Geophysical Research Letters
RESEARCH LETTERA 2 Year Forecast for a 60–80% Chance of La Niña in 2017–2018

10.1002/2017GL074904
Key Points:• Thermocline discharge driven bystrong El Niño requires several years
of La Niña conditions to return to aneutral state• Forecast initialization at the peak of astrong El Niño event leads to skillful
predictions of subsequent 2 year La
Niña event• Returning La Nina conditions in 2018
have 60% probability based on a
dynamical model and 80% probability
based on an empirical model

Supporting Information:• Supporting Information S1

Correspondence to:P. N. DiNezio,
pdn@ig.utexas.edu

Citation:
DiNezio, P. N., Deser, C.,
Karspeck, A., Yeager, S., Okumura, Y.,
Danabasoglu, G., … Meehl, G. A.
(2017). A 2 year forecast fora 60–80% chance of La Niña
in 2017–2018. GeophysicalResearch Letters, 44, 11,624–11,635.
https://doi.org/10.1002/2017GL074904

Received 17 JUL 2017
Accepted 1 NOV 2017
Accepted article online 6 NOV 2017
Published online 27 NOV 2017©2017. American Geophysical Union.
All Rights Reserved.

DINEZIO ET AL.

Pedro N. DiNezio1 , Clara Deser2 , Alicia Karspeck2 , Stephen Yeager2 , Yuko Okumura1
Gokhan Danabasoglu2 , Nan Rosenbloom2 , Julie Caron2 , and Gerald A. Meehl2,

1 Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, Austin, TX, USA, 2 Climate andGlobal Dynamics Division, National Center for Atmospheric Research, Boulder, CO, USA

Abstract Historical observations show that one in two La Niña events last for two consecutive years.
Despite their outsized impacts on drought, these 2 year La Niña are not predicted on a routine basis.
Here we assess their predictability using retrospective forecasts performed with a climate model thatsimulates realistic multiyear events, as well as with an empirical model based on observed predictors.
The skill of the retrospective forecasts allows us to make predictions for the upcoming 2017–2018 boreal
winter starting from conditions in November 2015. These 2 year forecasts indicate that the return of La Niña
is more likely than not, with a 60% probability based on the climate model and an 80% probability basedon the empirical model; the likelihood of El Niño is less than 8% in both cases. These results demonstrate
the feasibility of predictions of the duration of La Niña.
Plain Language Summary Historical observations show that cold La Niña events in the tropicalPaciﬁc often reintensify for a second year. Despite their outsized climate impacts throughout the world,
these 2 year La Niña events are not routinely predicted. Our study demonstrates that long-term forecastsof these events are feasible. Moreover, we show increased likelihood of returning La Niña for next boreal
winter, a result that is directly relevant for assessing climate risks throughout the world, for instance, overthe southern tier of the U.S., where La Niña events create extreme seasonal heat and drought during winterand spring, and the Maritime Continent and Northern Australia, where it causes excess rainfall and ﬂooding.

1. Introduction
Historical observations show that most El Niño events last for several seasons after which they quickly transition into La Niña, the cold phase of the El Niño–Southern Oscillation (ENSO) phenomenon in the tropicalPaciﬁc. In contrast, La Niña events often reintensify for a second year (Kessler, 2002; Okumura & Deser, 2010).
These 2 year La Niña events are not routinely predicted because operational forecasts are limited to leadtimes of less than 9 months (Barnston et al., 2010). Furthermore, these operational forecasts have provento be unreliable at shorter lead times, for instance, during the most recent 2 year La Niña of 2010–2012. In
particular, operational predictions initialized in June 2011 showed a range of positive and negative ENSO
states in the following boreal winter 2011/2012, providing an unreliable picture of the evolution of La Niña
that year (ENSO forecasts initialized in June 2011 available online at http://iri.columbia.edu/our-expertise/
climate/forecasts/enso/archive/201106/SST_table.html). Indeed, La Niña ultimately reemerged during late2011 to early 2012, favoring the occurrence of one of the most persistent droughts over the southeastern U.S.
and Texas (Hoerling et al., 2013). Despite the weaker magnitude of the tropical cooling during the second
year, the rainfall deﬁcits over the southern U.S. are, on average, comparable in magnitude to the ﬁrst year andextend further north, potentially exacerbating droughts associated with 2 year La Niña (Okumura, DiNezio,
et al., 2017).
The onset of La Niña is highly predictable since it is governed by slowly propagating variations in the depthof the thermocline, the boundary separating the warm surface waters from deeper and colder waters. This
process is initiated by wind patterns associated with the preceding El Niño, which drive a shoaling of the equatorial thermocline across the Paciﬁc. These large-scale anomalies are typically characterized as a reduction in
upper ocean heat content or “discharge” of equatorial heat content. The thermocline shoaling maximizes a
few seasons after the peak of El Niño and is highly eﬀective at cooling the ocean’s surface over the central
equatorial Paciﬁc via increased entrainment and upwelling of cold subsurface waters. This lead-lag relationship is the basis of well-established theories explaining the onset and decay of ENSO events (Jin, 1997;
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Figure 1. Observed ENSO events and associated thermocline depth anomalies. (a) Niño-3.4 sea surface temperature(SST) gradient index and (b) zonally averaged thermocline depth index from multiple observational data sets. SST data
are from the ERSST3b reconstruction, and thermocline depth is computed from potential temperature data from the
ORAS-4, GECCO2, and GODAS reanalyses and in situ observations from the TAO/TRITON moored array.

