4048

JOURNAL OF CLIMATEVOLUME 25

Observed Antarctic Interannual Climate Variability and Tropical Linkages
DAVID P. SCHNEIDER, YUKO OKUMURA, AND CLARA DESER
Climate and Global Dynamics Division, Earth System Laboratory, National Center for Atmospheric Research,* Boulder, Colorado(Manuscript received 7 May 2011, in final form 21 November 2011)
ABSTRACTThis study reviews the mechanisms associated with Antarctic–tropical climate linkages and presents new
analyses of the seasonality and spatial patterns of tropical climate signals in the Antarctic for the late 1950s to
the present. Tropical climate signals are primarily communicated to the Antarctic via the Pacific–SouthAmerica (PSA) pattern and the southern annular mode (SAM). The impacts of these circulation patterns andtheir tropical linkages are evident in regressions of seasonally stratified Antarctic station temperature data
and annually resolved ice core records on global fields of sea surface temperature, sea level pressure, andprecipitation. Temperature and ice core anomalies in the Antarctic Peninsula region and adjoining areas ofWest Antarctica are significantly impacted by the PSA, interpreted as a Rossby wave train driven byanomalous tropical deep convection during ENSO events. This pattern is most evident in the austral spring,consistent with recent studies, suggesting that atmospheric conditions for Rossby wave propagation are mostfavorable during this season. During austral summer at the peak of the ENSO cycle, temperature anomalies at
East Antarctic coastal stations exhibit significant correlations with tropical Pacific anomalies. This linkage
reflects the influence of anomalous tropical heating on the position and strength of the subtropical jets and isconsistent with changes in eddy momentum fluxes that alter the mean meridional circulation associated with
the SAM. Of the ice cores that exhibit tropical linkages, most tend to be associated with the PSA teleconnection. The implications of the study’s findings for understanding Antarctic climate variability and climate change from seasonal to decadal time scales are also discussed.

1. Introduction
El Niño–Southern Oscillation (ENSO) arises fromair–sea interactions in the tropical Pacific Ocean and is
widely considered to be the dominant source of global
climate variability on interannual to decadal time scales(e.g., Trenberth et al. 1998; Neelin et al. 1998; Zhang
et al. 1997). The presence of ENSO signals in the highlatitude Southern Hemisphere (SH) in a variety ofvariables has been recognized for some time (e.g.,Trenberth 1975; Mo and White 1985; Savage et al. 1988;
Karoly 1989), but the associated teleconnection patterns
and their impacts are not widely appreciated. Givenevidence that changes in tropical climate are influencing* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: David P. Schneider, Climate andGlobal Dynamics Division, Earth System Laboratory, National Center for Atmospheric Research, Box 3000, Boulder, CO 80307-3000.
E-mail: dschneid@ucar.edu
DOI: 10.1175/JCLI-D-11-00273.1
Ó 2012 American Meteorological Society

trends in Antarctic temperature, sea ice (Ding et al.
2011; Schneider et al. 2012), and possibly the ice sheet
itself (Steig et al. 2012), it is important to place our understanding of Antarctic–tropical linkages on firmer
ground than has been previously possible (Turner
2004).
A well-known ENSO teleconnection pattern is the
Pacific–North America (PNA) pattern (e.g., Wallace
and Gutzler 1981). Although the PNA can vary independently of ENSO, the Rossby wave-forcing generated
by ENSO events projects onto the PNA. An El Niñoevent is associated with the positive phase of the PNA
pattern, characterized by a deep Aleutian low, highpressure over western Canada, and low pressure anomalies over the southern tier of the United States (e.g.,
Wallace and Gutzler 1981).
The SH’s counterpart to the PNA is the Pacific–SouthAmerica (PSA) pattern (e.g., Ghil and Mo 1991; Mo and
Higgins 1998; Mo 2000). Some authors have described
two PSA patterns in quadrature as being part of a propagating wave that can be excited by anomalous tropicalconvection (e.g., Mo and Higgins 1998; Mo and Paegle

15 JUNE 2012

SCHNEIDER ET AL.

