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Effects of stratospheric variability on El Niño teleconnections

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2015 Environ. Res. Lett. 10 124021(http://iopscience.iop.org/1748-9326/10/12/124021)
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Environ. Res. Lett. 10 (2015) 124021

doi:10.1088/1748-9326/10/12/124021

LETTER

OPEN ACCESS

Effects of stratospheric variability on El Niño teleconnections
J H Richter1, C Deser1 and L Sun2

RECEIVED

7 August 2015
REVISED

23 November 2015
ACCEPTED FOR PUBLICATION

25 November 2015
PUBLISHED

17 December 2015

1
2

Climate and Global Dynamics Laboratory, National Center for Atmospheric Research, PO Box 3000, Boulder, CO 80307, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder and NOAA Earth System ResearchLaboratory, Boulder, CO, USA

E-mail: jrichter@ucar.edu
Keywords: El Niño teleconnections, stratospheric variability, sudden stratospheric warmings, quasi-biennial oscillation, El Niño, El Niño
impactSupplementary material for this article is available online

Content from this work
may be used under theterms of the Creative
Commons Attribution 3.0
licence.

Abstract
The effects of the tropical Paciﬁc El Niño Southern Oscillation (ENSO) phenomenon are
Any further distribution ofcommunicated to the rest of the globe via atmospheric teleconnections. Traditionally, ENSO
this work must maintainattribution to theteleconnections have been viewed as tropospheric phenomena, propagating to higher latitudes as
author(s) and the title of
the work, journal citation Rossby waves. Recent studies, however, suggest an inﬂuence of the stratosphere on extra-tropicaland DOI.
ENSO teleconnections. The stratosphere is highly variable: in the tropics, the primary mode ofvariability is the quasi-biennial oscillation (QBO), and in the extra-tropics sudden stratosphericwarmings (SSWs) regularly perturb the mean state. Here, we conduct a 10-member ensemble ofsimulations with a stratosphere-resolving atmospheric general circulation model forced with theobserved evolution of sea surface temperatures during 1952–2001 to examine the effects of the QBO
and SSWs on the zonal-mean circulation and temperature response to El Niño, with a focus on thenorthern extra-tropics during winter. We ﬁnd that SSWs have a larger impact than the QBO on thecomposite El Niño responses. During El Niño winters with SSWs, the polar stratosphere shows
positive temperature anomalies that propagate downward to the surface where they are associatedwith increased sea-level pressure over the Arctic. During El Niño winters without SSWs, thestratosphere and upper troposphere show negative temperature anomalies but these do not reach thesurface. The QBO modulates the El Niño teleconnection primarily in winters without SSWs: thenegative temperature anomalies in the polar stratosphere and upper troposphere are twice as large
during QBO West compared to QBO East years. In addition, El Niño winters that coincide with the
QBO West phase show stronger positive sea-level pressure anomalies over the eastern Atlantic andNorthern Europe than those in the QBO East phase. The results imply that the stratosphere imparts
considerable variability to ENSO teleconnections.

1. Introduction
The tropical El Niño Southern Oscillation (ENSO)
phenomenon provides an important source of seasonal-to-interannual climate predictability worldwide(e.g., Ropelewski and Halpert 1987, Philander 1990,
Tippett and Barnston 2008). Although ENSO is
generated in the tropical Indo-Paciﬁc via coupled airsea interactions, its effects are transmitted to the rest of
the globe via large-scale atmospheric circulation teleconnection patterns excited by tropical atmosphericheating anomalies (e.g.: Horel and Wallace 1981,© 2015 IOP Publishing Ltd

Trenberth et al 1998, Alexander et al 2002, and manyothers). A prominent example in the northern hemisphere (NH) is the intensiﬁcation of the wintertime
Aleutian Low pressure system in response to the warm
phase of ENSO, which brings above normal temperatures and precipitation to parts of western North
America. The response of the Aleutian Low andassociated downstream centers-of-action of the Paciﬁc-North American (PNA) teleconnection pattern(Wallace and Gutzler 1981, Quadrelli and Wallace 2004) to ENSO is caused by tropospheric Rossby
waves forced by anomalous deep convection in theEnviron. Res. Lett. 10 (2015) 124021

tropical Indo-Paciﬁc, with subsequent feedbacks fromtransient-eddy momentum ﬂuxes (e.g.: Trenberth
et al 1998, Held et al 2002, Branstator 2003). Similar
mechanisms underlie ENSO teleconnections in otherareas, including the southern hemisphere (Jin andKirtman 2009).
In addition to ENSO, numerous studies haveshown that the stratospheric polar vortex can inﬂuence the extra-tropical tropospheric circulation, particularly in the Arctic and North Atlantic sectors(Baldwin and Dunkerton 2001, Thompson and Wallace 2001). For example, a weak polar vortex is often
accompanied by stratospheric sudden warming (SSW)
events during which the winds reverse from easterly towesterly and the polar stratosphere warms by tens ofdegrees within a few days (Chartlon and Polvani2007). SSWs may in turn inﬂuence the troposphericcirculation by inducing the negative phase of theNorthern Annular Mode or North Atlantic Oscillation(NAO) for up to 60 days following an event, providing
a source of tropospheric predictability (Baldwin and
Dunkerton 2001, Gerber et al 2009, Sigmondet al 2013, Tripathi et al 2014, Scaife et al 2015). The
precise mechanisms for this linkage are complex andstill not fully understood, but involve stratospheric‘downward control’ and tropospheric eddy momentum feedback (see Kidston et al 2015 and references
therein). The quasi-biennial oscillation (QBO), a prominent zonal wind oscillation in the tropical lower
stratosphere with an average period of 28 months(Baldwin et al 2001), can modulate the strength of thestratospheric polar vortex and thus consequently
affect the tropospheric circulation (Thompson andWallace 2001, Scaife et al 2014). As shown in the earlyobservational studies of Holton and Tan (1980, 1982),
when tropical zonal winds at 50 hPa are westerly (e.g.,west phase of the QBO), the stratospheric polar night
jet is stronger than when the QBO is in its east phase.
An overview of the generation mechanisms, modeling,
and impacts of the QBO is provided in Baldwin
et al (2001).
Because ENSO, SSWs and the QBO independently
affect the extra-tropical tropospheric circulation, El
Niño events that coincide with the occurrence of an
SSW or with a particular phase of the QBO may be
accompanied by a different pattern of circulation
anomalies than El Niño events in which SSWs areabsent or the QBO is in a neutral phase. For example,
Ineson and Scaife (2009) showed that the response to
El Niño is ampliﬁed during winters with SSWs compared to winters without SSWs based on simulations
with the vertically-extended version of the Met Ofﬁce
Hadley Centre climate model, HadGAM1. Based on
close analysis of observations, Ineson and Scaife (2009)
suggested distinct tropospheric and stratosphericpathways by which ENSO can inﬂuence the northern
extra-tropics. According to Butler et al (2014), thestratospheric teleconnection is primarily via SSWs.
Recently, Iza and Calvo (2015) noted that in the2

