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Mechanisms behind the Springtime North Pacific ENSO Teleconnection
Bias in Climate Models
RUYAN CHEN,a,b ISLA R. SIMPSON,c CLARA DESER,c BIN WANG,b,d AND YAN DUaaState Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology,
Chinese Academy of Science, Guangzhou, Chinab
Ministry of Education Key Laboratory for Earth System Modeling and Department of Earth System Science,
Tsinghua University, Beijing, ChinacNational Center for Atmospheric Research, Boulder, ColoradodState Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics,
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China(Manuscript received 28 April 2022, in ﬁnal form 29 July 2022)
ABSTRACT: Previous studies have shown that models overestimate the strength of ENSO teleconnections to the North
Paciﬁc during springtime, but the underlying reasons for this bias remain unknown. In this work, the relative contributions
from basic-state and thermodynamic/dynamic forcing factors are disentangled through idealized experiments with
the Community Earth System Model and a range of stationary wave modeling experiments. It is revealed that in
CESM1 the diabatic heating biases over the tropical Indian Ocean and tropical central-western Paciﬁc jointly favor a
cyclonic (anticyclonic) circulation bias to occur in the North Paciﬁc during the springtime of El Niño (La Niña) events.
On one hand, the difference in the modeled and observed climatological basic state does not lead to the bias formation
directly, as the diabatic heating biases are the primary cause. On the other hand, the springtime basic state is conducive toa more vigorous stationary wave response to the biased diabatic heating than the wintertime state, and this explains why
the teleconnection bias occurs during springtime but not in winter. An iterative bias-correction approach is then implemented in the atmospheric model component of CESM1 to verify the linkage between the tropical diabatic heating bias
and the teleconnection bias. Moreover, this explanation is shown to be relevant in other models of phase 5 of the Coupled
Model Intercomparison Project (CMIP5) as a strong relationship is found between biases in ENSO-related tropical centralwestern Paciﬁc/Indian Ocean precipitation and North Paciﬁc circulation across models in spring.
SIGNIFICANCE STATEMENT: The purpose of this study is to explain why climate models tend to overestimate
the springtime ENSO teleconnection to the North Paciﬁc. Through both simpliﬁed and comprehensive model experiments, we found that the diabatic heating biases over the tropical Indian Ocean and central-western Paciﬁc basins are
the main cause behind the circulation bias. Although similar heating biases also occur in winter, the spring mean climate state is more sensitive to the biased heating than the winter mean state. These ﬁndings are useful for developing
future climate models that would better simulate the springtime climate response during the ENSO events, as the sameproblem can be found in many other models.
KEYWORDS: Atmospheric circulation; ENSO; Teleconnections; Climate models; Bias; Spring season

1. Introduction
El Niño–Southern Oscillation (ENSO) is the most prominent ocean–atmosphere coupled mode of tropical interannual
variability (Bjerknes 1969; McPhaden et al. 2006). Throughthe modulation of tropical convection, ENSO-related sea surface temperature (SST) anomalies induce a large-scale atmospheric circulation response (e.g., Horel and Wallace 1981;
Held and Kang 1987; Zhang et al. 1996; Seager et al. 2003;
L’Heureux and Thompson 2006; Deser et al. 2017; Domeisen
et al. 2019), which has a profound global climate impact (e.g.,

Supplemental information related to this paper is available
at the Journals Online website: https://doi.org/10.1175/JCLI-D-220304.s1.
Corresponding author: Ruyan Chen, chenruyan@scsio.ac.cn

Ropelewski and Halpert 1986; Trenberth et al. 2002; Lyon
and Barnston 2005; Cai et al. 2011; Deser et al. 2018). These
ENSO-triggered anomalous atmospheric circulation patterns
are usually called “ENSO teleconnections” due to their remote inﬂuence, far from the SST anomalies that drive them.
Many different aspects of the ENSO teleconnection patterns
have now been discovered (Horel and Wallace 1981; Karoly
1989; Ghil and Mo 1991; Wang et al. 2000), among which theNorth Paciﬁc ENSO teleconnection is perhaps the most important pathway through which the inﬂuence of ENSO is transmitted to the middle and high latitudes of the Northern Hemisphere(Bjerknes 1966; Ropelewski and Halpert 1986; Kim et al. 2019;
Herceg-Bulić and Kucharski 2012; Herceg-Bulić et al. 2017).
During the warm (cold) phase of ENSO, the North Paciﬁc
ENSO teleconnection refers to the anomalous cyclonic (anticyclonic) circulation response centered over the North Paciﬁc region. Although its intensity usually peaks in winter and decays inthe following spring corresponding to the life cycle of ENSO

DOI: 10.1175/JCLI-D-22-0304.1
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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JOURNAL OF CLIMATE(Spencer and Slingo 2003; Chen et al. 2020), it should be notedthat the potential predictability of its climate impact could behigher during spring because the background noise during thatseason is substantially reduced compared to midwinter (Kumar
and Hoerling 1998). Moreover, some other studies have foundthat some anomalous weather events during springtime werelikely linked to a prolonged inﬂuence from ENSO maturing in
the previous winter (Wolter et al. 1999; Schmidt et al. 2001; Allenet al. 2015; Jong et al. 2016). Therefore, an accurate representation of the springtime North Paciﬁc ENSO teleconnection inclimate models is important for attribution and seasonal forecasting of anomalous weather/climate events due to ENSO.
While most prior studies that have examined model biases
in ENSO teleconnections have focused on the winter months,
Alexander et al. (2002) and Spencer and Slingo (2003) independently pointed out that the North Paciﬁc circulation
anomalies during the springtime of ENSO tend to be overestimated in models. A more recent assessment of models by
Chen et al. (2020) further veriﬁed that this overly strong
springtime North Paciﬁc ENSO teleconnection bias can beubiquitously detected in many different climate models (i.e.,
CMIP5/CMIP6) and different conﬁgurations (such as coupled, atmosphere-only, or pacemaker experiments). To ruleout the possibility that the bias is purely caused by sampling
error associated with internal variability, the signiﬁcance wastested through a bootstrapping technique as introduced byDeser et al. (2017, 2018). As illustrated in Chen et al. (2020),
the overly strong teleconnection signal during the springtime
of ENSO in models can further bias the simulated climate inﬂuence over North America, manifested as an overly prolonged springtime ENSO effect. Furthermore, no relationshipwas found between the bias strength and model-top height,
extratropical SST variability, atmospheric chemistry, biochemistry, or model resolution. At this stage, no explanation
has been given for the origins of this springtime North Paciﬁc
ENSO teleconnection bias.
Based on stationary wave theory, the large-scale quasistationary wave pattern can be interpreted as a steady atmospheric response to anomalous wave source forcing under aspeciﬁc climatic basic state (Ting and Sardeshmukh 1993;
Sardeshmukh and Hoskins 1988; Ting and Yu 1998; Held et al.
2002). From this perspective, the fundamental question is todetermine which part takes the greatest responsibility for theformation of the springtime North Paciﬁc ENSO teleconnection bias. Is it biases in the tropical heating? Biases in the basic state that affect the wave source? Biases in the basic statethat affect the wave propagation? Or biases in extratropical
processes or feedbacks that may impact stationary wave formation and evolution? It is difﬁcult to disentangle the different roles played by each factor through composite analyses
alone. In this paper, we use a stationary wave (SW) model(Ting and Held 1989; Held et al. 2002) to identify the relativeroles of tropical diabatic heating biases and basic-state biases
in contributing to the ENSO teleconnection bias. We then apply an “iterative bias-correction” approach in a full generalcirculation model (GCM) to verify the conclusion indicatedby the SW model. The potential cause of the “seasonal preference” of the bias (i.e., its tendency to occur exclusively during

VOLUME 35

the springtime) is also explored. The uniformity of the biasformation mechanism in different climate models is addressedin a preliminary manner by multimodel regression.
The rest of this paper is organized as follows. Section 2 describes the data and models used. Section 3 presents the stationary wave modeling results for exploring the key factors
behind the teleconnection bias. In section 4 an iterative biascorrection technique in the Community Atmosphere Modelversion 5 (CAM5) is introduced and its effectiveness at alleviating the ENSO teleconnection bias is discussed. A summaryand discussion are provided in section 5.

