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Chen Nan, Thual Sulian and Stuecker Malte F, El Niño and the Southern Oscillation: Theory, Reference Module in Earth Systems and Environmental Sciences, Elsevier, 2019. 30-Jan-19 doi: 10.1016/B978-0-12-409548-9.11765-8.
El Niño and the Southern Oscillation: TheoryNan Chen, University of Wisconsin—Madison, Madison, WI, United StatesSulian Thual, Fudan University, Shanghai, ChinaMalte F Stuecker, Institute for Basic Science (IBS), Busan, Republic of Korea; Pusan National University, Busan, Republic of Korea
Introduction 1Basic Features of ENSO 1The Climatological Mean State of the Tropical Pacific 1El Niño and La Niña 1The Turnabout of ENSO and the Genesis of Its Oscillation 2Intermediate Complexity Models of the Equatorial Pacific 3ENSO Triggers, Inhibitors, and Nonlinearities 5ENSO Complexity 6ENSO Asymmetry 6ENSO Seasonality 6ENSO Diversity 7Summary and Outlook 7Further Reading 8
Introduction
The El Niño Southern Oscillation (ENSO) is the most prominent interannual climate variation on Earth with large ecological andsocietal impacts. For instance, El Niño events can induce extreme weather events such as floods in Peru and Ecuador, droughts inIndonesia and Papua New Guinea, or decreased hurricane activity in the eastern Pacific. The ENSO consists of a cycle of anomalousconditions in winds and sea surface temperatures (SST) over the tropical eastern Pacific ocean that affect weather and climate acrossthe entire globe. The warm phase of the SST is known as El Niño and the cold one as La Niña. The Southern Oscillation is theaccompanying atmospheric component that covaries with the SST variations. El Niño is accompanied by weakened trade winds inthe tropical Pacific, while La Niña is accompanied by enhanced trade winds. ENSO exhibits considerable irregularity in amplitude,duration, temporal evolution, and spatial structure; which is referred to as ENSO complexity.
Basic Features of ENSO
The Climatological Mean State of the Tropical Pacific
The existence of ENSO can be explained by a strong dynamical coupling between the equatorial Pacific ocean and the overlyingatmosphere. Fig. 1A shows the normal climatological conditions of the equatorial Pacific ocean. Strong trade winds blow along theequator from the east to the west, which result in a pronounced zonal slope of the thermocline—the strong stratification in theocean that separates the surface layer of warm water from the colder (and denser) water below (Fig. 2). Due to the thermoclineslope, the trade winds lead to upwelling of cold subsurface water in the eastern equatorial Pacific and an accumulation of the largestamount of warm water (SST >28
�C) on the planet in the western equatorial Pacific (known as the warm pool region). The warm
surface water in the west drives intense deep atmospheric convection, while the east is characterized by atmospheric subsidence. Theatmospheric circulation cell driven by these regions of convection and subsidence is named the Walker circulation, which in turn isresponsible for the trade winds and the slope of the thermocline. This strong coupling between the thermocline, the SST, and thesurface winds in the tropical Pacific has been described by Jacob Bjerknes in his seminal 1969 paper and is the reason why ENSOexists.
El Niño and La Niña
El Niño and La Niña episodes represent periods of above- and below-average SSTs in the eastern equatorial Pacific (see Fig. 1B andC). Both El Niño and La Niña can be regarded as the destabilization from the normal climatological condition due to a positivefeedback loop proposed by Jacob Bjerknes and therefore termed Bjerknes feedback. An initial slight weakening of the trade windsdue would result in a reduced slope of the thermocline, and thereby upwelling of anomalously warm water in the eastern equatorialPacific (thermocline feedback), as well as anomalous advection of warm surface water towards the east (zonal advective feedback).The warming of the eastern equatorial Pacific will in turn further reduce the trade winds and thereby constitute a positive feedbackloop and result in an El Niño event. A similar reasoning can be followed for a slight initial increase of the trade winds that is
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Fig. 1 Schematic representation of (A) normal, (B) El Niño, and (C) La Niña conditions. Red colors correspond to warm water temperatures, and blue colors tocolder subsurface water temperatures. Adapted from McPhaden, M.J. et al. (1998). The Tropical Ocean-Global Atmosphere observing system: A decade of progress.Journal of Geophysical Research, 103(C7), 14169–14240.
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amplified via the positive Bjerknes feedback and will result in La Niña. The turnabout of ENSO requires an additional negativefeedback mechanism, which will be discussed in the next section.