2001), though others view the PSA patterns as geographically fixed standing waves that are only correlatedwith ENSO-related tropical convection anomalies in
austral spring (Robertson and Mechoso 2003). The first
PSA pattern is characterized as a wave train extending
from New Zealand across the South Pacific to southern
South America. The second PSA pattern has a characteristic zonal wave-3 structure over the Southern Ocean(Mo and Higgins 1998). As we will show, the first PSA
pattern comes out directly through regressions of atmospheric circulation data upon tropical indices [e.g., theSouthern Oscillation index (SOI)]; it is also the mainfeature in the SH of circulation anomalies for composites of El Niño or La Niña events (e.g., Deser et al. 2010).
Therefore, we focus only on the first PSA pattern, in
accordance with the view that it is the main response to
ENSO in the SH extratropics on the interannual timescale (e.g., Mo and Paegle 2001).
This paper also highlights the significant interactionsof the southern annular mode (SAM) and ENSO activity (e.g., Seager et al. 2003; L’Heureux and Thompson
2006; Fogt et al. 2011), and their importance to Antarctic
temperature anomalies in the austral summer. The SAM
is a seesaw of atmospheric mass between the mid- andhigh latitudes of the SH (e.g., Thompson et al. 2000; Fogt
and Bromwich 2006). The recognition that the SAM islinked with ENSO variability is relatively new; although
Karoly (1989) described a SAM-like zonally symmetricstructure in the austral summer atmospheric circulation
anomalies associated with ENSO events, he linked thisto Rossby wave forcing. However, Seager et al. (2003)
promoted a new hypothesis that ENSO variability influences the extratropical atmospheric circulation via amechanism distinct from a Rossby wave train. This alternative mechanism involves eddy–mean flow interactions and associated anomalies in the zonally symmetriccirculation driven by anomalous heating throughout thetroposphere in the tropics. As discussed below, otherstudies have since refined this idea (e.g., Seager et al.
2005, 2010; L’Heureux and Thompson 2006; Fogt et al.
2011). We present corroborating evidence that thismechanism is real and that it has a significant influenceon temperature anomalies over Antarctica.
Concerns have been raised about the strength andconsistency of ENSO teleconnections over Antarctic
and the Southern Ocean (e.g., Turner 2004; Cullather
et al. 1996; Fogt and Bromwich 2006; Gregory andNoone 2008). Composites of seasonal mean atmosphericcirculation anomalies indicate that ENSO teleconnections are present year-round in the Pacific sector of the
Antarctic (e.g., Trenberth and Caron 2000; Deser et al.
2010). However, the circulation patterns exhibit significant changes in strength and shape in different seasons,4049

and may be different from one event or decadal epoch
to the next (e.g., Turner 2004; Cullather et al. 1996; Fogt
and Bromwich 2006; Gregory and Noone 2008). The
apparent instability or weakness of ENSO-related circulation anomalies in the Antarctic has been partly
attributed to the relative dominance of the SAM. It
is common in analyses of SH atmospheric circulation
anomalies to treat the SAM and PSA patterns as distinct
modes with their own time scales and physics (e.g., Mo
2000; Cai and Watterson 2002). Indeed, the SAM and
PSA patterns are often defined as the first and second empirical orthogonal functions (EOFs) of SH geopotential height or sea level pressure (SLP) anomalies,
respectively (Mo 2000; Cai and Watterson 2002; Schneider
et al. 2004). The SAM is an intrinsic mode of atmospheric variability (e.g., Limpasuvan and Hartmann 2000)
and as such can vary independently of ENSO.
Part of the apparent inconsistency of the PSA patternin association with ENSO events may be attributed tothe seasonality of the SH atmospheric circulation, which
has largely not been considered in previous studies of
ENSO teleconnections in Antarctica and the Southern
Ocean. For example, the subtropical and polar front jetschange in their relative positions and strengths (e.g.,
Bals-Elsholz et al. 2001; Williams et al. 2007), which has
implications for the interactions of Rossby wave energy
with the storm tracks. An interesting observation is that
although the peak amplitude of ENSO-related tropical
SST anomalies occurs in the austral summer [December–
February (DJF)], the peak amplitude of the PSA anomalies during ENSO episodes occurs in the austral spring[September–November (SON); e.g., Jin and Kirtman2009)]. The analysis of Jin and Kirtman (2009) showedthat the SH Rossby wave source is strongest in the austral
spring and abruptly weakens in the austral summer.
Therefore, the primary SH response to ENSO—namely,
the PSA wave train—precedes the peak amplitude of
SST anomalies in the tropical Pacific by about one season.
In support of this hypothesis, Mo (2000) showed the PSA
pattern to be most significantly correlated with tropical
SSTs in the austral spring. In modeling experiments with
ENSO forcing shifted by 6 months, the NH response
preceded ENSO peak forcing, suggesting that the extratropical patterns forced by ENSO depend on the seasonality of the extratropical atmosphere (Jin and Kirtman2009).
Describing a second mechanism of tropical–extratropicallinkages, Seager et al. (2003) argued that during an El
Niño event, anomalous tropical heating strengthens themeridional temperature gradient, enhancing the subtropical jets, which act as waveguides to steer transient
eddies on a more equatorward and zonal path than in the
climatological mean flow. Associated with the equatorward

