Content uploaded by Hisayuki Kubota
Author content
All content in this area was uploaded by Hisayuki Kubota on Mar 13, 2014
Content may be subject to copyright.
Interdecadal variability of tropical cyclone landfall in the Philippines
from 1902 to 2005
Hisayuki Kubota
1
and Johnny C. L. Chan
2
Received 20 March 2009; revised 17 May 2009; accepted 21 May 2009; published 17 June 2009.
[1] A dataset of tropical cyclone landfall numbers in the
Philippines (TLP) is created from a combination of
historical observation records of the Monthly Bulletins of
Philippine Weather Bureau and Joint Warning Typhoon
Center best-track data for the period of 1902 to 2005.
Interdecadal variability of TLP is found to be related to
different phases of the El Nin˜o/Southern Oscillation
(ENSO) and the Pacific Decadal Oscillation (PDO). The
annual TLP has an apparent oscillation of about 32 years
before 1939 and an oscillation of about 10– 22 years after
1945. No long-term trend is found. During the low PDO phase,
the annual TLP decreases (increases) significantly in El Nin˜o
(La Nin˜a) years. During high PDO phase, however, the
difference in annual TLP between different ENSO phases
becomes unclear. These results suggest that natural variability
related to ENSO and PDO phases appears to prevail in
the interdecadal variability of TLP. Citation: Kubota, H., and
J. C. L. Chan (2009), Interdecadal variability of tropical cyclone
landfall in the Philippines from 1902 to 2005, Geophys. Res. Lett.,36,
L12802, doi:10.1029/2009GL038108.
1. Introduction
[2] Tropical cyclone (TC) provides precious fresh water
to the land but it can also cause disaster when it makes
landfall due to strong winds, heavy rain and storm
surge. Recently, the variability of TC activity (including
the frequency of occurrence and intensity) has become a
great concern because it may be affected by global warm-
ing. The number of intense TCs appeared to have increased
in association with the increase of sea-surface temperature
(SST) during the past 30 years [Webster et a l . ,2005].
However, TC occurrence also shows natural interdecadal
variability [Yumoto and Matsuura, 2001]. Several numerical
simulations under the assumption of future global warming
suggested that the TC frequency of occurrence would
decrease but their intensity would tend to increase [e.g.,
Oouchi et al., 2006; Gualdi et al., 2008].
[3] To identify the variability of the TC activity associ-
ated with climate change, a TC database of more than
100 years of observations over the Atlantic basin was
created [Landsea et al., 2004]. Mann and Emanuel [2006]
found a positive correlation between SST and Atlantic TC
frequency since 1871. However, the reliability of counting
TC remains questionable because the areas covered by
observations to detect TC over the Atlantic Ocean were
not sufficiently wide until geostationary satellite observa-
tions began in 1966 [Landsea, 2007]. For the western North
Pacific (WNP) basin, best-track data are available from
1945 from the Joint Typhoon Warning Center (JTWC).
The available TC data over the WNP basin is not sufficient
to answer the question whether the observed interdecadal
variability of TC is natural or anthropogenic, although Chan
[2008] pointed out that the natural oscillation is likely
dominant based on the data after 1960.
[4] In this study, historical observation records of TC
track during the period 1901 to 1940 were collected from
the Monthly Bulletins of the Philippine Weather Bureau
(MBP). This TC dataset traces TC tracks back to the
beginning of the 20th century west of 150°Eoverthe
WNP. Weather stations in the Philippines were established
and the MBP was published from the late 19th century by
Spanish and then American meteorologists [Udı´as, 1996].
In fact, the historical documents of TCs around the
Philippines can be traced back to the 16th century [Garcı´a-
Herrera et al., 2007]. The first observation record of TC
was in September 1865 in the first observation year of
Manila Observatory [Deppermann, 1939]. However, we had
to wait until the beginning of the 20th century when the
Philippine Weather Bureau deployed many weather stations
in the Philippines to capture the behaviors of typhoons in its
vicinity.
[5] We focus on the TC landfall numbers in the
Philippines (TLP) in this study because high quality data
of the TC tracks near the Philippines is available from the
MBP. Saunders et al. [2000] showed a significant decrease
(increase) in TLP during the autumn of El Nin˜ o (La Nin˜a).
The decrease in TLP is associated with the eastward shift of
TC tracks over the WNP during El Nin˜ o according to Wang
and Chan [2002]. Over the Pacific, Mantua et al. [1997]
also identified SST changes over a time scale of 20–30
years, known as the Pacific Decadal Oscillation (PDO).
