8ENSO Low-Frequency Modulation and Mean State Interactions
Alexey V. Fedorov1,2, Shineng Hu3, Andrew T. Wittenberg4, Aaron F. Z. Levine5, and Clara Deser6

ABSTRACTIs El Niño changing with global warming? Can we anticipate decades with extreme El Niño events? To answer
these questions confidently, we need to understand the modulation of the El Niño Southern Oscillation
phenomenon (ENSO) that occur on decadal and multidecadal timescales and involve changes in El Niño
amplitude, periodicity, dominant “flavors”, shifts in the Intertropical Convergence Zone, and other properties.
As major progress has been made in understanding various factors that can affect these characteristics of El
Niño, two main paradigms have emerged to explain the observed modulation of ENSO: (i) internally generated
variations due to the chaotic nature of the ocean‐atmosphere coupled system and (ii) externally driven varia­
tions due to cyclic or secular changes in the properties of the tropical background state such as mean winds orocean thermocline depth. This article reviews these two paradigms in the context of available observations, ide­alized models, and comprehensive general circulation models describing El Niño. Which paradigm will domi­
nate in the coming decades and whether global warming is already affecting El Niño remains unclear.

8.1. INTRODUCTION
With the continuing progress in observing and under­
standing El Niño over the past two decades (e.g. Meinen& McPhaden, 2000; Fedorov & Philander, 2000, 2001;
Fedorov et al., 2003; Lengaigne et al., 2006; Jin et al.,2003, 2006; Guilyardi et al., 2009, 2012b; Collins et al.,2010; Wittenberg, 2009; Vecchi & Wittenberg, 2010;
McPhaden et al., 2011; Capotondi et al., 2015a; Takahashi& Dewitte, 2016; C. Wang et al., 2017; Santoso et al., 2017;
Earth and Planetary Sciences, Yale University, New Haven,
CT, USA2
LOCEAN/IPSL, Sorbonne University, Paris, France
3
Lamont-Doherty Earth Observatory, Columbia University,
Palisades, NY, USA
4
NOAA Geophysical Fluid Dynamics Laboratory, Princeton,
NJ, USA
5
Department of Atmospheric Sciences, University ofWashington, Seattle, WA, USA
6
NCAR, Climate and Global Dynamics Division, Boulder,
CO, USA1

Hu & Fedorov, 2017a, 2018; Xie et al., 2018; Timmermann
et al., 2018; and many other important studies), it has
become apparent that each El Niño event is unique andthat ENSO variability as a whole undergoes pronounced
decadal and multidecadal modulations. These modula­
tions involve changes in El Niño amplitude, periodicity,
the propagation direction of SST anomalies, the domi­
nating El Niño “flavors,” the behavior of the Intertropical
Convergence Zone (ITCZ), and changes in other El Niño
properties as well as global impacts and teleconnections.
These climate modulations are evident in the observa­
tional SST record of the past 150 years (Figure 8.1). For
example, ENSO variability of moderate intensity was
typical of the late 19th and early 20th centuries, which
was followed by very weak variability in the 1930s and1940s (with the standard deviation of Niño3 index, STD≈ 0.5°C). The gradual growth in ENSO amplitude sincethe 1950s resulted in two decades of particularly strong
ENSO: the 1980s and 1990s (STD ≈ 0.9°C). Those two
decades saw the two strongest El Niño events of theobservational record to date: the extreme events of 1982

El Niño Southern Oscillation in a Changing Climate, Geophysical Monograph 253, First Edition.
Edited by Michael J. McPhaden, Agus Santoso, and Wenju Cai.
© 2021 American Geophysical Union. Published 2021 by John Wiley & Sons, Inc.
173

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE(a)Temperature, o C

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Figure 8.1 (a) Interannual variations in sea surface temperatures (SST) in the eastern equatorial Pacific Niño3
region (5°S–5°N, 150°W–90°W) shown on the background of decadal changes (in °C). El Niño conditions correspond to warmer temperatures. Note El Niño events of 1982, 1997, and 2015, the strongest in the instrumental
record. The annual cycle and higher‐frequency variations are removed from the data. The baseline is obtained byapplying a decadal low‐pass filter to the data. Updated after Fedorov and Philander (2000). (b) Standard deviationof the above record computed as a 20‐year running mean (blue line). The red line shows this data after smoothing.
The dashed line shows smoothed variations in standard deviation for the Niño3 SST annual cycle. (c) A Hovmoller
diagram of eastern Pacific precipitation anomalies averaged within 150°W–90°W since 1982; Niño3 variations
are also shown (black line, in ºC, axis on the right). (d) Standard deviation of SST anomalies along the equator(averaged 5°S–5°N) for two intervals: the 1980s and 1990s vs the early 21st century. After Hu and Fedorov (2018).

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

and 1997, which occurred as part of the much‐discussed
climate shift of the late 1970s that affected many compo­
nents of the climate system (e.g. Fedorov & Philander,
2000, Meehl et al., 2009). A multicomponent linear
inverse model has confirmed that statistically significant
systemic changes did occur in ENSO dynamics in theearly 1980s (Capotondi & Sardeshmukh, 2017).
The regularity and periodicity of events have also beenchanging through time. One can notice the clockwork
development of El Niño in the early 20th century withwarm events happening every 3 years, rare and irregular El
Niño occurrence in the mid 20th century, and then excep­
tionally strong events separated by about 5 years in the
1980s and the 1990s. Since the power spectrum of ENSO
variability has a broad peak with a maximum at about 3–5
years (e.g. Deser et al., 2009; Manucharyan & Fedorov,2014), one cannot conclude without any additional
assumptions whether these variations are random or forcedby external factors. While the cold phase of the oscillation,
La Niña, varies broadly as well, ENSO modulations arise
primarily from variations in the characteristics of El Niño.
The first two decades of the 21st century produced
relatively weak El Niño events with the exception of thestrong event of 2015 (Figure 8.1). This can be interpreted
as another climate shift of the early 2000s that went in the
opposite direction to what happened in the latter 1970s.
During 2000–2014 the Niño3 STD was reduced by 20%–30% relative to the previous two decades. This ENSO
weakening has been accompanied by a shift from stronger
eastern Pacific (EP) El Niño events to weaker central
Pacific (CP) events, which have their maximum SST
anomaly located farther west (Ashok et al., 2007; Kuget al., 2009; Kao & Yu, 2009; see Figure 8.1d and chapter 4of this book for more detail).
Furthermore, the ITCZ precipitation band, whose mean
climatological position is north of the equator and which
would typically migrate towards the equator during EP El
Niño events, failed to do so during the El Niños of 2000–
2016 (Figure 8.1c; Hu & Fedorov, 2018). Other termi­
nologies for CP and EP El Niño events are also used, forexample, El Niño Modoki versus conventional El Niño
(Ashok et al., 2007) or warm pool versus cold tongue El
Niño (Kug et al., 2009). The global impacts of CP and EP
events differ significantly (chapter 4 and section VI of thisbook). While the 2015–2016 El Niño is considered an EP
event based on its extreme warming magnitude in the east­
ern Pacific (Levine & McPhaden, 2016; Hu & Fedorov,2017a), it shared properties of both types throughout its
development (Hu & Fedorov, 2017a; Santoso et al., 2017).
Concurrently with the weakening of El Niño, the tropical
Pacific experienced a negative phase of the Interdecadal
Pacific Oscillation, characterized by a broad cooling in theeastern tropical Pacific and stronger trade winds (England
et al., 2014). In addition, the mean cross‐equatorialsoutherly winds in the eastern Pacific also strengthened

175

significantly (Hu & Fedorov, 2018). These mean statechanges and the ENSO weakening may be connected, butwhich one causes the other remains debatable, as ENSO
modulations themselves can cause changes in the meanstate (Ogata et al., 2013). The issue is how to distinguish
this nonlinear rectification of El Niño/La Niña signals
onto the mean state and any independent mean statechanges that can cause ENSO modulations (the subjectof the next two sections). In other words, are decadalchanges in the baseline in Figure 8.1a caused by ENSO
rectification or by factors external to ENSO?
The overall strengthening of ENSO variability sincethe 1950s, which culminated in three extreme El Niño
events, coincided with rapidly increasing atmospheric CO2
concentration and rising global mean temperatures, whichraises the question of whether this increase in El Niño
amplitude could be due to global warming (Fedorov &Philander, 2000, 2001; Collins et al,, 2010; Cai et al., 2015;
Capotondi & Sardeshmukh, 2017; also see chapter 13 ofthis book). To answer this question, we need to understandwhat causes ENSO modulations and also develop tools to
distinguish those externally forced from internally gener­
ated, which would help detect emerging ENSO changes
induced by global warming (e.g. Timmermann, 1999).
It is noteworthy that ENSO modulations are accompa­
nied by modulations in the seasonal cycle in the tropics(dashed line in Figure 8.1b). Earlier studies noticed that
ENSO activity and the strength of the annual cycle may
be anticorrelated: strong ENSO would go together with aweak annual cycle and vice versa (e.g. Chang et al., 1994;
Fedorov & Philander, 2001). Indeed, the interval of weak
ENSO in the mid‐20th century coincides with the stron­
gest annual cycle in the observations. However, the rela­tionship between the strengths of ENSO and the annual
cycle is not as straightforward, especially in the second
half of the 20th century, and is further complicated by
nonlinearities (Hannachi et al., 2003).
Strong ENSO modulations also occurred in the past on
different timescales, as suggested by paleo records (e.g.
Cobb et al., 2003; Cane, 2005; also see chapter 5), andobservational and modeling evidence indicate that robust
El Niños with different characteristics persisted acrosswide changes in climate (for a review see Manucharyan &Fedorov, 2014). Nevertheless, there are indications that
ENSO variability during the second half of the 20th
century might have been the strongest not only in themodern record but also in the entire Holocene (e.g. Cobb
et al., 2013; McGregor et al., 2013; White et al., 2018).
This makes the question of the effect of global warming
on ENSO even more compelling.
Two main paradigms have emerged to explain theobserved modulation of ENSO: (i) internally generated
variations due to the chaotic nature of the atmosphere
and/or the ocean‐atmosphere coupled system in theequatorial Pacific and (ii) externally driven variations

