9350

JOURNAL OF CLIMATEVOLUME 28

Comparing the Impacts of Tropical SST Variability and Polar Stratospheric
Ozone Loss on the Southern Ocean Westerly Winds*
DAVID P. SCHNEIDER AND CLARA DESER
Climate and Global Dynamics Laboratory, NCAR,1 Boulder, Colorado

TINGTING FANClimate and Global Dynamics Laboratory, NCAR, Boulder, Colorado, and Key Laboratory of Physical Oceanography,
Ocean University of China, Qingdao, China(Manuscript received 30 January 2015, in final form 2 September 2015)
ABSTRACT
Westerly wind trends at 850 hPa over the Southern Ocean during 1979–2011 exhibit strong regional andseasonal asymmetries. On an annual basis, trends in the Pacific sector (408–608S, 708–1608W) are 3 times larger
than zonal-mean trends related to the increase in the southern annular mode (SAM). Seasonally, the SAMrelated trend is largest in austral summer, and many studies have linked this trend with stratospheric ozone
depletion. In contrast, the Pacific sector trends are largest in austral autumn. It is proposed that these asymmetries
can be explained by a combination of tropical teleconnections and polar ozone depletion. Six ensembles oftransient atmospheric model experiments, each forced with different combinations of time-dependent radiative
forcings and SSTs, support this idea. In summer, the model simulates a positive SAM-like pattern, to which ozone
depletion and tropical SSTs (which contain signatures of internal variability and warming from greenhousegasses) contribute. In autumn, the ensemble-mean response consists of stronger westerlies over the Pacific sector,
explained by a Rossby wave originating from the central equatorial Pacific. While these responses resemble
observations, attribution is complicated by intrinsic atmospheric variability. In the experiments forced only withtropical SSTs, individual ensemble members exhibit wind trend patterns that mimic the forced response to ozone.
When the analysis presented herein is applied to 1960–2000, the primary period of ozone loss, ozone depletion
largely explains the model’s SAM-like zonal wind trend. The time-varying importance of these different drivers
has implications for relating the historical experiments of free-running, coupled models to observations.

1. Introduction* Supplemental information related to this paper is available at the
Journals Online website: http://dx.doi.org/10.1175/JCLI-D-15-0090.s1.
1
The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

the southern annular mode (SAM), which is commonly
defined as the leading empirical orthogonal function (EOF) of extratropical, lower-tropospheric geopotential height anomalies (Thompson and Wallace2000). The positive trend in the SAM has been linked
with a reduction in the Southern Ocean carbon sink(e.g., Le Quéré et al. 2007; Lovenduski et al. 2008), shifts
in the spatial distribution of precipitation (Kang et al.
2011; Previdi and Polvani 2014), and cooling trends inEast Antarctic surface climate (Marshall 2007; Nicolasand Bromwich 2014). There have also been significant
regional, nonzonal trends in the atmospheric circulation
at middle-to-high latitudes, contributing to Antarctic
sea ice anomalies (Holland and Kwok 2012; Li et al.

Corresponding author address: David P. Schneider, NCAR Climate and Global Dynamics Laboratory, P.O. Box 3000, Boulder,
CO 80307-3000.
E-mail: dschneid@ucar.edu

Publisher’s Note: This article was revised on 9 December 2015 tocorrect the online version of Figure 4, which was mistakenly replacedwhen originally published.

One of the most discussed aspects of Southern
Hemisphere (SH) climate change of the past several
decades is the intensification and poleward shift of themidlatitude jet, reflected in stronger westerly winds between 508 and 708S throughout the depth of the troposphere (e.g., Thompson et al. 2011; Previdi and Polvani2014). This wind shift projects on the positive phase of

DOI: 10.1175/JCLI-D-15-0090.1
Ó 2015 American Meteorological Society1 DECEMBER 2015

SCHNEIDER ET AL.

2014) and to warming trends on the Antarctic Peninsula(Ding and Steig 2013) and in West Antarctica (Dinget al. 2011; Schneider et al. 2012a).
The most cited cause of the increase in the SAM since
the early 1960s is polar stratospheric ozone depletion (e.g.,Thompson et al. 2011; Previdi and Polvani 2014). In observations over Antarctica, the austral summer increase in
the SAM during 1969–98 broadly parallels the drop in
total column ozone concentration (Thompson andSolomon 2002). The response to ozone depletion—a positive trend in the SAM—is consistent across a wide variety
of models and experimental designs (e.g., Gillett andThompson 2003; Son et al. 2010; Polvani et al. 2011;
Eyring et al. 2013). Generally, the circulation response toozone depletion has been attributed to polar stratosphericcooling, which strengthens the meridional temperature
gradient between the polar and middle latitudes.
This leads to a thermal wind response, but the exact
mechanisms of the SAM response remain unclear(e.g., Thompson et al. 2011). The zonal wind response
to ozone depletion also resembles the circulation response to tropical heating anomalies associated with
global warming or ENSO (e.g., Lu et al. 2008; Sun et al.
2013; Vallis et al. 2014). At least one study has argued
that rising SSTs, rather than ozone depletion, were themain cause of zonally symmetric circulation trends in
the SH during the twentieth century (Staten et al. 2012).
As with the response to ozone depletion, there is noconsensus on the most important mechanisms of the
response to tropical warming (Vallis et al. 2014). The
prevailing argument is that the rising SSTs lead to ameridionally broad, midtropospheric warming in thetropics, which alters the meridional temperature gradient, pushing the latitude of baroclinicity poleward, resulting in a poleward shift of the jet (Vallis et al. 2014).
Apart from Staten et al. (2012), the role of tropical SST
forcing in the context of observed SH circulation trends has
been interpreted in terms of poleward-propagating Rossby
waves, which are associated with zonally asymmetric circulation patterns at middle and high latitudes. Thesewaves, fueled by anomalous deep convection and latentheating in the tropics, are the main pathway of tropical–
Antarctic linkages. Several studies, including Ding et al.
(2011), Ding and Steig (2013), Li et al. (2014), Simpkins
et al. (2014), and Schneider et al. (2012a), have linked recent trends in the zonally asymmetric high-latitude circulation with Rossby waves generated in the tropics.
The recent trends in the tropics reflect, in part, a shift tothe negative phase of the Pacific decadal oscillation(PDO) since the late 1990s, characterized by decadalscale cooling of the central and eastern tropical Pacific(e.g., Trenberth et al. 2014) and strengthening of the
Pacific trade winds (England et al. 2014). Missing PDO

9351

and ENSO teleconnections may be a reason for the underestimation of late-twentieth-century SH zonal wind
trends by coupled models participating in phase 5 of the
Coupled Model Intercomparison Project (CMIP5) historical experiment (Mahlstein et al. 2013). In these experiments, the models generate their own tropicalvariability. If this variability does not sync with the phasesof the actual PDO and related phenomena like ENSO,
these model simulations are unlikely to exhibit the trends
in the tropically driven teleconnections that explain alarge part of recent Antarctic climate trends (e.g., Ding
et al. 2011; Ding and Steig 2013; Schneider et al. 2012a).
Another source of deficiency in CMIP5 simulations
may be traceable to the external forcing datasets prescribed. In particular, ozone depletion might be underestimated in some forcing datasets because of sparsesource data (Eyring et al. 2013; Hassler et al. 2013) or tolimited spatial (Waugh et al. 2009) or temporal resolution (Neely et al. 2014). The sensitivity experiments ofYoung et al. (2014) suggest that the stronger the ozone
depletion prescribed to a given model, the stronger thecirculation response.
Over the past several decades, a negative trend in thePDO (reflecting the shift from the positive phase tonegative phase in 1998; see Trenberth et al. 2014) coincided with a decline of ozone concentration over
Antarctica to historically low values. In this study, weinvestigate the impacts of these two trends on the SH
atmospheric circulation using multiple ensembles oftransient atmospheric model integrations in which observed tropical SSTs (which capture the variability of
the PDO) and different combinations of radiative forcings are prescribed. While previous work provides descriptions of the responses to ozone depletion andtropical SSTs separately, few studies have directlycompared and quantified these two responses and assessed the extent to which they combine to explain theobserved trend patterns. We also highlight the role ofintrinsic variability through inclusion of results fromindividual ensemble members and discuss how it cancomplicate attribution.
For the response to ozone depletion, the sensitivities
to two different forcing datasets are compared; one ofthese has relatively aggressive ozone loss, while theother has been widely used but criticized for its relatively weak ozone depletion. We group nonozone radiative forcings [including greenhouse gasses (GHGs),
natural and anthropogenic aerosols, solar variability,etc.] into a single category of ‘‘other’’ forcing. This is
motivated by the fact that the direct impacts of theseother radiative forcings (i.e., the components of the response not mediated by SSTs) have been previously
found to be very small relative to the effects of ozone

9352

JOURNAL OF CLIMATEVOLUME 28

TABLE 1. Summary of the experiments conducted with CAM4. The nonozone radiative forcings are consistent with those used in the
CMIP historical simulations with the CCSM4 (Gent et al. 2011). Further details are given in section 2 of the main text.
Expt No. and nameTime-varying forcing

1) Ozone (SPARC)SPARC O3.

2) Ozone (WACCM)

WACCM O3.

3) Tropical SST

Tropical SSTs (288N–288S).

4) Partial Forcing

Tropical SSTs (288N–288S) and all radiative
forcings except O3.
Tropical SSTs (288N–288S) and all radiative
forcings using SPARC O3.
Tropical SSTs (288N–288S) and all radiative
forcings using WACCM O3.

5) Full Forcing (SPARC)
6) Full Forcing (WACCM)

and SSTs (e.g., Deser and Phillips 2009; Polvani et al.
2011; Staten et al. 2012; Grise and Polvani 2014). Our
experiments do not address the extent to which thetropical SSTs themselves have changed in response toexternal forcing or, conversely, how tropically generated
variability might have influenced stratospheric ozone
concentrations.
Although the bulk of our analysis is focused on the
period 1979–2011, our experiments are conducted over alonger time period with a view toward future work as
well as an examination of sensitivity to the time periodof analysis. Different time periods are characterized bydifferent superpositions of tropical variability and ozone
loss than is 1979–2011, with implications for the relativeroles of different forcings.