Meinen & McPhaden, 2000) and explains the consistent and predictable occurrence of La Niña after El Niño.
Conversely, these theories propose that wind patterns associated with La Niña would drive a delayed deepening of the thermocline, or thermocline “recharge,” that would warm the ocean’s surface, terminating La Niña,
and potentially triggering a subsequent El Niño. This response appears to be ineﬀective, since La Niña events
rarely transition directly into an El Niño (Kessler, 2002). This breakdown of ENSO’s oscillatory behavior limits
our ability to predict La Niña’s termination and hence its duration.
Recent studies suggest that the magnitude and persistence of thermal anomalies below the ocean’s surface
could aid in the prediction of La Niña’s duration (DiNezio et al., 2017). According to that study, forecasts initialized at the peak of a strong El Niño reliably predict the occurrence of a subsequent 2 year La Niña in acoupled model. This occurs because strong El Niño events drive a thermocline shoaling that is disproportionally large, thus requiring several years of La Niña conditions to return the system to a neutral ENSO state. Thisidea is consistent with the observed evolution of the El Niño events of 1982 and 1997—the strongest on
record at the time. Both events were followed by thermocline shoaling of more than 30 m averaged across theequatorial Paciﬁc (Figure 1, blue curve). We posit that these thermocline depth anomalies—also the largest
on record—played a dominant role on the multiyear persistence of the subsequent La Niña (Figure 1, red
curves). A thermocline shoaling of more than 20 m was also observed during the spring and summer of 2016,
after the peak of the recent record-breaking El Niño (Figure 1), suggesting that this year’s La Niña could evolve
according to similar dynamics.
Here we present retrospective forecasts of the multiyear evolution of past El Niño events that resulted in subsequent 2 year La Niña. Furthermore, we present predictions for the upcoming boreal winter of 2017–2018
that are initialized at the peak of the most recent El Niño, that is, 2 years in advance. Our forecasts are performed with an empirical statistical model and a comprehensive coupled climate model. The coupled climate
model forecasts were performed with the Community Earth System Model Version 1 (CESM1)—a model capable of simulating very realistic El Niño–Southern Oscillation (ENSO) events, particularly the asymmetries inamplitude and duration between El Niño and La Niña events (DiNezio et al., 2017). The empirical model isbased on a statistical analysis of multiple observational data sets combined with probabilities derived from along control simulation performed with CESM1.
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We ﬁrst present the statistical analysis of precursors of 2 year La Niña from observations and the CESM1 control.
We then assess retrospective skill of the CESM1 at predicting historical 2 year La Niña over the 1954–2014
period. To conclude, we use the forecasts initialized in November 2015 to estimate the probability of a 2 year La
Niña following the most recent El Niño. The target season for these forecasts is November-December-Januaryof 2017/2018, a full 2 years after initialization.

