57

Monitoring and Predicting ENSO Events and Sea Temperature
Structure of the Warm Pool in the Western Pacific Ocean
Ding YIHUI, Li QINGQUAN, Zhang ZUQIANG and Zhai PANMAO
National Climate Center, China Meteorological Administration,
Beijing 100081
e-mail: dingyh@cma.gov.cn

Abstract
Numerous studies were undertaken for scientific support of the APN-funded project of the network
system for monitoring and predicting ENSO events and the sea temperature structure of the warm poolin the western Pacific. This paper is a brief summary of scientific highlights derived from this project
which include: (1) Development of some new indices for monitoring ENSO events and sea temperatures
and associated convective activity over the warm pool in the western Pacific. (2) Development of anumber of dynamic and statistical models for prediction of ENSO events on the basis of which
multi-model ensemble predictions for both dynamical and statistical models are made. Verfication ofpredictive skills shows good model performance. (3) A proposal for an air-sea interactive conceptualmodel applicable to predicting sea temperatures over the warm pool by using the subsurface currents
and temperature dataset from the TOGA-TAO array. Observational evidence is presented to reveal theformation mechanism and eastward propagation features of subsurface current anomalies driven by thestrong westerly wind anomalies over the warm pool in the western Pacific Ocean.
Key words: APN, ENSO events, multi-model ensemble prediction, warm pool

1. Introduction
It is well known that there is a close relationshipbetween ENSO events and anomalous climates overmost of the world. Numerous investigators (Huang &Wu, 1989; Li, 1990; Zhang et al., 1996; Huang et al.,1999) have shown that ENSO cycles (consisting of El
Niño and La Niña phases) have significant relations
with and impacts on climates, the environment, andsocio-economic aspects of Asian and western Pacificcountries and regions, such as Indonesia, the Philippines, Malaysia, Vietnam, Thailand, Australia, Japan,
Korea and China. They may cause droughts, floods,
cold injuries, heat waves and landfall of typhoons, aswell as forest fires in the Asian and Pacific region,
thus bringing about huge socio-economic losses anddeterioration of the environment in these regions. For
instance, recent statistics have shown that the impactof the 1997/1998 El Niño event brought abnormal
global and regional climate changes and large-scale
severe natural disasters, which left five million
persons homeless and brought direct economic losses
to US$33.9 billion. It has been found that there aresome definite correlative relationships between theonset, maturity and decay of ENSO events and winterand summer monsoon activity over Asia (Wang & Li,
Global Environmental Research9(1)/2005: 57-68
printed in Japan

2004).
On the other hand, the sea surface temperature
anomaly (SSTA) and associated convective activities
in the western Pacific Ocean may exert a teleconnective effect on the weather and climate in East Asia
through the PJ (Pacific-Japan) pattern (Nitta, 1987;
Huang & Li, 1987). Li et al. (1999) also pointed outthat the thermal regime of the warm pool and convective activity in the region around the Philippine Sea
play important roles in affecting the seasonal andinterannual variability of the East Asian summer monsoon. Therefore, from a unique regional viewpoint, it
is important to monitor and predict sea temperature of
the warm pool in the western Pacific Ocean and theirrelationship to summer monsoon activity. In order toimprove our understanding and knowledge in thesefields, it is essential to enhance monitoring and prediction of ENSO events and the SSTA of the warm poolover the western Pacific Ocean on seasonal and interannual time scales with necessary related information,
products and data sets distributed to APN countries/
regions via the Internet, thus leading to improvementof seasonal and interannual prediction of monsoon
activity and significant climate events in this region.
Through three year’s efforts (1999-2001), fundedby the APN, a network and associated web-page©2005 AIRIES

58

Ding YIHUI et al.