Chan [2008] indicated that intense typhoon frequency over
the WNP has oscillations of 16 – 32 years with a linkage to
ENSO and PDO phases during 1960– 2005. The question is
whether or not TLP changes during different phases of the
PDO?
[6] The objectives of this study are to create a unique
dataset of TLP from 1902 to 1939, to combine it with the
JTWC TLP data during the period of 1945–2005, and to
investigate the interdecadal variability of TLP during the
past 100 years in order to understand how such variability
may be related to different phases of ENSO and PDO.
The data used in this study are described in section 2.
Definitions of TLP and interdecadal variability of TLP are
discussed in section 3. The relationship among TLP, ENSO
GEOPHYSICAL RESEARCH LETTERS, VOL. 36, L12802, doi:10.1029/2009GL038108, 2009
Click
Here
for
Full
A
rticl
e
1
Research Institute for Global Change, Japan Agency for Marine-Earth
Science and Technology, Yokosuka, Japan.
2
Guy Carpenter Asia-Pacific Climate Impact Centre, City University of
Hong Kong, Hong Kong, China.
Copyright 2009 by the American Geophysical Union.
0094-8276/09/2009GL038108$05.00
L12802 1of4
and PDO is examined in section 4, followed by a summary
in section 5.
2. Data
[7] MBP reported surface weather station data in the
Philippines and TC tracks over the WNP region during
January 1901 – August 1940. The TC track was determined
using surface observations (wind, pressure, temperature, rain-
fall, etc.) at weather stations and ships, reports of calm weather
by the passage of TC eye, and damages to ships, houses and
other structures on land. TC tracks of MBP used in this study
were manually counted from the Bulletins. In this study, the
MBP data from 1902 to 1939 are used only when the TC
record and station surface data are available throughout an
entire year. Surface wind and pressure were available at
43 stations in 1902 and increased to 63 stations in 1939.
[8] After 1945, Philippine weather stations were rebuilt
by the Philippine Weather Bureau. Best-track data for TC
center locations from 1945 to 2005 are from the JTWC. The
JTWC best-track data and the MBP are combined to create a
TLP dataset of 100 years. Best track data from 1951 to 2005
are also available from the Regional Specialized Meteoro-
logical Center (RSMC) Tokyo-Typhoon Center in the Japan
Meteorological Agency (JMA), which are used for valida-
tion of the JTWC best-track data. Classification of El Nin˜o
and La Nin˜ a years follows Trenberth [1997]. The PDO
index is defined as the leading principal component of
North Pacific monthly SST variability poleward of 20°N
[Mantua et al., 1997].
3. Interdecadal Variability of TLP
3.1. Definitions for TC Landfall Numbers
in the Philippines
[9] The targeted area for TC landfall in the Philippines is
shown in Figure 1. For 1945 to 2005, TLP is defined by a TC
with tropical storm (TS) intensity of 35 kt or higher in the
maximum surface wind speed at TC center (based on the
JTWC best-track dataset) that passed the Philippines area of
Figure 1. For 1902 to 1939, surface wind at TC center is not
available. If a TC passed the Philippines area during that period
and the nearest minimum station pressure was observed to be
less than 750 mmHg (about 1000 hPa), one TLP is counted.
[10] The empirical relationship between pressure and
maximum wind speed at TC center was established by
Atkinson and Holliday [1977], which was applied in Dvorak
Technique for TC analysis [Dvorak, 1975]. The threshold of
1000 hPa used for TS criteria before 1939 is consistent
with this empirical relationship and therefore reasonable.
Nyoumura [1979] investigated 728 TSs from 1951 to 1977
using the JMA best-track data. The percentage of TSs with a
central pressure above 1000 hPa was 5.4 during this period.
The data of the two definitions of TLP proposed in this study
are available from 1951 to 1978 from the tropical cyclone
summaries by Bonjoc [1978]. (Tropical cyclone summaries
are available from 1948, however many weather stations only
reported from 1951 in the Philippines. Station pressure data
were used from 1951 in this study.) During this period, the
number of TSs that made landfall in the Philippines by the
definition of JTWC and JMA best-track data were both 124.
