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Quantifying and Understanding Forced Changes to Unforced Modes of AtmosphericCirculation Variability over the North Pacific in a Coupled Model Large Ensemble
JOHN P. O’BRIENa,b AND CLARA DESERaab

Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California(Manuscript received 11 February 2022, in ﬁnal form 29 July 2022)

ABSTRACT: While much attention has been given to understanding how anthropogenic radiative forcing inﬂuences themean state of the climate system, far less scrutiny has been paid to how it may modulate naturally occurring modes of variability. This study investigates forced changes to unforced modes of wintertime atmospheric circulation variability and associated impacts on precipitation over the North Paciﬁc and adjacent regions based on the 40-member Community Earth
System Model version 1 Large Ensemble across the 1920–2100 period. Each simulation is subject to the same radiative
forcing protocol but starts from a slightly different initial condition, leading to different sequences of internal variability.
Evolving forced changes in the amplitude and spatial character of the leading internal modes of 500-hPa geopotential
height variability are determined by applying empirical orthogonal function analysis across the ensemble dimension ateach time step. The results show that the leading modes of internal variability intensify and expand their region of inﬂuence
in response to anthropogenic forcing, with concomitant impacts on precipitation. Linkages between the Paciﬁc and Atlantic
sectors, and between the tropics and extratropics, are also enhanced in the future. These projected changes are driven
partly by teleconnections from ampliﬁed ENSO activity and partly by dynamical processes intrinsic to the extratropical atmosphere. The marked inﬂuence of anthropogenic forcing on the characteristics of internal extratropical atmospheric circulation variability presents fundamental societal challenges to future water resource planning, ﬂood control, and drought
mitigation.
KEYWORDS: El Nino; ENSO; Paciﬁc-North American pattern/oscillation; Teleconnections; Climate variability;
Internal variability

1. Introduction
Anthropogenic emissions of well-mixed greenhouse gases,
aerosols and other radiatively active constituents are causing
rapid and measurable changes to the mean state of the global
climate system despite uncertainty arising from complex physical processes (IPCC 2007, 2013, 2021). While much attention
has been given to understanding these mean-state changes,
far less scrutiny has been paid to how such emissions modulate the character of internally generated climate variability.
Fundamentally, the coupled ocean–atmosphere–land system
is governed by nonlinear dynamics, which gives rise to chaotic
behavior that deﬁnes a distribution of possible outcomes about
its mean state (Lorenz 1963). Such internally generated variability is an inherent feature of the climate system and places practical limits on predictability across a range of space and timescales (Branstator and Teng 2010; Deser et al. 2012a; DelSole
and Tippett 2018).

Denotes content that is immediately available upon publication as open access.
Supplemental information related to this paper is available
at the Journals Online website: https://doi.org/10.1175/JCLI-D-220101.s1.
Corresponding author: John P. O’Brien, jpobrien@ucar.edu

An important source of internally generated climate variability derives from ﬂuctuations in recurrent large-scalepatterns of atmospheric circulation known as teleconnections (Wallace and Gutzler 1981; Barnston and Livezey
1987; Feldstein 2000; Feldstein and Franzke 2017). These
atmospheric modes of variability arise from internal atmospheric dynamics (e.g., Branstator and Teng 2017) as wellas coupled ocean–atmosphere (DeWeaver and Nigam 2004;
Stan et al. 2017; Dong et al. 2018; Thomson and Vallis
2018) and land–atmosphere (Teng et al. 2019) interactions,and are important modulators of regional weather and climate on subseasonal to decadal time scales (Ropelewski
and Halpert 1987; Leathers et al. 1991; Hurrell 1995; Diazet al. 2001; Fasullo et al. 2020). Well-studied examples include the Paciﬁc–North American (PNA; Wallace and Gutzler
1981) pattern, North Atlantic Oscillation (NAO; Hurrell et al.
2003), and teleconnections arising from El Niño–Southern
Oscillation (ENSO; Bjerknes 1969).
It is likely that these intrinsic structures of variability will
undergo changes in amplitude, spatial pattern and temporal
behavior as a result of anthropogenic forcing (e.g., Branstator
and Selten 2009; Shepherd 2014; Deser et al. 2018), and theirremote linkages and climate impacts may change as well. For
example, recent studies indicate that the PNA pattern may
amplify (Chen et al. 2018) and its center-of-action may shiftnortheastward (Michel et al. 2020) in response to anthropogenic climate change. NAO variability is also expected to increase in a warming climate; however, model dependence
and multidecadal sampling ﬂuctuations impart considerable

DOI: 10.1175/JCLI-D-22-0101.1
Ó 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
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uncertainty and may obscure such forced changes (Visbeck
et al. 2001; Gillett et al. 2003; Deser et al. 2017a; Huang et al.
2018; McKenna and Maycock 2021). The interaction of teleconnection patterns with mean-state changes in backgroundﬂow and stationary and nonstationary wave characteristics[e.g., the Madden–Julian oscillation (MJO)] further complicates their response to anthropogenic forcing (Brandefelt
and Körnich 2008; Simpson et al. 2014; Mann et al. 2017;
Wills et al. 2019; Zhou et al. 2020).
Despite intense investigation, future changes to the characteristics of internally generated ENSO variability remain uncertain (e.g., Collins et al. 2010; Kohyama et al. 2018; Bellenger
et al. 2014; Cai et al. 2020; Wengel et al. 2021), although there isa growing consensus that ENSO-related tropical precipitation
variability will increase in a warmer climate (Power et al. 2013;
Cai et al. 2014; Huang and Xie 2015; Cai et al. 2020; Yun et al.
2021), impacting ENSO-induced teleconnections (Zhou et al.
2014; Drouard and Cassou 2019; Beverley et al. 2021). A recent
study by Cai et al. (2021) shows that there is high intermodel
agreement that ENSO sea surface temperature (SST) variability will increase in the twenty-ﬁrst century compared to thetwentieth century, but only when sampling ﬂuctuations due tolimited record length are adequately considered. However,model resolution and mean-state biases may affect this conclusion (e.g., Kohyama et al. 2018; Stevenson et al. 2021; Wengel
et al. 2021), underscoring the need for improved process-level
understanding.
In addition to the myriad physical processes that can complicate a robust quantiﬁcation of simulated teleconnection
patterns and their response to climate change, there are alsononphysical constraints such as model parameterizations andﬁnite computational resources. Moreover, the relatively shortobservational record hinders a robust statistical assessment ofteleconnection properties and possible forced changes thereof,
constraining model evaluation efforts (Hawkins and Sutton
2009; Deser et al. 2017a,b; Maher et al. 2018; Deser et al. 2020;
Fasullo et al. 2020). The historical climate record is special inthat it is the only one we have, or ever will experience; however, it also represents just one realization of an inﬁnite number of ways our climate history could have unfolded (Deser
2020; Mankin et al. 2020). Coupled climate modeling can overcome this limitation through the generation of multiple realizations under identical forcing scenarios with inﬁnitesimal
perturbations to the initial conditions. Due to the governing
chaotic dynamics, these realizations diverge and their extratropical atmospheric circulation states become largely uncorrelated after a few weeks (Lorenz 1963; Deser et al.
2012b). Such “initial condition” large ensembles (LEs) areintended to span the range of simulated internal variability
and allow us to probe alternate realities in which our climate
history could have unfolded differently than it has (Tebaldi
and Knutti 2007; Deser et al. 2012b). As such, LEs provide
an ideal test bed for assessing changes in internal climate
variability resulting from external forcing and are being
used accordingly (Deser et al. 2020).
A beneﬁt of LEs is the large number of samples of (simulated) internal variability at any given instant in time. This
unique attribute of model LEs can be exploited with suitable

