Asia-Pac. J. Atmos. Sci., 50(1), 69-81, 2014
DOI:10.1007/s13143-014-0028-3
REVIEW
Recent Progress on Two Types of El Niño: Observations, Dynamics, and Future
Changes
Sang-Wook Yeh1, Jong-Seong Kug2, and Soon-Il An3
1
Department of Marine Sciences and Convergent Technology, Hanyang University, Ansan, Korea
Korea Institute of Ocean Science and Technology, Ansan, Korea
3
Department of Atmospheric Sciences, Yonsei University, Seoul, Korea
2
(Manuscript received 26 November 2013; accepted 8 January 2014)
© The Korean Meteorological Society and Springer 2014
Abstract: The climate community has made significant progress in
observing, understanding and predicting El Niño and Southern
Oscillation (ENSO) over the last 30 years. In spite of that, unresolved questions still remain, including ENSO diversity and extreme
events, decadal modulation, predictability, teleconnection, and the
interaction of ENSO with other climate phenomena. In particular, the
existence of a different type of El Niño from the conventional El
Niño has been proposed. This type of El Niño has occurred more
frequently during the recent decades and received a great attention in
the climate community. This review provides recent progresses on
dynamics, decadal variability and future projection of El Niño, with
a focus on the two types of El Niño.
Key words: ENSO, two types of El Niño, conventional El Niño,
ENSO diversity
1. Introduction
The El Niño-Southern Oscillation (ENSO) is a naturally
occurring fluctuation of sea surface temperature (SST) in the
tropical Pacific on interannual timescales (Ropelewski and
Halpert, 1987). Though the ENSO phenomenon starts in the
tropical Pacific, it can impact agriculture and hydrological
cycle, and cause severe weather events in the globe scale
through the ocean and atmospheric teleconnection (Alexander
et al., 2002; McPhaden et al., 2006; Spencer and Braswell,
2013; Ham et al., 2014). Furthermore, the ENSO is able to
influence the marine ecosystem in the Pacific through changing
the nutrient supply, along with the year-to-year variability in
global atmospheric carbon concentration (Chavez et al., 1999;
Murtugudde et al., 1999; Picaut et al., 2001; McPhaden et al.,
2006; Park et al., 2011). Therefore, the ENSO phenomenon
including its climate impacts and socio-economic consequences
has been a key scientific topic to understand natural climate
variability and, by extension, climate change (McPhaden et al.,
Corresponding Author: Jong-Seong Kug, Korean Institute of
Ocean Science and Technology, 787 Haean-ro, Sangnok-gu, Ansan
426-744, Korea.
E-mail : [email protected]
Corresponding Author: Soon-Il An, Department of Atmospheric
Sciences, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul
120-749, Korea.
E-mail: [email protected]
2006).
Since the big El Niño event of 1982/83, the climate community has made significant progress in observing, understanding and predicting ENSO, in particular after the launch of
a real-time observing system known as the Tropical OceanGlobal Atmosphere/Tropical Atmosphere Ocean (TOGA/TAO)
Program that further promoted such progress. These efforts
have advanced our understanding of the ocean-atmosphere
coupled feedback processes that are essential for the ENSO
evolution (Zebiak and Cane, 1987; Jin, 1997a, b; Latif et al.,
1998; Kirtman and Schopf, 1998; An and Wang, 2000;
Fedorov and Philander, 2000; Kug et al., 2003; An et al.,
2005; Dewitte et al., 2012). Furthermore, the current climate
system models from the third and fifth Coupled Models
Intercomparision Project (CMIP3 and CMIP5) provided an
invaluable data base to investigate the ENSO processes, in
particular those associated to its sensitivity to long-term
changes in mean states (van Oldenborgh et al., 2005; Guilyardi
et al., 2009; Collins et al., 2010; Ham and Kug, 2012; Kug et
al., 2012; Yeh et al., 2012).
In spite of all the progress, questions remain, including
ENSO diversity and extreme events, decadal modulation,
predictability, teleconnection and the interaction of ENSO with
other climate phenomena. Moreover, Bellenger et al. (2013)
argued that the CMIP5 multi-model ensemble displayed no
quantum leap in ENSO performance compared to the CMIP3,
indicating that there exists much room to improve the ability of
CMIP models to simulate coupled feedback processes. For
example, the state-of-the-art climate models have still difficulties in simulating the probability density function of the
observed SST in the tropical Pacific, which is related to
deficiency in simulating the observed El Niño diversity (Yu
and Kim, 2010; Ham and Kug, 2012; Kug et al., 2012; Jang et
al., 2013).
Many recent studies have suggested that there exists a
different type of El Niño from the conventional El Niño in
terms of spatial pattern, including the thermocline depth, zonal
current, and the location of convection (Larkin and Harrison,
2005; Ashok et al., 2007; Kao and Yu, 2009; Kug et al.,
2009a; Takahashi et al., 2011). For example, the 2009/10 El
Niño, which is the one of the strongest El Niño event on
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record, was characterized by the maximum anomalous sea
surface temperature (SST) in the central equatorial Pacific.
The 2009/10 El Niño was quite different from the conventional
El Niño event in terms of its spatial structure and associated
teleconnection pattern from the tropics to the extratropics,
which is associated with the tropical diabatic forcing in
relation to precipitation. This type of El Niño has occurred
more frequently during the recent decades (Yeh et al., 2009;
Lee and McPhaden, 2010; Na et al., 2011). After several
studies (Yeh et al., 2009; Lee and McPhaden, 2010; McPhaden
et al., 2011), such El Niño, the so-called “warm pool El Niño,”
received a great deal of attention in the climate community. In
this article, we review the recent progresses on the observational evidence, dynamics, and their changes, with a focus
on the two types of El Niño.
