15 MARCH 2017

POLVANI ET AL.

1959

Distinguishing Stratospheric Sudden Warmings from ENSO as Key Drivers ofWintertime Climate Variability over the North Atlantic and Eurasia
LORENZO M. POLVANIColumbia University, New York, New YorkLANTAO SUN AND AMY H. BUTLER
CIRES/University of Colorado, and NOAA/ESRL, Boulder, Colorado

JADWIGA H. RICHTER AND CLARA DESER
National Center for Atmospheric Research,a Boulder, Colorado(Manuscript received 4 April 2016, in final form 5 October 2016)
ABSTRACT
Stratospheric conditions are increasingly being recognized as an important driver of North Atlantic andEurasian climate variability. Mindful that the observational record is relatively short, and that internal climate
variability can be large, the authors here analyze a new 10-member ensemble of integrations of a stratosphereresolving, atmospheric general circulation model, forced with the observed evolution of sea surface temperature (SST) during 1952–2003. Previous studies are confirmed, showing that El Niño conditions enhance
the frequency of occurrence of stratospheric sudden warmings (SSWs), whereas La Niña conditions do notappear to affect it. However, large differences are noted among ensemble members, suggesting caution wheninterpreting the relatively short observational record. More importantly, it is emphasized that the majority ofSSWs are not caused by anomalous tropical Pacific SSTs. Comparing composites of winters with and without
SSWs in each ENSO phase separately, it is demonstrated that stratospheric variability gives rise to large andstatistically significant anomalies in tropospheric circulation and surface conditions over the North Atlantic
and Eurasia. This indicates that, for those regions, climate variability of stratospheric origin is comparable inmagnitude to variability originating from tropical Pacific SSTs, so that the occurrence of a single SSW in agiven winter is able to completely alter seasonal climate predictions based solely on ENSO conditions. Thesefindings, corroborating other recent studies, highlight the importance of accurately forecasting SSWs for
improved seasonal prediction of North Atlantic and Eurasian climate.

1. Introduction
El Niño–Southern Oscillation (ENSO) is the largest
driver of climate variability on interannual time scales,
and its effects are felt in many regions of the world (see,e.g., Trenberth et al. 1998; Alexander et al. 2002).
However, its influences on the North Atlantic sector and

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/
JCLI-D-16-0277.s1.
a
The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author e-mail: Lorenzo M. Polvani, lmp@
columbia.edu

the Eurasian continent have remained, until recently,
somewhat elusive (Brönnimann 2007; RodríguezFonseca et al. 2016). While a few studies have suggested
the possible existence of tropospheric ENSO teleconnections reaching into the North Atlantic (Toniazzo
and Scaife 2006; Graf and Zanchettin 2012), the notionof a ‘‘stratospheric pathway’’ has gained much attention
in recent years. What is generally meant by that expression is that the stratosphere acts as a bridge, communicating tropical Pacific Ocean variability to thoseremote regions, and representing an alternate ENSO
teleconnection path, distinct from the canonical Pacific–
North America pattern (Horel and Wallace 1981).
It is worth recalling that, on seasonal time scales, theinfluence of ENSO on the Northern Hemisphere polar
stratosphere is now well established. Shifts in tropical

DOI: 10.1175/JCLI-D-16-0277.1
Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright
Policy (www.ametsoc.org/PUBSReuseLicenses).