176

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

due to longer‐term cyclic or secular changes in the prop­
erties of the tropical background state such as the mean
winds or ocean thermocline depth. A swinging pendulum
provides a partial analogy. When randomly hit fromdifferent sides, a pendulum would typically exhibit alter­
nating intervals with stronger or weaker amplitude of
motion. As long as we consider the random forcing aspart of the dynamics of the pendulum, this scenario issimilar to the first paradigm. Now, if one starts graduallychanging the length of the pendulum, that will also mod­ulate the motion. This latter example is related to thesecond paradigm. Here, we will review these two paradigms
in the context of available observations, idealized models,and comprehensive general circulation models (GCM)
describing El Niño. It is noteworthy that whether ENSO
modulations are externally forced or internally generated
typically cannot be assessed solely based on the relatively
short observational records; therefore, many of our con­clusions will rely on models of various complexities.
8.2. INTRINSICALLY GENERATED MODULATIONOF ENSO
8.2.1. Decadal Modulation of ENSO in Idealized Models
Given the challenges of understanding the causes ofmultidecadal variability of ENSO in the observed record
and in comprehensive GCMs, models of reduced com­
plexity are broadly used to help interpret the observations
and understand the different potential causes of this mul­
tidecadal variability (chapter 6). In such models, decadal
modulation can arise as part of linear or quasi‐linear
dynamics if the dominant internal ENSO mode is weakly
damped or slightly unstable with stochastic forcing (e.g.
Kirtman & Schopf 1998; Thompson & Battisti, 2000,
2001; Fedorov, 2002; Fedorov et al., 2003; Philander &Fedorov, 2003; Flügel et al., 2004) or as part of nonlinear
dynamics of a low‐order deterministic chaotic system(e.g. Tziperman et al., 1995; Jin et al., 1996; Timmermann
et al., 2003; Roberts et al., 2016). Low‐order determin­
istic chaos means that the system can be described by sev­
eral ordinary differential equations exhibiting chaotic
behavior such that small changes in initial conditions at
finite times lead to a different solution without any noise
forcing (e.g. Lorenz, 1963; Saltzman, 1962).
The simplest theoretical models of ENSO are linear andstochastically forced. The imposed stochastic forcing
reflects the effect of westerly and easterly wind bursts (orsurges) that frequently occur in the western/central tropical
Pacific (e.g. Fedorov, 2002; Harrison & Chiodi, 2009; Huet al., 2014; Fedorov et al., 2015; D. Chen et al., 2015; Hu& Fedorov, 2016, 2017a; Levine et al., 2017a) and otheratmospheric and oceanic high‐frequency processes.
However, without additional modifications, these simple
models struggle to replicate a key feature of ENSO – its

asymmetries. The SST anomalies for El Niño tend to bestronger, shorter in duration, and transition more robustly
into the opposite phase (La Niña) than the corresponding
SST anomalies for La Niña (Okumura & Deser, 2010;
K.‐Y. Choi et al., 2013; DiNezio & Deser, 2014; also see
chapter 7). The El Niño/La Niña amplitude asymmetry,
which is reflected in a positive skewness of about 0.85 of
the SST timeseries in Figure 8.1a, varies greatly with themultidecadal modulation of ENSO. When extending
conceptual models to account for the asymmetry, nonline­
arity is added to the equations. In stochastically forcedmodels, nonlinearity can be added as state‐dependent
noise (Eisenman et al., 2005; Levine & Jin, 2010; Levine
et al., 2016) or a nonlinear growth rate (K.‐Y. Choi et al.,2013; Takahashi & Dewitte, 2016).
More complicated nonlinear formulations of the
dynamics can result in a low‐order chaotic ENSO
(B. Wang & Fang 1996; Timmermann et al., 2003; Muet al., 2007). Earlier studies also emphasized nonlinear
interactions between ENSO and the seasonal cycle toexplain the chaotic behavior (Jin et al. 1994, Tziperman
et al. 1994). We note, however, that in many of these low‐order models without stochastic forcing, a transition tochaotic behavior typically requires unrealistically strong
nonlinearity. Thus, ENSO is more commonly viewed as aweakly damped/slightly unstable low‐frequency tropicalcoupled linear mode affected by moderate nonlinearity
and sustained by stochastic wind forcing (Thompson& Battisti, 2000, 2001; Fedorov et al., 2003; Philander& Fedorov, 2003; or more recently Capotondi et al., 2018).
Examples of ENSO modulations generated by a diverse
selection of simple models are shown in Figure 8.2,
including a recharge oscillator with state‐dependent noise
forcing (Levine et al., 2016), a delayed oscillator with anonlinear growth rate and noise forcing (K.‐Y. Choiet al., 2013), and a low‐order deterministic chaotic non­linear oscillator (Mu et al., 2007). The recharge oscillator
model generates an oscillation between SST in the easternequatorial Pacific and mean thermocline depth describedby two ordinary differential equations (Jin 1997, Meinen& McPhaden, 2000). The delayed oscillator is based on adifferential equation for SST with an explicit time delay
in one of the terms (Suarez & Schopf, 1988). The two
oscillator models emerge as different approximations ofan integrodifferential equation describing ENSO in a
low-frequency limit (Fedorov, 2010). For further details,
see chapters 6 and 7.
Given the asymmetries between El Niño and La Niña,
observed changes in the tropical Pacific mean state on mul­
tidecadal timescales might be as simple as differences in rateof occurrence of large El Niño events (Schopf & Burgman,
2006; Ogata et al., 2013; Atwood et al., 2017). However,these changes in the mean state have been shown to influence
ENSO growth rate and frequency (e.g. Fedorov & Philander,
2000, 2001; Wittenberg, 2002; Bejarano & Jin, 2008),

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

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Figure 8.2 Examples of ENSO modulations in 100-year simulations of eastern equatorial Pacific SST anomalies(in ºC) from different idealized models. (a) A recharge oscillator with state‐dependent noise forcing (Levine et al.,2016), (b) a delayed oscillator with a nonlinear growth rate (K.‐Y. Choi et al., 2013), (c) a low‐order chaotic nonlinear oscillator (Mu et al., 2007), and (d) a version of the Cane-Zebiak model (Bejarano & Jin, 2008). Black lines
are 10‐day outputs, grey lines are 11‐year averages. For details, see text and chapters 6 and 7.

leading to potential feedbacks between changes in themean state and changes in ENSO and making it difficult toseparate forced and random changes in ENSO variability(Burgman et al., 2008). Noise‐forced variability in ENSO is
less predictable on decadal timescales where changes in themean state are forced by the inherently random nature of
the weather forcing being integrated by the ocean, which inturn ­modifies ENSO statistics by modulating the growthrate and frequency. Low‐order deterministic chaos, on theother hand, suggests that there may be predictable modu­
lations of ENSO on these decadal timescales depending
on how well we know the initial conditions, as shown inthe study of Karspeck et al. (2004) using the intermediate
coupled model of Zebiak & Cane (1987), hereafter theCane‐Zebiak (CZ) model (see chapter 6).
The CZ model can produce a deterministically chaotic
ENSO (e.g. Figure 8.2d), which is related to the existenceof two unstable modes and strong nonlinearity in someof the model’s versions. In the original nonlinear CZ

configuration, there are two distinct sets of phases of themultidecadal variability, one with a high ENSO varianceand another with low ENSO variance, and the transitions
from a low‐variance state to a high variance state are
more predictable than the transitions from high‐varianceto low‐variance (Ramesh & Cane, 2019). Note, however,this result is different from the behavior of the GFDL
CM2.1 general circulation model, which shows no clear
predictability of decadal ENSO amplitude (see next
section and Wittenberg et al., 2014).
In contrast to the original version of the CZ model,
some newer configurations of this model have only one,weakly‐damped mode and exhibit quasi‐linear dynamics.
Likewise, linear inverse modeling (LIM) of ENSO assumes
that nonlinearities evolve fast enough to be treated as partof additive stochastic forcing in the LIM (Penland et al.,2000, Newman et al., 2016, and chapter 9). Consequently,
it is unclear where on the spectrum between linear sto­
chastic and low‐order chaos the ­observations sit, and it is178

