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The Equatorial Pacific Cold Tongue Bias in CESM1and Its Influence on ENSO Forecasts
XIAN WU,a YUKO M. OKUMURA,b PEDRO N. DINEZIO,c STEPHEN G. YEAGER,a AND CLARA DESERaab

Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado
Institute for Geophysics, Jackson School of Geosciences, The University of Texas at Austin, Austin, Texasc
Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder, Boulder, Colorado(Manuscript received 21 June 2021, in ﬁnal form 24 January 2022)

ABSTRACT: The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) areinvestigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1,
for 1954–2015. The equatorial Paciﬁc cold tongue in the forecasts is too strong and extends excessively westward due to a
combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the ﬁrst year than that inherent to the model due to the imbalance between initialsubsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also
the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature
anomalies related to ENSO extend to the far western equatorial Paciﬁc during boreal summer when the cold tongue bias isstrong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Paciﬁc. The forecast errors ofpattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative
thermocline feedback in the equatorial Paciﬁc and tropical interbasin adjustments during the mature phase of ENSO.
These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface airtemperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more
pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensiﬁcation of the
Bjerknes feedback.
KEYWORDS: Paciﬁc Ocean; ENSO; Seasonal forecasting; Model errors

1. Introduction
The tropical Paciﬁc Ocean exhibits zonal contrast in meansea surface temperature (SST) between the western warm
pool and the eastern equatorial cold tongue. The easternequatorial Paciﬁc cold tongue controls climatological SST andrainfall patterns across a large area of the tropics. The cold
tongue simulated by the successive generations of Coupled
Model Intercomparison Project (CMIP) models is commonly
too cold and extends excessively westward compared toobservations (Mechoso et al. 1995; Davey et al. 2002; Zheng
et al. 2012; Brown et al. 2014; Bellenger et al. 2014; Planton
et al. 2020; Guilyardi et al. 2020; Jiang et al. 2021). This cold
tongue SST bias in models has been attributed to the misrepresentations of dynamic and thermodynamic processes inthe equatorial Paciﬁc, including too strong equatorial zonal
surface winds, too strong oceanic horizontal and verticaltemperature advection, too little SST damping due to erroneously positive cloud–shortwave radiation feedback, andDenotes 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-210470.s1.
Corresponding author: Xian Wu, xianwu@ucar.edu

misrepresentations of subseasonal variability, such as tropical instability waves (Mechoso et al. 1995; Davey et al. 2002;
Wittenberg et al. 2006; Zheng et al. 2012; Vannière et al.
2013; Ray et al. 2018; Siongco et al. 2020). The cold tongue
SST bias in coupled models is also related to the bias of theintertropical convergence zone (ITCZ) simulated in atmospheric models, which inﬂuences the surface wind bias in
the equatorial Paciﬁc (Lin 2007; de Szoeke and Xie 2008; Li
and Xie 2014). Besides the annual mean bias, most CMIP5
models erroneously simulate the timing and amplitude of
the annual cycle of the eastern equatorial Paciﬁc SSTs (e.g.,
Chen and Jin 2017; Wengel et al. 2019).
The climatological cold tongue bias has been suggested toaffect the simulations of tropical climate variations, especiallythe leading mode of interannual climate variability}El
Niño–Southern Oscillation (ENSO). In association with thewestward extension of the cold tongue, equatorial SST, surface wind, and precipitation variations related to ENSO
events are shifted too far west in climate models compared toobservations (Wittenberg et al. 2006; Taschetto et al. 2014;
Graham et al. 2017; Planton et al. 2020; Jiang et al. 2021). Thisis primarily because the enhanced climatological zonal temperature gradient induces a stronger zonal advective feedbackin the western equatorial Paciﬁc and favors the westward
extension of ENSO SST anomalies (Graham et al. 2017; Jiang
et al. 2021). Besides affecting the oceanic feedback, the cold
tongue bias could shift the rising branch of Walker circulation
westward. As a result, the wind–SST feedback is overestimated in the western equatorial Paciﬁc but underestimated in

DOI: 10.1175/JCLI-D-21-0470.1
Ó 2022 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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the whole equatorial Paciﬁc in some models (Ham and Kug
2011; Bayr et al. 2018). The inﬂuence of mean-state bias on
ENSO amplitude is complex and masked in many models dueto the error compensation of ENSO feedbacks. For example,
a model can still simulate realistic ENSO amplitude when
underestimating both the positive wind–SST feedback andnegative heat ﬂux–SST feedback (Bellenger et al. 2014; Bayret al. 2019a).
The cold tongue bias can also degrade the models’ ability
to predict ENSO. Models with a cold tongue bias show errors
in predicting the pattern of ENSO; in particular, they show
negative correlation skill in their ENSO forecasts of SST
anomalies in the western Paciﬁc in both dynamical (Ham et al.
2014) and statistical forecasts (Ding et al. 2018). Forecast
errors of ENSO amplitude are also found to be related to thebiases of SST and upper-ocean temperature in the equatorial
Paciﬁc (Manganello and Huang 2009; Kim et al. 2017).
Besides the forecast errors of pattern and amplitude of ENSO
events, a recent study by Wu et al. (2021a) notes that the low
skill in predicting the termination of El Niño events and thesubsequent transition to La Niña is related to the strong cold
tongue bias of the retrospective forecasts conducted with
Community Earth System Model, version 1 (CESM1). However, the errors regarding the phase transition of El Niño areabsent in the “perfect model” prediction experiments performed with the same model (Wu et al. 2021b) because such
idealized predictions do not have the issues that arise in the
retrospective forecasts summarized below.
Errors in operational dynamical forecasts arise not only
from the inherent model biases discussed above, but also
from the initialization methods, including uncertainties in
reproducing the true initial conditions and incompatibilities
between the initial states and the model dynamics. Whenforecasts are initialized with full-ﬁeld oceanic states derived
from observations, the forecasts will drift toward the model’s
own climatology as the forecasts progress (Misra et al. 2008;
Magnusson et al. 2013). In addition to the gradual model drift,
the full-ﬁeld initialization may lead to a rapid initial adjustment of the model if there is a large imbalance between theprescribed initial conditions and the model dynamics, known
as the initialization shock problem (Magnusson et al. 2013).
Understanding the causes of cold tongue bias and related
ENSO forecast errors is important from the standpoint of seasonal prediction skill in ENSO-related tropical rainfall andextratropical teleconnections (Bayr et al. 2019b; Ding et al.
2020). In this paper, we analyze the climatological bias of theequatorial Paciﬁc and its impact on ENSO forecasts in a suiteof multiyear CESM1 forecasts initialized with full-ﬁeld oceanic states in March, June, and November during 1954–2015.
First, we examine in detail the origins of cold tongue bias in
the CESM1 forecasts, including the inherent climatologicalbias of CESM1, imbalance between the initial conditions andmodel dynamics, and errors in the ocean initial conditions.
Then we investigate the forecast errors in ENSO characteristics and teleconnections, and their relationship with the cold
tongue bias of the forecasts and initial condition error.
The rest of this paper is organized as follows. The model,
experiments, and analysis are described in section 2. Section 3

VOLUME 35

presents the analyses of cold tongue bias and ENSO errors in
the forecasts and the dynamical processes underlying theseerrors. Results are summarized and discussed in section 4.