The Turnabout of ENSO and the Genesis of Its Oscillation
The negative feedback that is responsible for the turnabout of ENSO and an oscillation was proposed by Klaus Wyrtki in 1985 andinvolves slow adjustments of the thermocline in addition to the fast positive feedback that Bjerknes hypothesized. During an ElNiño event, ocean heat content is slowly discharged from the equator to the off-equatorial region, which allows for a shallowing ofthe zonal-mean equatorial thermocline and thus upwelling of colder subsurface waters in the eastern equatorial Pacific that result ina termination of El Niño and a transition to either neutral or La Niña conditions.
Fig. 3A shows the evolution of the two indices that are widely utilized to monitor the state of the equatorial Pacific from Fig. 1.These two indices are: (1) the Niño 3SST index, which is the area-averaged value of SST anomalies over the eastern Pacific(150�W–90�W and 5�S–5�N), and (2) the Warm Water Volume (WWV), which is the averaged value of thermocline depth(isothermal 20�C) representing the heat content over the entire Pacific basin (120�E–80�W and 5�S–5�N). The indices are definedas anomalies from the mean state. It is clear from Fig. 3A that the ENSO has a significant cyclic behavior (with a clockwise evolution
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Fig. 3 (A): Time evolution of Niño3 SST and WWV indices. Niño3 SST is the average of sea surface temperature (SST) anomalies in the region 150�W–90�W and5�N–5�S (Eastern Pacific). WWV (warm water volume) is the average of the thermocline depth anomalies (isothermal 20
�C) in the region 120�E–80�W and 5�N–5�S
(i.e., zonal mean). (B): the PDF of the Niño3 SST index and its Gaussian fit. Data source: NOAA/NCDC ERSST.v4 data and NOAA/PMEL TAO Project.
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in the phase space diagram), alternating between El Niño and La Niña conditions. As shown in Fig. 4 for individual events, El Niñotends to start with increased ocean heat content (top left quadrant) and then transitions to La Niña when the heat content isdepleted (bottom left quadrant).
This behavior was conceptualized in the recharge oscillator model by Fei-Fei Jin in 1997:
dtT ¼ �lT þ oHdtH ¼ �oT,
(1)
where T represents the eastern Pacific SST anomalies and H is the anomalous warm pool heat content, and l, o are constantcoefficients. Simple oscillators as the one in (1) are able to exhibit the key oscillating behavior as shown in Figs. 3–4 with a period ofaround 2–7 years.
Crucially, it is the combined effects of the fast positive feedbacks and delayed negative feedbacks that drive the system tooscillate. Here, the positive feedback is the Bjerknes feedback (encompassing the thermocline and zonal advective feedbacks)described in Section “El Niño and La Niña,” in which the Walker circulation and the zonal ocean temperature gradient intensify orattenuate each other (parameter l > 0 in (1)). Meanwhile, the delayed negative feedback is commonly attributed to the sloweradjustment of the ocean (parameter o > 0 in (1)).
Given an disturbance of the coupled tropical Pacific ocean-atmosphere system, a fast positive feedback results in the growth of anEl Niño event, which is then followed by a delayed negative feedback that reverses the initial growth to its opposite phase. The samegrowth/inversion mechanism applies for La Niña and the result is an oscillation. Note that different models have been developed toexplain the negative feedback. Despite the difference in the processes involved in those models, the underlying principles aresimilar. For example, while the equations in (1) explicitly emphasize the recharge/discharge of ocean heat content H, such amechanism can also be presented implicitly by a delayed oscillator equation:
dtT ¼ aT tð Þ � bT t � tð Þ,with fixed delay t that captures the negative feedbacks. Notably, the ENSO cycle in Figs. 3–4 shows additional features, such as amarked asymmetry with El Niño phases being of greater intensity and shorter duration than La Niña phases. This observational factallows the consideration that ENSO might be an incomplete cycle that would start with an El Niño event (top right) and end afterthe discharge of the thermal ocean heat content, allowing a role for deterministic nonlinear processes and/or stochastic processes inaddition to the linear dynamics discussed above. Importantly, the simple models introduced here can be extended to includerepresentations of these processes.
Intermediate Complexity Models of the Equatorial Pacific
Intermediate complexity models for ENSO describe the fluid dynamics and thermodynamics of the ocean and atmosphere withsome reasonable simplifications. The intermediate models allow a more complete and quantitative understanding of the funda-mental ENSO dynamics while they remain computationally efficient. Notably, the most prominent of these models—the Zebiak-Cane (ZC) model—was used to successfully predict the 1986/87 El Niño and thereby initiated decades of ENSO research thereafter.