2. Experiments, data, and methods
a. Model and experiments
This study uses the Community Atmosphere Model,
version 4 (CAM4), configured at a 0.98 latitude 3 1.258
longitude horizontal resolution with finite volume dynamical core and 26 vertical levels (Neale et al. 2013).
CAM4 simulates realistic SH circulation responses totropical variability (Wilson et al. 2014). Its predecessor,
CAM3, has been widely used to study the responseto stratospheric ozone depletion (Polvani et al. 2011;
Young et al. 2014).
Six ensembles of transient experiments are conductedwith CAM4, as outlined in Table 1. The first two ensembles consist of ozone sensitivity experiments, forcedby time-varying ozone concentrations from two different
ozone datasets described below. In these experiments,
SSTs, sea ice concentrations, and nonozone radiative
forcings are held to seasonally varying climatologies. We
refer to these first two experiments together as the Ozone
ensemble. The remaining four ensembles use common

Climatological forcing

Years and No. of runs

All SSTs and sea ice and all
non-O3 radiative forcings.
All SSTs and sea ice and all
non-O3 radiative forcings.
Extratropical SSTs and sea ice
and all radiative forcings.
Extratropical SSTs and sea
ice and O3.
Extratropical SSTs and sea ice.

1950–2011, 5 runs

1950–2011, 5 runs

Extratropical SSTs and sea ice.

1950–2011, 5 runs

1950–2011, 5 runs
1900–2011, 5 runs
1950–2011, 5 runs

SST and sea ice concentration lower boundary conditions:Observed time-varying tropical SSTs are prescribed over
288N–288S, and a seasonally varying climatology for SSTs
and sea ice concentrations is used poleward of 358. Between 288 and 358 latitude in both hemispheres, the SST
anomalies are tapered by adding damped anomalies (linearly weighted by latitude) to the climatologies. The
Tropical SST (TSST) experiment is forced by time-varying
tropical SSTs only; the radiative forcings have no timedependence beyond the seasonal cycle. The Partial Forcing experiment is forced by time-varying tropical SSTs
plus all radiative forcings except ozone. The final experiments, referred to as the Full Forcing experiments, areforced by time-varying tropical SSTs and the full suite ofradiative forcings, including ozone. In all experiments, theexternal forcings without time dependence beyond theseasonal cycle are set to climatological values for the year
2000; the SST and sea ice concentration climatologies are
based on the period 1900–2010. All experiments have fiveensemble members, initialized from different atmosphericstates taken from a control simulation of the coupled
version of CAM4, the Community Climate System Model,
version 4 (CCSM4; Gent et al. 2011).

b. Boundary conditions, reanalysis, and observations
The SST and sea ice concentration boundary conditions are from the dataset of Hurrell et al. (2008). For thenonozone external forcings, the datasets are the same as
those used for the CMIP5 twentieth-century historicalexperiments with CCSM4 (Gent et al. 2011). The external forcings are extended beyond the year 2005 withvalues from the representative concentration pathway
8.5 (van Vuuren et al. 2011).
The first ozone dataset, referred to as the Stratosphere–
Troposphere Processes and Their Role in Climate(SPARC) dataset, was developed for and widelyused by numerous models for their CMIP5 historical experiments (Cionni et al. 2011; Eyring et al.

1 DECEMBER 2015

SCHNEIDER ET AL.

9353

FIG. 1. Seasonal-mean time series of relative ozone concentration over 658–908S at 50 hPa in the SPARC and
WACCM forcing datasets for (a) SON SPARC, (b) SON WACCM, (c) DJF SPARC, and (d) DJF WACCM. The
values are percentages relative to the 1979–2011 long-term mean, calculated for each record separately.

2013). The SPARC data are monthly mean, zonal-mean,
vertically resolved ozone fields. For the primary period
of interest here (1979–2011), the SPARC stratosphericozone data are based on regression fits (representing
different time scales and processes of variability) toozonesonde observations. However, these observations
are sparse: SPARC included observations from only one
location in the SH polar region, Syowa station (69.08S,
39.68E). As Syowa is located near the edge of the polar
vortex, it does not experience the severe ozone depletion that can occur in the deep interior of the polar
vortex (Eyring et al. 2013). Comparing the SPARC
austral spring polar stratospheric ozone values (658–
908S; 100–10 hPa) with a more complete set of in situ
ozonesonde measurements, Hassler et al. (2013) findthat SPARC values lie within but toward the high end of
the observed range.
The second ozone dataset consists of monthly averaged ozone concentrations calculated interactively
by the Whole Atmosphere Community Climate Model(WACCM), a high-top chemistry–climate model (Marshet al. 2013). This dataset, referred to here as the WACCM
ozone dataset, is independent of the SPARC dataset,as it is based on a model experiment with specified
emissions of ozone-depleting substances (Marsh et al.

2013), rather than on direct ozonesonde observations.
Unlike the zonal-mean SPARC data, the WACCM
data vary with latitude and longitude. In austral spring,
WACCM-estimated ozone concentrations agree very
well with Antarctic ozonesonde observations over the
1979–2005 period, while in summer, WACCM’s ozone
depletion is too severe, likely due to the model’s seasonal polar vortex persisting too long (Marsh et al.
2013). Kay et al. (2015) describe the transformation of
the WACCM-simulated ozone into a prescribed forcing dataset. Externally forced ozone variations areemphasized over the model-generated internal variability by applying a 10-yr running mean to the WACCM
ozone concentrations, using separate time series foreach month.
Figure 1 compares WACCM and SPARC time seriesof relative ozone concentration over 658–908S at 50 hPa
in austral spring [September–November (SON)] andsummer [December–February (DJF)]. Both datasets
are temporally smooth and exhibit a broadly similar
progression of ozone loss in SON, with the maximum
values occurring at the beginning of the period and theminimum values occurring in the mid-1990s. WACCM
ozone loss is more rapid than SPARC ozone loss. For
example, during the 1980s, WACCM ozone declines by9354

JOURNAL OF CLIMATE

about 70% of its long-term mean value, but SPARC
ozone declines by only about 40% of its long-term meanvalue. From the mid-1990s onward, SPARC ozone recovers somewhat, but WACCM values remain very low.
In DJF, the difference between the WACCM and
SPARC time series is more striking than in SON:WACCM exhibits strong ozone loss while SPARC has
almost no trend.
Trends in zonal wind and air temperature are computed from monthly fields of the ERA- Interim (ERAI;
Dee et al. 2011). To place the reanalysis winds trends inthe context of other climate variables and observationaldata, the winds are compared with SSTs from the
Hurrell et al. (2008) dataset mentioned above, andrainfall data from the Global Precipitation Climatology
Project, version 2.2 (Adler et al. 2003). The ERAI
data are chosen because, among the major reanalyses,
ERAI is superior in terms of its depiction of the major
circulation features at high SH latitudes and shows
trends consistent with those in station-based, largescale climate indices (Bracegirdle and Marshall 2012;
Bracegirdle 2013). For temperature, the main regionof interest here is the polar lower stratosphere. The
ERAI temperature anomalies at 100 hPa over 658–908S
show excellent agreement in terms of interannual variability and trends during their period of overlap withthe station-average upper-air temperature time seriescompiled by Screen and Simmonds (2012) (see Fig. S1in the supplemental material). As the Screen andSimmonds (2012) data have coarse temporal resolution
and limited spatial coverage, they are not directly usedin this this study.

c. Methods
The sensitivity experiments described above enable
two methods to infer the role of ozone forcing. First, theforced response to ozone can be obtained directly fromthe ensemble means of the first two sets of experiments
in Table 1. A second approach is to difference the ensemble means of the Full Forcing and Partial Forcing
integrations. Both approaches are applied for the
SPARC- and WACCM-forced ensembles separately
and in combination. The austral summer, 1960–2000
zonal-mean zonal wind and temperature trends for these
two methods and two ozone datasets are compared inFig. S2 of the supplemental material. In general, there
are more noticeable differences associated with the
choice of dataset than with the method. The approximate linearity of responses to external forcing, also
found in previous studies (e.g., Deser and Phillips 2009),
supports use of the differencing method to infer the direct impacts of nonozone radiative forcings (herein referred to as Other Forcing). The response to OtherVOLUME 28

Forcing is obtained by differencing the ensemble meansof the Partial Forcing and TSST integrations.
For a robust estimate of the forced response to ozonethat leverages the strengths and weaknesses to the twodatasets, we use the mean of the responses to SPARC
and WACCM ozone: that is, the ensemble mean of all 10
single-forcing integrations (5 runs using WACCM ozone
and 5 runs using SPARC ozone). Similarly, we will discuss the Full Forcing response as the ensemble mean of
all 10 of the Full Forcing integrations. However, we will
also highlight the aspects in which the responses to
SPARC and WACCM ozone forcing are distinct, basedon comparisons of the 5-member ensembles.
Linear least squares analysis is used to compute trends
and regression coefficients. Throughout this paper, thecalculation of statistical significance is based on the twosided Student’s t test methodology and adjustment for
autocorrelation outlined by Santer et al. (2000). Boththe sample size and the degrees of freedom for indexing
the critical t value are adjusted according to the lag-1
autocorrelation of the residuals. Note that this significance test is based only on the trend and variance of theobservations or the ensemble mean of the model simulations. Another metric for determining if a forced trend
may be considered detectable is the signal-to-noise ratio(SNR), where the signal is the ensemble-mean trend andthe noise is the standard deviation of the trends acrossensemble members. This second approach better accounts for the internal variability as estimated by themodel than does the ensemble-mean time series approach; however, it is typically more applicable for large
ensemble sizes where the SNR is better constrained(e.g., Deser et al. 2014). For the results emphasized
here—namely, the austral summer response of the circumpolar zonal winds to tropical SSTs and ozone depletion and the austral autumn response of the zonal
winds in the South Pacific basin to tropical SSTs—weestimate the SNRs in addition to applying the t test.
Seasons in this paper refer to the austral seasons (e.g.,summer is DJF). Nonconventional 3-month seasonalmeans are also used, referred to by the first letters of thecorresponding month names [e.g., January–Marchis JFM].