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JOURNAL OF CLIMATE

convective heating during El Niño and La Niña drive
poleward-propagating planetary-scale Rossby waves.
During El Niño, these anomalous planetary waves tend
to constructively align with the climatological background wave pattern and amplify with height into thestratosphere (Fletcher and Kushner 2011; Smith andKushner 2012; Fletcher and Minokhin 2015). Planetary
waves break in the stratosphere (McIntyre and Palmer
1984), and the increased wave breaking during El Niño
tends to warm the polar stratosphere and weaken thepolar vortex. This has been shown in numerous observational studies (van Loon and Labitzke 1987; Garfinkel
and Hartmann 2007; Free and Seidel 2009). Opposite
but smaller anomalies are observed during La Niña
winters.
Many modeling studies have confirmed these observations (Sassi et al. 2004; García-Herrera et al. 2006;
Manzini et al. 2006; Cagnazzo et al. 2009; Calvo andMarsh 2011, just to cite a few). One needs to keep inmind, however, that these ENSO-induced seasonal
anomalies—even if well observed and robustly modeled—are largely confined to the stratosphere. It is only when
the stratosphere is severely perturbed, notably by theoccurrence of a stratospheric sudden warming (SSW)
event in midwinter, that stratospheric variability iscommunicated to the surface.
In winters with one or more SSWs, anomalous surface
conditions are seen for several weeks following these
extreme events, and the surface signal is strongest over
the North Atlantic sector and Eurasia (Baldwin and
Dunkerton 2001; Polvani and Waugh 2004; Charlton
and Polvani 2007; Hitchcock and Simpson 2014). The
importance of SSWs in bridging tropical Pacific anomalies into the North Atlantic and Eurasia was highlighted in a modeling study by Ineson and Scaife (2009),
who showed that El Niño conditions lead to anomalous
European climate only in winters when SSWs occur.
This result has recently been confirmed in studies using
different models (Domeisen et al. 2015; Richteret al. 2015).
Beyond modeling studies, some observational evidence for the existence of a stratospheric pathway waspresented in Butler et al. (2014, hereafter BPD14). The
fundamental limitation of that study, however, resides in
the length of the observational record: using the entire
period 1958–2013, after partitioning the different ENSO
phases by SSW occurrence, several composites consistedof less than 10 events. One key goal of this paper,
therefore, is to transcend that limitation.
To that end, we here supplement the observations
with a 10-member ensemble of integrations with astratosphere-resolving atmospheric general circulation
model forced by the observed evolution of SSTs during

VOLUME 30

1952–2003, performed and analyzed by Richter et al.
(2015). Rather than focusing on how SSWs alter the El
Niño stratospheric pathway as done in Richter et al.
(2015), we here focus on separating and contrasting thedistinct signatures of ENSO and SSWs. After detailing
our methods in the next section, we first consider theeffect of ENSO on the frequency of SSWs and discuss
the most recent observations in the context of our model
ensemble. Then, leaving that effect aside, we demonstrate that the surface signatures of SSWs on North
Atlantic and Eurasian winter climate are large, so muchso that they clearly stand out irrespective of the ENSO
phase. Finally, we attempt to quantify the relative importance of ENSO and SSWs on North Atlantic andEurasian surface climate and we show that, in fact, they
are of comparable magnitude. This implies that, for agiven winter, the occurrence of a single SSW can be
sufficient to considerably alter a seasonal forecast
predicated uniquely on the phase of ENSO. We conclude with a brief summary and a discussion of thecurrent challenges.

2. Methods
The model integrations analyzed here were performed with a 46-level version of the Community Atmospheric model, version 5 (denoted 46LCAM5), with
an approximate horizontal resolution of 100 km. The
model top is located at 0.3 hPa, so that the model isable to capture stratospheric variability, and notably
SSWs. Furthermore, the gravity wave schemes areadjusted so as to produce a realistic quasi-biennial
oscillation in the lower stratosphere. For further details on the model setup, the reader may consult
Richter et al. (2015).
An ensemble of 10 integrations was performed with
46LCAM5, covering the 51-yr period 1952–2003. These
integrations were forced with observed monthly sea
surface temperatures (SSTs) and sea ice concentrations
from Hurrell et al. (2008), in addition to known naturaland anthropogenic radiative forcings (greenhouse gases,
aerosols, volcanoes, and the solar cycle).
To identify SSWs we follow the simple algorithm ofCharlton and Polvani (2007): between November andMarch, the central date of an event is set as the day when
the zonal mean zonal winds at 10 hPa and 608N become
easterly. To avoid double counting events, once a
warming is identified an interval of 20 consecutive days
with westerly winds must exist before the next central
date can be defined (Charlton-Perez and Polvani 2011).
Also, simple criteria are applied to avoid so-called‘‘final’’ warmings, when stratospheric winds return toeasterlies in the late spring following the seasonal cycle

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POLVANI ET AL.