A sketch of the main components in intermediate complexity models is as follows. First, the shallow water equations are usuallyutilized in these models to describe the temporal evolution of the equatorial ocean. Consistent with the observations in Fig. 2, thesemodels assume that the ocean is well stratified, that is, the thermocline separates two layers of different densities. A basicnondimensional example of this type of the dynamics reads:
In (2), x is longitude, y is latitude, and t is time; U and V are anomalies of zonal and meridional currents in the upper layer,respectively, andH represents thermocline depth anomalies. Eq. (2) describes the essential ocean dynamics required for ENSO. Thefirst two equations in (2) describe the balance between the horizontal acceleration @tU, the Coriolis force �yU, the pressure created
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Fig. 4 Evolution of several El Niño events in the phase space of the Niño3-SST and WWV indices from Fig. 2. The Niño, Niña, recharge and discharge phases areindicated. Evolution is mainly clockwise. Data source: NOAA/NCDC ERSST.v4 data and NOAA/PMEL TAO Project.
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by the thermocline gradient @xH, and the friction by winds at the surface tx. The last equation describes the conservation of massabove the thermocline.
Second, a thermodynamical budget is considered to describe the evolution of the SST anomalies in a thin and well-mixed surfacelayer (around 50m). A typical dynamical model is given by:
@t þ eð ÞT þU@x�T þ �W@zTsub ¼ 0, (3)
where T is the SST anomalies, U is the zonal current anomalies, and W is the vertical current anomalies (also called upwelling).Meanwhile, �T, �U, and �W are the corresponding background climatological mean values that are usually prescribed. The mostsignificant heating contributions as retained in (3) result from the anomalous zonal advection of mean temperatures U@x
�T (zonaladvective feedback), the mean vertical advection of subsurface temperature anomalies �W@zT (thermocline feedback), and Newto-nian damping �eT of SST anomalies. Some other potentially important processes have been omitted in (3) for simplicity.An important approximation is to relate subsurface temperatures to the deepening or shallowing of the thermocline such thatthe vertical advection term in (3) can be expressed as �W@zTsub � �WgH where g is a constant.
Finally, the shallow water equations may also be utilized to describe the evolution of the equatorial atmosphere, which aresimilar in notations to (2) but describe instead the evolution of surface wind and geopotential height anomalies in response todiabatic heating due to deep convection induced by SST anomalies. It is reasonable to assume that the diabatic heating release isproportional to the SST anomalies and has a fixed vertical structure such that the dynamics can be described by a single verticalbaroclinic mode. In addition, the atmosphere adjustment (several days or weeks) is usually neglected because it is comparativelyfaster than the adjustment of the upper ocean (several months), which leads to a simple steady-state function for the expression ofwind stress in (2) with:
tx; ty� � ¼ g Tð Þ: (4)
Note that instead statistical descriptions of the wind stress response to SST anomalies can also be adopted in hybrid intermediatecomplexity models. Intermediate complexity models are able to describe the two essential components of the ENSO cycles in aquantitative way, namely the development of El Niño and La Niña episodes and the transition between these episodes.
First, the delayed negative feedback allows the interchange between El Niño and La Niña conditions, which is attributed to theocean adjustment. In fact, the shallow water Eq. (2) admits the equatorial wave solutions, namely the Kelvin and the Rossby wavesas sketched in Fig. 5, to be responsible for such an adjustment. These waves have a planetary scale (>1000km) in the zonal direction
Fig. 5 Sketch of oceanic equatorial waves propagations.
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but they remain confined in a narrow equatorial band (around 5�N–5�S in latitude) due to the Coriolis force. The Kelvin andRossby waves propagate eastward and westward, respectively, and are partially reflected at the equatorial Pacific boundaries that arethe South American coast (�80�W), as well as the maritime continent (�130�E).
It is the surface wind stress that triggers both the Kelvin and Rossby waves, which carry oceanic properties of opposite signeastward and westward and eventually reverse the conditions after propagation and reflection, resulting in delayed negativefeedbacks. The oceanic Kelvin waves take around 2 months to cross the entire equatorial Pacific while the oceanic Rossby wavestake about 6 months. One example of the delayed negative feedback is the net effect of equatorial waves that recharges or dischargesthe overall heat content in the equatorial Pacific (defined as the zonal mean of thermocline depth or the western Pacific warm watervolume).