3. Resultsa. Zonal wind trends in the reanalysis
The pattern of 850-hPa zonal wind trends during
1979–2011 using all months (Fig. 2a) suggests that thelargest westerly (positive) wind trends on Earth haveoccurred over the Southern Ocean, particularly in the
Pacific sector (outlined by the black box in Fig. 2a).

1 DECEMBER 2015

SCHNEIDER ET AL.

9355

FIG. 2. Trends over 1979–2011, using all months, for (a) 850-hPa zonal wind and (b) SST and tropical rainfall. The
SST trends correspond with the color shading. Negative rainfall trends are in brown contours, and positive rainfall
trends are in magenta [contoured at 61.0, 61.5, and 62.0 mm day21 (33 yr)21]. The (c) first and (d) second EOF
patterns of the monthly 850-hPa zonal wind anomalies over 308–708S, the region bounded by the lines in (c). The
patterns were obtained by regressing the global zonal wind anomalies onto the standardized PC time series. For all
panels, white stippling indicates that the trend or regression coefficient is significant at or above the 95% level. The
white and black boxes in (a) correspond with the areas used to represent winds in the equatorial Pacific and in the
Pacific sector of the Southern Ocean, respectively. The latitudes bounding the time-varying tropical SST forcing of
the model integrations are indicated in (b). (e) The trend in 850-hPa zonal wind that is congruent with the first two
EOFs; (f) the global SST trends with the zonal-mean trend removed at each latitude.

Westerly trends extend across the Southern Ocean at
approximately 608S, though they are not statisticallysignificant at all longitudes. Easterly (negative) wind
trends cover most of the Pacific basin between 308N and
308S, with the largest values occurring in the central andwestern equatorial Pacific (white box in Fig. 2a).

Negative SST trends (Fig. 2b) over much of theSouthern Ocean accompany the westerly wind trends.
The negative SST trends are consistent with oceanic
responses to stronger westerlies via anomalies in mixed
layer depth, Ekman transport, and air–sea heat fluxes(e.g., Sallée et al. 2010). In the Pacific basin of both

9356

JOURNAL OF CLIMATE

hemispheres, the pattern of negative SST trends in theeast and positive SST trends in the west resembles thenegative phase of the PDO (England et al. 2014;
Trenberth et al. 2014). The east–west contrast in SST
trends is consistent with stronger trade winds and faster
subtropical oceanic gyres (England et al. 2014). The
negative rainfall trends over the central tropical Pacific (Fig. 2b) imply strongly negative diabatic heating
anomalies, giving rise to the expectation of an atmospheric Rossby wave response that may impact the SH
atmospheric circulation (Trenberth et al. 2014). With
the zonal-mean trend removed, the central and eastern
Pacific cooling stands out as anomalous (Fig. 2f), whichsets the stage for the circulation response in the model
simulations described below.
To relate the observed wind trend pattern to the twoleading modes of SH atmospheric circulation variability,
an EOF analysis of the monthly zonal wind anomalies
over 308–708S is performed. The first EOF (Fig. 2c) explains 21% of the variance and is associated withanomalies of one sign between 308 and 508S and of the
opposite sign between 508 and 708S. Wind anomalies
associated with the second EOF (Fig. 2d), which explains 13% of the variance, are largely confined to the
Pacific and are consistent with a wave train extending
from the tropics to the Antarctic. The zonal wind
anomalies related to the second EOF reflect a cyclonic
circulation feature centered near 608S, 1208W. This is
the approximate average position of the Amundsen Sea
low, a semipermanent low pressure center that has
deepened in recent decades (Raphael et al. 2015), consistent with the zonal wind trends in the Pacific sector.
The principal component (PC) time series of the twoleading modes of zonal wind variability (Figs. 3a,b) both
exhibit positive trends, but the trend in PC2 is larger
than the trend in PC1, signaling the relatively larger
trends in the Pacific sector of the Southern Ocean. PC1
exhibits relatively high-frequency variability and ishighly correlated (r 5 0.88; significant at 99%) with the
SLP-based SAM index of Marshall (2003), which isdisplayed in Fig. 3c. Anomalies in PC2 exhibit greater
persistence than anomalies in PC1, consistent with theidea that much of the variance in PC2 arises fromtropical variability (both PDO and ENSO). Indeed,
there is a correlation of r 5 0.55 (significant at 95%)
between PC2 and the time series of zonal wind from thecentral equatorial Pacific (Fig. 3d), which in turn iscorrelated with the PDO index at r 5 0.45 (significant at95%). Although PC2 has more high-frequency noise
than the equatorial Pacific index because of its extratropical emphasis, both time series show multiyear periods of positive anomalies, especially after the late
1990s when the PDO transitioned to its negative phase.

VOLUME 28

The EOF analysis reveals that the spatial pattern of
the wind trend indeed projects onto the leading modesof variability. The zonal band of positive values between
508 and 708S projects onto the first mode, and thebroader latitudinal extent of stronger positive values in
the Pacific sector projects onto the second mode. The
trend pattern linearly congruent with the sum of the twomodes, obtained by multiplying the leading two regression patterns (based on detrended data) by the trendof their respective PCs and summing the two patterns(Fig. 2e) has a correlation of r 5 0.84 with the observed
trend pattern (Fig. 2a) over 308–708S. The distinct timeseries corresponding to these leading modes suggeststhat two processes may be at play to explain the observed zonal wind trend, the first largely focused on thezonal-mean circulation at high latitudes, and the second
focused regionally on the Pacific sector, with a significant connection to the tropics.
For comparison with the model integrations, we construct two indices of the monthly zonal wind field,
guided by the observed EOF and trend patterns. The
first index, correlated at r 5 0.96 with PC1, is the areal
average of the wind field over 508–708S across all longitudes; this index is analogous to the SAM index, andwe will refer to its spatial signature as the SAM-likepattern. Second, a Pacific sector index, correlated at r 5
0.87 with PC2, is calculated as the areal average over theregion outlined in Fig. 2a (408–608S, 708–1608W). Unlike
the PCs, these indices are not necessarily orthogonal,
and there is a small overlap in their domain. Nonetheless, these indices (Figs. 3e,f) exhibit temporal characteristics similar to their PC counterparts (Figs. 3a,b),with sufficient distinction to relate them to different
driving processes.
Another defining characteristic of the zonal wind
trend is its seasonality. Trends in the SAM-like pattern
are highly seasonally dependent (Fig. 4), with the largest
positive trend occurring in DJF and near-zero or negative trends occurring in JJA through SON. Trends in the
Pacific sector are less seasonally dependent, with positive trends year-round, peaking and most significant inautumn. Annually, the Pacific sector trend is 3 times
larger than the trend related to the SAM. The spatialand seasonal asymmetries of the zonal wind trends are
the key aspects that we highlight as we relate the observed trends, as estimated by the reanalysis, to themodel experiments.

b. Modeled zonal wind trends: Spatial patterns andtheir seasonality
Figure 5 depicts the simulated ensemble-mean trends
during 1979–2011. In response to Full Forcing, significant westerly trends extend across the Southern Ocean,1 DECEMBER 2015

SCHNEIDER ET AL.

9357

FIG. 3. Monthly mean time series of (a) PC1 of 850-hPa zonal wind corresponding to the pattern in Fig. 2c; (b) PC2
of 850-hPa zonal wind corresponding to the pattern in Fig. 2d; (c) the SAM index of Marshall (2003); (d) equatorial
Pacific wind anomalies averaged over the white box in Fig. 2a; (e) indices of zonal wind anomalies over 508–708S; and(f) the Pacific sector. All time series are smoothed with a 4-month running mean.

and easterly trends cover the central and western tropical Pacific (Fig. 5a). In the SH, the largest westerly
trends occur in the Pacific sector. This pattern is broadly
similar to the trends in ERAI (pattern correlation over
308–708S is 0.72 and globally 0.62).
The trend pattern in the TSST ensemble (Fig. 5b)resembles that of the Full Forcing ensemble, showing
again westerly trends across the Southern Ocean withstronger trends in the Pacific sector. However, thesetrends are somewhat weaker than the trends in the Full
Forcing ensemble. This difference is explained mainlyby the ozone response (Fig. 5c), characterized by westerly trends between 508 and 708S and easterly trends
between 308 and 508S, resembling the SAM pattern in

Fig. 2c. The sum of the TSST and Ozone ensemble
trends (Fig. 5e) exhibits a strong pattern correlation (r 50.80) over 308–708S with the reanalysis trend and closely
resembles the Full Forcing trend. This illustrates theapproximate linearity of the responses to tropical SSTs
and ozone and suggests a minor role for Other Forcing.
Therefore, the remainder of this analysis focuses on the
responses to tropical SSTs and ozone.
Figures 6 and 7 characterize the modeled circulation patterns in terms of the two zonal wind indicesand stratify their trends by season. Trends in the
SAM-like pattern (Fig. 6) exhibit marked seasonality
in the Full Forcing ensemble mean (Fig. 6a), both interms of magnitude and statistical significance. The largest

9358

JOURNAL OF CLIMATE

FIG. 4. Seasonal-mean 850-hPa zonal wind trends (from ERAI)
during 1979–2011 for 508–708S (solid line) and the Pacific sector(dashed line). Markers on the seasonal lines indicate statisticalsignificance at or above 90%. Annual (ANN) trends are indicatedon the right side. The annual trend for 508–708S is not statisticallysignificant.