2. Models and data used
a. Stationary wave model
1) MODEL INTRODUCTION
Stationary wave theory tells that the background state andanomalous forcing work together in determining the stationary wave response. To isolate the relative contributions ofbiases in the forcing, and biases in the basic-state circulation,
in forming the springtime North Paciﬁc ENSO teleconnection
bias, a baroclinic stationary wave (SW) model is adopted inthis paper (Ting and Yu 1998; Held et al. 2002). The SW
model is based on the three-dimensional (3D) primitive equations and all the variables are described as deviations fromthe zonal mean. The model has rhomboidal wavenumber-30
truncation in the horizontal and 14 vertical s coordinate levels. The time scale of the Rayleigh friction and Newtonian
cooling is 15 days at all levels, and the biharmonic diffusion
coefﬁcient is set to 1 3 1017 m4 s21. These dampings are necessary to reach a steady-state model solution. Given that thismodel produces a steady-state solution, it does not simulatetransient eddy inﬂuence on the mean ﬂow. Instead, the transient eddies’ inﬂuence is prescribed via forcing terms. In our
experiment design, anomalous diabatic heating and transient
momentum ﬂux during the ENSO years are added as the forcing terms onto the climatological three-dimensional basic
state. Note that the contribution from orographic forcing isembedded in the basic state as the basic state is prescribed as
the climatological 3D state as opposed to the zonal mean.
Thus, nonlinearities between the orography and anomalous
heating and transients are not represented. By integrating themodel for a short period [usually after tens of days; in Ting
and Yu (1998) about 30 days], the stationary wave response
during ENSO can be obtained. Compared with an atmospheric general circulation model (AGCM), the SW model
does not incorporate sophisticated physical processes andfeedback mechanisms. However, the model has the capabilityof capturing the climatological large-scale stationary wave
patterns, as well as depicting the anomalous response to tropical diabatic heating anomalies related to ENSO (Ting and Yu
1998; Held et al. 1989; Ting and Held 1989; Ting andSardeshmukh 1993; Held et al. 2002; Jong et al. 2020). The
stationary wave model is much easier to interpret than a full
AGCM and provides the opportunity to pull apart the different contributions to stationary wave anomalies. It is also computationally inexpensive, which makes it practical to carry out a

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large suite of sensitivity experiments to isolate the role playedby different aspects. That being said, the lack of two-way interactions between the anomalous forcing and circulation response
inevitably leads to its oversimpliﬁcation of the response, and insome cases it can be challenging to isolate cause and effect,
where feedbacks are involved. We, therefore, complement thestationary wave modeling approach with an iterative bias correction procedure within a comprehensive climate model.

2) DATA FOR RUNNING SW MODEL EXPERIMENTS
In this paper, the input ﬁelds for the SW model consistof two categories: observation-based and model-based. To
calculate the observation-based input ﬁelds, the 55-year Reanalysis dataset JRA-55 provided by the Japan Meteorological Agency (JMA) is used (Kobayashi et al. 2015; Haradaet al. 2016). This dataset provides high-quality climate reanalysis data spanning 1958 to the present. Based on the 4D-Var
data assimilation scheme, a larger number of observational
data sources are incorporated in JRA-55. It also has a relatively high spatial (T319 3 60 levels) and temporal resolution (6 hourly). To prepare the input ﬁelds for model-based
simulations, the ensemble mean of a 10-member set of
CESM1 Tropical Ocean–Global Atmosphere (TOGA) simulations is used based on the period 1958–2010. Following
convention (e.g., Lau and Nath 1994; Trenberth et al. 1998),
SSTs in the TOGA simulations are prescribed with observed
time-evolving SSTs in the tropics (308–308N) and the observed climatological seasonal cycle elsewhere. The TOGA
conﬁguration has been widely adopted to study the model’s
response to the observed tropical SST evolutions, especiallythose related to ENSO (e.g., Deser et al. 2017; Thirumalai
et al. 2017; Lehner et al. 2018).
The diabatic heating forcing needed by the SW model iscalculated as the residual of the thermodynamic equation as
follows:


­T
T ­T
1 V · =T 2 v k 2
;
(1)
Q5
­t
p
­p
where Q stands for the diabatic heating and ­T/­t is the local
temperature change; 2V · =T and v(kT/p 2 ­T/­t) are thehorizontal and vertical thermal advection term respectively,
where T represents the air temperature, V is the horizontal
winds vector, and v is the vertical velocity in pressure coordinates; k 5 Rd/Cp, where Rd is the speciﬁc gas constant fordry air and equals 287 J kg21 K21, and Cp is the speciﬁc heatat constant pressure, which equals 1004.7 J kg21 K21. For
calculating the observed diabatic heating, all the variables
required by Eq. (1) are taken from JRA-55 monthly reanalyses. As a result, the heating transport by submonthly transients is included in the calculated ﬁeld. It is reasonable toinclude the contribution from transients given that the SW
model cannot simulate that effect independently (Held et al.
2002). Comparison with the Q calculated using 6-hourly data
reveals major differences over the extratropical storm tracks.
In the tropics, however, the heat ﬂux convergence by submonthly transients is relatively small compared to the diabatic heating (not shown). We prefer to use the monthly

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data to estimate the diabatic heating because not all necessary ﬁelds are available at a higher time resolution for the
TOGA simulations, and it is a reasonable approach for the
SW model. However, we caution that, although it is a good
approximation for the tropical area, the extratropical“diabatic heating” presented in this paper will be greatly inﬂuenced by the transient heat ﬂux convergence. To verify
the reliability of the calculated diabatic heating based on
JRA-55, other reanalysis data including NCEP–NCAR (R1)
(Kalnay et al. 1996), ERA20C (Poli et al. 2016), ERAInterim (Dee et al. 2011), ERA5 (Hersbach et al. 2020), and
CFSR (Saha et al. 2010) are also used for comparison.
The convergence of the transient vorticity ﬂux (2DV z )is used to represent the transient momentum forcing, where z
represents the vorticity (Hurrell 1995; Ting and Yu 1998). The
overbar represents a monthly average and the prime indicatesﬂuctuations about the monthly mean. Assuming the continuity equation holds, 2DV z can be rewritten as (­2 u y  /­x2 2­2 u u /­x­y 1 ­2 y  y  /­x­y 2 ­2 u y  /­y2 ) 3 (21), which allows
it to be calculated from the monthly accumulated ﬂuxes
available from the TOGA output. The transient divergence
ﬂux convergence (2DV div ) is not included because it could
not be straightforwardly calculated from the available
TOGA data, and it is not an important forcing term in thereanalysis (not shown). For each SW modeling experiment,
the model is integrated for 60 days, and the average of thelast 20 days is used for analysis.

b. Community Atmosphere Model version 51) MODEL INTRODUCTION
Developed primarily at the National Center for AtmosphericResearch (NCAR), the Community Atmosphere Model (CAM)
is one of the most widely used global atmospheric models forweather and climate research (Danabasoglu et al. 2020). The
latest release reaches version 6.3 (CAM6.3) within the framework of the Community Earth System Model, version 2(CESM2) (Bogenschutz et al. 2018; Danabasoglu et al. 2020). To
be consistent with the CESM1 TOGA simulations, the previous
version, CAM5 (Neale et al. 2012), is adopted in this paper (Kayet al. 2015; Deser et al. 2018).