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analysis techniques. For example, teleconnection patterns areoften determined through the lens of empirical orthogonal
function (EOF) analysis, a statistical technique designed tocapture spatially coherent patterns that explain the greatest
proportion of temporal variability across the domain of interest (Wallace and Gutzler 1981). However, with an LE, provided there are enough realizations, the EOF approach may
be applied over the ensemble dimension rather than the time
dimension, allowing a direct assessment of the evolving impact of external forcing on modes of internal variability. This
novel technique, known as ensemble-EOFs (Maher et al.
2018) or snapshot-EOFs (SEOFs; Haszpra et al. 2020a), hasonly recently been introduced to climate science and is particularly well suited to the analysis of LEs. Since the mean acrossthe ensemble (i.e., the forced response) is removed prior to
calculating the SEOFs, only the internal component of variability in each ensemble member at each time step is retainedin the analysis. While the forced response to the mean state isremoved at each time step, externally forced changes to internal variability are retained. Applications of SEOF analysis todate include the study of forced changes in ENSO and related
teleconnection patterns (Maher et al. 2018; Bódai et al. 2020;
Haszpra et al. 2020a), the NAO (Herein et al. 2016, 2017),
and the Arctic Oscillation (AO; Haszpra et al. 2020b).
In this study, we apply the SEOF methodology to quantify
and understand evolving forced changes in intrinsic patternsof wintertime atmospheric circulation variability over the
North Paciﬁc and concomitant changes in precipitation, including those over North America, during the period 1920–2100
as simulated by the 40-member LE conducted with the fully
coupled Community Earth System Model version 1 (CESM1;
hereafter CESM1-LE; Kay et al. 2015). We further decompose
these forced changes into their tropical and extratropical origins
via ensemble linear correlations with the leading SEOF modes
of internal tropical Paciﬁc SST variability. We also assess the inﬂuence of anthropogenic forcing on linkages between internal
circulation variability over the North Paciﬁc and North Atlantic,
and the role of the tropics in mediating such connections.
Finally, we quantify the relative magnitudes of forced changes
in modes of internal variability versus forced changes in themean state. We also discuss the extent to which these modesof variability are symmetric with respect to polarity.
The remainder of the paper is organized as follows. In
section 2, we describe the data and analysis methods. In
section 3, we present our results, including a validation of ourapproach to isolating tropical versus extratropical sources offorced changes in internal variability. In section 4, we synthesize
and discuss our ﬁndings, including implications for future environmental change and human systems.

2. Data and methods
a. DataThis study employs the 40-member CESM1-LE conducted
at a spatial resolution of 18 in both latitude and longitude(Kay et al. 2015). The historical portion of these simulations(1920–2006) is subject to observed natural and anthropogenic

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radiative forcings, while the future period (2007–2100) uses the
RCP8.5 emissions scenario, which is characterized by high rates
of fossil fuel consumption with no climate mitigations (Riahi
et al. 2011; van Vuuren et al. 2011). Each ensemble member isgiven a random perturbation to the initial atmospheric temperature ﬁeld (order of 10214 K), which serves to create ensemble
spread due to the internal chaotic dynamics of the coupled system (Lorenz 1963; Deser et al. 2012a). Once the memory of
the initial conditions is lost, the individual members follow independent sequences of internal variability superimposed upon
a common forced response. With a sufﬁcient number of ensemble members, the ensemble-mean represents the externallyforced response at any given time (Milinski et al. 2020).
We analyze the following monthly output ﬁelds from the
CESM1-LE: 500-hPa geopotential height (Z500), total precipitation (large-scale plus convective; PRECT), and surface
temperature (TS); the latter is equivalent to SST at oceanic
grid boxes. All quantities are seasonally averaged over boreal
winter December–February (DJF). We also employ a 2600-yr
atmosphere-only control simulation conducted as part of the
CESM1-LE Project. This simulation uses preindustrial (1850)
radiative forcing conditions and monthly varying SST and sea
ice concentration (SIC) climatologies taken from the 1800-yr
fully coupled preindustrial control simulation (see Kay et al.
2015 for further information).

b. Modes of variability
We apply SEOF analysis to DJF Z500 anomalies over the
extratropical (208–908N) North Paciﬁc (NPac: 1108–2608E)
and North Atlantic (NAtl: 808W–108E) domains, and to DJF
SST anomalies over the tropical Paciﬁc (TPac: 358S–358N,
1408–2958E). These domains are outlined in Figs. S1 and S2
in the online supplemental material. We consider the threeleading Z500 SEOFs over the NPac region, which we shallrefer to as NPac_Z500_SEOF1, NPac_Z500_SEOF2, and
NPac_Z500_SEOF3. As will become apparent, the ﬁrst modeis equivalent to the Paciﬁc–North American pattern (PNA;
Barnston and Livezey 1987), the second mode to the North
Paciﬁc Oscillation (NPO; Linkin and Nigam 2008), and thethird mode to the east Paciﬁc pattern (EPP; Barnston andLivezey 1987; Yuan et al. 2015). We also consider the leading
Z500 SEOF over the NAtl region (NAtl_Z500_SEOF1),
which represents the NAO (Hurrell et al. 2003), and the twoleading SST SEOFs over the TPac region. TPac_TS_SEOF1
represents the canonical ENSO pattern, whose positive phaseis closely associated with eastern Paciﬁc El Niño events (Kaoand Yu 2009), and TPac_TS_SEOF2, in its positive phase,is synonymous with central Paciﬁc El Niño (aka Modoki)
events (Capotondi et al. 2015). Note that while we refer toTPac_TS_SEOF1/2 as east Paciﬁc/central Paciﬁc (Modoki)
ENSO, respectively, it should be recognized that these are only
naming conventions useful for referring to speciﬁc parts of thenonlinear continuum that is ENSO (Takahashi et al. 2011; Dommenget et al. 2013; Williams and Patricola 2018; also see row
1 of Fig. S3). All four modes of Z500 variability consideredin this study, and their associated impacts on precipitation,
are realistically simulated in the CESM1-LE, as shown in

19

Figs. S6 and S7 of Guo et al. (2019) and Fig. 3 of Deser et al.
(2017b) based on traditional space–time EOF analysis over thehistorical period (see also Fasullo et al. 2020). The ENSO
modes are also generally well represented in terms of amplitude
and spectral characteristics, with a slight westward extension of
their equatorial Paciﬁc SST anomalies due to the well-known
mean state bias of the cold tongue (see Capotondi et al. 2020;
Fasullo et al. 2020; Wu et al. 2022).

