011 minus
1979–1994). Shading indicates
significant regions at the 95 %
confidence level. Difference
between two GPI spatial patterns obtained by use of mean
states of all variables for the
period 1979–2011 except for b
absolute vorticity at 850-hPa,
c relative humidity at 700-hPa,
d potential intensity, e vertical
wind shear, and f omega at
500-hPa for the periods both
1979–1994 and 1995–2011
their surrounding environments through their journeys. For
instance, the longer their life spans over warm oceans, the
more opportunity TCs have to intensify. Thus, TC genesis
location is a useful indicator of potential intensification
(Wang and Chan 2002; Chan 2008; Ha et al. 2012).
A significant westward movement of the averaged
genesis location on the decadal time scale occurred in
1994/1995 at the 95 % confidence level by using a changepoint detection method of Pettitt (1979). The decrease of
typhoon frequency and the northward movement of the
averaged genesis location can be also seen since the mid1990. In addition, typhoon frequency has decreased since
1994 which is consistent with change in zonal direction
of genesis location. Thus, we divided the analysis period
(1979–2011) into P1 (1979–1994) and P2 (1995–2011) to
investigate the causal relationship between the interdecadal changes in typhoon activity and environmental conditions for formation. The mean genesis location has moved
northward by 1° and westward by 4.3° from P1 [13.6°N,
146.9°E] to P2 [14.6°N, 142.6°E]. The change in the
typhoon activity since the mid-2000s will be also analyzed
in conjunction with the change since the mid-1990s.
3.3 Environmental states
The modified GPI (Murakami and Wang 2010), which is
useful in understanding typhoon formation conditions from
large-scale environmental mean states, was adopted to analyze the mean state changes over the main formation region.
Despite the favorable oceanic condition (Fig. 3a), the modified GPI mean difference between P1 and P2 (1995–2011
minus 1979–1994) indicates that atmospheric conditions
13
are not as favorable to typhoon formation over the eastern part of the main formation region during P2 (Fig. 5a).
Differences between the two GPI states obtained by use
of mean states of all variables for the period 1979–2011,
except for absolute vorticity at 850-hPa, relative humidity
at 700-hPa, PI, vertical wind shear, and omega at 500-hPa
for both P1 and P2. They represent each contribution to the
changes in the modified GPI (Fig. 5b–f). The changes in
the relative humidity at 700-hPa, PI, and omega at 500-hPa
lead to the northward movement of a favorable formation
region. Their contributions show strong meridional asymmetries in the vicinity of 15 N°. The absolute vorticity at
850-hPa shows an overall decrease over the main formation
region.
Among the factors, the change in the magnitude of vertical wind shear mostly contributed to decreases of the
modified GPI over the eastern part of the formation region,
showing a strong zonal asymmetry. This result is different
from that negative vorticity anomaly is the primary modulator for the late season typhoon frequency (Hsu et al.
2014). Vertical wind shear is the primary dynamic factor controlling TC activity (Gray 1979; Goldenberg et al.
2001) because the strong vertical wind shear inhibits formation of a warm core, a requirement for TC development
(Park et al. 2012). Figure 6 supports the fact that a relative
role of vertical wind shear in TC formation significantly
increased during P2, especially over the eastern part of the
formation region. The strong vertical wind shear during P2
is negatively correlated with both typhoon frequency and
a zonal movement of averaged genesis location in situ,
whereas it is positively correlated with a meridional movement of averaged genesis location in situ. It implies that the
Interdecadal change in typhoon genesis condition
3249
Fig. 6 Spatial patterns of correlation coefficients between
9-year moving averaged JASON
mean magnitude of vertical
wind shear and JASON typhoon
(categories 1–3) activity for the
periods P1 (1979–1994) and
P2 (1995–2011). The averaged
best track dataset was used in
both typhoon (a, b) frequency
and genesis locations in (c,
d) meridional and (e, f) zonal
directions. Shading indicates
significant regions at the 95 %
confidence level
northwestward movement of the favorable condition for TC
formation results in the decrease of typhoon frequency. The
critical role of vertical wind shear in inactive TC activity
also pointed out in Liu and Chan (2013).