2) DATA FOR CAM5 SIMULATIONS
Two initial experiments are carried out using CAM5: 1) thecontrol simulation (CTRL), in which the climatological SSTs
are prescribed and taken from the 1800-yr preindustrial simulations of the CESM Large Ensemble Project (Kay et al.
2015), and 2) the El Niño SST forcing simulation (EN), in
which the SST forcing is obtained by superimposing thecomposited SST anomalies (ERSSTv3b; Smith et al. 2008;
408S–408N) during El Niño events onto the CTRL’s climatological SSTs. The El Niño events are deﬁned when the DJFmean Niño-3.4 index is greater than one standard deviationfrom the mean (Chen et al. 2020), and only the events during
1958–2010 (the overlapping period of TOGA and JRA-55)
are selected. We branch off on 1 October of each year of CTRL
and run an ensemble of 20 EN simulations from October toApril. Therefore, the response to El Niño SST anomalies is

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FIG. 1. Stationary wave components of the FM averaged (left) 1000- and (right) 300-hPa streamfunction anomalies
of El Niño events during 1958–2010: (a),(d) the observational ﬁelds based on JRA-55, (b),(e) the outputs fromTOGA simulations, and (c),(f) their difference.

available as the difference between the 20-member ensemble
mean of the EN and CTRL simulations (this EN minus CTRL
difference is referred to as “NINO” hereafter).
We then run a series of iterative bias-correction simulations
using CAM5. The purpose of these bias-correction simulations
is to artiﬁcially eliminate or alleviate the diabatic heating bias
in the model by adding a correction term into the model integration process. The correction term is calculated from the difference between the observed (using JRA-55) and modeleddiabatic heating ﬁelds. Technical details of applying the iterative bias-correction approach will be given in section 4.

paper, the springtime of ENSO speciﬁcally refers to the mean ofFebruary–March (FM) following the ENSO peaking winter,when the North Paciﬁc circulation bias is most evident (Chenet al. 2020). Although the FM season is more often called thelate-winter or early-spring, the term “springtime” is preferred inthis work because it is a long-standing bias feature that was ﬁrst
discovered in the spring months’ average (March–May), until
Chen et al. (2020) pointed out that it is most signiﬁcantly seen
during February and March. So we continue to refer to thespringtime bias for consistency with previous literature.

c. Other model simulations and observational data

3. Stationary wave modeling results

The precipitation and sea level pressure (SLP) ﬁelds simulated by the Coupled Model Intercomparison Project phase 5(CMIP5) models’ preindustrial (piControl) simulations are
adopted for investigating the relationship between the tropical precipitation bias and the springtime North Paciﬁc ENSO
teleconnection bias across different models. The observed
precipitation during 1979–2010 is taken from the Global Precipitation Climatology Project (GPCP) version 2 (Adler et al.
2003). The observed SLP is based on the European Centre for
Medium-Range Weather Forecasts (ECMWF) twentieth-century
reanalysis (ERA20C; Poli et al. 2016), in which surface pressure
and winds are assimilated using ICOADS observations, andour conclusions would remain qualitatively unchanged if otherreanalysis datasets were used instead (Chen et al. 2020). In thisa. Model validation
Figure 1 shows the stationary wave component (i.e., deviation from the zonal mean) of the streamfunction anomalies at1000 and 300 hPa composited during FM of the El Niñospring in JRA-55 and CESM1 TOGA simulations separately.
Their differences (i.e., the bias ﬁelds to be explained) are
shown in Figs. 1c and 1f. The panels for the upper-level states(right column of Fig. 1) are colored and expanded in the display area to show the global stationary wave structure. In thelower troposphere near the surface, the North Paciﬁc cyclonic
circulation anomaly simulated by TOGA is signiﬁcantly stronger than that in observation. In addition, a westward shift bias
is noticeable for its central position (cf. Figs. 1a,b). In the

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TABLE 1. Stationary wave modeling conﬁgurations for model
validation and isolating the roles of anomalous forcing and basic state.
The basic state is the FM climatology of winds, surface pressure, andtemperature during 1958–2010. Diabatic heating and momentum
forcing by the transients are composited anomalies during FM of the
El Niño years.
Experiment

Basic state Heating Transients

JRA_all
TOGA_all

JRA
TOGA

JRA
TOGA

JRA
TOGA

TOGA_H1JRA_T1JRA_B
JRA_H1TOGA_T1JRA_B
JRA_H1JRA_T1TOGA_B

JRA
JRA
TOGA

TOGA
JRA
JRA

JRA
TOGA
JRA

upper troposphere, the observations exhibit a wave train
structure propagating from the equatorial central Paciﬁc tothe mid- to high latitudes of the North Paciﬁc and North
America (Fig. 1d). In the TOGA simulations, the wave train
structure is similar to that in observations but with a stronger
amplitude. By differing the observed and modeled ﬁelds, theupper level exhibits a quadrupole bias pattern covering theNorth Paciﬁc region (Fig. 1f).
The feasibility of using the SW model to trace the bias
source relies on its ability to reproduce the above-displayedbias features. To test that, two independent simulations
are carried out using the SW model (Table 1). One is the7695

observation-based simulation (“JRA_all”), which means the basic state and anomalous forcing are all taken from the JRA-55.
The other is the model-based simulation (“TOGA_all”), whichmeans the corresponding ﬁelds needed by the SW model arederived from the CESM1 TOGA outputs. Speciﬁcally, the basic
state refers to the February–March mean of the temperature,surface pressure, and horizontal wind ﬁelds during 1958–2010
based on either JRA-55 or CESM TOGA simulations, and theforcing terms are calculated by compositing the observed ormodeled anomalous diabatic heating and transient momentum
for the springtime (FM) of the El Niño years. The diabatic heating and transient forcing effects in the Southern Hemisphereand the Arctic regions are not included, but the modeling
results would remain qualitatively unchanged if they are employed globally (not shown).
The modeling results shown in Fig. 2 indicate that the SW
model can reproduce the ﬁelds shown in Fig. 1 reasonably
well. For the lower-level circulation response (Figs. 2a–c),
TOGA_all simulates a stronger cyclonic anomaly thanJRA_all does over the North Paciﬁc. The upper-level wave
structures are also well captured by the SW model for boththe observation-based (JRA_all; Fig. 2d) and the modelbased (TOGA_all; Fig. 2e) simulations. Compared to the observed pattern, the teleconnection pattern simulated by the
TOGA_all experiment is not only stronger but also extends
westward (Figs. 2d,e). The quadrupole bias pattern covering
the North Paciﬁc region is also well reproduced by the SW

FIG. 2. Stationary wave components of the (left) 1000- and (right) 300-hPa streamfunction anomalies simulated by the(a),(d) JRA_all experiments and (b),(e) TOGA_all experiments in Table 1, and (c),(f) their difference.

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FIG. 3. Stationary wave components of the (left) 1000- and (right) 300-hPa streamfunction anomalies simulated by(a),(d) the difference between TOGA_H1JRA_T1JRA_B and JRA_all experiments for isolating the role of diabatic heating, (b),(e) the difference between JRA_H1TOGA_T1JRA_B and JRA_all experiments for isolating therole of transient momentum forcing, and (c),(f) the difference between JRA_H1JRA_T1TOGA_B and JRA_all experiments for isolating the role of basic state.

model as manifested in Fig. 2f. Therefore, the springtime
North Paciﬁc ENSO teleconnection bias in TOGA can be reproduced by the SW model to a great extent, although theamplitude of the biased cyclonic circulation over the North
Paciﬁc is a little weaker in the SW model. Substantial differences between the actual TOGA bias and the SW model bias
can be seen in the north Indian Ocean–Asian region. Notethat nonlinear interactions of the anomalous wave with the topography are not included, but we also tried starting from thezonal mean basic state and using the orography as an independent forcing component. The corresponding SW modeling
results suggest that the nonlinear effects caused by the interactions with orography are negligible (not shown). We will
use the stationary wave model to disentangle the relative contributions from anomalous forcing and basic state to the biased circulation response to El Niño in the next step.