2. Model experiments and analysis methods
a. CESM1
The simulations and forecasts used in this study were runwith the CESM1, an earth system model consisting of interactive atmosphere, ocean, land, and ice components (Hurrell
et al. 2013). All of model components were run at nominal 18
horizontal resolution, including the Community Atmosphere
Model, version 5 (CAM5; Neale et al. 2012) with 30 verticallevels; the Parallel Ocean Program, version 2 (POP2; Smithet al. 2010) with 60 vertical levels; the Community Land
Model, version 4 (CLM4; Lawrence et al. 2011); and the Los
Alamos National Laboratory Community Ice Code, version 4(CICE4; Hunke and Lipscomb 2008).
The CESM1 produces one of the most realistic simulationsof the ENSO phenomenon among global climate models(Bellenger et al. 2014). It reproduces the broad spectral peakof ENSO in the 3–6-yr band, the asymmetric amplitude andpattern between El Niño and La Niña, and the wide range of
durations of ENSO events (DiNezio et al. 2017a; Wu et al.
2019). The CESM1 shows high skill in predicting the durationof El Niño and La Niña up to two years in advance when initialized from particular ocean states (DiNezio et al. 2017a,b;
Wu et al. 2021a,b). However, the free-running preindustrial
control simulations of CESM1 overestimate the amplitude of
ENSO by about 20% and show an excessive extension of
ENSO anomalies into the western equatorial Paciﬁc compared to observations (DiNezio et al. 2017a; Capotondi et al.
2020). The initialized forecasts of CESM1 show large biases in
predicting ENSO pattern and duration especially when the
climatological cold tongue bias is strong in the forecasts initialized in particular seasons (Wu et al. 2021a).

b. CESM1 retrospective forecasts and uninitialized
simulations
We analyze three sets of multiyear CESM1 ensemble forecasts initialized on the ﬁrst day of March, June, and November
for each year from 1954 to 2015. The November-initialized
forecasts are taken from the CESM Decadal Prediction Large
Ensemble (Yeager et al. 2018) and the ensembles initialized in
March and June are from Wu et al. (2021a). All forecast
ensembles use the same model and component conﬁguration
as in the CESM1 Large Ensemble Project (CESM1 LE; Kayet al. 2015). For each year and calendar month, ensemble forecasts are initialized with identical ocean and sea ice conditions
for the initialization date, and the ensemble spread is generated by adding small perturbations of an order of 10214 to theatmospheric initial conditions. The ensembles initialized in
March, June, and November have 10, 20, and 40 members andare integrated over 30, 27, and 34 months, respectively. The
initialization months and forecast length were selected toinvestigate the predictability of ENSO events with lead times
up to 2 years and the seasonal dependence of forecast skill.

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A bootstrap analysis of the 40-member November-initialized
forecasts showed that an ensemble size of 10 was sufﬁcient toestimate the ensemble mean signal of ENSO predictions,although the ensemble spread is slightly underestimated compared to the 40-member forecasts (Wu et al. 2021a). The
ocean and sea ice initial conditions for all ensembles were generated by forcing the ocean (POP2) and sea ice (CICE4) component models with historical atmospheric and surface ﬂux
ﬁelds. This “forced ocean–sea ice simulation” (FOSI) canreproduce key features of observed ocean and sea ice (e.g.,
Yeager and Danabasoglu 2014; Yeager et al. 2015), but its
deﬁciency in simulating the equatorial Paciﬁc mean climate
and ENSO variability will be examined. The atmosphere andland initial conditions are obtained from the CESM1 LE (Kayet al. 2015) for the November-initialized forecasts and from an
atmosphere–land model (CAM5–CLM4) simulation prescribed with monthly ocean and sea ice ﬁelds simulated by the
FOSI for the March- and June-initialized forecasts. The different techniques for initializing atmosphere and land models are
not expected affect the predictions of tropical Paciﬁc SSTs,
whose predictability is mostly governed by oceanic memory.
All forecasts follow historical (1954–2005) and representative
concentration pathway 8.5 (RCP8.5; 2006–15) radiative forcings.
We further compare initialized forecasts with uninitialized
CESM1 LE to estimate the mean-state errors that arise frominitialization problems. It is noted that the CESM1 LE only
has model bias, and the initialized forecasts suffer from bothmodel bias and initialization problems. We make use of the
40-member CESM1 LE during the forecast period 1954–2015.
The ﬁrst member of the CESM1 LE is initialized with oceanicand atmospheric conditions of year 401 of the CESM1 preindustrial control simulation and integrated from 1850 to 2100
under the historical (prior to 2006) and RCP8.5 (2006–2100)
forcings. The other 39 members are branched from the ﬁrst
member on 1 January 1920 with small perturbations applied
to the atmospheric initial conditions (order of 10214 K).

c. Observational datasets
We compare the forecasts, uninitialized simulations, andocean initial conditions used for initializing the forecasts
against several observational and reanalysis datasets over theperiod of 1954–2017. Observed SSTs are taken from the Hadley Centre Sea Ice and SST dataset (HadISST; Rayner et al.
2003) at 18 spatial resolution. The near-surface air temperature over land is taken from the Berkeley Earth Surface Temperature (BEST) station-based dataset at 18 spatial resolution(Rohde et al. 2013). We deﬁne the thermocline depth as thedepth of maximum vertical ocean temperature gradientderived based on the ocean temperature from European Centre for Medium-Range Weather Forecasts (ECMWF) Ocean
Reanalysis System 4 (ORAS4; Balmaseda et al. 2013) on 18
horizontal resolution with 42 vertical levels. Several otherocean reanalysis datasets are also used to validate the estimation of observed equatorial Paciﬁc thermocline depth (Fig. S1in the online supplemental material), including the Simple
Ocean Data Assimilation reanalysis, version 2.2.4 (SODA;
Carton and Giese 2008), version 4 of the Met Ofﬁce Hadley