2. Observations
a. SST patterns of the two types of El Niño
The diversity of El Niño has received increasing attention
over the last decade because of the frequent emergence of a
different type of El Niño, named “Dateline El Niño,” “El Niño
Modoki,” “Warm Pool El Niño,” and “Central Pacific El
Niño” (Larkin and Harrison, 2005; Ashok et al., 2007; Kao
and Yu, 2009; Kug et al., 2009a). These El Niños have different
characteristics compared to “conventional” El Niño. In particular, their maximum sea surface temperature (SST) anomalies
appear over the central Pacific rather than the eastern Pacific.
Hereafter, we will refer to this type of El Niño as warm pool
(WP) El Niño, and refer to the conventional El Niño as cold
tongue (CT) El Niño, following Kug et al. (2009a).
Using both ERSST.v3 and HadISST for the period of 19702013, we classify both CT and WP El Niño events. The
methodology to classify these events is the same as that used
by Kug et al. (2009a). The CT and WP El Niño events are
included if either NINO3 SST index or NINO4 SST index
during the boreal winter (December-February, or DJF) is
greater than its corresponding one standard deviation. And
then, the CT (WP) El Niño is defined as the year when the
NINO3 (NINO4) SST index is greater than the normalized
NINO4 (NINO3) SST index. Note that the NINO3 and
NINO4 SST indices are defined as the anomalous SST
averaged in 5oS-5oN, 90oW-150oW and 5oS-5oN, 160oE-150oW,
respectively.
There are some differences in CT and WP El Niño years
when using the two SST datasets, but both datasets show that
the occurrence frequency of WP El Niño is significantly larger
than that of CT El Niño. For example, the occurrence frequencies of WP and CT El Niño are 0.27/year and 0.11/year
for the period of 1970-2013, respectively, based on the
HadISST, that is, the occurrence frequency of WP El Niño is
twice of that of CT El Niño over the last 40 years. In particular,
all El Niño events since 2000 belong to the WP El Niño in
both HadISST and ERSST.v3, except for the El Niño in 2003.
This is consistent with previous studies (Kug et al., 2009a; Yeh
et al., 2009; Lee and McPhaden, 2010), which claimed that the
occurrence of WP El Niño increased since the late 1990s.
Figure 1 displays the seasonal evolutions of CT El Niño
(Figs. 1a-f) and WP El Niño (Figs. 1g-l) in the HadISST. Note
that the same composite analysis is applied to the ERSST.v3
and negligible differences exist in their spatial structures (not
shown). As previous studies pointed out, the most striking
difference between the CT and WP El Niños is the longitudinal
displacement of maximum anomalous SST along the equator
in their mature stages. The CT El Niño is characterized by the
maximum anomalous SST in the eastern equatorial Pacific
during winter (Fig. 1d), while the maximum SST center of the
WP El Niño during winter (Fig. 1j) is located near the dateline
in the central equatorial Pacific. The spatial structure of the CT
El Niño is zonally extended from the eastern equatorial Pacific
to the central equatorial Pacific, while the WP El Niño is
zonally confined within the central equatorial Pacific.
During seasonal evolution from the developing stage (i.e.,
spring (March-May, MAM(0)), summer (June-August, JJA(0))
and fall (September-November, SON(0))) to the decaying
stage (i.e., MAM(+1) and JJA(+1)), there are also differences
between the CT and WP El Niños. For the CT El Niño,
anomalously warm SST develops during summer in the fareastern equatorial Pacific (Fig. 1b). From summer to fall,
anomalously warm SST has been rapidly developing in the
eastern equatorial Pacific, and then slowly grows to the maximum during winter (Figs. 1c-d). The westward migration of
maximum anomalous SST is apparent from summer to winter
(Figs. 1b-d). For the decaying stage, the anomalous SST
abruptly decreases in the central and eastern equatorial Pacific
(Fig. 1e), and negative SST anomalies appear in the central
equatorial Pacific during JJA(+1) (Fig. 1f).
In contrast, the seasonal evolution of the WP El Niño is
quite different from the CT El Niño (Figs. 1g-l). The anomalous SST appears from the eastern subtropics to the central
equatorial Pacific during spring (MAM(0)) and summer
(JJA(0)) although its magnitude is weak (Figs. 1h-i). As the
WP El Niño develops further from summer to fall (SON(0)),
the anomalously warm SST in the central equatorial Pacific
becomes stronger and the center of the maximum anomalous
SST is persistently located in the central equatorial Pacific.
The prominent structure in the WP El Niño is detectable with
the anomalous SST zonally confined within the central equatorial Pacific during fall (Fig. 1i). The anomalously warm SST
is gradually growing from fall to winter and it reaches its
maximum intensity during winter (Fig. 1j). For the decaying
stage, the anomalously warm SST persists from MAM(+1) to
JJA(+1) (Figs. 1k-l), and anomalously cold SST appears in the
far-eastern tropical Pacific.
Overall, the WP El Niño occurs more frequently than the CT
El Niño over the last 30 years. In addition, the WP El Niño is
quite different from the CT El Niño in terms of its spatial
structure, intensity and seasonal evolution.