b. Isolating the roles of anomalous forcing and basic state
To separate the relative contributions from basic-state biases
and anomalous forcing biases in forming the springtime North
Paciﬁc ENSO teleconnection bias as reproduced by the TOGA_all
experiment, three perturbation experiments are carried out:
“TOGA_H1JRA_T1JRA_B”, “JRA_H1TOGA_T1JRA_B”,

and “JRA_H1JRA_T1TOGA_B” (Table 1). Based on JRA_all,
only one component among the basic state [denoted by “(·)_B”],
anomalous diabatic heating [denoted by “(·)_H”], and anomaloustransient momentum forcing [denoted by “(·)_T”] is changed fromthe JRA-55-based ﬁeld to the CESM1 TOGA-based ﬁeld for each
perturbation experiment. For example, TOGA_H1JRA_T1
JRA_B uses the anomalous diabatic heating calculated fromCESM1 TOGA (i.e., the “TOGA_H” component), while the basic
state and transient momentum forcing are still taken from JRA-55(i.e., the “JRA_T1JRA_B” component). As a result, by differing
the stationary wave responses simulated by TOGA_H1
JRA_T1JRA_B (see Figs. S1a,d in the online supplemental
material) and JRA_all (Figs. 2a,d), the contribution from the difference in diabatic heating can be isolated as shown in Figs. 3a and 3d.
In a similar way, the contribution from anomalous transient
momentum forcing bias can be extracted by comparing
JRA_H1TOGA_T1JRA_B (Figs. S1b,e) and JRA_all as given
in Figs. 3b and 3e. Similarly, the role played by the difference in thebasic state can be derived by comparing JRA_H1JRA_T1TOGA_B (Figs. S1c,f) and JRA_all as shown in Figs. 3c and 3f.
It is clear that the bias ﬁeld (i.e., Figs. 1c,f; see also Figs. 2c,f)
is mostly explained by the difference in the modeled and simulated diabatic heating anomalies (Figs. 3a,d). To be speciﬁc,
TOGA_H1JRA_T1JRA_B is accompanied by an overly

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FIG. 4. Stationary wave components of the 1000- and 300-hPa streamfunction anomalies simulated by (a),(b) the
difference between TOGA_trop and JRA_trop experiments in Table 2 for isolating the role of tropical diabatic heating and (d),(e) the difference between TOGA_xtrop and JRA_xtrop experiments for isolating the role of extratropical diabatic heating. (c),(f) The 500-hPa diabatic heating bias in TOGA composited over the tropical (158S–158N) andextratropical (158–808N) region respectively.

strong lower-level North Paciﬁc cyclonic response comparedto JRA_all (Fig. S1a and Fig. 3a), which is very similar to thestationary wave response simulated by the TOGA_all experiment (Figs. 2b,c). By contrast, the response caused by the difference in transient momentum (Fig. 3b) or basic state (Fig. 3c)
is much weaker. At the upper level, the biased wave train structure (Figs. 1f and 2f) is most clearly seen in Fig. 3d, as a stronger
wave propagating from the equatorial central Paciﬁc to themid- to high latitudes of the North Paciﬁc and North America.
At the lower level, no evident biased stationary wave propagation pattern can be detected when only the contribution fromtransient momentum forcing (Fig. 3e) or basic state (Fig. 3f) is
considered in the SW model.
Note that the distinct quadrupole pattern as shown by Figs. 1f
and 2f is inadequately simulated by Fig. 3d, which can be explained by the lack of interaction with transient forcing (Fig. S2).
It is well known that there is a two-way interaction between transient eddy activities and the large-scale wave patterns, but the
SW model is incapable of simulating this. Therefore, whether
the contribution from transient momentum forcing shown by the
SW model is a feedback onto the stationary wave anomalies produced by the heating bias is unclear from the stationary wave

model alone. We will use a series of comprehensive GCM simulations to further validate the ﬁndings given by the SW model.
At this point, we can conclude that the diabatic heating bias is
the dominant factor leading to the biased circulation in TOGA.
As shown in Figs. 4c and 4f, signiﬁcant diabatic heating biases
can be detected over both the tropical and extratropical regions.
Therefore, the next step is to isolate the key biased heating region with the aid of the SW modeling method.

c. Isolating the key biased heating region
To explore the relative importance of the tropical(158S–158N; Fig. 4c) and extratropical (158–808N; Fig. 4f) diabatic heating biases, four heating perturbation experiments arecarried out as summarized in Table 2. As the role played by thetransient momentum forcing is trivial compared to diabatic
heating, in this section the contribution from transient momentum forcing is not considered. For the basic state, all the experiments are based on the CESM1 TOGA simulations, but themodeling results do not change qualitatively if the basic state istaken from JRA-55. The modeling results for the tropical heating-only (“JRA_trop” and “TOGA_trop”) and extratropicalheating-only (“JRA_xtrop” and “TOGA_xtrop”) experiments

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7698

JOURNAL OF CLIMATETABLE 2. Stationary wave modeling conﬁgurations for isolating
the key biased heating region. Basic state is the FM climatology ofwinds and temperature during 1958–2010 from the CESM1 TOGA
simulations. Diabatic heating anomalies are composited during
FM of the El Niño years.
ExperimentHeating

JRA_trop
TOGA_trop
JRA_xtrop
TOGA_xtrop

JRA (158S–158N)TOGA (158S–158N)
JRA (158–808N)
TOGA (158–808N)

TOGA_IO
JRA_IOTOGA_WP
JRA_WP
TOGA_CP
JRA_CPTOGA (158S–158N, 408–1108E)
JRA (158S–158N, 408–1108E)
TOGA (158S–158N, 1108E–1808)
JRA (158S–158N, 1108E–1808)
TOGA (158S–158N, 1808–1108W)
JRA (158S–158N, 1808–1108W)

are given in Figs. S3 and S4, respectively. Their relative contribution for the North Paciﬁc circulation bias can be more directly seen using Fig. 4. It is striking that the upper-level wave
propagation features simulated by the “trop” experiments(Fig. 4b) are very close to the bias ﬁeld derived directly fromJRA-55 and TOGA simulations (Fig. 1f). Compared to Fig. 2,
the north Indian Ocean–Asian region is even better simulated
in the tropical heating-only conﬁguration, suggesting that unrealistic tropical–extratropical interaction and transient feedback in the SW model is likely the main reason for the limited
model accuracy over this region. The extratropical diabatic
heating difference also contributes to the enhancement of thecirculation bias at the lower level near the surface (Fig. 4d). In the

VOLUME 35

upper layer, although a strong anticyclonic response to extratropical diabatic heating can be seen over the west coast of North
America, no signiﬁcant bias signal can be detected to its west over
the North Paciﬁc region (Fig. 4e).
Recall that in our calculation the diabatic heating is derived
from Eq. (1) using monthly data, so that the heat transport bytransients is included. We can remove the contribution fromtransients by recalculating the observed diabatic heating from
Eq. (1) at a higher frequency (6 hourly). For the TOGA simulation, the diabatic heating without contribution from transient heat transport is available as the sum of the shortwave
heating rate (QRS), longwave heating rate (QRL), temperature tendency due to moist process (DTCOND), and vertical
diffusion (DTV) in the model output variables. After removing the contribution from transients, the signal caused by extratropical heating is greatly reduced (not shown). Therefore,
the extratropical feedback shown here is mainly associatedwith the transient eddy heat ﬂux convergence.
The structure of the springtime tropical diabatic heating
bias is given in Fig. 5b. Signiﬁcant bias signals can be detected
over three major ocean basins: a negative bias over the tropical Indian Ocean (IO; 158S–158N, 408–1108E), a positive bias
over the tropical central-western Paciﬁc (WP; 158S–258N,
1108E–1808), and a negative bias over the tropical central
Paciﬁc (CP; 158S–158N, 1808–1108W). According to Fig. S5,
the negative diabatic heating bias over the IO is caused by the
extra anomalous cooling simulated by TOGA but not present
in the reanalyses. The positive bias signal over the WP can be
explained by the fact that the TOGA simulated diabatic cooling anomalies are too weak compared to that in JRA-55. The
bias distribution over the CP consists of an antisymmetric

FIG. 5. (a) DJ and (b) FM-averaged tropical diabatic heating bias in CESM1 TOGA simulations during FM of
El Niño events from 1958 to 2010. (top) The vertical cross section averaged over 158S–158N. (bottom) The horizontal
distribution at 500 hPa. The area that exceeds signiﬁcance at the 95% conﬁdence level is stippled.