TABLE 1. The number of observed stratospheric sudden warmings (SSWs), over the period 1958–2013, for the different ENSO phases. The
values earlier reported in Butler et al. (2014) are reproduced in the bottom row.

(BPD14)

Reanalysis

SSTs

All winters

El Niño

La Niña

Neutral

ERA-40/ERA-I
NCEP–NCAR
NCEP–NCARERSSTv4
ERSSTv4
ERSSTv3b

38
35
35

16
16
15

10
10
13

12
9
7

[for details, see Charlton and Polvani (2007)]. In addition, the results from 46LCAM5 are compared to reanalyses: we make use of both NCEP–NCAR (Kalnay
et al. 1996) and ERA-40/ERA-Interim data (Deeet al. 2011).
Finally, to identify the ENSO phases we follow
NOAA’s Climate Prediction Center (CPC) procedure, as
follows: when the time series of 3-month running mean
anomalies in SSTs [we use ERSSTv4 (Huang et al. 2015)]
over the Niño-3.4 region (58N–58S, 1708–1208W) exceeds
either 10.58 or 20.58C for a minimum of five consecutive
overlapping 3-month ‘‘seasons’’ (November–January,
December–February, etc.) an El Niño or La Niña event isdefined. Mindful that ENSO events are often definedusing a more stringent criterion (61 standard deviation of
the Niño 3.4 SSTs), we repeated the analysis presented
below with this higher threshold, but found no qualitative
differences of consequence.

3. Revisiting the ‘‘stratospheric pathway’’
We start by revisiting the observational record, andupdating the findings originally reported in Butler
and Polvani (2011) and BPD14. Using the identical
1958–2013 period, we list in Table 1 the number ofobserved SSWs for each ENSO phase. We have computed these numbers using both NCEP–NCAR and
ERA-40/ERA-Interim, to ensure that the results are
not overly sensitive to the choice of reanalysis. For thesake of completeness, we report the actual dates ofindividual SSWs in online supplemental Tables S1 andS2, for NCEP–NCAR and ERA-40/ERA-Interim respectively; these can be directly compared with Table 1of BPD14.
The key result that emerges from Table 1 is that while
El Niño appears to considerably enhance the occurrenceof SSWs, La Niña does not seem to affect it. This conclusion differs from the one in BPD14, who reported
that only 7 of the 35 SSWs in the record occurred during
ENSO-neutral winters, with nearly double that number
for both El Niño and La Niña. The correction comesfrom the fact that we have here used the updated
ERSSTv4 dataset (Huang et al. 2015). In changing from
ERSSTv3b (used in BPD14) to the updated ERSSTv4,
three weak La Niña winters with SSWs (1983/84, 2005/06,

and 2008/09) were reclassified by the CPC as ENSOneutral, and one ENSO-neutral winter with an SSW(1979/80) was reclassified as an El Niño.
Having revised the observational evidence, whichappears to indicate that La Niña does not alter thefrequency of SSWs, we now examine the 10-member
ensemble of 51-yr-long model integrations. For each
ENSO phase, the ‘‘frequency of SSWs’’ (measured inevents per decade) is listed in Table 2 for each memberof the ensemble, as well as for the ensemble mean.
Using the frequency of SSWs, instead of the absolutecount, allows for direct comparison between the modeland the observations (which cover periods of different
lengths).
The most striking aspect in Table 2 is how large thevariability across the ensemble is. For instance, member 4 shows an overall frequency of 4.1 SSWs per decade, while member 6 shows almost 7 SSWs per
decade, a more than 60% increase. Or consider this:member 5 shows a robust reduction in SSW frequency
in both El Niño and La Niña years compared to ENSOneutral years, while member 8 shows the opposite effect. One key lesson, therefore, to be gathered fromTABLE 2. Frequency of stratospheric sudden warmings (SSWs), in
events per decade, across the different ENSO phases. Each member ofthe 46LCAM5 ensemble is shown, as well as the ensemble mean (boldfont) and standard deviation (italic font). For the reanalyses (bottomrows), we use the period 1958–2013, for comparison with BPD14.
MemberAll winters