Within the 124 TCs, 107 and 105 TSs satisfied the threshold
(<1000 hPa) of station pressure in the Philippines for JTWC
and JMA best-track data respectively. The definition of the
nearest minimum station pressure tends to underestimate the
numbers of TCs compared to that of the maximum surface
wind speed at TC center. However the difference between
these two TLP definitions of the maximum surface wind
speed at TC center and the nearest minimum station pressure
was 13.7% and 15.3% respectively, for JTWC and JMA best-
track data. This kind of difference is equivalent to only about
0.6 TLP per year, and therefore is acceptable.
3.2. Interdecadal Variability
[11] Figure 2a shows annual TLP from 1902 to 2005.
After 1945 and 1951, best-track data sets of JTWC and
Figure 1. The landfall area in the Philippines used in this
study.
Figure 2. (a) Annual TLP using MBP (from 1902 to 1939;
red curves), JTWC (from 1945 to 2005; blue curves), and
JMA (from 1951 to 2005; green curves), where the thick
curves are for 10-year running mean. (b) Wavelet power
spectrum of annual TLP using real-valued Mexican-hat
wavelet. Wavelet is performed separately before 1939 using
TLP from MBP and after 1945 using TLP from JTWC data.
The thick curve indicates the edge effects.
L12802 KUBOTA AND CHAN: VARIABILITY OF TROPICAL CYCLONE L12802
2of4
JMA are plotted. Both best-track data show similar inter-
annual and interdecadal variability of TLP until 1980s. This
difference from 1990s is due to the deactivation of the US
Air Force aircraft reconnaissance [Kamahori et al., 2006].
We will use the JTWC best-track data for the analysis after
1945.
[12] Before 1939, TLP was low around 1920 and high
around 1910 and the mid-1930s. Data are missing between
1940 and 1944. After 1945, the year-to-year variability
becomes high from the mid-1960s to the mid-1970s. Since
1990, TLP has been decreasing. However, even considering
the difference between the two TLP definitions of the
maximum surface wind speed at TC center after 1945 and
the nearest minimum station pressure before 1939, no trend
can be seen in the annual TLP from 1902 to 2005. A real-
valued Mexican-hat wavelet analysis of annual TLP per-
formed separately for the periods before 1939 and after
1945 [Torrence and Compo, 1998] shows a dominant
periodicity of about 32 years around 1920, and a 10 –
22 years periodicity around the 1960s and after the 1990s
(Figure 2b). On the other hand, shorter periods of less than
10 years prevailed from the mid-1960s to the mid-1980s.
Our results indicate that the 32-year periodicity of TLP was
also dominant before 1940.
4. Relationship of TLP to ENSO and PDO
[13]Saunders et al. [2000] noted that the TLP has an
interannual variability associated with ENSO. However, the
effect of ENSO can be modified by that of the PDO [e.g.,
Chan and Zhou, 2005]. Is TLP then also affected by the
PDO? In this section, the annual TLP is estimated from June
to the following May based on the combination of Asian
monsoon and ENSO [Yasunari, 1991]. The average of
annual TLP during low PDO (1945– 1976) and high PDO
phases (1902– 1939 and 1977 – 2005) are therefore com-
pared (Table 1). (Mantua et al. [1997] defined low PDO
period until 1925. However our results did not show
significant difference before and after 1925.) During the
low PDO phase, the difference in annual TLP between
El Nin˜ o and La Nin˜a years is significant at the 95% confi-
dence level. However, during high PDO phases, such a dif-
ference disappears and annual TLP number is slightly higher
in El Nin˜ o years.
[14] A difference in monthly TLP between different
ENSO phases is seen from September to November during
both low and high PDO phases (Figure 3), with TLP
numbers being generally higher during La Nin˜a years.
However, the difference of monthly TLP between different
ENSO phases becomes smaller in autumn (October and
November) and has opposite signs during summer (June
and July) and winter (December and January) of high PDO
phases. These results offset the difference of annual TLP
between different ENSO phases.
[15] Interannual variability of TC activity is likely influ-
enced by the intensity of the anomalous Philippine Sea
anticyclone. During autumn of El Nin˜o developing year,
Philippine Sea anticyclone anomalies are established [Wang
and Zhang, 2002], which suppresses the TC activity over
this region and reduces TLP. During the low PDO phase
before 1976, the contrast of anomalous Philippine Sea
anticyclone in autumn between ENSO phases was intensi-
fied. However, after 1977 of the high PDO phase, this
contrast was weakened; in fact Philippine Sea cyclonic
anomaly was strengthened during summer of the El Nin˜o
developing year [Wang, 1995]. The modulation of anoma-
lous Philippine Sea anticyclone behaviors between ENSO
phases may influence TLP during different PDO phases.