2. Data and Methods
2.1. Observational Data
We use the Extended Reconstructed Sea Surface Temperature version 3b (ERSST3b) (Smith et al., 2008) to compute sea surface temperature anomalies (SSTA) for the 1954 to present period. SSTA are computed relative
to the monthly mean seasonal cycle over the 1986–2015 period, the same baseline used in NOAA’s operational deﬁnition of ENSO events (http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuﬀ/
ONI_change.shtml). We use ocean subsurface temperature data to estimate the depth of the thermocline,
ZTC , as the location of the maximum vertical temperature gradient (DiNezio et al., 2017). The following ocean
reanalysis data are used for diﬀerent periods: ORA-S4 (Balmaseda et al., 2013) (1958–2014), GECCO2 (Kohl &Stammer, 2008) (1954–2010), GODAS (Huang et al., 2010) (1982–2016), and direct observations from the
TAO/TRITON array (1986–2016). The monthly mean climatology over the common 1986–2010 period is′removed from each data set to compute ZTC anomalies, ZTC. Sea level pressure (SLP) data used to asses therealism of ENSO teleconnections are from the twentieth Century Reanalysis (Compo et al., 2011).
2.2. ENSO IndicesWe track ENSO events using an index deﬁned as the diﬀerence between the SSTA averaged over the Nino-3.4
region in the central equatorial Paciﬁc (170∘ W–120∘ W, 5∘ S–5∘ N) minus SSTA averaged over the westernequatorial Paciﬁc (120∘ E–150∘ E, 5∘ S–5∘ N). This “gradient” Niño-3.4 SST index (hereinafter Niño-3.4g) captures the weaker or stronger zonal SST gradient associated with El Niño and La Niña and is highly correlated(r = 0.98) with the conventional Niño-3.4 SST index (Figure S1a in the supporting information). We use thisindex because it is less sensitive to long-term warming than the conventional Niño-3.4 SST index. The westernequatorial Paciﬁc region (120∘ W–170∘ W, 5∘ S–5∘ N) shows a consistent warming trend of 0.81 K (63 year)−1(Figure S1b). Referencing Niño-3.4 SSTA relative to this region isolates ENSO-related variability from thelong-term warming trends, which can be of considerable magnitude and highly uncertain (Cane et al., 1997;
Cravatte et al., 2009; Deser et al., 2010; Latif et al., 1997). As a result of this normalization, the observed
Niño-3.4 SST gradient index shows a negligible trend over the 1954-to-present period; in contrast to a warming trend of 0.44 K (63 year)−1 using the conventional deﬁnition (Figure S1a, red and blue curves). We use
a threshold of 0.6 K to deﬁne El Niño and La Niña commensurate with the larger standard deviation of ourNiño-3.4g SST index (Table S1). The thermocline discharge and recharge are estimated using an index deﬁned′′
across the equatorial Paciﬁc (140∘ E–80∘ W, 5∘ S–5∘ N). This zonal-mean ZTC
index
as the area-averaged ZTC′(hereinafter Z̄ TC ) captures the timing and magnitude of the thermocline depth variations in all four data
sets, particularly the large negative departures associated with the thermocline discharge following El Niño
events (Figure S2c). We detrend and then average the Z̄′ TC index from each data set to obtain a multidata set
mean (Figure 1, blue curve). Observational uncertainty is computed as the minimum-maximum range of theoverlapping data sets (Figure 1, blue shading).
2.3. Empirical Model
We developed an empirical model for predicting 2 year La Niña combining observed predictors with probabilities from a 1,800 year-long control simulation performed with CESM1 (hereinafter CESM-CTL). CESM-CTL
simulates realistic characteristics of ENSO events, including their spatial patterns and global teleconnections(Figure S2), amplitude (Table S1), and importantly the same frequency of 2 year La Niña events as observed.
First, we selected 251 El Niño events from the CESM-CTL and computed a set of predictors following themethodology of DiNezio et al. (2017). The 2 year La Niña predictand (Niño − 3.4gNDJ+2 ) is deﬁned as the magnitude of the Niño-3.4g index 2 years after the peak of an El Niño event. A “peak El Niño” predictor is deﬁned
as the Niño-3.4g SST index during the peak November-December-January (NDJ) season (Niño − 3.4gNDJ 0 )of each simulated El Niño event. A discharge predictor is deﬁned as the Z̄′ TC index averaged from February
FMAMJJ+1
to July following the peak of an El Niño event (Z̄′ TC). We binned the 251 events according to the values of the “El Niño peak” and “thermocline discharge” predictors using overlapping bins of 1 K and 10 m
width, respectively. The cumulative density function (CDF) of the Niño − 3.4gNDJ+2 predictand was estimated
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Geophysical Research Letters10.1002/2017GL074904

Precursors of 2−year La Nina
Historical observations and ocean reanalysis data

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Figure 2. Statistical precursors of observed 2 year La Niña events in nature and in the CESM-DP-LE. Scatterplots
showing correlations between the observed and predicted Niño-3.4g index in the year following the ﬁrst peak of
La Niña (Niño-3.4gNDJ+2 , y axis) and the following two predictors (x axis): (a and c) the Niño-3.4g index at the peak of the
El Niño event preceding La Niña and (b and d) the thermocline discharge during the transition between El Niño to
La Niña. Returning La Niña conditions for a second year, that is, a 2 year La Niña, are given by Niño-3.4gNDJ+2 values(y axis) less than −0.6 K (blue shading). El Niño conditions during the second year, that is, a 1 year La Niña are given by
Niño-3.4gNDJ+2 values larger than 0.6 K (red shading). Each dot is labeled according to the year of the initial El Niño
event (based on December). Figures 2a and 2b, and 2c and 2d show precursors derived from observational data and the
CESM-DP-LE, respectively. Blue dots in Figures 2a and 2b identify events for which there is a CESM-DP-LE hindcast.
Gray contours show the cumulative probability of Niño-3.4gNDJ+2 predictor as a function of the precursors computedfrom the uninitialized CESM1 control simulation. Ellipses indicate observational uncertainty for observed precursors,
computed as the min-max range among the three data sets, and ensemble spread for hindcast precursors, computed as
the ensemble standard deviation. For the CESM-DP-LE, the discharge predictor is computed from the ensemble-mean
FMAMJJ+1
Z̄′ TC index averaged from February to July of the year after initialization, which we label Z̄′ TC.