4 Possible causes
4.1 The dominant mode of the interdecadal variability
To investigate possible causes of the interdecadal changes
in typhoon activity related with the change in environmental factors, we adopted Empirical Orthogonal Function
(EOF) analysis to identify their dominant modes. An independent spatio-temporal pattern was verified via a rule of
thumb suggested in North et al. (1982).
Figure 7 shows that the first leading mode of the magnitude of vertical wind shear during the period of 1979–
2011. A significant change in a zonal asymmetric pattern
of the vertical wind shear is detected in 1994/1995 at the
95 % confidence level by using a change-point detection method of Pettitt (1979). Its change-point is consistent with that of the genesis location in zonal direction. We
point out that the 9-year moving averaged first principal
component (PC1) of vertical wind shear, which is a significantly independent mode (48.71 %), is negatively correlated with 9-year moving averaged typhoon frequency
during P2 (r = −0.91), whereas is not significant during P1
Fig. 7 The first leading mode of JASON mean magnitude of vertical wind shear for the period 1979–2011. a Spatial pattern, b percentage variance for each EOF modes, and c the corresponding principal
component (PC) time series (black bar) and its 9-year moving average (gray solid line)
(r = 0.23). The temporal correlation coefficients become
significant between the PC1 of vertical wind shear and
typhoon genesis location during P2 (Table 2). These results
also support the fact that the relative role of vertical wind
13
3250
Y. Choi et al.
Table 2 Temporal correlation coefficients between 9-year moving averages of both typhoon activity and environmental factors during P1
(1979–1994) and P2 (1995–2011)
Periods
Typhoon frequency
Typhoon genesis location in zonal direction
Typhoon
genesis location
in meridional
direction
P1
P2
P1
P2
P1
P2
SST averaged over the main formation region (5°–20°N, 120° E–180°)
0.92**
0.1
0.23
0.23
−0.91**
−0.53*
0.08
The 1st PC of Vertical wind shear (0°–25°N, 120E°–180°)
−0.88**
0.28
−0.16
−0.37
0.87**
Numbers in bold indicate significant correlation coefficients (* and ** indicate at 95 and 99 % confidence levels, respectively)
Fig. 8 The two leading modes
of JASON mean SST for the
period 1979–2011. a, c Spatial
patterns, b, d the corresponding
principal component (PC) time
series (black bar) and its 9-year
moving average (gray solid
line), and e percentage variance
for each EOF modes
shear in typhoon formation condition becomes significant
in recent decades, as the magnitude of vertical wind shear
becomes stronger over the eastern part of the main formation region.
To understand possible causes of the increase in eastward gradient of the magnitude of vertical wind shear since
the mid-1990s, we performed EOF analysis of the local
SST (0°–25°N, 120°E–180°) including the main formation
region where shows an oceanic warm state in recent decades (Fig. 3a). The SST averaged over the main formation
region cannot represent the recent decrease of typhoon frequency and change in genesis location (Table 2).
The first leading mode (44.22 %) displays westward
warming and eastward cooling with respect to 160°E over
the main formation region, which manifests in the increase
of the zonal SST gradient (Fig. 8a, b). The 9-year moving
averaged corresponding PC time series reveals a significant
13
correlation coefficient (r = 0.92) with that of vertical wind
shear during the period 1979–2011. A significant changepoint of the first leading mode of local SST is the same
as that of vertical wind shear. Furthermore, the dominant
mode of local SST, which shows a strong zonal asymmetry, is highly correlated with the typhoon activity during
P2, whereas is not meaningful during P1. Thus, the zonal
asymmetry in local SST can be the most important modulator of typhoon frequency through a movement of favorable genesis location, changing vertical wind shear over the
main formation region since the mid-1990s.