4050

JOURNAL OF CLIMATETABLE 1. Names of locations corresponding to labels in Fig. 2.
Boldface names indicated records used for the one-point regression maps in Figs. 4, 5, 8, and 9. Parentheses in the stations
section list start year of record as used in this study, latitude, andlongitude. Parentheses in the ice cores section list end year of therecord as used in this study, latitude, and longitude.
Stations
1. Amundsen–Scott South Pole (1958; 908S, 08W)
2. Novolazarevskaya (1961; 70.88S, 11.88E)
3. Syowa (1966; 69.08S, 39.68E)
4. Mawson (1958; 67.68S, 62.98E)
5. Davis (1958; 68.68S, 78.08E)
6. Mirny (1958; 66.58S, 93.08E)
7. Vostok (1958; 78.58S, 106.98E)
8. Casey (1959; 66.38S, 110.58E)
9. Dumont D’Urville (1958; 66.78S, 140.08E)
10. Scott Base (1958; 77.98S, 166.78E)
11. Rothera (1977; 67.58S, 68.18W)
12. Faraday (1958; 65.48S, 64.48W)
13. O’Higgins (1963; 63.38S, 57.98W)
14. Esperanza (1958; 63.48S, 57.08W)
15. Marambio (1971; 64.28S, 56.78W)
16. Orcadas (1958; 63.38S, 44.78W)
17. Halley (1958; 75.58S, 26.48W)
18. Neumayer (1982; 70.78S, 8.48W)
Ice cores
A. Dronning Maud Land (1997; 70.668S, 08E)
B. Vostok snow pit (1999; 78.08S, 1068E)
C. Law Dome 2000 (1999; 66.788S, 112.828E)
D. Talos Dome (1996; 72.88S, 159.758E)
E. Siple Dome A (1993; 81.658S, 148.818W)
F. ITASE 00–5 (1999; 77.688S, 123.998W)
G. ITASE 00–1 (1999; 79.388S, 111.238W)
H. ITASE 02–4 (1999; 86.58S, 107.998W)
I. ITASE 01–2 (1999; 77.848S, 102.918W)
J. ITASE 01–3 (1999; 78.128S, 95.658W)
K. ITASE 01–5 (1999; 77.068S, 89.148W)
L. Siple station (1983; 75.928S, 84.158W)
M. Gomez (1980; 74.028S, 70.638W)
N. Dyer Plateau (1989; 70.668S, 64.58W)
O. Dolleman Island (1981; 60.58S, 70.308W)
P. James Ross Island (1999; 64.28S, 57.678W)
Q. Berkner Island (1990; 79.618S, 45.728W)

shift in the storm track, anomalous ascent occurs overthe midlatitudes, causing cooling, while descent occurs
at polar latitudes, causing warming. L’Heureux andThompson (2006) described variations on this mechanism and found a significant relationship between the
SAM and SST anomalies associated with ENSO in theeastern equatorial Pacific for November–February. Theirwork implied that a negative SAM phase, or higher
pressure over the Antarctic, is associated with a warm
ENSO event, consistent with the equatorward shift of thestorm tracks described by Seager et al. (2003, 2005, 2010).
To explain SAM–ENSO correlations, Fogt et al.
(2011) investigated the relative phasing of SAM and