trends, significant at the p , 0.01 level, occur in the latespring through early autumn, peaking in November–
January (NDJ) and DJF. The minimum trends occur inwinter and early spring, similar to the reanalysis. The
ensemble spread is also indicated in Fig. 6a by the thinlines; the trends in ERAI (Fig. 6a, repeated from Fig. 4)
lie within the range of individual ensemble members
during all seasons, and all members have positive trends
from October–December (OND) thru DJF.
The ensemble-mean TSST trends (Fig. 6b) show lessseasonal dependence than the Full Forcing trends, withslightly larger trends from spring through late summer
than in autumn and winter. A more distinct seasonality
is evident in the Ozone ensemble mean (Fig. 6c), whichhas positive trends from late spring through late summer and near-zero trends in autumn and winter. The
ensemble-mean trend is significant (at p , 0.05) in DJF.
The amplitude and seasonality of the SAM-like response to Full Forcing is largely explained by the combination of Ozone and TSST experiments. The DJF
trend in the SAM-like pattern in the Full Forcing
ensemble mean is 1.14 m s21 (33 yr)21, compared to
0.56 m s21 (33 yr)21 for TSST and 0.43 m s21 (33 yr)21
for Ozone. The SNR in DJF for the Full Forcing ensemble is 2.5, while it is 1.6 for the TSST ensemble andless than one for the Ozone ensemble.
For the Pacific sector index (Fig. 7), trends in both
the Full Forcing ensemble (Fig. 7a) and the TSST ensemble (Fig. 7b) are consistently positive year-round in
the individual ensemble members. The Full Forcing
ensemble-mean trend is most significant (at p , 0.01) inautumn. The TSST ensemble-mean trend is also significant in autumn. In contrast, the Ozone ensemble-mean

VOLUME 28

trends are close to zero for most of the year, but both the
SPARC and WACCM ensembles are slightly positive in
winter. The comparison of ensemble-mean trends in
Fig. 7b illustrates that tropical SSTs largely account for
the trend in the Full Forcing ensemble mean in autumn,
from February–April (FMA) through April–June(AMJ). In March–May (MAM), the SNR is larger for
the TSST ensemble mean (2.0) than it is for the Full
Forcing ensemble mean (1.4), underscoring the important role of tropical SSTs in the wind trends in the Pacificsector. In winter and spring, external forcings enhance
the trend in the Pacific sector, although the ensemble
spread is largest in these seasons.
The maps in Fig. 8 provide a complimentary view ofthe seasonal zonal wind responses to Full Forcing,
Tropical SST, and Ozone experiments. In summer(DJF), both tropical SST and ozone contribute to thetrend pattern of the Full Forcing ensemble mean, which
in turn is statistically significant and well correlated withthe observed trend pattern (pattern correlation over 308–
708S is 0.74). The pattern correlations in DJF are lower
considering TSST or Ozone experiments separately. In
autumn (MAM), only tropical SST makes a significant
contribution to the trend pattern of the Full Forcing
ensemble mean, and this trend is concentrated in the
Pacific sector, as it is in the reanalysis, giving a patterncorrelation over 308–708S of 0.76. In the winter andspring, the forced responses associated with TSST and
Full Forcing show roughly similar patterns to MAM, butthere are not high pattern correlations with the trends in
ERAI. Reasons for the less significant responses in JJA
and SON are discussed in section 3d below.

c. Summer (DJF) zonal wind response and the rolesof tropical SSTs and ozone
Following from the result that tropical SST and ozone
depletion contribute jointly to the Full Forcing zonal
wind response in DJF and that the trend pattern at
850 hPa is roughly zonally symmetric (Fig. 8b), thezonal-mean zonal wind and temperature trends associated with these forcings are displayed in Fig. 9 as afunction of height and latitude. In ERAI (Fig. 9a),westerly wind trends are centered at about 608S andextend throughout the depth of the troposphere andlower stratosphere, mainly on the poleward side of theclimatological jet. The wind trends are accompaniedby stratospheric cooling, strongest at approximately
100 hPa over the pole, and by tropospheric warming in
the tropics and midlatitudes (Fig. 9b). The patterns ofwind and temperature trends in the Full Forcing ensemble mean (Figs. 9c,d) are similar to the reanalysis.
Both tropical SSTs (Fig. 9e) and ozone depletion(Fig. 9g) are associated with westerly trends centered at

1 DECEMBER 2015

SCHNEIDER ET AL.

9359

FIG. 5. Maps of simulated ensemble-mean trends in 850-hPa zonal wind over 1979–2011, using all months, for the(a) Full Forcing, (b) TSST, (c) Ozone, (d) Other Forcing, and (e) sum of Tropical SST and Ozone ensembles. For all
panels, white stippling indicates that the trend is significant at or above the 95% level. The numbers at the top right ofeach map in (a)–(e) are the pattern correlations with (f) the ERAI trend pattern over 308–708S and globally.

608S that extend throughout the troposphere. Although
the ozone-induced wind trends are generally larger than
the trends associated with tropical SSTs, they do notaccount for the entire magnitude of the trends in the Full
Forcing ensemble mean. In the lower troposphere, notethat the ozone-induced trends, while equal to or stronger than the trends associated with tropical SSTs, occupy
a relatively narrow meridional extent. The broader extent
of the TSST-induced trends helps to explain their relatively large magnitude when calculated at 850 hPa over
508–708S, as discussed above.
The individual roles of tropical SSTs and ozone are
evident in the zonal-mean temperature trends (Figs.
9d,f,h). The main thermal signature of ozone depletion
resides in the polar lower stratosphere, where significant
cooling occurs (Fig. 9h). Tropospheric warming in thetropics and midlatitudes, evident in the reanalysis(Fig. 9b) and Full Forcing ensemble mean (Fig. 9d), canonly be explained by tropical SSTs (Fig. 9f), not byozone (Fig. 9h). As discussed in the introduction, an
intensification of the westerlies at high latitudes in response to tropical warming has been found by a numberof studies (e.g., Kushner et al. 2001; Lu et al. 2008; Sunet al. 2013; Staten et al. 2012).
Cooling of the lower polar stratosphere associatedwith ozone depletion is most pronounced in spring (e.g.,Thompson et al. 2011). To confirm that this expectedseasonal signature of ozone loss occurs in these experiments, Fig. 10 illustrates the seasonal polar stratospheric(658–908S; 100 hPa) temperature trends in association
with the different forcings. ERAI (gray line, Fig. 10a)shows year-round cooling, significant at better than p ,0.05 from late spring through early winter. The maximum cooling of about 68C occurs in OND and
NDJ. Trends in the Full Forcing ensemble are in excellent agreement, showing the same seasonal pattern in9360

JOURNAL OF CLIMATE

FIG. 6. Seasonal-mean trends in 850-hPa zonal wind for 1979–
2011, averaged over 508–708S, for the (a) Full Forcing, (b) TSST,
and (c) Ozone ensembles. The thin lines correspond to individual
ensemble members, and the thick lines represent the ensemblemean trends. The ensemble-mean trend associated with Full
Forcing (red line) is repeated in (b) and (c). The gray line in(a) represents the reanalysis. The significance of the ensemblemean trends is indicated by markers.

all ensemble members. Comparing the stratospherictemperature trends of the Full Forcing ensemble mean
with the responses to tropical SSTs (Fig. 10b) and ozone(Fig. 10c), it is clear that ozone largely explains theVOLUME 28

FIG. 7. As in Fig. 6, but for the Pacific sector (408–608S, 708–1608W)
zonal winds.

cooling signal. The seasonality of peak cooling in latespring and early summer in the Full Forcing ensemble(Fig. 10a) mirrors that in the Ozone ensemble (Fig. 10c).
Significant cooling in NDJ occurs in response to tropical
SSTs (Fig. 10b), but considering individual members,cooling is not as consistent in the TSST ensemble as it isin the Ozone ensemble.
Figures 10a and 10c reveal differences between the
responses to SPARC and WACCM ozone forcing.

1 DECEMBER 2015

SCHNEIDER ET AL.

9361

FIG. 8. Global maps of (top)–(bottom) seasonal-mean trends in 850-hPa zonal wind during 1979–2011, for (a) ERAI, (b) Full Forcing,(c) TSST, (d) Ozone. For all panels, white stippling indicates that the trend is significant at or above the 95% level. The numbers at the top
right of each map are the pattern correlations with the ERAI trend pattern over 308–708S and globally.

These trend differences are most distinct in DJF and
JFM, with WACCM showing more cooling than
SPARC. During these seasons, each individual memberof the WACCM ensemble cools more than any memberof the SPARC ensemble. The persistence of strong
cooling in the WACCM-forced runs through the summer likely arises from a bias in WACCM’s polar vortex(e.g., Marsh et al. 2013). The seasonal timing of thecooling in ERAI, with peak values in OND and NDJ, isbetter replicated in the SPARC ensemble than in the
WACCM ensemble. The SPARC ensemble mean actually has more cooling than does WACCM in SON and
OND, although the individual ensemble members
overlap. The differences in the responses to SPARC and
WACCM ozone forcing can be seen more clearly in
Fig. S3 of the supplemental material. The WACCM
ensemble mean generally exhibits larger cooling, although the season of maximum cooling is delayed relative to SPARC.
Late spring stratospheric cooling associated withozone loss has often been linked directly with a responseof the midlatitude jet and near-surface westerlies insummer (e.g., Gillett and Thompson 2003; Polvani et al.
2011; Eyring et al. 2013). Ensemble-mean time series ofpolar stratospheric temperatures in spring (OND) and
850-hPa zonal winds in DJF from the Ozone and TSST
experiments are shown in Fig. 11. In the Full Forcing
ensemble mean, the OND polar stratospheric cooling
trend is pronounced from the early 1980s through theearly 1990s, after which the trend levels out (Fig. 11a).
Ozone forcing (Fig. 11c) explains most of this trend.
However, there is also a small but significant cooling
trend in response to tropical SST variability (Fig. 11b),with a slightly different timing than that in response toozone forcing. In particular, positive anomalies associated with tropical SST changes persist into the early
1990s, influencing the trend of the Full Forcing ensemble
mean (Fig. 11a). Summer zonal wind anomalies from theFull Forcing ensemble mean (Fig. 11d) evolve nearly instep with the spring stratospheric temperature anomalies, increasing from the early 1980s through the early
1990s, then leveling out. The spring stratospheric temperature and summer tropospheric wind time series arehighly correlated (r 5 20.93), even after detrending(r 5 20.82).
Inspection of individual ensemble members reveals
that lower-stratospheric cooling is not always associatedwith an intensification of the tropospheric westerly