While the ocean adjustment is essential for the delayed reversal of El Niño or La Niña conditions, the picture is certainlyincomplete without accounting for the key roles played by the coupling between ocean and atmosphere. The identification andclassification of positive and negative feedbacks from the equatorial Pacific can be described in terms of instabilities. The behaviorof the coupled system is essentially controlled by the dominant instabilities, which typically lead to an initial disturbance. Thesedominant instabilities are usually identified by using (but not limited to) linear stability analysis which is able to quantify the scales,structures, evolutions, and the driving processes of these instabilities.
Note that the prevailing instabilities in the equatorial Pacific are coupled, that is, they result from an interaction between theocean and the atmosphere. In fact, if the ocean and atmosphere were decoupled, then small perturbations in either of them wouldquickly decay. ENSO is believed to reside in amost interesting dynamical regime where the scales and adjustment times of the oceanand atmosphere are comparable to each other (i.e., involving positive and negative feedbacks of comparable strengths). Theassociated instabilities have a mixed behavior of free oceanic waves and the modes that depend primarily on ocean-atmosphereinteractions such as the Bjerknes feedback as described above.
ENSO Triggers, Inhibitors, and Nonlinearities
Beyond the analysis of coupled instabilities, several other important ingredients need to be included into the coupled model inorder to describe the ENSO dynamics in an accurate fashion. Fundamental questions about the dynamical evolutions of ENSOinclude: how is the ENSO cycle triggered, maintained, and terminated? Different scenarios can be expected depending on theinstability mechanisms and the coupling between the atmosphere and ocean components. For example, if the system is stronglydamped, then ENSO is primarily a response to external forcing. On the other hand, if the system is strongly unstable, then the ENSOshould be limited by internal nonlinearities.
A broad range of atmospheric disturbances in the tropics may be considered as possible triggers or inhibitors to ENSO variability.These atmospheric disturbances originate mainly from the enhanced convection over the western Pacific warm pool (due to thewarm SST typically above 28
�C) although sometimes the atmospheric disturbances also have extratropical origins. An example is
shown in Fig. 6.
Fig. 6 Spatio-temporal evolution of anomalous zonal winds during the El Niño event of 1997/98 (NCEP/NCAR reanalysis data). The zonal winds are decomposedinto low-frequency wind variability associated with ENSO (computed as a 90 days running mean) and high-frequency intraseasonal wind bursts (computed as theresidual). The average in the western Pacific region (140�E–180�E) shows the complete reconstruction (low-frequency wind variability in black, westerly wind burstsin red, and easterly wind bursts in blue).
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Those disturbances are classified depending on the orientation of the anomalous zonal winds near the equator, as either westerlywind bursts (with winds blowing eastward) or easterly wind bursts (with winds blowing westward). The most relevant wind burstshave a zonal wind velocity amplitude of around 5 � 20 ms�1 with a large zonal fetch and these wind bursts typically last5–30 days. This intraseasonal variability is largely stochastic and disorganized. However, it is able to trigger or inhibit El Niño orLa Niña events by destabilizing the slower interannual coupling in the equatorial Pacific. Such a behavior is conceivable from thepoint of view of a slightly dissipated oscillating coupled system with stochastic perturbations. In fact, a basic modification of (1)that encompasses this behavior reads:
dtT ¼ �lT þ oH þ x Tð Þ,dtH ¼ �oT,
(5)
where x describes a stochastic noise source. Note that the noise is usually considered to be multiplicative (In other words, the noisedepends on the state variable T ). The physical reasoning is that increased SST in the western Pacific enhances the probability of deepconvection and the strength of wind bursts. Due to this dependency, atmospheric wind bursts are more prominent at the onset ofthe El Niño events and have a significant impact on the evolution of the events. An illustrative example is the major El Niño eventthat occurred in 2015/2016. In fact, an El Niño event was forecasted to develop and peak in 2014/2015. However, one hypothesisfor why it did not develop in that winter and instead grew to a large El Niño in the subsequent year are the occurrence of strongeasterly bursts during boreal spring 2014.