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

possible that during some periods, ENSO is better
simulated as a linear or quasi‐linear stochastic system andothers as a nonlinear low‐order chaotic system. This issue
has an important bearing on the behavior of comprehen­sive GCMs, as discussed next.
8.2.2. Decadal Modulation of ENSO in GCMs
Coupled GCM simulations have demonstrated that
ENSO can undergo interdecadal modulations of its
behavior, not unlike those seen in some simpler models,even in the absence of changes in external forcings. These
modulations can emerge spontaneously, even in control
runs where the external radiative forcing is held fixed(Rodgers et al., 2004, Wittenberg, 2009, Deser et al.,2012, Borlace et al., 2013, Vega‐Westhoff & Sriver, 2017).
An unforced control simulation described in Wittenberg(2009) and shown in Figure 8.3 exhibits strong intrinsic
modulation of ENSO, with multidecadal epochs of verystrong (epoch M7), weak (M5), regular (M2), or irregular(a)

NINO3 SST (°C)
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(M6) SST variability. The simulation can also generate
epochs (e.g. M1 and M6) that resemble historical obser­
vations (e.g. R1 and R2), making it difficult to reject themodel outright even if the simulated ENSO is generally
stronger than observed. ENSO modulation is also evi­
dent in ensembles of historical simulations, where differ­ent ensemble members with identical external forcings
can exhibit different ENSO behaviors during the same
forcing decade (Newman et al., 2018).
Intrinsically generated modulation has been found toaffect multiple aspects of ENSO: the amplitude, period,and spectrum (Wittenberg et al., 2006; Wittenberg, 2009;
Stevenson et al., 2012; Borlace et al., 2013); spatio‐
temporal patterns (Wittenberg et al., 2014; Wittenberg,
2015; Capotondi et al., 2015a; C. Chen et al., 2017; Leeet al., 2014); teleconnections (Lee et al., 2016, 2018; Deseret al., 2017b), dynamical mechanisms and nonlinearity(Kug et al., 2010; Atwood et al., 2017), and predictability(Karamperidou et al., 2014; Wittenberg et al., 2014; Ding
et al., 2018). The existence of strong intrinsic modulation

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Figure 8.3 SST (°C) averaged over the Niño3 region (150°W–90°W, 5°S–5°N) from (a) the observational reconstruction of Smith et al. (2008) and (b) 20 centuries from the GFDL‐CM2.1 preindustrial control run (Delworth
et al., 2006; Wittenberg et al., 2006; Wittenberg, 2009). Red/blue shading highlights departures of the runningannual‐mean SST from a 20‐year low‐passed climatology. Gray shading indicates the 2000‐year mean. Highlighted
epochs (R1‐2, M1‐7) are discussed in the text. From Wittenberg (2009).

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LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

begs for caution when assessing ENSO simulations.
Multiple centuries of simulation, and/or numerous
ensemble members, may be required to robustly detectand attribute changes in ENSO arising from differingmodel formulations or changes in external forcings(C. Chen et al., 2017; Newman et al., 2018; Maher et al.,2018). The long timescales of ENSO modulation also sug­gest that existing instrumental records might not yet besufficient to falsify some climate models, which motivates
the use of paleoclimate proxies to help extend climate
records deeper into the past (Vecchi & Wittenberg, 2010).
Intrinsic ENSO modulation can also imprint onto var­iability at decadal timescales, both through nonlinear rec­tification and by temporally “blurring” the interannual
undulations of climatological features like the oceanic
thermocline and atmospheric convergence zones (Vimont,
2005; Di Lorenzo et al., 2010; Wittenberg, 2015). A wood­
working analogy for the temporal blurring effect would
be switching on a power‐sander: the tool’s rapid oscilla­tions appear to blur its edges into gradual visual transi­
tions, even though the actual edges remain sharp at any
given instant. As a result of this effect, model control sim­
ulations (Watanabe & Wittenberg, 2012; Watanabe et al.,2012; Ogata et al., 2013; Atwood et al., 2017) have shownthat compared to weak‐ENSO epochs, strong‐ENSO
epochs are associated with warmer time‐mean SSTs in
the far east Pacific, cooler time‐mean SSTs at the easternedge of the warm pool, intensified time‐mean rainfall inthe eastern equatorial Pacific, and a more diffuse(“blurred”) time‐mean vertical structure of the ocean
thermocline. This can be seen in Figure 8.3, where relative
to the weak‐ENSO epoch (M5) the strong‐ENSO epoch(M7) shows a clear warming of the multidecadal mean
SST in the eastern equatorial Pacific.
Using a linearized version of the CZ model fit to the
GFDL CM2.1 long control run of Figure 8.3, Atwoodet al. (2017) concluded that the ENSO‐induced multi­
decadal changes in time‐mean ocean climate, in particular
the climatological SST, surface winds, thermal stratifica­tion, currents, and upwelling, actually acted to damp thesimulated ENSO during active‐ENSO epochs, suggestingthat the decadal ocean changes were a symptom rather
than a cause of the amplified ENSO. However, CM2.1’s
active‐ENSO epochs also tended to be linked to stronger
and more zonally extensive stochastic wind forcing, and amore nonlinear wind stress response to SST anomalies(SSTA). This suggests that atmospheric nonlinearity,
which was neglected from the intermediate model ofAtwood et al. (2017), may be essential to maintaining thestrong ENSO modulation in CM2.1. These results are
consistent with the idea that both individual ENSO
events and active‐ENSO decades can be generated at
random, by tropical Pacific wind stress noise and its
nonlinear dependence on SST in the western/central­

179

equatorial Pacific (Vecchi et al., 2006; Gebbie et al., 2007;
Zavala‐Garay et al., 2008, K.‐Y. Choi et al., 2013, Fedorov
et al., 2015, Capotondi et al., 2018).
On the other hand, Borlace et al. (2013) argued that intheir 1000‐year run using CSIRO Mk3L, the generated
multidecadal ENSO modulations emerged as a result ofgradual modulations of the tropical mean state in the modelthat modulated the strength of the thermocline feedback inthe climate model, as assessed from the Bjerknes stability
index (Jin et al., 2006); the effect on ENSO of mean statechanges is discussed in more detail in the next subsection.
Atwood et al. (2017) further found that for the CM2.1
model, ENSO SST variance is roughly chi‐square distrib­
uted, consistent with the hypothesis that the ENSO
amplitude in one epoch is unrelated to that in its neigh­boring epochs. This may also explain why models withstrong ENSO activity also tend to exhibit strong inter­decadal modulation of ENSO amplitude (J. Choi et al.,2013; C. Chen et al., 2017): essentially, strong‐ENSO
models can generate both weak and strong epochs, while
weak‐ENSO models can only generate weak epochs. This
motivates using amplitude modulation relative to thelong‐term average ENSO amplitude when comparing dif­ferent simulations, evaluating models against observa­
tions, or assessing the impacts of external forcings.
Despite these results, a key question remains: Are theintrinsically generated modulations of ENSO in GCMs,
seen for example in Figure 8.3, driven by the sameprocesses as those seen in the observations (Figure 8.1),
or are they an artifact of too strong an amplitude of thesimulated ENSO, perhaps associated with the dominantinternal mode being too unstable?
8.3. EXTERNALLY DRIVEN MODULATIONOF ENSO
8.3.1. The Role of Tropical Pacific Mean State Changes
In contrast to the internally generated ENSO modula­
tions that arise solely within the tropical ocean‐
atmosphere system due to intrinsic physics of El Niño
and La Niña and potentially atmospheric noise, exter­
nally driven modulations require a mechanism or forcing
different from ENSO. This “external” forcing should gen­erate changes in the tropical Pacific on timescales longer
than the ENSO characteristic timescales and can be due
to a number of climate phenomena (e.g. the Pacific
Decadal Oscillation, decadal variations in the Pacific
Meridional Modes, the effects of other ocean basins on
the tropical Pacific, changes caused by rising concentra­
tions of atmospheric CO2, etc.). Such a forcing modifies
the mean state of the tropical Pacific on decadal andlonger timescales, which in turn can give rise to low‐­frequency ENSO modulation.