(http://ncc.cma.gov.cn) of APN projects (#99012,#2000-12 & #2001-12) have been successively set up,
with their catalogues and information residing in aworkstation (Ding, 2002). The National Climate
Center (NCC) of the China Meteorological Administration is responsible for maintenance and improvement of this Internet network, as well as collecting
necessary information and updating the network on amonthly basis. Other participating countries/regions
furnish information and datasets. In the network, a
special emphasis is placed on oceanic and meteorological conditions over the warm pool and the South
China Sea, the impact of ENSO events and the SSTAof the warm pool on the weather and climate of APN
countries (including tropical cyclones and monsoon),which have often been neglected in current global
studies and networks. Besides historical perspectives,
predictions and outlook for the tropical SSTA and El
Niño/La Niña events as well as related data and information associated with the ENSO events and thewarm pool have been widely collected, including
TOGA-TAO and SCSMEX (the South China Sea
Monsoon Experiment) buoy data, oceanic data of thewarm pool and the South China Sea, the oceanic observations of Japan along 137°E, and satellite datasuch as OLR and TBB, and most of these have been
put onto the network. This is the first distributed network for climate monitoring and prediction established successfully in the APN region.

2. New Indices for Monitoring ENSO Eventsand Sea Surface Temperatures (SST) in
the Warm Pool
A number of indices have been used to monitor theevolution of ENSO events, including their onset, development, maturity and decay. Among them, themost widely accepted indices are SSTA (SST anomaly) indices in regions Nino1+2, Nino 3 and Nino 4,
and the Southern Oscillation Index (SOI). Based onthese indices, a set of criteria to define the onset time,
duration, intensity and mode or type of development
has been developed. By using these indices and criteria, an ENSO event can be identified in early stages.
Then, at later stages its intensity and duration may beobjectively defined. Here, a new SSTA index (Nino
1+2+3+4 and SOI), the Synthesized Ocean-Atmospheric Index, for ENSO monitoring is suggested. It
can be used to define different types of warm episodes
which initially occur in either the equatorial central
Pacific or the equatorial eastern Pacific. Based on thisnew index, if the average SSTA in these regions
exceeds 0.5°C for at least six months, a warm episodemay be defined to occur. In contrast, the criterion for
occurrence of a cold episode (La Niña) is set at lessthan – 0.4°C. Then, the ENSO duration is defined as
total number of months from the onset month toending month of an ENSO event. The mature phase isdefined as the time period with SSTA ≥ 1.0°C (SSTA≤ – 0.8) in Nino 1-4 regions for a warm (cold) event.
The regions of initial occurrence of an ENSO event
vary from case to case (Rasmussen & Carpenter,
1982), but they generally fall into two types: theeastern onset type with positive SSTA starting fromthe Nino 1+2 region and the central onset type withpositive SSTA starting from the Nino 4 region.
It can be concluded that the index of Nino
1+2+3+4 can capture all the major features demonstrated separately in regions Nino 1+2, Nino 3 andNino 4. Therefore, this new index can characterize
ENSO events of different regions of origin with asingle composed index, without missing any cold or
warm episodes. Figure 1 shows an intercomparison
among the various ENSO indices used in the monitor-

SSTA indices in Nino regions(1991.1 — 2003.06)
Nino 1+2

Nino 4

Nino 3

Nino 1+2+3+4

Fig. 1 An intercomparison among the various ENSO indices from 1991 to 2003
(Zhai et al., 2002).

Monitoring and Predicting ENSO Events

ing and diagnostic study of ENSO events.
The intensity index for ENSO episodes may be defined with this accumulated SSTA of regions Nino 1-4
during the entire period of cold or warm episodes.
Based on this index, all the episodes during the past
50 years are classified into different intensity categories. Table 1 indicates the intensity classification andother related parameters of 15 warm episodes and 11
cold episodes.
The connection between convection and SSTAs in
the tropical Pacific has been studied, based on satellite
measured Outgoing Longwave Radiation (OLR) data59

and by applying SVD technique. Results have revealed that convection is very closely related to ENSO
events. According to a study of typical pattern of OLR
fields characterizing convection during ENSO episodes, a convection oscillation index (COI) similar to
the SOI has been put forward for ENSO monitoring.
The correlation between the COI and SSTA index in
the Nino 3 region is higher than that between the SOIand SSTA index of the same region (Fig. 2).
In addition, a synthesized ocean-atmospheric index
for ENSO monitoring has also been derived (see SYN.
Intensity in Table 1), which is defined as the

Table 1 Classification of ENSO events from 1951 to 2000 (Zhai et al., 2002).

