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Two-Year Dynamical Predictions of ENSO Event Duration during 1954–2015
XIAN WU,a YUKO M. OKUMURA,a CLARA DESER,b AND PEDRO N. DINEZIOca

Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texas
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, ColoradocDepartment of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Coloradob(Manuscript received 5 August 2020, in final form 7 February 2021)
ABSTRACT: El Niño and La Niña events show a wide range of durations over the historical record. The predictability ofevent duration has remained largely unknown, although multiyear events could prolong their climate impacts. To explore
the predictability of El Niño and La Niña event duration, multiyear ensemble forecasts are conducted with the Community
Earth System Model, version 1 (CESM1). The 10–40-member forecasts are initialized with observed oceanic conditions on
1 March, 1 June, and 1 November of each year during 1954–2015; ensemble spread is created through slight perturbations to theatmospheric initial conditions. The CESM1 predicts the duration of individual El Niño and La Niña events with lead times
ranging from 6 to 25 months. In particular, forecasts initialized in November, near the first peak of El Niño or La Niña, canskillfully predict whether the event continues through the second year with 1-yr lead time. The occurrence of multiyear La Niña
events can be predicted even earlier with lead times up to 25 months, especially when they are preceded by strong El Niño. The
predictability of event duration arises from initial thermocline depth anomalies in the equatorial Pacific, as well as sea surface
temperature anomalies within and outside the tropical Pacific. The forecast error growth, on the other hand, originates mainly
from atmospheric variability over the North Pacific in boreal winter. The high predictability of event duration indicates thepotential for extending 12-month operational forecasts of El Niño and La Niña events by one additional year.
KEYWORDS: Pacific Ocean; Tropics; Atmosphere-ocean interaction; ENSO; Climate prediction

1. Introduction
The El Niño–Southern Oscillation (ENSO) phenomenon arises
from dynamic and thermodynamic interactions of the ocean andatmosphere in the tropical Pacific and is the largest source of climate variability on interannual time scales (e.g., Wallace et al. 1998;
Neelin et al. 1998; Wang and Picaut 2004; Chang et al. 2006).
Anomalously warm (El Niño) and cold (La Niña) sea surface
temperature (SST) conditions in the central and eastern equatorial
Pacific associated with ENSO alter the distribution of atmospheric
convection in the tropics and influence global weather patterns via
atmospheric teleconnections (e.g., Trenberth et al. 1998; Alexander
et al. 2002; Taschetto et al. 2020). Climate forecast centers aroundthe world have been making routine ENSO predictions since the1980s, which provide the basis for seasonal forecasts of temperatureand precipitation over North America and Europe (e.g., Kumar
and Hoerling 2000; Shukla et al. 2000; Kumar et al. 2014; Scaife
et al. 2014; L’Heureux et al. 2015, 2020). Early studies on ENSO
prediction focused on the onset and amplitude of ENSO events(Latif et al. 1998; Kirtman et al. 2002; Chen and Cane 2008; Jin et al.
2008). More recent research has expanded to include predictions ofDenotes content that is immediately available upon publication as open access.
Supplemental information related to this paper is available
at the Journals Online website: https://doi.org/10.1175/JCLI-D-200619.s1.
Corresponding author: Dr. Xian Wu, xianwu@ucar.edu

the spatial pattern of ENSO-related SST anomalies throughout thetropical Pacific basin (Hendon et al. 2009; Capotondi et al. 2015;
Imada et al. 2015). Long-range forecasts of the duration of ENSO
events have, however, received much less attention, with only a few
case studies focused on multiyear La Niña events and a single initialization month (DiNezio et al. 2017a,b; Luo et al. 2017). The
duration of ENSO events could affect the length of their associatedclimate and socioeconomic impacts (e.g., Hoerling and Kumar
2003; Okumura et al. 2017; Deepak et al. 2019), calling for accurate
predictions of ENSO event duration with sufficient lead times.
Recent progress has been made in understanding the characteristics and mechanisms of ENSO event duration. ENSO
events usually start to develop during boreal spring to summer
and peak in boreal winter. After the peak, individual ENSO
events show a wide range of temporal evolution in the subsequent year. About one in three El Niño events and one out of
two La Niña events persist for a second year (Wu et al. 2019).
Multiyear events account for a larger fraction for La Niña than
El Niño events, in agreement with the longer average durationof La Niña than El Niño shown by previous studies (Kessler
2002; Larkin and Harrison 2002; McPhaden and Zhang 2009;
Ohba and Ueda 2009; Okumura and Deser 2010; Wu et al.
2010; Dommenget et al. 2013; An et al. 2020). Previous studies
suggest that the primary factor controlling the duration of individual El Niño events is the onset timing (Horii and Hanawa
2004; Lee et al. 2014; Wu et al. 2019), while the duration ofindividual La Niña events is strongly affected by the amplitudeof preceding warm event (DiNezio and Deser 2014; Wu et al.
2019). These factors influence the duration of individual ENSO
events by modulating the timing and magnitude of negative
feedbacks within the tropical Pacific, as well as from the other

DOI: 10.1175/JCLI-D-20-0619.1
Ó 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).
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tropical oceans. El Niño events that develop early in boreal
summer tend to terminate shortly after their peak in boreal
winter, whereas those that develop in late boreal summer tofall usually last two years. The early onset timing of El Niño
leads to the earlier occurrence of Rossby wave reflection andsooner adjustment of tropical Atlantic and Indian Oceans by
the mature phase, both of which effectively terminate the eventby the following summer (Wu et al. 2019). In the meantime,
when La Niña follows strong El Niño, initial large thermocline
shoaling across the equatorial Pacific and strong warming of
the Atlantic and Indian Oceans together act to prolong the La
Niña duration, with the latter enhancing easterly wind anomalies over the western Pacific (DiNezio and Deser 2014; Wu
et al. 2019).
Do the long-lead precursors and associated dynamicalmechanisms for ENSO event duration indicate its degree ofpredictability? The potential predictability of ENSO event
duration has been explored in the perfect model predictions
conducted with the Community Earth System Model, version 1(CESM1; Kay et al. 2015), a climate model that realisticallysimulates the diverse temporal evolution of ENSO events(DiNezio et al. 2017a; Wu et al. 2021). In these idealized prediction experiments, the model predicts its own trajectories in
the control simulation with perfect initialization of oceanic, sea
ice, and land states and with roundoff level errors added toatmospheric initial conditions. Thus, these predictions are notaffected by the issues common in the operational ENSO
forecasts, including the errors in initial conditions and model
drift to its own climatology. Under the perfect model setting,
the CESM1 successfully predicts the termination of early-onset
El Niño and the multiyear persistence of late-onset El Niño
when initialized with oceanic conditions in the onset months(Wu et al. 2021). Multiyear La Niña can also be predicted when
the CESM1 is initialized around the peak of strong El Niño
(DiNezio et al. 2017a).
The real-world prediction skill of ENSO event duration remains largely unexplored other than the case study of the 2017–18 La Niña by DiNezio et al. (2017b). Current operational
ENSO forecasts are run forward for up to 12 months (Barnston
et al. 2012, 2019), thus precluding forecasts of multiyear ENSO
events. In addition, operational forecasts show difficulty predicting the reintensification of multiyear La Niña in winters of
2008, 2011, and 2017, even when initialized in boreal summer
before the second La Niña peak (https://iri.columbia.edu/ourexpertise/climate/forecasts/enso/archive/). The limited skill of
the operational forecasts in predicting the return of La Niña
could partly arise from the difficulties of climate models in
simulating the asymmetric duration of El Niño and La Niña
(Ohba et al. 2010). Using the retrospective forecasts conductedwith the CESM1, DiNezio et al. (2017b) showed that multiyear
La Niña events during 1954–2015 can be predicted 2 years inadvance when the model is initialized with observed oceanic
conditions near the peak of strong El Niño events. The predictability of these multiyear La Niña events is attributed tosubsurface oceanic memory associated with strong El Niño.
However, some multiyear La Niña events follow weak-tomoderate El Niño events (e.g., 1970–72 and 2007–09); conversely, the La Niña event that followed the relatively strong El