El Niño

La Niña

Neutral

1
2
3
4
5
6
7
8
9
10

6.5
4.7
7.5
4.1
5.7
6.7
6.3
6.3
6.3
5.3

7.4
5.8
9.0
5.3
5.8
7.9
7.4
8.4
6.8
5.8

3.3
5.3
6.0
2.7
3.3
6.7
5.3
6.0
6.0
6.0

8.2
2.9
7.1
4.1
7.7
5.3
5.9
4.1
5.9
4.1

Ensemble mean
Ensemble std. dev.
NCEP–NCAR
ERA-40/ERA-I
BPD14

5.9
1.0
6.2
6.8
6.3

7.0
1.3
8.0
8.0
7.9

5.1
1.4
6.3
6.3
7.2

5.5
1.8
4.5
6.0
3.7

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FIG. 1. (a) Frequency of stratospheric sudden warmings (in events per decade) for different
ENSO phases. (b) Ratio of SSW frequency, for El Niño and La Niña, to ENSO-neutral
frequency. Box plots: median (horizontal line), 25th and 75th percentile (box), and maximum
and minimum values (whiskers) for the 10-member CAM5 ensemble. Red and blue markers
show NCEP–NCAR and ERA-40/ERA-Interim reanalyses, respectively, for the period
1958–2013.

our ensemble is that the internal variability1 of thecoupled stratosphere–troposphere system is large.
This implies that extreme caution is needed whendrawing conclusions from the relatively short observational record. Recall that the latter is equivalent to asingle member taken from what appears to be a rather
broad distribution, assuming our model is faithfully
capturing the internal variability of the stratosphere–
troposphere system.
Next, we ask how the frequencies of SSWs in our modelcompare with the ones in the reanalyses. The question
here is not whether the ensemble mean ‘‘matches’’ any one
set of observations but rather whether the observations(which carry their own uncertainties) fall within the distribution provided by the ensemble of model integrations.
As seen from Fig. 1a, the answer is yes: for all phases of
ENSO the observed frequencies are bracketed by our
10-member ensemble (the numerical values of the observed
frequencies are given in the bottom rows of Table 2).
With this validation, and armed with a 10-member
ensemble of model integrations, we now revisit thequestion: does ENSO alter the frequency of SSWs? The
answer appears to be yes, but only in one phase. As seenin Fig. 1b, El Niño (but not La Ninã) years are associatedwith an increased frequency of SSWs compared to

1
The drivers of this large variability remain poorly understood.
There is some evidence that the quasi-biennial oscillation may be aplayer (Garfinkel and Hartmann 2007; Richter et al. 2011; Taguchi2015). However, studies have suggested possible contributions
from the Madden–Julian oscillation (Garfinkel et al. 2012b), thePacific decadal oscillation (Woo et al. 2015; Kren et al. 2016), andeven Arctic sea ice conditions in early winter (Kim et al. 2014).

ENSO-neutral years, both in our model and in the observations. Note, however, that this increase (1.5 additional
SSWs per decade, in the ensemble mean; cf. Table 2)
is slightly smaller than the standard deviation across theensemble in the neutral ENSO phase (61.8 SSWs per
decade). In our model ensemble, therefore, the enhanced occurrence of SSWs during El Niño is at theborder of statistical significance. Statistics notwithstanding, we believe that this effect is robust, as it has
been reported in earlier studies. In particular, our 27%
increase in SSW frequency during El Niño seen (from5.5 to 7.0 events per decade) is in excellent agreementwith the 30% increase reported in Bell et al. (2009) andthe values reported by Garfinkel et al. (2012a), for a
handful of models. Increase in SSW frequency during El
Niño was originally suggested by Taguchi and Hartmann(2006), but with a nonstandard methodology.
In summary: the weight of the evidence is that, whileinternal variability may be quite large, La Niña does notappear to affect the frequency of occurrence SSWs
whereas El Niño does enhance it, and the current
modeling estimate is that roughly 30% more SSWs occur during El Niño than during ENSO-neutral winters.
At this point, one is naturally led to ask: is this of practical importance? We believe so, and we attempt todemonstrate this in the next section.