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FIG. 6. Stationary wave components of the 300-hPa streamfunction anomalies simulated by the single-basin diabatic heating forcing experiments in Table 2 with input ﬁelds based on (a)–(c) JRA-55 reanalyses and (d)–(f) CESM1 TOGA simulations, and (g)–(i) their differences, forced by (left) IO heating only, (center) WP heating only, and (right) CP heating only.

structure about the equator (i.e., positive to the north but negative to the south). As can be seen from Fig. S5, this antisymmetric structure can be explained by the overly strong andlimited southward extension of the heating ﬁeld in CESM1
TOGA. A similar bias structure can be consistently detectedbased on different reanalyses data [NCEP–NCAR (R1),
ERA20C, ERA-Interim (ERAI), ERA5, and CFSR] as shownin Fig. S6.
We can further assess the role played by the diabatic heating bias in each tropical basin by imposing the IO, WP, or CP
diabatic heating exclusively in the SW model (Table 2). The
modeling results indicate that the diabatic heating differences
over the IO and WP positively contribute to the cyclonic circulation bias over the North Paciﬁc (Figs. 6g,h). Speciﬁcally,
the extra cooling effects over the IO in TOGA cause a wave
train response with anticyclonic anomalies over the tropicalwestern Paciﬁc and cyclonic anomalies over the North Paciﬁc.
As no obvious IO heating anomalies are seen in JRA-55(Fig. S5), its wave response is accordingly much weaker in
Fig. 6a. As a result, the contribution revealed by Fig. 6g is
mostly a manifestation of the IO cooling effects in TOGA.
For the WP, the modeled wave train response forced by the
TOGA-based heating anomalies (Fig. 6e) is similar to thatforced by the IO (Fig. 6d) but with a weaker amplitude. The
insensitivity of the stationary wave response to the location of

the tropical heating source has been found by many previous
studies (Simmons et al. 1983; Branstator 1985; Ting and Yu
1998). When forced by the WP diabatic heating anomalies
based on JRA-55 (Fig. 6b), signiﬁcant circulation anomalies
can be found as a cyclonic system located to the east of thePhilippines. The eastern part of the North Paciﬁc is associatedwith anticyclonic anomalies while its western part displays acyclonic response. When looking at the JRA_CP and TOGA_
CP experiments, the stationary wave response modeled by
TOGA CP heating (Fig. 6f) is weaker than that modeled by
JRA-55 (Fig. 6c). As a result, the circulation anomalies
caused by their heating difference consist of an anticyclone response over the North Paciﬁc region (Fig. 6i), which opposes
the biased circulation as a whole. As can be seen from Fig. 4c,the diabatic heating bias over the CP is of opposite sign in thenorth and south of the equator. To investigate the relative inﬂuence of these two regions, two more simulations are carriedout by specifying the northern and southern parts of the CP
heating bias into the SW model separately. The result indicates that the North Paciﬁc anticyclonic anomalies shown in
by Fig. 6i are mostly due to the lack of southern warming effects (i.e., cooling bias) in TOGA (Fig. S7). In summary, during the springtime (FM) after the peak of El Niño events, thediabatic heating bias over the IO and WP jointly lead to theoverestimation of the North Paciﬁc cyclonic circulation

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7700

JOURNAL OF CLIMATETABLE 3. Stationary wave modeling conﬁgurations forunderstanding seasonality of the teleconnection bias. The basic
state is the DJ/FM climatology of winds and temperature during
1958–2010. Diabatic heating forcing is the difference between
TOGA and JRA-55 composited anomalies (i.e., diabatic heating
bias in TOGA) during DJ/FM of the El Niño years.
Experiment

Basic state

Heating

FM_H1FM_B
DJ_H1FM_B
FM_H1DJ_B

TOGA FMTOGA FMTOGA DJFM (TOGA-JRA)
DJ (TOGA-JRA)FM (TOGA-JRA)

response in the model. The weak cooling bias over the CP,
when the tropical diabatic heating anomalies during ENSO
force the extratropical teleconnection, partially offsets therole played by the IO and WP heating bias.

d. Understanding the seasonality of theteleconnection bias
Chen et al. (2020) demonstrated that the overly strong
North Paciﬁc ENSO teleconnection bias appears during thespringtime (FM) and is not present during the peak ENSO
months [December–January (DJ)]. What causes this seasonality in the bias? Based on the above analyses, a reasonablespeculation would be the differences in the tropical diabatic
heating bias. However, the tropical diabatic heating bias

VOLUME 35

averaged over the previous winter months (Fig. 5a) suggeststhat a signiﬁcant heating bias has already built up in winterand the distribution of the bias signal during DJ is very similar
to that during FM, as a negative–positive–negative structure
spanning the IO, WP, and CP basins, respectively. However,its amplitude over the WP (IO) during DJ is weaker (stronger) than that during FM.
To investigate why the bias appears in FM and not DJ,
three additional experiments are carried out using the SW
model. Considering the fact the nonlinear effects between
the diabatic heating and basic state are weak, instead of carrying out two independent simulations as “JRA_trop” and“TOGA_trop”, the tropical diabatic heating biases are ﬁrst
calculated and put into the SW model directly in this section(Table 3). The modeling results suggest that only minor differences can be detected between these two approaches(cf. Figs. 4a,b and 7a,b). By sticking to the FM basic state(“FM_B”) and combining it with the diabatic heating biases
during FM or DJ (“FM_H” or “DJ_H”), we ﬁnd that themodeled stationary wave response to the DJ-averaged tropical heating bias is even stronger than that to the FM heating
bias (cf. Figs. 7a,b or 7d,e). Thus, the difference in the DJ and
FM diabatic heating bias is not the main reason for the seasonality of the bias occurrence. However, if the FM diabatic
heating bias is added to the DJ-averaged basic state in the SW
model (“FM_H1DJ_B”), it is obvious that the wave response

FIG. 7. Stationary wave components of the (left) 1000- and (right) 300-hPa streamfunction anomalies simulated bythe (a),(d) FM_H1FM_B, (b),(e) FM_H1DJ_B and (c),(f) DJ_H1FM_B experiments for understanding seasonalityof the teleconnection bias in Table 3.

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FIG. 8. FM-averaged SLP anomalies (contour lines) during El Niño events for (a) ERA20C and (b) CESM1TOGA simulations, and (c) the difference between EN and CTRL (i.e., NINO) experiments in CAM5. Shading in(b) and (c) is the bias ﬁelds compared to ERA20C. (d) Seasonal evolution of the bootstrapped NPI (hPa) calculated
from ERA20C (red), CESM1 TOGA simulations (black), and CAM5 NINO experiments (blue). The box height indicates the 25%–75% conﬁdence interval of the NPI composites. See Chen et al. (2020) for more details. Also shown
are FM-averaged tropical (158S–158N) diabatic heating bias during El Niño events for (e) CESM1 TOGA simulationsand (f) CAM5 NINO experiments. Regions that exceed the 95% conﬁdence level of signiﬁcance are stippled.

is weakened considerably (cf. Figs. 7a,b or 7d,e). Therefore,
the springtime basic state is conducive to a larger amplitude
stationary wave response than the winter basic state evenwhen forced by the same diabatic heating anomalies. In otherwords, the seasonal evolution of the background state, rather
than the difference in the diabatic heating ﬁeld between DJ
and FM, leads to the ENSO teleconnection bias occurring
preferentially in spring. The question then becomes: Why is thespringtime basic state conducive to a larger-amplitude stationary
wave response than winter? Possible answers to this question will
be provided in the discussion section of this paper.

