Predictability of Baiu Precipitation in the Western North Pacific*Tomohiko Tomita,1,2 Takao Yoshikane,2 and Tetsuzo Yasunari2,3(1: Faculty of Science, Kumamoto Univ., 2: FRCGC/JAMSTEC, 3: HyARC, Nagoya Univ.)*Faculty of Science, Kumamoto Univ., Kurokami 2-39-1, Kumamoto, 860-8555, Japan.
e-mail: tomita@sci.kumamoto-u.ac.jp
Abstract
The baiu is specified by a rainy transition period between northern spring and summer in East Asia. The interannual
variability of the baiu progress is examined using the extended empirical orthogonal function (EEOF) analysis and theregression or correlation techniques based on the score. Data used are the globally gridded GPCP precipitation and theatmospheric parameters estimated in the NCEP-DOE AMIP-II reanalysis for the period of 1979-2003.
The interannual variability of the baiu progress is represented by the following three types. Type A indicates thereversal of precipitation anomalies from May to June and the persistence of anomalies during June and July. The large
amplitudes appear after 1989. Type B identifies the specific precipitation anomalies only in June, and the large scores
are concentrated in the period of 1993-1999. In Type C, the precipitation anomalies appear from May to July near thebaiu front, which is predominant before 1992.
Type A concurrents with the SSTAs off the Philippines between 135°E and 180. It is suggested that the anomalous
meridional circulation induced by the SSTAs forces the anomalous north-south shift of the subtropical Pacific high andthe resulting precipitation anomalies near the baiu front. The precursor of Type B is the SSTAs appearing from theSouth China Sea to the south of Japan in the pre-baiu season. The SSTAs seem to directly excite the anomalous upward
flow near the baiu front and to control the following anomalous baiu precipitation. In Type C, the SSTAs persist from
May to July near the Philippines to the west of 145°E. The SSTAs seem to contribute to smaller water vapor transport
from the tropics. It is found that the baiu progress is quite sensitive to the spatial distribution of SSTAs in the western
North Pacific.

1. Introduction
Baiu (in Japanese, Meiyu in Chinese, Changma in
Korean) characterizes a rainy transition period from
northern spring to summer in East Asia. Precipitation inthe baiu season is one of prerequisite water resources inthe East Asian countries. It is, therefore, of quite
importance to diagnose the interannual variability of thebaiu phenomenon.
The baiu front, which appears from early June
through late July near Japan, climatologically extends
along the northwestern periphery of the North Pacificsubtropical high (the Pacific high) as part of the Asiansummer monsoon (Fig. 1, Murakami and Matsumoto
1994; Wang and LinHo 2002). Therefore, the interannual
variability could be primarily controlled by thedevelopment of the Pacific high, the Asian summermonsoon, the large-scale heat low over the Asian
continent, and the circulations in mid-high latitudes
including the Okhotsku high. In addition, the remote
effects through the El Niño/Southern Oscillation (ENSO)
may indirectly modulate the interannual variability (Nitta1989; Tanaka 1997). Quite recently, Tomita et al. (2004)
found that the meridional variation of the baiu front
indicates the two interannual variations; one is thebiennial (2-3 yr) and the other is the lower-frequency(5-6 yr) variations. The most notable differences between
them appear in the spatial development of sea surface
temperature anomalies (SSTAs) from northern winter toearly summer in the western North Pacific, which furthersuggests a potential predictability of the baiu
phenomenon.
The present study examines the predictability,
particularly focusing on the anomaly fields in late spring.
Furthermore, this work divides the baiu season into two,Fig. 1: Long-term mean of June precipitation (color, mmday-1) and the standard deviation (line, mm day-1).
that is, June and July, since the season is put in transition
from northern spring to summer and the rainy periodmay easily shift back and forth. The findings derived
from the present work would contribute to raise theseasonal prediction of the baiu phenomenon.
In the remaining sections, section 2 introduces theemployed datasets and analysis procedures briefly.
Section 3 exhibits the interannual tendencies of baiu
transition, and the precursors and physical mechanisms
are examined in section 4. Section 5 gives a summary
and further discussion.

2. Data and analysis procedures
The present study uses the following two datasets: (1)global precipitation estimated in the Global PrecipitationClimatology Project (GPCP; Adler et al. 2003), and (2)atmospheric parameters assimilated in the National
Centers for Environmental Prediction (NCEP) Department of Energy (DOE) Atmospheric ModelFig. 2: Eigen vectors of EEOF1(11.7%; (a) May, (b) June, and (c)July; contour), and (d) the scoretime series. The time series arenormalized, while the standard
deviation (SD) is imposed on theeigen vectors. Color indicates theSD of monthly precipitation with aunit of mm/day, and the color scaleis put near the bottom of (c).