4. SSWs: A large source of climate variability over
the North Atlantic and Eurasia
Because ENSO is the dominant driver of variability for agreat many aspects of the climate system, the majority ofprevious studies (e.g., García-Herrera et al. 2006; Ineson

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and Scaife 2009; Bell et al. 2009; Domeisen et al. 2015, andseveral others) have focused on investigating how SSWs
might enhance the impacts of ENSO, notably by providing
an alternative pathway for climate variability actuallyoriginating in the tropical Pacific to reach into the North
Atlantic and all the way into Eurasia via the stratosphere.
It is entirely consistent with observations, however, to lookat things from a different perspective, realizing that SSWs
are—to a very large extent—an independent source ofclimate variability.
Consider the following simple question: what fractionof SSWs are actually ‘‘caused’’ by ENSO? Let us attempt to estimate this number using the results of theprevious section. For simplicity, let us assume that thethree phases of ENSO occur with approximately similarfrequency (which is roughly the case for the 1958–2013
period; see Tables S1 and S2). Then let us assume that
La Niña does not affect the frequency of SSWs, whereas
El Niño increases it by 30%. Now, let n be the number of
SSWs in the ENSO-neutral phase; the number of SSWs
during La Niña is then also n, while the number for El
Niño is 1:3n. The total number of SSWs is thus 3:3n (thesum of the three phases): of these, however, only 0:3n
are due to ENSO. The point here is that a whopping
91% of SSWs (3 O 3.3) in our model are not directly
attributable to ENSO.
Of course such an estimate is very crude and rests on
several assumptions. Nonetheless, the qualitative conclusion is clearly robust: the majority of SSWs occur
irrespective of ENSO. Even if El Niño were able todouble the frequency of SSWs, still 75% of them would
occur irrespective of ENSO (using the same method as
above). This is an important point, and it argues that thesurface impacts of these events needs to be evaluatedand understood in their own right. Hence, the goal ofthis section: to demonstrate that North Atlantic andEurasian climate variability associated with SSWs isactually very large and, in fact, able to overwhelm the
ENSO signal over those regions.
To evaluate the impact of SSWs, we need to disentangle it from the impact of ENSO. But this is easily
accomplished: we just contrast winters with and without
SSWs for each phase of ENSO separately. We start byexamining ENSO-neutral winters, as this extracts thepurest signal of SSWs, uncontaminated by any tropical
Pacific SST anomalies (although we note that, owing to
the seasonal averaging, some variability from sources
other than SSWs may be aliased in).
Using our 10-member ensemble, in Fig. 2 we show
composites of 500-hPa geopotential (Z500, top row),surface temperature (Ts , middle row) and precipitation(P, bottom row) anomalies, during the extended wintermonths [November–March (NDJFM)] for years in the

1963

model with a neutral ENSO phase. Compositing all such
winters (Fig. 2a) is not very informative as the cancellation between strong and weak stratospheric polar
vortices, in addition to the absence of ENSO anomalies,
leaves little of statistical significance. The interesting
results are seen in the composite of ENSO-neutral
winters with one or more SSWs (Fig. 2b), where threenotable features stand out:
1) A clear dipole in Z500 over the North Atlantic,
typical of the negative phase of the North Atlantic
Oscillation (NAO) or the northern annularmode (NAM).
2) Higher temperatures over Greenland, southern Europe, North Africa, and southern Eurasia, accompanied by cold anomalies over northern Europe and,especially, over Siberia.
3) A clear dipole in precipitation over the North
Atlantic (see the highlighted red sector) bringing
increased precipitation over southern Europe anddrier conditions over northern Europe.
These features are easily understood to result from theanomalous dipolar tropospheric circulation over the
North Atlantic, which is well known to be a consequenceof the severely perturbed stratospheric circulation during SSWs (Baldwin and Dunkerton 2001; Polvani and
Waugh 2004).
In ENSO-neutral winters without SSWs oppositesigned features, but of weaker amplitude, can be seen(Fig. 2c). While many of those winters exhibit ananomalously strong polar vortex, the tropospheric impacts accompanying such a vortex are smaller than thoseof SSWs [as recently noted by Scaife et al. (2016)].
Furthermore, those impacts are here partially obscured
by naively compositing all winters without SSWs,
whether the polar vortex is strong2 or not. Be that as it
may, the strong signal of SSWs is clearly amplified bysubtracting the winters without SSWs (Fig. 2d), and thefeatures listed above become larger and more significant
in the difference plots.
We next demonstrate that those same features are foundeven during nonneutral ENSO winters, again exploiting
the large number of events present in our 10-member