c. Snapshot-EOF analysis
An ever-increasing concern with applying conventional
EOF analysis to climate data in the time domain is the issue
of non-stationarity resulting from anthropogenically forced
climate change (Milly et al. 2008; AghaKouchak et al. 2013;
Sarhadi et al. 2018). Non-stationarity occurs in the presenceof aperiodic or time-dependent climate forcings, which resultsin time-dependent statistics. Thus, in the case of traditional
EOF analysis, one must always subjectively choose the time
window over which to calculate the EOF, which in the presence of nonstationary data can result in time-dependent representations of the phenomenon in question.
Here, we compute the leading modes of variability discussed in section 2b using the SEOF approach. We calculate
the SEOFs across the ensemble dimension of a four-dimensional
data object consisting of ensemble, time, latitude, and longitude dimensions. By deﬁnition, EOF analysis operates ondata from which the mean has been removed; in the case of
SEOF analysis, the ensemble-mean (i.e., the forced response) is removed from the data at each time step, leaving
only the internal component for analysis. In addition, as is
standard with EOF analysis, the data are weighted by thesquare root of the cosine of the latitude to account for converging longitudes and decreasing grid areas approaching
the poles. The resulting SEOFs, computed independently
for each time step across the 40 realizations of the CESM1-LE,
describe the leading modes of unforced variability at a given instant in time (Drótos et al. 2015; Herein et al. 2017). We verify
that no mode-mixing occurs among the SEOFs and thathigher-order modes are well separated according to the North
test (Fig. S4; North et al. 1982).
For added robustness, we increase the number of samples
used in each SEOF calculation from 40 to 200 by including all
winters within a 5-yr running time window (note that we do
not apply a running mean to the data; rather, we pool the
5 discrete winters within the 5-yr time window). This approach assumes the climate is roughly stationary with respect
to external forcings across the windowed period, which is areasonable assumption in this application. We then assign the
resulting SEOFs to the year on which the window is centered.
For example, the ﬁrst SEOF calculation includes the years
1921–25 and is assigned to the year 1923. Therefore, for thefull 1921–2100 time period, we calculate 5-yr windowed
SEOFs whose central years span from 1923 to 2098 (176 central dates in total).
Like traditional space–time EOF analysis, which results inmutually orthogonal components in both space (the EOFs)
and time [the principal components (PCs)], the equivalent

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FIG. 1. DJF regression maps of precipitation (mm day21; color shading) and Z500 (contour interval 5 10 m; negativevalues shown as dashed contours) during (left) 1923–33 and (right) 2088–98 for (a)–(f) the three leading SEOFs ofNorth Paciﬁc Z500 variability and (g),(h) the leading SEOF of North Atlantic Z500 variability. Stippling indicates statistically signiﬁcant values (0.01 signiﬁcance level) in at least 7 of 10 years. See text for details of the SEOF methodology and Fig. S1 for the SEOF domains.

space-ensemble SEOF components are likewise mutually orthogonal in both the space and ensemble dimensions. Eachsnapshot principal component (SPC) describes the amplitude
and sign of the corresponding SEOF in each ensemble member. We create global regression maps for each standardized
SPC by linearly regressing anomalies (departures from the ensemble mean) of a particular ﬁeld (Z500, PRECT, or TS) ineach ensemble member onto the SPC, and repeat this procedure for each windowed time step. Statistical signiﬁcance foreach SEOF (unless otherwise noted) is indicated by stipplingwhere the p value on the SPC ensemble regression is less thanor equal to the 0.01 signiﬁcance level. In addition, we also employ a procedure for multiple hypothesis testing to control for
false positive discoveries (q* 5 0.01) to ensure the robustnessof statistically signiﬁcant patterns (Benjamini and Hochberg
1995). For all map-view ﬁgures, statistical signiﬁcance stippling always corresponds to the color-ﬁlled ﬁeld.

3. Resultsa. Forced changes in variability
Figure 1 displays regression maps of Z500 (contours) superimposed upon precipitation (color shading) for each of thefour extratropical Z500 SEOFs deﬁned in section 2. The left
column shows results averaged over the ﬁrst 10 years of theanalysis period (1923–33) and the right column shows results
averaged over the last 10 years (2088–98). While the phase ofeach mode is arbitrary, we have chosen to display them suchthat the associated precipitation anomalies over the equatorial
Paciﬁc are positive, with the exception of NPac_Z500_SEOF2,
which is deﬁned to be consistent with its correlation to central Paciﬁc Modoki as discussed in greater detail below. In
the early period (left column), NPac_Z500_SEOF1 (PNA;
Fig. 1a) shows a wave-like circulation structure with a prominent trough centered over the North Paciﬁc just south of the
Aleutian peninsula. NPac_Z500_SEOF2 (NPO; Fig. 1c) is characterized by a meridionally oriented dipole structure featuring azonally elongated trough centered over the subtropical Paciﬁcwith an associated ridge centered over the Bering Strait region.
NPac_Z500_SEOF3 (EPP; Fig. 1e) displays a well-developed
trough over the eastern North Paciﬁc oriented such that it represents a pathway for subtropical moisture transport to thewestern United States, with an associated ridge centered overthe Hudson Bay region. Finally, NAtl_Z500_SEOF1 (NAO;
Fig. 1g) most prominently depicts a meridionally oriented dipole structure centered over the North Atlantic.

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FIG. 2. As in Fig. 1, but for the ﬁrst two SEOFs of tropical Paciﬁc SST variability (see Fig. S2 for the SEOF domains).

The right column of Fig. 1 displays the transformation eachmode undergoes by the end of the twenty-ﬁrst century. Of thethree NPac modes, NPac_Z500_SEOFs 1 and 3 both show apronounced ampliﬁcation/deepening of their respective circulation features as well as an enhancement of their associatedprecipitation anomalies over the North Paciﬁc and along the
U.S. West Coast. NPac_Z500_SEOF3 is a mode that has received less attention; however, we ﬁnd that it plays an increasingly
important role in a future warmer climate. NPac_Z500_SEOF3 ismost prominently associated with subtropical moisture transport originating near the Hawaiian Islands (Figs. 1e,f). Moistureladen storms originating in this region are commonly known asatmospheric rivers (ARs), speciﬁcally, ARs of the “Pineapple
Express” variety (Rutz et al. 2019; O’Brien et al. 2020). Figure 1f
indicates that in a future warmer climate, this mode is both ampliﬁed and shifted southeastward, suggesting a greater likelihood for future AR activity for the western United States. Thisis consistent with previous studies that show AR frequency andintensity increase globally with climate warming (Payne et al.
2020), with disproportionate increases in the Paciﬁc basin(O’Brien et al. 2022), and in particular California (Gershunov
et al. 2019; Rhoades et al. 2021). NPac_Z500_SEOF2 shows
more subdued changes, with modest strengthening of thetrough over the central North Paciﬁc and associated precipitation increase. In addition, this mode develops a more prominent semi-hemispherical wave train character spanning fromthe North Paciﬁc to the North Atlantic arcing over the polar
region. NAtl_Z500_SEOF1 ampliﬁes considerably by the endof the twenty-ﬁrst century, not only over the Atlantic sector
but even more conspicuously in its connection to the PNA
over the North Paciﬁc, with an associated enhancement of precipitation over the western and southeastern United States.
(For a more detailed look at the circulation patterns and theirstatistical signiﬁcance, see Fig. S1.)
Common to all the Z500 SEOF modes is the striking increase in their associated amplitudes of tropical Indo-Paciﬁc
precipitation variability from the early twentieth century to
the late twenty-ﬁrst century (cf. left and right columns of Fig. 1).
This suggests that enhanced tropical precipitation variability