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Niño in 1986–87 lasted only one year. The predictability andunderlying mechanisms of these particular La Niña events remain unclear. Moreover, the predictability of El Niño durationin the real world has not yet been investigated.
In this study, we analyze a suite of multiyear ensemble
forecasts performed with the CESM1 and initialized with observed oceanic conditions on 1 March, 1 June, and 1 Novemberof each year from 1954 to 2015. The 3 3 62 ensemble forecasts
can provide a more comprehensive assessment of the predictability and mechanisms of observed ENSO event duration at
different lead times up to 25 months, as well as an assessment of
seasonal dependence of forecast skill. In particular, we address
the following questions. To what extent can we predict the
duration of El Niño and La Niña events in the real world?
What is the maximum lead time of skillful predictions,and what are the mechanisms affecting the predictability
and forecast error growth? Is it feasible to extend the current
12-month operational ENSO forecasts by one additional year?
The rest of the paper is organized as follows. Section 2 describes the model, forecast experiments, and analysis methods.
Section 3 estimates the predictability and error growth of
ENSO event duration in the forecasts and investigates theunderlying oceanic and atmospheric mechanisms. Section 4summarizes the main results and discusses the implications for
operational ENSO forecasts and outlook for future studies.

2. Model experiments and analysis methods
a. CESM1
We conduct a suite of multiyear ensemble forecasts with theCommunity Earth System Model, version 1 (CESM1), a stateof-the-art climate model developed at the National Center for
Atmospheric Research. The CESM1 consists of atmosphere,
ocean, land surface, and sea ice components linked by a flux
coupler (Hurrell et al. 2013; Kay et al. 2015). The atmosphericcomponent is the Community Atmosphere Model, version 5(CAM5) that uses a finite-volume dynamical core at a horizontal resolution of 0.98 latitude 3 1.258 longitude with 30
levels in vertical (Neale et al. 2012). The oceanic component,
the Parallel Ocean Program, version 2 (POP2; Smith et al.
2010), has meridional resolutions increasing from 0.658 at 608N
to 0.278 at the equator and 60 levels in the vertical. The landmodel is version 4 of the Community Land Model (CLM4;
Lawrence et al. 2011) that is run on the same horizontal grid as
the atmosphere model. The sea ice model, the Los Alamos
National Laboratory Community Ice Code, version 4 (Hunkeand Lipscomb 2008), uses the same horizontal grid as theocean model.
The CESM1 reproduces many important characteristics ofobserved ENSO behavior, including the broad spectral peak inthe 3–6-yr band, asymmetries between El Niño and La Niña,
and diversity in the amplitude, pattern, and temporal evolutionof individual events; however, the CESM1 overestimates theoverall amplitude of ENSO by about 20% (DiNezio et al.
2017a; Wu et al. 2019; Capotondi et al. 2020; Planton et al.
2021). Importantly, the CESM1 captures the observed ratio of
single and multiyear ENSO events and the associated oceanic

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TABLE 1. A summary of the forecast ensembles used in this study. Forecasts are initialized on 1 Mar, 1 Jun, and 1 Nov of each year during
1954–2015.
Initialization month Simulation length Ensemble size Ocean and sea ice initial conditions Atmosphere and land initial conditions
March
June
November

30 months
27 months
122 months

10
20
40

CORE-forced FOSI
CORE-forced FOSI
CORE-forced FOSI

and atmospheric mechanisms (Wu et al. 2019). Nevertheless,
ENSO anomalies simulated in the CESM1 show an excessive
extension to the western equatorial Pacific than observed(Capotondi et al. 2020), which is a common problem among
climate models and related to too cold climatological SST in
the equatorial Pacific (e.g., Bellenger et al. 2014; Graham et al.
2017; Planton et al. 2021). The potential effect of these model
biases on our forecasts of ENSO event duration will be briefly
discussed in section 3b.

b. Forecast experiments
To explore the predictability of ENSO event duration at different lead times, we analyze CESM1 multiyear ensemble forecasts initialized with observed oceanic conditions on 1 March,
1 June, and 1 November in each year during 1954–2015 (Table 1).
The forecasts initialized in November are part of the CESM
Decadal Prediction Large Ensemble (CESM-DPLE; Yeager
et al. 2018), which consists of 40 members of 10-yr simulations foreach initialization year. We use the first 30 months of forecasts
from the CESM-DPLE for our analysis. Following the CESMDPLE protocol, we conduct two additional sets of ensemble
forecasts initialized on 1 March and 1 June of each year. The
March- and June-initialized forecasts are integrated for 30 and
27 months, respectively, and consist of 10 and 20 members foreach initialization year, respectively. The three initialization
months correspond to the peak, decay, and onset phases of ENSO
events, and the comparison of forecasts initialized before and after
spring can show the influence of the spring predictability barrier.
The bootstrap analysis of 40-member November-initialized forecasts suggests that an ensemble size of 10 is sufficient to estimate
the ensemble mean and spread for 2-yr lead forecasts (see Fig. S2
in the online supplemental material).
In all three sets of forecasts, each ensemble is initialized withthe same ocean and sea ice conditions obtained from a simulation in which the ocean and sea ice components of CESM1
are forced with observed atmospheric and surface flux fields[hereafter called a forced ocean–sea ice simulation (FOSI)].
The surface fluxes are computed using bulk formulas based on
observed atmospheric fields from the Coordinated Ocean-Ice
Reference Experiment (CORE) forcing dataset (Yeager et al.
2015). Please refer to Yeager et al. (2018) for details of theocean and sea ice initialization. The atmosphere and land initial conditions for the CESM-DPLE are based on one ensemble member of the CESM Large Ensemble Project (Kayet al. 2015), in which ocean and sea ice conditions evolve freely.
For the March- and June-initialized forecasts, the atmosphereand land initial conditions are generated using a CAM5-CLM4
simulation forced with observed monthly ocean and sea ice
fields from the FOSI. This change in the initialization scheme