2
We have, in fact, separately composited winters with a strong
polar vortex, but found only slightly larger anomalies than those in
Fig. 2c. This is not surprising since, obviously, the vortex becomesanomalously strong in most winters without SSWs. Such compositing, however, requires choosing a threshold for the vortex
strength, which is unavoidably arbitrary. To eschew arbitrary choices
and unnecessary complexity, therefore, we have decided to retain thesimpler binary procedure (with or without SSWs), which emphasizes
the key role of SSWs.

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FIG. 2. Composite NDJFM anomalies for ENSO-neutral years in the 46LCAM5 ensemble. (a) All ENSO-neutral winters, (b) ENSOneutral winters with SSWs, (c) ENSO-neutral winters without SSWs, and (d) the difference between (b) and (c). (top) 500-hPa geopotential height (contour interval 10 m). (middle) 1000-hPa temperature (in K). (bottom) Precipitation (in mm month21). Anomalies arecomputed with respect to the entire period of model integration (1952–2003). Stippling indicates statistical significance at the 95% level,
based on a two-sided t test. The red box delimits the area with the clearest SSW influence on North Atlantic and European precipitation.
The brackets at the top of each column indicate the number of composited winters.

ensemble. Let us consider El Niño first. Over the Pacificand the North American continent (Fig. 3a) El Niño is thedominant winter climate driver and the expected anomaly
patterns are clearly reproduced in our model: one sees thefamiliar Pacific–North American (PNA) teleconnection in
Z500, warmer anomalies in Canada, and cold anomalies
with increased precipitation over the lower United States.
Note that these features are also present in Figs. 3b and 3c(i.e., whether SSWs occur or not) since SSWs have a relatively small impact over the Pacific and North America.
In stark contrast, over the North Atlantic and Eurasia
the wintertime anomalies change sign depending on theoccurrence of SSWs. Compare Figs. 3b and 3c over thoseregions, and note how the NAO, surface temperatures
over Europe, North Africa and Asia, and precipitationin the highlighted sector show statistically significant but
opposite-signed anomalies. The large impact of SSWs iseven clearer in the difference plots (Fig. 3d). More importantly, these impacts are nearly identical to those in
Fig. 2d for the ENSO-neutral winters. This suggests thatthe phase of ENSO may not be the primary driver ofclimate variability of those regions.
This key finding is confirmed in Fig. 4, where we show
similar composites during La Niña winters. First,
compare Figs. 4a and 3a and note how the anomalies
flip sign over the Pacific and North America: this confirms the well-known fact that ENSO is the dominant
driver of climate variability in those regions. Next,
contrast Figs. 4d and 3d and see how the anomalies do
not flip sign over the North Atlantic and Eurasia. This

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POLVANI ET AL.