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with MATLAB’s ksdensity code using data from each bin. This resulted in a set of CDFs for diﬀerent values of
MAMJJA+1
the Niño − 3.4gNDJ 0 and Z̄′ TC
predictors. This dependence is plotted as contours in Figures 2a and 2b.
Last, we computed observed predictors using all available observational products following the same deﬁnitions as for the CESM-CTL. An averaging interval March to August is used for the observed thermocline disMAMJJA+1
charge predictor (Z̄′ TC) based on timing of the peak of the observed discharge. Analysis of Niño-3.4gNDJ 0
includes observed events since 1920, the period when all three data sets agree on the amplitude of El Niño
events. Observational uncertainty is computed as the minimum-maximum range of the overlapping datasetsfor both the “El Niño peak” and “thermocline discharge” predictors, as well as for the 2 year La Niña predictand, shown in Figures 2a and 2b as ellipses. For each observed event, the probability of 2 year La Niña is givenby value of the CDF for Niño-3.4gNDJ+2 = −0.6 K and the magnitude of the observed predictor.
2.4. Dynamical Model
The CESM1 forecasts belong to the Decadal Prediction Large Ensemble (hereinafter CESM-DP-LE), a suiteof ensemble forecasts run on the Cheyenne platform at the National Center for Atmospheric Research(NCAR)-Wyoming Supercomputer Center (Computational and Information Systems Laboratory, 2017). The
suite consist of 61 ensemble hindcasts (retrospective forecasts) of global climate initialized in every
1 November from years 1954 to 2014, and an additional ensemble forecast initialized in 1 November 2015. We
used the former for assessing the retrospective predictability of 2 year La Niña events since 1954, and the latter for predicting the evolution of the current La Niña. Ocean and sea ice initial conditions were obtained froma simulation performed with POP2 and CICE—the CESM ocean and sea ice models—forced at the surface
with atmospheric reanalysis data deﬁned by the Coordinated Ocean-ice Reference Experiments (CORE) phase
II as in Yeager et al. (2015). This CESM-CORE simulation captures key ocean dynamical processes involved in
the onset and decay of 2 year La Niña in agreement with the ORAS-S4 reanalysis (Figure S3). The land andatmosphere were initialized from an uninitialized historical simulation.
Ensembles were generated for each initial date perturbing each member’s atmospheric initial conditions witha unique round-oﬀ level diﬀerence (order of 1014 K) to the air temperature ﬁeld. Each ensemble consists of
40 members run for 10 years providing forecasts with suﬃcient length to explore the evolution of 2 year La
Niña. We used the 1954- to 2014-initialized hindcasts also to compute and remove forecast drift following
the methodology of International CLIVAR Project Oﬃce (2011) extensively used in seasonal and decadal climate forecasts (Hazeleger et al., 2013; Meehl & Teng, 2012; Yeager et al., 2012). Further details are available
in section 2 of the supporting information. This approach removes the drift that is common to all ensembles,
isolating internally generated and externally forced climate anomalies. The latter can be quite pronounced
over a 62 year period such as 1954–2015 and thus introduce biases in the amplitude of ENSO events. For
instance, the ensemble-mean SSTA between earlier and recent events reveals a pronounced warming trend
over the tropical Paciﬁc in the CESM-DP-LE (Figures S4 and S5, left). A similar warming trend is evident in theobserved SSTA computed relative to the same 1964–2014 baseline (Figures S4 and S5, right). Previous studies
have addressed this issue by removing the forced component from the hindcasts (e.g., van Oldenborgh et al.,2012). We circumvent this issue by using the Niño-3.4g SST index deﬁned in section 2.2.
Lastly, retrospective veriﬁcation is required to demonstrate the skill of a forecast system prior to its use for
actual predictions (Barnston et al., 2010; Ham et al., 2014; Kirtman et al., 2001; Wang et al., 2010; Tang et al.,2005; Tippett et al., 2011). Our analysis shows that that the CESM-DP-LE is particularly skillful at predicting theduration of historical La Niña with 2 and 1 year lead times when initialized from the peak of the preceding
El Niño and from the ﬁrst peak of La Niña, respectively (Figures S7 and S8). Further details available in section 3of the SI. To conclude, we use the 2015-initialized forecasts to predict the evolution of this year’s event andestimate the likelihood of returning La Niña during the target season of November-December-January (NDJ)
of 2017/2018.
2.5. ENSO Prediction Plumes
We computed the mean Niño-3.4g and Z̄′ TC indices among all members of each ensemble to quantify thepredictable component of each forecast. We deﬁne a second year La Niña when the Niño-3.4g SST index isless than −0.60 K, during the November-December-January (NDJ) season, 2 years after the forecast start date
(NDJ+2). Probability of 2 year La Niña are computed as the fraction of ensemble members showing Niño-3.4g
SST index under the −0.60 K threshold during NDJ+2. A composite of events starting from strong and moderate El Niño shows that on average the CESM-DP-LE hindcasts capture the evolution of the subsequent
La Niña events in terms of timing and amplitude (Figures S6a and S6b).
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3. Results
3.1. Observed Precursors