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Fig. 1. Evolution of composite SST during the CT El Niño in spring (March-May, MAM(0)), summer (June-August, JJA(0)), fall
(September-November, SON(0)), winter (December-February, D(0)JF(+1)), and the following spring (MAM(+1)) and summer
(JJA(1)) (panels (a)-(f)). Panels (g)-(l) are the same as panels (a)-(f), except for evolution of the WP El Niño. The monthly SST
data is from the Hadley Centre Sea Ice and SST data set (HadISST) with a 1.0 × 1.0 resolution from 1970 to 2013 (Rayner et al.,
2003). The SST used in the present study is de-trended by removing the linear trend before analysis.
b. Impacts of two types of El Niño
Why do we need to study two types of El Niño? This is
mainly because of the location of the warming center, which
can lead to significant differences in the associated teleconnection from the tropics to the extratropics (Larking and
Harrison, 2005; Kumar et al., 2006; Ashok et al., 2007; Kug et
al., 2010a; Wang and Wang, 2013). In other words, the
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location of maximum anomalous SST determines the spatial
pattern of precipitation in relation to the two types of El Niño,
which significantly influences the atmospheric teleconnection
emanated from the tropics. According to Kug et al. (2009a)
and Yeh et al. (2009), the composite rainfall patterns corresponding to the CT El Niño and the WP El Niño are quite
different. For the CT El Niño, for example, the center of
maximum anomalous precipitation is around the dateline. For
the WP El Niño, it is shifted westward to around 165oE.
Therefore, the anomalous precipitation is largely enhanced in
the western equatorial Pacific and reduced in the central and
eastern equatorial Pacific during the WP El Niño, compared to
that during the CT El Niño.
Changes in the spatial pattern of tropical convection between
the two types of El Niño lead to different impacts on weather
and climate variability from regional to global scale via the
atmospheric teleconnection (Cai and Cowan, 2009; Kim et al.,
2009; Lee et al., 2010; Kug et al., 2010a; Graf and Zanchettin,
2011; Song et al., 2011; Yoon et al., 2012; Yu et al., 2012;
Wang and Wang, 2013). The two types of El Niño have substantially different impacts on the frequency and tracks of
North Atlantic cyclones by modulating vertical shear in their
main development regions (Kim et al., 2009). Furthermore, the
frequency of tropical cyclone is significantly positively correlated with the WP El Niño, whereas the frequency of tropical
cyclones in the northwestern North Pacific has a markedly
negatively correlated with the CT El Niño (Chen and Tam,
2010). In addition, both surface temperature and rainfall
anomalies over North America in response to the two types of
El Niño are also quite different in terms of spatial pattern and
sign (Larkin and Harrison, 2005; Mo et al., 2010; Yu et al.;
2012). Several studies also argued that the WP El Niño causes
different precipitation variability over Australia, Eurasia and
India compared to the CT El Niño (Kumar et al., 2006;
Hendon et al., 2009; Graf and Zanchettin, 2012). There is
evidence that the atmospheric teleconnection from the tropics
to the high latitude is also different in the Southern Hemisphere
during different types of El Niño. Song et al. (2011) showed
that the dipole pattern of Antarctica sea ice persisted until
austral winter after the El Niño mature season during the WP
El Niño, but not during the CT El Niño. This is associated with
the fact that the WP El Niño contributes to a strong Rossby
wave response and the weakening of the polar-front jet that
yields strong Antarctic dipole pattern in austral spring in the
Southern Hemisphere.
Finally, the impact on a specific region can be considerably
different between the two types of El Niño. For example, the
impact of El Niño on the Korean Peninsula highly depends on
the type of El Niño (Kug et al., 2010a). Without considering
the type of El Niño, the relation between various climate
variables over South Korea and El Niño was quite weak due to
the different impacts from the two types of El Niño. In fact,
most regions in South Korea tend to experience cold climate
during the developing period of the CT El Niño, while these
regions experience warm climate during the developing period
of the WP El Niño. This example suggests that the climate
impact of El Niño should be investigated according to the type
of El Niño.
3. Dynamics
a. A step forward beyond classical dynamics of El Niño
The understanding of ENSO dynamics has moved forward
beyond “delayed oscillator theory” (Suarez and Schopf, 1988;
Battisti and Hirst, 1989) or “recharge oscillator theory” (Jin,
1997a, b). First, the atmospheric stochastic forcing including
the westerly wind bursts that were known to be a trigger of El
Niño is in turn revealed to be influenced by the ENSO
(Eisenman et al., 2005; Gebbie et al., 2007; Gebbie and
Tziperman, 2009); and thus the atmospheric stochastic forcing
used to be treated as additive noise now expands its meaning
to multiplicative noise as playing a positive feedback (Jin et
al., 2007; Kug et al., 2008, 2009b; Levine and Jin, 2010). The
oceanic noises, such as the tropical instability waves (TIWs),
have significant influences as well, and are influenced by
tropical Pacific climate variability including the seasonal cycle
and El Niño (Yu and Liu, 2003; An, 2008; Kug et al., 2010b;
Ham and Kang, 2011). As a result, the intensification of TIWs
during the La Niña and the suppression of these waves during
the El Niño are considered as one of causes of El Niño/La
Niña amplitude asymmetry (An, 2008).
Second, critical characteristics of El Niño that were hardly
explained by the linear dynamics require nonlinear dynamics.