indicative of stronger ENSO activity in the future may be playing a role in driving the changes seen in the extratropical circulation and hydroclimate patterns. To evaluate this supposition, we
calculate the SEOFs corresponding to the ﬁrst two leading
modes of tropical Paciﬁc (TPac) SST variability, which together
explain approximately 70% of the interannual SST variability ateach time step (note, that like SEOFs themselves, the variancethey explain is also an implicit function of time: see Fig. 3).
Figure 2 displays the precipitation and Z500 regression
maps for each TPac SEOF mode in the early (1923–33) and late(2088–98) periods. In its positive phase, TPac_TS_SEOF1 bears
resemblance to east Paciﬁc (EP) El Niño and TPac_TS_SEOF2
to central Paciﬁc El Niño (Modoki), as shown in Fig. S2, although there is not a unique one-to-one mapping (see section 4
for additional discussion of the nonlinear interactions between
these modes.)In the early period, TPac_TS_SEOF1 displays a zonallyelongated pattern of below normal Z500 heights over the
North Paciﬁc extending into the North Atlantic, with oppositesigned anomalies over Canada and the subpolar North Atlantic,
while TPac_TS_SEOF2 shows a meridionally oriented dipole structure over the North Paciﬁc resembling the NPO(Figs. 2a,c, respectively); both Z500 patterns are statistically signiﬁcant (Fig. S5). TPac_TS_SEOF1 is associatedwith positive precipitation anomalies over the western halfof the equatorial Paciﬁc and Indian Oceans typical of thewarm phase of ENSO (Fig. 2a), while TPac_TS_SEOF2 isaccompanied by weak negative precipitation anomalies
over the central equatorial Paciﬁc (Fig. 2c).
The most pronounced end-of-century changes are seen for
TPac_TS_SEOF1, which shows enhanced variability in tropical precipitation, a deepened and wider extratropical circulation response that spans nearly the entire Northern Hemisphere,
and an ampliﬁed extratropical hydroclimate response most apparent over the western and southeastern United States extending out across the North Atlantic and into western Europe(Fig. 2b). End-of-century changes in TPac_TS_SEOF2 include
intensiﬁed and slightly eastward-shifted tropical Paciﬁc precipitation variability, along with a commensurate ampliﬁcation

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JOURNAL OF CLIMATEand eastward shift of the North Paciﬁc trough and associatedprecipitation signal off the western United States (Fig. 2d).
These changes indicate that early in the record, the regional
western U.S. response to events associated with TPac_TS_SEOF2
is largely insigniﬁcant (Fig. 2c), consistent with the ﬁndingsof previous studies (Ashok et al. 2007; He et al. 2021).
However, by the end of the twenty-ﬁrst century, these
events are intensiﬁed, leading to a stronger and statisticallysigniﬁcant hydroclimate response in the western United States,
particularly over California (Fig. 2d). Note that while we focus
on describing the changes associated with the positive phase ofeach mode, the SEOFs presented here are symmetric by construction and therefore describe variability of both polarities.
As such the negative phase impacts are also implicit in our
descriptions. For example, considering TPac_TS_SEOF1(EP ENSO), while the positive phase (El Niño) shows increasing precipitation over the central/east Paciﬁc, westernand southeastern North America, and western Europe, thenegative phase (La Niña) will have equal and opposite impacts. The implicit symmetry in our study is a consequenceof the linear regression used in representing the SEOFs andthus does not capture any asymmetries known to exist for
ENSO (e.g., Hoerling et al. 1997; Frauen and Dommenget
2010; Takahashi et al. 2011; Dommenget et al. 2013). For an
accounting of nonlinear asymmetric impacts associated with
ENSO, see Figs. S14–S17 as well as Patricola et al. (2020).
Overall, Fig. 2 indicates that ENSO-related tropical precipitation variability in the CESM1-LE increases substantially ina future warmer climate, along with associated atmosphericteleconnections and hydroclimate impacts across much of theNorthern Hemisphere. The similarity between the changes
depicted in Figs. 1 and 2 suggest that, ENSO forcing will impart a more pronounced signature on the extratropical circulation modes and exert a greater inﬂuence on global weather
and climate toward the end of the twenty-ﬁrst century. This ismost prominently seen in NAtl_Z500_SEOF1 (NAO) whichdisplays end-of-century precipitation and atmospheric anomaly patterns that are nearly identical to those associated with
TPac_TS_SEOF1 for the same time period (cf. Figs. 1h and 2b).
This is a particularly signiﬁcant change as it suggests that regions
like western Europe, which historically have seen minimal impacts from ENSO events, in particular the positive El Niñophase, will increasingly be subject to atmospheric perturbations
arising from east Paciﬁc SST variability via a novel transatlantic
teleconnection. This future ampliﬁed teleconnection from thetropical Paciﬁc to the North Atlantic has been recently documented in CMIP5 models (Müller and Roeckner 2008; Fereday
et al. 2020), and may be related to the eastward shift of El Niñoevents in a warmer climate (Williams and Patricola 2018; Zhang
et al. 2019). Together this suggests a greater ENSO–NAO teleconnection in the future with potential El Niño impacts to western Europe that are without historical precedent (see also
Drouard and Cassou 2019).
Figure 3 shows the fractions of variance explained as wellas the total domain variance (red curve; right vertical axis) byeach of the SEOF modes as a function of time. Total domain
variance increases substantially in the TPac and NPac domains while only rising modestly in the NAtl domain. The

VOLUME 36

increase in total domain variance is mostly reﬂected in thefractional variance explained by the leading mode in each domain. That is, it is only the leading mode in each domain thatshows an increase in their fractional variance explained, which
implies that these modes have and will continue to dominate
the contribution to overall atmospheric/oceanic variability intheir respective regions of inﬂuence. For the lower-ordermodes fractional variance explained across time remains relatively constant. However, because total variance increases ineach domain, each individual mode accounts for more total
variance across time. It is interesting to note the apparent
leveling off in the total variance in the TPac domain. This
could indicate a point of saturation in the region that may berelated to an asymmetric background warming which reduces
zonal and meridional temperature gradients, which are criticalfor ENSO-related processes (Lindzen and Nigam 1987; Back andBretherton 2009). As a companion to Fig. 3, Fig. S4 shows the associated North Test errors (North et al. 1982) for each of the individual SEOF modes to establish their separability across time.
To explicitly evaluate the time-varying changes in tropical–
extratropical teleconnections, we calculate the ensemble correlations of the extratropical SPCs against the two tropical SPCs.
Figure 4a shows the ensemble correlations of the extratropical
Z500 modes with TPac_TS_SEOF1 (EP El Niño), while Fig. 4bshows those with TPac_TS_SEOF2 (El Niño Modoki). Reﬂecting what can be visually inferred from Figs. 1 and 2, tropical–
extratropical correlations between ENSO and the extratropical
Z500 modes increase in a future warmer climate (Fig. 4). In
the 1920s, TPac_TS_SEOF1 is most highly correlated with
NPac_Z500_SEOF1 (PNA) (r values ; 0.47 on average) which
implies TPac_TS_SEOF1 explains approximately 25% of
the variability in NPac_Z500_SEOF1; correlations between
TPac_TS_SEOF1 and the other Z500 modes are muchsmaller (r , 0.3). By the end of the twenty-ﬁrst century,
TPac_TS_SEOF1 correlations with NPac_Z500_SEOF1 aremodestly higher (r ; 0.57), but those with the other Z500
modes change more substantially. For example, Fig. 4a
indicates that by the end of the twenty-ﬁrst century,
NPac_Z500_SEOF3 will become just as important (if not
more so) to the North Paciﬁc El Niño teleconnection as the
PNA. What this suggests is that future strong El Niño events
will preferentially excite not only the PNA mode, which is associated with enhanced extratropical cyclone activity (Hoerling
and Ting 1994), but also NPac_Z500_SEOF3 (EPP), increasing the probability for enhanced subtropical moisturetransport and AR activity.
It is interesting to note that the correlations between
TPac_TS_SEOF1 and NPac_Z500_SEOF3 are highly variable
during the twentieth century, ranging from ,0 to about 0.5
depending on the year, unlike those with NPac_Z500_SEOF1,
which remain consistently strong throughout (r ; 0.5; Fig. 4a).
This indicates that over the historical period, there is substantial uncertainty as to whether tropical forcing excites both the
PNA and EPP teleconnections, or only the PNA. Interestingly, the large variability in the model’s historical ENSO–EPP
connection mostly disappears by 2050/60, after which NPac_
Z500_SEOF3 becomes a quasi-permanent feature of the model’s ENSO teleconnection and is excited with equal probability