9362

JOURNAL OF CLIMATEVOLUME 28

FIG. 9. Latitude–height plots of DJF (left) zonal-mean zonal wind and (right) temperature trends for 1979–2011:(a),(b) ERAI, and the ensemble-mean trends associated with (c),(d) Full Forcing, (e),(f) TSST, and (i),(j) Ozone
ensembles. In all panels, color shading corresponds to the trend; black contours indicate the climatology, and green
stippling indicates that the trend is significant at or above the 95% level. Note the nonlinear color scale. The zonal
wind contour interval is 3.0 m s21.

winds over 508–708S. Although all 10 Ozone ensemble
members exhibit lower-stratospheric cooling in thelate spring and early summer (Fig. 10c), the lowertropospheric wind trends are more variable (Fig. 6c).
Figure 12 compares the summer zonal-mean temperature and zonal wind trends in the Ozone ensemble
member with the weakest 850-hPa wind trend and the

TSST ensemble member with the strongest 850-hPa
wind trend. The single TSST member exhibits a pattern of large zonal wind trends (Fig. 12c) poleward of308S that resembles the reanalysis (Fig. 9a) as well as
the forced response to ozone depletion (Fig. 9g). In
contrast, wind trends in the single Ozone ensemble
member (Fig. 12e) are negative in the middle and lower

1 DECEMBER 2015

SCHNEIDER ET AL.

9363

FIG. 10. As in Fig. 6, but for the lower polar stratospherictemperature (100 hPa; 658–908S).

troposphere over 508–708S, despite some significant
polar stratospheric cooling (Fig. 12f). These results illustrate the role of intrinsic variability, which is important to recognize when relating the model results to theobserved trends. While it can be said that the observed
zonal wind trend is generally consistent with the forced
response to ozone depletion, the wind field by itself does
not provide a unique fingerprint of ozone depletion.

FIG. 11. Time series of ensemble-mean OND polar lower
stratospheric temperature (100 hPa; 658–908S) associated withthe (a) Full Forcing, (b) TSST, and (c) Ozone ensembles.
(d) Ensemble-mean time series of DJF zonal winds averaged
over 508–708S associated with Full Forcing.

9364

JOURNAL OF CLIMATEVOLUME 28

FIG. 12. As in Fig. 9, but showing trends associated with single ensemble members of the TSST and Ozone ensembles
instead of the ensemble-mean trends.

d. Pacific sector zonal wind variability and its causes
Returning to the Pacific sector, tropical variability
dominates the variability of the winds in both reanalysis
and the model runs. As discussed earlier, there is a significant correlation of r 5 0.55 between the tropical winds(Fig. 13a) and the Pacific sector winds (Fig. 13b). In themodel simulations, the time series of the Pacific sector
winds from the Full Forcing ensemble mean (Fig. 13c) iscorrelated at r 5 0.83 (with 2-month smoothing) with thereanalysis tropical wind time series. The correlation isnearly as high if only the TSST ensemble mean is used
(Fig. 13d, r 5 0.81). The decadal-scale variability in these
indices is consistent with the PDO (Fig. 13e). Interannual
ENSO variability, as measured by the Niño-3.4 index(Fig. 13f), is also reflected in these time series, but the
ENSO index lacks the shift in the late 1990s that is captured by the PDO index (Fig. 13e) and the central
equatorial Pacific zonal winds (Fig. 13a).

The robust atmospheric circulation response to theprescribed tropical SSTs can be understood by analogy
to a La Niña state, in which stronger-than-normal tradewinds are associated with enhanced zonal gradients in
SSTs and SLP. The cooling imposed in the central andeastern tropical Pacific leads to higher SLP by reducing
the flux of heat to the atmosphere, decreasing buoyancy,and vice versa for the warming imposed in the western
tropical Pacific and other ocean basins. The low-levelwinds respond to the enhanced pressure gradients,
leading to a region of low-level divergence over thecentral Pacific (Fig. 8b). As seen in Fig. 14, this divergence is associated with reduced convective rainfall,
which in turns provides the anomalous latent heatingthat drives the Rossby wave response.
One aspect of the results that we have not yet examined is the seasonality of the tropical forcing and why themost significant SH wind response to tropical SSTs occurs in autumn (MAM). Figure 14 shows seasonal maps

1 DECEMBER 2015

SCHNEIDER ET AL.

9365

FIG. 13. (a) Time series of ERAI 850-hPa zonal wind anomalies from the equatorial Pacific repeated from Fig. 3d.
The 850-hPa zonal wind anomalies from the Pacific sector of the Southern Ocean for (b) ERAI, and the ensemblemeans associated with (c) Full Forcing and (d) TSST. (e) The monthly PDO index based on the first EOF of detrended North Pacific SST anomalies (see Trenberth et al. 2014 for definition); (f) the Niño-3.4 index from the
NOAA/Climate Prediction Center. For clarity, each time series is smoothed by a 2-month running mean.

of the prescribed SST trends together with diagnostics ofthe responses in tropical convection and upper-levelatmospheric circulation. Consistent with the seasonalityof the Pacific sector wind response to tropical SST
forcing, the tropical convective and Rossby wave responses are also seasonal. In MAM (Fig. 14c), the200-hPa streamfunction field bears clear signatures ofpoleward-propagating Rossby waves from the tropics
into both hemispheres. In the SH high latitudes, the200-hPa streamfunction anomalies are focused on the
Pacific sector in MAM, in contrast to other seasons,when they are more diffuse and zonally symmetric,
consistent with the lower-tropospheric wind anomalies

in Fig. 8c. The waves originate from the cyclonicstreamfunction anomalies straddling the equator overthe cool SSTs between 1208W and the date line. As indicated by the velocity potential trends (Fig. 14d),
upper-level convergence is focused on this region, consistent with the reduced rainfall. Enhanced rainfall andupper-level divergence occur across the western Pacific,
completing an east–west dipole pattern that resembles
the Southern Oscillation.
The convective responses are different in JJA andSON than in MAM, even though the underlying SST
trend patterns are similar. In JJA and SON, the largest
negative convective rainfall trends and the largest

9366

JOURNAL OF CLIMATEVOLUME 28

FIG. 14. Maps showing diagnostics of the seasonal-mean convective and Rossby wave responses to tropical SST
forcing during 1979–2011. (left) Prescribed SST trends (color scale) and simulated 200-hPa streamfunction trends(gray contours); negative contours (dashed) and positive contours (solid), with an interval of 62 3 106 m2 s21(33 yr)21. (right) The 200-hPa velocity potential x trends (color bar), convective rainfall trends (colored contours),
and 200-hPa zonal wind climatology (gray contours), all from the model. Positive x trends indicate convergence,and negative trends indicate divergence. Negative rainfall trends are in green contours and positive rainfall trends
are in magenta contours, with intervals of 61.25, 61.5, and 61.75 mm day21 (33 yr)21. Zonal wind is contoured at
5 m s21, starting at 30 m s21.

upper-level convergence trends occur north of theequator (Figs. 14f,h), minimizing their influence on theSH circulation: the cyclonic center of action immediately south of the equator in the streamfunction field ishalf the magnitude it is in MAM. The seasonality of theconvective responses could be a function of the meanstate and the migration of the ITCZ into the summer
hemisphere. Additionally, Rossby wave propagation
depends on the location of convection with respect tothe position and strength of the subtropical jets (e.g., Seo

1 DECEMBER 2015

SCHNEIDER ET AL.

and Son 2012; Bao and Hartmann 2014; Li et al. 2015),
which are also shown in Fig. 14. There is a split jet in
MAM (Fig. 14d) and strong subtropical jets in JJA and
SON (Figs. 14f,h). Recent observational analyses(Adames and Wallace 2014) and model studies (Bao andHartmann 2014) suggest that Rossby waves extract energy from the exit regions of split jets but can be trappedin the tropics by strong subtropical jets.
In DJF, both the convective responses in the tropics(Fig. 14b) and the Rossby wave propagation in the SH(Fig. 14c) are rather weak. The weak subtropical jet in
DJF (Fig. 14b) likely plays a role in the lack of wave
propagation (e.g., Jin and Kirtman 2009; Ding et al.
2012). Instead, the summer response to tropical SST
anomalies projects onto the SAM, consistent with observational studies (e.g., L’Heureux and Thompson
2006; Ciasto et al. 2015). We conclude that the seasonality of the Pacific sector wind response to tropical SSTs
largely reflects the seasonality of the convective responses in the tropical Pacific, as well as the seasonalityof Rossby wave propagation.