W
A
R
M
E
V
E
E
T
S

C
O
L
D
E
V
E
N
T
S

8
8
16
7
11
16
11
7
6
17
17
15
9
6
14
7
11
16
11
7
6
17
17
15
9
6
14

Peak
SSTA(°C)
1.0
0.9
1.4
0.8
1.3
1.1
1.9
0.9
0.9
2.5
1.6
1.4
1.1
1.2
2.8
0.8
1.3
1.1
1.9
0.9
0.9
2.5
1.6
1.4
1.1
1.2
2.8

Peak
Month
11
9
1
10
12
5
12
10
9
12
9
4
5
12
12
10
12
5
12
10
9
12
9
4
5
12
12

SSTA
Intensity
W
VW
S
W
M
M
S
W
VW
VS
VS
S
W
W
VS
W
M
M
S
W
VW
VS
VS
S
W
W
VS

SYN.
Intensity
W
W
M
W
M
M
S
W
VW
VS
VS
S
M
W
VS
W
M
M
S
W
VW
VS
VS
S
M
W
VS

Onset
Type
E
E
E
E
E
C
E
E
E
C
C
C
C
C
E
E
E
C
E
E
E
C
C
C
C
C
E

25
6
11
10
19
12
19
13
14
8
18

-1.7
-0.7
-1.1
-0.7
-1.4
-1.4
-1.5
-0.9
-1.6
-0.5
-1.3

11
9
12
2
12
12
12
12
12
11
1

VS
VW
M
W
S
M
S
M
S
VW
S

VS
W
W
VW
S
S
VS
W
S
VW
S

E
C
E
E
E
E
C
E
E
E
C

Onset & end

Duration

1951.06-1952.01
1953.04-1953.11
1957.04-1953.11
1963.70-1964.01
1965.05-1966.03
1968.10-1970.01
1972.04-1973.02
1976.07-1977.01
1979.09-1980.02
1982.05-1983.09
1986.09-1988.01
1991.05-1992.07
1993.03-1993.11
1994.09-1995.02
1997.04-1998.05
1963.70-1964.01
1965.05-1966.03
1968.10-1970.01
1972.04-1973.02
1976.07-1977.01
1979.09-1980.02
1982.05-1983.09
1986.09-1988.01
1991.05-1992.07
1993.03-1993.11
1994.09-1995.02
1997.04-1998.05
1954.04-1956.04
1956.07-1956.12
1964.03-1965.01
1967.08-1968.05
1970.06-1971.12
1973.06-1974.05
1974.09-1976.03
1984.10-1985.10
1988.04-1989.05
1995.09-1996.04
1998.10-2000.03

Remarks: VS-very strong with an intensity index of ≥18.0°C (≤–16.0°C) for warm (cold) episode; VW-very weakwith an intensity of ≤4.5°C (≥–3.5°C) for warm (cold) episodes; S- strong with an intensity index of
17.9-14.0°C (–15.9 - –12.0°C) for warm (cold) episodes; M-moderate with an intensity index of
13.9-7.0°C (–11.9 - –6.0°C) for warm (cold episodes); and W-weak with an intensity index of 6.9-4.6°C(–5.9 - –3.6°C) for warm (cold) episodes. E-eastern Pacific onset; C-central Pacific onset.

60

Ding YIHUI et al.
Nino 3 SSTA

Fig. 2 Time series of the SSTA index in Nino 3, SOI and
COI. SSTA index in Nino 3: equatorial Pacific sea
surface temperature anomaly index averaged over
the area for Nino 3 region 90°W-150°W, 5°N-5°S(green). COI: Convective activities oscillation index
over equatorial Pacific (red). COI = standardized(standardized (ECI) – standardized (WI)). ECI
stands for the OLR anomaly index over theequatorial eastern and central Pacific (5°N-5°S,
170°E-90°W) and WI stands for the OLR anomalyindex over the equatorial western Pacific (5°N-5°S,
110°E-140°E). Anomalies are departure from theaverages of the 1979-1998 period. SOI =standardized (standardized (SLPA (TAHITI)) –standardized (SLPA (DARWIN)) (blue)).