FOSI-forced CAM5-CLM4
FOSI-forced CAM5-CLM4
CESM1 uninitialized

was introduced to produce more realistic land initial conditions
over the tropics and midlatitude areas where atmosphericconditions are strongly controlled by SST forcing. A moreaccurate initialization of land features (e.g., soil moisture) may
improve predictions of hydroclimate variability over land.
However, we expect that most of the predictability of ENSO
events arises from oceanic initial states due to its longmemory. All ensemble members were initialized with atmospheric initial conditions modified with a unique perturbation
at the roundoff level of the computer (order 10214). The
smallest possible perturbations result in entirely uncorrelated
weather after about a week among the members, generating
the ensemble spread. All forecasts are run under the Coupled
Model Intercomparison Project phase 5 (CMIP5) ‘‘historical’’ forcing for 1954–2005 and the CMIP5 representative
concentration pathway 8.5 forcing for 2006–15. Table 1
summarizes the information on the forecast ensembles usedin this study.

c. Drift correction and detrending methods
Because the CESM1 does not perfectly reproduce the observed climatology, the observed initial conditions cause themodel to drift toward its own climatology as the simulation
proceeds. To remove this effect, a given output variable fromthe ensemble forecast, Y, is adjusted following a method thathas been extensively used for correcting the full-field initialization forecasts (CLIVAR 2011; Meehl and Teng 2012; Yeager
et al. 2012; Hazeleger et al. 2013). For each ensemble mean
forecasts initialized in year j, the variable Y is averaged acrossmembers 1 to m for lead time t (1.1). The resulting ensemble
mean variable is then averaged across all initialization years to
obtain a ‘‘drifting’’ baseline as a function of lead time (1.2). The‘‘drifting climatology’’ is then subtracted from each member ineach ensemble to yield drift-adjusted anomalies (1.3):
Yjt 5

1 m
åY ,
mk51 jkt

(1.1)

Yt 5

1n
åY ,
nj51 jt(1.2)

0
5 Yjkt 2 Yt ,
Yjkt

(1.3)

Here, Yjkt represents a given variable Y at lead time t from amember k of an ensemble initialized in year j, Yjt is the ensemble mean of Yjkt, and Yt is the ‘‘drifting’’ baseline common
0is the drift-corrected Yjkt. The
to all ensemble members; Yjkt‘‘drifting climatology’’ is calculated for the period of 1958–2015
for each lead time, to be consistent with the period of observational data used for forecast verification. To remove theUnauthenticated | Downloaded 09/25/23 04:38 PM UTC

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JOURNAL OF CLIMATETABLE 2. A list of 1- and 2-yr El Niño and La Niña events during
1954–2015, denoted by the years when the events first develop.
Years
1-yr El Niño
2-yr El Niño
1-yr La Niña
2-yr La Niña

1963, 1965, 1972, 1982, 1991, 1994, 1997,
2002, 2006, 2009
1957, 1968, 1976, 1986, 2014
1964, 1988, 1995, 2005
1954, 1970, 1973, 1983, 1998, 2007,2010, 2016

effect of global warming, we calculate the quadratic trend of
ensemble means at each lead time and subtract it from eachensemble member.

d. Observational data and forecast verification
The forecasts of ENSO events are verified against several observational datasets through composite and correlation analyses. For
SST, we use the Hadley Centre Sea Ice and SST dataset (Rayner
et al. 2003) available for 1870–2019 on a 18 grid. The thermocline
depth, which is defined as the depth of maximum vertical temperature gradient, is derived from the European Centre for MediumRange Weather Forecasts (ECMWF) Ocean Reanalysis System 4(Balmaseda et al. 2013) available for 1958–2017 on a 18 grid with 42
levels in the vertical. For surface wind components, we make use of
the National Centers for Environmental Prediction (NCEP)–
National Center for Atmospheric Research (NCAR) reanalysis(Kalnay et al. 1996) available for 1948–2017 on a 2.58 grid. As for theensemble forecasts, observed monthly climatology is calculated for
1958–2015 and monthly anomalies are quadratically detrended.
We select observed ENSO events for forecast verification
based on SST anomalies averaged over the Niño-3.4 region(1708–1208W, 58S–58N; hereafter called the Niño-3.4 index).
The year when an ENSO event first develops is denoted as year
0 and the months of the year, using the first three letters of eachmonth name, as Jan0, Feb0, . . . , and so on to Dec0. Following
Wu et al. (2019), El Niño and La Niña events are defined when
the Niño-3.4 index smoothed with a 3-month running mean
filter is greater than 0.75 standard deviations and less than 20.75
standard deviations in any month from Oct0 to Feb11 (i.e.
February of the following year), respectively. The standard deviation of the smoothed Niño-3.4 index is calculated separately
for each month, ranging from 0.898 to 1.098C during October–
February. El Niño and La Niña events are further classified into
2-yr events when the smoothed Niño-3.4 index remains above
0.5 standard deviations and less than 20.5 standard deviations in
any month from Oct11 to Feb12, respectively, and otherwise
into 1-yr events. The weaker threshold value used for the second
compared to the first year is based on the observed features of
1-yr and 2-yr ENSO events (Wu et al. 2019). During the period
for which the CESM1 ensemble forecasts are available (1954–2017), there are ten 1-yr El Niño, five 2-yr El Niño, four 1-yr La
Niña, and eight 2-yr La Niña events (Table 2). Figure 1 andFig. S1 show the time series of the Niño-3.4 index in observations
and forecasts for individual 1- and 2-yr El Niño/La Niña events.
Using the three sets of CESM1 forecast ensembles initialized in March, June, and November, we examine how far inadvance the duration of observed ENSO events can be

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predicted. The Niño-3.4 index in Dec11 is used as an indicator of
the ENSO event duration, and the forecast lead time is defined
relative to Dec11. For simplicity, we use threshold values of 0.58C
and 20.58C, which is close to 0.5 standard deviation of the Dec11
Niño-3.4 index in CESM1 (0.588C), to determine the predicted
duration of El Niño and La Niña events, respectively. For all El
Niño and La Niña events during 1954–2015, we compare thetemporal evolution of the Niño-3.4 index between observations
and ensemble forecasts initialized in Nov21, Mar0, Jun0, Nov0,
Mar11, and Jun11, which give lead times ranging from 25 to
6 months. To assess the ability of CESM1 in predicting the duration of El Niño and La Niña, we compare observed and predicted
evolutions of the Niño-3.4 index and other oceanic and atmospheric variables composited for all 1- and 2-yr El Niño and La
Niña events. The CESM1’s prediction skill is also assessed using
the correlation coefficient and root-mean-square error (RMSE)of the Niño-3.4 index between observations and ensemble mean
forecasts as a function of lead time. We compare the prediction
skill of CESM1 forecasts with the persistence forecasts, whichassume that the observed anomaly in the month before initialization remains unchanged for the period of forecasts. We also
compare the RMSE with the root-mean-square (RMS) of theobserved Niño-3.4 index to assess the prediction skill in terms of
relative amplitude. The statistical significance of the composite
and correlation analyses is tested through a two-tailed Student’s
t test at the confidence level of 95% for both observations andforecasts. The relatively small sample size of 2-yr El Niño and 1yr La Niña events in observations makes it difficult to obtainrobust signals. However, the composite features of 1- and 2-yr
events are in overall agreement with our previous findings basedon the extended period 1900–2017 (Wu et al. 2019).