observational record, the stratospheric and upper tropospheric polar vortex warms and weakens only during El Niño winters with SSWs and cools andstrengthens during El Niño winters without SSWs.
Domeisen et al (2015) found that 500 hPa geopotential
height anomalies for El Niño winters resemble a typical NAO pattern only for winters with SSWs in the
Max Plank Institute-Earth System Model (MPI-ESM)
seasonal prediction system.
The representation of the QBO has been a challenge for many general circulation models (GCMs).
Calvo et al (2009) used the Middle Atmosphere
ECHAM5 model to examine the effect of the QBO on
the 80°N zonal mean temperature and 60°N zonal
wind response to a single strong El Niño event. They
found that the onset of the stratospheric extra-tropicalanomalies associated with El Niño is delayed when the
QBO is in its westerly phase compared to its easterly
phase. Garﬁnkel and Hartman (2010) examined theinﬂuence of the QBO on El Niño teleconnections over
the North Paciﬁc in reanalysis data and simulations
with the Whole Atmosphere Community Climate
Model, a model with a well-resolved stratosphere anda lid near 140 km. They found a stronger El Niño teleconnection during the westerly QBO phase as compared to the easterly phase. They suggested that the
QBO has an inﬂuence on tropospheric wave propagation, and hence on the teleconnection pattern.
In this study, we examine the effects of both the
QBO and SSWs on the zonal-mean NH winter atmospheric circulation response to El Niño events using a
new, higher-top conﬁguration of the Community
Atmosphere Model, version 5 (CAM5). This version of
the model produces an internally-generated QBO as
well as a realistic frequency of occurrence of SSWs.
Our results are based on a 10-member ensemble ofsimulations forced with the observed evolution of sea
surface temperatures during 1952–2001. The large
sample size allows for a robust assessment of the inﬂuence of the QBO and SSWs on the response to El Niño.
Note that this study does not attempt to address causality among ENSO, QBO and SSWs. Rather, its purpose is to highlight how the superposition of these
phenomena in various combinations contributes to awide range of El Niño teleconnections.

2. Model description and methods
We use a new version of CAM5 (Richteret al 2014a, 2014b) that has 46 vertical levels and amodel top at 0.3 hPa instead of the standard conﬁguration (Neale et al 2012) with 30 levels and a model
top at ∼2 hPa (see ﬁgure S1 for details of the verticalgrid). We use a spectral element dynamical core(Dennis et al 2012) with a horizontal resolution of
approximately 100 km. The 46-level version of CAM5,
hereafter referred to as 46LCAM5, includes the Richteret al (2010) parameterization of non-orographic

Environ. Res. Lett. 10 (2015) 124021

gravity waves, similar to that in Richter et al (2014a).
The convective gravity wave efﬁciency was adjusted so
as to produce a realistic QBO period in the lower
stratosphere.
We conducted a 10-member ensemble of simulations with 46LCAM5 for the period 1952–2001, using
the observed evolution of global monthly sea surface
temperatures and sea ice conditions as lower boundary conditions from Hurrell et al (2008) and observed
external radiative forcings (solar, greenhouse gases,
volcanoes, and aerosols). The ﬁrst 6 ensemble members were initialized with the same wind proﬁle in the
QBO neutral phase and small (order 10−14 K) perturbations to the initial temperature proﬁle. The additional four ensemble members start with a tropicalzonal wind proﬁle in the easterly QBO phase (plussmall perturbations to the temperature proﬁle) inorder to change the relationship between the phase of
ENSO and QBO in the various ensemble members.
This experimental design allows us to study El Niño
teleconnections subject to different phases of the
QBO; however, we note that the simulated pairings
between individual El Niño events and QBO phase
may not be as observed.
We deﬁne El Niño events based on years when the
December value of the monthly Niño 3.4 SST anomaly
exceeds 1 standard deviation after smoothing with a
3-point binomial ﬁlter following Deser et al (2010).
With this criterion, 8 El Niño winters are identiﬁed
during our period of analysis: 1957/58, 1965/66,
1972/73, 1982/83, 1986/87, 1991/92, 1994/95, and1997/98.
Following Butler et al (2014), we identify an SSW
when the zonal-mean westerly wind at 10 hPa and 60°
N reverses sign during the months November throughMarch. Zonal-mean winds are required to return towesterly for 20 consecutive days before a new SSW
event can be deﬁned. In our SSW counts, we do notinclude ﬁnal warmings, in which the zonal-mean
zonal wind becomes easterly and does not return towesterly for at least 10 consecutive days before 30
April.
We deﬁne westerly (easterly) QBO winters as thosein which the December-February (DJF) average zonalmean equatorial (2°S–2°N) zonal wind at 30 hPa is>2.5 m s−1 (< −2.5 m s−1). Using a stricter deﬁnitionof the QBO (±5 m s−1 threshold) yields similar results(not shown).
We form composites over all 8 El Niño winters,
and also sub-divide these composites according to thephase of the QBO and occurrence of SSWs. The signiﬁcance of the results is assessed using a student-t
test, using the mean and standard deviation of theevents that make up each composite and comparing
these with the mean and standard deviation of all
years. We compare the model results to zonal-mean
zonal winds and temperatures from the ERA-40 Reanalysis (ERA-40; Uppala et al 2005) and sea level3