difference between the normalized accumulatedmonthly SSTAs of the Nino 1-4 regions and thenormalized accumulated SOI. This index reflects the
combined effect of the SSTA and the large-scalesurface pressure fields. Intensity categories of ENSO
events classified with this index show somedifferences from the results derived with SSTA
Intensity, as indicated in Table 1.
The interannual variability of SSTs in the warm
pool region is very small, but the spatial coverage of
the warm pool changes significantly. A new index
related to the areal extent of SST greater than 28°C
and the SST departure from 28°C were calculated tomeasure the intensity of the warm pool. They are
named the area index and the intensity index of thewarm pool, respectively. Figure 3 shows time series ofthese two indices since 1991. It can be seen that theintensity index and the area anomaly index have good
positive correlation, i.e., the expansion of area of thewarm pool corresponds to stronger anomaly indices.
These two indices also show an obvious decadal variability, with positive anomalies found in the period
from 1994-2003 and negative anomalies found before
1994. Anomalous warming and an expanding warm
pool can exert an important effect on the teleconnective mode by exciting active convective activity (Nitta,
1986; Huang, et al., 1987). In the past decade, themajor seasonal rain belt in China has shifted to theregion of the Yangtze River basin and South China. It

Fig. 3 Time series of the area anomaly index (upper panel) and intensity anomalyindex (lower panel) from 1991 to 2003.

Monitoring and Predicting ENSO Events

is not clear yet whether they have a possible correlative relationship on an interdecadal time scale. This
problem needs to be further studied. However, forsome individual years, one does observe remote
effects of convective activity over the warm pool. As
shown in Fig. 4, in the summer of 2004, the surface
air temperature anomaly was characterized by a northeast-southwest-oriented ‘sandwich’ pattern, namely
above normal over the southeast coast of China, below
normal over central China and above normal over
Northeast China and Northwest China. In particular,
the southeast coast of China suffered from a severe
hot wave this summer, which was probably associatedwith active convective activities in subtropical regionof the northwest Pacific Ocean (Fig. 5). Thatlarge-scale convection persisted throughout the wholesummer, which induced a JP pattern-like teleconnec-

61

tive mode over East Asia, with a compensative subsidence to the north of the active convective region in
the warm pool. Such thermal forcing enhanced thesubtropical high over the West-Pacific and associatedsubsidence which is attributed to occurrence of the hot
wave over the southeast coast of China and lower valley of the Yangtze River (Fig. 6).

3.

ENSO Predictions and Verifications

A simplified air-sea dynamical model system(SAOMS) of the ZC type (Zebiak & Cane, 1987) anda statistical model system have been developed andused to predict sea surface temperature anomalies over
the tropical Pacific Ocean. The dynamical model system includes three components: initialization, modeling, and prediction and verification. In this system,

Fig. 4 Surface air temperature anomalies (unit: 0.10oC) in June, Julyand August (JJA) of 2004 (from the Division of Climate
Diagnosis and Prediction of National Climate Center, China
Meteorological Administration).

Fig. 5 Outgoing Longwave Radiation (OLR) anomaly (Unit: W/m2) in JJA of 2004.

62

Ding YIHUI et al.