The second leading mode (24.24 %), which is a significantly independent mode of the first mode, displays eastward warming over the region (Fig. 8c–e). The enhanced
eastward warming decreases the SST gradient over the
main formation region during P1, whereas the reduced
eastward warming has increased the SST gradient since
Interdecadal change in typhoon genesis condition
3251
Fig. 9 The 9-year moving averaged JASON mean magnitude
of vertical wind shear and wind
fields at (a, b) 200-hPa and
(c, d) 850-hPa regressed onto
the 9-year moving averaged
first leading principal component (PC) time series of SST
(Fig. 8b) for the periods (a, c)
P1 (1979–1994) and (b, d) P2
(1995–2011). Contours denote
the magnitude of vertical wind
shear and shading indicates
significant regions at the 95 %
confidence level. The wind
vectors are shown at the 95 %
confidence level in either the
zonal or meridional component.
The vector length indicates the
wind speed (m s−1) based on a
vector scale in the upper-righthand corner of each figure
both the mid-1990s and the mid-2000s. Thus, the increased
westward SST gradient has contributed to the strong vertical wind shear since both the mid-1990s and the mid-2000s
over the eastern part of the main formation region. The
present result indicates that the zonal asymmetric pattern,
not magnitude, of local SST can mainly explain the recent
change in typhoon genesis condition.
4.2 Influence of zonal asymmetry of SST on vertical wind
shear
To investigate influence of zonal asymmetry of local SST
on large-scale wind fields regarding the change in vertical
wind shear, we present regressed wind fields and vertical
wind shear onto the two leading mode of local SST.
The regressed wind fields onto the first PC time series of
local SST manifest an enhanced linkage between the vertical wind shear and zonal asymmetry of local SST over the
main formation region since the mid-1990s (Fig. 9). During
P1, upper level anticyclonic wind anomaly is located in the
southeastern part of Japan and weak lower level westerly
wind anomaly occurs in the south of about 15°N in the consideration of the sign of PC time series (Fig. 9a, c). During
P2, zonally elongated cyclonic wind anomaly at upper level
is located in the northeastern part of the main formation
region and lower level easterly anomaly much strengthened compared to that of P1 (Fig. 9b, d). Compared to the
climatology of wind fields based on the period 1979–2011
(Fig. 10), lower level easterly and upper level westerly
winds linked with the zonal SST distribution (Fig. 9b, d)
enhance (reduce) climatological westerly (easterly) vertical
Fig. 10 JASON mean horizontal wind climatology based on the
period 1979–2011. a 200-hPa and b 850-hPa horizontal winds. The
vector length indicates the wind speed (m s−1) based on a vector scale
in the upper-right-hand corner of each figure
wind shear over the eastern (western) part of the main formation region during P2. These anomalous wind fields contribute to the intensified eastward gradient of vertical wind
shear since the mid-1990s. The first PC time series of local
SST is highly correlated (r = 0.77) with the SST gradient which is defined by eastward region [5°–20°N, 180°]
13
3252
Y. Choi et al.
Fig. 11 Same as Fig. 9 except
for the second leading principal
component (PC) time series of
SST (Fig. 8d)
minus westward region [5°–20°N, 120°E] (not shown). The
regression fields onto the SST gradient are similar to that of
the first PC time series of local SST (not shown). Thus, it
implies that the first PC time series of local SST is a good
indicator of zonal asymmetry of local SST.
