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Model Biases in the Simulation of the Springtime North Pacific ENSO Teleconnection
RUYAN CHEN,a ISLA R. SIMPSON,b CLARA DESER,b AND BIN WANG,a,c,daMinistry of Education Key Laboratory for Earth System Modeling, and Department of Earth System Science, Tsinghua University,
Beijing, China; b National Center for Atmospheric Research, Boulder, Colorado; c State Key Laboratory of Numerical Modelingfor Atmospheric Sciences annind Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics,
Chinese Academy of Sciences, Beijing, China; d College of Ocean Science, University of Chinese Academy of Sciences, Beijing, China(Manuscript received 30 December 2019, in final form 14 August 2020)
ABSTRACT: The wintertime ENSO teleconnection over the North Pacific region consists of an intensified (weakened)
low pressure center during El Niño (La Niña) events both in observations and in climate models. Here, it is demonstrated
that this teleconnection persists too strongly into late winter and spring in the Community Earth System Model (CESM).
This discrepancy arises in both fully coupled and atmosphere-only configurations, when observed SSTs are specified, and isshown to be robust when accounting for the sampling uncertainty due to internal variability. Furthermore, a similar problem
is found in many other models from piControl simulations of the Coupled Model Intercomparison Project (23 out of 43 inphase 5 and 11 out of 20 in phase 6). The implications of this bias for the simulation of surface climate anomalies over North
America are assessed. The overall effect on the ENSO composite field (El Niño minus La Niña) resembles an overly
prolonged influence of ENSO into the spring with anomalously high temperatures over Alaska and western Canada, andwet (dry) biases over California (southwest Canada). Further studies are still needed to disentangle the relative roles playedby diabatic heating, background flow, and other possible contributions in determining the overly strong springtime ENSO
teleconnection intensity over the North Pacific.
KEYWORDS: ENSO; Stationary waves; Teleconnections; Climate models; Model errors; Model evaluation/performance

1. Introduction
Successful seasonal forecasts have great economic and societal benefits (Meinke and Stone 2005; Lemos and Dilling2007; Kumar 2010). Despite the unpredictable variability
produced by internal atmospheric dynamics, the response totropical sea surface temperature (SST) anomalies, especiallythose related to the El Niño–Southern Oscillation (ENSO)
phenomenon, has long been considered a major source of
seasonal predictability for different parts of the world (Palmer
and Anderson 1994; Barnett et al. 1994; Livezey et al. 1996).
Based on this boundary forcing conception, some efforts havebeen made in using atmosphere-only models (Rowell 1998;
Kumar and Hoerling 2000; Kumar et al. 2001; Barnston et al.
2005), as well as initialized coupled models, which include twoway air–sea interaction and the prediction of the future state of
the sea surface (Stockdale et al. 1998; Chakraborty andKrishnamurti 2006; Peng et al. 2011; Gleixner et al. 2017), todevelop seasonal prediction systems.
By integrating a general circulation model (GCM) withvaried initial conditions but identical boundary forcing, Shukla(1981) suggested that the prediction skill of the seasonal mean
is determined by the evolution of low-frequency planetary
waves. Thus, the ENSO-induced predictability inevitably relieson the models’ ability to simulate the large-scale atmospheric
response to tropical SST anomalies. In the presence of an El

Supplemental information related to this paper is available atthe Journals Online website: https://doi.org/10.1175/JCLI-D-191004.s1.
Corresponding author: Ruyan Chen, cry15@mails.tsinghua.edu.cn

Niño (La Niña) event, one of the most dominant features in the
Northern Hemisphere circulation response is a deepened (weakened) low pressure center over the North Pacific (Bjerknes 1966,
1969; Namias 1976), which can be explained by the propagation of
Rossby waves from the tropics (Hoskins and Karoly 1981; Webster
1981; Horel and Wallace 1981). Through this pathway, ENSO’s
effects can be transmitted to the North Pacific and North America(Ropelewski and Halpert 1986; Papineau 2001; Schubert et al.
2008; Johnson et al. 2014). This ENSO–North Pacific relationshipis considered a fundamental process that should be reasonably
reproduced in models, despite the diversity of model configurations, resolutions, numerical methods, and parameterization
schemes (Held and Kang 1987; Stoner et al. 2009; Hurwitz et al.
2014; Deser et al. 2017).
Most previous studies have focused on the peak season of
ENSO [December–February (DJF)] when the amplitudes of
SST anomalies and teleconnections are largest (Gershunov
and Barnett 1998; Yang and DelSole 2012; Bellenger et al.
2014), with a few other studies having also looked at the otherseasons before or after the peak (Alexander et al. 2002;
Spencer and Slingo 2003; Bladé et al. 2008; Lee et al. 2014; Jonget al. 2016). According to Kumar and Hoerling (1998), thepotential for seasonal predictability over North America isstronger during the late winter and spring season, as the SSTforced signals remain strong enough but the background noise
is substantially reduced. By looking at the relationship between
California precipitation and ENSO events, Jong et al. (2016)
found that the influence of El Niño on California precipitation
becomes stronger when the SST anomalies weaken in thespring, and suggested that this can be attributed to a warming
climatological mean sea surface temperature, which is favorable for deep convection. Some other studies have attributed

DOI: 10.1175/JCLI-D-19-1004.1
Ó 2020 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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extreme events over North America during the spring season
to the prolonged influence of ENSO (Wolter et al. 1999; Bateset al. 2001; Schmidt et al. 2001). Therefore, the evaluation of
the models’ performance in simulating the springtime ENSO
teleconnection is of great practical value but has received relatively little attention.
Two notable prior studies that examined the model fidelityof springtime ENSO teleconnection are those of Alexander
et al. (2002) and Spencer and Slingo (2003). They each pointed
out that, compared to observations, the modeled sea levelpressure (SLP) anomalies over the North Pacific remain too
strong during the springtime after the peak of ENSO events.
Alexander et al.’s (2002) conclusions were drawn from an ensemble of experiments with an atmospheric GCM coupled to a
one-dimensional entraining ocean mixed layer model beneatheach atmospheric grid cell, with observed SSTs specified in theeastern equatorial Pacific. They speculated that the model’s
bias toward a stronger North Pacific SLP response during
spring was likely caused by the lack of ocean dynamics in theirmodel setup, although sampling due to internal variability may
have also contributed. Spencer and Slingo (2003), on the other
hand, used an ensemble of HadAM3 atmospheric GCM simulations forced by observed SSTs. They also found that the
ENSO anomalies in the simulated North Pacific SLP fieldpersisted too strongly into March–May (MAM), and argued
that this could be due to the model’s limitation in simulating
the tropical precipitation accurately, even in the presence of
the imposed observed SSTs. In particular, the model was foundto overestimate the spring precipitation over the warmest
tropical SSTs, leading to a stronger extratropical wave train
response. The lack of two-way coupling and deficiencies in theconvective scheme used were suggested as possible reasons
behind the bias in simulated precipitation and the bias was notfound to be reduced significantly by increasing the vertical andhorizontal resolutions.
The aim of the present study is to evaluate the springtime ENSO
teleconnection in current generations of Earth system models(ESMs) and assess the extent to which the overly strong springtime
teleconnection bias discussed in the aforementioned studies, almost
two decades ago, remains ubiquitous among models and model
configurations. In addition, we discuss the implications of this bias
for the simulation of ENSO-induced surface climate anomalies
over North America. More specifically, our aims are 1) to accurately place the magnitude of the bias within the context of theinternal variability that is present in both the observational recordand model simulations; 2) to examine, within one model, the dependence of the bias on experimental design (e.g., prescribed SSTs
vs coupled ocean); 3) to assess the fidelity of springtime ENSO
teleconnections across a large suite of state-of-art global ESMs; and4) to assess the implications of the bias for the simulated surface
climate response to ENSO over North America. To achieve these,
we make use of nine sets of simulations from the Community Earth
System Model (CESM), preindustrial (piControl) simulations fromthe Coupled Model Intercomparison Project phases 5 and 6(CMIP5 and CMIP6), and Atmospheric Model Intercomparison
Project (AMIP) simulations from CMIP6.
The rest of this study is organized as follows. The data andmethods used are described in section 2. The assessments of the