Fig. 3: As in Fig. 2 but for EEOF4(8.7%).

Intercomparison Project II (AMIP-II) reanalysis(Kanamitsu et al. 2002). Both datasets are gridded
globally, and the monthly mean data are employed forthe updated period from 1979 to 2003.
In order to investigate the interannual variability ofbaiu progress, we first applied extended empiricalorthogonal function analysis (EEOF; Weare and Nasstrom
1982) to the continuous three-month (May-June-July)
precipitation field in the western North Pacific. Then theprecursors and the associated physical processes areexamined using regression and correlation techniquesbased on the score time series, where the significance ofcorrelation coefficients is tested by the 95% level ofStudent t-test. In this work, we particularly focused on asurface temperature (SFT) field, which corresponds tosea surface temperature (SST) in oceans.

3. Interannual variability of baiu progress
Since the baiu indicates a rainy transition period from
northern spring to summer, it is necessary to investigate
the variability of progress but not of the temporal mean.
For this purpose, we utilized the EEOF analysis for thecontinuous three-month precipitation fields from latespring to early summer (May-June-July). However, since
the main rainy period is June, we chose the three EEOF
modes based on how well the eigen vectors explained thelarge-scale precipitation anomalies near the baiu front inJune. The selected EEOF modes are first (EEOF1;

Fig. 4: As in Fig. 2 but for EEOF5(7.2%)

11.7%), fourth (EEOF4; 8.2%), and fifth (EEOF5; 7.2%)
ones (Figs. 2, 3, and 4). The second EEOF mode (10.7%;
not shown) explains the precipitation anomalies in Mayand July well but not those in June, while the third mode
(8.7%; not shown) demonstrates the anomalies only inJuly. The other lower modes seem to reflect smaller-scaledisturbances in precipitation anomalies. The tendency ofsmall contribution rates would be ascribed to that thesmall-scale disturbances can largely contribute toprecipitation anomalies in midlatitudes and that theanalyzed domain covers somewhat larger area than thatof action centers. The local contribution rates of thechosen EEOF modes could be larger near the baiu frontin June.
The baiu progress associated with the three EEOF
modes are summarized as follows. Type A defined by theEEOF1 is characterized by the reversal of precipitation
anomalies from May to June (Figs. 2a and 2b). Then theanomalous baiu precipitation continues in July with thesame sign (Fig. 2c). The large amplitude tends to appear
after 1989 (Fig. 2d). Type B shown in the EEOF4 isfeatured by the temporally local precipitation anomalies
in June (Fig. 3b). In May and July (Figs. 3a and 3c), theprecipitation is not well organized. The large amplitude
is concentrated in the period of 1993-1999 (Fig. 3d). The
precipitation anomalies persisting from May to July (Fig.
4a, 4b, and 4c) distinguish Type C of the EEOF5 fromthe former two. The score time series (Fig. 4d) clearlyFig. 5: Correlation (color)and regression (contour)
coefficients of surface
temperature with the scoretime series of EEOF1.
Positively (negatively)
significant correlations at
95% level are colored withred (blue).

Fig. 6: As in Fig. 5 but for
the EEOF4.

Fig. 7: As in Fig. 5 but for
the EEOF5.

exhibit that the Type C was dominant before 1992. As
such, the baiu progress is systematically changed on
decadal or interdecadal timescales.

4. Development of surface temperature field
In order to find out the precursors of anomalous baiu
progress and to examine the physical processes from
May to July, we employed the correlation and regression
techniques based on the score time series. Figures 5, 6,
and 7 show the anomalous surface temperature fields
associated with each type of baiu progress.
Type A concurrents with positive SSTAs off thePhilippines between 135°E and the international date line(Fig. 5). Although the regression coefficients get small asmonth passes, the correlations remain significant with apositive sign in the region for the three months. On theother hand, the sign of SSTAs is changed from May toJune near Japan corresponding to the change ofprecipitation anomalies (Figs. 2a and 2b). The
continuous SSTAs off the Philippines should be regarded
as a precursor of Type A. The anomalous meridional
circulation inferred from the positive SSTAs, that is, ananomalous circulation cell with ascent off the Philippinesand descent near Japan, may indicate the anomalous
northward shift of the subtropical Pacific high in thewestern North Pacific and the negative precipitation
anomalies in the baiu front.
When Type B is dominant, the significant positive
SSTAs appear from the northern part of the South China