Figure 11 displays that the wind fields related with the
second PC time series of local SST significantly contribute
to the zonal asymmetry in vertical wind shear over the main
formation region during P2, whereas is not meaningful during P1. This second PC time series is significantly correlated with typhoon frequency during P1 but not with vertical wind shear over the main formation region (Fig. 11a,
c). This result implies that the eastward warming increased
until the mid-1990s could influence on the increase in
typhoon frequency thermodynamically, not through the
modulation of vertical wind shear. During P2, easterly vertical wind shear anomaly reduces (enhances) climatological
vertical wind shear over the eastern (western) part of the
main formation region (Fig. 11b, d). In other words, these
wind fields offset vertical wind shear induced by the westward SST gradient. However, its influence on the decrease
in vertical wind shear has reduced since both the mid1990s and the mid-2000s. Thus, it can be interpreted that
the combined effect of the two leading modes of local SST
has contributed to the increases of the magnitude of vertical wind shear since both the mid-1990s and the mid-2000s
over the eastern part of the main formation region.
The influence of zonal asymmetry of tropical Pacific
SST (i.e. ENSO) on typhoon activity has been studied as an
important modulator on the interannual time scale (Wang
and Chan 2002; Chan 2008; Kim et al. 2011; Zhan et al.
13
2011; Ha et al. 2012). Yeh et al. (2010) pointed out that a
relative role of oceanic and atmospheric conditions in TC
frequency reversely changed around 1990, which is consistent with the change in relationship between the WNP
TC frequency and ENSO on the decadal time scale. Thus,
we further investigate the relationship between ENSO and
typhoon activity, looking into changes in wind fields.
The temporal correlation coefficients between 9-year
moving averaged typhoon activity and ENSO indices consisted of Nino 3 (5°S–5°N, 150°–90°W), Nino 3.4 (5°S–
5°N, 170°–120°W), and Nino 4 (5°S–5°N, 160°E–150°W)
indicate that both Nino 3.4 and Nino 4 index (Fig. 12e)
recently have a key role in typhoon activity. Nino 4 index
particularly shows a significant correlation coefficient with
the second PC time series of local SST. The regressed wind
fields onto Nino 4 index show a remarkably enhanced linkage between the vertical wind shear and zonal asymmetry
in tropical central Pacific SST over the main formation
region since the mid-1990s (Fig. 12). This feature is similar
to that of the second PC time series of local SST, which
shows an increased correlation with Nino 4 index during P2
(Figs. 11, 12). The reduced vertical wind shear related to
the tropical central Pacific SST over the eastern part of the
main formation region has decreased since the mid-1990s
and the mid-2000s. Thus, the large-scale circulation related
to tropical central Pacific SST can suppresses typhoon formation through the modulation of vertical wind shear in
recent decades. When it comes to ENSO indices showing
an increase in significant correlation coefficients with zonal
movement of genesis location during P2, they reveal significant correlation coefficients with typhoon frequency.
Interdecadal change in typhoon genesis condition
3253
Fig. 12 The 9-year moving averaged JASON mean
magnitude of vertical wind
shear and wind fields at (a, b)
200-hPa and (c, d) 850-hPa
regressed onto (e) the 9-year
moving averaged the time series
of simultaneous Nino 4 index
(5°S–5°N, 160°E–150°W) (gray
solid line) for the periods (a, c)
P1 (1979–1994) and (b, d) P2
(1995–2011). Contours denote
the magnitude of vertical wind
shear and shading indicates
significant regions at the 95 %
confidence level. The wind vectors are shown at the 95 % confidence level in either the zonal
or meridional component. The
vector length indicates the wind
speed (m s−1) based on a vector
scale in the upper-right-hand
corner of each figure. e Red
solid line denotes the 9-year
moving averaged PC2 time
series of local SST (Fig. 8d).
Black bar denotes JASON Nino
4 index on the interannual time
scale. Red and blue dotted lines
are drawn at ±0.5 °C
Thus, it can be interpreted that interdecadal variability of
ENSO partly influences typhoon frequency through the
zonal movement of genesis location, which is effectively
modulated by the change in vertical wind shear. The present result supports the fact that the change in characteristics of tropical Pacific SST in the recent decade is not the
main cause for the unfavorable formation conditions, which
is suggested in Liu and Chan (2013) and Hsu et al. (2014).