pressure (SLP) from the 20th Century Reanalysis version 2 (20CR; Compo et al 2011).

3. SSWs and QBO in CAM5
Realistic simulation of SSWs and the QBO is critical to
studying their inﬂuence on tropospheric climate. The
frequency of occurrence of SSWs based on the NCAR/
NCEP Reanalysis (Kalnay et al 1996), for which reliablestratospheric data are available starting in 1957, isapproximately 0.6 per year (Charlton et al 2007). The
SSW frequency averaged over the 10-member
46LCAM5 ensemble is also 0.6 per year. Anotherimportant aspect of SSWs is their seasonal distribution. The frequency of observed SSWs peaks in January
and February (1.5 per decade; ﬁgure S2). The
mean±one standard deviation of SSW frequency inthe 46LCAM5 ensemble encompasses the observations in each winter month (November–March),
indicating that the model simulates a realistic seasonal
distribution. On average, the simulated SSW frequency is slightly higher in November, December andMarch and slightly smaller in January and Februarycompared to observations (ﬁgure S2).
The internally-generated QBO in 46LCAM5 exhibits an average period of 27 months, very similar to theobserved mean of 28 months (ﬁgure S3). The amplitude of the westerly phase of the QBO in 46LCAM5 isrealistic (15–20 m s−1), but the easterly phase is
weaker than observed (−20 to −28 m s−1 compared to−25 to −35 m s−1 at 20 hPa: ﬁgure S3).

4. El Niño teleconnections
4.1. Mean response and variability
Following previous studies (Manzini et al 2006 andCalvo et al 2009), ﬁgure 1 shows the observed andsimulated El Niño composites of zonal mean anomalies of temperature at 80°N (hereafter T80N) and zonal
wind at 60°N (hereafter U60N) from October throughApril, between 1000 and 1 hPa. Note that observations
are based on 8 events, while the model results are basedon 80 events obtained by combining all 10 simulations.
In observations, El Niño elicits a warming of the polar
vortex that peaks in February with a maximum
amplitude of approximately 10 K (ﬁgure 1(a)). The
polar stratospheric warming is associated with aweakening of the midlatitude jet by up to 17 m s−1(ﬁgure 1(b)). The warming of the polar vortex
descends into the lower stratosphere and tropospherewith time, arriving at the surface in March. The
stratospheric signals are signiﬁcant at the 95% level,
whereas those in the troposphere are not. The El Niño
composite in the 46LCAM5 ensemble based on all 80
events shows similar features as in ERA-40, but withweaker amplitude and stronger connection to thesurface (ﬁgures 1(c) and (d)). In addition, the model
response exhibits higher statistical signiﬁcanceEnviron. Res. Lett. 10 (2015) 124021

Figure 1. El Niño composites of zonal mean anomalies of temperature at 80°N (top) and zonal wind at 60°N (bottom) from Octoberthrough April for ERA-40 (left column) and the 46LCAM5 10-member ensemble (right column). The number of El Niño events
included in each panel’s average is depicted in parenthesis in the title of each panel. Contour interval for temperature anomalies is
1.0 K for ERA-40 and 0.5 K for 46LCAM5. Contour interval for zonal wind anomalies is 2.0 m s−1 for ERA-40 and 1.0 m s−1 for
46LCAM5. The statistical signiﬁcance of the signal based on the student t-test at the 85% and 95% levels are depicted by white and redlines, respectively.

Figure 2. As in ﬁgure 1(c) but for each of the 10 46LCAM5 ensemble members. Contour interval is 1.0 K. Statistical signiﬁcance of thesignal based on the student t-test at the 85% and 95% levels are depicted by the white and red lines, respectively.

(>95%) throughout the stratosphere and tropospherecompared to observations, due mainly to the larger
sample size. In the 46LCAM5 ensemble mean, the4maximum T80N is ∼2.5 K compared to 10 K in ERA40, and the maximum U60N is 4 m s−1 compared to
17 m s−1 in ERA-40.