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FIG. 3. Total, fractional, and cumulative ﬁeld variance for the SEOFs over their respective domains. (top) TPac_TS: total variance (red), fractional variance for SEOF1 (blue)
and SEOF2 (orange), and their sum (cyan). (middle) NPac_Z500: total variance (red), fractionalvariance for SEOF1 (blue), SEOF2 (orange), and SEOF3 (green), and their sum (cyan). (bottom)
NAtl_Z500: total variance (red) and fractional variance for SEOF1 (blue).

as NPac_Z500_SEOF1. This behavior suggests that the excitation of both the PNA and EPP by strong east Paciﬁc El Niñoevents may become a regular occurrence toward the end of
the twenty-ﬁrst century. Similarly, the TPac_TS_SEOF1 correlations with NAtl_Z500_SEOF1 become as strong as thosewith the PNA and the EPP by the middle of the twenty-ﬁrst
century, suggesting the potential for damaging El Niño related
ﬂooding in western Europe, similar to that which has occurredin the western United States (Corringham and Cayan 2019).
Figure 4b shows the correlations between the extratropical
Z500 modes and TPac_TS_SEOF2 (El Niño Modoki). As waspreviously noted, there is a strong and robust relationshipbetween TPac_TS_SEOF2 and NPac_Z500_SEOF2 (NPO)that remains effectively unchanged in a future warmed climate. The other extratropical modes show little, if any, correlation with TPac_TS_SEOF2. In addition, only two of themodes, NPac_Z500_SEOF1 (PNA) and NPac_Z500_SEOF3(EPP), show a statistically signiﬁcant change in correlation
across the simulation period. The relationship with NPac_
Z500_SEOF1 becomes slightly more positive, increasing fromr ; 0.12 to 0.25. Given that the relationship with NPac_
Z500_SEOF1 and western U.S. hydroclimate is to increase
precipitation, the increasing positive correlation with TPac_
TS_SEOF2 would act to enhance precipitation over the region

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FIG. 4. Time-varying correlation coefﬁcients between the SPC of each extratropical Z500 SEOF
mode and the SPC of (a) TPac_TS_SEOF1 and (b) TPac_TS_SEOF2. The best-ﬁt linear trend line
and its associated 95% conﬁdence interval (color shading) are also shown for each curve.

and is likely what explains the positive precipitation anomalies
over the western United States, and in particular California,
seen in Fig. 2d. This issue will be revisited and expanded uponfurther in the following section. For completeness, Fig. S6 shows
the full set of correlation pairs for each mode considered.

b. Separating tropical and extratropical variability
Our results thus far indicate that tropical Paciﬁc variability
is projected to play an increasingly important role in driving
patterns of atmospheric circulation variability over both theNorth Paciﬁc and North Atlantic basins by the end of thetwenty-ﬁrst century. However, it is still unclear how much of
the forced change in extratropical variability is due to tropicalinﬂuences vs. intrinsic midlatitude dynamics. We now aim to
separate the direct response of extratropical circulation variability to anthropogenic forcing from the indirect, tropicallymediated response, referred to as “Pathway 1” and “Pathway
2,” respectively, in the schematic shown in Fig. 5. To accomplish
this, we remove the linear correlations with TPac_TS_SPC1
and TPac_TS_SPC2 from the global Z500 ﬁeld at each time
step, and then apply SEOF analysis to the tropical-residual

Z500 ﬁeld using the same NPac and NAtl domains as before. The resulting tropical-residual Z500 SEOFs (hereafter,
TRZ500 SEOFs) are largely devoid of tropical precipitation
variability in both the early and late periods, as expected(Fig. 6). Nonetheless, they exhibit robust forced changes intheir patterns and amplitudes, albeit less dramatic thanthose of the full Z500 SEOFs shown in Fig. 1. For example,
NPac_TRZ500_SEOF1 shows an ampliﬁcation of the maximum depth of the North Paciﬁc trough by about 31% between the two periods (Figs. 6a,b; see Fig. S7 for signiﬁcanceof the Z500 ﬁelds). Comparing this to the deepening that occurs for NPac_Z500_SEOF1 (;44%: Figs. 1a,b) indicates thatthe direct effect of external forcing is substantial relative to
tropically mediated forcing (Pathway 2). On the other hand,
NPac_TRZ500_SEOF2 shows very little change in its circulation or precipitation patterns between the early and lateperiods, indicating little sensitivity to the radiative effects
associated with Pathway 1. However, when comparing the
spatial characteristics of this mode with what is shown in
Figs. 1c and 1d, it is evident that there are signiﬁcant differences in its expression. This suggests that NPac_Z500_

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FIG. 5. Schematic diagram of the direct and indirect inﬂuences of external (i.e., anthropogenic)
forcing on internal extratropical circulation variability. Pathway 1 represents the direct inﬂuence
on intrinsic midlatitude dynamics. Pathway 2 depicts the indirect (tropically mediated) inﬂuence
whereby external forcing acts directly on the tropics, which then drives teleconnections to theextratropics.

SEOF2 (NPO) depends strongly on tropical ocean variability for its existence.
NPac_TRZ500_SEOF3, which is associated with subtropical moisture transport, shows a modest increase in amplitude

and a northeastward shift in the future, independent of tropical ENSO inﬂuence (Figs. 6e,f). In a box encompassing
California, Oregon, and Washington, where the majority ofthe precipitation associated with NPac_TRZ500_SEOF3 isFIG. 6. As in Fig. 1 after removing the linear inﬂuence of the two leading SEOFs of tropical Paciﬁc SST variability.