Centre EN4 (EN4; Good et al. 2013) with two different sets ofbias corrections by Levitus et al. (2009; EN4l09) and Gouretski
and Reseghetti (2010; EN4g10), Estimating the Circulation
and Climate of the Ocean Version 4, Release 4 (ECCOV4R4;Forget et al. 2015), global ocean data assimilation system(GODAS; Behringer and Xue 2004), and the global objective
analyses of ocean data from the International Comprehensive
Ocean and Atmosphere Dataset and the Kobe Collection(ICOADS_Kobe; Ishii et al. 2005). The surface wind components, sea level pressure (SLP), precipitation, and 200-hPa
geopotential height (Z200) are taken from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al.
1996) on a 2.58 grid. The shortwave ﬂux, latent heat ﬂux, andnet heat ﬂux at surface are taken from the Objectively Analyzed Air–Sea Fluxes (OAFlux; Yu and Weller 2007) on a
18 grid for 1984–2009. We also test the robustness of atmospheric composites results in Fig. 7 using the Japanese 55-year
Reanalysis (JRA55; Kobayashi et al. 2015) for surface wind,
SLP, ocean precipitation, and Z200, and the Climatic Research
Unit time series (CRU; Harris et al. 2014) for land precipitation during 1958–2017 (Fig. S9). We regrid the observational
data to the same grid as the model output before calculating
their difference.

d. Analysis methods
The drifting climatology of the ensemble forecasts is calculated separately for each initialization month by averaging theensemble mean forecasts across the initialization years from1958 to 2015 for each lead time. We deﬁne forecast anomalies
as deviations from the climatology. The effect of externalradiative forcing is removed by subtracting a quadratic trend
computed from the ensemble mean anomalies for each leadtime following Yeager et al. (2018). We remove a quadratic
trend instead of a linear trend because the forecasts after 2000
show a more pronounced warming trend than the earlier
period. The observations are treated in a similar manner, withthe monthly climatology calculated for 1958–2015, and quadratically detrending applied to the monthly anomalies. The
inﬂuence of external forcing on the ENSO forecast skill issmall for short lead times but becomes signiﬁcant for leadtime longer than one year (not shown). The monthly climatology inherent to the CESM1 is calculated using the 40-member
ensemble mean of CESM1 LE for the forecast period of
1958–2015. The effect of radiative forcing is removed by subtracting the monthly ensemble mean anomalies from individual ensemble members.
The temporal evolution of ENSO events is tracked using
the Niño-3.4 index deﬁned as SST anomalies averaged over
the Niño-3.4 region (58S–58N, 1708–1208W). Following Wu
et al. (2019, 2021a), observed ENSO events are deﬁned when
the absolute value of the detrended Niño-3.4 (|Niño-3.4|)
index smoothed with a 3-month running mean ﬁlter is greaterthan 0.75 standard deviations in any month from October to
February. The standard deviation is computed separately foreach month during October to February over the period
1954–2017. We denote the year of initial ENSO development

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as year 0 and the months of the year as Jan0, Feb0, … , andDec0. We composite ensemble forecasts initialized in Nov21,
Mar0, Jun0, Nov0, Mar11, and Jun11 of observed ENSO
events. ENSO events are further classiﬁed into 1-yr events
when the smoothed |Niño-3.4| index drops below 0.5 standard
deviations in any month from Oct11 to Feb12, and otherwise
into 2-yr events. During 1954–2017, we identify ten 1-yr El
Niño (1963, 1965, 1972, 1982, 1991, 1994, 1997, 2002, 2006,
and 2009), ﬁve 2-yr El Niño (1957, 1968, 1976, 1986, and2014), four 1-yr La Niña (1964, 1988, 1995, and 2005), andeight 2-yr La Niña events (1954, 1970, 1973, 1983, 1998, 2007,2010, and 2016), with the list of years indicating year 0. Twoyear ENSO events are selected based on the Niño-3.4 index
in two consecutive winter seasons, which does not guarantee
the continuity during the summer between the two winters.
We deﬁne the onset and termination of ENSO events in theforecasts when the |Niño-3.4| ﬁrst exceeds and drops below
0.58C, respectively. Readers are referred to Wu et al. (2021a)
for further details of the analysis methods. The ENSO variability in the CESM1 LE is evaluated based on the ENSO
events identiﬁed using the same method as for observations
applied to all 40 members during 1954–2015. We compare theclimatology and composite ENSO events in the initialized
CESM1 forecasts with those in observations and the CESM1
LE. The statistical signiﬁcance of the composite anomalies isassessed using a two-tailed Student’s t test at the conﬁdence
level of 95%.

3. Resultsa. Climatological errors in the equatorial Pacific
The climatological seasonal cycle of SST, surface wind, andthermocline depth in the equatorial Paciﬁc for the forecasts
initialized in November, March, and June are compared withobservations and CESM1 LE (Fig. 1). The three sets of forecasts capture the seasonal intensiﬁcation of the SST cold
tongue during boreal summer and autumn (Figs. 1a–d). In
both the forecasts and observations (Figs. 1a–d), the SST
cooling intensiﬁes in the eastern equatorial Paciﬁc when theequatorial southerly winds start to develop in late boreal
spring to early summer (May–June) in association with theseasonal northward migration of the ITCZ (Mitchell andWallace 1992; Xie 1994). During summer and early autumn(June–September), the eastern equatorial Paciﬁc SST coolingand southeasterly winds extend to the central-western equatorial Paciﬁc. The intensiﬁcation and westward extension of the
Paciﬁc cold tongue and associated climatological easterly
winds are overestimated in the forecasts (Fig. 1e). Climatological SSTs in the forecasts show a cooling bias of up to 24.38C
around Sep0 in the ﬁrst year of the March-initialized forecasts,
together with an excessive westward shift of the Paciﬁc warm
pool edge (288C) by up to 358 of longitude during Sep0–Mar11of the March- and June-initialized forecasts relative to observations (Fig. 1e). The SST cooling and easterly wind biases
over the western-central equatorial Paciﬁc linger into early
boreal spring in the forecasts, resulting in a 2-month delayof the warmest season in the equatorial eastern Paciﬁc