5. Conclusions
[16] A dataset of TLP is constructed by using historical
observation records of MBP from 1902 to 1939 and the
Table 1. Annual TLP From June to the Following May Divided
According to PDO and ENSO Phases
a
1902 – 1939
High PDO
1945 – 1976
Low PDO
1977 – 2005
High PDO
El Nin˜ o years 4.1 3.6 4.5
La Nin˜ a years 3.9 6.0 4.3
Normal years 4.7 4.3 5.1
a
Bold numbers represent they are statistically significant at 95%
confidence level of the difference between El Nin˜ o and La Nin˜ a years.
Figure 3. Monthly TLP during high PDO phases of
(a) 1902–1939 and (c) 1977 – 2005 and (b) low PDO period
of 1945– 1976. In each PDO phase, monthly TLP from June
to the following May is averaged in El Nin˜ o (solid curves),
La Nin˜ a (dashed curves), and normal years (dotted-dashed
curves).
L12802 KUBOTA AND CHAN: VARIABILITY OF TROPICAL CYCLONE L12802
3of4
JTWC best-track data from 1945 to 2005. MBP TC dataset
can trace TC tracks back to the beginning of the 20th
century. With modern TC best-track data from 1945 over
the WNP basin, a unique dataset is created to investigate the
interdecadal variability of TLP during the past 100 years.
From 1902 to 1939, TLP is defined by TC track that passed
over the Philippine area and the nearest minimum Philippines
station pressure was observed to be less than 1000 hPa by
MBP. After 1945, TLP is defined by TS that passed the
Philippines area based on the JTWC best-track data. The
difference between the two TLP definitions is less than
16%. Annual TLP from 1902 to 2005 using the two
definitions shows dominant periodicity of about 32 years
before 1940 and of about 10 – 22 years after 1945; however,
no trend is found. Instead, the annual TLP has a significant
difference between different ENSO phases during a low
PDO phase, with a higher number during La Nin˜ a con-
ditions. However, such differences disappear during high
PDO phases. The difference in monthly TLP between
different ENSO phases become weaker in autumn (October
and November) and has opposite signs during summer
(June and July) and winter (December and January) during
high PDO phases. Therefore, natural variability related to
ENSO and PDO phases appears to prevail in the interdeca-
dal variability of TLP.
[17]Acknowledgments. We thank Jun Matsumoto of Tokyo Metro-
politan University, Yoshiyuki Kajikawa, Shang-Ping Xie, and Axel
Timmermann of University of Hawaii to obtain Monthly Bulletins of
Philippine Weather Bureau maintained by the University of Hawaii. We
thank Cynthia P. Celebre of Philippine Atmospheric, Geophysical and
Astronomical Services Administration for providing Bonjoc [1978]. This
research was supported by ‘‘Global Environment Research Fund by the
Ministry of the Environment Japan’’ B-061, ‘‘Data Integration & Analysis
System’’ funded by the National Key Technology, Ministry of Education,
Culture, Sports, Science and Technology (MEXT), and Grant-in-Aid for
Scientific Research 20240075 of the MEXT (Leader: Jun Matsumoto).
GFD-DENNOU library was used for drawing the figures. Work of the
second author began when he was a Visiting Professor at the Center of
Climate Systems Research of the University of Tokyo, whose support is
gratefully acknowledged.
References
Atkinson, G. D., and C. R. Holliday (1977), Tropical cyclone minimum sea
level pressure/maximum sustained wind relationship for the western
North Pacific, Mon. Weather Rev.,105, 421 – 427.
Bonjoc, M. C. (1978), Tropical Cyclone Summaries From 1948 to 1978,
360 pp., Philipp. Atmos. Geophys. and Astron. Serv. Admin., Quezon
City, Philippines.
Chan, J. C. L. (2008), Decadal variations of intense typhoon occurrence in
the western North Pacific, Proc. R. Soc., Ser. A.,464, 249 – 272.
Chan, J. C. L., and W. Zhou (2005), PDO, ENSO and the early summer
monsoon rainfall over south China, Geophys. Res. Lett.,32, L08810,
doi:10.1029/2004GL022015.
Deppermann, C. E. (1939), Some characteristics of Philippine typhoons,
128 pp., Weather Bur., Manila.
Dvorak, V. F. (1975), Tropical cyclone intensity analysis and forecasting
from satellite imagery, Mon. Weather Rev.,103, 420 – 430.