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suite of CESM configurations and other model simulations(CMIP5, CMIP6, and ERA20CM) are presented in section 3.
The implications for the modeled surface temperature andprecipitation anomalies over North America are addressed in
section 4. In section 5, we briefly discuss the possible origin of
the bias before a summary and conclusions are provided in
section 6.

2. Data and methods
a. Observation-based products
Our primary period of analysis is 1920 to 2010. The SST dataset
used to identify the ENSO events is the Extended Reconstructed
Sea Surface Temperature version 3b (ERSSTv3b; Smith et al.
2008) from the National Oceanographic and Atmospheric
Administration (NOAA), which provides monthly analysis from1854 with a 28 3 28 horizontal resolution. Three SLP datasets areused for evaluating the ENSO-related circulation anomalies:
the European Centre for Medium-Range Weather Forecasts(ECMWF) twentieth-century reanalysis (ERA20C; Poli et al.
2016), the NOAA Twentieth Century Reanalysis version 2(20CRv2; Compo et al. 2011), and the Met Office Hadley
Center’s mean sea level pressure dataset (HadSLP2r; Allanand Ansell 2006). Note that the interpolation procedure applied in HadSLP2r could introduce uncertainties over regionswith sparse observations. ERA20C and 20CRv2 each assimilate surface pressure observations, while ERA20C additionallyassimilates marine surface winds and has a higher resolution.
We primarily use ERA20C, but the same qualitative conclusions can be drawn from the other two datasets. The ERA20Cbased horizontal winds and specific humidity on pressure levels
are also used. Our primary near-surface air temperaturedataset is the monthly Berkeley Earth Surface Temperature(BEST; Rohde et al. 2013) dataset, which provides 18 3 18
gridded temperature analysis created with a large sampling ofin situ thermometer measurements. For precipitation data overland, we use the Global Precipitation Climatology Center(GPCC) Full Data Product version 7 (Schneider et al. 2014) at
18 3 18 horizontal resolution, which is derived from a combination of rain gauge–based analyses and remote sensing. Boththe monthly global precipitation products of ERA20C and the
Global Precipitation Climatology Project (GPCP) version 2(Adler et al. 2003) are also used. The GPCP precipitation
product is available on a 2.58 3 2.58 grid from 1979 to thepresent and is based on rain gauge stations, satellites, andsounding observations.

b. Model simulations
1) CESM SIMULATIONS
We make use of a wide array of atmosphere-only and fully
coupled simulations with the Community Earth System Model(Hurrell et al. 2013). These simulations are summarized in Table 1
and are available for download from http://www.cesm.ucar.edu/
experiments/. For the atmosphere-only simulations, we include
three sets of 10-member Tropical Ocean–Global Atmospheresimulations (TOGA; Lau and Nath 1994; Trenberth et al. 1998;
Deser et al. 2018) with CESM version 1 (CESM1) that only differ

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TABLE 1. List of the length of simulations, and the number of El Niño (EN) and La Niña (LN) events identified in the CMIP5 andCMIP6 piControl simulations (with CESM-related ones marked by asterisks), for nine sets of CESM simulations and ERA20CM from
ECMWF as introduced in section 2. The CMIP6 models that are also included for their AMIP simulations are marked by boldface font.
CMIP5 model

Total length (yr)EN (yr)

LN (yr)CMIP6 model

Total length (yr)EN (yr)

LN (yr)ACCESS1.0
ACCESS1.3
BCC-CSM1.1
BCC-CSM1.1-M
BNU-ESM
CanESM2*CCSM4*CESM1-BGC*CESM1-CAM5*CESM1-FASTCHEM*CESM1-WACCM
CMCC-CESM
CMCC-CM
CMCC-CMS
CNRM-CM5
CNRM-CM5.2
CSIRO-Mk3.6.0
FGOAlS-g2
FGOAlS-s2
FIO-ESMGFDL CM3
GFDL-ESM2G
GFDL-ESM2M
GISS-E2-H
GISS-E2-H-CC
GISS-E2-R
GISS-E2-R-CC
HadGEM2-AO
HadGEM2-CC
HadGEM2-ES
INM-CM4
IPSL-CM5A-LR
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC5
MIROC-ESM
MIROC-ESM-CHEM
MPI-ESM-LR
MPI-ESM-MR
MPI-ESM-P
MRI-CGCM3
NorESM1-M
NorESM1-ME

250
500
500
400
559
996
1051
500
319
222
200
277
330
500
850
359
500
700
501
800
500
500
500
780
251
850
251
700
240
575
500
1000
300
300
670
630
255
1000
1000
1156
500
501
252

47
81
79
69
110
156
179
87
46
39
39
43
52
87
161
61
73
123
87
181
81
65
78
119
39
139
38
113
37
93
85
162
46
53
90
95
41
147
159
177
76
82
41

36
70
82
67
100
176
179
74
52
31
34
45
55
78
149
52
76 7
124
95
162
84
76
67
123
46
137
50
108
35
94
79
170
41
47
96
99
38
161
154
185
82
89
40

BCC-CSM2-MR
BCC-ESM1
CAMS-CSM1–0
CanESM5
CESM2
CESM2-WACCM
CNRM-CM6–1
CNRM-ESM2–1
E3SM-1–0
EC-Earth3-Veg
GISS-E2–1-G
GISS-E2–1-H
HadGEM3-GC31-LL
HadGEM3-GC31-MM
IPSL-CM6A-LR
MIROC6
MRI-ESM2–0
NESM3
SAM0-UNICON
UKESM1–0-LL

600
451
250
1000
1200
499
500
500
500
500
851
401
500
500
1200
800
701
200
700
750