The nonlinear properties of El Niño include the asymmetric
behaviors between El Niño and La Niña in terms of amplitude,
propagation, onset, and seasonal amplitude locking. Such properties may be explained by nonlinear dynamical processes
(e.g., An, 2009). The nonlinear statistical principal component
analysis developed based on the neural network (NLPCA; Wu
and Hsieh, 2003) objectively produced the asymmetric oscillatory features seen in the observations and coupled general
circulation models (GCMs) (Wu and Hsieh, 2003; An et al.,
2005). The asymmetric oscillatory mode obtained from the
NLPCA shows that a stronger El Niño is mostly confined in
the eastern equatorial Pacific while a relatively weaker La Niña
is mostly confined in the eastern-to-central equatorial Pacific.
Furthermore, the heat recharge/discharge as a precursor of each
event is opposite to the SST change, so that less heat recharged
state for the precursor of El Niño and more heat discharged
state for the precursor of La Niña (An et al., 2005).
Asymmetric amplitude between El Niño and La Niña was
explained by various sorts of nonlinear processes, such as the
nonlinear dynamical heating, particularly stressed on the nonlinear vertical advection (An and Jin, 2004; An et al., 2005;
An, 2009) or the nonlinear horizontal advection (Su et al.,
2010); atmospheric nonlinear responses with respect to El
Niño and La Niña (Kang and Kug, 2002, Choi et al., 2013);
dynamical heat flux due to the TIWs that lead to a stronger
warming during La Niña and weaker cooling during El Niño
31 January 2014
Sang-Wook Yeh et al.
(Swenson and Hansen, 1999; Jochum and Murtugudde, 2004;
An, 2008); the vertical mixing in the ocean mixed layer that is
nonlinearly constrained by vertical stratification (An, 2009);
nonlinear rectification of the tropical intra-seasonal oscillation
onto an ENSO cycle (Kessler and Kleeman, 2000); nonlinear
interaction between ENSO and the stochastic forcing including
the westerly wind bursts that are more active during the El
Niño as part of a positive feedback (Eisenman et al., 2005);
and biological-physical feedback influencing mixed layer temperature via modifying the solar energy absorbing by the
phytoplankton (Timmerann and Jin, 2002, Park et al., 2014).
All these possible nonlinear processes are different, but
commonly support the observations of stronger El Niño and
weaker La Niña.
The asymmetric transition between El Niño and La Niña due
to nonlinear atmospheric responses to SST was mentioned
(Ohba and Ueda, 2009). The asymmetric duration between El
Niño and La Niña was also reported, noting that an El Niño
event usually takes a year from late spring/ summer to the next
summer, while La Niña persists more than a year (Okumura
and Deser, 2010). Also, the upper ocean equatorial currents can
cause the propagation asymmetry between extreme El Niño and
La Niña events, such that the westward current is prevailing for
the westward propagating La Niña and opposite for the
eastward propagating extreme El Niño (Santoso et al. 2013).
b. Dynamics of two types of El Niño
As discussed in section 2.1, two-types of El Niño are
distinguished based on the SST patterns, i.e. the location of
maximum SST anomalies. Over the equatorial Pacific, the SST
anomalies are developed by several physical processes, whose
relative importance depends on the zonal location (An et al.,
1999; An and Jin, 2001; Kang et al., 2001) because the oceanic
and atmospheric basic states are highly zonally-asymmetric
over the tropical Pacific. Over the eastern Pacific, the mean
thermocline is quite shallow and the mixed layer is also
shallow due to strong static stability. In addition, the equatorial
upwelling prevails owing to the trade wind. Therefore, anomalous thermocline variation influences SST strongly; thus, the
thermocline feedback prevails there. When the thermocline
change leads to the SST change through modifying the
upwelled subsurface water property, it is called the thermocline
feedback (Jin and An, 1999).
On the other hand, over the central Pacific, the role of
thermocline in changing SST is limited because the mean
thermocline and mixed layer are relatively deep there. Instead,
the zonal SST gradient is relatively strong because of the warm
pool to the west and cold tongue to the east. Therefore, El
Niño-related zonal current anomalies can effectively induce
SST anomalies through zonal advection in the region. The
reinforced SST anomalies in turn induce additional surface
winds, and so does the zonal current that will amplify the
initial SST anomalies, that is the so-called “zonal advection
feedback. In addition, the higher mean SST over the central
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Pacific leads to the latent heat flux change to be more sensitive
to the wind speed change. For example, the anomalous
westerlies induced by the positive SST perturbation during El
Niño actually reduce the latent heat release from the ocean
surface, i.e., inducing anomalously surface warming and enhancing the positive SST anomaly, so-called “latent heat
feedback,” and the reduced cooling rate is strong when the
mean SST is high. On the whole, owing to the location of the
action center of SST anomalies, the thermocline feedback
plays a major role in evolving SST anomalies of CT El Niño,
especially when it develops over the eastern Pacific; in contrast, the WP El Niño is mainly controlled by the zonal
advection feedback and heat flux feedback (Kug et al., 2009a,
2010c; Yu et al., 2010).