VOLUME 35(February–April in observations and April–June in all forecast ensembles). These climatological SST and wind biases
are partly inherent to the model’s climatology (Fig. 1f), since
the deviations of forecasts from the CESM1 LE (Fig. 1g) aresmaller than that from the observations (Fig. 1e). The biases
in all three sets of forecasts are statistically signiﬁcant at the
99.5% conﬁdence level despite the different ensemble sizes,
according to the bootstrap method across the ensemble members (Fig. S2, top).
The development of the SST cooling bias in the easternequatorial Paciﬁc is preceded by a negative (shallow) thermocline depth bias that propagates eastward from the westerncentral equatorial Paciﬁc after the initialization (Fig. 1e). The
initial “shallow” thermocline bias is rooted in errors in theinitial condition data from FOSI, which simulates shallower
climatological thermocline in the western-central Paciﬁc compared to the ORAS4 (Fig. 2) and other six ocean reanalysis
datasets (Fig. S1a). The equatorial Paciﬁc SST simulated by
the FOSI is overall warmer than the observations, suggestingthat the quick development of cold tongue SST bias in the
March- and June-initialized forecasts is not an ampliﬁcationof the initial SST errors. The causes of FOSI errors in simulating the observed thermocline depth and SST in the equatorial
Paciﬁc require further examination. It could be partly causedby the CESM1’s inherent bias in simulating the depth andslope of the thermocline in the equatorial Paciﬁc (Fig. 1f), theerrors in surface winds in the equatorial and off-equatorial
Paciﬁc used to force the FOSI [not shown; see Yeager et al.
(2018) for more details about the wind datasets used in
FOSI], or/and the deﬁciencies in simulating other processes
that inﬂuence climatological SSTs.
The cold tongue SST bias in year 0 is larger in the Marchand June-initialized forecasts compared to the Novemberinitialized forecasts (Fig. 1e), and this difference among thethree forecast sets is well beyond the range of uncertainty
due to sampling of ensemble members (Fig. S2, bottom). The
dependence of cold tongue bias on the initialization month of
the forecasts is associated with the strong seasonality ofthermocline–SST feedback in the equatorial eastern Paciﬁc(Fig. 3a). In observations, CESM1 LE, and forecasts, thestrength of the feedback, measured by the regression coefﬁcient of SST on the thermocline depth in the eastern equatorial Paciﬁc (Niño-3 region), is smallest during boreal spring(March–May; 20.028C m21 in CESM1 LE) and largest in latesummer–autumn (August–November; 0.118C m21 in CESM1
LE). The seasonality of the SST sensitivity to thermocline
depth is associated with the seasonal change in climatologicalupwelling in the eastern equatorial Paciﬁc (not shown). In the
March- and June-initialized forecasts, the initial thermocline
shoaling bias in the western-central equatorial Paciﬁc propagates into the eastern basin during boreal summer–autumn,
inducing large negative SST bias (Fig. 1e). In the Novemberinitialized forecasts, by contrast, the thermocline shoaling bias
arriving in boreal spring produces a smaller SST bias in theeastern equatorial Paciﬁc. Note that the correlation between
the thermocline and SST mainly represents interannual variability, as the seasonal cycle of cold tongue involves littlechange in the thermocline depth (Chang and Philander 1994;

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FIG. 1. Longitude–time sections of climatological monthly SST (8C; shading), thermocline depth (contours at intervals of 30 m), and surface wind (m s21; vectors) in the equatorial Paciﬁc (38S–38N) for the ensemble forecasts initialized in (a) November, (b) March, and (c) June (superscripts on the months along the y axis indicate the ﬁrst year
when all forecasts overlap), (d) observations (HadISST, ORAS4, and NCEP–NCAR) and (f) CESM1 LE during
1958–2015. Note that the climatologies of the forecasts are a function of lead time, whereas those of the observations
and CESM1 LE are simply repeated for clarity. The deviations of the forecast climatologies from (e) observations and(g) CESM1 LE for SST (8C; shading), thermocline depth (contours at intervals of 10 m; zero contours thickened andnegative contours dotted), and surface wind (m s21; vectors). The thermocline depth is smoothed with a nine-point
running-mean ﬁlter in the longitudinal direction. The statistical signiﬁcance of Fig. 1e is shown in Fig. S2.

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FIG. 2. (a) Longitude–time sections of climatological SST (8C; shading) and thermocline depth(contours at intervals of 30 m) in the equatorial Paciﬁc (38S–38N) during 1958–2015 for the FOSI.
(b) The difference of climatological SST (8C; shading) and thermocline depth (contours at intervals of 10 m; zero contours thickened and negative contours dotted) between the FOSI andobservations (HadISST and ORAS4). The thermocline depth is smoothed with a nine-point running-mean ﬁlter in the longitudinal direction.

Xie 1994). However, the thermocline–SST feedback that
operates in interannual variability appears to be important for
amplifying the inﬂuence of initial climatological thermocline
shoaling bias on climatological SST bias. Our results are consistent with a recent study by Siongco et al. (2020), which also
shows that the cold tongue bias of CESM1 hindcasts develops
faster during boreal summer–autumn than other seasons dueto the strengthening of vertical temperature advection in theeastern equatorial Paciﬁc.
The three sets of forecasts, especially those initialized in
March and June, display a pronounced cooling bias in theﬁrst year (year 0) relative to subsequent years of the forecast(Fig. 1e). This strong initial cooling bias is associated with theimbalance of the initial ocean state and the model’s dynamics
as shown in the difference between forecasts and CESM1 LE(Fig. 1g). The initial thermocline shoaling anomalies acrossthe equatorial Paciﬁc leads to an SST cooling response in year
0 of the forecasts relative to CESM1 LE. As a result, the climatology of forecasts shows pronounced deviations fromthe model’s climatology in year 0 before stabilizing. The

difference in climatological surface winds between forecasts
and CESM1 LE is consistently very small throughout the 2-yr
forecasts, suggesting that the initial model adjustment ismostly driven by oceanic processes.

b. Forecast errors of ENSO characteristics
Forecast errors of the temporal evolution and amplitude of
ENSO events, and the dependency of error development on
the initialization season, are assessed based on the predictionsof Niño-3.4 SST index. Figures 4a and 4b show the time seriesof the Niño-3.4 index composited for all El Niño and La Niñaevents during 1954–2015 in observations and ensemblemean forecasts initialized at different lead times. The onset
of El Niño and La Niña (deﬁned using a threshold of|Niño-3.4| . 0.58C) in the summer of year 0 is not captured in
the forecasts initialized in Nov21, which is probably related tothe overall low predictability of the onset of ENSO events
before boreal spring (e.g., Torrence and Webster 1998; Tippettand L’Heureux 2020) rather than the model errors. One
exception from this composite result is the onset of La Niña

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FIG. 3. Seasonal mean regression coefﬁcients between different variables in the observations (black curves; HadISST, ORAS4,
NCEP–NCAR, and OAFlux), CESM1 LE (yellow curves), November- (red curves), March- (blue curves), and June-initialized (green
curves) forecasts during 1954–2015 (1984–2009 for observed surface ﬂuxes). (a) Regression of SST on thermocline depth (8C m21),(b) regression of thermocline depth on surface wind (s), (c) regression of surface wind on SST (m s21 8C21), (d) regression of net surface
heat ﬂux on SST (W m22 8C21), (e) regression of surface shortwave ﬂux on SST (W m22 8C21), and (f) regression of surface latent heatﬂux on SST (W m22 8C21). The surface winds are averaged over the Niño-4 region (58S–58N, 1608E–1508W), and all other variables areaveraged over the Niño-3 region (58S–58N, 1508–908W). Note that the regression coefﬁcients in the forecasts are a function of lead time,
whereas those of the observations and CESM1 LE are simply repeated for clarity.