4. Bias correction in CAM5
The SW model is useful for diagnosing the relative contributions from different factors driving the stationary wave response. However, it is highly simpliﬁed compared to acomprehensive GCM. As found using the SW model, thetransient feedback over the extratropical region appears toplay a role in enhancing the bias circulation. However, theanomalous transient eddy activities may have a tropical origin
in a comprehensive GCM, where the transients are able to respond to the tropical forcing. To test the hypothesis that thefull circulation bias has an ultimate origin in the tropics, aseries of bias-correction experiments are carried out in thissection. The model used is CAM5, which is the same as theone used in the CESM1 TOGA simulation. The El Niño response is determined using the NINO simulations (see section
2 for details).
The ensemble mean of the North Paciﬁc anomalies in
CAM5 NINO are compared to the ERA20C, and the CESM1TOGA simulations in Figs. 8a–c. The overly strong negative
SLP anomalies over the North Paciﬁc seen in the TOGA experiments are reproduced by the NINO experiments. Following Chen et al. (2020), the North Paciﬁc Index (NPI) is
calculated using area-averaged SLP anomalies over 358–608N,
1658E–1458W. Figure 8d displays the seasonal evolution of
the NPI in observations, TOGA simulations, and NINO experiments. The uncertainties induced by the sampling error
are estimated by bootstrapping (Deser et al. 2017, 2018; Chenet al. 2020). By resampling the ENSO events with replacement repeatedly, the bootstrapping technique aims to form
synthetic ENSO composites to characterize the uncertainties
caused by sampling ﬂuctuation arising from internal variability. For TOGA and NINO, the NPI bias is most signiﬁcantfrom February to March. In the TOGA simulations, thelowest value of NPI (i.e., the largest amplitude) occurs in
February, which is one month delayed compared to

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JOURNAL OF CLIMATEVOLUME 35

FIG. 9. Evolution of the 500-hPa tropical diabatic heating bias by applying the iterative bias-correction technique in CAM5.

observations. In the NINO experiments, the NPI amplitude
peaks one month even later in March. Therefore, by forcing
the model with a canonical El Niño SST pattern and comparing to a simulation with prescribed climatological SSTs, themajor characteristics of the springtime North Paciﬁc ENSO
teleconnection bias that was found in CESM1 TOGA can belargely reproduced. The stronger circulation bias in the NINO
experiment compared to TOGA is in agreement with its
stronger tropical diabatic heating bias seen over the tropicalIndian Ocean (IO) and central-western Paciﬁc (WP; cf. Figs.
8e and 8f). We will now use this setup to correct the ENSOinduced diabatic heating biases in the comprehensive GCM
and demonstrate that the tropical heating biases are the ultimate driver of the extratropical teleconnection bias.

a. Iterative bias-correction approach
To artiﬁcially “correct” the tropical diabatic heating in
NINO, we add a bias-correction term to the EN experiments’
thermodynamic equation as a modiﬁcation of the temperaturetendency produced at the end of the physics time step (i.e.,every 30 min). This bias correction tendency is calculated foreach month (October to April) and imposed over the tropicalband within 238S–238N, which is a little wider than the region
considered by the SW model in previous sections. We cannot
simply impose the heating bias multiplied by 21 in the model,
because the model would nonlinearly amplify the modiﬁcation effects due to feedback mechanisms. This “overreacting
consequence” inhibits us from ideally correcting the bias ﬁeld

in one step. The solution proposed is by adopting an “iterative
approach,” which consists of two key steps: 1) a scaling factor
is adopted to scale down the correction term to partially offset
the ampliﬁcation effects caused by positive feedbacks in themodel and 2) the initial guess of the bias-correction term is
optimized iteratively according to the residual bias in each iteration step. The bias-correction term used in each step is estimated by⎪⎧⎪ 21 3 (QNINO
3 0:25),
bias
⎪⎪
⎪⎨
(i)
(i)
(i21)
Qcrct 5 ⎪ Qcrct 5 Qcrct 2 Q(i21)bias 3 0:25,⎪⎪⎪⎪ (i)⎩ Q 5 Q(i21) 2 Q(i21) 3 0:1,
crct
crct
bias

when i 5 1
when 2 # i # 6 ,(2)

when 7 # i # 9

where Q(i)crct is the bias correction term for modifying the temperature tendency in the “iter i” experiment, where i is the iteration step. The scaling factor is set to 0.25 from step 1 tostep 6, and changed to 0.1 from step 7 to step 9 as the residualis the initial tropicalbias becomes smaller. The term QNINO
bias
heating bias in the NINO experiment, and Q(i21)bias is the residual bias after applying (i 2 1) steps of corrections. The speciﬁc
deﬁnition for Qbias in each step is
5 (QEN 2 QCTRL ) 2 Qobs
QNINO
bias
(i)
Q(i)
bias 5 (Qiter 2 QCTRL ) 2 Qobs

(3)

where Qobs is the tropical diabatic heating anomalies during
the El Niño events based on JRA-55 reanalyses. The tropical

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FIG. 10. (a) The root-mean-square error (RMSE) of the diabatic heating ﬁelds at 500 hPa in each iteration step for the whole tropicalband (red dots), the tropical Indian Ocean (IO; 238S–238N, 408–1108E; blue stars), the tropical central-western Paciﬁc (WP; 38S–238N,
1108E–1808; orange squares), and the tropical central Paciﬁc (CP; 238S–238N, 1808–1108W; green circles) respectively. (b)–(d) Comparisonof the heating proﬁle for each basin before and after applying the bias-correction technique. Red is for the observation based on JRA-55,
black is for CAM5 experiments before bias correction (EN minus CTRL), and green is for the CAM5 experiments after 9 steps of iterative bias-correction (iter 9 minus CTRL).

diabatic heating ﬁelds in the CTRL, EN, and iter i experiments are denoted as QCTRL, QEN, and Q(i)iter respectively. ForQ(i)iter , it is the sum of the model generated heating and theQ(i)crct term added into the model.

b. Effectiveness of the bias-correction approach
To illustrate the effectiveness of the iterative bias-correction
approach, the 500-hPa tropical diabatic heating bias (Qbias)
at each iteration step is given in Fig. 9. Generally, the amplitude of the bias becomes smaller as the iteration stepincreases. Most of the signiﬁcant heating bias has been wipedout after nine steps of iterative correction, except for the
extra cooling signals remaining over western New Guinea(Fig. 9i). The improvements can be more quantitatively evaluated by calculating the root-mean-square error (RMSE) ineach iteration step as shown in Fig. 10a. For the tropical region as a whole, the RMSE value displays a monotonicallydecreasing trend with a slight increase seen in iter 7 (the seventh iteration) due to the increase of the bias signal over WP
and CP. The trend in RMSE for each single ocean basin isless monotonic. The ultimate goal of conducting the iterative

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JOURNAL OF CLIMATE

FIG. 11. FM-averaged SLP simulated by (a) the difference between iter 9 and CTRL experiments (contour lines) and the bias ofthis difference with respect to observations (ERA20C; color shading). (b) Seasonal evolution of the bootstrapped NPI (hPa) calculated from ERA20C (red) and CESM1 TOGA simulations (black),
and the difference between iter 9 and CTRL experiments (blue).
The box height indicates the 25%–75% conﬁdence interval of the
NPI composites. See Chen et al. (2020) for more details.

bias-correction approach is to obtain one simulation whose
tropical diabatic heating bias is largely eliminated, and we
consider this to have been achieved by iter 9. Signiﬁcant improvement can be detected at all vertical levels, especially inthe middle and upper troposphere where the original bias ismost evident (Fig. 10; see also Fig. S8). Continuing this iterative process further does not produce substantial improvements (not shown). Therefore, the iter 9 experiment is taken
as the one that meets the expectation of a successful bias-correction outcome in CAM5.