The coupled system of the equatorial Pacific also includes several fundamental nonlinearities that are essential for ENSOdynamics. Such nonlinearities may explain the unique nature of ENSO as seen in Fig. 3, with SST anomalies growing to largeamplitudes during El Niño events compared to typically weaker but longer lasting La Niña conditions. Such a behavior can forinstance be described by a coupled system transiting back and forth between an unstable regime and a stable one (called a Hopfbifurcation). A straightforward modification of (1) that encompasses this behavior reads:
dtT ¼ �lT þ o1H if H < ddtT ¼ �lT þ o2H if H � ddtH ¼ �oT,
(6)
where the parameter o in (1) now takes different values o1 and o2 that depend on the system stateH relative to a given threshold d.For H < d, the parameter o is given by o ¼ o1, and the coupled system is unstable that allows a spontaneous growth of El Niñoevents. When H � d the system transits to a stable regime with o ¼ o2 that leads to the relaxation to La Niña conditions. Thetransition between the stable and unstable regimes allows the system to reach a limit cycle. We emphasize that many other types ofnonlinearities are involved in simple and intermediate complexity models in addition to the example given in (6).
ENSO Complexity
Despite a relatively clear understanding of the basic mechanisms such as the positive Bjerknes feedback and the slow recharge/discharge of equatorial ocean heat content, ENSO involves other remarkable multiscale and nonlinear features that greatly increaseits overall spatial and temporal complexity.
ENSO Asymmetry
One of the notable features of ENSO is its asymmetry between El Niño and La Niña phases in terms of amplitude, duration,temporal evolution, and spatial structure. As mentioned earlier, El Niño events typically have a larger amplitude and shorterduration compared to La Niña events, which is clearly seen in Fig. 3A. This characteristic also leads to a strongly non-Gaussianprobability density function (PDF) of the SST anomalies averaged over the eastern equatorial Pacific. The PDF exhibits positiveskewness due to the El Niño—La Niña asymmetry and has a one-sided fat tail that is associated with extreme El Niño events(Fig. 3B).
This asymmetry can be explained by the fundamental nonlinearities and multiplicative (state-dependent) noise in the coupledatmosphere-ocean system. For instance, one pronounced nonlinearity is the outcropping of the thermocline in the eastern Pacificthat limits the maximum potential amplitude of La Niña events. Moreover, nonlinear dynamical heating can lead to the SSTanomaly asymmetry between El Niño and La Niña. Another possible mechanism results from the aforementioned state-dependentnoise. The probability for deep atmospheric convection to occur increases significantly in the western Pacific when SST are above athreshold of �28
�C. The associated frequency and strength of intraseasonal wind bursts in turn increases, which is a typical feature
seen during the onset of strong El Niño events. In contrast, much weaker and less frequent wind bursts typically occur during theonset phase of La Niña.
ENSO Seasonality
Another pronounced feature of ENSO is its seasonal synchronization. Fig. 7 shows a composite representation of El Niño events as afunction of calendar months. El Niño events tend to develop around boreal spring and peak in boreal winter. A likely hypothesis to
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explain this phenomenon are seasonal variations in the atmospheric and oceanic conditions, which are able to modulate the linearstability of the equatorial Pacific. Therefore, certain months of the year are more favorable to the development of ENSO.
Furthermore, the termination of ENSO events are also seasonally-paced due to its nonlinear interactions with the seasonal cycle.For example, a pronounced southward shift of anomalous equatorial winds typically occurs in boreal winter during El Niño events,which is driven by the seasonal migration of the western Pacific warm pool. The sudden relaxation of the wind anomalies on theequator acts to accelerate the termination of El Niño and its transition to La Niña, thereby contribution to the seasonalsynchronization of ENSO. This interaction between interannual ENSO variability and the seasonal cycle leads to additionaldeterministic modes of climate variability, so-called ENSO combination modes (C-modes), which are characterized by predomi-nantly near-annual timescales. These C-modes play an important role in bridging ENSO’s impact for instance to the East Asianmonsoon region. In addition, in an unstable nonlinear ENSO regime, the seasonal cycle can lead to frequency locking anddeterministic chaos in the coupled system. However, it seems that ENSO likely operates in a stable damped regime in observations.