5 Summary and discussion
The interdecadal changes in typhoon (categories 1–3) frequency and its genesis condition over the WNP during the
period 1979–2011 are investigated with consideration for
TC data quality issues. To reduce the uncertainty resulted
from discrepancies among the best track datasets, a detection-produced dataset by use of MERRA reanalysis data
was utilized as a homogeneous dataset with five available
best track datasets. The causal linkage between the typhoon
frequency and environmental changes in recent decades
is examined in terms of typhoon formation conditions. To
examine how changes in environmental conditions have
contributed to typhoon activity, we divided the analysis
period (1979–2011) into P1 (1979–1994) and P2 (1995–
2011) based on the significant westward movement of the
averaged typhoon genesis location by using a change-point
detection method of Pettitt (1979), which manifests the
consistent decrease of typhoon frequency.
It is noted that typhoon frequency has decreased since
the mid-1990s even under the oceanic warm state. The
decrease of typhoon frequency can be explained with the
northwestward movement of favorable genesis location.
This movement indicates that TCs generated in recent
decades hardly reach typhoon intensity because they
lost opportunity to stay over warm oceans through their
13
3254
journeys. Among the environmental factors, we demonstrate that vertical wind shear is the most important modulator, showing a strong zonal asymmetry. The eastward
gradient of vertical wind shear over the main formation
region effectively influences the decrease of typhoon frequency, showing the northwestward movement of the averaged genesis location in recent decades. Liu and Chan
(2013) and Hsu et al. (2014) previously pointed out that
the decrease of TC frequency over the southeastern part
of WNP, where Chan (2008) determined the most effective region to modulate intense TCs activity. Liu and Chan
(2013) demonstrated that the strong vertical wind shear and
strong subtropical high are the main causes for the recent
low TC frequency (June–October) which is more than tropical storm category. From the present result, the change in
typhoon category, which has the largest portion of WNP
TCs, can be responsible for the decrease of TC frequency
shown in Liu and Chan (2013). Hsu et al. (2014) focused
on the change in late season typhoon activity related to that
in low-level vorticity anomaly. However, the region showing mostly unfavorable environmental conditions does not
exactly correspond to the genesis location in which late
season typhoon frequency decreases.
The change in vertical wind shear over the main formation region is strongly linked with zonal asymmetry of
local SST. We demonstrate that the westward gradient of
local SST can be the most important modulator of typhoon
frequency through a movement of favorable genesis location, changing vertical wind shear over the main formation
region since the mid-1990s. The present results indicate
that the horizontal distribution, not magnitude, of local SST
can be a key factor for prediction of future typhoon activity.
Yeh et al. (2010) showed the decadal change in relationship between the WNP TC frequency and ENSO, which is
consistent with the fact that a relative role of oceanic and
atmospheric conditions in TC frequency reversely changed
around 1990. We manifest that interdecadal variability of
ENSO partly influences typhoon frequency through the
zonal movement of genesis location, which is modulated
by the change in vertical wind shear. Our result supports
the fact that the recent change in characteristics of tropical Pacific SST is not the main cause for the unfavorable
formation conditions during P2. Liu and Chan (2013)
explained that vertical wind shear patterns related to tropical SST shows insignificant difference between central
Pacific warming and eastern Pacific (EP) warming. Hsu
et al. (2014) demonstrated that the unfavorable dynamic
condition for typhoon genesis is influenced by both a local
Walker circulation induced by WNP warming and anticyclonic anomaly enhanced by EP cooling. They considered
the EP cooling as secondary modulator.
The long-term TC activity prediction plays a critical role in sustainable infrastructure design and economic
13
Y. Choi et al.
development plans. Although the reason for the zonal
asymmetry in the local SST change is still unclear and
could be result of both long-term internal variability and
anthropogenic effect (Chan 2008; Knutson et al. 2010),
the present results suggest that not the magnitude of local
SST alone, but also its horizontal pattern is an important
parameter for the long-term typhoon activity prediction.