c. Improvement in circulation response
To look at the corresponding change in the circulation response, the anomalies are composited as the difference between the ensemble mean of the iter 9 and CTRL simulationsin Fig. 11a. Also given is the bootstrapped NPI evolution in
Fig. 11b. After “correcting” the tropical diabatic heating, thesimulated North Paciﬁc circulation response is signiﬁcantly
weakened, although it still overestimates the amplitude compared to ERA20C (color shading in Fig. 11a). In addition,
the anomaly center is located farther eastward in the iter 9
experiment, so that the westward shifting bias is erased aswell. The improvement is also seen in the seasonal evolution features. For the CESM1 TOGA simulation and the
CAM5 NINO simulations, the peak time of the NPIVOLUME 35

amplitude is delayed compared to that in observations. In
the iter 9 experiment, the NPI peaks in January, the samemonth as observations. The residual bias in the heating ﬁeld
reveals that there are weak positive biases over the CP(Fig. 9i). Taking the residual heating bias in the iter 9 as theanomalous forcing in the SW model, we ﬁnd that the weakheating bias over the CP is the main cause of the residualNorth Paciﬁc cyclonic circulation bias seen in iter 9 (seeFig. S9).
The upper-level anomalous circulation as depicted by the
300-hPa streamfunction and 150-hPa velocity potential isshown in Fig. 12 for observations, NINO and iter 9. In NINO,
the upper-level circulation bias is manifested as a convergencecenter over the tropical Indian Ocean (Fig. 12h). In observations, the anomalous convergence center is located over the
Maritime Continent with another divergent center to the east
(Fig. 12g). The strong tropical Indian Ocean convergence bias
can be explained by the diabatic cooling bias over the IO,
which would induce sinking motion and upper-level convergence. For the anomalous upper-level streamfunction ﬁeld, a
cyclonic bias can be seen to the northwest of the Arabian
Peninsula in NINO (Fig. 12e), which is also linked to the IO
diabatic cooling bias by a geostrophic adjustment mechanism tothe sinking motion. The observed streamfunction anomalies
feature a negative–positive–negative wave structure around
1408–1508E, propagating from the tropics to the middle andhigh latitudes of the North Paciﬁc (Fig. 12d). A similar tripole
pattern can also be detected in the NINO experiment but witha westward shift and an intensiﬁcation of the northernmost cyclonic anomalies. After “removing” the tropical diabatic heating bias, the dipole structure in the anomalous potential
velocity ﬁeld and the tripole structure in the anomalous streamfunction ﬁeld are approaching the observations as shown in
Figs. 12i and 12f. At 1000 hPa (Figs. 12a–c), the cyclonic anomalies over the North Paciﬁc region are weakened accordingly initer 9 compared to NINO, but still stronger than that in observations, consistent with the SLP ﬁelds shown in Fig. 11a. The
improvement upon bias correction can be reasonably traced
back to the wave dynamics at upper levels which is directly
linked to the improvements in the tropical forcing (Figs. 9
and 10). Furthermore, it is also proven by the iter 9 experimentthat the extratropical feedback is tied to the tropical diabatic
heating bias, as the extratropical heating bias is much weakened
after alleviating the tropical bias (see Fig. S10). In conclusion,
the bias-correction experiment carried out in CAM5 further
proves that the springtime North Paciﬁc ENSO teleconnection
bias in CESM1 is of tropical origin.

5. Summary and discussion
The North Paciﬁc ENSO teleconnection communicates theinﬂuence of tropical SST anomalies to the higher latitudes of
the Northern Hemisphere. Although the ENSO-related SST
anomalies and the induced North Paciﬁc circulation response
usually peak during the winter season, previous studies have attributed some extreme events happening during spring to theprolonged effects of ENSO (Jong et al. 2016; Wolter et al. 1999;
Schmidt et al. 2001; Cook et al. 2017). There is also evidence

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FIG. 12. FM-averaged streamfunctions at 1000 hPa for (a) JRA-55’s El Niño years’ composite anomalies, (b) the difference between EN
and CTRL experiments, and (c) the difference between the iter 9 and CTRL experiments. (d)–(f) As in (a)–(c), but for streamfunctions at
300 hPa. (g)–(i) As in (a)–(c), but for velocity potential at 150 hPa.

showing that the potential predictability for the ENSO climate
inﬂuence is even higher in spring (Kumar and Hoerling 1998).
Therefore, an accurate simulation of the springtime North
Paciﬁc ENSO teleconnection by climate models is of great
importance for the attribution and forecasting of springtime
anomalous events related to ENSO. However, the modeled
springtime North Paciﬁc ENSO teleconnection is overly strong
compared to observations (Alexander et al. 2002; Spencer andSlingo 2003; Chen et al. 2020). Here, we have investigated theorigins of this bias.
With the aid of the stationary wave (SW) model, the relative
contributions from the basic state, the anomalous diabatic heating, and the anomalous transient forcing are disentangled. It isfound that the deﬁciency in the simulated tropical Indian Ocean(IO) and tropical central-western Paciﬁc (WP) diabatic heating
anomalies is the main cause of the springtime North Paciﬁccyclonic bias during the El Niño events in CESM1 TOGA
simulations. In contrast, the central Paciﬁc (CP) diabatic
heating bias counteracts that produced by the other two
regions.
Although the difference between the modeled and observed basic state does not give rise to the bias occurrence directly, the natural seasonal evolution of the basic state in bothobservations and CAM dictates the seasonality of the bias.
This is because the springtime basic state favors a stronger
stationary wave response to the tropical diabatic heating
anomalies associated with ENSO than the wintertime basic
state, as elucidated in our SW modeling experiments.
The role of transient eddies is composed of two parts:
1) Transient heat transport could locally reinforce the lowerlevel bias circulation. Speciﬁcally, the transient eddy heatﬂux convergence is essential in forming the extratropicalheating bias seen in Fig. 4f, which contributes to the enhancement of the North Paciﬁc circulation bias shown in
Fig. 4d. 2) Transient vorticity ﬂux is essential for modulating the wave response structure, as the biased North Paciﬁc
quadrupole pattern is better simulated after including thetransient momentum forcing (cf. Fig. S2 and Fig. 3). However, the SW model is incapable of simulating the two-way
interactions between transient eddy activities and thelarge-scale wave structure. Through comprehensive GCM
simulations, it is further shown that the transient eddy activities are inseparable from the large-scale wave response
triggered by the tropical diabatic heating anomalies. Therefore, the role played by the transients is considered to besecondary.
To verify the conclusions drawn from the SW model, additional experiments are carried out with a full general circulation model (CAM5). An iterative bias-correction approach isadopted to modify the CAM5 diabatic heating response to observed El Niño events such that the ultimate diabatic heatingﬁeld resembles observations. This approach is shown to effectively eliminate the tropical diabatic heating bias over all thethree ocean basins (i.e., the IO, the WP, and the CP). In response to the “improved” tropical heating forcing ﬁeld, theNorth Paciﬁc circulation response is signiﬁcantly improved: its
intensity, center location, and seasonal evolution are all muchcloser to the observations, further indicating that the springtime North Paciﬁc ENSO teleconnection bias is tied to thetropical diabatic heating bias in the model.
Our ﬁndings raise a number of additional questions:
1) Are the bias formation mechanisms for the La Niñaevents equal and opposite to those for El Niño? To

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FIG. 13. Scatterplots of the relationship between the tropical precipitation biases and the NPI biases across CMIP5
models during FM of (left) El Niño and (right) La Niña composite events. The precipitation biases are area averaged
for the (a),(d) IO, (b),(e) WP, and (c),(f) CP.

answer this question, the tropical diabatic heating bias
during La Niña events are composited in TOGA andshown in Fig. S11. It is clear that the tropical diabatic
heating bias over the three ocean basins has a similar pattern to that during El Niño but with opposite sign, i.e., theIO and the CP are associated with positive heating bias,
while the WP displays an overly strong cooling effect.
When the biased heating ﬁelds for composite La Niñaevents are imposed in the SW model (Table S1), theresults suggest that the positive diabatic heating bias overthe IO and the negative bias over the WP positively contribute to the formation of the North Paciﬁc anticyclonicbias during La Niña events (Fig. S12). In contrast, the diabatic heating bias over the CP has an opposite effect byleading to a weak cyclonic response. It is concluded thatthe bias formation mechanism behind La Niña events canbe roughly viewed as equal and opposite to that during El
Niño.