ENSO Diversity
A common way to highlight ENSO diversity is to contrast the anomalous SST patterns at the height of different ENSO events.In addition to the canonical El Niño (also known as the eastern Pacific (EP) El Niño) as shown in Fig. 1B, for which the anomalouspositive SST are maximized in the equatorial eastern Pacific ocean, a renewed interest in ENSO diversity is stimulated by a differenttype of El Niño that has been frequently observed in recent decades and is called the central Pacific (CP) El Niño or El Niño Modoki(a Japanese word that means similar but different). CP El Niño events are characterized by positive SST anomalies confined to thecentral Pacific (Fig. 8). Such zonal SST gradients result in an anomalous two-cell Walker circulation over the tropical Pacific, withstrong anomalous convection located in the central Pacific. Note that the heat content recharge and discharge as shown in Fig. 4 isweaker during CP El Niño events (in particular from years 2000–10). This observation suggests that different driving mechanismsother than the thermocline feedback provide the dominant positive feedback for CP events. In fact, it has been shown that zonaladvective feedback is the dominant mechanism for CP El Niño. Notably, a consensus seems to emerge that these two types of ElNiño events are not dynamically distinct. Instead, ENSO likely resides in a continuum between the two types of events. Here, thecanonical EP type, usually stronger in intensity, is believed to be an extreme realization within the continuum involving strongernonlinear processes.
Summary and Outlook
ENSO is the most predictable low-frequency climate phenomenon in the world besides the seasonal cycle. A hierarchy of modelspresented in Sections “The Turnabout of ENSO and the Genesis of Its Oscillation” and “Intermediate Complexity Models of theEquatorial Pacific” has been utilized to both study ENSO’s fundamental dynamics as well as forecast its future evolution. Most ofthese models, despite their differences in complexity, perform considerably better than persistence forecasts in predicting typicalENSO indices at lead times between 6 and 12 months.
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Fig. 8 SST anomalies during December 1997 (EP El Niño) and December 2009 (CP El Niño), as a function of longitude and latitude. Data is from the ERSSTreanalysis V5.
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This predictability originates from the deterministic nature of ENSO, in which the inertia of the coupled system (especially of theupper ocean) provides the source of predictability. Despite demonstrated predictive skill of seasonal forecasts, the ENSO predict-ability limits remain unknown. Other features of the climate system that are known to be associated with ENSO, such as SSTanomalies in the off-equatorial Pacific and in other ocean basins, might potentially provide additional sources of predictability andare active areas of current research.
One important feature of ENSO predictability is that it varies strongly in different seasons. As discussed earlier, El Niño eventstypically peak during boreal winter and then decay or transition to La Niña during the following spring or summer. Seasonalforecasts initialized in boreal spring typically display the lowest skill, a phenomenon long known as the spring predictability barrier.In addition, the predictability of ENSO seems to have decreased during recent decades despite advancements in both observing andforecasting systems. It is hypothesized that the more frequent emergence of CP ENSO events in recent decades might havecontributed to this decrease in skill. Furthermore, it is still strongly debated how potential predictability depends on the phase ofENSO (i.e., during El Niño and during La Niña). How ENSO changed in the past and how it might change in the future in responseto greenhouse gas forcing are additional important areas of active research that provide a powerful testbed for the different ENSOtheories discussed here.
Further Reading
Bjerknes J (1969) Atmospheric teleconnections from the equatorial Pacific. Monthly Weather Review 97(3): 163–172.Wyrtki K (1985) Water displacements in the Pacific and the genesis of El Niño cycles. Journal of Geophysical Research: Oceans 90(C4): 7129–7132.Jin F-F (1997) An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. Journal of the Atmospheric Sciences 54(7): 811–829.Zebiak SE and Cane MA (1987) A model El Niño-southern oscillation. Monthly Weather Review 115(10): 2262–2278.Clarke AJ (2008) An introduction to the dynamics of El Niño and the southern oscillation, 324 pp. Academic Press.Dijkstra HA (2006) The ENSO phenomenon: Theory and mechanisms. Advances in Geosciences 6: 3–15. https://doi.org/10.5194/adgeo-6-3-2006.Gill AE (1982) Atmosphere-Ocean dynamics. New York, NY: Academic Press.McPhaden MJ, et al. (1998) The Tropical Ocean-global atmosphere observing system: A decade of progress. Journal of Geophysical Research 103(C7): 14169–14240.Neelin JD, Battisti DS, Hirst AC, et al. (1998) ENSO theory. Journal of Geophysical Research 103: 14261–14290.Philander SGH (1990) El Niño, La Niña and the southern oscillation. New York, NY: Academic Press.Timmerman A, et al. (2018) El Niño–southern oscillation complexity. Nature. https://doi.org/10.1038/s41586-018-0252-6.Wang C and Picaut J (2004) Understanding ENSO physics: A review, geophysical monograph, vol. 147, pp. 21–48.
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