The difference in spatial pattern of SST anomalies leads to
different ocean adjustment processes between the two types of
El Niño via distinct atmospheric responses. The zonal shift of
the SST anomalies produces different atmosphere response
patterns, including patterns of precipitation and zonal wind
anomalies: more to the west during the WP El Niño and more
to the east during the CT El Niño. The zonal location of wind
stress is critical for the equatorial ocean adjustment, which
determines the instability (Jin et al., 1997a) and the time scale
of El Niño (An and Wang, 2000). Kug et al. (2010c) performed a sensitivity experiment of ocean adjustment to the
location of the wind stress forcing using the intermediate
ocean model of Zebiak and Cane (1987), in which the periodic
windstress forcing having its maximum loading fixed at a
certain longitude is adopted. When the center of windstress
forcing is located at the dateline, the deepening of zonal mean
thermocline over the Pacific basin tends to lead the westerly
anomaly by about a quarter cycle of the period of the
prescribed windstress. However, when the center of windstress
forcing is shifted to 140oE, the zonal mean thermocline is
almost in phase with the wind stress forcing. Since the
atmospheric response is almost simultaneous as the SST, the
zonal mean thermocline can be a precursor for the CT El Niño
as mentioned in Jin (1997a, b) and Meinen and McPhaden
(2000), but not for the WP El Niño. Therefore, the results of
Kug et al. (2010c) can be directly applied to the dynamical
ocean adjustment of two types of El Niño.
When the westerly anomalies are located in the central
Pacific, the east-west seesaw pattern of the equatorial thermocline becomes dominant. The seesaw pattern can be understood by the contribution of the forced upwelling Rossby wave
and downwelling Kelvin wave responses. Due to the zonal
contrast of thermocline and the resultant poleward geostrophic
current, the discharge process of mass and thermal energy
occurs quite effectively (Jin et al., 1997a); consequently, the
tendency of zonal mean thermocline is in-phase with SST
anomaly. This indicates that the zonal mean thermocline can
lead SST anomaly by about a quarter cycle, as the recharge
oscillator theory proposed (Ren and Jin, 2012). Kug et al.
(2009a, 2010a) showed that the positive zonal mean heat
content tends to lead SST anomalies by several months for the
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Fig. 2. Composites of sea level (cm) averaged over 140oE-90oW for
(a) CT El Niño and (b) WP El Niño events. This figure is a modified
version of Fig. 8 of Kug et al. (2009a) by adding 2009/10 to the WP
El Niño composite.
CT El Niño case in both observations and model simulations
(also see Fig. 2a).
On the other hand, when the westerly anomalies are close to
the western boundary, the forced upwelling Rossby wave
responses, which are generated to the west of the forcing,
become weaker due to the western boundary. In addition, the
forced downwelling Kelvin wave responses exist in the western
Pacific, so the westerly forcing hardly generates any shoaling
response of the equatorial thermocline over the western Pacific,
compared to that of the central Pacific windstress forcing.
Instead, the forced downwelling Kelvin wave responses are
overwhelming in the equatorial basin, which is the main part
of the zonal mean heat content. In addition, the maximum
thermocline anomalies appear over the central Pacific, so the
discharge process is relatively weaker (Kug et al., 2009a).
Therefore, the zonal mean thermocline depth tends to be inphase with the wind anomalies when the westerly wind is
shifted to the west. It is quite consistent with the WP El Niño
case. The observational and model results showed that the
zonal mean heat content is almost in-phase with the SST
anomaly (Fig. 2b), and the mass discharge is quite weak in the
WP El Niño composite (Kug et al., 2009a, 2010a). Since the
mass discharge is weak, the shoaling tendency of the equatorial
thermocline is weak and it hardly leads to a La Niña phase in
the following year. In other words, almost no time lag between
zonal mean thermocline and SST anomalies infers weak instability in terms of the recharge oscillator model (Jin et al.,
1997a). Therefore, the WP El Niño events hardly belong to the
self-sustained oscillatory mode; they likely belong to the
damped oscillatory mode, so that an external forcing or stronger
stochastic forcing is necessary for the excitation of the WP El
Niño.
It has been suggested that the extratropical connection can
be important for the occurrence of the WP El Niño events. A
seasonal footprinting mechanism (Vimont et al., 2001, 2003,
2009) provided a clue on how mid-latitude atmospheric vari-
ability could lead to tropical ENSO variability via atmosphereocean coupling in the subtropics, especially leading to the
onset of El Niño events (Anderson, 2003, 2004; Chang et al.,
2007, Park et al., 2013). Yu et al. (2010) and Yu and Kim
(2011) showed that the seasonal footprinting mechanism tend
to trigger the WP El Niño rather than the CT El Niño. These
studies further suggested that the WP El Niño is an extratropically excited mode of the tropical Pacific variability.
Note that this process is more effective for the cold phase of
the central Pacific SST, particularly for the following year of
strong CT El Niño events.
Recently, Ham et al. (2013a) suggested another triggering
mechanism of WP El Niño events. They showed that the
surface cooling in the north tropical Atlantic during boreal
spring tends to lead to WP El Niño events in the following
winter. The signals in the tropical Atlantic are conveyed along
the Pacific ITCZ, and their coupled convective responses accompany anticyclonic and cyclonic responses over the western
Pacific and eastern Pacific, respectively. As a result, there are
anomalous equatorial westerlies and easterlies over the western
Pacific and eastern Pacific during the El Niño developing
period, respectively. The western Pacific westerlies induce the
central Pacific warming, and the eastern Pacific easterlies
suppress the eastern Pacific warming, which leads to the development of WP El Niño. Unlike the North tropical Atlantic
SST, the Atlantic Niño tends to enhance the development of
CT El Niño (Ham et al., 2013b; Keenside et al., 2013), because
it affects tropical Pacific by modulating the Walker circulation
rather than the off-equatorial connection.