Sea to the south of Japan in the pre-baiu season (Fig. 6a).
It is conceivable that the positive SSTAs induce theupward motion resulting in the positive precipitation
anomalies near the baiu front of June (Fig. 3b). The
precipitation anomalies themselves modify the SSTAs in
June, and the neutral temperature condition seems toappear in June and July (Figs. 6b and 6c). We could
consider the positive SSTAs to the south of Japan in May
as a precursor of Type B.
Type C is characterized by the significant negative
SSTAs near the Philippines to the west of 135°E
remaining for the three months (Fig. 7), although thesignal is somewhat weak in May (Fig. 7a). Unlike thecase of Type A, the negative SSTAs could contribute tosmaller water vapor transport along the eastern coast ofthe Asian Continent resulting in the weaker-than-normal
baiu activity. It is worth noting here that the SSTAs near
the Philippines can have opposite effects on the baiu
activity due to the zonal distributions (compare Figs. 5band 7b).
Nitta (1989) found that the so-called Pacific-Japan
pattern had a potential to alter the baiu precipitation
anomalies through the anomalous meridional circulation.
Tomita et al. (2004) recently pointed out that the spatial
distribution of the SSTAs in the western North Pacificmodified the meridional location of anomalous baiu
precipitation. The present study proposes the SSTAs justto the south of Japan and the zonal distribution along thelatitude of the Philippines as the precursors to theanomalous baiu progress.

5. Summary
A rainy transition period is specified between
northern spring and summer in East Asia, which is called
Baiu (in Japanese, Meiyu in Chinese, Changma in
Korean). The precipitation in this period is a prerequisite
for the summer water resources in the East Asiancountries. The present study examines the interannual
variability of the baiu progress from May to July using
the EEOF analysis. Then the precursors and theassociated physical processes are discussed.
Data used in this work are the GPCP precipitation
and atmospheric parameters estimated from theNCEP-DOE AMIP-II reanalysis. Both datasets areglobally gridded, and the monthly mean data are used forthe period of 1979-2003.
Based on the eigen vectors near the baiu front in June,
the three EEOF modes are distinguished, i.e., EEOF1(11.7%; Type A), EEOF4 (8.2%; Type B), and EEOF5(7.2%; Type C). Type A is characterized by the reversalof precipitation anomalies from May to June and by thepersistence of anomalies during June and July. The large
scores tend to appear after 1989. On the other hand, Type
B is featured by the specific precipitation anomalies onlyin June, and the large scores are concentrated in theperiod of 1993-1999. The persisting precipitation
anomalies for the three months distinguish Type C fromthe other two types, which is predominant before 1992. It
is of interest that the baiu progress indicates the decadal
or interdecadal variability.
Type A concurrents with significant positive SSTAs
off the Philippines between 135°E and the international
date line, while the sign of SSTAs is changed from Mayto June near Japan. The significant SSTAs to the east of135°E in the subtropical western Pacific are regarded asa precursor of Type A. The anomalous meridional
circulation induced by the positive SSTAs could force
the anomalous northward shift of the subtropical Pacifichigh in the western part and the resulting negative
precipitation anomalies near the baiu front. The precursorof Type B is the positive SSTAs appearing from theSouth China Sea to the south of Japan in the pre-baiu
season. The positive SSTAs would directly excite theanomalous upward flow resulting in the positive
precipitation anomalies in the baiu front of June.
However, the neutral SST condition continues in thefollowing June and July. Type C is featured by thenegative SSTAs near the Philippines to the west of 135°E
remaining for the three months from May to July. The
negative SSTAs would contribute to smaller water vapor
transport from the tropics rather than modifying themeridional circulation in the western Pacific. The baiu
activity seems to be very sensitive to the spatial
distribution of SSTAs near the Philippines. The
precursors would contribute to raise the seasonal
prediction of baiu progress.
The physical mechanisms how the spatial distributionof SSTAs is determined in the western North Pacificremain controversial. Wang et al. (2000) and Wang andZhang (2002) proposed a specific air-sea interaction withhorseshoe SSTAs extending in the entire tropical Pacific,
which is forced by the teleconnection associated with theENSO. Diagnosing the output from GCM experiments,
Lau and Nath (2000) proposed another physicalmechanism that was based on the Rossby-type
atmospheric response on the SSTAs in the tropicalwestern Pacific. Tomita and Yasunari (1996) and Tomitaet al. (2004) pointed out that the spatial pattern ofhorseshoe SSTAs that is associated with the biennial
oscillation of the Asian-Australian monsoon is different
from that appearing with the ENSO. Further study isneeded to identify the effects of the ENSO and of theAsian-Australian monsoon on the interannual variabilityof baiu progress.

Acknowledgements
The present study has been supported by the Frontier
Research Center for Global Change, Japan Agency for
Marine-Earth Science and Technology and by theGrant-in-Aid for Scientific Research (14740283) of theMinistry of Education, Culture, Sports, Science andTechnology (MEXT) of Japan.

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