4. Discussion
We have highlighted the seasonal and regional
asymmetries in the westerly wind trends over theSouthern Ocean during 1979–2011 and discussed thesein the context of the forced responses to tropical SSTs
and stratospheric ozone depletion as given by CAM4.
Although both low-frequency tropical variability andozone depletion have been linked with Antarctic climate change, ours is one of the first studies to directlycompare and quantify their relative importance. In lightof the simultaneous trends in tropical SSTs and in polar
stratospheric ozone depletion, 1979–2011 is a particularly interesting time period for our analysis. The tropical SST trends include the signature of the negative
phase of the PDO, marked by cooling in the eastern
Pacific. Warming trends across the rest of the tropicaloceans include the impacts of rising GHGs.
We find that both tropical SSTs and ozone depletion
contribute to the intensification of the westerly winds
over the Southern Ocean, with the largest trends in the
Pacific sector. In our experiments, nonozone radiative
forcings (including GHGs) do not have a significant rolein directly forcing the winds. Seasonally, the most significant trends in both the reanalysis and the simulated
zonally symmetric, SAM-like pattern occur in NDJ and
DJF. Depending on the ozone dataset, the ozonerelated contribution to the westerly wind trend in DJF
ranges from about 0.18 to 0.62 m s21 (33 yr)21, comparedwith the tropical SST contribution of about 0.56 m s21(33 yr)21 (Fig. 6). In the Pacific sector, both the model

9367

and observations show significant trends in the autumn,
and the model experiments suggest that the autumn
trend is largely driven by tropical SSTs.
Our results extend recent work suggesting an influence of the tropics on the summer SAM. A number ofstudies have shown significant interannual correlations
between eastern Pacific tropical SSTs and the SAM index in observations (e.g., L’Heureux and Thompson
2006; Ding et al. 2012; Schneider et al. 2012b; Ciasto
et al. 2015). Ding et al. (2014a) go a step further by removing the signal of ENSO variability from the observations and suggest that the SAM trend is driven in partby tropical warming, thereby providing observational
support for the modeling work of Lu et al. (2008) andSun et al. (2013). In a follow-up modeling study, Ding
et al. (2014b) show a tropical influence on the summer
SAM trend by relaxing the atmospheric state to observations in the tropics. Our results broadly confirm thisinfluence using a different model and methodology.
However, our analysis does not break down the zonallysymmetric response to tropical SST anomalies in termsof an ENSO-like and a global warming response (e.g.,
Lu et al. 2008). This will be the subject of future work.
This study joins a number of studies that have linked
recent atmospheric circulation trends over the Southern
Ocean and Antarctica to tropical variability (e.g., Ding
et al. 2011; Ding and Steig 2013; Li et al. 2014; 2015;
Simpkins et al. 2014; Schneider et al. 2012a). The general physical arguments explaining these linkages arebroadly consistent across the studies. For example, Ding
and Steig (2013) link a cyclonic circulation trend overthe Bellingshausen Sea with rapid warming on the
Antarctic Peninsula during 1979–2009, showing thatboth the circulation and warming trends are strongest in
MAM (Ding and Steig 2013). Consistent with our work
explaining the strengthening zonal winds in the Pacificsector, Ding and Steig (2013) attribute this circulation
trend to tropical Pacific forcing, using an atmosphericmodel coupled to a slab ocean in the extratropics. Similar to our TSST experiments, Ding and Steig (2013)
prescribe observed SSTs in the tropics. Both studies
attribute autumn circulation trends in the Pacific sector to a poleward-propagating Rossby wave originating over the equatorial Pacific between about 1208W
and 1808.
Earlier work by a similar team of authors (Ding et al.
2011) suggested that central tropical Pacific warming
during austral winter has driven an atmospheric Rossby
wave response, leading to anomalous high pressure in
the Amundsen–Bellingshausen Sea region. This agrees
with our work in illustrating that the high-latitude SH
circulation is sensitive to anomalous forcing in the central tropical Pacific. However, the scenario of central

9368

JOURNAL OF CLIMATE

Pacific warming and a strong wintertime anticyclonic
circulation trend in the high-latitude SH does not fit thereanalysis or SST trends during 1979–2011. The SH
circulation trends in JJA are characterized by a small,
negative SAM trend with stronger centers of action overthe continent than over the ocean (their Fig. 1 and ourFig. 4). The negative SAM trend is within the ensemble
spread of our experiments (Fig. 6a). In contrast, Ding
et al.’s (2011) central Pacific warming experimentproduces a localized wave train pattern in the South
Pacific that does not resemble the SAM (their Fig. 4).
The discrepancy in the sign of the central tropical Pacific
SST change can be reconciled by the different time periods and methods used. Ding et al. (2011) apply a superposed epoch analysis to the SSTs, differencing theperiods 1979–93 and 1994–2009. The latter period mixes
the two phases of the PDO (Fig. 13e), and the epoch
difference shows a small warming in the central tropical
Pacific. If the latter epoch instead starts in 1999, then the
PDO pattern, with cooling in the central and eastern
Pacific and warming in the western Pacific, is evident in
the epoch difference map (see Fig. 3 in Trenberth et al.
2014). Ding et al.’s (2011) experiment is highly idealized
in that a monopole of warming is prescribed over thecentral tropical Pacific, the magnitude of which is scaled
to one of the leading patterns of SST covariability with
SH geopotential height, not to the actual SST trend. In
contrast, our TSST experiment is based on the observed
SST field, and the anomalous negative heating over thecentral tropical Pacific arises from the zonal SST gradient. This leads to an extratropical atmospheric circulation response that resembles the trends in the reanalysis(Fig. 5).
In our Tropical SST experiment, the Rossby wave
response is weaker in winter and spring than in autumn,
resulting in an insignificant wind response in the SH.
This seems inconsistent with the findings of Li et al.
(2014) and Simpkins et al. (2014), who connected tropical Atlantic SST trends with SH winter and spring circulation trends, respectively. Our experiments show
some support for a role of the Atlantic in JJA and SON
evident in the increased convective rainfall and upperlevel divergence during these seasons over the Atlantic(Figs. 14f,h). However, the Atlantic trends are muchsmaller than the convective anomalies over the Pacific,
suggesting that the Pacific plays the stronger role. Also,
unlike Li et al. (2014), who suggest that tropical Pacific–
Antarctic connections are limited to interannual ENSO
time scales, we find significant decadal variability in thetropical Pacific is clearly linked with circulation anomalies over the Southern Ocean in both the observations
and our model experiments (Fig. 13). While our results
do not rule out a role for the tropical Atlantic in the SH

VOLUME 28

circulation trends, care must be taken to carefullycompare the results of idealized experiments to observations. In general, if one region is forced in an AGCM
experiment, the rest of the global atmosphere will respond, and the response patterns will tend to project
onto the preferred modes of variability, such as thesecond mode of zonal wind variability (Fig. 2d), which isrelated to fluctuations in the Amundsen Sea low. The Li
et al. (2014) and Simpkins et al. (2014) experiments are
more similar to Ding et al. (2011) than to the TSST experiment described here, in that only one region of thetropical ocean surface is forced. Finally, while theseprevious studies have associated Rossby wave forcing
with anomalous heating over the tropical oceans,anomalous cooling is equally effective at driving wavelike responses (e.g., Trenberth et al. 2014).
Our results highlighting the role of tropical SST variability in SH zonal wind trends since 1979 might seem tocontradict previous work suggesting that ozone depletion has been the main driver of SH atmosphericcirculation trends (e.g., Polvani et al. 2011; Previdi andPolvani 2014; Thompson et al. 2011). We hypothesizedthat the coincidence of ozone loss with the PDO trend isimportant for understanding the zonal wind trends over
the Southern Ocean during 1979–2011. The studies
emphasizing ozone’s dominant role in the wind trends
analyzed somewhat different time periods. According to
the forcing datasets, the downward trend in ozone concentration largely occurred between the late 1960s andmid-1990s, with a large fraction of the total ozone loss
occurring before the 1979 start date of our analysis(Fig. 1.). In contrast, the PDO was positive throughout much of the 1980s and 1990s before shifting to its
negative phase in 1998 (Fig. 13e). Thus, the period ofstrongest ozone loss occurred prior to the most recent
major shift in tropical climate.
As a point of discussion, we briefly investigate
whether ozone has a larger relative influence on thecirculation over the primary period of ozone loss, theperiod 1960–2000 (consistent with Polvani et al. 2011).
During this period, there is very little trend in the PDO,
as its two phases (negative in the 1960s–70s, positive inthe 1980s–90s) largely cancel (see Fig. 2 in Trenberth
et al. 2014). For the SAM-like pattern (Fig. 15), the Full
Forcing ensemble-mean trend has seasonality similar to
the 1979–2011 period, with the largest trends in the latespring and summer and smallest trends in winter(Fig. 15a). Comparing this trend to the ensemble-mean
trends in the TSST (Fig. 15b) and Ozone ensembles(Fig. 15c), it is apparent that ozone dominates theoverall trend in the Full Forcing ensemble. The TSST
ensemble mean has no significant trend in any season,
while the Ozone ensemble mean has positive and1 DECEMBER 2015

SCHNEIDER ET AL.

FIG. 15. As in Fig. 6, but 1960–2000 is used instead of 1979–2011.
The observed zonal wind trends are inferred from the trend of the
Marshall (2003) SAM index: the detrended monthly SAM index
was regressed on the detrended index of 850-hPa zonal wind from
ERAI (Fig. 3e), and the regression coefficient was multiplied by
the 1960–2000 seasonal-mean trends in the SAM index.

significant trends from the late spring through latesummer. The SNRs in DJF for the Full Forcing andOzone ensembles are 2.5 and 1.6, respectively. In the
Pacific sector, the ensemble-mean trends are largely
insignificant during 1960–2000 (not shown). Thus, weconclude that our results are consistent with previous