five dynamic models (NCCo, NCCn, NCC/STI,
NCC/NIM and CAMS/NJU) are used to make ensemble predictions of ENSO events.
The model NCCo is based on the ZC87 model(Zebiak & Cane, 1987). Several computational functions have been changed. Both pre- and post-processing procedures have been constructed. A new initialization has been used in the NCCo, i.e., both computed
SSTA and wind stress anomalies are replaced by theobserved values during the initialization (Li & Zhao,
2000). The model NCCn has been modified based on
the NCCo without initialization. In particular, the upwelling and advection in the oceanic component of the
NCCo have been improved (Zhang & Zhao, 2000).
The model NCC/STI was based on the NCCo without
initialization. An adjoint assimilation has been createdand used to perform multiple-seasonal predictions(Duan et al., 2000). The model NCC/NIM is based on
an Oxford model (Balmaseda et al., 1994). Both theclimate background and the heat flux estimate in theatmospheric component of the model have been improved (Zhang & Ding, 2000). The dynamic framework of the model CAMS/NJU is based on the ZC87.
It covers three oceanic basins (the tropical Pacific
Ocean, Indian Ocean and tropical Atlantic Ocean).
New parameters in the Indian Ocean and Atlantic
Ocean have been selected after several experiments(Yin et al., 2000).
A hindcast experiment from 1980 to 1996 was
carried out to test the performance of the SAOMS incomparison with the persistent prediction (Fig. 7). It
can be seen that the model system shows a certain
skill at predicting the SSTA over the tropical Pacific
Ocean, for 6-12 months in advance, with correlation
coefficients considerably exceeding the persistence
prediction. Particularly for predictions with a leadtime of 9-12 months, all the models perform better
than the persistence prediction. In this hindcast experiment, three El Niño events (1982/83, 1986/87,
1991/92) and three La Niña events (1984/85, 1988/89
are 1995/96) were included. Therefore, the prediction
skill shown in Fig. 7 mainly reflects the predictive
performance for ENSO events.
Comparison of Observed and Predicted Nino3 SSTA

Figure 8 shows real-time prediction results for the
SSTA in the Nino 3 region during 1997-2004.
Multi-model ensemble predictions were made basedon five models which started from January of 1997 to
2004. It can be seen that the model system predicted
both El Niño (1997/98) and La Niña (1998/2000)
events reasonably. Among the five models, only one
model (NCC/STI) captured the 2002 El Niño event,whereas the other four models missed this El Niño
event. Thus, the multi-model ensemble prediction also
500

Fig. 6 500 hPa geopotential height anomaly (unit: 10 gpm)
in JJA of 2004.

Fig. 7 Temporal correlation coefficients between observed andpredicted Nino3 SSTA in 1980-1996 by the SAOMS.
Corr Coeff of Pred and Observe Nino3 SSTA
during 1997-2004

Fig. 8 Verification of predictions starting from January of 1997-2004 (left) and anomaly correlation
coefficients between prediction and observation (right).

Monitoring and Predicting ENSO Events

missed the forecast of the 2002 warm episode in early
2002. In January 2004, the SAOMS predicted that
Nino 3 SSTA might increase in the following seasons(see Fig. 8). Although the predicted warming occurred
three months earlier than observations, the prediction
captured the weak warm process in 2004. During
1997-2004, the correlation coefficient between themulti-model ensemble predictions and observations
was 0.67. The correlation coefficients at 1-3 and 6-11
lead months were all above 0.5 (see Fig. 8).
The statistical model system consists of four statistical models (PC-CCA, SSA, ANA and FLT). An ensemble prediction can be made based on individual
predictions derived from different statistical models.
The Principal Component Canonical Correlation
Analysis Model (PC-CCA) has been established byapplying EEOF, the PRESS principle and ensemble
forecast techniques (Jiang et al. 1999). In the Maximum Entropy Singular Spectrum Analysis Model(SSA), principal components with significant periods
have been generated, so the ENSO cycle has been reconstructed by using singular spectrum analysis (Zhai,
2000). This maximum entropy singular spectrum
FORECAST OF Nino3 SST ANOMALIES (SSA_ME)

FORECAST OF Nino3 SST ANOMALIES (PC_CCA)