events following strong El Niño, which can be predicted in
the forecasts initialized from the preceding El Niño peak (notshown). The onset of El Niño and La Niña is ﬁrst seen in the
Mar0-initialized forecasts, although the Niño-3.4 SST anomalies quickly dissipate in the following autumn. The Jun0-initialized forecasts show improvement, but the predicted peakof El Niño and La Niña is weaker and occurs too early compared to observations. The shift in the timing of peak is associated with the extensive westward shift of maximum ENSO
SST anomalies in the forecasts (cf. Fig. 5).
After the peak, all forecasts of El Niño show a warm SST
bias in year 11 of about 0.68C, while all forecasts of La Niñacapture the returning of La Niña as seen in observations(Figs. 4a,b). To assess the forecast errors of ENSO event duration, we classify El Niño and La Niña events into those lasting
one year (Figs. 4c,d) and two years (Figs. 4e,f). The forecasts
initialized in and after Jun0 successfully capture the termination of 1-yr El Niño and 1-yr La Niña events (deﬁned using athreshold of |Niño-3.4| , 0.58C), but do not capture the transition to the opposite ENSO state in year 11 (Figs. 4c,d). On theother hand, the forecasts show good performance in predicting
the continuation of 2-yr ENSO events. The reintensiﬁcation of
2-yr El Niño events is captured in the forecasts initialized
in/after Nov0, and the persistence of 2-yr La Niña events ispredicted in all six sets of forecasts (Figs. 4e,f). The mechanisms driving the long-term predictability of 1-yr and 2-yr
ENSO events have been explored by Wu et al. (2021a) and
DiNezio et al. (2017b). Because 1-yr El Niño accounts for alarge portion of El Niño events (10 out of 15) and 2-yr La Niñadominates the signal of all La Niña events (8 out of 12), themodel shows better performance in predicting the overall duration of La Niña than El Niño events. In the remainder of thissection, we show the results of our dynamical analysis for 1-yr
El Niño events and discuss the similarities and differences withthe other types of ENSO events whose results are shown in the
supplemental materials.
To illustrate the dynamical processes underpinning theforecast errors in Niño-3.4 SST index, the spatiotemporal evolutions of SST, surface wind, and thermocline depth anomalies in the forecasts of 1-yr El Niño are compared withobservations (Fig. 5; see Fig. S3 for the results of other typesof ENSO events and Fig. S4 for the statistical signiﬁcance ofthe composites). In the Mar0- and Jun0-initialized forecasts(Figs. 5b,c), the SST warming and surface westerly wind
anomalies associated with 1-yr El Niño quickly extend toward
the far western Paciﬁc during boreal summer (Jun0–Aug0),
when the mean-state cold tongue bias intensiﬁes (Fig. 1e) andcauses biased oceanic and atmospheric feedbacks in the

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FIG. 4. Time series of the Niño-3.4 index (8C) composited for (a) all El Niño, (b) all La Niña, (c) 1-yr El Niño, (d) 1-yr La Niña, (e) 2-yr
El Niño, and (f) 2-yr La Niña events in the observations (HadISST; black curves) and ensemble-mean forecasts (colored curves) during
1954–2015. The forecasts are initialized in Nov21 (pink), Mar0 (red), Jun0 (yellow), Nov0 (green), Mar11 (light blue), and Jun11 (darkblue). The dashed horizonal lines denote the 60.58C threshold. The solid portions of the colored curves indicate that the composite forecasts are signiﬁcantly different from zero at the 95% conﬁdence level.

equatorial Paciﬁc (Fig. 3). The difference between the forecasts and observations during Jun0–Apr11 displays a zonal
dipole pattern in the equatorial Paciﬁc, indicating a westward
shift of the SST and wind anomalies (Fig. 5e). In the Mar0and Jun0-initialized forecasts, the weaker SST warming overthe central-eastern equatorial Paciﬁc (1808–808W) during the
El Niño development phase is related to the underestimatedwind–SST feedback during SON0–DJF11 (Fig. 3c) and weaker
positive thermocline depth anomalies in the eastern equatorial
Paciﬁc compared to the observations (Fig. 5e). The weakened
thermocline deepening is linked to the simultaneous weakened
westerly wind anomalies over the western-central Paciﬁc(1608E–1808) and the underestimated wind–thermocline feedback in the eastern equatorial Paciﬁc in forecasts (Fig. 3b).
Furthermore, the errors in the initial thermocline depth anomalies also contribute to weaker El Niño amplitude, which show
weaker thermocline deepening in the central-eastern equatorial Paciﬁc at the initialization in the Mar0- and Jun0-initialized
forecasts than observations.

Besides the pattern and amplitude biases of 1-yr El Niñoforecasts during Jun0–Apr11, the biased SST warming andwesterly wind anomalies over the western equatorial Paciﬁc
persist after the mature phase. In association with the lingering westerly anomalies and weaker peak amplitude of 1-yr
El Niño, the thermocline shoaling over the western equatorial
Paciﬁc is too weak and does not propagate into the easternequatorial Paciﬁc after the mature phase in the Mar0- andJun0-initialized forecasts, resulting in the failure of predicting
the La Niña state in year 11. The lingering ENSO SST andwind anomalies in the western Paciﬁc is likely driven by theoverestimated wind–SST feedback during AMJ11–JAS11 in
the forecasts (Fig. 3c). In addition, the damping effect of net
surface heat ﬂux on SST in the eastern equatorial Paciﬁc isunderestimated in the Mar0- and Jun0-initialized forecasts(Fig. 3d), which could contribute to the lingering ENSO
duration. The underestimation of the heat ﬂux–SST feedback is largely contributed by the shortwave–SST feedback(Fig. 3e) and partly by the latent heat–SST feedback during

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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 for
1-yr El Niño in the (a) observations (HadISST, ORAS4, and NCEP–NCAR), ensemble-mean forecasts initialized in (b) Mar0, (c) Jun0,(d) Nov0, and (e) the difference between the forecasts and observations. The thermocline depth anomalies are smoothed with a nine-point
running-mean ﬁlter in the longitudinal direction and a 1–2–1 ﬁlter in the time direction in both observations and forecasts. The composites
for other types of ENSO events are shown in Fig. S3, and the statistical signiﬁcance of these composite anomalies is shown in Fig. S4.