9369

studies that emphasized the dominant role of ozone
depletion in the SH atmospheric circulation trends
during 1960–2000. Further, our work extends this previous research to illustrate that the relative roles oftropical SSTs and ozone depletion are dependent on thetime period considered.
The choice of the SPARC or WACCM ozone dataset
does not change the key results in either the 1979–2011or the 1960–2000 period. For 1979–2011, ozone forcing
alone is insufficient to explain the DJF SAM-like trend
in the Full Forcing ensemble mean. WACCM ozone
produces a larger ensemble-mean wind response thandoes SPARC ozone, but there is considerable overlap
among the ensemble members (Fig. 6c), even though thestratospheric cooling trends are more distinguishable(Fig. 10c). Large intrinsic variability in the troposphericwind field blurs the distinctions between the responses
to the two ozone datasets. For the longer 1960–2000
period, when the intrinsic variability is somewhat more
muted and the ensemble-mean ozone-induced trends
are more statistically significant, the zonal wind responses to SPARC and WACCM ozone forcing areeven more similar (Fig. 15c and Fig. S2 in the supplemental material). For the 850-hPa 508–708S DJF zonal
wind index, the 1960–2000 trend associated withSPARC is 0.24 m s21 (10 yr)21, while the trend associated with WACCM is approximately 9% larger,
0.26 m s21 (10 yr)21. Of the 10 Ozone ensemble members, only one exhibits a negative trend, and this one
belongs to the WACCM ensemble (Fig. 15c).
The importance of the relative phasing of tropical
decadal variability with ozone depletion has implications for the interpretation of free-running, coupledmodels, whose externally forced (e.g., ensemble mean)
circulation trends over the Southern Ocean do not
capture the zonal asymmetry of recent changes(Haumann et al. 2014) nor the strength of the zonal wind
trend in summer (Mahlstein et al. 2013). While thesemodels could be missing or misrepresenting key physics,
one process they likely lack is the observed evolution oftropical climate, which we find could explain both ofthese deficiencies in their circulation trends over theSouthern Ocean. In turn, the wind field is important for
many other aspects of Southern Ocean climate dynamics. For instance, sea ice–ocean models suggest that recent trends in the Southern Ocean sea ice cover are a
response to the wind forcing (Zhang 2014; Holland
et al. 2014).
The time period dependence of the relative roles oftropical SSTs and ozone, and the difficulties in distinguishing the responses to the two ozone datasets, inpart reflects the role of intrinsic, unforced atmosphericvariability in shaping circulation trends on regional

9370

JOURNAL OF CLIMATE

scales. In interpreting the observed SAM trend, it isimportant to recall that the SAM is the leading mode ofvariability in the SH atmospheric circulation and can
exhibit trends over various time scales that arise simply
from internal dynamics. Therefore, caution should be
exercised when associating observed trends in recent
decades with the forced response to ozone (e.g., Kang
et al. 2011), as the observed trends may also be explained by a combination of intrinsic variability and aforced response to tropical SST trends. Clearly identifying the signature of ozone depletion in SH climate
requires a long time period or an evaluation of multiple
variables, such as the lower polar stratospheric temperature. According to our results, the most robust evidence for a significant role of ozone depletion in the
tropospheric circulation trend lies in the combination of:(i) the seasonality of the trend in the SAM-like pattern(e.g., Fig. 6); (ii) the seasonal signature of ozone in thelower polar stratospheric temperature (e.g., Fig. 10); and(iii) the correlation of the zonal wind time series with thelower polar stratospheric temperature (e.g., Fig. 11).
As the interaction of ozone depletion and tropicallygenerated variability with intrinsic, unforced atmospheric variability has played a key role in shaping
Antarctic and Southern Ocean climate change over the
past 30 years, it is almost certain that similar interactions
will play a key role in climate change over the next 30
years. Such interactions may involve a different combination of external forcings than was important for the
recent past. For example, stratospheric ozone levels are
expected to recover, while GHGs are likely to rapidly
increase. Understanding the interactions of these trends
with internal variability should continue to be a topic of
ongoing study.
Acknowledgments. The authors thank three anonymous reviewers for constructive reviews that led to amuch improved paper. We thank Adam Phillips for
conducting the Tropical SST ensemble, for assistancewith configuring and postprocessing the other simulations, and for computing the monthly PDO index. Lorenzo Polvani engaged in valuable discussions that led tothe direct comparison of SPARC- and WACCM-driven
simulations. James Screen kindly provided Antarctic
upper-air temperature data based on Screen andSimmonds (2012). The Niño-3.4 index was obtained
from the NOAA/Climate Prediction Center (http://
www.cpc.ncep.noaa.gov/data/indices/sstoi.indices). David
Schneider was supported by National Science Foundation (NSF) Grants 1048899 and 1235231. Tinting Fan
was supported by the China Scholarship Council. The
figures were produced with the NCAR Command
Language software package. The simulations discussed

VOLUME 28

here can be accessed through the Climate Variability
and Change Working Group of the Community Earth
System Model (http://www.cesm.ucar.edu/working_groups/
Climate/experiments/cesm1.0/).
REFERENCES
Adames, Á. F., and J. M. Wallace, 2014: Three-dimensional
structure and evolution of the MJO and its relation to themean flow. J. Atmos. Sci., 71, 2007–2026, doi:10.1175/
JAS-D-13-0254.1.
Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation
analysis (1979–present). J. Hydrometeor., 4, 1147–1167,
doi:10.1175/1525-7541(2003)004,1147:TVGPCP.2.0.CO;2.
Bao, M., and D. L. Hartmann, 2014: The response to MJO-likeforcing in a nonlinear shallow-water model. Geophys. Res.
Lett., 41, 1322–1328, doi:10.1002/2013GL057683.
Bracegirdle, T. J., 2013: Climatology and recent increase of westerly winds over the Amundsen Sea derived from six reanalyses. Int. J. Climatol., 33, 843–851, doi:10.1002/joc.3473.
——, and G. J. Marshall, 2012: The reliability of Antarctic tropospheric pressure and temperature in the latest global
reanalyses. J. Climate, 25, 7138–7146, doi:10.1175/
JCLI-D-11-00685.1.
Ciasto, L. M., G. R. Simpkins, and M. H. England, 2015: Teleconnections between tropical Pacific SST anomalies andextratropical Southern Hemisphere climate. J. Climate, 28,
56–65, doi:10.1175/JCLI-D-14-00438.1.
Cionni, I., and Coauthors, 2011: Ozone database in support of
CMIP5 simulations: Results and corresponding radiative
forcing. Atmos. Chem. Phys., 11, 11 267–11 292, doi:10.5194/
acp-11-11267-2011.
Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis:
Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/
qj.828.
Deser, C., and A. S. Phillips, 2009: Atmospheric circulation trends,
1950–2000: The relative roles of sea surface temperature
forcing and direct atmospheric radiative forcing. J. Climate,22, 396–413, doi:10.1175/2008JCLI2453.1.
——, ——, M. A. Alexander, and B. V. Smoliak, 2014: Projecting
North American climate over the next 50 years: Uncertainty
due to internal variability. J. Climate, 27, 2271–2296,
doi:10.1175/JCLI-D-13-00451.1.
Ding, H., R. J. Greatbatch, and G. Gollan, 2014a: Tropical impact
on the interannual variability and long-term trend of theSouthern Annular Mode during austral summer from 1960/
1961 to 2001/2002. Climate Dyn., 44, 2215–2228, doi:10.1007/
s00382-014-2299-x.
——, ——, and ——, 2014b: Tropical influence independent of
ENSO on the austral summer Southern Annular Mode. Geophys. Res. Lett., 41, 3643–3648, doi:10.1002/2014GL059987.
Ding, Q., and E. J. Steig, 2013: Temperature change on the Antarctic Peninsula linked to the tropical Pacific. J. Climate, 26,
7570–7585, doi:10.1175/JCLI-D-12-00729.1.
——, ——, D. S. Battisti, and M. Küttel, 2011: Winter warming inWest Antarctica caused by central tropical Pacific warming.
Nat. Geosci., 4, 398–403, doi:10.1038/ngeo1129.
——, ——, ——, and J. M. Wallace, 2012: Influence of the tropics
on the southern annular mode. J. Climate, 25, 6330–6348,
doi:10.1175/JCLI-D-11-00523.1.

1 DECEMBER 2015

SCHNEIDER ET AL.

England, M. H., and Coauthors, 2014: Recent intensification of winddriven circulation in the Pacific and the ongoing warming hiatus.
Nat. Climate Change, 4, 222–227, doi:10.1038/nclimate2106.
Eyring, V., and Coauthors, 2013: Long-term ozone changes andassociated climate impacts in CMIP5 simulations. J. Geophys.
Res., 118, 5029–5060, doi:10.1002/jgrd.50316.
Gent, P. R., and Coauthors, 2011: The Community Climate System
Model version 4. J. Climate, 24, 4973–4991, doi:10.1175/
2011JCLI4083.1.
Gillett, N. P., and D. W. J. Thompson, 2003: Simulation of recent
Southern Hemisphere climate change. Science, 302, 273–275,
doi:10.1126/science.1087440.
Grise, K. M., and L. M. Polvani, 2014: The response of midlatitude
jets to increased CO2 : Distinguishing the roles of sea surface
temperature and direct radiative forcing. Geophys. Res. Lett.,
41, 6863–6871, doi:10.1002/2014GL061638.
Hassler, B., P. J. Young, R. W. Portmann, G. E. Bodeker, J. S.
Daniel, K. H. Rosenlof, and S. Solomon, 2013: Comparison of
three vertically resolved ozone data sets: Climatology, trends
and radiative forcings. Atmos. Chem. Phys., 13, 5533–5550,
doi:10.5194/acp-13-5533-2013.
Haumann, F. A., D. Notz, and H. Schmidt, 2014: Anthropogenic
influence on recent circulation-driven Antarctic sea ice changes.
Geophys. Res. Lett., 41, 8429–8437, doi:10.1002/2014GL061659.
Holland, P. R., and R. Kwok, 2012: Wind-driven trends in Antarctic
sea-ice drift. Nat. Geosci., 5, 872–875, doi:10.1038/ngeo1627.
——, N. Bruneau, C. Enright, M. Losch, N. T. Kurtz, and R. Kwok,
2014: Modeled trends in Antarctic sea ice thickness. J. Climate,27, 3784–3801, doi:10.1175/JCLI-D-13-00301.1.
Hurrell, J. W., J. J. Hack, D. Shea, J. M. Caron, and J. Rosinski,
2008: A new sea surface temperature and sea ice boundary
dataset for the Community Atmosphere Model. J. Climate, 21,5145–5153, doi:10.1175/2008JCLI2292.1.
Jin, D., and B. P. Kirtman, 2009: Why the Southern Hemisphere
ENSO responses lead ENSO. J. Geophys. Res., 114, D23101,
doi:10.1029/2009JD012657.
Kang, S. M., L. M. Polvani, J. C. Fyfe, and M. Sigmond, 2011:
Impact of polar ozone depletion on subtropical precipitation.
Science, 332, 951–954, doi:10.1126/science.1202131.
Kay, J. E., and Coauthors, 2015: The Community Earth System
Model (CESM) Large Ensemble project: A community resource for studying climate change in the presence of internal
climate variability. Bull. Amer. Meteor. Soc., 96, 1333–1349,
doi:10.1175/BAMS-D-13-00255.1.
Kushner, P. J., I. M. Held, and T. L. Delworth, 2001: Southern
Hemisphere atmospheric circulation response to global
warming. J. Climate, 14, 2238–2249, doi:10.1175/
1520-0442(2001)014,0001:SHACRT.2.0.CO;2.
Le Quéré, C., and Coauthors, 2007: Saturation of the Southern
Ocean CO2 sink due to recent climate change. Science, 316,
1735–1738, doi:10.1126/science.1136188.
L’Heureux, M. L., and D. W. J. Thompson, 2006: Observed relationships between the El Niño–Southern Oscillation and theextratropical zonal-mean circulation. J. Climate, 19, 276–287,
doi:10.1175/JCLI3617.1.
Li, X., D. M. Holland, E. P. Gerber, and C. Yoo, 2014: Impacts of thenorth and tropical Atlantic Ocean on the Antarctic Peninsulaand sea ice. Nature, 505, 538–542, doi:10.1038/nature12945.
——, E. P. Gerber, D. M. Holland, and C. Yoo, 2015: A Rossby
wave bridge from the tropical Atlantic to West Antarctica.
J. Climate, 28, 2256–2273, doi:10.1175/JCLI-D-14-00450.1.
Lovenduski, N. S., N. Gruber, and S. C. Doney, 2008: Toward amechanistic understanding of the decadal trends in the