63

analysis model was established after applying the
maximum entropy AR (p) technique. In the Analogue
Prediction Model (ANA), firstly ten simple indices fororiginal series similarity, trend similarity and acceleration similarity were designed on the basis of the dynamic analysis of ENSO cycle. Then a synthetic index
was generated from the ten simple indices, establishing the so-called analogue forecast model (Ren &Zhou, 2000). The idea of the Optimum Filtering
Assembly Model (FLT) was based on an ENSO component analysis theory, in which the ENSO cycle wasseparated into three time-scale oscillations – QBO,
low frequency mode of 3-5 years and interdecadal
variance using filtering techniques. A new statistical
ENSO model, the optimum filtering assembly model,
was established by applying four statistical time series
analysis models (Ren et al., 1998). The statisticalmodel system is used to perform real-time operational
forecast of Nino 3 SSTA each month at the National
Climate Center of China. Figure 9 shows the individual predictions of Nino 3 SSTAs for the subsequent
year (from July 2003 to July 2004) using four statistical models and the multi-statistical model ensemble
FORECAST OF Nino SST ANOMALIES (FILTER)FORECAST OF Nino3 SST ANOMALIES (ANALOGUE)FORECAST OF Nino3 SST ANOMALIES

Fig. 9 Real-time forecasts of the Nino 3 SSTA using 4 statistical models and the ensemble ofmulti-statistical model forecasts. Singular Spectral Analysis (upper left), Canonical Correlation
Analysis (lower left), Analogue Prediction (lower right), Optimum Filter Assembly (upper
right), and the ensemble prediction (bottom).

64

Ding YIHUI et al.

prediction. It seems that for the future 12 months, theSSTA in the Nino 3 region will stay at a normal condition. This is the same as the general trend predicted by
the Australian National Climate Center Coupledmodel. At least 30 forecasts of Nino 3 SST anomalies,
which started from July-August 2003, have predicted
a neutral condition (–0.8-0.8°C). Most SSTA predictions made by dynamic and statistical models aroundthe world starting from August 2003 such as CPC,
COLA(2), ECMWF, LDEO(4), NCEP, NOAA Linear
Inverse, Seripps/MPI, NSIPP/NASA, JMA and
CLIPER have predicted a neutral condition throughApril 2004 (NOAA/NCEP, 2003).

4. An Air-sea Interactive Conceptual Model
Applicable to Predicting the Sea Temperature Structure Over the Warm Pool
The warm pool is located in an extensive tropicaloceanic region extending from the Philippines to thedateline, with the maximum SSTs observed. During awarm episode, adjustment of the thermocline results

Fig. 10in enhanced negative SST anomalies in the equatorial
western Pacific, while, during a cold episode, the opposite situation occurs in the equatorial western
Pacific. Therefore, in order to predict SSTAs and subsurface sea temperatures over the warm pool, thephysical processes responsible for thermocline adjustment must be studied. This problem is closely related
to air-sea interaction between the wind field and thethermocline. A longer time series of the oceanic datasets for the equatorial ocean current profile and thermocline depth (from 1988 to 2001), derived from thein site TOGA-TOA buoy array (McPhaden, 1995) was
employed to investigate the ENSO-related atmosphere-ocean interaction in this region.
To highlight the effects of low-level wind anomalies on the subsurface ocean, the mature phases ofcold and warm episodes, e.g. December 1996 andDecember 1997, were chosen for investigating potential linkages between atmospheric wind anomalies andthermodynamic variations in the equatorial ocean profile (Fig. 10). Anomalies of all elements here are relative to the 1992-2001 climatological average. At theend of 1996, enhanced trade winds met anomalous

(a)

(d)

(b)

(e)

(c)

(f)

Monthly 850 hPa zonal wind anomalies over the tropical Pacific (upper panels, m/s), equatorial profiles ofanomalous ocean currents (middle panels, cm/s) and of sea temperature anomalies (bottom panels, °C).