3. Resultsa. Predictability of 1-yr and 2-yr ENSO events
The CESM1 successfully predicts the temporal evolution of
ENSO events with lead times up to 25 months, well beyond the
maximum lead time of 12 months in the current operational
ENSO forecasts. The ensemble forecasts initialized at six different lead times relative to Dec11 are shown for four representative 1- and 2-yr ENSO events in Fig. 1 (see Fig. S1 for allother events). The development and termination of the strong
1-yr El Niño in 1972 are predicted in nearly all members of theensemble forecasts initialized in Mar0 with a 21-month leadtime, although the subsequent development of La Niña in 1973
is not captured by the ensemble forecasts until the model isinitialized in Nov0. The development of the 2-yr El Niño in
1986–87 is first predicted by the ensemble forecasts initializedin Nov0 due to the late onset. The Nov0-initialized forecasts
further predict the continuation of El Niño into 1987 in theensemble mean with a 13-month lead time, consistent withobservations. The development and termination of the strong
1-yr La Niña in 1988 are predicted in the ensemble forecasts
initialized in Jun0 with an 18-month lead time. Most prominently, the development and continuation of the multiyear La
Niña in 1998–2000 are predicted with a 25-month lead time inthe ensemble forecasts initialized as early as in Nov21, aroundthe peak of the preceding strong El Niño in 1997.

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FIG. 1. Time series of the Niño-3.4 index in observations (black curves) and ensemble forecasts (colored curves) for (first column) 1-yr
El Niño in 1972, (second column) 2-yr El Niño in 1986–87, (third column) 1-yr La Niña in 1988, and (fourth column) 2-yr La Niña in 1998–
99. The ensemble forecasts are initialized in (first row) Nov21, (second row) Mar0, (third row) Jun0, (fourth row) Nov0, (fifth row) Mar11,
and (sixth row) Jun11. Dec0 denotes the first peak of El Niño/La Niña events. The ensemble mean and individual member forecasts areindicated by the thick and light colored curves, respectively. See Fig. S1 for predictions of all other 1- and 2-yr ENSO events during
1954–2015.

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JOURNAL OF CLIMATETo assess the overall ability of CESM1 in predicting the
ENSO event duration, the observed and predicted Niño-3.4
index are composited for all 1-yr and 2-yr El Niño and La Niña
events during 1954–2015 (Table 2; Fig. 2, left). On average, thedevelopment and termination of both 1-yr El Niño and La Niña
composites are predicted in the ensemble forecasts initializedin Jun0, when these composite events tend to begin. The
transition from 1-yr El Niño to La Niña is not predicted until
Jun11, partly due to the CESM1’s bias in simulating prolonged
El Niño as discussed further in section 3b. The development
and continuation of 2-yr El Niño are, on the other hand, predicted by Nov0, providing up to 13 months of lead time relative
to the second peak in Dec11. The development and continuation of 2-yr La Niña are predicted even one year earlier in
Nov21 with lead time of 25 months. The predicted first peak of
La Niña occurs too early compared to observations, but thisapparent forecast error is an artifact of the extensive westward
shift of maximum equatorial cooling during boreal summer-fallin the CESM1 (cf. Fig. 6). The predicted Dec11 Niño-3.4 index
for all ensemble forecast members shows a similar frequency
distribution for all lead times, indicating high multiyear predictability of 2-yr La Niña (Fig. 2, right). In contrast, the frequency distribution of the Dec11 Niño-3.4 index becomes wider
with longer lead times for other event categories. Overall, theCESM1 can predict the event duration for all categories when
initialized in Nov0 with a lead time of 13 months.
We examine the robustness of the composite forecasts bycomparing the observed and predicted ensemble-mean Niño3.4 index in Dec0 and Dec11 for the individual ENSO events
that make up each composite (Fig. 3). The event termination in
Dec11 is predicted in the Nov0-initialized forecasts in seven outof ten 1-yr El Niño events and three out of four 1-yr La Niña
events. The forecasts initialized before Nov0 show a positive
bias of the Dec11 Niño-3.4 index for most 1-yr El Niño events,
consistent with the composite forecasts (Fig. 2). The event
continuation in Dec11 is predicted in the Nov0-initialized
forecasts in four out of five 2-yr El Niño events and in the
Nov21- or Mar0-initialized forecasts in all 2-yr La Niña events.
The notable consistency of multiyear predictability of 2-yr La
Niña agrees with the earlier study by DiNezio et al. (2017a,b).
It is noted that some forecasts initialized before Nov0 fail topredict the development of ENSO by Dec0 and thus are unsuitable for the prediction of ENSO event duration.
Figure 4 shows the correlation skill and RMSE of the Niño3.4 index predicted by the CESM1 for all years, El Niño years,
and La Niña years during 1954–2015. In general, the correlation skill decreases quickly in boreal spring (Mar0–Jun0 and
Mar11–Jun11), reflecting the well-known spring predictability
barrier of ENSO (e.g., Torrence and Webster 1998). The
CESM1 forecasts show higher correlation skill than the persistence forecasts at most lead times. The correlation skill
based on all years remains above 0.5 within 13, 13, and11 months in the forecasts initialized in Nov21, Mar0, and Jun0,
respectively. When the analysis is limited to El Niño years, theNov0-initialized forecasts show correlation skills above 0.5
during Dec11–May12 with 13–18-month lead times, indicating
long-range predictability of El Niño duration. The analysis
limited to La Niña years shows even higher correlation skill,

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exceeding 0.8 during Aug11–Mar12 in the Nov0-initialized forecasts (9–16-month lead times) and during Dec11–Feb12 in the
Jun0-initialized forecasts (13–17-month lead times). The correlation skill increases slightly during boreal summer to winter of thesecond year (Jun11–Dec11) of El Niño and La Niña in the forecasts initialized in Nov0–Jun11, which may be caused by seasonal
growth of predictable SST anomalies [a similar result was reported in Chen et al. (2004)]. The higher prediction skill for La
Niña than El Niño years is likely related to the CESM1’s error ofoverestimating the duration of 1-yr El Niño (Fig. 2).
The RMSE of the forecasts, on the other hand, grows rapidly
in boreal summer and fall (Jun0–Nov0 and Jun11–Nov11) due
to the seasonal intensification of the Bjerknes feedback (e.g.,
Neelin et al. 1998). In general, the forecasts show smaller
RMSE when the correlation skill is higher. The RMSE issmaller than the RMS of observed Niño-3.4 index in Dec11
when the correlation skill is above 0.6 in the forecasts initialized in and after Nov0 for El Niño and La Niña years, indicatingskillful predictions. These results show that the CESM1 has
high predictive skill of ENSO event duration with lead times ofmore than one year when initialized at certain ENSO states,
owing to the model’s ability to simulate the dynamical processes associated with ENSO events.