VOLUME 25

ENSO events and associated anomalous poleward eddy
momentum fluxes. Although supporting the generalarguments of Seager et al. (2003, 2005) and L’Heureux
and Thompson (2006), Fogt et al. (2011) also suggested
that in certain combinations of positive and negative
SAM and ENSO phases, the eddy momentum fluxes
associated with SAM and ENSO can be opposing,weakening the high-latitude ENSO signal. However, theoccurrence of opposing eddy momentum fluxes has aseasonal dependence, occurring more often in austral
winter and early spring, while strong, reinforcing interactions tend to occur more often in November–February(Fogt et al. 2011). The impacts of the ENSO modulationof the SH zonally symmetric circulation have not been
widely discussed.
The primary contribution of this study is providing
a concise description of the seasonality and spatial patterns of tropical signals in the Antarctic, with a view
toward a better physical understanding of observed
Antarctic interannual climate variability in the context
of the dynamical mechanisms discussed in previous work
(Seager et al. 2003; L’Heureux and Thompson 2006; Jin
and Kirtman 2009; Fogt et al. 2011). Our focus is theperiod from the late 1950s to the present, representing
the era of continuous meteorological records from Antarctic research stations (e.g., Turner et al. 2004). We firstpresent an overview of atmospheric anomalies related toindices of SAM and ENSO variability, followed by asurvey of teleconnections associated with one-point regression maps of Antarctic station records, and discuss
the consistency of the two approaches. The zonal symmetry of the tropical teleconnection in austral summer isthen further explored using reanalysis data to show thatthe surface signals in the Antarctic are coherent withcirculation anomalies throughout the troposphere fromthe tropics to the Antarctic. Additionally, we compare
tropical linkages evident in Antarctic meteorologicalrecords with those in ice core records over the course of
the instrumental era, so that ice core records may bebetter applied to extending the knowledge of Antarctic–
tropical linkages prior to the instrumental era.

2. Data and methods
a. Antarctic data
For Antarctic observational data, we use monthly-mean
observations of surface air temperature (SAT) from theReference Antarctic Data for Environmental Research(READER) archive (Turner et al. 2004). As indicatedin Table 1, the majority of the 18 station records weselect have nearly complete records from 1958 to present. In the case of missing data, the missing years or

15 JUNE 2012

SCHNEIDER ET AL.

4051

FIG. 1. Map of the Antarctic study region. Locations of long-term monthly SAT records fromthe READER archive are marked by solid black circles. Locations of annual ice core records
discussed in the text are indicated as open black or white circles. Background color scale is theannual mean surface mass balance (mm, water equivalent per year) from van de Berg et al.
(2006), as provided by A. Monaghan and used with permission.

seasonal means are not included in the regressions discussed below. The station data were downloaded inearly 2010 (from http://www.antarctica.ac.uk/met/
READER/data.html). The monthly index of the SAM isused from Marshall (2003) and was obtained online (athttp://www.antarctica.ac.uk/met/gjma/sam.html).
Knowledge of ENSO signals in the high latitudes of
the SH has been hampered in part by the short durationand sparseness of the observational network. Annuallyor subannually resolved ice cores (e.g., Steig et al. 2005)
present a valuable opportunity to address the observational gaps. ENSO signals in Antarctic ice core records
have been reported by several studies (e.g., Bromwich
et al. 2000; Ichiyanagi et al. 2002; Schneider and Steig
2008; Gregory and Noone 2008; Divine et al. 2009; Naiket al. 2010). Here, we use the Antarctic set of annual
mean ice core records of water isotopes (d18O and dD),
generally a proxy for temperature, that were compiled

and evaluated by Schneider and Noone (2007) and described in references therein. Two additional records,
provided by Dr. R. Mulvaney of the British Antarctic
Survey, are from Gomez and Dolleman Island (Peel et al.
1988). Figure 1 indicates the locations of station and ice
core records. As indicated on Table 1, the records havevarious ending dates; most end in 1999 but several end in
the 1980s. All of the ice core records go back to the 1958
start year of our analysis. The original ice core time seriesof water isotope data (d18O or dD) are used in standardized form for the purposes of this study.
Although ice core records significantly broaden thespatial coverage and temporal length of Antarctic climate
datasets, there remain ambiguities in their interpretation
because of the reliance on annual data, uncertainties indating, seasonality of deposition, and multiple physicalcontrols on isotopic fractionation (e.g., Masson-Delmotte
et al. 2008). Our approach to understanding these records