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FIG. 14. (left) Climatological zonal (contour lines) and meridional (color shaded) winds at 300 hPa from TOGA
simulations during (a) DJ and (b) FM of 1958–2010, with (c) their difference (FM minus DJ). (right) Stationary wave
components of the 300-hPa streamfunction anomalies simulated by the (d) “FM_H1(DJ_VT1FM_U)_B”,(e) “FM_H1(DJ_UT1FM_V)_B”, and (f) “FM_H1(DJ_UV1FM_T)_B” experiments in Table 4.

2) Is there evidence for similar origins of the bias in othermodels? According to Fig. 6 of Chen et al. (2020), theNorth Paciﬁc ENSO teleconnection bias can be detectedin most CMIP5 models. As a preliminary investigation
into the universality of the cause of bias in these different
models, a scatterplot of the relationship between the tropical precipitation bias and the NPI bias across 43 CMIP5
models [Table 1 of Chen et al. (2020)] is plotted inFig. 13. A signiﬁcant correlation is found between boththe IO (positive correlation) and WP (negative correlation) precipitation bias and the NPI bias for both El Niñoand La Niña in spring (FM). However, no signiﬁcant correlation is found between the CP precipitation and NPI
biases. This suggests that the bias formation mechanism
revealed in CESM1 TOGA simulations may also exist
in other models. For further model development aiming
at improving the springtime ENSO teleconnections, it issuggested that efforts should be made to optimize theprecipitation simulation across different tropical ocean
basins simultaneously.
3) Why is the springtime basic state more sensitive to tropicalanomalous diabatic heating anomalies compared to thewintertime basic state? According to the stationary wave
modeling results in section 3, the springtime basic state ismore favorable for wave propagation into the North Paciﬁcthan the wintertime basic state, even when forced by thesame diabatic heating anomalies. However, the underlying
mechanism has not been addressed. One possibility is dueto the seasonal change in the jet stream. Another possible
mechanism is the mean seasonal change in atmosphericstratiﬁcation. These two possibilities are investigated in apreliminary manner in Fig. 14 by using three additional SW
modeling experiments according to Table 4. Starting fromthe “FM_H1FM_B” and “FM_H1DJ_B” modeling results
shown in Fig. 7, the role of FM basic state is decomposed into
its zonal winds [through the “FM_H1(DJ_VT1FM_U)_B”
experiment], meridional winds [through the “FM_H1(DJ_UT1FM_V)_B” experiment], and temperature [throughthe “FM_H1(DJ_UV1FM_T)_B” experiment] components
separately. As suggested by Figs. 14d–f, the difference in the
background meridional winds is crucial in determining a moresensitive response seen in the FM basic state (Fig. 7e). In particular, the Rossby wave train emanating from the western tropical Paciﬁc and arcing over the North Paciﬁc is approximately
doubled in strength under the FM meridional wind basic statecompared to the FM zonal wind or temperature basic states(cf. Fig. 14e with Figs. 14d,f). As shown in Figs. 14a–c, the subtropical jet stream during FM is signiﬁcantly weakened

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JOURNAL OF CLIMATEVOLUME 35

TABLE 4. As in Table 3, but the basic state is reconstructed as follows: meridional winds and temperature from the DJ average,
and zonal winds from the FM average for FM_H1(DJ_VT1FM_U)_B; zonal winds and temperature from DJ-average, andmeridional winds from FM average for FM_H1(DJ_UT1FM_V)_B; and zonal and meridional winds from DJ-average, andtemperature from FM average for FM_H1(DJ_UV1FM_T)_B.
Experiment

Basic state

Heating

FM_H1(DJ_VT1FM_U)_B
FM_H1(DJ_UT1FM_V)_B
FM_H1(DJ_UV1FM_T)_B

DJ VT but FM U
DJ UT but FM V
DJ UV but FM T

FM (TOGA-JRA)FM (TOGA-JRA)FM (TOGA-JRA)compared to DJ. On one hand, the zonal wind speed is reducedby 8 m s21 in its core region. On the other hand, the convergent(divergent) meridional winds around the jet entrance (exit) region are weakened accordingly, suggesting the mutually coupled characteristics in the zonal and meridional components of
the upper-level winds along the jet stream. Therefore, the SW
modeling results here indicate that the meridional wind aspectof the climatological basic state is important in determining theseasonality of the teleconnection bias as discussed in section 3,
which may be further linked to a weakened subtropical jet
steam during FM compared to DJ.
Despite continued improvements in the simulation of
ENSO and associated teleconnections, further understandingof the air–sea coupled system outside of the central-eastern
Paciﬁc is necessary for eliminating the springtime North Paciﬁc
ENSO teleconnection bias in the models. Great challenges
may exist because the IO and WP precipitation anomalies during ENSO events can be inﬂuenced by many different factors(e.g., local air–sea feedback, remote inﬂuence via an atmospheric bridge from other tropical or extratropical regions, andthermodynamic/dynamic processes in the ocean). These myriad inﬂuences should be considered in an integrated fashion
for their joint contributions to the simulation and predictabilityof ENSO in climate models. As pointed out by many previous
studies (Lau and Nath 2003; Wu and Kirtman 2005), air–sea
coupling plays a vital role in regulating the IO–WP region precipitation variability. Note that this bias can be signiﬁcantly
found even in the coupled conﬁguration of the CMIP models(Chen et al. 2020), indicating that AMIP simulations do not
produce the bias as a result of the lack of coupling and that if
air–sea coupling issues are playing a role then it must be processes that are misrepresented in both coupled and AMIP simulations. In addition, although the difference in the modeled
and observed basic state is not the determining factor leading
to the springtime ENSO teleconnection bias over the North
Paciﬁc region, seasonal variation in the subtropical circulation
mean state explains why the bias is most signiﬁcantly seen during the springtime (FM). The results of this paper suggest thata better understanding of both the tropical forcing and its interaction with the basic state is crucial for advancing the simulation and predictability of springtime ENSO teleconnections.
Acknowledgments. We appreciate the three anonymous reviewers for their thoughtful comments. The CESM project is
supported primarily by the National Science Foundation (NSF).
This work is supported by the National Center for AtmosphericResearch, which is a major facility sponsored by the National
Science Foundation under Cooperative Agreement (1852977),
the National Natural Science Foundation of China (41875127),
and the Chinese Academy of Sciences (133244KYSB20190031,
183311KYSB20200015, ISEE2021PY02, ISEE2021ZD01). Computing and data storage resources, including the Cheyenne supercomputer (doi:10.5065/D6RX99HX), were provided by theComputational and Information Systems Laboratory (CISL) at
NCAR. Ruyan Chen was supported by the graduate visitor program of the Advanced Study Program at NCAR. We acknowledge the World Climate Research Programme’s Working Groupon Coupled Modelling, which is responsible for CMIP, and we
thank the climate modeling groups for producing and making
available their model output. For CMIP, the U.S. Departmentof Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led developmentof software infrastructure in partnership with the Global Organization for Earth System Science Portals.
Data availability statement. Data access to JRA-55 atmospheric reanalyses is available at https://jra.kishou.go.jp/JRA-55/
index_en.html#jra-55. The long-term sea level pressure provided
by ERA20C is available from the ERA-20C ECMWF PublicDatasets web interface: https://www.ecmwf.int/en/forecasts/
datasets. The CMIP5 model outputs are available through the
Earth System Grid Federation at http://esgf-node.llnl.gov/. The
CESM1 TOGA simulation are available for download fromhttp://www.cesm.ucar.edu/experiments/. The datasets generated
through the stationary wave modeling processes and CAM5
experiments in this study are available from the corresponding
author on reasonable request.

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