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

That changes in the mean (or background) state of thetropical Pacific ocean‐atmosphere system can affect
ENSO has been known from the first studies of ENSO
using the Cane‐Zebiak model (1987) and its subsequentversions (Battisti & Hirst, 1989; Jin & Neelin, 1993;
Fedorov & Philander, 2000, 2001; Chen & Cane, 2008).
Several parameters characterizing the mean tropical state
emerged as the key factors controlling ENSO characteris­
tics, such as its amplitude, period, the structure anddirection of propagation of SSTA, or the structure of
precipitation anomalies. Among the most important aremean zonal winds, mean thermocline depth, ocean
vertical stratification, or meridional temperature contrast
across the equator, as was demonstrated by a stability
analysis examining the eigenmodes of the tropical ocean‐
atmosphere system within models similar to the CZ
model (Fedorov & Philander, 2000, 2001), see Figure 8.4,

and other studies (An & Jin, 2000; Wittenberg, 2002;
Guilyardi, 2006; Collins et al., 2010).
How do changes in those characteristics of the tropicalmean state affect ENSO? They do it by altering major
feedbacks that control the period and the growth rates of
the weakly damped or marginally unstable modes thatgive rise to ENSO. To understand their effects, one canconsider the linearized equation for temperature rate ofchange written either for the ocean surface or mixed
layer:Tt

uTx

u Tx vTy

v Ty wTz

w Tz Q

1.4

8

1.2

6

1

4

2

0.8
110

120

130

140

Mean thermocline depth (m)

(b)
1.4

0.5

1.2
Mean wind

Period (years)

Mean wind

(a)

100

0
1

0.8
100

–0.5
110

120

(8.1)

Here, T is temperature; x and y are horizontal coordi­
nates (corresponding to longitude and latitude respec­tively), z is ocean depth, and u, v, and w are velocity

Growth rate (1/year)

180

130

140

Mean thermocline depth (m)Figure 8.4 (a) The period (in years) and (b) growth rates (in 1/year) of the least damped/most unstable ENSO modein an intermediate coupled model as a function of thermocline depth (in meters) along the horizontal axis andthe intensity of easterly equatorial wind stress. Wind stress is given in nondimensional units such that 1 roughly
corresponds to mean wind stress in the 1990s. Dashed lines indicate zero growth rate or neutral stability; there
are no ENSO-like modes within the stable white area. Transitions from point B to A and then from A back to B
would imply changes in ENSO similar to those that occurred in the late 1970s and then in the early 2000s. After
Fedorov and Philander (2000, 2001).

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

components corresponding to those coordinates; t is
time, subscripts indicate derivatives; bars and primes
indicate mean and anomalous values, respectively. The
terms on the right‐hand‐side of this equation describe
various advective terms, except for the last one, which
describes net atmospheric damping of temperature
anomalies by surface heat fluxes.
Each of the advective/upwelling terms incorporates aparticular feedback affecting ENSO. For example, wTz
describes mean upwelling of anomalous temperature andcontains the thermocline feedback, while w Tz describes
anomalous upwelling of mean temperature and hence
contains the so‐called Ekman feedback. The thermocline
feedback is critical for ENSO as it communicates ther­
mocline information to the ocean surface, making pos­sible a dynamical coupling between the ocean and theatmosphere. It also largely controls the period of ENSO
being close to 3–5 years. The Ekman feedback is weaker
but can still be important in many models. Other terms of
the equation, uTx and u Tx , vTy and v Ty , containzonal and meridional advection feedbacks and also affect
ENSO. At the same time, many of these terms, and Q′,
have components associated with damping of SST anom­alies. Note that nonlinear terms neglected in Eq. 8.1 canalso play an important role during strong El Niño events.
All of the aforementioned feedbacks are affected bychanges in background conditions. In a linear framework,for example, the mean deepening of the thermocline or
the strengthening of thermocline stratification mightfavor the thermocline feedback and produce longer
periods and a stronger ENSO (e.g. Fedorov & Philander,
2000, 2001; Zhao & Fedorov, 2020). However, the rela­tionship between particular changes in the mean state and
ENSO is not straightforward because (i) the stability
characteristics of the dominant ENSO mode(s) can varynonmonotonically; (ii) mean state changes could simulta­
neously affect different feedbacks, damping effects, andpossible nonlinearities; and (iii) changes in different char­acteristics of the mean state typically occur in parallel.
Actual changes in the mean thermocline depth andstratification are difficult to diagnose confidently, espe­
cially before the 1990s, given large variations due to
ENSO and the sparsity of subsurface data; however,changes in mean winds appear to be measured moreaccurately for the past 30–40 years since the introductionof the in‐situ buoys and the satellite scatterometer winddata. These measurements suggest a gradual strength­
ening of the zonal winds, in particular in the central‐western tropical Pacific (Figure 8.5a) over the past three
decades, suggesting a strengthening of the Walker
circulation (McGregor et al., 2014; Hu & Fedorov, 2018).
Figure 8.5a also shows a multidecadal strengthening ofcross‐equatorial winds in the eastern Pacific, which is
confirmed by in‐situ, satellite, and atmospheric reanalysis

181

data (Hu & Fedorov, 2018). This gradual strengtheningof meridional winds is unlikely to be caused by ENSO
changes and contains signals forced both locally andfrom outside the tropical Pacific, possibly from thetropical North Atlantic.
According to earlier studies with intermediate models
focusing on zonal winds, and more recent studies withcomprehensive GCMs focusing on both zonal and merid­
ional winds, such a wind strengthening should lead toweaker ENSO variability. Indeed, imposing the strength­
ening of zonal or cross‐equatorial wind anomalies
comparable to that observed during the past three decades
within perturbation experiments using a comprehensive
climate GCM, for example CESM (Hu & Fedorov, 2018;
Zhao & Fedorov, 2020), weakens the ENSO cycle(Figure 8.5d,e), producing changes similar to the observed
shift in El Niño characteristics after the year 2000: atransition to CP events, the suppression of the ITCZ
meridional migrations, and a shift toward more westward
propagation of SST anomalies.
Finally, it is important to note again that even thoughchanges in the mean state can clearly cause ENSO modu­
lations, modulations of the same magnitude can occur inthe comprehensive models without any changes in themean state (as discussed in section 2), and the question of
how to distinguish inherently generated versus externallydriven ENSO modulations remains unresolved.
8.3.2. The Role of Other Ocean Basins
How do the changes in the mean state of the tropical
Pacific discussed in the previous subsection come about?
One possibility, to be discussed in section 8.4, is by inter­acting with decadal/multidecadal modes of climate vari­ability in the Pacific. Another possibility involves theinfluence on the Pacific of other oceans (also see
chapter 11). Model experiments and long proxy analyses
suggest that other ocean basins have the ability to impact
ENSO statistics on multidecadal timescales. The remotely
forced changes to the tropical Pacific typically come via
an atmospheric bridge driving changes in the Walker
circulation. From the 1980s to the early 2010s, the Walker
circulation strengthened (McGregor et al., 2014). Recent
work on Pacific decadal trends and variability has shownthat warming SSTs in the tropical Atlantic force addi­
tional convection locally and anomalous subsidence inthe eastern tropical Pacific. The anomalous subsidence inthe tropical Pacific increases the trade wind strength andthe SST gradient across equatorial Pacific, essentiallyenhancing the Walker circulation and increasing the ther­
mocline slope (McGregor et al., 2014, Kang et al., 2014,
Li et al., 2016).
A warmer tropical Indian Ocean also can enhance theWalker circulation by driving additional Indian Ocean

182

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

Multidecadal trend

(a)

0.4
0.3
0.2
0.1
0
–0.1
–0.2
–0.3
–0.4

20N

0

20S
1
120E

150E

150W

180

(b)

90W

120W

60W

(c)

TAUX

TAUY
2.4

20N

20N

0

0

1.8
1.2
0.6
0
–0.6
–1.2

20S

20S

–1.8
–2.4

120E

150E

180

150W

120W

90W

60W

120E

150E

180

150W

120W

90W

60W

SST anom. std. dev. (°C)

1.4
1.2
1.0
0.8
0.6

CTL

0.4

TAUX

SST anom. std. dev. (°C)

(e)

(d)

1.4
1.2
1.0
0.8
0.6

CTL

0.4

TAUY

0.2

0.2
135E

180

135W

90W

Longitude

135E

180

135W

90W

Longitude

Figure 8.5 (a) Observed multidecadal trends in SST (colors; °C/decade), surface winds (vectors; m/s/decade), andprecipitation (contours; 0.25 mm/day/decade interval) over the period of 1982–2015. For precipitation anomalies, blue, red, and white contours indicate positive, negative, and zero values, respectively. (b) Zonal and (c)meridional wind stress anomalies (10–2 N/m2) superimposed in a coupled climate model (CESM) in the experiments named TAUX and TAUY, respectively. The magnitude of the superimposed wind stress anomalies is
equivalent to three decades of the multidecadal trend during 1982–2015. (d,e) Standard deviations of equatorial(5°S–5°N) SST anomalies for (d) TAUX and (e) TAUY, in comparison with the control simulation (CTL). Note thereduction of ENSO variability in the eastern Pacific in both experiments. Panels a, c, and e are modified from Hu
and Fedorov (2018).

convection and has also been suggested as a contributor
to the changes in the tropical Pacific over the first decadeand a half of the 21st century (Luo et al., 2012, Chikamoto
et al., 2015, L. Dong & McPhaden, 2017b; Hu & Fedorov,2019). Studies of the Indian Ocean forced trends in theWalker circulation implicate anthropogenic climatechange (Luo et al., 2012; L. Dong & McPhaden, 2017b).
Changes to the Walker circulation and upper ocean cur­rents and stratification in the equatorial Pacific have beenshown to change ENSO amplitude and frequency inmodels of intermediate complexity (Fedorov & Philander,
2000, 2001; Wittenberg, 2002; Bejarano & Jin, 2008).