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FIG. 7. Temporal linear regressions across the set of 176 time representations (years 1923–2098) of each SEOF mode (rows) based on(left) full ﬁelds and (center) tropical-residual ﬁelds. (right) The difference between the left and center columns. Precipitation is shown incolor shading [mm day21 ha21, where ha 5 hectoannum (or century)], and Z500 is contoured (contour interval 5 4 m ha21; negativevalues dashed). Stippling indicates statistically signiﬁcant values, determined when at least 50% of the years SEOF time slices have both
statistically signiﬁcant (p # 0.1) correlations across both the ensemble and time dimensions. Stippling in the difference ﬁeld represents signiﬁcance at p # 0.1 signiﬁcance level according to a two-tailed t test.

focused, there is a ;40% increase in winter rainfall variability
from the early to the late period. This suggests that in the future, even in years without strong ENSO activity, there exists
a greater likelihood that this circulation mode could produce
extremely wet or dry winters characterized by anomalous AR
activity capable of rapidly exacerbating or ameliorating antecedent drought conditions (Dettinger 2013). Indeed, the western United States just experienced a poignant example of thistype of atmospheric variability in the winter of 2017, when despite ENSO neutral conditions prevailing, California received
record breaking winter precipitation driven by an anomalous
number of ARs (Vahedifard et al. 2017; Vano et al. 2019;
Patricola et al. 2020).
Future changes in NAtl_TRZ500_SEOF1 are relatively
modest over the Atlantic sector, with a slight diminution in
amplitude and eastward shift of its main centers of action,bringing enhanced precipitation variability to the eastern
Mediterranean and western Scandinavia (Figs. 6g,h). Largerchanges in this mode are evident over the North Paciﬁc where

they are reminiscent of the PNA, suggesting that external
forcing will serve to better link the North Paciﬁc and North
Atlantic basins in a future warmer climate, independent offorced changes in ENSO, consistent with the ﬁndings ofDrouard and Cassou (2019). However, most of the forcedchanges in NAtl_Z500_SEOF1 stem from resonant excitation
by ENSO, as can be seen by comparing Figs. 1h and 6h.
Since the SEOF methodology yields a representation of unforced variability at each time step, we can concisely display
the projected temporal changes in each unforced mode by linearly regressing the SEOFs across the time dimension(1923–2098). Figure 7 compares the temporal linear regression maps for the total (left column) and TPac Residual (center column) versions of the Z500 SEOF modes; analogous
temporal regression maps for the two TPac_TS_SEOF modes
based on total ﬁelds are shown in Figs. 7a and 7b. The rightcolumn of Fig. 7 shows the difference between the maps inthe left and middle columns. These difference maps depict
how forced trends in internal modes of TPac SST variability

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27

FIG. 8. DJF regression maps of precipitation (mm day21; color shading) and Z500 (contour interval 5 10 m; negative values shown asdashed contours) for (a)–(c),(e)–(g),(i)–(k) the three leading SEOFs of North Paciﬁc Z500 variability and (d),(h),(l) the leading SEOF ofNorth Atlantic Z500 variability. Results based on the (left) CESM1-LE full and (center) tropical Paciﬁc residual ﬁelds during 1923–33 areshown. (right) Analogous results based on the 2600-yr preindustrial atmosphere control simulation, formed by compositing 10 randomly
sampled EOF calculations computed from 200-yr segments for consistency with the 10-yr averaging used for the SEOFs. See text for details. Stippling indicates that at least 7 out of 10 years had statistically signiﬁcant regressions at the p # 0.01 signiﬁcance level. Inset numbers show the pattern correlation and RMSE value (top and bottom numbers, respectively) of the precipitation regression ﬁeld in the
SEOF mode vs the corresponding EOF mode from the atmosphere-only control simulation in the right column.

modify forced trends in internal modes of Z500 variability. It isclear by comparing the columns of Fig. 7 that the relative contributions of direct external forcing (Pathway 1; center column)
and tropically mediated forcing (Pathway 2; right column) depends on which SEOF mode is considered. NPac_Z500_SEOF1
shows roughly equal contributions from the two pathways, withsome differences in their spatial patterns (cf. Figs. 7i,o). The
tropically induced component (Fig. 7o) displays a strong spatial
resemblance to the teleconnections associated with TPac_TS_
SEOF1 (Fig. 7a), as expected based on the results shown in
Fig. 4. Trends in NPac_Z500_SEOF2 are mainly due to thetropically mediated pathway over the North Paciﬁc and toboth pathways over the North Atlantic (Figs. 7d,j,p).
Interestingly, the modes that show the largest temporal trends
are NPac_Z500_SEOF3 and NAtl_Z500_SEOF1 (Figs. 7e,f).
Not coincidentally, these modes also exhibit the largest projected increases in linkages to TPac_TS_SEOF1 (recall Fig. 4a).
While the tropically mediated pathway exerts a strong inﬂuenceon the trends in these modes (Figs. 7q,r), intrinsic extratropicalatmospheric dynamics also makes a substantial contribution(Figs. 7k,l). For example, the tropical-residual version of NPac_
Z500_SEOF3 shows a prominent ampliﬁcation of Z500 variability centered over British Columbia (maximum amplitudes
around 14 m per century; Fig. 7k), while the tropically mediatedcomponent of this mode exhibits similar magnitude trends of

opposite sign between Alaska and the western United States(Fig. 7q). It is also noteworthy that tropical inﬂuences explain
approximately two-thirds of the NAO-like trends in this mode
(cf. Figs. 7e,q).
As mentioned earlier, trends in Z500 variability associatedwith NAtl_Z500_SEOF1 are largest over the North Paciﬁcand North America, areas outside the domain used to deﬁne
the mode (Fig. 7f). This is due to the two pathways having opposing inﬂuences over the North Atlantic sector and constructive inﬂuences over the North Paciﬁc region (Figs. 7l,r). The
tropical Paciﬁc inﬂuence on the trends in NAtl_Z500_SEOF1(Fig. 7r) bears a strong resemblance to the trends associatedwith TPac_TS_SEOF1 (Fig. 7a), consistent with Fig. 4a. In
summary, forced trends in all four modes of extratropical circulation variability and their associated impacts on precipitation are due to a combination of intrinsic atmospheric dynamics
and tropically driven teleconnections. Forced changes in thepreferred patterns of circulation variability driven by intrinsic
atmospheric dynamics are likely to arise from forced changes in
the circulation mean state, as demonstrated by Branstator and
Selten (2009).
To verify our methodology for isolating intrinsic extratropicalatmospheric dynamics, we leverage the 2600-yr atmosphereonly preindustrial control (atm-pictl) simulation described in
section 2a. Figure 8 compares the full (left column) and TPac-

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FIG. 9. Comparison of forced changes in the mean state vs forced changes in internal variability. (a) Ensemble-mean
future (2085–100) minus past (1921–35) precipitation (color shading; mm day21) and Z500 (contour interval 5 10 m,
negative contours dashed). (b)–(e) Z500 SEOF modes averaged over the period 2088–98 (repeated from the right column of Fig. 1); color scale and contour interval are the same as in (a). (f) Ensemble-mean linear trends (1921–2100) of
precipitation (color shading; mm day21 century21) and Z500 (contour interval 5 5 m century21; negative contours
dashed). (g)–(j) Linear trends (1923–2098) of each Z500 SEOF mode (repeated from Figs. 7c–f); color scale and contour interval are the same as in (f).

residual (center column) Z500 SEOF modes during the ﬁrst
10 years of the simulation with the atm-pictl EOFs (right column). Recall that the atm-pictl simulation has no interannual
SST variability and therefore no ENSO of any kind. The TPac
residual regression maps strongly mimic the atm-only results, indicating the residual regression methodology is effectively controlling for the effects of tropical SST variability on extratropicalcirculation and hydroclimate. In particular, the precipitation pattern correlations are considerably higher (and the spatial rootmean-squared errors (RMSE) are substantially lower) between
the atm-pictl EOFs and TPac-residual SEOFs than they are between atm-pictl and the full Z500 SEOFs (see numbers in thepanel insets to Figs. 8a–h), providing quantitative evidence forthe efﬁcacy of our methodology. For example, the precipitation
pattern correlation for NPac_Z500_SEOF3 is 0.62 between the
atm-pictl and the TPac-residual SEOF, and only 0.31 between
the atm-pictl and the full SEOF, while the corresponding RMSE
values are 0.20 and 0.11, respectively. These results are robust to
sampling uncertainty as shown Fig. S8.