Analysis of precursors of historical ENSO events supports a link between the occurrence of 2 year La Niña andthe strength of the preceding El Niño as proposed by previous theoretical and modeling studies (DiNezio &Deser, 2014; DiNezio et al., 2017; Okumura, Sun, et al., 2017). The peak amplitude of El Niño events, measured
by the gradient Niño-3.4g SST index during the NDJ season (Niño-3.4gNDJ 0 ), is anticorrelated (r = −0.33,
p = 0.11) with the magnitude of the Niño-3.4g index 2 years later (Niño-3.4gNDJ+2 ). A correlation of this magnitude would not warrant a skillful prediction; however, the scatterplot between these quantities suggests thatthe likelihood of 2 year La Niña increases with the strength of the preceding El Niño (Figure 2a). For instance,
the observed SST data show that a returning El Niño after La Niña would be highly unlikely when the preceding El Niño has a peak amplitude larger than 1.5 K (Figure 2a, Niño-3.4gNDJ+2 > 1.5 K). Conversely, 2 year
La Niña are more likely to occur following strong El Niño events since two out of the three strongest El Niño
events were followed by La Niña conditions 2 years later and none by a returning El Niño. The remaining event,the El Niño of 1972/1973, showed neutral ENSO conditions during the second year.
Our empirical model based on the CESM-CTL shows increasing likelihood of 2 year La Niña as a function of thestrength of the preceding El Niño (Figure 2a, gray contours). According to this model, La Niña events precededby El Niño amplitudes within the range observed during NDJ 2015 (Figure 2a, green shading) would have a
65% to 85% chance of lasting 2 years (An 80% chance for the averaged Niño-3.4gNDJ 0 predictor among thethree SST data sets). The magnitude of the thermocline discharge can also be used as a predictor for 2 year
MAMJJA+1
La Niña. The observed Z̄′ TC
predictor is also correlated (r = 0.39, p = 0.14) with the amplitude of the
Niño-3.4g index in NDJ of the following year. Furthermore, the probabilities from the CESM-CTL (Figure 2b,
gray contours) show that La Niña events preceded by a discharge predictor within the range observed during
2016 (Figure 2b, green shading) would have a 80% to 90% chance of becoming a 2 year event.
3.2. Retrospective Predictions
The CESM-DP-LE shows a similar link between predictions of past 2 year La Niña events and the peak amplitude and thermocline discharge of the preceding El Niño as in the CESM-CTL. Hindcasts initialized during
El Niño Novembers show ensemble-mean Niño-3.4gNDJ+2 values 2 years later that are anticorrelated withthe magnitude of the ensemble-mean Niño-3.4gNDJ 0 predictor (r = −0.58, p = 0.01). The ensemble-mean
Niño-3.4gNDJ+2 predictand is also highly correlated (r = 0.60, p = 0.01) with the discharge predictor computedfrom the ensemble-mean Z̄′ TC index averaged from February to July of the year after initialization. The hindcasts show higher correlation than the observational data because the ensemble-mean predictors averageout random variability. This suggests that in the CESM-DP-LE, the magnitude of the thermocline discharge
determines a large fraction of the predictable component of 2 year La Niña. This result also suggest thatmany observed 2 year La Niña preceded by weak El Niño/discharge were inﬂuenced by unpredictable factors(i.e., atmospheric noise). Moreover, the CESM-DP-LE supports our conclusion that the likelihood of 2 year
La Niña increases after strong El Niño events. The hindcasts initialized in November of 1972, 1982, and 1997,
at the peak of the strongest El Niño events in our period of study, show highly predictable negative SST
anomalies during the NDJ season 2 years later, as seen by the ensemble mean (Figures 2c and 2d, circles) andensemble spread (Figures 2c and 2d, ellipses) of the Niño-3.4g SST index. Forecasts initialized from states characterized by weaker El Niño and associated discharge lead to less predictable 2 year La Niña due to a smaller
ensemble-mean signal, and also due to larger ensemble spread.
3.3. Predictions of the Current Event
The CESM-DP-LE initialized in November 2015 predicts an ensemble-mean evolution similar to previous 2 year
La Niña events preceded by strong or moderate El Niño events (Figures 3a and 3b). In particular, our model
predicted the observed onset of La Niña during boreal fall of 2016 and its subsequent peak near the end of
2016, but more importantly, it also predicts continued persistence of La Niña conditions through the upcoming boreal winter (Figure 3a). The ensemble-mean Niño-3.4g SST index remains under the −0.6 K thresholdused to deﬁne La Niña conditions throughout our target forecast season NDJ of 2017/2018 (Figure 3a, solid red
curve) as well as the preceding August-September-October season, thus satisfying operational criteria used
to classify ENSO events (NOAA’s Climate Prediction Center deﬁnes ENSO events when Niño-3.4 SST anomalies
exceed a 0.5 K threshold for ﬁve consecutive 3 month running mean seasons). The 2015-initialized forecast
also shows zonally averaged thermocline depth anomalies evolving with the same magnitude and timing as
observed (Figure 3b, blue versus green curves), with thermocline discharge peaking during February to Julyof 2016, shown by Z̄′ TC values of about −27 m, slightly shallower than observed.
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Current predictions of 2−year La Nina
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−1
−2
−3