91
73
48
156
188
78
89
77
75
77
200
63
82
91
196
109
113
31
105
119

101
81
42
166
199
73
85
84
75
83
160
55
83
79
192
89
112
35
10
117

CESM model
TOGA-ERSSTv3b
TOGA-ERSSTv4
TOGA-ERSSTv5
GOGA-ERSSTv4
CESM2-CAM6
CESM2-WACCM6PACEMAKER
LENS-his
LENS-pi

Total length (yr)(1920–2010) 3 10(1920–2010) 3 10(1920–2010) 3 10(1920–2010) 3 10(1950–2010) 3 3(1950–2010) 3 3(1920–2010) 3 20(1920–2005) 3 40
1801

EN (yr)16 3 1016 3 1016 3 1016 3 10
11 3 3
11 3 316 3 20506
277

LN (yr)14 3 1014 3 1014 3 1014 3 10933
933
14 3 20
484
243

ECMWF
ERA20CM

Total length (yr)(1920–2010) 3 10

EN (yr)16 3 10LN (yr)14 3 10in the SST dataset used: ERSSTv3b (Smith et al. 2008), ERSSTv4(Huang et al. 2015), and ERSSTv5 (Huang et al. 2017) SSTs,
henceforth referred to as TOGA-ERSSTv3b, TOGA-ERSSTv4,
and TOGA-ERSSTv5, respectively. By prescribing observed
SSTs within the tropical belt (from 288S to 288N) and using theclimatological seasonal cycle elsewhere, the TOGA configuration
focuses on the model’s response to the observed historical evolution of tropical SST anomalies. We also use a 10-member
ensemble of Global Ocean–Global Atmosphere (GOGA) simulations with CESM1 with observed time-varying SSTs from
ERSSTv4 imposed globally (GOGA-ERSSTv4). In addition to
the CESM1 simulations, we assess two three-member GOGA

ensembles with the newly developed CESM version 2 (CESM2)(Danabasoglu et al. 2020). One of them employs the low-top
Community Atmosphere Model (CESM2-CAM6) (Bogenschutz
et al. 2018) and the other employs the high-top Whole Atmosphere
Community Climate Model (CESM2-WACCM6) (Gettelman
et al. 2019). Note that these two CESM2 simulations cover ashorter period from 1950 to 2010 (Table 1).
For the fully coupled configurations, we use a 20-member
ensemble of the tropical Pacific pacemaker simulations performed with CESM1 (PACEMAKER; Kosaka and Xie 2013;
Deser et al. 2017). In the PACEMAKER simulations, theobserved evolution of the ENSO events is prescribed by

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JOURNAL OF CLIMATETABLE 2. The El Niño (EN) and La Niña (LN) years identified inobservation based on ERSSTv3b during 1920–2010. Extremely
strong events are marked by boldface font.
EN years

LN years

1925/26, 1930/31, 1939/40, 1940/41,
1941/42, 1957/58, 1965/66, 1968/
69, 1972/73, 1982/83, 1986/87,
1991/92, 1994/95, 1997/98, 2002/
03, 2009/10
1924/25, 1933/34, 1938/39, 1942/43,
1949/95, 1955/56, 1970/71, 1973/
74, 1975/76, 1984/85, 1988/89,
1998/99, 1999/10, 2007/08

nudging the eastern equatorial Pacific (208S–208N, 1808–808W)
SST anomalies toward observations. We also make use of a 40member ensemble of fully coupled CESM1 historical simulations (LENS-his) and a 1801-yr-long piControl (LENS-pi)
simulation conducted as part of the CESM1 Large Ensemble(LENS) project (Kay et al. 2015). With the exception of LENSpi, all of the above simulations are forced with historical atmospheric forcings (Hurrell et al. 2013).

2) OTHER MODEL SIMULATIONS (CMIP5, CMIP6, AND
ERA20CM)We also assess the piControl simulations from 43 CMIP5
and 20 CMIP6 models (listed in Table 1). The piControl simulations typically offer a larger sample of ENSO events thanhistorical simulations, which is helpful for accurately diagnosing the ENSO forced response in the presence of internalvariability. To access the behavior of the CMIP models withprescribed observed SSTs we also use the AMIP simulations
from selected CMIP6 models. To ensure a sufficient sample
size, only models that provide more than three AMIP members
from 1979 or 1950 to 2010 or at least one member that covers
the whole period of 1920–2010 are included, such that the
AMIP composites include at least as many events as observations. This leaves nine CMIP6 models with AMIP simulations(boldface font in Table 1), in addition to the CESM2-CAM6
and CESM2-WACCM6 simulations described above. We also
make use of a 10-member ensemble of atmospheric model
integrations known as ERA20CM (Hersbach et al. 2015)
available from ECMWF. It uses prescribed historical SSTs andsea ice cover from HadISST2 (Titchner and Rayner 2014), andwith no assimilation of atmospheric observations.

c. Methods
1) THE DEFINITION OF ENSO EVENTSIn this study, the El Niño (EN) and La Niña (LN) events are
identified by first calculating the Niño-3.4 index [defined byBarnston et al. (1997) as the area-averaged SST anomalies over
58S–58N, 1208–1708W] for each month during 1920–2010. A
three-point binomial filter is used to smooth the indices before
the DJF mean is calculated and linear detrending is subsequently performed. The EN (LN) events are defined as when
the detrended DJF Niño-3.4 index is higher (lower) than plus
(minus) one standard deviation (Okumura and Deser 2010;
Deser et al. 2017). We identifited 16 EN events and 14 LN

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events based on ERSSTv3b as observed ENSO events (Table 2).
We use the same events in the simulations with prescribed historical SSTs and the PACEMAKER simulations. Since theCESM2 simulations begin in 1950, only events after 1950 are
included in those. A summary of the number of events used foreach model or ensemble is given in Table 1.
The composite of anomalies during the EN (LN) years are
obtained by first removing the annual cycle of the monthly
climatological mean during 1920–2010 for the observational
record, and during the total simulation length used for eachmodel (Table 1) at each grid point, then compositing based on
the identified EN (LN) winters. For the coupled models andAMIP simulations shorter than 1920–2010, to take into accountany differences from observed ENSO amplitude, the composite anomalies are further normalized by scaling by the ratioof the DJF composite mean EN or LN Niño-3.4 anomaly inobservations (ERSSTv3b) over 1920–2010 to that in themodels over whatever simulation period is available.

2) STATISTICAL TESTING
To evaluate the statistical significance of the difference between the modeled and observed ENSO response in the presence of sampling uncertainty, a random sampling technique isemployed following Deser et al. (2017, 2018). For observations,
we randomly sample with replacement (bootstrap) 16 EN and 14
LN events from the observed events. This is repeated 1000 times
to form 1000 resampled ENSO composites for observations. The
same procedure is applied to the modeled ENSO events, still
keeping the sample size of the EN and LN events the same asthat in observations (16 for the EN and 14 for the LN). Note thatfor the model simulations with more than one member, the EN(LN) events of each member are put into one single large‘‘sampling pool’’ before bootstrapping.