Garcı´a-Herrera, R., P. Ribera, E. Herna´ ndez, and L. Gimeno (2007), North-
west Pacific typhoons documented by the Philippine Jesuits, 1566 – 1900,
J. Geophys. Res.,112 , D06108, doi:10.1029/2006JD007370.
Gualdi, S., E. Scoccimarro, and A. Navarra (2008), Changes in tropical
cyclone activity due to global warming: Results from a high-resolution
coupled general circulation model, J. Clim.,21, 5204 – 5228.
Kamahori, H., N. Yamazaki, N. Mannoji, and K. Takahashi (2006), Varia-
bility in intense tropical cyclone days in the western North Pacific, Sci.
Online Lett. Atmos.,2, 104 – 107.
Landsea, C. W. (2007), Counting Atlantic tropical cyclones back to 1900,
Eos Trans. AGU,88(18), doi:10.1029/2007EO180001.
Landsea, C. W., C. Anderson, N. Charles, G. Clark, J. Dunion,
J. Fernandez-Partagas, P. Hungerford, C. Neumann, and M. Zimmer
(2004), The Atlantic hurricane database re-analysis project: Documen-
tation for the 1851 – 1910 alternations and additions to the HURDAT
database, in Hurricanes and Typhoons: Past, Present, and Future,
edited by R. J. Murnane and K.-B. Liu, pp. 177 – 221, Columbia Univ.
Press, New York.
Mann, M. E., and K. A. Emanuel (2006), Atlantic hurricane trends linked to
climate change, Eos Trans. AGU,87(24), doi:10.1029/2006EO240001.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis
(1997), A Pacific interdecadal climate oscillation with impacts on salmon
production, Bull. Am. Meteorol. Soc.,78, 1069 – 1079.
Nyoumura, Y. (1979), Statistics of annual frequency and minimum pressure
of typhoon, Weather Serv. Bull. Jpn. Meteorol. Agency,46, 263 – 270.
Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S. Kusunoki,
and A. Noda (2006), Tropical cyclone climatology in a global-warming
climate as simulated in a 20 km-mesh global atmospheric model: Fre-
quency and wind intensity analyses, J. Meteorol. Soc. Jpn.,84, 259– 276.
Saunders, M. A., R. E. Chandler, C. J. Merchant, and F. P. Roberts (2000),
Atlantic hurricanes and NW Pacific typhoons: ENSO spatial impacts on
occurrence and landfall, Geophys. Res. Lett.,27, 1147 – 1150.
Torrence, C., and G. P. Compo (1998), A practical guide to wavelet ana-
lysis, Bull. Am. Meteorol. Soc.,79, 61 – 78.
Trenberth, K. E. (1997), The definition of El Nin˜o, Bull. Am. Meteorol.
Soc.,78, 2771 – 2777.
Udı´as, A. (1996), Jesuits’ contribution to meteorology, Bull. Am. Meteorol.
Soc.,77, 2307 – 2315.
Wang, B. (1995), Interdecadal changes in El Nin˜ o onset in the last four
decades, J. Clim.,8, 267 – 285.
Wang, B., and J. C. L. Chan (2002), How strong ENSO events affect
tropical storm activity over the western north Pacific, J. Clim.,15,
1643 – 1658.
Wang, B., and Q. Zhang (2002), Pacific-East Asia teleconnection. Part II:
How the Philippine sea anomalous anticyclone is established during El
Nin˜ o development, J. Clim.,15, 3252 – 3265.
Webster, P. J., G. J. Holland, J. A. Curry, and H.-R. Chang (2005), Changes
in tropical cyclone number, duration, and intensity in a warming envir-
onment, Science,309, 1844 – 1846.
Yasunari, T. (1991), The monsoon year—A new concept of the climatic
year in the tropics, Bull. Am. Meteorol. Soc.,72, 1331 – 1338.
Yumoto, M., and T. Matsuura (2001), Interannual variability of tropical
cyclone activity in the western North Pacific, J. Meteorol. Soc. Jpn.,
79, 22 – 35.
J. C. L. Chan, Guy Carpenter Asia-Pacific Climate Impact Centre, City
University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong,
China.
H. Kubota, Research Institute for Global Change, Japan Agency for
Marine-Earth Science and Technology, 2-15 Natsushima-cho, Yokosuka,
Kanagawa 237-0061, Japan. (kubota@jamstec.go.jp)
L12802 KUBOTA AND CHAN: VARIABILITY OF TROPICAL CYCLONE L12802
4of4