TC

3
(e)
2
1
0
−1
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10

# of members

1015# of members

3
2
1
0
−1
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98 999 000
2
1

5

19

3
(i)
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10

15

# of members

3
2
1
0
−1
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−3
10 011 012
2
2

20

index (m)

3
(c)
2
1
0
−1
−2
−3

zonal−mean Z

Nino−3.4g SST index (K)Retrospective predictions of 2−year La Nina
30 (d)
20
10
0
−10
−20
−30
73 974 975
1
1

19

30 (f)
20
10
0
−10
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−30
83 984 985
11

1930 (h)
20
10
0
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−30
98 999 000
21

1930 (j)
20
10
0
−10
−20
−30
5

10

15

# of members

10 011 012
2
2

20

Figure 3. Current and retrospective predictions of La Niña events performed with the CESM1. Predicted and observed(a, c, e, g, and i) Nino-3.4 SST index and (b, d, f, h, and j) zonally averaged thermocline depth, ZTC , index, of ENSO events
initiated in 1972, 1982, 1997, 2009, and 2015. Histograms show the distribution of predicted Niño-3.4g SST values
for the target season of November-December-January 2 years after initializations (NDJ+2). Figures 3a and 3b show theprediction plumes from the forecast initialized in November 2015, at the peak of the El Niño preceding the current
La Niña. Figures 3c–3j show retrospective forecasts of previous events initiated by strong and moderate El Niño. Solidgreen curves are the observed indices. Refer to section 2 for details on how these indices are computed from multiple
available observational data sets. Red and blue thick curves identify the predicted ensemble-mean indices. Thin curves
identify the indices predicted by each individual ensemble member. Pink and lilac curves correspond to ensemble
members that show returning El Niño conditions and are not considered in the forecasts (see supporting text section 3.4
for more details on how these members are identiﬁed).

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Predicted and observed rainfall anomalies
DJF 1975
30˚N (a)

(b)

0˚
30˚S

DJF 1985
30˚N (c)

(d)

0˚
30˚S

DJF 2000
30˚N (e)

(f)

0˚
30˚S

DJF 2012
30˚N (g)

(h)

0˚
30˚S

DJF 2018
30˚N (i)
0˚
30˚S
60˚E

120˚E

180˚

120˚W

−100 −50 −25 −10

60˚W

−5

0

5

10

25

50

100

rainfall anomaly (mm/mon)Figure 4. Predicted and observed rainfall anomalies during the second year peak of La Niña events. (a, c, e, g, and i)
Ensemble-mean and (b, d, f, and h) observed rainfall anomalies corresponding to the target December-January-February
forecast season of selected 2 year La Niña that were preceded by strong or moderate El Niño events. Stippling indicateslocations where the ensemble signal to noise ratio is larger than 0.5. Observed rainfall anomalies prior to and after 1979
are from ERA40 reanalysis data (Uppala et al., 2005) and GPCPv2 satellite observations (Adler et al., 2003), respectively.

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Geophysical Research Letters10.1002/2017GL074904