4052

JOURNAL OF CLIMATEVOLUME 25

FIG. 2. Reference map indicating the locations of station records (numbered, solid circles) andice core records (letters, open circles) corresponding to the names in Table 1.

is through direct comparison with station data. It has beensuggested (Turner 2004) that ice core records are only
useful for climate studies after a certain amount of lowpass smoothing because of low snow deposition rates in
the Antarctic. However, as illustrated in Fig. 1, the annual
surface mass balance (precipitation minus sublimationand snowmelt; van de Berg et al. 2006) varies considerably across the Antarctic continent. In general, annual ice
core time series can be recovered where the snow accumulation exceeds 100 mm yr21; this includes essentiallyall of West Antarctica and Law Dome in East Antarctica.
The dating uncertainty tends to be lower for higher accumulation rate cores and the amount of material perseveredis greater, potentially enhancing the climate signal that ispreserved.

b. Global and tropical data
We compute one-point, least squares regression
and correlation maps using a globally complete, 28 3 28
latitude–longitude gridded dataset of SST anomalies
known as the National Oceanic and Atmospheric Administration’s (NOAA) Extended Reconstructed SST,
version 3b (ERSSTv3b) (Smith et al. 2008), and a globally complete, 58 3 58 latitude–longitude gridded dataset
of SLP, the Hadley Centre SLP, version 2 (HadSLP2;
Allan and Ansell 2006). Both gridded datasets havemonthly resolution, span the main 1958–2008 period of
our analyses, and were obtained from the Physical Science Division of the NOAA Earth System ResearchLaboratory in Boulder, Colorado (http://www.esrl.noaa.
gov/psd/data/). Prior to computing the regression maps,
the local monthly climatology was subtracted from eachgrid point for a common base period, and the local leastsquares linear trends were removed from the resulting
monthly anomaly time series. Then, the SST or SLP
anomalies were spatially smoothed with a local nine-point
filter to reduce the small-scale noise in the regression
patterns. The Antarctic in situ SAT data are treated in thesame manner but with no spatial smoothing.
Additionally, we compute regressions or correlationsof Antarctic time series with two precipitation datasets;
a dataset of land station precipitation (Hulme et al.
1998), provided on a 2.58 3 3.758 latitude–longitude gridwith no interpolation or infilling; and a global gridded
dataset, the Global Precipitation Climatology Project,version 2.1 (GPCP) (Adler et al. 2003), obtained fromthe National Center for Atmospheric Research (NCAR)(http://www.cgd.ucar.edu/cas/catalog/satellite/gpcp/gpcp_
v21.html). The Hulme et al. (1998) network of stations isrelatively sparse, but it provides longer temporal coveragethan the GPCP, which only covers 1979–present.
We calculate a standardized SOI from the HadSLP2
data, subtracting the area-averaged SLP anomalies between 208N and 208S and all longitudes except 1808–
808W (‘‘Darwin’’) from those between 208N and 208S
and 1808 and 808W (‘‘Tahiti’’). Results are very similarusing the SOI of Trenberth (1984). Similarly, we use theSST data to compute the Niño-3.4 index as the areaaveraged SST anomaly in the region 58–58S, 1208–
1708W. Additional data include three-dimensionaltemperature and zonal mean wind fields from the National Centers for Environmental Prediction, version 2(NCEP2), reanalysis for 1979–2008 (Kanamitsu et al.
2002), obtained from the NOAA Earth System Research Laboratory (http://www.esrl.noaa.gov/psd/data/
gridded/).
To illustrate the organized spatial patterns associatedwith Antarctic temperature or ice core anomalies, ourregression maps show the entire field of regression values. Our discussion and interpretations are focused onfeatures that are robust and consistent across several
physical variables (e.g., SST, SLP, precipitation). Statistical significance is assessed using a two-tailed t test, andsignificant ( p , 0.05) regression values in the SST field
are indicated in the regression maps by white stippling. Because of the sometimes limited overlap between the ice core data and the Hulme et al. (1998)
precipitation data, an additional requirement was imposed that the sample size be at least 20 values. Figure 2
indicates the locations of the Antarctic ice core and station data using the same map projection as the regression
maps; Table 1 lists the corresponding names and latitude–
longitude information.