Monitoring and Predicting ENSO Events

westerly winds over the warm pool area. As a consequence, the upper-layer of water in the central andequatorial eastern Pacific was driven westward,
whereas the western counterpart moved in an opposite
direction. A pile-up of warm water occurred in thevicinity of the dateline. Therefore, a subsidence of
upper-layer water would be expected, reducing such apeaking tendency of the sea surface level. Owing to a
weak vertical temperature difference in the mixed
layer, it was possible for the upper-layer of sea water
to descend to a deeper level in the mixed layer. However, in the neighborhood of the thermocline, where
the environmental sea temperature drops sharply withdepth, further subsidence in the mixed layer was
greatly hampered by rapidly increasing upward buoyancy. In this case, the sinking water had to diverge
over the sloping thermocline surface. This is why thezonal currents below 80 meters were almost the reverse of those above that level, as shown in Fig. 10b.
On the other hand, the subsidence of the upper layerof water not only caused a warm advection to the bottom of the mixed layer, but also reduced the upwellingof cold water below the mixed layer, ultimately inducing a depressed thermocline. This illustration gives areasonable interpretation for the emergence of a positive temperature anomaly maximum sloping fromwest to east across the equatorial basin in Fig. 10c.
As shown in the right-hand column in Fig. 10, thecase of 1997 was almost the mirror of that in 1996. A
record-breaking strong El Niño reached maturity atthe end of 1997. A dominant westerly anomaly prevailed over nearly the entire equatorial Pacific except
for west of 150°E. Consistent with the low-levelwinds, the upper-layer of water over the whole equatorial Pacific traveled eastward, with a speed core of
40 cm/s observed to the east of the dateline. Thismeans that, despite the anomalous zonal winds and theupper-layer zonal current, a true zonal divergence wasfound to the west of the dateline, which induced an
opposite flow in the layers below 80 meters (Fig. 10).
This pattern favored an upwelling in the mixed layer
and a consequently shoaled thermocline, which created a cold water core as much as 4°C lower in the
80-180 m layer of the warm pool.
Figure 11 shows temporal evolutions of low-level
zonal winds and related zonal divergence ( ∂u / ∂x ), andof the thermocline depth anomaly, which represents
the thermal condition of the subsurface ocean or themixed layer thickness. As shown in Fig. 11, considerable temperature anomalies in the subsurface warm
pool occurred unexceptionally before the onset ofeach ENSO event. Of great importance is that thevariation in zonal wind divergence, rather than thezonal wind anomaly itself, matches that of the warm
pool subsurface thermal conditions well. Althoughwesterly anomalies remained over the warm pool from1992 through 1994, as an example, the original thin
mixed layer became thicker than normal in early 1994.
In fact, the centers of the westerly winds were located65

to the east of the warm pool during the period of 1992
to 1993, thus zonal divergence did prevail at that time,
favoring an upwelling in the mixed layer and an uplifted thermocline. In early 1994, recovering tradewinds in the central Pacific resulted in the replacement of zonal divergence with zonal convergence, asign that a change in the mixed layer thickness anomaly would take place in the warm pool soon.
Considerable disagreement between changes inzonal wind anomalies and subsurface thermal conditions have been found in recent years. Westerlies in
the western Pacific were replaced by easterlies in late1997, while the thermocline was not pushed down to alevel below normal until one year later. In the springof 2001, the original easterly wind anomaly sustaining
over the warm pool for three years came to an end, but
the depressed thermocline persisted till the summer of
2002. Thermal variations in the warm pool subsurface
ocean can be easily explained from the viewpoint of
zonal wind convergence. In the boreal winter of
1997/98, the easterlies to the east of 160°E whichbrought the 1997/98 El Niño to an end several months
later, together with the westerlies west of 160°E,
caused zonal divergence over the warm pool, so thesubsurface ocean stayed in a cold condition. By theautumn of 1998, a newly developed cold episode triggered a reversal of the zonal wind anomalies over thecentral and eastern Pacific, and thus a consequentzonal convergence and thickening of the mixed layer
commenced in the warm pool region. In the spring of
2001, westerlies appeared again over the western
Pacific, but the zonal confluence still remained, except
for a slightly eastward shift, because of the persistenceof above-normal trade winds east of the dateline. In
this regard, the thermocline did not uplift to a shallower-than-normal depth until the spring of 2002
when the westerlies migrated eastward and associatedzonal divergence occurred east of 160°E. In October
2002, a flattened thermocline along the equator was
built up.
Finally, a schematic illustration of the atmosphere-ocean coupling process along the equatorial
Pacific, with the mature phase of La Niña as an example, is shown in Fig. 12. An intensified Walker Circulation and active western Pacific convection created apositive feedback to zonal convergence of low-level
wind stress over the warm pool. More surface layerwater is transferred to the warm pool by the windstress-induced ocean current, favoring the subsidenceof surface layer water. Due to the relatively weak sea
temperature difference with depth in the mixed layer,
at the depth A, the imposed buoyancy on the sinking
water is not strong enough to counteract its further
drop. However, in the vicinity of the thermocline, thatis, depth B, the upward buoyancy increases rapidly
because of the strong vertical temperature gradient,which finally exhausts the sinking momentum of thewater. In this case, the thermocline, something like arigid boundary, impels the subsiding water to diverge

66

Ding YIHUI et al.