Murakami et al. (2011) highlighted that environmental conditions for WNP TC formation significantly depend on SST
spatial pattern in future projections. The present results
can contribute to natural disaster mitigation and climate
change adaptation strategies in future and assist in evaluation of anthropogenic effects versus natural variability on
the interdecadal time scale.
Acknowledgments This work was supported by GRL Grant of the
National Research Foundation (NRF) funded by the Korean Government (MEST 2011-0021927).
References
Atkinson GD (1974) Investigation of gust factors in tropical cyclones.
FLEWEACENTechnical note JTWC 74-1, Fleet Weather Center,
Guam, 9 pp
Barcikowska M, Feser F, Storch HV (2012) Usability of best track
data in climate statistics in the western North Pacific. Mon Wea
Rev 140:2818–2830
Bengtsson L, Hodges KI, Esch M, Keenlyside N, Kornblueh L, Luo
JJ, Yamagata T (2007) How may tropical cyclones change in a
warmer climate? Tellus 59A:539–561
Camargo SJ, Zebiak SE (2002) Improving the detection and tracking
of tropical cyclones in atmospheric general circulation models.
Weather Forecast 17:1152–1162
Chan JCL (2006) Comment on “Changes in tropical cyclone number, duration, and intensity in a warming environment”. Science
311:1713
Chan JCL (2008) Decadal variations of intense typhoon occurrence in
the western North Pacific. Proc R Soc A 464:249–272
Chan JCL (2009) Thermodynamic control on the climate of intense
tropical cyclones. Proc R Soc A 465:3011–3021
Emanuel KA (1995) Sensitivity of tropical cyclones to surface
exchange coefficients and a revised steady-state modelincorporating eye dynamics. J Atmos Sci 52:3969–3976
Emanuel KA (2007) Environmental factors affecting tropical cyclone
power dissipation. J Clim 20:5497–5509
Emanuel KA, Nolan DS (2004) Tropical cyclone activity and global
climate. In: 26th conference on hurricanes and tropical meteorology, American Meteorology Society, Miami, FL, pp 240–241
(preprints)
Frank WM, Young GS (2007) The interannual variability of tropical
cyclones. Mon Wea Rev 135:3587–3598
Goldenberg SB, Landsea CW, Mestas-Nuñes AM, Gray WM (2001)
The recent increase in Atlantic Hurricane activity: causes and
implications. Science 293:474–479
Gray WM (1979) Hurricanes: their formation, structure, and likely
role in the tropical circulation. In: Shaw DB (Ed) Meteorology over the tropical oceans. Royal Meteorological Society, pp
155–218
Ha KJ, Yoon SJ, Yun KS, Kug JS, Jang YS, Chan JCL (2012) Dependency of typhoon intensity and genesis locations on El Niño phase
Interdecadal change in typhoon genesis condition
and SST shift over the western North Pacific. Theor Appl Climatol 109:383–395
Hsu PC, Chu PS, Murakami H, Zhao X (2014) An abrupt decrease in
the late-season typhoon activity over the western North Pacific. J
Clim. doi:10.1175/JCLI-D-13-00417.1
Kim HM, Webster PJ, Curry JA (2011) Modulation of North Pacific
tropical cyclone activity by three phases of ENSO. J Clim
24:1839–1849
Knapp KR, Kruk MC (2010) Quantifying interagency differences in
tropical cyclone best-track wind speed estimates. Mon Wea Rev
138:1459–1473
Knapp KR, Kruk MC, Levinson DH, Diamond HJ, Neumann CJ
(2010) The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. Bull Amer
Meteor Soc 91:363–376
Knutson TR, McBride JL, Chan JCL, Emanuel K, Holland G, Landsea C, Held I, Kossin JP, Srivastava AK, Sugi M (2010) Tropical
cyclones and climate change. Nat Geosci 3:157–163
Kossin JP, Knapp KR, Vimont DJ, Murnane RJ, Harper BA (2007) A
globally consistent reanalysis of hurricane variability and trends.