3. Evaluation of the modeled springtime North Pacific
ENSO teleconnectiona. CESM simulations
To depict the North Pacific circulation related to ENSO, wedefine a North Pacific index (NPI) after Trenberth and Hurrell(1994) and Deser et al. (2017) as the area-averaged SLP
anomalies over 358–608N, 1658E–1458W. The seasonal evolution of the observed and modeled NPI is illustrated in Fig. 1 for
EN-LN and EN/LN alone. Here, we include the results from
ERA20C, HadSLP2r, and 20CRv2 reanalyses, as well as all nine
CESM configurations. The response shown by the HadSLP2r is
weaker than that displayed by the ERA20C and 20CRv2, while
the characteristics of its seasonal evolution are similar: the intensification of the observed anomalies occurs in November andpeaks in January, before decaying rapidly over the next 2 months(February and March). The peak value in the models is about
50% larger than the observed, and occurs in February, not
January (i.e., one month delayed compared to observations).
When considering the timing of the peak SLP anomaly during
individual events, it is only for EN events where there is a consistent difference between models and observations (Fig. S1 in
the online supplemental material). For LN events, there is no
consistent difference between the models and observations in

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FIG. 1. Seasonal evolution of the NPI anomalies (hPa) for (a) the
difference between El Niño and La Niña and (b) El Niño and (c) La
Niña events. Red shows the observation-based datasets and grayshows the nine CESM simulations with the gray shading demarcating their range.

the probability of the timing of the peak SLP anomaly (Fig. S2),
but this can be reconciled with the differences in Fig. 1c by thefact that, in the model, the NPI peak value is on average larger
for February-peak events than for January-peak events, whereas
the opposite is true in observations (not shown). The discrepancy shown in Fig. 1 appears from February to March in the
EN-LN and EN composites, and from February to May in the
LN composite. In either EN or LN, that discrepancy indicatesstronger North Pacific circulation anomalies in the models
compared to those in observations. Note that in the LN composite, the difference between the models and observations ismuch smaller during April–May compared to that during
February–March (FM). Although the peak magnitude of NPI islarger for the EN composite, the bias in the EN–LN composite is9989

dominated by that in the LN composite. Given that prior studies
have suggested that the North Pacific circulation response may
not scale linearly with ENSO amplitude (Frauen et al. 2014;
Garfinkel et al. 2019; Jiménez-Esteve and Domeisen 2019), we
further check whether this springtime bias is heavily dependent
on the inclusion of the strongest ENSO event of the record in thecomposite. The results are shown in Fig. S3, where it can be seenthat the bias remains when the extreme events of 1982/83 and1997/98 are excluded. In fact, if anything, the bias does not seemto be present in the extreme events, but the limited sample sizein observations inhibits our ability to draw any strong conclusions in this regard. This bias is, therefore, a characteristic feature of ENSO in general and is not dominated by the extreme
events and we proceed to consider all ENSO events within thetime series together.
To test the significance of the difference between the simulated and observed EN–LN NPI, the random sampling methodof Deser et al. (2017) (see section 2) is applied to generate thebox plots in Fig. 2. As depicted in Fig. 2c, the bottom and top ofeach box represent the 25th and 75th percentiles of the sorted
bootstrapped NPI composites, respectively, and the middle
line is the average of the 1000 bootstrapped NPI composites.
The 5th and 95th percentiles are marked by the whiskers. For
model simulations with more than one member, the composite
anomaly of each individual member is marked by a blue circle.
For the reanalysis and the simulation with only one member(LENS-pi), the blue circle represents the value of the composited NPI for that one time series. The height of the box andwhiskers can be interpreted as the range of the uncertainty due
to internal variability for a sample size that is equivalent to thatof observations. The difference between the 75th and 25th
percentiles of the bootstrapped NPI composites varies from
2.47 hPa in DJF to 1.38 hPa in MAM for ERA20C, and from
2.68 hPa in DJF to 1.71 hPa in MAM for the average of nine
CESM simulations. Thus, both in observations and model
simulations, the internal variability during the winter season is
generally larger than that during the spring, which is consistent
with previous studies (Kumar and Hoerling 1998). During
February and March, the interquartile ranges of the observational and model samples do not overlap and for the majorityof model simulations the 95% confidence intervals do not evenoverlap, which indicates a very significant difference between themodel and observations during these months in the sense that theamplitude of the ENSO teleconnection is larger in the model.
Quantitatively, where the 95% confidence interval of the bootstrapped samples of the model does not overlap with the observed
value, which is true in every case in February and March, there isless than a 5% chance that the observed value would be sampledfrom the model distribution and, therefore, less than a 5% chancethat the model is behaving as observations.
Having found the bias in the NPI index to be the largest in
FM, we show a more detailed view of the spatial distribution of thebias in this season in Fig. 3. The observed (based on ERA20C) andsimulated (TOGA-ERSSTv3b and PACEMAKER) SLP anomalies over the North Pacific during FM of the EN and LN events, andtheir difference (EN–LN) are shown by the contours in Fig. 3,
whereas for the CESM simulations the difference in the composite
anomaly from observed is depicted by shading. The corresponding

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FIG. 2. Box plots of bootstrapped ENSO composites (EN–LN) of NPI (hPa) in observations(red), and nine CESM simulations (black) in (a) each month from December to May, and(b) 2-month averages. Box and whiskers represent the uncertainty range for an equivalent sample
size to that of observations (16 EN and 14 LN). (c) A legend for (a) and (b). The height of each box
indicates the 25th–75th percentile of the sorted 1000 resampled NPI composites, and the middle

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FIG. 3. Composites of SLP anomalies (contours) over FM during (d)–(f) El Niño (EN), (g)–(i) La Niña (LN) events, and (a)–(c) theirdifference (EN–LN) for the (left) ERA20C dataset and (middle) TOGA-ERSSTv3b and (right) PACEMAKER simulations fromCESM1. The contour interval is 2 hPa with negative values dashed. Shadings are the difference fields compared to ERA20C. The red box
marks the region used for defining the North Pacific index (NPI).

composite results for the other CESM simulations are similar to theones shown here (Fig. S4). The box area used for defining NPI isroughly centered on the EN–LN composite anomalies of the nine
CESM simulations throughout the DJ–FM seasons, and covers the
maximum SLP anomaly difference from observations in FM (Fig. 3;
see also Figs. S4 and S5). Note that the observed center of action(1508W) is about 208 east of the center of NPI [1708W, the same asthat in Trenberth and Hurrell (1994)], but moving the NPI box 208
to the east (so that it is instead centered on the observed anomaly)
does not qualitatively change the results here. The exception is thatfor the composites of LENS-his and LENS-pi simulations, the relative weak NPI bias during EN events (Fig. 1) is mainly a shift in theanomaly center away from our NPI box. Overall, the conclusions
are not strongly dependent on the averaging region used and thebias is clear from the density of contours in Fig. 3.
In observations, the negative values during EN years over
the North Pacific dominate the EN–LN composite difference(cf. Figs. 3d,g). This asymmetric feature in the amplitude of the