1965

FIG. 3. As in Fig. 2, but for El Niño winters.

confirms that SSWs are a major driver of climate variability over the North Atlantic and Eurasia, so muchso that their impact is the same irrespective of the
ENSO phase.
That said, we hasten to emphasize that the phase of
ENSO is also surely important for the NAO and for
Mediterranean precipitation, although perhaps less so for
East Asian temperature. Compare the Z500 fields in
Figs. 3b and 4b: in the first we see a strong negative NAO
pattern, but in the latter we see no anomalies at all. So,
obviously, the ENSO phase matters as well. The way tounderstand these differences, is that in Fig. 3 El Niño adds
to the SSW-induced anomalies (hence the large negative
signal), whereas in Fig. 4 La Niña cancels those anomalies. The same addition/cancellation is clearly seen in thebottom row for Mediterranean precipitation anomalies.
Of course it is not surprising that ENSO would be alarge driver of variability: what we wish to emphasize
here, however, is that the impacts of SSWs are of acomparable magnitude as those of ENSO. Just to beclear, compare Figs. 4b and 4c, and see how the La Niña–
induced positive NAO (Fig. 4c, top), the Eurasian
warming (Fig. 4c, middle), and the precipitation anomalies (Fig. 4c, bottom) basically disappear when SSWs
are present (Fig. 4b). This is telling us that, in a given
winter, the occurrence of a SSW may be able to cancelout any ENSO-induced anomalies.
To conclude then, we feel compelled to ask: can one
quantify how large is the climate variability associatedwith SSWs compared to the variability associated with
ENSO, over the North Atlantic and Eurasia? The precise
answer is difficult to compute, and we acknowledge thatthe question is slightly ill posed, given that ENSO does infact increase the frequency of SSWs somewhat. Nonetheless, as we have explained above, that increase is nothuge, so that the great majority of SSWs are clearly not
related to ENSO: thus ENSO and SSWs can be thought ofas largely independent sources of variability, at least for a
first stab at the answer, which is given by the quantities
shown in Table 3.

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FIG. 4. As in Fig. 2, but for La Niña winters.

From the model output of our 10-member ensemble, welist in Table 3 the (top) standardized NAO index in JFM,
and (middle) the Eurasian surface temperature and (bottom) precipitation anomalies in NDJFM for all phases of
ENSO and all stratospheric conditions. Using the NCEP–
NCAR reanalysis, identical quantities were reported inTable 3 of BPD14 (except for Mediterranean precipitation, which we have added here). To answer thequestion above, we suggest comparing, for each of thethree quantities in Table 3 herein, the difference between
the two bold entries in the first numerical column of thattable (El Niño minus La Niña winters, averaged over all
stratospheric conditions) with the differences between thetwo bold entries in the top row of each panel (winters with
SSWs minus winters without SSWs, averaged over all
ENSO phases). We realize this is terribly crude: nonetheless, the point of the exercise is simply to note that thedifferences between winters with and without SSWs arelarger than the differences between El Niño and La Niña

winter. In other words, stratosphere-induced anomalies
over the North Atlantic and Eurasia (on average) appear
to be larger than ENSO-induced anomalies (on average).
This is especially true for Eurasian surface temperatures,
where the effect of SSWs appears to be more than an orderof magnitude larger than the effect of ENSO. Of course,
since some fraction of SSWs (say, about 10%, if we are tobelieve our model) are associated with ENSO, the variability associated with ENSO over those regions is
possibly a little larger than our crude estimate. Nonetheless, the key point remains: the polar stratosphereprovides a large source of variability, whose amplitude iscomparable to that associated with ENSO over large regions of the Northern Hemisphere.

5. Summary and discussion
Confirming a number of recent studies, the resultspresented here offer new, compelling modeling evidence15 MARCH 20171967

POLVANI ET AL.

TABLE 3. North Atlantic and Eurasian climate variability indices associated with ENSO and SSWs, computed from our 10-member
ensemble of 46LCAM5 integrations. (top) January–March North Atlantic Oscillation (NAO) index from NCEP/CPC, standardized using
the January–March mean and standard deviation. (middle) Mean NDJFM surface temperature anomaly (K) for northern Eurasia (608–758N and 308–1208E). (bottom) NDJFM mean Mediterranean (358–458N, 108–258E) mean precipitation rate anomaly (mm month21). For
each of these three quantities, we report the values for different ENSO phases in separate rows and for different stratospheric winterconditions in separate columns, as indicated by the labels. See text for explanation of bold font.
All winters
All winters
El Niño
La Niña
Neutral