Environ. Res. Lett. 10 (2015) 124021

An immediate conclusion that might be drawn
from this comparison is that 46LCAM5 underestimates the magnitude of the extra-tropical El Niño
response in the stratosphere, and overestimates thedownward connection to the surface. However, it isimportant to consider the variability of the El Niño
response across the ensemble members, since observations contain the equivalent of one realization of
46LCAM5. Figure 2 shows that there is considerable
diversity in the response across the 10 ensemble members. Some (1, 2, 6, 7, and 9) show clear evidence ofdownward propagation of positive T80N from thestratosphere to the troposphere albeit with differences
in magnitude and timing, while others (3, 5, 10) lackcoherent warming and downward inﬂuence. Similar
results are found for U60N (not shown). These results
suggest that a downward propagating T80N (or
U60N) signal averaged over a sample of 8 events during 1957–2001 is not a universal response to El Niño,
at least according to 46LCAM5. Of all the ensemble
members, number 7 shows the closest match to observations in terms of T80 pattern and amplitude (andU60N; not shown), indicating that the model is capable of producing approximately the ‘correct’ signal;
however the large spread across the ensemble members suggests that this response is not necessarily
expected in any single realization. We speculate thatthe overall weaker-than-observed El Niño response in
46LCAM5 might also be partially caused by the deﬁcient amplitude of the resolved tropical wave spectrum
related to the convection parameterization (Zhang
and McFarlane 1995).
The extra-tropical El Niño response in the Middle
Atmosphere ECHAM5 model shown by Calvo et al(2009) is nearly three times stronger than in
46LCAM5. However, the El Niño response in Calvo
et al (2009) was based on a single, very strong El Niño
event (1997–1998). We ﬁnd a similar amplitude
response in the 46LCAM5 when we restrict our El
Niño composite to the two strongest events (1982–83
and 1997–1998), with a maximum value of T80N
(U60N) in the stratosphere of 8 K (−14 m s−1) in January, very similar to the values reported in Calvo et al(2009). This demonstrates that the strength of the stratospheric response is modulated by the strength of the
El Niño event, and that 46LCAM5 produces an El
Niño response consistent with previous studies.
The 8 El Niño events that make up our composites
are based on a 1 standard deviation threshold of the
Niño 3.4 Index (recall section 2). We have repeated
our analysis using a more lenient criterion (a 0.5 °C
threshold of the Nino3.4 index for 5 consecutive seasons following Butler et al 2014) and ﬁnd generally
similar results, although the timing of the stratospheric T80N anomalies is 1 to 2 months earlier for
weak events compared to strong events (not shown).
This may be due to the fact that the Niño 3.4 SST
anomalies tend to peak earlier during weak events
compared to strong events (October to November
5

versus December to January: not shown), but further
work is needed to establish causality.
4.2. Inﬂuence of SSWs
Here we examine the sensitivity of the composite El
Niño teleconnections to SSW occurrence by dividing
the El Niño winters into those that contain at least one
SSW and those without any SSWs. Of the 8 El Niño
winters sampled in the observational record, 4 wereaccompanied by at least one SSW and 4 had no SSWs.
The observed composite of El Niño winters with SSWs
shows a positive T80N propagating from the stratosphere to the upper troposphere between January andMarch, followed by a similar downward-propagating
negative T80N signal (ﬁgure 3(a)). In contrast, during
winters without SSWs, the warming is conﬁned near
10 hPa during January and February, accompanied bycooling throughout most of the lower stratosphere in
the early part of the winter and in the upper troposphere over the entire winter (ﬁgure 3(b)). Of the 80 El
Niño winters sampled in the 46LCAM5 ensemble, 46
were accompanied by at least one SSW and 34 had no
SSW occurrences. Overall, the 46LCAM5 composites
with and without SSWs are similar to their observedcounterparts (ﬁgure 3). For example, El Niño winterswith SSWs show evidence of downward propagationof T80N from the stratosphere to the tropospherebeginning in January (ﬁgure 3(c)). This downward
propagation extends all the way to the surface in themodel, unlike observations, with maximum andstatistically signiﬁcant warming at 1000 hPa in March(ﬁgure 3(c)). El Niño winters without SSWs in themodel show cooling in the lower stratosphere andupper troposphere in early winter, persisting into late
winter in the upper troposphere, similar to observations (ﬁgure 3(d)). Unlike observations, the simulatedcooling is followed in January by warming near 2 hPathat subsequently descends into the troposphere as
spring progresses; the observed warming maximum at
10 hPa in January is not simulated by the model. The
differences in the composite of El Niño events withand without SSWs are also clear from the U60N
anomalies as shown in ﬁgure S4.
4.3. Inﬂuence of the QBO
Due to the substantial inﬂuence of SSWs on El Niño
teleconnections, we consider the inﬂuence of the QBO
separately for winters with and without SSWs. The
overall structure of T80N during winters with SSWs issimilar during QBOE (12 events; ﬁgure 3(e)) and
QBOW (26 events; ﬁgure 3(g)); however, the timing ofindividual features differs slightly. During QBOE
years, stratospheric T80N starts increasing in December reaching its maximum in the stratosphere near
10 hPa in January, whereas during QBOW years T80N
increases beginning in January and reaches it’s maximum near 10 hPa in February. These differences in
T80N between QBOW and QBOE are signiﬁcant at

Environ. Res. Lett. 10 (2015) 124021

Figure 3. El Niño composites of zonal mean temperature anomalies at 80°N from October through April for ERA40 (ﬁrst column)
and 46LCAM5 simulations (remaining columns). Top panels show composites of winters with SSWs whereas the bottom panels show
winters without SSWs. The ﬁrst two columns show an average over all QBO phases, whereas columns 3 and 4 show QBOE and QBOW
phases, respectively. Statistical signiﬁcance of the signal based on the student t-test at the 85% and 95% levels are depicted by the whiteand red lines, respectively.