EN and LN anomalies can either be attributed to a true
asymmetry in the nature of the ENSO response or to internalvariability–induced uncertainty associated with the relatively
small sample size in observations (Zhang et al. 2014; Deseret al. 2017). By examining the distribution of 1000 bootstrapped NPI composites for the EN and LN (inverted) fromobservations, the significance of this asymmetry can be tested.
As shown in Fig. 4b, the 10th percentile of the bootstrapped
inverted LN composite is greater than the 90th percentile of
the EN composite (i.e., this is a strong indication that there is areal asymmetry between EN and LN responses in this season).
By excluding the extreme EN events of 1982/83 and 1997/98,
this asymmetric feature becomes less significant (Fig. 4d),suggesting that the contribution from extreme EN events ingiving an overall stronger North Pacific response in EN eventsis important. As shown in Fig. 4a, a similar asymmetric response over the North Pacific can also be seen in DJ, but, as
concluded in Deser et al. (2017) for the DJF season, it does notline is the average. The 5th and 95th percentiles are indicated by the whiskers. The blue dot
represents the mean for each individual member in the simulations with more than one member
and the one value for the observations and LENS-pi. The red-shaded region extends the 25th–
75th-percentile range for ERA20C across the plot for reference.

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FIG. 4. Histograms of the bootstrapped North Pacific index (NPI; hPa) over (a) DJ and (b) FM during El Niño
(red) and La Niña (blue) events. (c),(d) As in (a) and (b), but with extreme events (i.e., 1982/83 and 1997/98)
excluded. La Niña values are multiplied by 21. The 10th and 90th percentiles are indicated by the dashed lines.

pass significance tests. The modeled responses in FM are also
generally stronger in EN years than in LN years (although lessdramatic than observed) in most of the CESM simulations(Fig. 3; see also Fig. S4). Given the reduced importance ofinternal variability in the model composites due to the larger
sample size, this lends further support to the asymmetric natureof the EN and LN responses in this season. Despite theasymmetric absolute response, the consistency of both sets of
CESM simulations in Fig. 3 in giving a stronger response over
the North Pacific than observations in FM during both EN and
LN is obvious.

b. Simulations with other models
Figures 5a and 5c show the composite of the FM SLP
anomalies (EN–LN) over the North Pacific area for the 43
CMIP5 models and 20 CMIP6 models from the coupled
piControl simulations, and Fig. 5e shows the same but for theCMIP6 AMIP simulations. Similar to CESM (Fig. 3; see also
Fig. S4), a stronger amplitude of the ENSO response is observed in the multimodel mean composites of both CMIP5 andCMIP6 compared to observations and the discrepancy islargest in FM. In addition, the modeled response is centered
farther west than that in observations, much like CESM. The
seasonal evolution of the NPI (Figs. 5b,d, f) in the multimodel
mean (blue lines) of the CMIP5 and CMIP6 simulations also
shows that the modeled NPI tends to peak one month (twomonths in CMIP6’s piControl simulations) later thanobservations. As was also found in CESM, the bias in the
CMIP5/CMIP6 models is dominated by the bias during La
Niña events, with the simulated springtime teleconnection
during El Niño events being closer to observed (Fig. S6).
However, it is not appropriate to conclude that the models are
better in simulating the NPI during EN events given that themodeled DJ NPI in EN events is generally weaker than observed. It is speculated that if the modeled NPI during DJ of
the EN events were closer to the observational value, the CMIP
models may exhibit stronger North Pacific circulation anomalies in
the FM season as well. This also indicates that the seasonal averagecommonly used for model evaluation (i.e., DJF) may average out
compensating errors in the subseasonal time scale. Similar to what
was found in CESM (Fig. S3b), the discrepancy shown in EN events
becomes larger after excluding extremes in the CMIP models (notshown). To summarize, the biases in the distribution and evolutionof North Pacific SLP anomalies during ENSO events in the CMIP
models are consistent with those revealed by the CESM simulationsin the previous section.
The difference between modeled and observed (MODOBS) NPI for the EN–LN composite during FM is summarized for the individual models in Fig. 6. Here, the 5th–95th(25th–75th) confidence intervals in the modeled and observed
NPI anomalies are generated using the same random samplingtechnique as in Fig. 2. The models marked by red plus signs arethose with composite NPI anomalies lying outside of the 5th–
95th bootstrapped composites of observational values—that is,

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FIG. 5. (left) Composites of EN-LN SLP anomalies (contours) over FM during ENSO events averaged over (a) 43
CMIP5, (c) 20 CMIP6 models’ piControl simulations, and (e) 9 CMIP6 models’ AMIP simulations. Shadings are thedifference fields compared to ERA20C as in Fig. 3. (right) Evolution of the NPI (hPa) in observations (red) andeach of the (b) 43 CMIP5, (d) 20 CMIP6 models’ piControl simulations, and (f) 9 CMIP6 models’ AMIP simulations(gray). Blue is for the multimodel mean and gray shading depicts the model range.

with less than 5% chance that the modeled NPIs could be sampled from the observed distribution given the observational uncertainty. To test based on the uncertainty range in the models, ablue plus sign is used to indicate when the observed value lies
outside of the 5th–95th confidence interval of a given model.
These two assessments of significance will only differ if themodel’s sampling uncertainty differs from that in observation. It
is obvious that the majority of the CMIP models tend to give astronger rather than weaker ENSO response over the North
Pacific area compared to observations. Specifically, only three
CMIP5 models and one CMIP6 model are associated with apositive (although not significant) bias in NPI. After taking into
account the sampling uncertainty, the composite FM NPI from 23
out of 43 CMIP5 models and 11 out of 20 CMIP6 models’
piControl simulations is significantly (exceeding 95% confidence
level against the uncertainties in both observations and models)more negative than observed, further emphasizing that the bias
identified in CESM is present in many other models as well.
Here we are cognizant of the fact that the bias in coupledruns could arise from a bias in the response to the ENSO SST

anomalies, a bias in the representation of the SST anomalies
themselves, or both. For example, there is a well-known biasthat modeled ENSO SST anomalies tend to extend too far west
(Kiehl and Gent 2004; Li and Xie 2014). Despite the possible
contribution of biases in the simulation of the actual ENSO
SSTs, in these coupled model composites, it is shown thatsimilar biases exist when observed SSTs are prescribed in thesemodels too (Figs. 5 and 6), indicating that the primary issue lies
in how the atmosphere responds to ENSO SST anomalies.
Recall that to partially eliminate the potential influence fromthe difference in the amplitude of the ENSO events, the NPIfrom the coupled simulations has been scaled by the ratio of thecomposite mean Niño-3.4 anomalies in ERSSTv3b to that in
the model. The corresponding ratio of the DJF mean Niño-3.4
index in each model to that in observations is marked by agreen dot in Fig. 6. This shows that there is no clear relationshipbetween a model’s NPI index bias and its ENSO amplitude. The
conclusions here are not sensitive to the season used for scaling,
namely replacing the DJF Niño-3.4 indices with the FM/FMA
index gives qualitatively the same results (not shown). It is clear