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

But studies of the Atlantic are more mixed in cause.
Previous model studies imposing a large cooling in the
subpolar north Atlantic similar to the melting of theGreenland ice sheet lead to a decrease in ENSO amplitudeand frequency (B. Dong et al., 2006, B. Dong & Sutton,
2007, Timmermann et al., 2007), while a recent study
using an updated version of one of the same models
found that changes in the noise forcing kept ENSO
amplitude from changing noticeably, highlighting thechallenge of distinguishing ENSO modulation fromrandom fluctuation (Williamson et al., 2018).
More important in the context of the present‐day cli­mate is the effect on ENSO of natural SST variability in
the Atlantic, known as the Atlantic multidecadal oscilla­tion or variability (AMO or AMV; e.g. Enfield et al.,
2001; Clement et al., 2015; R. Zhang et al., 2016). A
combination of paleo and model evidence suggests thatthe changes in the north Atlantic SST due to the AMV
could partially explain the multidecadal variability in
ENSO through forced changes to the seasonal cycle ofwinds and currents in the equatorial Pacific (B. Donget al., 2006, Zanchettin et al., 2016, Levine et al., 2017b,
2018, Hu & Fedorov, 2018). The changes in the seasonal
cycle are strongly linked to changes in the ITCZ such as
the strength and northward progression of the ITCZ
during the boreal summer and fall (Levine et al., 2018).
The AMV can also affect ENSO by inducing changes in
the mean zonal and especially cross‐equatorial winds in
the Pacific (Hu & Fedorov, 2018). The latter study has
shown that imposing a warming in the subtropical North
Atlantic, a region strongly affected by the AMV, would
induce a strong southerly cross‐equatorial wind anomaly
in the Pacific as well as a weak easterly wind anomaly,
which would reduce ENSO amplitude and suppress thesouthward migration of the ITCZ, among other effects(Figure 8.6).
8.3.3. The Role of Model Biases
Another issue directly related to the effects of meanstate changes on ENSO is the role of model climatolog­
ical biases that affect their simulations of ENSO andhence the fidelity of ENSO decadal modulations
simulated by coupled GCMs. These biases typically
include a Pacific equatorial cold tongue (ECT) that is too
intense and extends too far west (Burls et al., 2017; C.
Chen et al., 2017; Thomas & Fedorov, 2017; Santoso
et al., 2019). This ECT cold bias inhibits eastward andequatorward shifts of atmospheric deep convection dur­ing El Niño, leading to response patterns of rainfall,clouds, and westerly wind stress anomalies that are too
weak and too far west (K.‐Y. Choi et al., 2015; Ham &Kug, 2015; Capotondi et al., 2015b). This weakens both
the wind stress coupling to SSTA, and the damping of

183

SSTA due to cloud shading in the equatorial Pacific,
altering the balance of surface feedbacks during El Niño
(Bellenger et al., 2014). It also reduces the positive (west­erly) skewness of the equatorial wind stress response to
SSTA, which contributes to the inability of most models
to fully capture the observed amplitude, duration, andtransition asymmetries between El Niño and La Niña
(K.‐Y. Choi et al., 2013), as well as the longer‐term
impacts of these ENSO asymmetries on decadal‐scalevariability (Lin et al., 2018).
A simulated ECT that extends too far west will also
displace the region with large values of Tx (associatedwith the ECT/warm pool boundary) to the west. This
westward displacement leads to excessive zonal separa­tion of the SSTA induced by the zonal advective andthermocline feedbacks (related to the terms u Tx and
wTz , respectively, in Eq. [8.1]), which then causes
models to produce unrealistic “double‐peaked” El Niño
events (Graham et al., 2017). Models with stronger ECTs
also tend to show excessive westward propagation of
SSTA, due to an altered balance of subsurface advective
feedbacks (Ham & Kug, 2015).
Biases in atmospheric processes can also directly
influence ENSO’s feedbacks. Most models still underesti­
mate the meridional width of the equatorial westerly
wind stress anomalies during El Niño, which hastens thedischarge of equatorial subsurface heat content during
warm events, weakens the events and accelerates theirlifecycle, and shortens the ENSO period (Capotondi
et al., 2006). Studies have shown that improving theatmosphere component’s representation of the verticaltransport of horizontal momentum by cumulus
convection, through either enhanced atmospheric resolu­
tion or improved subgrid parameterizations, is one way
to help broaden the meridional extent of the equatorial
zonal wind stress anomalies and thereby lengthen andimprove the simulated ENSO period (Kim et al., 2008;
Neale et al., 2008).
Community efforts are underway to understand andaddress model climatological and ENSO biases in thetropical Pacific via improved ENSO diagnostics and met­
rics, enhanced model resolution, and better parameteri­zation of subgridscale processes in the ocean and cloudphysics in the atmosphere (Guilyardi et al., 2012a, 2016;
Burls et al., 2017; also see chapter 9).
8.4. ENSO AND THE PACIFIC DECADALOSCILLATIONAs emphasized in the above discussion, changes in themean state of the tropical Pacific can lead to ENSO
modulation. On the other hand, the rectification of
ENSO modulation can lead to the appearance of decadal
variations in the tropical mean state. The major issue in

184

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE(a)

TNA - CTL
1
0.8
0.6
0.4
0.2
0
–0.2
–0.4
–0.6
–0.8
–1

20N

0

20S

2

120E

150E

180

150W

120W

90W

60W

30W

0

(b)
1.4

SST anom.std. dev. (°C)

1.2
1.0
0.8
0.6
CTL
0.4

TNA

0.2
135E

180

135W

90W

Figure 8.6 Tropical Atlantic warming as a cause for zonal and cross‐equatorial wind strengthening in the Pacificand the subsequent changes in ENSO characteristics. (a) Anomalies in SST (colors; °C) and surface wind (vectors;m/s) in response to a tropical North Atlantic warming imposed between 0 and 30ºN in a coupled climate model(the simulation is called TNA). The imposed warming is constant in time but its magnitude (~0.5ºC) is equivalent
to the observed trends over the past three decades. Note the simulated strengthening of mean zonal and meridional winds in the eastern Pacific. (b) Standard deviations of equatorial SST anomalies averaged within 5°S–5°N
as a function of longitude for the control and TNA experiments. Note a significant reduction in the ENSO
amplitude in the TNA experiment. The induced change in ENSO is very similar to that observed in the early 2000s(c.f. Figure 8.1d). After Hu and Fedorov (2018).

this context is the relationship between ENSO and thedecadal/multidecadal modes of SST variability in the
Pacific, and in particular its relationship with the domi­
nant pattern of variability of monthly SSTA over the
North Pacific (20–60°N) termed the “Pacific Decadal
Oscillation” (PDO; Mantua et al., 1997; Figure 8.7).
This basinwide structure, consisting of SSTA of one signin the central and western North Pacific and the opposite
sign along the eastern North Pacific, is linked to SSTA in
the tropical and South Pacific, reminiscent of the patternassociated with the interannual ENSO phenomenon,
albeit with weaker amplitude along the equator(Figure 8.7). This pan‐Pacific structure is also known as

the “Interdecadal Pacific Oscillation” (IPO), defined as
the leading mode of 13‐year low‐pass filtered Pacific
SSTA after the secular global warming mode (Poweret al., 1999).
The PDO/IPO is a coupled ocean‐atmosphere pheno­menon, as evidenced by its signature in surface winds(Figure 8.7). When the PDO/IPO is in its positive phase,
the Aleutian low‐pressure center is deeper than normal,
accompanied by westerly wind anomalies over the central
and western North Pacific and southerly wind anomalies
in the eastern North Pacific.
Although it is convenient to describe the PDO/IPO as asingle “mode” of variability with a unique spatial pattern

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

185

PDO
(a)

0.5 m/s(b)–0.6 –0.5 –0.4 –0.3 –0.2 –0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

4.0
3.0
2.0
1.0
0.0
–1.0
–2.0
–3.0
1890

1920

1950

1980

2010

Figure 8.7 Spatial pattern and temporal evolution of the PDO based on the ERSSTv5 SST data set during 1870–
2017. The PDO is defined following Mantua et al. (1997) as the leading empirical orthogonal function and associated principal component (PC) time series based on monthly SSTA over the North Pacific (20°N–60°N) after
subtracting the global mean SSTA. (a) Color shading shows global SSTA regressions (°C) and vectors show global
surface wind anomaly regressions (ms–1) upon the normalized PC time series. Winds are from the NOAA‐CIRES
20th Century Reanalysis v2c. (b) PC time series. The heavy black line indicates a 61‐month running mean.