the 85% level per the student-t test (ﬁgure S5(a)).
Similar differences in timing are also evident in the
U60N responses between QBOE and QBOW years(ﬁgures S4(e), S4(g)).
The phase of the QBO has a stronger inﬂuence on
the structure of the T80N and U60N responses to El
Niño during winters without SSWs compared to winters with SSWs (ﬁgures 3(f), (h), and S5(b)). During El
Niño winters without SSWs, between November andApril, T80N is weakly negative throughout the lower
stratosphere (up to ∼10 hPa) during QBOE (13events; ﬁgure 3(f)), and is positive above 10 hPa. During QBOW El Niño winters without SSWs (18 events;
ﬁgure 3(h)), T80N is also negative, but much stronger
in magnitude, with a cooling of −5 K in December
near 10 hPa, that descends into the lower stratosphereby February/March. U60N during QBOE is generally
2 m s−1 or less throughout the stratosphere between
October and April (ﬁgure S4(f)), whereas it is between
2 and 8 m s−1 in the stratosphere between November
and March during QBOW (ﬁgure S4(h)). In addition
to the differences in the stratosphere between QBOE
and QBOW years, El Niño winters without SSWs show
a statistically signiﬁcant 1 K T80N signal near the surface between January and March during QBOW years(ﬁgure 3(h)), a feature that is not present during
QBOE years (ﬁgure 3(f)). This warming is associatedwith the weakening of U60N in the mid-troposphereby up to 2 m s−1 in March during QBOW, signiﬁcantat the 95% level (ﬁgure S4(h)).
6

In order to ensure that the differences between
QBOE and QBOW signals shown in ﬁgure 3 areindeed a result of the QBO and not a result of sampling
different El Niño events, we have repeated our analysis
using the same set of El Niño events for both QBO
phases and found similar results (not shown).
Figure 3 highlights the importance of examining
the effects of the QBO in El Niño winters with andwithout SSWs separately. Although Calvo et al (2009)
examined the inﬂuence of the QBO on T80N during
the 1997 to 1998 El Niño event using a large ensembleof simulations, they did not stratify their results
according to the occurrence of SSWs, and hence theirresults cannot be directly compared to ours.
To summarize, our results suggest that the downward propagation of positive T80N (and negative
U60N) anomalies from the stratosphere to the troposphere occur primarily in El Niño winters with SSWs
as opposed to El Niño winters without SSWs with aslight modulation in timing by the QBO (ﬁgure 3).
However, during El Niño winters without SSWs, theQBO has a clear inﬂuence: during QBOW the stratospheric vortex is much cooler and stronger especiallybetween November and January as compared to
QBOE winters.
Several previous studies have investigated interactions between QBO, ENSO and stratospheric conditions. For example, Garﬁnkel and Hartmann (2007)
found that the polar vortex is stronger in QBOW years
compared to QBOE years during La Nina and neutral

Environ. Res. Lett. 10 (2015) 124021

Figure 4. El Niño composites of sea level pressure anomalies for January–March based on 20th Century Reanalysis (ﬁrst column) and
46LCAM5 (remaining columns). Top panels show composites of winters with SSWs whereas the bottom panels show winters without
SSWs. The ﬁrst two columns show an average over all QBO phases, whereas columns 3 and 4 show El Niño QBOE and QBOW phases,
respectively. Number of El Niño events included in the composite is noted in parenthesis next to the panel labels. Contour interval is
1.5 hPa. Statistical signiﬁcance of the signal based on the student t-test at the 85% and 95% levels are depicted by the white and redlines, respectively.

ENSO conditions, but not during El Niño conditions.
This nonlinearity was further explored in Garﬁnkel
and Hartmann (2008), who showed that when El Niño
and the QBOE phase occur together, the teleconnection pattern does not resemble the PNA pattern, andtherefore the upward wave propagation into the stratosphere is not enhanced. In our study we do not
examine the mechanisms behind the interactions of
the QBO and El Niño teleconnections. Possible
mechanisms were proposed by Garﬁnkel et al 2010
and Garﬁnkel and Hartmann (2010, 2011a, b). However, our study strongly suggests that the interactionsof El Niño and QBO are very different for winters withand those without SSWs, and hence should be examined separately.
4.4. Surface Teleconnections
Figure 4 shows the corresponding spatial distributionsof SLP anomalies during January–March (JFM) basedon observations (20th Century Reanalysis) and
46LCAM5. The JFM season was chosen based on when
the zonal-mean T80 and U60 signals reach the surface(recall ﬁgures 1 and 3). In observations, El Niño JFM
winters with SSWs show a signiﬁcant negative NAO
response, with positive SLP anomalies in the Arctic
and negative anomalies over the North Atlantic(maximum amplitudes ∼4.5 hPa in both regions;ﬁgure 4(a)). Surprisingly, there is little signal over the7

Aleutian Low region in the 4-event composite,
although 2 of the individual winters do show asigniﬁcant deepening of the Aleutian Low (notshown). Note that the 4 El Niño events with SSWs
(1957/58, 1965/66, 1972/73, 1986/87) are generally
weaker than those without SSWs (1982/83, 1991/92,
1994/95, 1997/98), which may explain the lack of asigniﬁcant Aleutian Low response in the former
compared to the latter. Farther south, the western
North Paciﬁc shows signiﬁcant positive SLP anomalies, as expected for El Niño events. The observed El
Niño JFM composite for winters without SSWs showsa strong and signiﬁcant deepening of the AleutianLow, and a signiﬁcant positive NAO-like response in
the far eastern Atlantic (ﬁgure 4(b)).
The model also shows differences in the JFM SLP
anomaly pattern between winters with SSWs (46events over the 10 ensemble members) and those
without SSWs (34 events over the 10 ensemble members) that resemble those in nature. During winterswith SSWs, 46LCAM5 shows positive SLP anomalies
in the Arctic (maximum amplitude ∼4 hPa) coupledwith negative anomalies over the North Atlantic andNorth Paciﬁc, all of which are statistically signiﬁcant at
the 95% conﬁdence level (ﬁgure 4(b)). During winters
without SSWs, the model shows a weak response in the
Arctic and Atlantic sectors, but the spatial pattern is
not unlike that found in observations (ﬁgure 4(d)).