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FIG. 6. (a) The difference between the EN-LN composites of modeled and observed NPI(NPI bias; hPa) over FM for each of the 43 CMIP5 models’ piControl simulations. The black
whiskers (red lines) indicate the 25%–75% confidence interval based on 1000 bootstrappedsamples from the models (observations), all sampling with an equivalent sample size to theobservations. The models that significantly differ from the observed value against the 5%–95% confidence interval in the observations (models) are marked by red (blue) plus signs. For
each model, the NPI bias averaged over December to January is marked by the red dot, andthe ratio of the DJF composite Niño-3.4 index to observations is in green (right axis). The
models developed by the same institution or that share a development history are shown inthe same color other than black. (b) As in (a), but for CMIP6 models’ piControl and AMIP
simulations, nine configurations of CESM, and ERA20CM simulations.

that the NPI bias in FM is not simply a persistence of a bias fromearly winter (DJ), as shown by the red dots in Fig. 6. There is asuggestion that the models that have less of an FM bias, actually
have a too-weak teleconnection during DJ (also see Fig. S6).
Therefore, the FM bias might be even more ubiquitous acrossthe models if those models had a more accurate early winterteleconnection.
The influence of coupling can be analyzed using CESM
multi-configuration experiments and piControl/AMIP simulations
from CMIP6. In CESM, the bias in the coupled LENS-his andLENS-pi simulations are significantly smaller (25th–75th confidence
intervals are not overlapped) than that in the TOGA-ERSSTv3b

and TOGA-ERSSTv4 simulations (Fig. 6b). However, no significant improvement is found in the PACEMAKER simulationcompared to other atmosphere-only simulations. Compared to
LENS-his and LENS-pi, PACEMAKER is also a coupled simulation while it has more realistic ENSO SST anomalies.
Therefore, the apparent improvement shown in LENS-his andLENS-pi simulations is not likely to be due to the two-way
coupling between atmosphere and ocean. A possibility is thatthe differences in the ENSO simulation in the coupled model
partially offset the atmospheric model errors. A comparison
between the AMIP-type simulations in CMIP6 and their corresponding coupled simulations shows that no significant

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improvement is found in the presence of the coupling, so the
AMIP simulations do not produce the bias as a result of thelack of coupling but rather do so due to errors in the atmospheric response to the SSTs. We also consider one set ofsimulations of ERA20CM, which is a 10-member ensemble ofprescribed historical SST simulations. These are analogous to
the CESM GOGA runs but with an entirely different model,
and it can be seen in Fig. 6b (far right) that this model has a very
similar springtime bias when forced by the observed ENSO
events. Therefore, it provides further evidence for the ubiquityof the bias in different climate models, even when forced withobserved SSTs. Furthermore, as different atmosphere-only
configurations of CESM displayed no significant difference
from each other, no evidence has been found that the influenceof extratropical SSTs (by comparing TOGA and GOGA), or
the model top height (by comparing CESM2-CAM6 andCESM2-WACCM6) has any bearing on the bias.
In most cases, the models developed by the same institution
resemble each other in terms of the amplitude of NPI biases, especially when the models only differ in atmospheric chemistry(MIROC-ESM and MIROC-ESM-CHEM), biochemistry (e.g.,
NorESM1-M and NorESM1-ME), and stratospheric representation (e.g., CMCC-CM and CMCC-CMS; CESM1-CAM5 andCESM1-WACCM; and CESM2-CAM6 and CESM2-WACCM6).
Model resolution, in general is not a determining factor for theoccurrence of the bias (e.g., IPSL-CM5A-LR and IPSL-CM5AMR; MPI-ESM-LR and MPI-ESM-MR), although there are somecases when improvements are seen (e.g., BCC-CSM-1.1 and BCCCSM-1.1-M in CMIP5, and HadGEM3-GC31-LL and HadGEM3GC31-MM in CMIP6). There are also cases when changes in thephysics lead to improvement (e.g., compare IPSL-CM5B-LR with
IPSL-CM5A-MR and IPSL-CM5A-LR).

4. Implications for the modeled climate response over
North America
The North Pacific atmospheric circulation can have a profound impact on the surface climate over North America. For
example, when the low pressure system around the AleutianIslands becomes deeper and shifted eastward, the southerly
and westerly winds along the west coast of North America areintensified, increasing the frequency of heavy daily precipitation events over the western United States (Kim et al. 2019),
and warming a large portion of Alaska (Papineau 2001) and thewestern United States (Favre and Gershunov 2009).
To investigate the influence of the circulation bias on thesimulation of the climate impacts over North America, we firstcompare the modeled EN–LN composites of surface air temperature and precipitation anomalies during FM in the CESM
PACEMAKER simulations with observations in Figs. 7a and
7c. The PACEMAKER simulations are selected over other
CESM configurations since they has a larger ensemble size
than TOGA and GOGA simulations (20 members compared
to 10 members), and two-way air–sea interaction is included.
Moreover, compared to the LENS-his and LENS-pi simulations, the SST anomalies during ENSO are more realistic. For
surface air temperature (TAS; Fig. 7a), significant warm biases
are found in the northwest covering Alaska to Nunavut. Significant
cold biases are mostly seen to the north and northeast of the Great

9995

Lakes. Weak warm biases also occur along the coastal area of
the Gulf of Mexico. The accompanying circulation anomalies at
850 hPa suggest that the warm bias over Alaska can be directly
linked to the northeast branch of the cyclonic circulation bias
over the North Pacific, and associated southerly warm advection.
The weak warm biases along the coastal area of the Gulf of
Mexico accompany a southerly wind bias in the western North
Atlantic. The anomalous cold air over northeastern Canada
south of Hudson Bay appears to be related to circulation
anomalies over the western Atlantic and Hudson Bay, rather
than directly influenced by the NPI. The distribution of precipitation (PREC; Fig. 7c) shows a patch of significant dry biases
along the west coast from southern Canada to the northern tierof the United States, and wet biases over the southwestern andthe southeastern United States. The moisture flux shown invectors suggests that there are offshore moisture transport biases
prevailing over the dry-bias region and southerly inshore moisture transport biases associated with the southwest United States
wet bias. Following Jong et al. (2016), we apply the composite
analysis to the extreme and nonextreme EN events separately
and find that California precipitation is markedly increased inboth types of events in PACEMAKER, in contrast to observations which show a larger signal during extreme EN events (notshown). A possible consequence is that the models would give an
overoptimistic estimation of the role played by moderate EN
events in relieving periods of drought of California.
Similar results are given by the multimodel mean of theCMIP5 models in Fig. 7b for surface air temperature and in
Fig. 7d for precipitation. Compared to PACEMAKER, thewarm biases shown in Alaska and western Canada are weaker in
the CMIP5 models, mainly because the North Pacific circulation
bias and the associated southerly anomalies (Figs. 7e,f) are
weaker in the CMIP5 ensemble mean. The cold biases south of
Hudson Bay, by contrast, are stronger in the CMIP5 models.
The major difference in the composite precipitation bias in the
CMIP5 models compared to PACEMAKER is that the wet bias
over California is missing, which can be explained by the limited
eastward extension of the cyclonic flow anomalies that preventthe inland moisture flux anomalies from reaching the southwest
United States in the CMIP5 models.
From another perspective, the linkage between the surface
climate response and the NPI bias can be established byregressing across the CMIP5 models. The regression maps
shown in Figs. 8a and 8b provide further evidence that the above
surface climate biases found over western North America are,indeed, connected to the NPI bias. Note that the sign of the NPI
bias has been inverted before calculating the regression coefficients, so that the sign of the regression coefficient matches thesign of the anomalies in the composite bias discussed above.
Figure 8a indicates that, in the CMIP5 models, a stronger North
Pacific ENSO teleconnection tends to be accompanied by awarmer anomaly over northwest North America and colder
anomaly in the southeast. Different from Fig. 7b, no significant
temperature biases to the north and northeast of the Great
Lakes are present. In addition, the weak cold biases seen overthe coastal area of the Gulf of Mexico in the regression map
across the CMIP5 models are of opposite sign compared to thecomposite bias. This suggests that these features found in the