0.00
-0.15
10.20
10.01

All winters
El Niño
La Niña
Neutral

0.00
10.09
10.14
20.22

All winters
El Niño
La Niña
Neutral

0.00
11.90
23.58
11.02

Winters w/ SSW
JFM NAO index (standardized)
20.22
20.35
20.05
20.19
NDJFM Eurasian surface temperature (K)
20.58
20.47
20.53
20.78
NDJFM Mediterranean precipitation (mm month21)
13.55
15.10
10.13
14.22

of the crucial role of the stratosphere in driving surface
climate variability over the North Atlantic and Eurasia.
One might ask, at this point, whether observational evidence can also be adduced to further strengthen theseresults. In many ways, the BPD14 study already provides
much of the present observational evidence and, in fact,
motivated the present modeling study. Nonetheless, since
the compositing in BPD14 was done in a different way(meant to emphasize the notion of a stratospheric pathway), for the sake of completeness we have produced the
NCEP–NCAR version of Figs. 3 and 4 (see supplementary Figs. S1 and S2, for El Niño and La Niña, respectively). It is important to stress how small the numberof events in each of the observational composites is (withless than 10 winters, in most cases). That said, these supplementary figures offer some measure of confirmation of
the modeling results.
Beyond the observational evidence, it is useful to put
our results in the context of other studies with different
models, several of which also emphasized the importance of SSWs for surface climate variability. Notably
Sigmond et al. (2013) performed retrospective ensemble forecasts with the Canadian Middle Atmosphere Model (CMAM), and demonstrated enhanced
seasonal skills only when the forecasts were initializednear the onset dates of SSWs. Similarly, Domeisen
et al. (2015) carried out a predictability study of El
Niño over Europe with the Max Plank Institute Earth
System Model (ECHAM6) and showed increased
predictability of the circulation up to 4 months ahead
but only for those El Niño winters that were accompanied by SSWs. And, last, Scaife et al. (2016) used theWinters w/o SSW
10.23
10.10
10.40
10.18
10.59
10.91
10.69
10.24
23.63
22.80
26.57
21.69

UK Met Office Global Seasonal forecast system(GloSea5) to compute hindcasts of the NAO, andconcluded that their seasonal NAO forecast skills actually vanish when members with SSWs are excluded
from the ensemble.
Finally, although we have here focused on highlighting SSWs as an independent source of climate variability, we conclude by revisiting the fact that El Niño doesin fact enhance the occurrence of SSWs. Our modeling
results closely agree with the previous study of Bell et al.
(2009) and suggest a 30% increase in the occurrence of
SSW during El Niño, with no significant impact during
La Niña. As for the observations: averaging the numbers
obtained from NCEP–NCAR and ERA-40/ERAInterim using the updated ERSSTv4 products (bottom
panel of Table 2), we see roughly 50% greater frequency
of SSW occurrence during El Niño than during ENSOneutral winters.
While, in a climatological sense, this increase in SSW
frequency with El Niño still leaves the majority of SSWs
unrelated to ENSO, we suggest such an increase is
nonetheless of considerable practical importance. The
reason for this is that individual SSWs are essentially notpredictable more than a couple of weeks ahead, in themost propitious circumstances (Tripathi et al. 2015,2016). However, when an El Niño event is underway one
can make a seasonal forecast (in the late summer or fall)of an increased probability of SSWs occurring the following winter. Since, as we have demonstrated above,
the occurrence of SSWs in a given winter is able tooverwhelm surface climate over the North Atlantic andEurasia, early awareness of an increased likelihood of1968

JOURNAL OF CLIMATE

SSWs would translate into an improved seasonal climate
forecast for those regions.
Acknowledgments. The authors thank the referees forthoughtful and constructive critical feedback, and are
grateful to the editor for extending the revision deadline
to accommodate some family difficulties. LMP is funded
by a grant from the National Science Foundation (NSF)
to Columbia University. LS is supported by NOAA’s
Climate Change Program Office. The authors acknowledge the use of computational resources at the
NCAR-Wyoming Supercomputing Center provided by
the NSF and the State of Wyoming, and supported by
NCAR’s Computational and Information Systems
Laboratory.
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