Environ. Res. Lett. 10 (2015) 124021

Figure 5. El Niño composites of sea level pressure anomalies for January–March based 20th Century Reanalysis (ﬁrst column) andthree individual ensemble members of 46LCAM5 (remaining columns). Top panels show composites of winters with SSWs whereas
the bottom panels show winters without SSWs. Number of El Niño events included in the average is noted in parenthesis next to thepanel labels. Contour interval is 1.5 hPa. Statistical signiﬁcance of the signal based on the student t-test at the 85% and 95% levels are
depicted by the white and red lines, respectively.

The weakness of the NAO-like pattern in ﬁgures 4(c)and (d) is partly due to the large variability of the
ENSO response in 46LCAM5. Figure 5 compares the
JFM SLP anomaly pattern for observations to three
individual ensemble members of 46LCAM5. These
three particular ensemble members show a NAO-likepattern, with positive SLP anomalies over Europe andEast Atlantic, and negative anomalies over the Arctic
during winters without SSWs, and an opposite patternduring winters with SSWs. The surface ENSO
response in these three selected ensemble members isvery similar to observations suggesting that the model
reproduces the observed ENSO teleconnections; however only half of our ensemble members show this pattern, further underlining the role of sampling
variability.
It is interesting to note that the observed El Niño
composite with SSWs shows a small but signiﬁcantnegative SLP response in the far northwestern subpolar Paciﬁc, unlike the observed El Niño composite
without SSWs, which is dominated by a strong deepening of the Aleutian Low (ﬁgures 5(a) and (b),respectively). This is consistent with Garﬁnkel et al(2012), who found that 500 hPa geopotential heightvariability in the northwestern subpolar Paciﬁc is crucially important for whether El Niño leads to an SSW
or not. The model ensemble members do not consistently show this result, although member number 4
comes closest to the observed SLP patterns in El Niño
8

years with SSWs and those without SSWs (ﬁgures 5(g)and (h), respectively).
The phase of the QBO also inﬂuences the SLP
response. During winters with SSWs these effects are
small and statistically insigniﬁcant as shown in ﬁgure S6(a). Consistent with the stronger tropospheric response
shown in ﬁgure 3(h), the inﬂuence of the QBO is larger
during El Niño winters without SSWs. In particular, thepositive anomaly center west of the UK is signiﬁcantly
stronger in QBOW compared to QBOE years(ﬁgures 4(f) and (h) and ﬁgure S6(b)). The SLP El Niño
response is also strengthened over Northern Eurasia in
QBOW years as compared to QBOE years. It is worth
noting that out of the 4 observed El Niño winters without SSWs, two were in the QBOW phase (with one eachin the QBO neutral and QBOE phases).

5. Discussion and conclusions
We have examined the northern hemisphere extratropical stratospheric and tropospheric response to El
Niño using a 10-member ensemble of simulations forthe period 1952–2001 based on 46LCAM5, a model that
produces an internally-generated QBO and has arealistic frequency and seasonal distribution of SSWs.
Overall, the simulated response to the composite of 80
El Niño events (8 events×10 simulations) resembles
the observed response based on 8 events, with a warmer

Environ. Res. Lett. 10 (2015) 124021

and weaker stratospheric vortex in January that propagates down to the troposphere, arriving at the surface
between February and March. However, individual
ensemble members show considerable diversity in theircomposite El Niño responses. For example, only 6 out
of 10 ensemble members show downward propagationof a warm and weak vortex signal from the stratosphereto the troposphere in response to El Niño, and thetiming and amplitude of this response is highly variable.
Thus, direct comparison of the El Niño response in themodel and observations must account for both theforced signal and internal atmospheric variability (e.g.,sampling variability).
We ﬁnd that the occurrence of SSWs, as comparedto the phase of the QBO, has a more pronounced effecton the simulated extra-tropical El Niño response. For
example, during El Niño winters with SSWs there is aclear downward propagation of positive T80N andnegative U60N from the stratosphere to the lower troposphere between January and March, whereas inwinters without SSWs, the stratosphere is cooler
between November and March and does not appear toinﬂuence the lower troposphere. These ﬁndings are inagreement with the observational study of Butler et al(2014) who attribute the stratospheric pathway of El
Niño teleconnections to SSWs, as well as the modelingstudy of Domeisen et al (2015) who showed that El
Niño teleconnections were different for winters withand without SSWs during the period 1981–2002.
We ﬁnd that the phase of the QBO has a small, but
noticeable, effect during El Niño winters with SSWs.
In particular, T80N reaches a maximum in stratosphere in January in QBOE winters, and in February in
QBOW winters. The descent of the T80N anomaly tothe troposphere is faster in QBOW winters as compared to QBOE winters. Calvo et al (2009) found that
T80N anomalies persisted longer during QBOW as
compared to QBOE, however they only studied the
response to the strong 1997/1998 El Niño event anddid not separate their ensemble members into winterswith and without SSWs, hence the results are notdirectly comparable.
We ﬁnd that the QBO primarily inﬂuences El Niño
teleconnections during winters without SSWs. Speciﬁcally, the stratospheric and upper tropospheric cooling between November and March at 80°N andstrengthening of the stratospheric jet at 60°N is twiceas large during QBOW as compared to QBOE for El
Niño years without SSWs. Previous studies have neverconsidered the combined inﬂuence of SSWs and QBO
on ENSO teleconnections. It is interesting to note that
Domeisen et al (2015) noted differences between themodeled and observed El Niño teleconnections during
winters without SSWs and speculated that this was due
to internal variability and limited observations. However, our study suggests that part of the differences
between their model and observations could be due tothe lack of a well represented QBO in their model.
9