(a)

(b)

(c)

Fig. 11 1992-2002 longitude-time cross sections of near-equatorial (a) 850 hPa monthly zonal wind anomalies (m/s), (b)thermocline depth fluctuation (m), and (c) the 9-point-smoothed zonal difference ∂u / ∂x (10-7/s).

Fig. 12 Schematic illustration of the equatorial Pacific atmosphere-ocean interaction during the mature phase of La Niña
event. The letters of A and B denote different depths of thedark shaded water box. ω and F represent the downward
velocity of the water box and the buoyancy imposed on it by
the surrounding ocean, respectively. Lengths of the arrows
indicate the intensities of factors. The solid thick line and thedashed line stand for the climate average and anomalousstates of the thermocline, repectively.

Monitoring and Predicting ENSO Events

over its own sloping surface. The consequent ocean
current profile in the equatorial mixed layer, convergence in the upper layer and divergence in the deeper
layer, produces persistent downwelling and reducescolder water upwelling beneath the mixed layer, ultimately inducing the maximum temperature anomaly
at the climatological depth of the thermocline.

5.

67

convective activity over the warm pool; use andverification of multiple model ensemble predictions
by dynamic and statistical models for ENSO events;
and a proposal for an air-sea conceptual model applicable to predicting the sea temperatures over the warm
pool in the western Pacific Ocean. These scientificresults provide a physically sound basis for supporting
this regional APN network.

Concluding Remarks

In recent years, NOAA of US, JMA, the Bureau of
Meteorology of Australia and the National Climate
Center of China have made considerable progress inthe monitoring and prediction of ENSO events. They
issue operational oceanic and atmospheric productswith regard to the tropical Pacific Ocean and carry out
monthly, seasonal, and interannual prediction of SSTA
using both statistical methods and dynamic models ona regular basis. These products provide useful guidance for other countries. The focus of most of theirmonitoring and prediction, however, is placed on theequatorial central and eastern Pacific Ocean, with lessattention paid to monitoring and prediction of thewestern Pacific warm pool or the impacts resulting
from the warm pool SSTA and ENSO. In 1999, under
the support of the APN, China, Japan, Korea, thePhilippines, Thailand, Malaysia, Vietnam, Indonesia
and Australia jointly proposed and undertook the APN
cooperative research project ‘Monitoring and prediction of ENSO events and SSTA over the warm pool in
the western Pacific Ocean’ (1999-2000) (Ding, 2002).
Since all the participating institutions are from APN
countries/regions, which are affected greatly by the
Asian monsoon and the warm pool, they have great
concern for ENSO events and the warm pool as wellas their impact on the East Asian monsoon. This is thecentral issue of our project. Therefore, under the
project’s goals and objectives, a close regional cooperative relationship has been established and has
worked jointly very well. Through the development
and establishment of an international network, thisproject has greatly increased the APN’s capability todisseminate and exchange monitoring and prediction
information on ENSO events and the warm pool to
APN countries. This kind of infrastructure building is
especially useful to developing countries in this region
for timely provision of information and warnings to
help them prepare for potential disasters. Their development of national capability in this regard will bealso enhanced. Therefore, this project contributes tothe fundamental APN goal of building regional andnational capacities for studying global environmental
change issues. On the other hand, the provided information can assist policymakers in taking necessary
action for prevention of potential disasters.
The present paper is a brief summary of scientifichighlights derived from this project, which include thedevelopment of some new indices for monitoring
ENSO events and sea temperatures and associatedAcknowledgements
Authors would like to express appreciation forcontinuous contributions from participating countries
and experts for this project.
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