c. Comparing forced changes in internal variability versus
forced changes in the mean state
In this section, we brieﬂy compare forced changes in the internal modes of atmospheric circulation variability withforced changes in the mean state. Figure 9a shows the differences in ensemble-mean Z500 and precipitation between the

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periods 2085–100 minus 1921–35, indicative of forced changes
in the mean state. Similar to other CMIP5 models, theCESM1-LE projects enhanced precipitation in the tropics andmiddle-to-high latitudes, and reduced precipitation in the subtropics, accompanied by decreased geopotential heights overthe northern North Paciﬁc and Atlantic, and increased heights
at lower latitudes. These mean state circulation changes are
similar in magnitude to the circulation anomalies associatedwith a one standard deviation departure of the internal modesof Z500 variability in the late twenty-ﬁrst century (Figs. 9b–e).
This indicates that the forced changes in mean circulation caneasily be obscured by internal variability, consistent with previous studies (e.g., Deser et al. 2012a; Shepherd 2014; Deseret al. 2017a).
Next, we compare forced trends in the mean state withforced trends in the internal modes of variability over the period 1920–2100. It is evident that forced trends in the internal
Z500 SEOF modes (Figs. 9g–j) are nonnegligible compared toforced trends in the Z500 mean state (Fig. 9f), especially for
NPac_Z500_SEOF3 and Natl_Z500_SEOF1. Two conclusions
regarding projected changes in extratropical circulation in thelate twenty-ﬁrst century can be drawn from these comparisons: 1) internal variability can obscure the mean state response to anthropogenic forcing; and 2) forced changes in themean state are commensurate with forced changes in the leading modes of internal variability.

4. Summary and discussion
In this study, we have examined forced changes to unforced
modes of extratropical atmospheric circulation variability over
the North Paciﬁc during winter as simulated by the 40-member
CESM1 large ensemble over the period 1920–2100 using anovel “snapshot-EOF” methodology. This approach allows us
to directly and succinctly quantify how anthropogenic emissions alter the spatial patterns and amplitudes of the leading
modes of internal circulation variability and their associatedimpacts on precipitation over the course of the 20th and 21st
centuries. We ﬁnd that the internal modes of variability intensify and expand their spatial footprint in response to futureclimate change, with enhanced linkages to the tropics andthe North Atlantic. Interestingly, it is North Paciﬁc mode 3,
which depicts the present-day “east Paciﬁc pattern,” that
displays the largest forced changes, rather than modes 1 and
2 (“Paciﬁc–North American” and “North Paciﬁc Oscillation” patterns, respectively). Like many other models,
CESM1 simulates future intensiﬁcation of the two leading
modes of tropical Paciﬁc SST variability (canonical east
Paciﬁc and central Paciﬁc Modoki ENSO) and an eastward
shift in their associated tropical Paciﬁc precipitation anomalies (Cai et al. 2014, 2020, 2021). These changes in ENSO activity contribute to the forced changes in the internal modesof extratropical circulation variability via tropically induced
teleconnections. However, intrinsic midlatitude atmospheric
dynamics independent of tropical inﬂuences also plays animportant role. Finally, forced changes in amplitude of theleading internal modes of extratropical circulation variability are similar to those of the mean state, highlighting the29

need for an integrated approach to the study of anthropogenic climate change.
The preponderance of model evidence to date indicatesthat ENSO-related SST variability will increase in a future
warmer climate (Cai et al. 2018; 2020, 2021) and display
greater longitudinal variation in the location of associatedtropical deep convection (Williams and Patricola 2018; Zhang
et al. 2019), although mean state biases in thermal stratiﬁcation may compromise the models’ projected changes(Kohyama et al. 2018). For an expanded view of other ENSO
indices (e.g., Niño-3.4, etc.) and metrics relating to this andother models/LEs the following references provide comprehensive summaries: Cai et al. (2018, 2020, 2021); Phillips et al.
(2020); Dieppois et al. (2021); and Planton et al. (2021). While
there are important exceptions to the projected increase in
ENSO variability (e.g., Wengel et al. 2021), our results suggestthat not only might the canonical ENSO teleconnections (e.g.,
the PNA) strengthen in the future, but presently variable/weakteleconnections may increase far more substantially. Indeed,
the relationship between ENSO and NPac_Z500_SEOF3,
which shows high interannual variability across the historicalperiod, displays a consistently high correlation, on par with oreven greater than that of the PNA by the end of the twenty-ﬁrst
century. This suggests that in the future NPac_Z500_SEOF3
will perhaps become an even more important component of the
ENSO teleconnection than the PNA (Fig. 4a). Such historicalintermittent excitation of NPac_Z500_SEOF3 by ENSO may
be relevant for explaining the observed greatly ampliﬁed western U.S. hydroclimate response to the 1983 and 1998 extreme
east Paciﬁc El Niño events in observations.
Figures 1 and 6 show that NPac_Z500_SEOF1 (PNA) is associated with moisture transport to the western United Statesfrom the North Paciﬁc, most likely in the form of extratropical
cyclones (ETCs), while NPac_Z500_SEOF3 is associated withsubtropical moisture transport, which is indicative of Pineapple
Express–type AR activity. Given that these two modes are orthogonal, they represent independent moisture transport pathways that may be excited either independently or jointly. Thus,
for example, considering the positive ENSO phase, the highvariability shown in the historical period in the relationship between TPac_TS_SEOF1 and NPac_Z500_SEOF3 (Fig. 4a) suggests that some historical El Niño events may have beencharacterized by both of these moisture transport pathways,
while others were not. Events such as these would likely haveshown a high propensity of both ETC and AR activity over
the winter period, and correspondingly, anomalously highrainfall, which could have led to severe and damaging ﬂooding,particularly for the western United States.
Figure 4 further shows that in the future, the high interannual variability associated with NPac_Z500_SEOF3 during
the historical period largely disappears toward the end of the
century and that it is excited with an equally high likelihood
as the PNA, suggesting a higher probability for damaging
ENSO events in the future according to the CESM1-LE. El
Niño events such as these would likely be more harmful thanbeneﬁcial and pose serious risks to water control infrastructureand ecosystems (Corringham and Cayan 2019; O’Brien et al.
2019; Persad et al. 2020), especially in environments that have