b. Processes controlling the predictability in the
Nov0-initialized forecasts
We first analyze the processes contributing to the high predictability of ENSO event duration in the Nov0-initialized
forecasts. Figure 5 compares the composite evolutions of SST,
thermocline depth, and surface wind anomalies in the equatorial Pacific between 1- and 2-yr El Niño and La Niña events inobservations and the Nov0-initialized forecasts (see Fig. S3 forthe statistical significance of these composites). In observations, SST warming associated with 1-yr El Niño exceeds 0.48C
around May0 in the western-central Pacific (1708E–1608W),
2–4 months earlier compared to 2-yr El Niño. 1-yr El Niño
becomes stronger during the mature phase compared to 2-yr El
Niño, with maximum amplitudes ;1.68C compared to 1.18C in
Dec0. Because of the earlier onset and stronger peak amplitude, upwelling equatorial Rossby waves that provide negative
feedback to the equatorial Pacific warming have larger amplitude and arrive at the western boundary earlier in 1-yr El
Niño compared to 2-yr El Niño (Wu et al. 2019). The equatorial thermocline starts to shoal in the western Pacific by latesummer of year 0 in 1-yr El Niño, but not until year 11 in 2-yr
El Niño; the magnitude of the shoaling is also less for 2-yr El
Niño compared to 1-yr El Niño. As a result, thermocline depth
anomalies in the western equatorial Pacific are of opposite signin Nov0 between 1- and 2-yr El Niño, and the ensemble forecasts initialized in that month show a distinct evolution. In
the 1-yr El Niño forecasts, the negative thermocline depth
anomalies propagate into the eastern Pacific during boreal
spring, leading to a rapid decay of SST anomalies. In the 2-yr El
Niño forecasts, by contrast, the delayed propagation of weaker
negative thermocline depth anomalies is not sufficient toterminate the event in spring, and the remnant SST warming
starts to reintensify during the equatorial cold season (June–
November). Thus, the timing and magnitude of delayed

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FIG. 2. (left) Time series of the Niño-3.4 index (8C) in observations (black curves) and ensemble-mean forecasts (coloredcurves) composited for all (first row) 1-yr El Niño, (second row) 2-yr El Niño, (third row) 1-yr La Niña, and (fourth row) 2-yr La
Niña events during 1954–2015. The forecasts are initialized in Nov21 (pink), Mar0 (red), Jun0 (yellow), Nov0 (green), Mar11(light blue), and Jun11 (dark blue), with lead times ranging from 25 (Nov21) to 6 (Jun11) months relative to Dec11. Dec0
denotes the first peak of El Niño/La Niña events. The solid colored curves indicate that the composite forecasts are significantly
different from zero at the 95% confidence level. (right) Histograms of the Niño-3.4 index (8C) in Dec11 constructed from all
ensemble forecast members of all events that make up each composite, expressed as a percentage of the total number.

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FIG. 3. Niño-3.4 index (color bar; 8C) in Dec0 (left columns) and Dec11 (right columns) for individual (a) 1-yr El Niño, (b) 2-yr El Niño,(c) 1-yr La Niña, and (d) 2-yr La Niña events in observations (bottom rows) and ensemble mean forecasts (other rows) with different leadtimes relative to Dec11 as indicated. Dec0 denotes the first peak of El Niño/La Niña events. White boxes indicate missing data.

negative oceanic feedback, which is reflected in the Nov0 initial
thermocline states, are key to the predictions of El Niño duration, confirming that the mechanisms identified in previous
diagnostic and perfect model studies also operate in the realworld forecasts (Wu et al. 2019, 2021).

The timing of delayed negative oceanic feedback also appears to be important for the predictions of La Niña duration(Fig. 5). In observations, 1-yr La Niña tends to develop aroundMar0 in the central-eastern equatorial Pacific (1408–808W),
about 3 months earlier than 2-yr La Niña. The different onset

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FIG. 4. (left) Correlation skill and (right) RMSE (8C) of the Niño-3.4 index in ensemble-mean forecasts initialized in different months
compared to observations (solid colored curves) as a function of lead time (x axis) for (top) all years, (middle) El Niño years, and (bottom) La
Niña years during 1954–2015. In the left panels, correlation skill values are indicated by filled circles are statistically significant at the 95%
confidence level. The correlation skill of ensemble-mean forecasts is also compared with correlation skill of persistence forecasts (dashed coloredcurves). In the right panels, the RMSE of ensemble-mean forecasts is compared with RMS of the observed Niño-3.4 index (dashed black curves).
The forecasts are initialized in Nov21 (pink), Mar0 (red), Jun0 (yellow), Nov0 (green), Mar11 (light blue), and Jun11 (dark blue), with lead times
ranging from 25 (Nov21) to 6 (Jun11) months relative to Dec11. Dec0 denotes the first peak of El Niño/La Niña events.

timing results in opposite signs of thermocline depth anomalies in
the western equatorial Pacific in Nov0. When initialized with these
distinct oceanic conditions in Nov0, the CESM1 successfully predicts
the evolution of 1- and 2-yr La Niña in year 11. The Nov0 oceanicstates are also strongly influenced by the magnitude of thermocline
shoaling across the equatorial Pacific in the preceding boreal spring–summer, which is in turn related to the amplitude of preceding El
Niño. In the 2-yr La Niña forecasts, the greater initial shoaling of thethermocline prevents the delayed oceanic feedback from reversing thesign of thermocline depth anomalies in the eastern equatorial Pacific.
The Nov0-initialized forecasts predict the early terminationof both 1-yr El Niño and La Niña events, but the timing is a few
months later than in observations (Fig. 5). For example, theSST warming in the western-central Pacific associated with 1-yr
El Niño is replaced by SST cooling in late boreal spring inobservation but persists into boreal summer in the forecasts.
Compared to observations, the predicted equatorial SST andwind anomalies extend too far west and linger too long into
summer of year 11 over the western Pacific. The westward
displacement of equatorial anomalies may make surface winds
over the western equatorial Pacific less susceptible to thenegative feedback from the tropical Indian Ocean via the atmospheric bridge and delay the event termination (Okumura andDeser, 2010; Okumura et al. 2011; Ohba and Watanabe 2012).
The westward displacement of equatorial anomalies is, in turn,likely caused by too strong Pacific cold tongue in CESM1, which