Though the ocean dynamical processes and triggering mechanisms as well as the SST patterns are distinctly different between the two types of El Niño, it is still controversial whether
the two types of El Niño are originated from physically
different modes. The El Niño diversity can be understood by
two different views. First, the El Niño diversity results from
random processes in a single physical mode, that is, a random
process from oceanic and atmospheric components modulates
the most unstable (or least damped) ENSO mode, and the
resultant coupled process can lead to diverse zonal locations of
SST anomaly. This is consistent with the idea of “El Niño
continuum” (Giese and Ray, 2011). Giese and Ray (2011)
analyzed many El Niño events from 1871 to 2008. Though
their results depended on the ocean reanalysis used, particularly for the early period when the observational data were
sparse, they clearly showed that the location of El Niño varied
considerably from the western to eastern Pacific, and that the
zonal distribution follows the Gaussian distribution to a large
extent, without a bimodal structure. This result seems to imply
that the location of El Niño is randomly distributed about a
mean value, of which a single unstable mode represents,
supporting the idea of “El Niño continuum.”
However, it is still possible that two distinct modes represent
a single peak of the distribution with random processes when
the action centers of the two modes are close to each other.
Several studies reported the possibility that the tropical Pacific
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Sang-Wook Yeh et al.
has fluent physical modes as well as the dominant ENSO
mode (Jin et al., 2001, 2003; Kang et al., 2004). Using a twostrip-down version of an intermediate atmosphere-ocean coupled
model, An and Jin (1999) showed the changes in the relative
contribution of thermocline feedback and zonal advective
feedback due to changes in the mean climate state lead to
different coupled modes; one is a mixed ocean adjustmentSST mode excited mainly by the thermocline feedback, and
the other is a gravest ocean basin mode excited mainly by the
zonal advective feedback. Again, this study suggested that the
relative importance of two feedbacks may destabilize and
bring into different physical modes.
As mentioned earlier, the zonal advective feedback is relatively important for the WP El Niño, while the thermocline
feedback, for the CT El Niño. These relative roles of the two
feedback processes can be linked to different physical modes,
as pointed out by Bejarano and Jin (2008). They found that the
two different leading modes can coexist and become unstable
under wide ranges of basic states and parameter conditions in a
linearized intermediate coupled model. One mode, called the
“quadrennial (QQ) mode,” is related to the slow ocean dynamic
adjustment of equatorial heat content with a dominant thermocline feedback, which is consistent with the recharge oscillator
theory. This mode mimics the characteristics of CT El Niño to
a large extent. The other mode, called the “quasi-biennial (QB)
mode,” involves a strong zonal advective feedback for phase
transition, which is similar to the advective-reflective oscillator.
The QB mode tends to be relatively less unstable, and tends to
capture the observed features of WP El Niño. The coexistence
of the QB and QQ modes is somewhat sensitive to small
changes in the basic state.
These theoretical studies support that the two types of El
Niño have different roots in the rich coupled modes over the
tropical Pacific. However, it should be noted that these
theoretical studies are mostly based on the linear system. In a
nonlinear system, two linear modes are not orthogonal any
longer, and El Niño anomalies can be produced by a nonlinear
combination of these leading modes. For example, the atmospheric response and feedback may play major roles in the
nonlinear combination. In particular, if the magnitudes of anomalies become bigger, the nonlinear interaction will be stronger;
then, it might be difficult to separate the contributions of two
linear modes. In this sense, it will be a great challenge to find
out whether or not the two types of El Niño are originally
based on different physical modes.
Most studies focused on the distinct features of the two types
of El Niño in their warm phases; a few (Ashok et al., 2007;
Kao and Yu, 2009) revealed the existence of a new type of
cold events (La Niña). However, the existence of the two types
of La Niña events remains uncertain. When the two types of
cold events are separated based on the same criteria for the
warm events, Kug and Ham (2011) found that the SST and
precipitation patterns for the cold events are less distinct than
those for the warm events, suggesting that the two types of La
Niña are hardly defined. This asymmetry in characters can
75
result from nonlinear atmospheric responses to the SST forcing.
Given different patterns in SST forcing, atmospheric responses
tend to be more distinct for the positive SST forcing, compared
to those to the negative SST forcing. This is because the
atmospheric convection is more sensitive to an increased SST
than to a decreased SST where the base convection is weak
(Desser and Wallace, 1990; Hoerling et al., 1997; Kang and
Kug, 2002). For example, the negative SST anomaly over the
eastern Pacific hardly induces anomalously negative convection. In this case, the atmospheric convection related to the
cold event only responds to the SST anomaly over the central
Pacific, where the climatological convection is relatively
stronger. Therefore, the atmospheric responses do not exhibit
much difference for the different patterns of negative SST
anomalies. This asymmetric character for two-types of ENSO
has a considerable implication for the impacts of different
ENSO.
4. Long-term change of ENSO
a. Decadal and longer timescale changes of ENSO variability
The significant, longer timescale amplitude modulation of
ENSO in the past century has been found in the natural records
(Cobb et al., 2003; Li et al., 2011; Yan et al., 2011) and
instrumental records (Gu and Philander, 1995; Wang, 1995;
Mitchell and Wallace, 1996; Wang and Wang, 1996; An and
Wang, 2000; Yeh and Kirtman, 2005). For example, the two
decades after the late 1970s’ climate shift were characterized
by larger amplitude and longer El Niño events compared to
those in the previous decades (An and Wang, 2000); and since
the late 1990s, which was also suggested as a climate shift
(Hong et al., 2013), the WP El Niño events occurred more
frequently than in the previous decades (e.g., Ashok et al.,
2007; Kao and Yu, 2009; Kug et al., 2009a; Yeh et al., 2009).