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been previously impacted by wildﬁres (Cannon and DeGraff
2009), which is an ever-growing problem in the western UnitedStates (Westerling 2016; Williams et al. 2019). Similarly, in
California, La Niña events would likely be characterized bydeeper and more persistent drought conditions. Taken together,
our results point to a future characterized by larger interannual
precipitation variability, consistent with previous ﬁndings(Pendergrass et al. 2017; Swain et al. 2018). In addition, Fig. 4aalso suggests that in both the historical and future climates, there
is still substantial variability in both the PNA and EPP that is not
explained by ENSO, and that it is not guaranteed that ENSO
will excite either of those modes, either individually or simultaneously, in future extreme El Niño or La Niña events. Thus, theresults from studying this LE imply that there will still be a relatively high probability for failed western U.S. hydroclimate responses to ENSO events, such as what happened in 2015/16(Patricola et al. 2020).
From a prediction viewpoint, this study has several implications. First, due to the increasingly disproportionate inﬂuence
ENSO will exert on global hydroclimate in the future, there
may be more ability to forecast weather related impacts stemming from ENSO events (Figs. 2a,b). Given the greater andmore consistent correlations between TPac_TS_SEOF1 andthe extratropical Z500 SEOFs (except the NPO) in the future(Fig. 4a), this effectively translates into a more consistent hydroclimate response, especially when quantifying ENSO with
an appropriate metric (Williams and Patricola 2018; Patricola
et al. 2020; Nigam and Sengupta 2021). On the other hand,when controlling for variability originating in the tropics, only
NPac_Z500_SEOF1 and NPac_Z500_SEOF3 show large androbust patterns of change (recall Figs. 6a,b,e,f, respectively).
This indicates that these internal modes of variability amplify ina warming climate, potentially making seasonal-to-subseasonal
prediction more challenging. For example, taken together, theexternally forced changes in these two intrinsic atmosphericmodes suggest that unpredictably wet (e.g., California winter2017; Patricola et al. 2020) or dry (e.g., California winter2013/14; Swain et al. 2014) winters will be more common in thewestern United States in a future warmer climate (Fig. 6). The
increasingly unpredictable nature of wet/dry winters perhaps
may be even more challenging from a water management perspective than an extreme El Niño/La Niña, since in the case of
the latter, we can forecast and monitor ENSO development,
and thus prepare. However, in the case of winters like 2014/17,
they largely catch us by surprise, presenting immediate
mitigation challenges that stress ecosystems and unprepared infrastructure to the point of failure (Vano et al.
2019; Persad et al. 2020). It is beyond the scope of thisstudy to assess whether the unforced excitation of the NPac_
Z500_SEOF1/3 modes were driving factors in the California
winter of 2017; however, it is becoming increasingly clearthat unforced variability plays an important and often disproportionate role in driving western U.S. precipitation(Dong et al. 2018; McKinnon and Deser 2021; Risser et al.
2021).
The results we have presented here implicitly assume thateach of the modes of variability and their associated precipitation impacts are symmetric with respect to sign: That is, their

VOLUME 36

patterns and amplitudes are equal and opposite between their
negative and positive phases. Here, we shall brieﬂy discuss
the degree to which this assumption holds for the extratropical Z500 modes and the two tropical Paciﬁc SST modes as
documented in Figs. S9–S17. Overall, we ﬁnd that this assumption holds reasonably well for the extratropical Z500
modes, especially in the early part of the twentieth century
and to varying degrees in the latter part of the twenty-ﬁrst
century; however, it does not hold for the tropical Paciﬁc SST
modes.
The distributions of the extratropical Z500 SPCs based onkernel density estimates (O’Brien et al. 2016) show a high degree of symmetry in both the ﬁrst 30 years and last 30 years of
the 1921–2100 analysis period, while those for the TPac SST
SPCs are asymmetric, consistent with previous studies (e.g.,
Dommenget et al. 2013; Fig. S9). Composite maps of theZ500, precipitation and temperature ﬁelds based on the upper(.90th percentile) and lower (,10th percentile) tails of the
SPC distributions for each mode are shown in Figs. S10–S17
for both the early 30-yr period and the late 30-yr period. The
sum of these composite maps, indicative of the degree ofstructural asymmetry between the positive and negative
phases, is also shown for each mode and time period. As canbe seen from Figs. S10 and S11, the asymmetric (nonlinear)component in the early period is generally small comparedto the symmetric (linear) component of each Z500 mode
(cf. magnitudes in the right column with those in the left
and middle columns), although some localized differences
are apparent. For example, the asymmetric component of
NPac_Z500_SEOF1 (PNA) shows a cyclonic circulation featurethat is highly reminiscent of the tropical Northern Hemisphere(TNH) pattern (Mo and Livezey 1986). While relatively small in
amplitude, its proximity to the U.S. West Coast and impact on
California precipitation highlights its potential importance tothis region. The asymmetries in the other Z500 SEOFs are lesspronounced in terms of their terrestrial precipitation impacts.
More pronounced asymmetries are evident in the late period,
although these nonlinearities are still generally weaker than thelinear component of the extratropical Z500 modes (Figs. S12
and S13). These asymmetries in the Z500 modes are generallyaccompanied by asymmetries in tropical Paciﬁc precipitation,suggestive of a tropical origin for the extratropical circulation
nonlinearities. Further analysis is needed to assess the statisticalsigniﬁcance of these asymmetries, and to probe their underlying
dynamical mechanisms.
The two TPac SST modes show clear evidence of asymmetry in both time periods, as expected based on previous studies(Takahashi et al. 2011; Dommenget et al. 2013; Figs. S14–S17).
A noteworthy feature is that the asymmetric component of
TPac_TS_SEOF1 (Figs. S14c–S17c) bears a strong resemblance
to the negative phase of TPac_TS_SEOF2 (Figs. S14e–S17e) inboth time periods. Indeed, the nonlinear relationship between
the two tropical modes shown in Fig. S3 indicates that it isvirtually impossible to have a strong El Niño or La Niñawithout a similarly strong contribution from the negative
phase of TPac_TS_SEOF2. On the other hand, TPac_TS_
SEOF2 can express itself in either its positive or negative
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becomes less likely in the late period evidenced by the narrowing of the distribution where the TPac_TS_SEOF1
SPCs are near zero (Fig. S3). Further analysis is needed toassess how these asymmetries in the two TPac SST modes
contribute to the asymmetries in the extratropical Z500
modes of variability discussed above.
Our results are based on a single model large ensemble,
and thus call into question the extent to which the results aremodel dependent. Applying the snapshot EOF approach toother model LEs such as those in the Multi-Model Large Ensemble Archive (Deser et al. 2020) would provide additional
insight into the robustness of future forced changes in thecharacteristics of internal modes of variability. Decomposing
the forced changes into components related to ENSO versus
intrinsic atmospheric dynamics would shed further light into
the origins of model discrepancies, and possibly provide avenues for exploring possible emergent constraints (e.g., Simpson
et al. 2021). Finally, there is scope for applying the frameworkused in this study to other regions and seasons.
Acknowledgments. We thank the members of NCAR’s
Climate Analysis Section, especially Isla R. Simpson, Adam
S. Phillips, and John T. Fasullo, as well as Mark D. Risser
of LBNL’s EESA, for their helpful insights throughout the
duration of this study. We also thank the editor and threeanonymous reviewers for their insightful and constructive
comments that greatly improved the quality of this manuscript. John. P. O’Brien was supported by the Climate andLarge-Scale Dynamics Program in the Division of Atmospheric and Geospace Sciences at the National Science
Foundation (NSF). This material is based upon work supported by the National Center for Atmospheric Research,which is a major facility sponsored by the NSF under Cooperative Agreement 1852977. The authors acknowledge highperformance computing support from Cheyenne (https://doi.org/10.5065/D6RX99HX) provided by the NCAR’s Computational and Information Systems Laboratory, sponsored by
the National Science Foundation. The authors know of no
conﬂicts of interest with this publication.
Data availability statement. All data used in this study are
openly available from the Earth System Grid (ESG; www.
earthsystemgrid.org). Further information and ongoing experiments related to the CESM1-LE Project can be found online (https://www.cesm.ucar.edu/projects/community-projects/
LENS/).

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