SON0–JJA11(Fig. 3f). Previous studies also ﬁnd that the
shortwave–SST feedback is important to the simulation of
ENSO phase locking (Wengel et al. 2018; Bayr et al. 2021).
In contrast, the biases of spatial pattern, peak amplitude,
and phase transition of 1-yr El Niño events are reduced in the
Nov0-initialized forecasts (Figs. 5d,e and Fig. S4), for which
the mean-state cold tongue bias is weaker and the initialization is closer to the El Niño peak than the Mar0- and Jun0-

initialized forecasts (Fig. 1e). In particular, the phase transition
from El Niño to La Niña is captured in the Nov0-initialized forecasts, although the amplitude of subsequent La Niña is underestimated presumably due to the smaller initial thermocline depth
anomalies in FOSI. The forecasts of the other three types of
ENSO events also show westward-shifted pattern bias andweaker amplitude of anomalies over the central-eastern Paciﬁc,and these biases are stronger in the Mar0- and Jun0-initialized

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forecasts than the Nov0-initialized forecasts (Fig. S3). Similar
to 1-yr El Niño, the Mar0 and Jun0 -initialized forecasts
fail to capture the phase transition of 1-yr La Niña but
show biased weak equatorial Paciﬁc cooling throughout
year 11. In contrast, the continuation of 2-yr El Niño ispredicted by the Nov0 -initialized forecasts, and the continuation of 2-yr La Niña events is predicted in all three setsof forecasts, despite the pattern and amplitude biases inyear 11.
To understand why the surface wind anomalies over the far
western equatorial Paciﬁc tend to linger into the second year
and prolong equatorial Paciﬁc warming in the 1-yr El Niñoforecasts, we examine SST anomalies in the key ocean basinsthat could inﬂuence these wind anomalies. Figure 6 compares
the longitude–time sections and time series of SST and surface wind anomalies over the equatorial Paciﬁc, the tropicalIndian Ocean (108S–08), and the tropical North Atlantic(08–208N) composited for 1-yr El Niño events in the observations, forecasts initialized in Jun0 and Nov0, and CESM1 LE(see Fig. S5 for the results of other types of ENSO events andFig. S6 for the statistical signiﬁcance of the composites). In
observations, the tropical Indian and Atlantic Oceans warm
up during the mature phase (Dec0–Feb11) of 1-yr El Niñodue to tropical atmospheric adjustments to the equatorial
Paciﬁc SST warming (Fig. 6a; Xie and Carton 2004; Schottet al. 2009). The Jun0-initialized forecasts, however, underestimate the warming of tropical Indian and Atlantic Oceans during Dec0–Feb11 presumably due to the weaker El Niñowarming over the eastern equatorial Paciﬁc (Figs. 6b,e,h; theMar0 forecasts show similar results). The underestimatedIndian and Atlantic Ocean warming and overestimatedwarming over the western equatorial Paciﬁc could work
together to modulate the interbasin SST contrast and contribute to the biased persistence of surface westerly wind
anomalies in the spring after the El Niño peak in the Jun0initialized forecasts. Figure 6h more quantitively show thatthe cooling bias of the Indian and Atlantic Oceans contributes to about 40% of the bias in the positive interbasin SST
contrast during Dec0–Feb11, while the SST warming bias
over the western Paciﬁc is the dominant factor. The Nov0initialized forecasts show similar SST and wind biases during Dec0–Feb11 as in the Jun0-initialized forecasts, indicating these biases are inherent to the model, but they aremuch reduced in the Nov0-initialized forecasts due to shortlead times (Figs. 6f,i).
We note that the westward shift of ENSO anomaly pattern
and SST warming bias over the western equatorial Paciﬁc is abias inherent to the CESM1 (Figs. 6d,g,j). Nonetheless, thismodel reproduces the observed frequency of 1-yr El Niñoevents in the control simulation (Wu et al. 2019). In CESM1
LE, the delayed warming of the tropical Indian and Atlantic
Oceans after the peak of 1-yr El Niño is much stronger thanobservations (Figs. 6d,g,j). The warming bias of the Indianand Atlantic Oceans balances with the SST warming bias over
the western-central equatorial Paciﬁc, resulting in a smaller
bias in the interbasin SST contrast and wind anomalies during
the boreal spring after the El Niño peak than that in the forecasts. This SST error compensation between the Paciﬁc and

VOLUME 35

other two tropical oceans appears to be important to thesmaller wind biases in CESM1 LE. The tropical interbasininteractions on El Niño termination has also been investigated in many previous studies [see Cai et al. (2019) for areview and references therein], but the causality and relative
contributions of tropical ocean basins in affecting the surface
wind anomalies and ENSO are needed to be examined in theforecast setting.

c. Forecast errors of ENSO teleconnections
The forecast errors of tropical SST and precipitation anomalies
during the peak of 1-yr El Niño also affect the wintertime atmospheric teleconnections in the Northern Hemisphere. Figure 7
compares SST, surface air temperature over land, surface wind,
SLP, precipitation, and Z200 anomalies over the tropics andNorthern Hemisphere during Dec0–Feb11 composited for the
1-yr El Niño events in observations and the Jun0- and Nov0initialized forecasts (see Fig. S7 for the results of other typesof ENSO events and Fig. S8 for the statistical signiﬁcance ofthe composites). The atmospheric composites in Fig. 7 arerobust to the choice observational datasets (Fig. S9). In
association with the tropical Paciﬁc SST warming, SLP
decreases over the North Paciﬁc, deepening the Aleutian low
in both observations and forecasts (Figs. 7a–c, left). In the
Jun0-initialized forecasts, the center of the circulation anomalies exhibits a westward shift of about 108 of longitude anda more zonally elongated pattern compared to observations(Figs. 7a,b, left).
The surface atmospheric biases are mirrored in theupper-tropospheric circulation anomalies (Figs. 7a,b, right).
In observations, the increased precipitation and resultant
diabatic heating induce a pair of anticyclones at 200 hPa straddling the central equatorial Paciﬁc and a Rossby wave train
propagating into the Northern Hemisphere. In the Jun0-initialized
forecasts, the response of extratropical Z200 anomalies isshifted westward and is more zonally elongated comparedto observations. The pattern difference between the Jun0initialized forecasts and observations displays a slightly tilted
zonal dipole pattern in both SLP and Z200 over the midlatitude North Paciﬁc (Fig. 7d). This Z200 anomaly dipole is partof a Rossby wave train originating in southeastern China,
which is forced by the biased positive precipitation anomalies
over the far western tropical Paciﬁc. In contrast, the biases
of extratropical atmospheric circulations are much reducedin the Nov0-initialized forecasts, consistent with the weakerbiases in the pattern and amplitude of tropical SST and precipitation anomalies due to shorter lead times (Fig. 7e).
Related to the improved predictions of atmospheric circulation anomalies over the North Paciﬁc, the forecast errors ofland surface air temperature anomalies over North America
are much weaker in the Nov0-initialized forecasts than the
Jun0-initialized forecasts (Figs. 7d,e). This result is consistent
with the ﬁndings by Bayr et al. (2019b), who show that those
CMIP5 models with a stronger cold tongue bias tend to simulate a westward shift of atmospheric convection in the tropicalPaciﬁc and associated atmospheric teleconnections to the North
Paciﬁc during ENSO events.