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FIG. 5. Longitude–time sections of SST (8C; shading), thermocline depth (contours at intervals of 8 m starting at 64 m; zero contours
thickened and negative contours dashed), and surface wind (m s21; vectors) anomalies in the equatorial Pacific (38S–38N) composited for1- and 2-yr El Niño and La Niña events in observations and ensemble-mean forecasts initialized in Nov0. The thermocline depth anomalies
are smoothed with a 1–2–1 filter in the time direction and a 9-point running-mean filter in the longitudinal direction in both observations
and forecasts. Dec0 denotes the first peak of El Niño/La Niña events. The statistical significance of these anomalies is shown in Fig. S5.

acts to shift atmospheric convection anomalies to the west (Hamand Kug 2012; Bayr et al. 2018). In support of this notion, theJun0-initialized forecasts show an even more pronounced bias
for protracted 1-yr El Niño and La Niña (cf. Fig. 8 and Fig. S4),
because the cold tongue bias develops faster in forecasts initialized during the equatorial cold season compared to thoseinitialized in other seasons (Fig. S5; Siongco et al. 2020).
Besides the oceanic feedback, observed surface wind anomalies
over the western equatorial Pacific show distinct evolutions after
the first peak of 1- and 2-yr El Niño and La Niña events (Fig. 5). In
particular, observed surface wind anomalies over the westernequatorial Pacific decay quickly after the peak of 1-yr events but
persist through year 11 of 2-yr events. The Nov0-initialized
forecasts simulate the different evolution of surface wind anomalies for 1- and 2-yr events, although the 1-yr events show a slower
decay compared to observations, consistent with the slower termination of these events in the model.
To examine the causes of different wind evolutions, we
compare the spatial patterns of SST and surface wind anomalies between 1- and 2-yr events during Nov0–Dec0 (Fig. 6; see
Fig. S6 for the statistical significance of these composites). In
observations, the equatorial Pacific warming is weaker in 2-yr

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FIG. 6. Maps of SST (8C; shading) and surface wind anomalies (m s21; vectors) composited for (top) El Niño and (bottom) La Niña basedon (left) observations and (right) ensemble-mean forecasts initialized in Nov0. For El Niño and La Niña maps, the top rows show resultsfor 1-yr events, second rows for 2-yr events, and third rows for their difference (2-yr minus 1-yr), all during Nov0–Dec0. The bottom rows
show the difference between 2- and 1-yr events during Jan11-Feb11. The statistical significance of these anomalies is shown in Fig. S6.

than 1-yr El Niño, with the center of warming shifted slightly to
the west. The tropical Atlantic and Indian Oceans also warm
less in 2-yr El Niño, presumably due to the late onset andweaker amplitude of equatorial Pacific warming. Anotherstriking difference is the SST warming in the northeast tropical

Pacific and off the west coast of the United States and Mexico
in 2-yr El Niño, which is nearly absent in 1-yr El Niño. The
difference maps between 2- and 1-yr El Niño events display thenortheast tropical Pacific warming and associated southwesterly wind anomalies in Nov 0 –Dec 0 . These extratropical

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2-yr El Niño and 1-yr La Niña forecasts show different patterns
from observations. We note that the observational composites
could be strongly influenced by internal atmospheric variability due to the small sample size (Fig. S6; Deser et al. 2017).

c. Multiyear predictability of 2-yr La Niña

FIG. 7. Time series of the Niño-3.4 index (8C) in observations(black curves) and ensemble-mean forecasts (colored curves) composited for 2-yr La Niña events following (top) strong (1972, 1982,
1997, and 2015) and (bottom) moderate (1953, 1969, 2006, and 2009)
El Niño events. The preceding El Niño is considered to be strong(moderate) when the Dec21 Niño-3.4 index is .2.08C (0.58–2.08C).
The forecasts are initialized in Nov21 (pink), Mar0 (red), Jun0 (yellow), Nov0 (green), Mar11 (light blue), and Jun11 (dark blue), withlead times ranging from 25 (Nov21) to 6 months (Jun11) relative to
Dec11. Dec0 denotes the first peak of El Niño/La Niña events. The
solid colored curves indicate that the composite forecasts are significantly different from zero at the 95% confidence level.

anomalies extend to the western equatorial Pacific in Jan11–
Feb11, indicative of the role of the North Pacific meridionalmode (NPMM; e.g., Vimont et al. 2001; Anderson 2003; Changet al. 2007; Alexander et al. 2010). The SST states in Nov0–
Dec0 and subsequent equatorward propagation associatedwith the NPMM are well captured by the Nov0-initialized
forecasts, although the northeast tropical Pacific warming is
weaker compared to observations. Similar differences arefound between 2- and 1-yr La Niña events in both observations
and the Nov0-initialized forecasts, except that the amplitude ofequatorial Pacific cooling is comparable between 2- and 1-yr
events and the equatorward propagation of northeast tropical
Pacific cooling is less clear in observations. These results suggest that in addition to the tropical interbasin adjustments the
NPMM plays an important role in maintaining surface wind
anomalies over the western equatorial Pacific during 2-yr El
Niño and La Niña. The processes that cause the different initial
NPMM states between 1- and 2-yr events in the first place remain unclear, which could be related to the internal atmospheric variability over the North Pacific or the tropical SST
forcing (Capotondi et al. 2019; Stuecker 2018; Wu et al. 2019;
Fang and Yu 2020). The North Pacific wind anomalies in the