The surface signatures of El Niño teleconnections
are also very different during winters with and without
SSWs. In agreement with modeling results of Ineson
and Scaife (2009), 46LCAM5 simulates a positive SLP
response in JFM over the polar cap during winterswith SSWs, a feature that is not present during winters
without SSWs. Ineson and Scaife (2009) also found astronger Aleutian Low response during winters with
SSWs compared to winters without SSWs. In
46LCAM5, we ﬁnd a similar magnitude of the Aleutian low response in JFM for winters with and without
SSWs; however, the response of the December–
January average is stronger during winters with SSWs
(not shown).
We found that QBOW winters intensify both thenegative SLP anomaly in the North East Paciﬁc as wellas the positive SLP anomaly in the north-eastern
Atlantic. Ineson and Scaife (2009) found a signiﬁcant
SLP anomaly over Northern Europe during winters
without SSWs, and a high SLP anomaly in the eastern
Atlantic; however their study did not consider theeffects of the QBO. In our 10-member ensemble mean
El Niño response, the NAO-like pattern is not apparent; however many of the ensemble members do show
an NAO-like response, highlighting the inﬂuence ofinternal variability and sampling.
In summary, we have demonstrated that both
SSWs and the phase of the QBO may inﬂuence El Niño
teleconnection patterns in 46LCAM5, and that evenwith 8 events, El Niño composites are subject to considerable sampling ﬂuctuations as a result of large
internal variability. In our study we do not separate El
Niño events into different ‘ﬂavors’; however that distinction could further inﬂuence our ﬁndings especiallyduring winters without SSWs as suggested by Iza andCalvo (2015). The combined effects of SSWs and QBO
on the El Niño teleconnections deserve more detailed
studies; however this work suggests that the lack of
explicit representation of these processes in GCMs
may lead to an underestimate of the variability in El
Niño teleconnection patterns.

Acknowledgments
We thank Julio Bacmeister for help with the development of the 46LCAM5 and Isla Simpson and Natalia
Calvo for valuable discussions. We also thank the threeanonymous reviewers for their thoughtful commentsthat have greatly improved this manuscript. The
National Center for Atmospheric Research is sponsored by the National Science Foundation. L Sun isfunded by a grant from the Ofﬁce of Polar Programs at
the National Science Foundation and by the Earth
System Modeling program at the NOAA/Climate
Program Ofﬁce. NCEP Reanalysis data provided by
the NOAA/OAR/ESRL PSD, Boulder, Colorado,
USA, from their Web site at http://esrl.noaa.gov/
psd/.

Environ. Res. Lett. 10 (2015) 124021

References
Alexander M A, Blade I, Newman M, Lanzante J R, Lau N C andScott J D 2002 The atmospheric bridge: the inﬂuence of
ENSO teleconnections on air–sea interaction over the global
oceans J. Clim. 15 2205–31
Baldwin M P et al 2001 The quasi-biennial oscillation Rev. Geophys.
39 179–229
Baldwin M P and Dunkerton T J 2001 Stratospheric harbingers ofanomalous weather regimes Science 294 581–4
Branstator G W 2003 Remote response to tropical heating Bull. Am.
Meteorol. Soc. 84 1006–7
Butler A H, Polvani L M and Deser C 2014 Separating thestratospheric and tropospheric pathways of El Niñ o–Southern Oscillation teleconnections Environ. Res. Lett. 9024014
Calvo N, Giorgetta M A, Garcia-Herrera R and Manzini E 2009
Nonlinearity of the combined warm ENSO and QBO
effects on the northern hemisphere polar vortex in
MAECHAM5 simulations J. Geophys. Res. 114
D13109Charlton A J and Polvani L M 2007 A new look at stratosphericsudden warmings. Part I. Climatology and modeling
benchmarks J. Clim. 20 449–69
Compo G P et al 2011 The twentieth century reanalysis project Q. J.
R. Meteorol. Soc. 137 1–28
Dennis J, Edwards J, Evans K, Guba O, Lauritzen P, Mirin A,
St-Cyr A, Taylor M and Worley P 2012 CAM-SE: a scalable
spectral element dynamical core for the community
atmosphere model Int. J. High Perform. Comput. Appl. 26
74–89
Deser C et al 2012 ENSO and paciﬁc decadal variability in thecommunity climate system model version 4 J. Clim. 25
2622–51
Domeisen D I V et al 2015 Seasonal predictability over Europe
arising from El Nino and stratospheric variability in the MPIESM seasonal prediction system J. Clim. 28 256–71
Garﬁnkel C I, Butler A H, Waugh D W, Hurwitz M M andPolvani L M 2012 Why might stratospheric sudden warmings
occur with similar frequency in El Niño and La Niña winters
J. Geophys. Res. 117 D19106
Garﬁnkel C I and Hartmann D L 2007 Effects of the El Nino
Southern Oscillation and the quasi-biennial oscillation onpolar temperatures in the stratosphere J. Geophys. Res. 112
D19112Garﬁnkel C I and Hartmann D L 2008 Different ENSO
teleconnections and their effects on the stratospheric polar
vortex J. Geophys. Res. 113 D18114
Garﬁnkel C I and Hartmann D L 2010 Inﬂuence of the quasibiennial oscillation on the North Paciﬁc and El Nino
teleconnections J. Geophys. Res. 115 D20116
Garﬁnkel C I and Hartmann D L 2011a The inﬂuence of the quasibiennial oscillation on the troposphere in winter in ahierarchy of models. Part I: simpliﬁed dry GCMs J. Atmos. Sci.
68 1273–89
Garﬁnkel C I and Hartmann D L 2011b The inﬂuence of the quasibiennial oscillation on the troposphere in winter in ahierarchy of models. Part 2: perpetual winter WACCM runs J.
Atmos. Sci. 68 2026–41
Garﬁnkel C I, Hartmann D L and Sassi F 2010 Troposphericprecursors of anomalous Northern Hemisphere stratosphericpolar vortices J. Clim. 23 3282–99
Gerber E P, Orbe C and Polvani L M 2009 Stratospheric inﬂuence on
the tropospheric circulation revealed by idealized ensemble
forecasts Geoph. Res. Lett. 36 L24801
Held I M, Mingfang T and Wang H 2002 Northern winter stationary
waves: theory and modeling J. Clim. 15 2125–44
Holton J R and Tan H 1980 The inﬂuence of the equatorial quasibiennial oscillation on the global circulation at 50 mb
J. Atmos. Sci. 37 2200–8
Holton J R and Tan H-C 1982 The quasi-biennial oscillation in thenorthern hemisphere stratosphere J. Meteorol. Soc. Japan 60
140–8