Geophys Res Lett 34:L04815. doi:10.1029/2006GL028836
Landsea CW, Harper BA, Hoarau K, Knaff JA (2006) Can we detect
trends in extreme tropical cyclones? Science 313:452–454
Lee MI, Schubert SD, Kim D (2011) Representation of tropical
storms in the Northwestern Pacific by the Modern-Era retrospective analysis for research and applications. Asia Pac J Atmos Sci
47(3):245–253
Lee TC, Knutson TR, Kamahori H, Ying M (2012) Impacts of climate
change on tropical cyclones in the western North Pacific Basin.
Part I: past observations. Trop Cyclone Res Rev 1(2):213–230
Liu KS, Chan JCL (2013) Inactive period of western North Pacific
tropical cyclone activity in 1998–2011. J Clim 26:2614–2630
Murakami H (2014) Tropical cyclones in reanalysis data sets. Geophys Res Lett 41:2133–2141
Murakami H, Wang B (2010) Future change of North Atlantic tropical
cyclone tracks: projection by a 20-km-mesh global atmospheric
model. J Clim 23:2699–2721
Murakami H, Wang B, Kitoh A (2011) Future change of western
North Pacific typhoons: projections by a 20-km-mesh global
atmospheric model*. J Clim 24:1154–1169
3255
North GR, Bell TL, Cahalan RF, Moeng FJ (1982) Sampling errors
in the estimation of empirial orthogonal functions Mon Wea
Rev110:699–706
Park MS, Elsberry RL, Harr PA (2012) Vertical wind shear and ocean
heat content as environmental modulators of western North
Pacific tropical cyclone intensification and decay. Trop Cyclone
Res Rev 1(4):448–457
Pettit A (1979) A non-parametric approach to the change-point problem. Appl Stat 28(2):126–135
Rayner NA, Parker DE, Horton EB et al (2003) Global analyses of
sea surface temperature, sea ice, and night marine air temperature
since the late nineteenth century. J Geophys Res 108:4407. doi:1
0.1029/2002JD002670
Rienecker MM, Suarez MJ, Gelaro R et al (2011) MERRA: NASA’s
modern-era retrospective analysis for research and applications. J
Clim 24:3624–3648
Song JJ, Wang Y, Wu L (2010) Trend discrepancies among three best
track data sets of western North Pacific tropical cyclones. J Geophys Res 115:D12128. doi:10.1029/2009JD013058
Vecchi GA, Knutson TR (2011) Estimating annual numbers of Atlantic hurricanes missing from the HURDAT database (1878–1965)
using ship track density. J Clim 24:1736–1746
Velden C, Harper B, Wells F et al (2006) The Dvorak tropical cyclone
intensity estimation technique: a satellite-based method that has
endured for over 30 years. Bull Am Meteorol Soc 87:1195–1210
Wang B, Chan JCL (2002) How strong ENSO events affect tropical
storm activity over the western North Pacific*. J Clim 15:1643–1658
Webster PJ, Holland GJ, Curry JA, Chang HR (2005) Changes in
tropical cyclone number, duration, and intensity in a warming
environment. Science 309:1844–1846
Wu MC, Yeung KH, Chang WL (2006) Trends in western North
Pacific tropical cyclone intensity. Eos Trans Am Geophys Union
87(48):537–548
Yeh SW, Kang SK, Kirtman BP, Kim JH, Kwon MH, Kim CH (2010)
Decadal change in relationship between western North Pacific
tropical cyclone frequency and the tropical Pacific SST. Meteorol
Atmos Phys 106:179–189
Zhan R, Wang Y, Lei X (2011) Contributions of ENSO and East
Indian Ocean SSTA to the interannual variability of northwest
pacific tropical cyclone frequency*. J Clim 24:509–521
13