The CESM1 shows high skill in predicting 2-yr La Niña
events with lead times up to 25 months (Fig. 2). DiNezio et al.
(2017a,b) suggest that the multiyear predictability of 2-yr La
Niña arises from large initial shoaling of the equatorial
Pacific thermocline caused by the preceding strong El Niño,
but not all observed 2-yr La Niña events are preceded bystrong El Niño. To explore other factors affecting the predictability, we further classify 2-yr La Niña events into those
preceded by strong and moderate El Niño (Fig. 7). Here, thepreceding El Niño is considered to be strong (moderate)
when the smoothed Dec21 Niño-3.4 index exceeds 2.08C(0.58–2.08C). In agreement with DiNezio et al. (2017a,b), theCESM1 predicts 2-yr La Niña when initialized around thepeak of strong El Niño in Nov21. The Nov21-initialized
forecasts fail to predict the onset of 2-yr La Niña preceded bymoderate El Niño, but the forecasts initialized after Mar0
consistently predict the development and duration of these
events. This result is in contrast to the 1-yr La Niña forecasts
initialized in and after Mar0, which consistently predict theevent termination in year 11 while the amplitude of thepreceding El Niño is comparable (i.e., moderate; Fig. 2).
Thus, there seem to be important factors other than theamplitude of preceding El Niño that affect the development
and duration of La Niña events.
Figure 8 shows the temporal evolution of ocean–atmosphereanomalies in the equatorial Pacific, the tropical North Atlantic,
and the tropical Indian Ocean for composites of 2-yr La Niña
preceded by strong El Niño, 2-yr La Niña preceded by moderate El Niño and all 1-yr La Niña events, based on observations and the ensemble forecasts initialized between Nov21
and Nov0 (see Fig. S7 for the statistical significance of thesecomposites). When initialized with strong El Niño conditionsin Nov21, the CESM1 predicts the evolution of the tropicalocean–atmosphere system in the subsequent two years of La
Niña surprisingly well, comparable to the forecasts initialized
in later months. In both observations and the Nov21-initialized forecasts, strong El Niño causes not only large shoalingof the equatorial Pacific thermocline, but also strong warmingof the tropical Atlantic and Indian Oceans, which delay theinterbasin atmospheric adjustments to La Niña cooling andmaintain the easterly wind anomalies. As stated earlier, theCESM1 first predicts the development and duration of 2-yr
La Niña preceded by moderate El Niño when initialized in
Mar0. The development and duration of 1-yr La Niña are notpredicted until Jun0, when the thermocline begins to shoal in
the central-eastern equatorial Pacific. Compared to 1-yr La
Niña, both observed and predicted 2-yr La Niña preceded bymoderate El Niño shows stronger equatorial Pacific cooling,with the center displaced to the west. Thus, the amplitude andlocation of equatorial Pacific cooling appear to affect the La
Niña duration and its predictability. It is interesting to notethat the tropical North Atlantic is warmer during Nov0–Apr0

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FIG. 8. Longitude–time sections of SST (8C; shading), thermocline depth (contours at intervals of 8 m starting at 64 m; zero contours thickened
and negative contours dashed), and surface wind (m s21; vectors) anomalies composited for (a) 2-yr La Niña events following strong El Niño, (b) 2-yr
La Niña events following moderate El Niño events, and (c) all 1-yr La Niña events in (first column) observations and (second to fifth columns)
ensemble-mean forecasts initialized in Nov21, Mar0, Jun0, and Nov0, respectively. Dec0 denotes the first peak of El Niño/La Niña events. The
anomalies are averaged between 38S and 38N in the Pacific (1208E2808W), between 108S and 08 in the Indian Ocean (408–1208E), and between 08
and 208N in the Atlantic (808W–108E). The thermocline depth anomalies are smoothed with a 1–2–1 filter in the time direction and a 9-point runningmean filter in the longitudinal direction in both observations and forecasts. The statistical significance of these anomalies is shown in Fig. S7.

of 2-yr La Niña preceded by moderate El Niño than 1-yr La
Niña. Recent studies suggest that tropical North Atlantic SST
anomalies significantly affect the development of ENSO
events during boreal spring via the atmospheric bridge (Hamet al. 2013; Ham and Kug 2015). It is plausible that themagnitude of tropical North Atlantic warming in boreal
spring modulates the amplitude and duration of subsequent
La Niña.

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FIG. 9. Ensemble mean (curves) and spread (shading; 61 standard
deviation) of the Niño-3.4 index (8C) in forecasts initialized in Nov21(pink), Mar0 (red), Jun0 (yellow), Nov0 (green), Mar11 (light blue),and Jun11 (dark blue) composited for (first row) 1-yr El Niño, (secondrow) 2-yr El Niño, (third row) 1-yr La Niña, and (fourth row) 2-yr La
Niña events during 1954–2015. Dec0 denotes the first peak of El Niño/
La Niña events. The composite ensemble spread is calculated bypooling all members of the forecast ensembles for each type of events
after removing the mean of each forecast ensemble from the individual
members of that ensemble. Only the composite forecasts that successfully predict the duration of ENSO events are shown.

d. Sources of ensemble spread
While the ensemble-mean forecasts reveal the predictablecomponent of the ENSO event duration, the ensemble spread
provides information on the uncertainty of predictions. Figure 9shows the growth of ensemble spread of the Niño-3.4 index
around the ensemble mean as a function of lead time for forecast
ensembles composited for 1- and 2-yr El Niño and La Niña events.
As expected, the ensemble spread in Dec11 generally becomes
smaller in forecasts with shorter lead times for all four types of
events, indicating decreased forecast uncertainty. The Nov0-initialized forecasts, however, show larger ensemble spread in Dec11
than the Jun0-initialized forecasts for 1-yr El Niño and La Niña,
which may be partly attributed to the larger ensemble size of the
Nov0-initialized forecasts (Fig. S2). It is interesting to note that theensemble spread in Dec11 is much larger for El Niño than La

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Niña in the forecasts initialized in or before Nov0. This result
suggests that the growth of ensemble spread may depend on theensemble mean state. Similar results are found in the perfectmodel predictions conducted with the CESM1 and its predecessor(Wu et al. 2021; Larson and Kirtman 2019).
To identify the sources of ensemble spread of event duration, we correlate the Dec11 Niño-3.4 index with global SST
and SLP anomalies in the preceding months using the forecast
ensembles of all El Niño and La Niña events (Fig. 10; see
Fig. S8 for the statistical significance of these correlations).
Before conducting the correlation analysis, we remove theensemble mean from individual members of each forecast
ensemble and then pool all forecast ensembles for each initialization month. In this way, we remove the predictable signal(given by the ensemble mean) and isolate the unpredictablecomponent (the residual from the ensemble mean) for our analysis. Here, the state dependence of the ensemble spread is notconsidered but needs to be investigated in future studies. In theforecast ensembles initialized between Nov21 and Nov0, the ensemble spread of the Dec11 Niño-3.4 index originates mainly
from a meridional dipole pattern of SLP anomalies over the North
Pacific in Dec0–Jan11 (Fig. 10b) and associated subtropical North
Pacific SST anomalies that peak in Mar11–Apr11 (Fig. 10c). This
result indicates that wintertime atmospheric variability over the
North Pacific and attendant ocean–atmosphere interactions contribute to the uncertainty of ENSO event duration, consistent
with a previous study suggesting the dominant role of the NPMM
in the ensemble spread of ENSO forecasts (Ma et al. 2017). In theforecasts initialized in Nov21 and Mar0, variability similar to theIndian Ocean dipole (IOD) mode in Sep0–Oct0 also weakly
contributes to the ensemble spread of the Dec11 Niño-3.4 index(Fig. 10a). In the forecasts initialized after the first peak of El Niño
and La Niña in Mar11 and Jun11, the ensemble spread appears tooriginate mainly from atmospheric variability over the South
Pacific, suggesting the role of the South Pacific meridional mode
(SPMM; Fig. 10d; DiNezio et al. 2017a; Larson et al. 2018).