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FIG. 7. Composites of the difference between the (left) PACEMAKER modeled and (right) CMIP5 multimodel
mean and the observed EN–LN anomalies in (a),(b) surface air temperature (TAS; shading) and horizontal winds
at 850 hPa (vectors), (c),(d) precipitation (PREC; shading) and moisture flux at 850 hPa (Qflux; vectors), and (e),(f)sea level pressure (SLP) over FM of ENSO events. Stippling indicates that the observed value lies outside of the
5th–95th percentiles from the bootstrapped composites by applying the observed confidence intervals to the model
simulations. See Deser et al. (2018) for more details.

composite bias are not directly tied to the NPI bias. The acrossmodel regression coefficient map between the precipitation composite bias and the NPI bias (Fig. 8b) exhibits significant positive
precipitation anomalies in California (90% confidence level) anddry biases to the north, which is in line with a strengthening of thecanonical precipitation response to ENSO as depicted by Mo and
Higgins (1998), Mo and Schemm (2008), and many other studies.
Note that compared to the composite results, the NPI regressions
show a weaker dry bias to the west of Hudson Bay and a morecoastal-confined wet bias over the southeastern United States.
In the EN–LN composite field of SLP anomalies, the springtime
anomalies in SLP in both the PACEMAKER simulations and thecoupled CMIP5 models exhibit a positive bias centered over the
North Atlantic (Figs. 7e,f). However, the regression of SLP
anomalies onto the NPI bias across the CMIP5 models makes clearthat the magnitude of this North Atlantic bias is not related to themagnitude of the NPI bias (Fig. 8c), suggesting the Atlantic andPacific biases may have distinct causes. The lack of significant circulation changes around Hudson Bay in the regression map is also
consistent with the weak climate impacts (both in TAS and PREC)
displayed over that area (Figs. 8a,b). The cold biases over thesoutheastern United States are under the influence of the prevailingof northerly wind brought by the weak southeastern North Atlantic
low pressure anomalies, which are linked to the NPI bias. In summary, the influence of the model biases in the simulation of a stronger
North Pacific ENSO teleconnection after the peak season is consistent with a prolonged ENSO impact on western North America inthe models (Mo and Higgins 1998; Papineau 2001; Deser et al. 2018).

5. Potential causes of the February–March NPI bias
It is well known that diabatic heating related to tropicalprecipitation anomalies plays a critical role in triggering the

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FIG. 8. Regression maps of (a) surface air temperature (TAS)
bias, (b) precipitation (PREC) bias, and (c) sea level pressure(SLP) bias on each grid point over FM of ENSO events onto 21 3
NPI bias across the CMIP5 models. Stippling indicates that the
regression coefficient exceeds 95% confidence level.

extratropical Rossby wave train pattern during ENSO events(Hoskins and Karoly 1981). Here we investigate whether there is any
evidence that this mechanism underlies the FM NPI bias in models.
The differences in the EN–LN composited anomalous precipitation
fields for CESM PACEMAKER, TOGA-ERSSTv3b, and themultimodel mean of the CMIP5 piControl simulations relative to
ERA20C averaged over FM are shown in Figs. 9a, 9c, and 9e, respectively. Figure 9g shows the regression, across CMIP5 models, ofthe precipitation simulation bias at each grid point over the ocean
onto the NPI bias. Since precipitation in ERA20C may have substantial influence from the underlying model, we also show anequivalent comparison for the shorter period from 1979–2010 using
the GPCP observational product in Fig. S7. These both give consistent conclusions.
According to Spencer and Slingo’s (2003) analysis, the simulated precipitation over the equatorial southwestern Pacific to9997

the east of New Guinea (central and western equatorial Pacific)
remains too high (low) during the springtime of EN (LN) events.
However, as can be seen in Figs. 9a, 9c, and 9e, in our study the
EN–LN composites of modeled precipitation to the east of New
Guinea around the date line are actually lower than observationsin FM. Furthermore, these negative precipitation anomalies are
not significantly correlated with the North Pacific SLP bias
across the CMIP5 models as indicated by Fig. 9g. Instead, acrossthe CMIP5 models, a weak positive correlation relationship with
the NPI index can be found in the precipitation anomalies fromthe western tropical Pacific around the Philippines into theequatorial western Pacific. A similar structure is detected in thecomposite fields where positive precipitation biases are detected
over the western tropical Pacific surrounding the Maritime
Continent, which extend farther east along the north of theequator in the TOGA-ERSSTv3b simulation. A significant relationship is also found for precipitation anomalies over the
midlatitude North Pacific (e.g., the dry biases around theKuroshio area and wet biases over the central North Pacific), butof course these are very likely due to the circulation bias as
opposed to causing it. Over the Indian Ocean, there is an overall
dry-bias signal accompanying the overly strong North Pacific
ENSO teleconnection in the models. The detailed structure reveals that while there is a consistent dry bias over the south
Indian Ocean in each composite analyses (Figs. 9a,c,e), significant negative correlations are found north of the equator in the
regression map across the CMIP5 models (Fig. 9g). Similar
dry biases are also seen over the north Indian Ocean in the
PACEMAKER differences from observations, but are notpresent in the TOGA experiments.
Although the precipitation-induced diabatic heating is essential for generating the ENSO teleconnection, the climatological background flow (e.g., the location and intensity of thejet stream) could modify the source of the waves and influence
the subsequent wave propagation (Simmons et al. 1983; Heldand Ting 1990; Hoskins et al. 1983; Sardeshmukh and Hoskins
1988). As a preliminary search for discrepancies in the mean
flow between models and observations, the zonal wind climatology (over all years) in FM at 200 hPa (U200) is examined(Figs. 9b,d,f,h). The results from the regression onto the NPI
across the CMIP5 models suggest that the strength of the climatological East Asian subtropical jet stream is positively
correlated with the NPI bias across the models. Compared toobservations, the East Asian jet stream over the North Pacificin the PACEMAKER simulations extends farther eastward
while the TOGA-ERSSTv3b simulation shows no obvious
difference from the observed background jet stream intensity.
Therefore, it is possible that the climatological U200 plays arole in some models, although there is not a consistent discrepancy in each case. It is possible that different mechanisms
are acting in different setups; for example, the north Indian
Ocean precipitation bias may be important in the coupledconfigurations like CMIP5 and PACEMAKER, while otherfactors may be important in TOGA. There are still several
other factors possibly involved in determining the ultimate
structure and intensity of the teleconnection pattern, such as
the role played by transient eddies, the detailed horizontal
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FIG. 9. (left) Composites of the difference of EN–LN ocean precipitation (PREC) anomalies between(a) PACEMAKER, (c) TOGA-ERSSTv3b, and (e) the multimodel mean of CMIP5 models and ERA20C over FM
of ENSO events. (g) Regression, across CMIP5 models, of ocean PREC bias on each grid point onto 21 3 NPI bias
during FM of ENSO events. (right) Climatological difference (all years) of the zonal wind at 200 hPa (U200)
between (b) PACEMAKER, (d) TOGA-ERSSTv3b, and (f) the multimodel mean of CMIP5 models and ERA20C
in FM. (h) Regression, across CMIP5 models, of FM U200 climatological mean (all years) at each grid point
onto 21 3 NPI bias. Blue contours delineate the observation values above 45 m s21; the interval is 5 m s21.
Stippling indicates that the composite difference or regression coefficient exceeds 95% confidence level.