and temporal history, it is now becoming apparent that a
multitude of physical processes contribute to its charac­
teristics (Newman et al., 2016). These include atmo­
spheric teleconnections to the North and South Pacificdriven by ENSO and their subsequent effects on theseasonal evolution of heat content anomalies in the upper
ocean mixed layer (Vimont, 2005; Newman et al., 2003);
stochastic atmospheric forcing of the North and South
Pacific oceanic wind‐driven gyre circulations via Rossby
wave dynamics coupled with deterministic feedbacks ofextratropical SSTA on to the atmosphere (Latif &Barnett, 1994; Deser et al., 1999; Schneider et al., 2002;
Kwon & Deser, 2007; L. Zhang & Delworth, 2015); andthe “seasonal footprinting mechanism,” whereby SSTAin the North and South Pacific influence the tropical
Pacific and the subsequent development of ENSO events(Vimont et al., 2001, 2003; DiLorenzo et al., 2008;
Alexander et al., 2010). In view of this multiplicity ofprocesses, it is not surprising that the temporal and spatial
characteristics of the PDO/IPO are challenging to define,understand, and predict. Indeed, alternate definitions of
the PDO/IPO are being proposed based on distinguishingspatial patterns based on frequency characteristics(X. Chen & Wallace, 2016; Wills et al., 2018).
The observational record is too short to assess whether
the temporal evolution of the PDO/IPO is truly oscilla­
tory with a robust and statistically significant spectral
peak, or simply part of a red noise continuum (Deseret al., 2004); it should be noted that the latter does not
exclude “regime shift” behavior (Newman et al., 2016).
Longer records of the PDO/IPO based on paleo‐climate
proxies are equivocal on this issue, with substantial dis­
agreement among the different types of proxy records(Ault et al., 2013; Newman et al., 2016). For this reason,
the term “Pacific decadal variability” is often used inplace of PDO/IPO.

186

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATEThe ability of global coupled climate models to sim­
ulate the observed characteristics of the PDO/IPO var­ies greatly across models (Newman et al., 2016). In
general, the models’ PDO/IPO tends to have a higher
autocorrelation and weaker linkages between the
North and tropical Pacific compared to observations.
In some long (millennial) control simulations withoutchanges in external forcing, the PDO/IPO exhibits adistinct spectral peak at approximately 20 years that is
statistically significant above a background red‐noisecontinuum of variability (Deser et al., 2006; Kwon &Deser, 2007; L. Zhang & Delworth, 2015). Thispreferred timescale appears to arise from within theextratropical coupled ocean‐atmosphere system,
although interactions with the tropics can enhance thevariability. Although the observed PDO/IPO timeseries exhibits a weak bidecadal spectral peak (Minobe,
1999; Deser et al., 2004), the most prominent timescaleof variability is nominally 50 years (in a 118‐year
record; recall Figure 8.7), a timescale that models do
not seem to capture. In one model, the PDO/IPO time­scale has been shown to decrease from approximately
20 years to approximately 12 years in response to global
warming, due to changes in ocean stratification andthe phase speed of internal Rossby waves (L. Zhang &Delworth, 2016).
The frequency and amplitude of El Niño and La Niña
events appear to be modulated by the phase of the PDO/
IPO (Fedorov & Philander, 2000, 2001; An & Wang,
2000; Timmermann et al., 2003; Okumura et al., 2017;
Lin et al., 2018) in both observations and models. Thisdependence can be explained by the sensitivity of ENSO
dynamics to the mean state as described in section 8.3, inparticular to changes in zonal winds evident in Figure 8.7.
In fact, the wind and SST trends of the past several
decades (Figure 8.5a) show a signature of the PDO/IPO
negative phase together with a strong asymmetric com­ponent associated with cross‐equatorial winds.
On the other hand, the connection between the fre­quency and amplitude of the ENSO cycle and the phaseof the PDO/IPO could be related to the rectification ofchanges in ENSO on the mean state (Rodgers et al., 2004;
Ogata et al., 2013; J. Choi et al., 2013; Wittenberg et al,2014), which might imply a possibility of two‐way interac­tions between ENSO and the PDO/IPO.
In addition to the PDO/IPO, other Pacific climate
modes with a decadal component can give rise to ENSO
modulations and ENSO/mean climate interactions. For
example, the North Pacific Meridional Mode (Chiang &Vimont, 2004), driven by the coupling between tropical/
subtropical meridional winds and SST variations, isknown to modify zonal winds in the equatorial Pacific,
thus linking the midlatitude North Pacific Oscillation to
the tropics (Di Lorenzo et al., 2015).

8.5. ENSO DECADAL MODULATION IN OCEAN
ENERGETICS
Recently, ocean energetics emerged as a powerful diag­nostic tool for studying variability and ocean‐atmosphereinteractions in the tropical Pacific, including ENSO
(Goddard & Philander, 2000; Fedorov, 2002; Fedorov
et al., 2003; Fedorov, 2007; Brown & Fedorov, 2008, 2010;
Brown et al., 2011; Hu et al., 2014; Kodama & Burls,2019). Here, we discuss how ENSO decadal modulations
are reflected in the energetics.
The ocean energetics describes the rate of change ofthe available potential energy (APE) induced by buoy­
ancy power generated by the divergence of surface wind‐driven currents; in turn, buoyancy power is closely related
to the generated wind power. Mathematically, the balanceof perturbation APE (or E for simplicity) can be repre­
sented as
dE
dt

W

(8.2)

E,

where W is perturbation wind power, γ is the efficiency ofwind power conversion to the APE (50%–60%), and ‐αE
represents APE damping (α is estimated in the range
0.5–1 year–1), see Brown and Fedorov (2010) and Shi et al.
(2020).
The perturbation E and W can be computed as follows:E

S2

dV , W

u

u

dA,

(8.3)

where ρ is potential density anomaly with respect to its
time‐mean horizontal average ρ*(z); S2 = − (1/g)dρ*(z)/dz
describes mean vertical stratification in the tropical
Pacific ocean; g is gravitational acceleration. Zonal ocean
current velocity and surface wind stress are represented
by u and τ, respectively; V and A are volume and surface
area, respectively. Thus, E is computed as a weighted
integral across the basin and with depth of density­
anomalies. In ENSO studies, this budget is typically­considered for the tropical Pacific basin (15°S–15°N,­
130°E–85°W). Vertical integration is conducted over theupper 400 m. Bars and primes describe the climatology
and perturbation components; nonlinear terms in theexpressions for E and W have been neglected.
Previous studies have shown that E describes changes
in the equatorial thermocline slope and is well correlatedwith Niño3 anomalies on interannual timescales(Goddard & Philander, 2000; Brown & Fedorov, 2010).
Typically, a positive (negative) E indicates a thermocline
steeper (flatter) along the equator than the climatology.
In accordance with Eq. 8.2, wind power leads E by
4–6 months with a correlation 0.6–0.7, providing an

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

alternative to ocean heat content as a precursor of El
Niño (e.g. Kodama & Burls, 2019).
A recent study suggests that E could be used to charac­terize different types of El Niño events within the ENSO
continuum; for example, higher negative E values corre­
spond to EP events, and lower negative values to CP
events (Hu et al., 2014). The same study also demon­
strates that the ocean energetics framework is useful forunderstanding the impacts of intraseasonal wind bursts
on the development, diversity, and predictability of El
Niño events.
Ocean energetics also shed light on decadal variations
in El Niño properties. The 1980s and 1990s witnessedstrong El Niño activity, but after the year 2000 El Niño
magnitude weakened (McPhaden et al., 2011; Hu &Fedorov, 2018; also see Figure 8.8a). Such decadal mod­ulation of ENSO is especially striking in the variations oftropical Pacific APE (Figure 8.8b). During the 1980s and1990s all El Niño events exhibit large negative values ofE, but it is not so for warm events after 2000. That leads
to a clear decadal shift in APE, even more pronounced
than that in Niño3, which suggests that the decadal shiftof El Niño properties involved much weaker subsurface
thermocline variations and a reduced thermocline
feedback.
Since direct calculations of E require ocean subsurface
information, an alternative way to estimate E is to use sea
surface height (SSH) information, as there is a tight con­nection between thermocline depth and SSH in thetropical Pacific (Shi et al., 2020). Accordingly, one canintroduce an SSH index such thatE