4. Summary and discussion
We have explored the predictability of ENSO event duration using three sets of CESM1 multiyear forecast ensembles
initialized with observed oceanic and sea ice conditions on
1 March, 1 June, and 1 November of each year during 1954–2015. The CESM1 shows high predictive skill of ENSO event
duration with lead times ranging from 6 to 25 months. In particular, the forecasts initialized in Nov0 near the first peak of El
Niño and La Niña can skillfully predict whether the events terminate or persist through the following year with a lead time of
13 months. The predictability arises from the timing and magnitude of delayed negative oceanic feedbacks and interbasin
adjustments, which are reflected in the initial thermocline depth
and SST anomalies. In addition, subtropical North Pacific SST
anomalies associated with the NPMM contribute to the predictability of ENSO event duration by affecting surface winds
over the western equatorial Pacific. The occurrence of multiyear
La Niña events can be predicted with lead times up to 25 months.
Multiyear La Niña events preceded by strong El Niño are predicted with the longest lead time of 25 months, owing to large

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FIG. 10. Correlation maps of SST (color shading) and sea level pressure (contours at intervals of 0.1; zero contours thickened andnegative contours dashed) anomalies with the Niño-3.4 index in Dec11 from the ensemble forecasts as a function of lead time [columns:
from (left) Nov21 to (right) Jun11 initialized forecasts] and verification time [rows: from (top) Dec21–Jan0 to (bottom) Dec11–Jan12]. All
members of the forecast ensembles for all El Niño and La Niña years are pooled together after removing the mean of each forecast
ensemble from the individual members of that ensemble. The boxes with labels (a)–(d) are used to denote the different processes that
contribute to the ensemble spread described in the text. The statistical significance of these anomalies is shown in Fig. S8.

adjustments of the equatorial Pacific thermocline and interbasin
SSTs associated with the preceding El Niño, in agreement with
DiNezio et al. (2017b). When La Niña events are preceded bymoderate-to-weak El Niño, springtime SST warming in thetropical North Atlantic may provide long-term predictability of
La Niña duration, although the role of oceanic conditions indifferent ocean basins requires further investigation.

The magnitude of error growth in our 2-yr forecasts shows
dependency on both lead times and ensemble mean state. The
forecast uncertainty is reduced in forecasts with shorter leadtimes and is smaller for La Niña events than El Niño events.
We investigated the general source of forecast error growth
without considering these dependencies. The forecast error
growth of event duration originates mainly from wintertime

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atmospheric variability over the North Pacific around the firstpeak of El Niño and La Niña (Dec0–Jan11) and attendant air–sea interactions in the tropical Pacific. In the forecasts initialized in Jun0, unforced variability of the IOD in the first boreal
fall also contributes to the ensemble spread. In the forecasts
initialized after the first peak of El Niño and La Niña (Mar11
and Jun11), the error growth of event duration is associatedwith atmospheric variability over the South Pacific during boreal spring to summer. The forecast errors originating fromthese tropical and extratropical phenomena grow faster duringlate boreal spring to summer than other seasons, when theseasonal cold tongue develops in the equatorial Pacific. The
forecast errors are also likely caused by intraseasonal windvariability over the western equatorial Pacific (e.g., Menkes
et al. 2014; Puy et al. 2017), which needs to be investigated infuture research.
The predictability of ENSO event duration primarily arises
from surface and subsurface oceanic conditions in the equatorial Pacific, which are also key to the predictability of onset
and amplitude of ENSO events (e.g., Wyrtki 1975; Meinen and
McPhaden 2000; Chen et al. 2004; Planton et al. 2018; Larson
and Kirtman 2019). However, in our CESM1 forecasts, theduration of ENSO events can be predicted with longer leadtimes than their first onset and peak phases. This result indicates that when the equatorial Pacific system is in an El Niño or
La Niña state, the ocean–atmosphere dynamics internal to thesystem, particularly the timing and magnitude of subsurface
oceanic processes in the equatorial Pacific, is able to overcome
the spring predictability barrier and provides long-term predictability. Most prominently, the strong thermocline shoaling
in the equatorial Pacific induced by strong El Niño can provide
2-yr lead predictability for the subsequent multiyear La Niña,
consistent with DiNezio et al. (2017b). Additional sources ofpredictability of ENSO event duration may come from remoteocean basins, including the tropical Indian and Atlantic as wellas the extratropical Pacific, consistent with recent studies ofbasin interactions as summarized by Cai et al. (2019) andAmaya (2019).
The capability of the CESM1 to predict the duration ofobserved ENSO events is inherently lower than that in theperfect model experiments, due to model biases in simulating
both the tropical Pacific mean state and ENSO (DiNezio et al.
2017a; Wu et al. 2021). As discussed earlier, the forecasts initialized with observed oceanic conditions systematically overestimate the duration of El Niño and La Niña events due to an
excessively strong cold tongue and resultant westward displacement of ENSO anomalies in the equatorial Pacific in thismodel. The prediction skills are also degraded by errors ininitial oceanic conditions. Recall that the oceanic initial conditions are derived from an ocean model simulation forced
with observed atmospheric and surface flux fields, which do not
perfectly agree with observations. For instance, in the forecastsof La Niña initialized with moderate El Niño conditions, theabsence of observed Indian Ocean warming may contribute to
the failure to predict the La Niña development in the following
year (Fig. 8).
Regardless of the caveats discussed above, the results of thisstudy indicate the potential of extending the current 12-month

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operational ENSO forecasts by one additional year. Our study
is based on a single model and the predictability is explored
only for selected initialization months. Further research isneeded to explore the model and seasonal dependencies of thepredictability of ENSO event duration. It is particularly important to assess the ability of models to reproduce the statistical characteristics of observed ENSO event duration using
multimodel historical or control simulations, which could influence the skill of models in predicting ENSO event duration.
Given the large computational costs, it may not be feasible tofully extend the operational ENSO forecasts by one additional
year for all models. However, our study highlights the benefitof extending the forecasts even for selected models and selected initialization months. Multiyear ENSO forecasts will
provide a new basis for predictions of prolonged climate
anomalies, such as persistent drought in the southern UnitedStates and widespread flooding in Australia during multiyear
La Niña events.
Acknowledgments. The authors thank the two anonymous
reviewers for constructive comments and suggestions that helped
to improve the manuscript. The observational datasets used in thisstudy are obtained online: HadISST from the Met Office Hadley
Center (http://www.metoffice.gov.uk/hadobs/), ECMWF Ocean
Reanalysis System 4 from ftp://ftp-icdc.cen.uni-hamburg.de/
EASYInit/ORA-S4, and NCEP–NCAR reanalysis from https://
psl.noaa.gov/data/gridded/data.ncep.reanalysis.html. The authors
thank Nan Rosenbloom for providing the oceanic and sea ice
initial conditions used for the forecasts. This study is supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office Modeling, Analysis,
Predictions, and Projections Program (NA17OAR4310149 andNA17OAR4310145) and the National Science Foundation(NSF) Physical Oceanography Program (OCE-1756883). The
CESM project is supported primarily by the NSF. This material
is based upon work supported by the National Center for
Atmospheric Research, which is a major facility sponsored by
the NSF under Cooperative Agreement 1852977.
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