Interestingly, the coupled general circulation model (CGCM)
simulations depicted that the decadal, even century-long timescale, modulation of ENSO characteristics could be generated
without invoking anthropogenic forcing (Wittenberg, 2009; Yeh
et al., 2011), inferring that such modulation of ENSO properties may not be related to the global warming.
The changes in mean tropical Pacific climate state can
critically modify ENSO characteristics through changing its
linear stability (An and Wang, 2000; Fedorov and Philander,
2000; Wang and An, 2001, 2002; Li et al., 2011), which is especially severe when the climate state is close to a bifurcation
point (Fedorov and Philander, 2000; Timmermann, 2003; Kim
and An, 2011). Therefore, even a small change in the climate
mean state could lead to a significant change in the ENSO
characteristics (An and Jin, 2000; Bejarano and Jin, 2008). The
decadal-to-interdecadal changes in the tropical climate mean
state can be internally induced by internal decadal mode of the
tropical Pacific that exists as the least damped normal mode
(Wang et al., 2003) or as an imported mode from a region
outside the tropical Pacific, all attributing to the decadal
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ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
change in ENSO. The internal decadal mode of the tropical
Pacific can be excited by stochastic forcing, or by an external
driver, such as the Atlantic multi-decadal oscillation (AMO)
that was proposed in many studies. The decadal-to-interdecadal
signals of the AMO may convey into the tropical Pacific Ocean
either via atmospheric teleconnection (Dong et al., 2006;
Timmeramann et al., 2007) or via oceanic teleconnection
(Timmermann et al., 2005, Thual et al., 2013).
The nonlinearity of the tropical climate system may also
contribute to the decadal modulation of ENSO through a
global bifurcation as a form of “homoclinic or heteroclinic connection,” which is used to explain the ENSO bursting phenomenon with more than a decadal interval (Timmermann et al.,
2003). Nonlinear dynamics further expanded to explain a complex interactive feedback between mean state and ENSO, in
which the residual effect of El Niño-La Niña asymmetry leads
to the tropical Pacific decadal variability (Sun et al., 2013),
which in turn modifies the ENSO characteristics by providing
a favorable condition for destabilizing a certain type of El
Niño (Choi et al., 2009, 2011, 2012; Ogata et al., 2013; Ye and
Hsieh, 2008). For example, Choi et al. (2012) showed that the
residual due to the frequent occurrence of WP (CP) El Niño
intensifies (reduces) the zonal contrast of the tropical Pacific
mean SST by analyzing a CGCM. The strong zonal contrast of
tropical Pacific SST results in more occurrence of WP El Niño
by enhancing zonal advection feedback. Such relationship between mean SST and majority of ENSO type is similar to the
tropical Pacific climate variation occurred during the last two
decades (see section 2.1). If the ENSO system is in a
subcritical regime, on the other hand, the chaotic variability in
the atmosphere could lead to decadal changes in ENSO
activity (Power and Colman, 2006; Power et al., 2006), inferring that the decadal modulation of ENSO is randomly
determined. On the whole, the change in the mean state is
correlated to the change in ENSO characteristics, including
their type, amplitude, etc.; but the causality is yet to be clearly
identified. Moreover, regardless of the change in the mean
state, the nonlinear dynamics and stochastic process are also
proposed as possible drivers of the decadal and longer timescale modulation of ENSO, though it is unclear how much
such processes can influence the type of ENSO.
b. ENSOs for the 20th and 21st centuries
In September 2013, the IPCC AR5 in its “summary for
Policymakers (SPM)” announced that “ENSO will remain the
dominant mode of interannual variability in the tropical Pacific;
natural variations of the amplitude and spatial pattern of ENSO
are large and thus confidence in any specific projected change
in ENSO and related regional phenomena for the 21st century
remains low.” Therefore, the prediction on future change in
ENSO is still challenging (Collins et al., 2010). This is because
the natural modulation range of ENSO may be close to or
more than the range of ENSO changes induced by the 21st
century climate forcing (An and Choi, 2013).
The long-term observation revealed that the ENSO amplitude
linearly increased during the recent century, although the
sampling was rather limited (Li et al., 2011). Some studies
proposed that the enhanced ENSO amplitude is related to the
global warming (e.g., Zhang et al., 2008; Kim and An, 2011;
Watanabe et al., 2012). The transient experiments of CGCMs
with imposed, increasing greenhouse-gas concentration confirmed the intensified ENSO activity to some extent. For
example, An et al. (2008) suggested that the intensified oceanic
vertical stratification due to the delayed subsurface response
compared to the ocean surface response to the global warming,
i.e., the fast warming at the ocean surface than at subsurface,
caused the intensified ENSO activity through the enhanced
thermocline feedback, particularly in an early stage of the
global warming. They further noted that ENSO activity would
be suppressed after the subsurface ocean has adjusted to the
global warming. However, because of uncertainties in the
nature of long-term modulation of ENSO (Wittenberg 2009),
whether the trend, or longer time-scale modulation, of ENSO
amplitude during the 20th century was due to the global
warming or natural variation is not yet clearly identified (e.g.,
Collins et al., 2010). In terms of the change in the 21st century
ENSO activity, the CMIP5 models produced rather diverse
results (Guilyardi et al., 2012; Kim and Yu, 2012; Stevenson et
al., 2012), and the multi-model ensemble of change in El Niño
amplitude in the experiments for the 20th century historical to
21st century Representative Concentration Pathway (RCP)
scenarios was statistically insignificant (Stevenson, 2012).