SSH index

hh dA,(8.4)

where h is SSH and the integral covers the same area ofthe tropical Pacific basin. Indeed, we find that this SSH
index correlates with E extremely well (r = 0.95), both oninterannual and decadal timescales (Figure 8.8c), althoughavailable satellite SSH measurements are relatively short.
These results suggest that the ocean energetics frame­
work can be useful for understanding ENSO decadal
modulations and potentially its decadal predictability.
Do the correlations and lag between W and E change on
decadal timescales? Does the efficiency γ change on
decadal timescales? These questions will require further
research.
8.6. PREDICTION OF ENSO DECADALMODULATION
When decades of unusual ENSO behavior are observedin nature or in models, it is tempting to try to attribute187

them to some deterministic cause, such as slow changes in
the climatological background state that might alter
ENSO stability. However, coupled GCM experiments
indicate that there is a large intrinsic component of
ENSO modulation that may be essentially unpredictableon decadal timescales, and this random ENSO modula­tion can actually drive decadal variations in the
background state.
For example, Wittenberg et al. (2014) examined thepredictability of epochs of extreme ENSO behavior in
the GFDL CM2.1 preindustrial control run. Figure 8.9highlights two of the extreme‐ENSO epochs from in
Figure 8.3: the weak epoch M5 in Figure 8.9a, and thestrong epoch M7 in Figure 8.9d. These two epochs display
stark differences in their spatiotemporal behavior. The
weak M5 epoch shows biennial SSTA variations, withwestward SSTA propagation and near symmetry of theamplitude and pattern of warm and cold events. In con­trast, the strong M7 epoch shows two distinct timescales(a quasi‐biennial warm‐to‐cold transition, and a quasi‐
quadrennial interval between major warm events), witheastward SSTA propagation during warm events, andstrong asymmetries between warm and cold events. Warm
events here are shorter, more extreme, farther east, andmore synchronized to the end of the calendar year thancold events, and warm‐to‐cold transitions are morerobust than cold‐to‐warm transitions. These ENSO
asymmetries in models and in nature have been attributed
to the asymmetric wind stress response to SSTA, associ­ated with the large equatorward and eastward shifts ofatmospheric convection during strong El Niño events(Lengaigne et al., 2006; K.‐Y. Choi et al., 2013, 2015).
Wittenberg et al. (2014) used a “perfect‐model”
approach, in which the model itself was used to “refore­cast” its own previously generated trajectories. Before
and during each extreme‐ENSO epoch, the model trajec­
tory was perturbed very slightly (adding 0.0001 K to asingle ocean temperature cell), yielding an ensemble of 40
slightly perturbed members. The perturbed ensemble wasthen allowed to evolve forward in time according to themodel dynamics.
Figure 8.9b shows that the weakest ENSO member
from each of the three 40‐member reforecasts does quali­
tatively reproduce the character of the weak M5 epoch.
However, Figure 8.9c shows that the strongest ENSO
member looks completely different, much more like thestrong M7 epoch in Figure 8.8d. Thus, a mere flap ofbutterfly wings can cause an otherwise quiet ENSO epoch
to explode into strong variability. Similarly, the weakest
ENSO member from the M7 reforecasts (Figure 8.9e)
demonstrates that butterflies can completely tame astrong‐ENSO epoch. Wittenberg et al. (2014) found thatthe initially tiny perturbations grew rapidly over the firstfew years, inevitably leading to a complete loss of ENSO

188

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

Nino3 (°C)

(a)
4.0
3.0
2.0
1.0
0.0
1.0
–2.0
–3.0
1980

1985

1990

1995

2000

2005

2010

2015

1980

1985

1990

1995

2000

2005

2010

2015

1980

1985

1990

1995

2000

2005

2010

2015

APE (1018J)

(b)
–5.0
–4.0
–3.0
–2.0
–1.0
0.0
1.0
2.0

SSH index (10–2 m2)

(c)
–3.0
–2.0
–1.0
0.0
1.0
2.0

Figure 8.8 Variations in (a) Niño3 index, (b) perturbation available potential energy (E), and (c) SSH index. E and
SSH index are defined in Eqs. (8.3) and (8.4). E is computed using the SODA ocean reanalysis. The vertical axesin (b) and (c) are reversed to facilitate comparison with the top panel. Note that decadal modulations of ENSO,
including the shift after the year 2000, are especially pronounced in the energetics. After Shi et al. (2020).

predictability on decadal scales. Beyond about 3 years(roughly one ENSO cycle), the perturbed ensemble’s
ENSO amplitude became statistically indistinguishable
from randomly selected states from the control run.
The results of Wittenberg et al. (2014) lend support tothe hypothesis that interdecadal modulation of ENSO
can arise even without variations in the ENSO’s under­
lying dynamics (Newman et al., 2011a, 2011b).
Realistically strong ENSO modulation is also found insimplified ENSO models that explicitly omit interactions
with other basins or with external climate modes (see sec­tion 8.2.1). Just as flipping a fair coin will occasionallygive an extended run of heads, an interannual ENSO
with no decadal memory, i.e. a Poisson process with aninterannual timescale, can occasionally produce multi­
decadal quiet or active epochs (Wittenberg, 2009).
Figure 8.9 also illustrates how the unpredictable
intrinsic component of ENSO modulation can impact

the multidecadal mean state of the equatorial Pacific.
The strong reforecast of the weak epoch (Figure 8.9c)shows warmer time‐mean SST in the east and cooler SST
in the west, associated with the asymmetry of ENSO
amplitude and pattern between warm and cold events.
The weak reforecast of the strong epoch (Figure 8.9e)
shows the opposite, with multidecadal cooling of the east
relative to the west. The key point is that substantial mul­
tidecadal changes in ENSO and the background climate
can arise at random, and thus must be considered whenattempting to attribute apparent changes to externalradiative forcings or intermodel differences (section 8.3).
Even if the intrinsic component of ENSO modulation
does turn out to be unpredictable on decadal scales, there
still appears to be untapped potential for improved long‐range ENSO forecasts at interannual scales. Ding et al.
(2018, 2019) find that many coupled GCM simulations
are now sufficiently realistic that their long control runs

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS

189

Perfect-model reforecasts of extreme ENSO epochs.
SST anomalies (averaged 5°S–5°N), running annual mean.
(d) Strong
epoch

1740
1735
1725
1720

1730

Simulation year

1170
1165

Simulation year

1160
1155
1150

120°E 180° 120°W 120°E 180° 120°W 120°E 180° 120°W(e) Weak (f) Strong
reforecast reforecast

1745

1750

(b) Weak (c) Strong
reforecast reforecast

1175

1180

(a) Weak
epoch

120°E 180° 120°W 120°E 180° 120°W 120°E 180° 120°W

Figure 8.9 Perfect‐model reforecasts (Wittenberg et al., 2014) of two 30‐year extreme‐ENSO epochs from themultimillennium GFDL‐CM2.1 coupled GCM preindustrial control run (Wittenberg, 2009). (a) Longitude‐timeplot of equatorial Pacific SSTA (averaged 5°S–5°N, running annual mean minus a 20‐year low‐pass climatology)
during one of the weakest ENSO epochs in the control run (M5 in Figure 8.3). (b) The weakest ENSO trajectoryfrom 40 perturbed reforecasts of the control run’s weak‐ENSO epoch, initialized at each of the three horizontal
green lines and integrated forward in time for 10 years; the 20‐year low‐pass control‐run climatology of (a) is
subtracted, to highlight both the decadal and ENSO changes. (c) As in (b) but for the strongest ENSO trajectoryfrom the 40 perturbed reforecasts. (d,e,f) As in (a,b,c) but for one of the strongest ENSO epochs in the control run(M7 in Figure 8.3); the 20‐year low‐pass control‐run climatology from (d) is subtracted from each of (d,e,f).
Contour interval is 1K, with shading incremented every half‐contour. The zero contour is omitted for clarity.

can be used as “libraries” of analogs of real‐world condi­
tions. The time evolution of these “model‐analogs” canthen be used as actual forecasts, which exhibit real‐world
skill that actually outperforms state‐of‐the‐art multi­
model forecasts (initialized via comprehensive data
assimilation) for the equatorial Pacific region. These fas­
cinating results point to an exciting future where existing
simulations could be used both to provide skillful andrapid ENSO forecasts and to reliably assess and attributedecadal‐scale variations in ENSO’s behavior andpredictability.
8.7. ENSO MODULATION AND THE GLOBALWARMING HIATUS
As is clear from the previous discussion, ENSO decadal
modulation typically manifests as decades of particularly
strong El Niño events (e.g. the 1980s and 1990s) alternat­

190

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

ing with decades of weak events (e.g. the 2000s). This
becomes important in the context of the global warming“hiatus” of 1998–2013, a time interval when global meansurface temperature (GMST) remained steady, or at thevery least the rate of GMST change was reduced (e.g.,Trenberth & Fasullo, 2013; Trenberth et al., 2014; Schmidt
et al., 2014; Fyfe et al., 2016). Similar “hiatus” intervals
are seen throughout the climate record of the 20th century.
Several physical mechanisms have been offered to
explain the most recent hiatus as well as other hiatuses,
including but not limited to eastern Pacific cooling,
Walker cell strengthening, enhanced ocean heat uptake,changes in stratospheric water vapor and aerosols, andatmospheric internal variability (e.g. Lyman et al., 2010;
Solomon et al., 2010; Kaufmann et al., 2011; Levitus
et al., 2012; Kosaka & Xie, 2013; Huber & Knutti, 2014;
England et al., 2014; Santer et al., 2014; Schmidt et al.,2014; Watanabe et al., 2014; Dai et al., 2015; Liu et al.,2016; R. Zhang et al., 2016; L. Dong & McPhaden,
2017a; Deser et al. 2017a), while some argue that the
hiatus itself depends on the datasets and the methods ofanalysis used (Karl et al., 2015).
Yet the simplest explanation for the hiatus comes fromENSO decadal modulation (Hu & Fedorov, 2017b). In
fact, because of huge heat release from the ocean to theatmosphere, strong El Niño events have significant
impacts on global mean temperatures, up to +0.2°C forsuch events as 1997 or 2015, which may last for a year or
longer (Figure 8.10a). Strong La Niña events cool theplanet, but the impacts are a little smaller. A sequence ofstrong and then weak El Niño events can easily create an
appearance of a flat plateau in the GMST record, while astrong event such as El Niño of 2015 can give rise to a
rapid warming. Consequently, ENSO decadal modula­
tions lead to decadal modulations of GMST on the orderof 1°C (Figure 8.10b).
A simple model that describes GMST and incorporates
atmospheric heat release due to ENSO is given by theequation below (Hu & Fedorov, 2017b):
dTg
dt