9371

Southern Ocean carbon sink. Global Biogeochem. Cycles, 22,
GB3016, doi:10.1029/2007GB003139.
Lu, J., G. Chen, and D. M. W. Frierson, 2008: Response of the zonal
mean atmospheric circulation to El Niño versus global warming. J. Climate, 21, 5835–5851, doi:10.1175/2008JCLI2200.1.
Mahlstein, I., P. R. Gent, and S. Solomon, 2013: Historical Antarctic mean sea ice area, sea ice trends, and winds in CMIP5
simulations. J. Geophys. Res., 118, 5105–5110, doi:10.1002/
jgrd.50443.
Marsh, D. R., M. J. Mills, D. E. Kinnison, J.-F. Lamarque,
N. Calvo, and L. M. Polvani, 2013: Climate change from 1850
to 2005 simulated in CESM1(WACCM). J. Climate, 26, 7372–
7391, doi:10.1175/JCLI-D-12-00558.1.
Marshall, G. J., 2003: Trends in the southern annular mode fromobservations and reanalyses. J. Climate, 16, 4134–4143,
doi:10.1175/1520-0442(2003)016,4134:TITSAM.2.0.CO;2.
——, 2007: Half-century seasonal relationships between theSouthern Annular mode and Antarctic temperatures. Int.
J. Climatol., 27, 373–383, doi:10.1002/joc.1407.
Neale, R. B., J. Richter, S. Park, P. H. Lauritzen, S. J. Vavrus, P. J.
Rasch, and M. Zhang, 2013: The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully
coupled experiments. J. Climate, 26, 5150–5168, doi:10.1175/
JCLI-D-12-00236.1.
Neely, R. R., D. R. Marsh, K. L. Smith, S. M. Davis, and L. M.
Polvani, 2014: Biases in Southern Hemisphere climate trends
induced by coarsely specifying the temporal resolution ofstratospheric ozone. Geophys. Res. Lett., 41, 8602–8610,
doi:10.1002/2014GL061627.
Nicolas, J. P., and D. H. Bromwich, 2014: New reconstruction of
Antarctic near-surface temperatures: Multidecadal trends andreliability of global reanalyses. J. Climate, 27, 8070–8093,
doi:10.1175/JCLI-D-13-00733.1.
Polvani, L. M., D. W. Waugh, G. J. P. Correa, and S.-W. Son, 2011:
Stratospheric ozone depletion: The main driver of twentiethcentury atmospheric circulation changes in the Southern Hemisphere. J. Climate, 24, 795–812, doi:10.1175/2010JCLI3772.1.
Previdi, M., and L. M. Polvani, 2014: Climate system response to
stratospheric ozone depletion and recovery. Quart. J. Roy.
Meteor. Soc., 140, 2401–2419, doi:10.1002/qj.2330.
Raphael, M., and Coauthors, 2015: The Amundsen Sea low: Variability, change, and impact on Antarctic climate. Bull. Amer.
Meteor. Soc., 97, doi:10.1175/BAMS-D-14-00018.1, in press.
Sallée, J. B., K. G. Speer, and S. R. Rintoul, 2010: Zonally asymmetric response of the Southern Ocean mixed-layer depth tothe southern annular mode. Nat. Geosci., 3, 273–279,
doi:10.1038/ngeo812.
Santer, B. D., T. M. L. Wigley, J. S. Boyle, D. J. Gaffen, J. J. Hnilo,
D. Nychka, D. E. Parker, and K. E. Taylor, 2000: Statisticalsignificance of trends and trend differences in layer-averageatmospheric temperature time series. J. Geophys. Res., 105,
7337–7356, doi:10.1029/1999JD901105.
Schneider, D. P., C. Deser, and Y. Okumura, 2012a: An assessment
and interpretation of the observed warming of West Antarctica in the austral spring. Climate Dyn., 38, 323–347,
doi:10.1007/s00382-010-0985-x.
——, Y. Okumura, and C. Deser, 2012b: Observed Antarctic interannual climate variability and tropical linkages. J. Climate,25, 4048–4066, doi:10.1175/JCLI-D-11-00273.1.
Screen, J. A., and I. Simmonds, 2012: Half-century air temperaturechange above Antarctica: Observed trends and spatial reconstructions. J. Geophys. Res., 117, D16108, doi:10.1029/
2012JD017885.

9372

JOURNAL OF CLIMATE

Seo, K.-H., and S.-W. Son, 2012: The global atmospheric circulation response to tropical diabatic heating associated with the
Madden–Julian oscillation during northern winter. J. Atmos.
Sci., 69, 79–96, doi:10.1175/2011JAS3686.1.
Simpkins, G. R., S. McGregor, A. S. Taschetto, L. M. Ciasto, andM. H. England, 2014: Tropical connections to climatic change
in the extratropical Southern Hemisphere: The role of Atlantic SST trends. J. Climate, 27, 4923–4936, doi:10.1175/
JCLI-D-13-00615.1.
Son, S.-W., and Coauthors, 2010: Impact of stratospheric ozone
on Southern Hemisphere circulation change: A multimodel
assessment. J. Geophys. Res., 115, D00M07, doi:10.1029/
2010JD014271.
Staten, P. W., J. J. Rutz, T. Reichler, and J. Lu, 2012: Breaking
down the tropospheric circulation response by forcing. Climate Dyn., 39, 2361–2375, doi:10.1007/s00382-011-1267-y.
Sun, L., G. Chen, and J. Lu, 2013: Sensitivities and mechanismsof the zonal mean atmospheric circulation response totropical warming. J. Atmos. Sci., 70, 2487–2504, doi:10.1175/
JAS-D-12-0298.1.
Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes inthe extratropical circulation. Part I: Month-to-month variability. J. Climate, 13, 1000–1016, doi:10.1175/1520-0442(2000)
013,1000:AMITEC.2.0.CO;2.
——, and S. Solomon, 2002: Interpretation of recent Southern
Hemisphere climate change. Science, 296, 895–899,
doi:10.1126/science.1069270.
——, ——, P. J. Kushner, M. H. England, K. M. Grise, and D. J.
Karoly, 2011: Signatures of the Antarctic ozone hole in

VOLUME 28

Southern Hemisphere surface climate change. Nat. Geosci., 4,
741–749, doi:10.1038/ngeo1296.
Trenberth, K. E., J. T. Fasullo, G. Branstator, and A. S. Phillips,
2014: Seasonal aspects of the recent pause in surface warming.
Nat. Climate Change, 4, 911–916, doi:10.1038/nclimate2341.
Vallis, G. K., P. Zurita-Gotor, C. Cairns, and J. Kidston, 2014:
Response of the large-scale structure of the atmosphere to
global warming. Quart. J. Roy. Meteor. Soc., 141, 1479–1501,
doi:10.1002/qj.2456.
van Vuuren, D. P., and Coauthors, 2011: The representative concentration pathways: An overview. Climatic Change, 109, 5–
31, doi:10.1007/s10584-011-0148-z.
Waugh, D. W., L. Oman, P. A. Newman, R. S. Stolarski, S. Pawson,
J. E. Nielsen, and J. Perlwitz, 2009: Effect of zonal asymmetries in stratospheric ozone on simulated Southern Hemisphere climate trends. Geophys. Res. Lett., 36, L18701,
doi:10.1029/2009GL040419.
Wilson, A. B., D. H. Bromwich, K. M. Hines, and S. Wang, 2014: El
Niño flavors and their simulated impacts on atmospheric circulation in the high southern latitudes. J. Climate, 27, 8934–
8955, doi:10.1175/JCLI-D-14-00296.1.
Young, P. J., S. M. Davis, B. Hassler, S. Solomon, and K. H.
Rosenlof, 2014: Modeling the climate impact of Southern
Hemisphere ozone depletion: The importance of the ozone
data set. Geophys. Res. Lett., 41, 9033–9039, doi:10.1002/
2014GL061738.
Zhang, J., 2014: Modeling the impact of wind intensification on
Antarctic sea ice volume. J. Climate, 27, 202–214, doi:10.1175/
JCLI-D-12-00139.1.