Independent from the future change in ENSO intensity, Yeh
et al. (2009) suggested that tropical Pacific warming in response to increased greenhouse forcing might lead to change
of dominant El Niño flavor: more frequent emergence of WP
El Niño events through a favorable condition for amplifying
feedback processes of central Pacific SST anomaly. As mentioned in section 3.b, the zonal advection feedback is a major
dynamical feedback process especially in the developing stage
of WP El Niño, and thus La Niña-like response of tropical
Pacific SST to the global warming, indicating an intensified
zonal SST gradient could reinforce the activity of WP El Niño
(Xiang et al., 2013).
Taschetto et al. (2013) argued, however, that the 27 CMIP5
models did not show any enhancement of the ratio in intensity
between WP and CT El Niño from the 20th century historical
to 21st century RCP8.5 scenarios. Different from the multimodel ensemble, Kim and Yu (2012) picked out several models
from the CMIP5, which simulated ENSO variability realistically, and showed that the variability of WP El Niño simulated
from the selected models actually increased under the global
warming. Therefore, the overall future prospect on which type
of El Niño will occur more frequently is rather uncertain, but
the intensification of WP El Niño as observed in several
objectively selected models remains a possible answer on how
the type of El Niño will be in a future.
31 January 2014
Sang-Wook Yeh et al.
77
Fig. 3. Schematic diagrams for the spatial patterns of CT El Niño (left) and WP El Niño (right). Filled and hollow arrows represent
anomalous atmospheric and oceanic circulations, respectively. Shadings at the air-sea interface denote SST anomaly. Dashed blue
line and solid red line represent the equatorial thermocline for climatological and El Niño conditions, respectively.
5. Summary
As we described above, the WP El Niño received a lot of
attention in terms of its mechanisms, its teleconnection from
the tropics to the mid latitude, its influence on weather and
climate variability, its prediction based on climate models, and
its future changes under the global warming. Despite recent
advances in our understanding of the two types of El Niño,
many aspects of their physics remain unknown. Here, we summarized the findings described in the previous sections as
follows.
• The WP El Niño events occurred more frequently than the
CT El Niño since the 1990s. The WP El Niño exhibits
distinct patterns in terms of atmospheric and oceanic processes from those of the CT El Niño (see the schematics in
Fig. 3). Since the SST pattern of WP El Niño is shifted to
the west, the resultant precipitation and surface wind anomalies are shifted westward. In response to the westward
shift of the wind forcing, the thermocline exhibits maximum deepening over the central Pacific, so the heat
discharge is relatively weaker.
• Many studies argued that the location of the warming
center, which is associated with the spatial pattern of precipitation in relation to tropical diabatic forcing, can lead
to significant difference in the associated teleconnection
from the tropics to the mid latitude between the two types
of El Niño. This results in different impacts on weather
and climate variability over the region and globe.
• In association with their different spatial patterns, the thermocline feedback is a dominant player in the phase transition of CT El Niño, while the zonal advective feedback is
more important for the evolution of WP El Niño.
• Since the equatorial ocean adjustment process is determined by the zonal location of the zonal wind stress,
westward shift of zonal wind stress anomalies in WP El
Niño leads to an almost-in-phase relationship between SST
and zonal mean thermocline, suggesting weak stability and
the need for external or stochastic forcing for the excitation
of WP El Niño.
• In spite of the controversial on the El Niño continuum,
several theoretical approaches have suggested that the two
types of El Niño are originated from different roots among
the rich coupled modes over the tropical Pacific.
• The decadal-to-interdecadal changes in ENSO characteristics, including El Niño flavors, are closely related to the
changes in the mean climate state through modifying the
linear stability. Regardless of the changes in the mean
climate states, the deterministic nonlinear process of the
tropical atmosphere-ocean coupled system or the stochastic
process in the atmosphere are also possible causes to
change ENSO characteristics. In particular, the intensified
zonal contrast of tropical Pacific mean SST, the deepening
of Pacific basin-wide thermocline, and the enhanced divergence in the central Pacific atmospheric boundary layer all
promote the WP El Niño events.
• The future changes in amplitude and spatial pattern of
ENSO are uncertain because of their strong natural modulation. Although the enhancement of the ratio in intensity
between WP and CT El Niño obtained from the CMIP5
multi-model ensemble was not significant, several models
that produced best ENSO simulations revealed that the
variability of WP El Niño increased under the global
warming.
Recently, the U.S. CLIVAR published a report on the U.S.
CLIVAR ENSO diversity workshop, which was held at
Boulder, Colorado, February 6-8, 2013 (U.S. CLIVAR report,
2013). This report notes outstanding issues and research priorities about the two types of El Niño as follows: 1) causes of
ENSO diversity regime changes, 2) precursors and triggers on
the two types of El Niño, 3) sustained and enhanced ocean
observations for ENSO, 4) teleconnection and its impacts, 5)
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ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES
assessment of climate model performance in simulating ENSO
diversity, and 6) prediction of the two types of El Niño. These
issues should be examined thoroughly to understand the
ENSO, the most dominant climate variability on Earth.
Acknowledgments. This work was funded by the Korea Meteorological Administration Research and Development Program
under Grant CATER 2012-3043.
Edited by: Song-You Hong, Kim and Yeh
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