10

Horel J D and Wallace J M 1981 Planetary-scale atmosphericphenomena associated with the Southern Oscillation Mon.
Weather Rev. 109 813–29
Hurrell J W, Hack J J, Shea D, Caron J M and Rosinski J 2008 A newsea surface temperature and sea ice boundary dataset for thecommunity atmosphere model J. Clim. 21 5145–53
Ineson S and Scaife A A 2009 The role of the stratosphere in theEuropean climate response to El Nino Nat. Geosci. 2 32–6
Iza M and Calvo N 2015 Role of stratospheric sudden warmings on the
response to central paciﬁc El Nino Geophys. Res. Lett. 42 2482–9
Jin D and Kirtman B P 2009 Why the Southern hemisphere ENSO
responses lead ENSO J. Geophys. Res. 114 D23101
Kalnay E et al 1996 The NCEP/NCAR 40-year reanalysis project
Bull. Am. Meteorol. Soc. 77 437–71
Kidston J et al 2015 Stratospheric inﬂuence on tropospheric jet
streams, storm tracks and surface weather Nat. Geosci. 8 433–40
Manzini E et al 2006 He inﬂuence of sea surface temperatures on thenorthern winter stratosphere: ensemble simulations with the
MAECHAM5 model J. Clim. 19 3863–81
Neale R B et al 2012 Description of the NCAR community
atmosphere model (CAM 5.0) NCAR Technical Note NCAR
TN 486 (www.cesm.ucar.edu/models/cesm1.0/cam/docs/
description/cam5_desc.pdf)
Philander S G 1990 El Nino, La Nina and the Southern Oscillation(New York: Academic) p 293
Quadrelli R and Wallace J M 2004 A simpliﬁed linear framework forinterpreting patterns of northern hemisphere wintertime
climate variability J. Clim. 17 3728–44
Richter J H, Bacmeister J T and Solomon A 2014a Effects of verticalresolution and non-orographic gravity wave drag on thesimulated climate in the community atmosphere model,
version 5 J. Adv. Model. Earth Systems 6 357–83
Richter J H, Bacmeister J T and Solomon A 2014b On the simulationof the quasi-biennial oscillation in the community
atmosphere model, version 5 J. Geophys. Res. 119 3045–62
Richter J H, Sassi F and Garcia R R 2010 Towards a physically based
gravity wave source parameterization in a general circulation
model J. Atmos. Sci. 67 136–56
Ropelewski C F and Halpert M S 1987 Global and regional scaleprecipitation patterns associated with the El Nino/Southern
Oscillation Mon. Weather Rev. 115 1606–26
Sassi F, Boville B, Kinnison D and Garcia R R 2005 The effects ofinteractive ozone chemistry on simulations of the middle
atmosphere Geophys. Res. Lett. 32 L07811
Scaife A A et al 2014 Predictability of the quasi-biennial oscillationand its northern winter teleconnection on seasonal to decadal
timescales Geophys. Res. Lett. 41 1752–8
Scaife A A et al 2015 Seasonal winter forecasts and the stratosphere
Atmos. Sci. Lett. (doi:10.1002/asl.598)
Sigmond M, Scinocca J F, Kharin V V and Shepherd T G 2013
Enhanced seasonal forecast skill following stratosphericsudden warmings Nat. Geosci. 6 98–102
Thompson D W J and Wallace J M 2001 Regional climate impacts of
the northern hemisphere annular mode Science 293 85–9
Tippett M K and Barnston A G 2008 Skill of multimodel ENSO
probability forecasts Mon. Weather Rev. 136 3933–46
Trenberth K E, Branstator G W, Karoly D, Kumar A, Lau N C and
Ropelewski C 1998 Progress during TOGA in understanding
and modeling global teleconnections associated with tropicalsea surface temperatures J. Geophys. Res. 103 14291–324
Tripathi O P et al 2014 The predictability of the extra- tropicalstratosphere on monthly timescales and its impacts on theskill of tropospheric forecasts Q. J. R. Meteorol. Soc. 141
987–1003
Uppala S et al 2005 The ERA-40 reanalysis. Q. J. Roy. Meteorol. Soc.
131 2961–3012
Wallace J M and Gutzler D S 1981 Teleconnections in the
Geopotential height ﬁeld during the northern hemisphere
winter Mon. Weather Rev. 109 784–812
Zhang G J and McFarlane N A 1995 Sensitivity of climate
simulations to the parameterization of cumulus convectionin the Canadian climate centre general circulation model
Atmos. Ocean 3 407–46