Is the evolution of the current event similar to that of the 1997 record-breaking El Niño? Both events show
the largest predicted thermocline discharge among our hindcasts (Figures 3b, 3d, 3f, 3h, and 3j and also 2d)
consistent with the record-breaking strength of the preceding El Niño. And both cases show the onset of La
Niña one year after the peak of El Niño, along with its return for a second year (Figure 3a). However, these
two events show striking diﬀerences in the consistency of the predictions. The onset of La Niña was predictedby all 40 members of the 1997-initialized ensemble (Figure 3h, pink curves), but only by 33 members of the
2015-initialized ensemble (Figure 3a, thin red curves). Twenty-four members predict the return of La Niña fornext winter (i.e., a 60% chance), in contrast to 38 members predicting La Niña conditions for NDJ of 2000
(a 95% chance of 2 year La Niña following the El Niño of 1997). Despite the strength of the preceding El Niño,
the forecasts of the current event show one of the weakest ensemble-mean La Niña predictions for the second
year among the events analyzed (Figures 3a, 3c, 3e, 3g, and 3 and also 2d). However, both sets of forecasts
provide a low probability of a returning El Niño during the second year (7.5% for the current event and 0% for
the 1997 event).
Several processes could explain the distinctions between the likelihood of a 2 year La Niña obtained from the
1997- and 2015-initialized forecasts. First, CESM1 predicts an ensemble-mean Niño-3.4g SST index of −0.61 K
for the target season of NDJ 2017, compared to the much larger value of −1.33 K for NDJ of 1999. This difference cannot be explained by diﬀerences in the thermocline discharge because both cases show the same
peak amplitude. CESM1 predicted not only a much stronger ﬁrst year La Niña relative to observed for 1998 but
also a much lower ensemble spread (Figure 3g). The diﬀerences with the 2015-initialized forecasts become
conspicuous by NDJ of 2017/2018 when the ensemble spread, measured by the standard deviation of the
Niño-3.4g SST index, is 0.77 K compared with 0.41 K for the 1997-initialized forecasts. The diﬀerent spread
between these cases could also be related to the strength of La Niña’s ﬁrst year peak, which would suppress
uncoupled atmospheric variability capable of causing forecast spread.
The CESM-DP-LE also predicted a 2 year La Niña following the El Niño events of 1972, 1983, and 2009
(Figures 3a, 3c, 3e, 3g, and 3i). Among these events, the 1972- and 1982-initialized hindcasts could be closer
analogs to the current predictions since they show similar ensemble mean and spread as the 2015-initialized
forecast (Figure 2d). However, these events show a weaker peak discharge in the CESM-DP-LE (Figure 2d). It is
possible that our deﬁnition of discharge, based on the depth of the maximum vertical temperature gradient,could be overestimating the ENSO-driven thermocline depth variations. For instance, the thermocline could
be experiencing long-term shoaling in response to global warming (DiNezio et al., 2009), thus biasing our definition of the Z̄′ TC index. This eﬀect would be more pronounced in the recent hindcasts where is diﬃcult toisolate externally forced trends from model drift. Note that one of these events, the El Niño of 1972, led to a
1 year La Niña in nature, followed by weak cold conditions during the target NDJ season of 1974/1975 that
did not meet the criterion for La Niña per se (Figure 3c, green curve). This is consistent with the predictions
for the current event, which has a 40% chance of being limited to 1 year.
3.4. Predicted Impacts
The CESM-DP-LE predicted typical La Niña rainfall anomalies for the 2 year events preceded by strong or moderate El Niño events (Figure 4). In particular, the predicted rainfall anomalies for the second year peak in boreal
winter show drying over the central equatorial Paciﬁc and wetter conditions over the Maritime Continent(Figures 4a, 4c, 4e, 4g, and 4i). These anomalies show a high ensemble signal to noise (Figure 4, stippling)suggesting reliable rainfall predictions over the tropics. Observations show similar patterns, albeit with more
spatial variability consistent with the more unpredictable nature of rainfall. Remote rainfall impacts associatedwith ENSO teleconnections, such as drying over the southeastern U.S., show signal to noise ratios smaller than
0.5. The one exception is boreal winter of 1999/2000, for which the CESM-DP-LE predictions show signal tonoise larger than 0.5 over the southern tier of the United States (Figure 4e, stippling). The rainfall predictions
for the upcoming boreal winter (Figure 4i) show large signal over the tropics, but not over North America,
highlighting the more unpredictable nature of extratropical ENSO teleconnections (Deser et al., 2017).

4. Conclusions
The results presented here demonstrate the feasibility of skillful forecasts of 2 year La Niña events at 2 year
lead times. These forecasts are skillful when initialized at the peak of strong El Niño events because morethan 1 year is required for the thermocline to return to neutral conditions after the large initial discharge.
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Geophysical Research Letters10.1002/2017GL074904