Tg

a log

CO2
CO2,refb TNINOc SAOD d , (8.5)

where Tg is GMST, τ is a relaxation time scale, CO2 andCO2, ref are carbon dioxide concentration and its reference
level, TNINO is SSTA averaged over the central‐easternequatorial Pacific, and SAOD is stratospheric aerosol
optical depth, which account for the impacts of volcanic
eruptions. Using the best‐fit model parameters a through dafter training, this simple model reproduces the GMST his­
tory since the late 19th century very well (Figure 8.10c),including the global warming hiatus intervals. However,when the ENSO forcing is suppressed, the model fails to

capture not only interannual variations as expected, butalso multidecadal variations, including the recent hiatus.
Computations with comprehensive climate models with
Niño3 temperature restored to the observed values alsosuggest the importance of ENSO modulations for GMST
variations (Kosaka & Xie, 2013, 2014), although atmo­
spheric internal variability and model biases in ENSO tele­connections must also be considered (Deser et al., 2017a).
8.8. CONCLUSIONS
Even though ENSO is the dominant climate mode on
interannual timescales, it bridges a broad range of time­scales, ranging from intraseasonal (westerly and easterly
wind bursts, the Madden Julian oscillation) to multi­
decadal (the PDO/IPO, global warming trends). These
different scales are not independent; for example, windbursts are known to be modulated by ENSO (Eisenman
et al., 2005; Yu et al., 2003) and possibly by decadal cli­mate variability (Hu & Fedorov, 2016). On the other
hand, a sequence of strong westerly wind bursts that
occur during a favorable state of the tropical ocean‐
atmosphere, e.g. an enhanced ocean heat content, a warm
pool extended to the east and/or reduced surface west­
ward current (McPhaden & Yu, 1999; Hu et al., 2014;
Fedorov et al., 2015; Hu & Fedorov, 2017a; Lengaigne
et al., 2004; Thual et al., 2016), can lead to an extreme El
Niño event or events, which may be sufficient to generate
ENSO decadal modulation (Gebbie et al., 2007). In turn,such decadal modulations can affect global mean surface
temperature and lead to either a transient rapid rise or
hiatus in GMST. Yet global warming itself can modulate
the properties of ENSO (chapter 13 in this book).
Overall, ENSO and tropical decadal variability areclosely connected. For example, the weak ENSO activity
since 2000, as compared to the 1980s and the 1990s, is sug­gested to be caused by the decadal strengthening of zonal
and cross‐equatorial winds in the eastern Pacific (e.g. Hu& Fedorov, 2018; Zhao & Fedorov, 2020). The zonal winds
strengthen as part of the negative phase of the PDO, whichshould play a role in modulating ENSO properties, but thecausality between the two phenomena is often hard totease out (McPhaden et al., 2011; Capotondi et al., 2015a).
The question of causality remains one of the key ques­
tions of ENSO modulation as changes in the mean state
affect the ENSO cycle, whereas ENSO modulation con­tributes to decadal variations. In fact, simple theories
often treat PDO/IPO as a result of reddening of the
ENSO signal (Newman et al., 2003) with additional
dynamics in midlatitudes. Comparing the timeseries of
the Niño3 SST (Figure 8.1a) and the PDO/IPO index(Figure 8.7b) one cannot help but notice that positive
phases of the PDO/IPO correspond to the intervals ofstrong El Niño events while negative phases to weak El

LOW-FREQUENCY MODULATION AND MEAN STATE INTERACTIONS
(a)
2.0
1.5

TNINO (°C)

1.0
0.5
0.0
–0.5
–1.0
–1.5
1880

1900

1920

1940

1980

2000

2020

1980

2000

2020

1980

2000

2020

Year

(b)
ENSO-forced GMST (°C)

1960

0.20
0.10
0.00
–0.10
–0.20
1880

1900

1920

1940

(c)
0.9

Observation
Model (all)

0.6
GMST (°C)

1960

Year

0.3
0.0
–0.3
–0.6
1880

1900

1920

1940

1960

Year

Figure 8.10 Modeled variations in (a) equatorial SST anomalies averaged within 5°S–5°N, 160°E–90°W (TNIÑO)and (b) GMST when forced solely by this ENSO signal (i.e. Eq. [8.5] with CO2 and volcanic forcing suppressed).
Brown lines indicate annual means, while green lines indicate 10‐year running means. Long‐term means are
removed from both variables. Note the modeled GMST has less power at higher frequencies. This reddening is aconsequence of the system’s thermal inertia. The ENSO forcing generates decadal variations in GMST withamplitude up to 0.1°C, which is sufficient to temporarily mask CO2‐induced trends of ~0.1°C per decade. (c)GMST variations estimated from the observations (red) and computed from the simple model (black) with all forcings in Eq. 8.5 active. After Hu and Fedorov (2017b).

191

192

EL NIÑO SOUTHERN OSCILLATION IN A CHANGING CLIMATE

Niño activity. Thus, a question arises: Is it the PDO/IPO
modulating ENSO, or is it ENSO modulation that is
responsible for the decadal variability?
Many other questions relevant to ENSO decadal mod­ulations also remain unresolved. It was discovered earlyin computation with the Cane‐Zebiak and otherintermediate and hybrid coupled models that ENSO isvery sensitive to small changes in various parameterssuch as the ocean‐atmosphere coupling strength (e.g.
Zebiak & Cane 1987; Battisiti & Hirst, 1989; Jin & Neelin,
1993; Fedorov & Philander, 2001; Wittenberg, 2002;
Fedorov 2010) or the meridional extent of wind anom­alies (Capotondi et al., 2006) or many different character­istics of the mean state as discussed in this chapter. It is
now well recognized that ENSO can be extremely sensitive
to the same parameters in complex GCMs and not neces­sarily in simple or intuitive ways. However, whether
ENSO has the same sensitivity in nature is not clear.
Likewise, it is not clear how unstable the internal mode
describing ENSO is. GCMs that have a strongly unstableinternal mode typically exhibit strong ENSO modula­
tions without any external forcing, but also have anapparently excessive amplitude of El Niño.
Furthermore, how would one distinguish between thetwo main paradigms explaining the observed modulationof ENSO, i.e. internally generated variations due to thechaotic nature of the tropical coupled system versus
externally driven variations due to forced changes in thetropical background state? This is a fundamental issue
that might remain unresolved for some time. While it canbe possible to answer this question in a particular climate
GCM, even though the question of causality may be
nontrivial, it is a much more difficult task to determine
the answer in nature, especially given the limited lengthand accuracy of the available observations (~150 years).
Just to obtain a statistically robust ENSO spectrum, one
may have to run a GCM for 500 years or longer (e.g.
Wittenberg, 2009). It is feasible that both paradigms
operate at the same time: in models, inherently generated
ENSO modulations are typically comparable in magni­
tude to ENSO changes caused by realistic changes in themean state.
As models continue to improve, we may be able to moreclearly distinguish the roles of external forcing frominternal ENSO modulation. Also important are improved
observations – including instrumental measurements toconstrain the processes and feedbacks operating in simu­
lations, paleo proxy records to illuminate the past
behavior of ENSO, and comprehensive multicentury
reanalyses to integrate all the diverse observations. Recent
advances in those areas are described throughout thisbook and offer hope for better predictions and projectionsof ENSO and its modulation in the future. The mosturgent question that we may be able to answer relatively

soon is whether global warming is already affecting
ENSO (chapter 13), as climate change represents perhaps
the strongest perturbation experiment we can observe.
ACKNOWLEDGMENTS
This study was supported by grants to A.V.F. from NSF(AGS‐1405272) and NASA (NNX17AH21G), by the
Guggenheim Fellowship, and the ARCHANGE project(ANR-18-MPGA-0001, France). S.H. was supported by
the Scripps Institutional Postdoctoral Fellowship andthe Lamont-Doherty Postdoctoral Fellowship. Figure 8.7
was produced by Adam Phillips at NCAR. We thankAntonietta Capotondi and another, anonymous reviewerof the paper, as well as participants of an ENSO workshop
at CSIRO in Hobart (2019), for their suggestions.
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