with the tropical precipitation anomalies (Ting and Sardeshmukh1993), feedbacks involving extratropical diabatic heating, and theinteractions between all these potential factors.

6. Summary and conclusions
The North Pacific ENSO teleconnection acts as an important atmospheric bridge that helps transmit the influence of
ENSO-related SST anomalies to North American surface climate (Alexander et al. 2002). Due to a larger forced signal
relative to background noise from internal variability, the late

winter to spring season is believed to have a potential forhigher seasonal predictability for North America compared tothe midwinter (Kumar and Hoerling 1998). However, thispredictability inevitably relies on the model’s capability toreasonably reproduce the SST-forced teleconnection patternover the North Pacific during that season. Here we find that
CESM1 and CESM2 display a significantly biased North
Pacific ENSO teleconnection intensity after the peak of El
Niño/La Niña events, especially in the 2-month average fromFebruary to March, regardless of how the experiments are

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configured (i.e., observed SSTs specified in the atmosphericmodel, eastern tropical Pacific SST anomalies nudged to observed in the coupled model, or free-running fully coupledsimulations). Specifically, the modeled SLP anomalies havelarger amplitude over the North Pacific, and maximize one
month later than in observations. A comparison with piControl
simulations from 43 CMIP5 and 20 CMIP6 models demonstrates
that more than half of the CMIP models exhibit a similar significant bias (i.e., they exhibit an overly strong teleconnection
response to ENSO in the FM season, which is not a carry-overphenomenon from the preceding December–January season).
Both CESM and the CMIP ensemble indicate that a stronger
North Pacific ENSO teleconnection after the peak of El Niño/
La Niña is associated with a bias in the simulation of anomalous surface climate over western North America. As revealedby the EN–LN composites, this takes the form of a warm bias
over Alaska, a wet bias over California, and a dry bias along thewest coast from southern Canada to the northern United Statescompared to observations. These features are expected basedon the large-scale circulation-induced thermal advection andmoisture transports that accompany the stronger North Pacificcirculation anomaly. In general, the models’ bias in displaying astronger North Pacific ENSO teleconnection during spring
likely falsely prolongs the influence of ENSO over North
America. In a broader sense, the ENSO forced signal duringthat season is overestimated in the models.
In the modeling work of Garfinkel et al. (2019), the early
spring North Pacific SLP response to ENSO events is of similar
magnitude to its winter counterpart (see Fig. 1 of their paper).
Our study suggests that this is a model deficiency and careshould be taken in interpreting the modeling results related tothe strength of springtime North Pacific circulation anomalies
during ENSO events. Further, understanding of the mechanism causing that bias is of critical importance to develop future models that can effectively be used for studying springtime
ENSO teleconnections, or providing seasonal forecasts. While both
Spencer and Slingo (2003) and Alexander et al. (2002) speculated
that a potential cause of the bias is the lack of dynamical ocean–
atmosphere coupling, our study suggests that most coupled models
also suffer from a similar problem. By examining the tropical Pacificprecipitation discrepancy in the CESM PACEMAKER, TOGAERSSTv3b, and CMIP5 models’ piControl simulations, we show thatthe central tropical Pacific precipitation bias in the PACEMAKER
and TOGA-ERSSTv3b simulations are of the opposite sign compared to Spencer and Slingo’s (2003) analysis. Moreover, no significant relationship is detected in the CMIP5 models between thebias in precipitation anomalies in the tropical Pacific and the North
Pacific circulation bias. Instead, the NPI biases in the CMIP5
models are found to be significantly negatively correlated with
precipitation biases over the tropical north Indian Ocean. However,such a precipitation bias is not present in the TOGA runs. There aremany other factors that could play a role, such as biases in transient
eddy feedbacks on the extratropical circulation or biases in thevertical structure of the heating perturbation and a detailed investigation of possibilities like these is needed to further understand theultimate reason behind this modeled deficiency.
Despite considerable improvements in coupled models overthe past 15 years, the springtime bias in ENSO teleconnections

to the North Pacific and attendant climate impacts over North
America remain ubiquitous and lack explanation. Further
work is needed to fully understand the bias and disentangle therole played by multiple components. Inspired by Held et al.
(2002) and the series of literature prior to that (Held and Kang
1987; Held et al. 1989; Ting and Sardeshmukh 1993), stationary
wave modeling that focuses on decomposing the stationary
field into multiple contributing components may be a useful
tool in future studies that aim for this understanding.
Acknowledgments. We appreciate the three anonymous reviewers for their thoughtful comments. The CESM project is
supported primarily by the National Science Foundation (NSF).
This material is supported by the National Natural Science
Foundation of China (41875127) and is based upon work supported by the National Center for Atmospheric Research, which
is a major facility sponsored by the National Science Foundation
under Cooperative Agreement 1852977. Computing and data
storage resources, including the Cheyenne supercomputer (doi:
10.5065/D6RX99HX), were provided by the Computational andInformation Systems Laboratory (CISL) at NCAR. Ruyan
Chen was supported by the graduate visitor program of theAdvanced Study Program at NCAR. We acknowledge theWorld Climate Research Programme’s Working Group on
Coupled Modelling, which is responsible for CMIP, and we
thank the climate modeling groups (listed in Table 1) for producing and making available their model output. For CMIP, theU.S. Department of Energy’s Program for Climate Model
Diagnosis and Intercomparison provides coordinating support
and led development of software infrastructure in partnershipwith the Global Organization for Earth System Science Portals.
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