Our statistical analysis shows that the magnitude of the peak thermocline discharge controls the predictablesignal for up to 2 years. The predictability of La Niña duration is consistent with processes governed by persistence, in which events driven by large initial perturbations will take longer to decay. The forecasts remainskillful at 2 year lead times because the ensemble-mean signal has suﬃciently large amplitude to overcome
the growing forecast spread. This conclusion is consistent with previous studies showing that ensemble
spread is not a good indicator of skill (Kirtman et al., 1997), a well-known diﬀerence with weather prediction, where low ensemble spread is critical for skillful forecasts. The striking similarity in the evolution of thethermocline depth anomalies in the hindcasts and observations suggest that the skillful prediction of past
2 year La Niña arises from CESM1’s realistic simulation of the dynamics of these events, which arises from
nonlinearities in dynamical feedbacks (Choi et al., 2013; DiNezio & Deser, 2014; DiNezio et al., 2017; Okumura
et al., 2011).
We estimate the probability of La Niña returning next boreal winter at between 60%, based on the
CESM-DP-LE forecast initialized in November 2015, and 80%, based on a statistical analysis of observed andsimulated predictors. We predict a rather weak La Niña based on the magnitude of the ensemble-mean
Niño-3.4g SST index (Figure 3a, red curve); however, its probability distribution is skewed toward more moderate La Niña conditions (Figure 3a, histogram). While the La Niña of 2016–2017 was considered weak by theconventional deﬁnition (Figure S1a, red curve), it was not in terms of the Niño-3.4g SST index (Figure S1a, blue
curve). Furthermore, the predicted rainfall patterns for the upcoming boreal winter are not much diﬀerent
from those of previous La Niña (Figures 4a, 4c, 4e, 4g, and 4i). This occurs because rainfall patterns are sensitive to SSTA gradients, and less to absolute SSTA, which increase with global warming. Therefore, the rainfall
anomalies more clearly show that the predicted SSTA for next winter will have La Niña-like impacts of considerable amplitude over the tropical Indian and Paciﬁc oceans. To date, our ensemble-mean predictions havetracked the observed evolution of the Niño-3.4g SST index (Figure 3a, blue and red curves). However, this does
not necessarily indicate that the event will continue to evolve according to the ensemble-mean predictions,as models and observations show that the magnitude of the ﬁrst and second year peaks are not correlated(DiNezio & Deser, 2014; DiNezio et al., 2017; Okumura, Sun, et al., 2017).
In addition, our forecasts show large spread, suggesting that the future evolution until our target season of
NDJ 2017/2018 could still be aﬀected by unpredictable processes in the Paciﬁc. Idealized predictions performed with CESM1 show that random wind variability in the western Paciﬁc and the state of the Southern
Oscillation have the largest short-term impact on forecast spread (DiNezio et al., 2017). Observations fromthe TAO/TRITON array captured easterly wind anomalies during February to April of 2017, which according tothese idealized forecasts will be more conducive to a 2 year La Niña. The Southern Oscillation index exhibited
values closer to neutral throughout the past austral winter and has turned positive since August of 2017 astate that would also be conducive for returning La Niña conditions. Future studies should assess the role ofthese stochastic processes in past 2 year La Niña.
The empirical model complements the dynamical forecasts because it shows that despite the large magnitude of the El Niño peak and thermocline discharge predictors, there is a nonnegligible chance (20%) that
La Niña will not return next boreal winter. Conversely, the dynamical model provides a lower bound to the likelihood of 2 year La Niña, which is comparable to the historical odds of a 50% chance, based on the observed
frequency of occurrence of 2 year La Niña events (13 out of 27 since 1880). The 60% prediction by CESM-DP-LE
is larger than the historical odds from the CESM-CTL, in which 2 year La Niña events occur with a 30% frequency. This supports our conclusion that that the initial conditions associated with the El Niño of 2015 haveincreased the odds of 2 year La Niña. Lastly, our forecasts show very low probability of El Niño conditions fornext winter. Additional work is needed to compare the skill of our predictions relative to that of operational
systems, which provide forecasts with shorter lead times. This is an important issue since operational forecasts
initialized in April of 2017 consistently predicted El Niño conditions for next boreal winter (Figure S9a). Since
then, these forecasts have shifted to predicting neutral conditions, when initialized in July 2017 (Figure S9b),
and to predicting returning La Niña conditions when initialized in September 2017 (Figure S9c). This drastic
shift in predicted outcomes could have arisen from limited predictive skill during spring, the so-called “spring
predictability barrier” (McPhaden, 2003; Webster & Yang, 1992), as well as from unrealistic dynamics of 2 year
La Niña in the operational models. For instance, models in which ENSO is excessively oscillatory would tend
to predict a subsequent El Niño when initialized right after the ﬁrst La Niña peak (e.g., April 2017). Such model
dependencies on dynamics of 2 year La Niña would need to be addressed in a multimodel framework. In sum,
all lines of evidence presented here point to a return of La Niña that is more likely than not, along with an
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Geophysical Research Letters10.1002/2017GL074904

unlikely return of El Niño. Despite the uncertainty inherent to any initialized climate forecast, the predictions
presented here could be useful for assessing risk of ﬂooding and drought during the upcoming boreal winterin areas throughout the world impacted by La Niña.
Acknowledgments
The authors would like to thankMichelle L’Heureux for her commentson this work and two anonymous
reviewers for their insightful comments and constructive criticism.
P. D. N., C. D., Y. O., A. K., andS. Y. are supported by the
National Oceanic and Atmospheric
Administration (NOAA) Climate
Program Oﬃce’s Modeling, Analysis,
Predictions, and Projections program (P. D. N. NA14OAR4310229
and C. D. NA14OAR4310228
and NA17OAR4310145, andY. O. NA14OAR4310227 andNA17OAR4310149) and Climate
Variability and Predictability Program(A. K. and S. Y. NA13OAR4310138).
A. K. and S. Y. are also supported by
the National Science Foundation (NSF)
Collaborative Research EaSM2 grantOCE-1243015. NCAR is sponsored by
the NSF. Computing resources were
provided by the Climate Simulation
Laboratory at NCAR’s Computationaland Information Systems Laboratory(CISL), sponsored by the National
Science Foundation and other agencies. The model output is available via
the Earth System Grid. We acknowledge the Global Tropical Moored
Buoy Array Project of NOAA/PMELfor providing subsurface ocean
temperature data.

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