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FIG. 6. Longitude–time sections of SST (8C; shading) and surface wind (m s21; vectors) anomalies in the equatorial Paciﬁc (38S–38N), theIndian Ocean (108S–08), and the Atlantic (08–208N) composited for 1-yr El Niño in the (a) observations (HadISST and NCEP–NCAR),(b) Jun0 forecasts, (c) Nov0 forecasts, (d) CESM1 LE, and the deviations of (e) Jun0 forecasts, (f) Nov0 forecasts and (g) CESM1 LE relative to the observations. The composites for other types of ENSO events are shown in Fig. S5, and the statistical signiﬁcance of these composite anomalies is shown in Fig. S6. (h)–(j) Time series of the ocean–atmosphere biases shown in (e) and (f) averaged in different ocean
basins: surface wind (m s21) over the western Paciﬁc (1308–1708E; pink curves), SST (8C) in the western-central Paciﬁc (1308E–1408W;
orange curves), the Indian Ocean (408–1208E; light blue curves), and the Atlantic (808W–08; dark blue curves), and the interbasin SST contrast (8C) between the western-central Paciﬁc and Indian/Atlantic Oceans (red curves). The time series are smoothed with a 3-month running ﬁlter.

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FIG. 7. Maps of (left) SST, land surface air temperature (8C; shading), surface wind (m s21, vectors) only fortropics and subtropics (308S–358N), SLP (contours at intervals of 0.8 hPa; zero contours thickened and negative contours dashed in gray), (right) precipitation (mm day21; shading), and Z200 (contours at intervals of 15 m; zero contours
thickened and negative contours dashed) anomalies composited for 1-yr El Niño events in the (a) observations(HadISST, BEST, ORAS4, and NCEP–NCAR), (b) Jun0 forecasts, (c) Nov0 forecasts, and deviations of the (d) Jun0
forecasts and (e) Nov0 forecasts from the observations. The composites for other types of ENSO events are shown in
Fig. S7, and the statistical signiﬁcance of these composite anomalies is shown in Fig. S8.

Besides the biases of atmospheric responses over the extratropical North Paciﬁc, we also observe that the anticyclonic
SLP anomalies over the western North Paciﬁc (WNP) aremuch weaker in the Jun0-initialized forecasts than observations. In observations, anticyclonic SLP anomalies develop
over the WNP mainly as a Rossby wave response to the SST
cooling and decreased precipitation anomalies over the far
western Paciﬁc during El Niño (Fig. 7a; e.g., Wang et al.
2000). The precipitation over the western Paciﬁc could be
suppressed not only by local SST cooling but also by the
Indian Ocean warming (e.g., Watanabe and Jin 2002). The
Jun0-initialized forecasts predict biased positive SST and precipitation anomalies over the far western equatorial Paciﬁc aswell as weaker Indian Ocean warming, resulting in a very
weak response of SLP anomalies over the WNP (Fig. 7b). In
observations, the surface southerly wind anomalies on thewestern ﬂank of the anticyclonic SLP anomalies increase theprecipitation over East Asia by bringing high moisture content from tropical oceans (Fig. 7a; e.g., Wu et al. 2003).
Instead, the Jun0-initialized forecasts fail to capture thesesoutherly wind anomalies and associated increased precipitation anomalies over East Asia (Figs. 7b,d). The predictions ofatmospheric circulation and rainfall anomalies over East Asia
are both improved in the Nov0-initialized forecasts when the
ENSO-related tropical SST and precipitation anomalies are
more realistically reproduced (Figs. 7c,e).

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These forecast errors of atmospheric teleconnections may,in turn, affect surface wind variability over the western equatorial Paciﬁc and the evolution of ENSO states in the second
year. In the difference map between the Jun0 forecasts andobservations, the biased SST warming and the southwesterly
wind anomalies over the subtropical Paciﬁc resembles theNorth Paciﬁc meridional mode (NPMM) and potentially contributes to the persistence of equatorial Paciﬁc warming inyear 11 (Fig. 7d; e.g., Vimont et al. 2003; Chang et al. 2007;
Fang and Yu 2020; Kim and Yu 2021). The weak development of the WNP anomalous anticyclone may also contributeto the ENSO forecast errors in year 11 because the easterly
wind anomalies on the southern ﬂank of this anticyclone weresuggested as an important factor for El Niño termination(Weisberg and Wang 1997).
Excessively weak tropical interbasin SST adjustments,
along with biases in the pattern and amplitude of Northern
Hemisphere teleconnections, are found for the forecasts of
the other three types of ENSO events (Figs. S5 and S7),
although the results for 1-yr La Niña and 2-yr El Niño in
observations are largely insigniﬁcant at the 95% conﬁdence
level due to the small number of events in the composites(Figs. S6 and S8). In contrast to degrading the forecasts of
1-yr ENSO events in year 11, the teleconnection errors overremote oceans during the mature phase of ENSO favor thepersistence of 2-yr ENSO events. For example, in the Jun0initialized forecasts of 2-yr La Niña (Figs. S5 and S7), the too
weak interbasin adjustment, biased negative NPMM, andweak WNP cyclonic SLP circulation may promote the persistence of easterly wind anomalies and SST cooling over thewestern equatorial Paciﬁc in the second year.

4. Summary and discussions
This study investigates mean-state biases in the equatorial
Paciﬁc and their impact on ENSO forecasts based on the retrospective forecasts performed with the CESM1. Three setsof forecasts are initialized in March, June, and November,respectively, to assess the seasonal dependency of the forecast
errors. All forecasts regardless of their initialization seasonsshow a strong cooling bias of the climatological SSTs in theeastern Paciﬁc cold tongue, along with a westward shift of theedge of the Paciﬁc warm pool and excessive climatologicaleasterly winds over the western-central Paciﬁc. This cold
tongue bias is ampliﬁed in the ﬁrst year of the forecasts before
the model stabilizes at its own climatology at longer leadtimes. This initial large forecast bias occurs because the initialconditions exhibit a shallower climatological thermocline
depth in the western-central equatorial Paciﬁc than the model’s climatology. This initial imbalance induces subsequent
SST cooling in the ﬁrst year. The magnitude of this initial
ampliﬁcation of the cold tongue bias depends on the initialization season. The cold tongue bias becomes larger in the
March- and June-initialized forecasts relative to the Novemberinitialized forecasts, because the initial thermocline depth
errors propagate into the eastern equatorial Paciﬁc when
the SST–thermocline feedback is strongest seasonally andthus ampliﬁes the model biases.

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Larger ENSO forecast errors are found in the March- andJune-initialized forecasts than in the November-initialized
forecasts. In the March- and June-initialized forecasts, SST
and wind anomalies associated with ENSO migrate excessively westward in the summer season when the cold tongue
bias corrupt the simulation of dynamic and thermodynamic
feedbacks in the equatorial Paciﬁc. These errors result in
weakened SST anomalies over the central-eastern Paciﬁc andhence weaker and earlier peak of ENSO events (as capturedby the Niño-3.4 SST index). These forecast errors of ENSO
amplitude and peak timing appear to arise from the initialization errors, since the CESM1 LE shows stronger ENSO
amplitude and comparable peak timing compared to observations. The underestimation of ENSO amplitude in the forecasts is also associated with weaker initial thermocline
anomalies simulated by the FOSI compared to the ocean
reanalysis data.
The errors in the pattern and amplitude of ENSO events
further affect the prediction of their termination. In particular, the excessively weak amplitude of El Niño events in theforecasts results in a weaker negative thermocline feedback inthe equatorial Paciﬁc together with weaker SST warming over
the tropical Indian and Atlantic Oceans}regions that are
known to inﬂuence the termination of ENSO events via atmospheric teleconnections (Cai et al. 2019). The weaker negative
feedback from these ocean basins and the too strong ENSO
SST anomalies in the western equatorial Paciﬁc lead to biased
persistent wind anomalies over the western equatorial Paciﬁc.
These biased oceanic and atmospheric processes may work
together to cause the excessive tendency of ENSO events tolast two years in the forecasts. The erroneous processes associated with the cold tongue bias tend to prolong the durationof ENSO, degrading the predictions of ENSO termination
but contributing to the high forecast skill of ENSO continuation for the wrong reason. This may explain why predictionsof 2-yr El Niño and 2-yr La Niña events are often better atlonger lead times (Figs. 4e,f), although it remains uncertain if
the differences among the forecasts are signiﬁcant consideringa small sample size of 2-yr ENSO events.
The ENSO forecast errors further inﬂuence the predictionsof atmospheric teleconnections, land surface air temperature,and rainfall in the Northern Hemisphere during the maturephase of ENSO. Related to the westward displacement of
ENSO SST and precipitation anomalies over the tropicalPaciﬁc, the tropospheric and surface atmospheric circulation
anomalies over the North Paciﬁc show a westward shift andbecome more zonally elongated in forecasts compared toobservations, leading to the forecast errors of land surface airtemperature over North America. Additionally, the patternbias of ENSO anomalies leads to SLP errors over the western
Paciﬁc and to precipitation errors over East Asia. Further
studies are needed to examine the forecast errors of ENSO
teleconnections in other seasons. These extratropical atmospheric errors during the mature phase of ENSO events may,in turn, affect the predictions of ENSO duration in the following year via the NPMM and WNP anomalous anticyclones/
cyclones.

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We emphasize the role of negative thermocline feedback
and ocean basin interactions, but further research is needed
to investigate the inﬂuence of other atmospheric and oceanic processes in affecting the forecasts of ENSO evolution.
For example, the meridional shift of wind anomalies could
fasten the termination of El Niño events (Lengaigne et al.
2006; McGregor et al. 2013), and the off-equatorial subsurface oceanic process are important for the occurrence ofmultiyear ENSO events (Iwakiri and Watanabe 2021; Wenet al. 2021).
Our study shows that errors in climatologies and ENSO
characteristics in full-ﬁeld initialized forecasts can be evenlarger than those in uninitialized simulations of the samemodel. The initialization imbalance between the “observed”(FOSI) oceanic initial conditions and model dynamics is
ampliﬁed by coupled feedbacks in the equatorial Paciﬁc,resulting in a stronger cold tongue bias in the forecasts thanthat inherent to the model. The traditional method of
removing the drifting climatology of forecasts cannot eliminate the inﬂuence of biased ocean–atmosphere feedbacks
on ENSO variability. As a result, these initialized forecasts
show ENSO errors that are absent from the uninitialized
simulations. For example, the CESM1 forecasts tend tooverestimate the duration of ENSO events, while the uninitialized simulations of the same model show realistic proportions of ENSO events lasting one and two years (Wuet al. 2019). It will be interesting to compare ENSO forecast
skill in CESM1 with that in its successor, CESM2, whichshows a reduced cold tongue bias (Capotondi et al. 2020).
More generally, the relationship between mean state biases
and ENSO biases warrants further examination using multimodel ensemble forecasts.
Our results reveal opportunities to improve the forecast
skill of ENSO events by improving the model’s mean state
and the accuracy of initial condition data. Despite upgradedphysics and spatial resolution across the generations of CMIP
models, systematic model errors in tropical Paciﬁc climatology and ENSO variability persist (see a recent review by
Guilyardi et al. 2020). The method of “ﬂux adjustment” wasadopted as an intermediate step to improve the simulation of
the tropical Paciﬁc mean state and its seasonal variation byprescribing a seasonally varying nudging climatology (e.g.,
Ray et al. 2018). Flux adjustment has been shown to improve
the simulation of equatorial Paciﬁc SSTs, ENSO-related climate impacts, and climatological land precipitation (Ferrett
and Collins 2019; Johnson et al. 2020; Zhang et al. 2021),
while some studies suggest it does not improve the representation of ENSO feedbacks (Spencer et al. 2007; Ferrett and
Collins 2019). Another method termed “anomaly initialization”
has been used to reduce the model drift and initialization shock,by which forecasts are initialized from the observed anomalies
added to the model’s climatology. However, it remains unclear
and model dependent which initialization method can produce
more skillful ENSO and seasonal climate predictions (Magnusson
et al. 2013; Smith et al. 2013; Hu et al. 2020). The ocean initialcondition data used for our forecasts show errors in reproducing both the climatology and variability of thermocline depth
in the equatorial Paciﬁc. These initial conditions are obtainedby forcing the ocean model of CESM1 with atmospheric forcing from the reanalysis data and are hence subject to themodel errors and uncertainties in the prescribed wind forcing.
Integrated efforts aimed at improving model simulations,observing systems and data assimilation methods are needed
to make progress on prediction skill of ENSO and its worldwide climate impacts.
Acknowledgments. The authors thank the three anonymous
reviewers for constructive comments and suggestions that
helped to improve the manuscript. Main observational datasets
used in this study are available online: HadISST from the Met
Ofﬁce Hadley Center (http://www.metofﬁce.gov.uk/hadobs/),
BEST from Berkeley Earth (http://berkeleyearth.org/data/),
ECMWF Ocean Reanalysis System 4 from ftp://ftp-icdc.cen.unihamburg.de/EASYInit/ORA-S4, OAFlux data from the Woods
Hole Oceanographic Institution (ftp://ftp.whoi.edu/pub/science/
oaﬂux/data_v3), and NCEP-NCAR reanalysis from https://psl.
noaa.gov/data/gridded/data.ncep.reanalysis.html. The CESM1
Large Ensemble simulations are available from https://www.
cesm.ucar.edu/projects/community-projects/LENS/. This work
is funded by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Ofﬁce Modeling, Analysis, Predictions, and Projections Program (NA17OAR4310149
and NA17OAR4310145) and the National Science Foundation (NSF) Physical Oceanography Program (OCE-1756883).
X. Wu is supported by an Advanced Study Program postdoctoral fellowship from then National Center for AtmosphericResearch (NCAR). The CESM project is supported primarily
by the NSF. This material is based upon work supported by
the NCAR, which is a major facility sponsored by the NSF
under Cooperative Agreement 1852977.

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