3. Resultsa. Comparison of SAM and ENSO signals
We examine the spatial patterns of anomalies in SLP,
SST, precipitation, and Antarctic station SAT and ice
core records associated with one standard deviation of

15 JUNE 2012

SCHNEIDER ET AL.

4053

FIG. 3. Regressions of seasonal mean anomalies of SST (1958–2008; gridded colors, 8C per standard deviation), SLP (1958–2008; black
contours, hPa per standard deviation; positive values solid and negative values dashed, interval of 0.4 hPa; white stippling indicates valuesof at least the 95% significance level), GPCP precipitation (1979–2008; green and orange contours for positive and negative precipitation
anomalies, respectively; lines drawn at 21.2, 20.6, 20.3, 0.3, 0.6, 1.2 mm day21) and Antarctic station data (1958–2008; squares, 8C per
standard deviation) upon corresponding (top to bottom) seasonal mean (left) SAM and (right) SOI indices. Also shown are correlations of
the seasonal SAM and SOI indices with the annual ice core anomalies (generally 1958–99; circles, units of correlation coefficients).

the SAM and SOI in each season in Fig. 3. Similar results
are obtained using alternative indices of ENSO variability, such as the Niño-3.4 index, and from composite
analysis (not shown). Throughout the year, the SAM is

associated with a zonally symmetric pattern of negative
SLP anomalies over the high-latitude SH and positive
SLP anomalies in the midlatitudes. The zonal symmetry
is most pronounced in SON and DJF, being interrupted

4054

JOURNAL OF CLIMATEin the midlatitude eastern Pacific sector in March–May(MAM) and June–August (JJA).
The characteristic ‘‘cold continent–warm peninsula’’
pattern associated with the positive phase of the SAM(Schneider et al. 2006) is clearly evident in the SAT
dataset. At coastal East Antarctica stations, SAT anomalies in excess of 20.88C occur in MAM and JJA, contrasted with anomalies greater than 10.78C in the western
Antarctic Peninsula. Weaker anomalies and a lesspronounced spatial contrast occur in SON and DJF;
East Antarctic coastal anomalies range from 20.28 to
20.68C, and only the northernmost Antarctic Peninsula
stations show positive temperature anomalies, in therange of 10.1 to 10.38C. The sign of the adjacent SST
anomalies is consistent with the Antarctic coastal stations, and the values are most significant in DJF. However, it should be noted that the SST dataset does notinclude air temperature anomalies over sea ice, which
would be expected to have anomalies of larger magnitude than SSTs (e.g., Yuan and Li. 2008; Kwok andComiso 2002). Individual ice core isotopic records are
not strongly correlated with the SAM: the largest correlations are in the range of 0.3–0.4 at the Berkner Island
and International Trans-Antarctic Scientific Expedition(ITASE) 02–4 sites. Similar values are obtained using
annual indices for the correlations—only the values atDronning Maud Land (DML) and ITASE 00–5 improveby about 0.1 compared to their maximum seasonalvalues (not shown).
For SLP, precipitation, and SST anomalies in the low
and midlatitudes, the patterns associated with the SOI,
on the right column of Fig. 3, are consistent with thoseshown in previous work associated with the Southern
Oscillation (e.g., Trenberth and Caron 2000) and withcomposites of El Niño and La Niña events (e.g., Deseret al. 2006, 2010). The circulation pattern in the SH extratropics strongly resembles the PSA 1 of Mo (2000);
as such, we use the term ‘‘PSA pattern’’ to refer to thisENSO teleconnection. The PSA is associated with significant SST anomalies in the South Pacific in all seasons.
As drawn, the maps exemplify La Niña conditions.
Specifically, in the tropics, the SOI is associated with aneast–west seesaw of SLP with nodes near the date lineand ;608W over South America. The largest east–west
pressure contrast occurs in DJF when the pressureanomalies are also most symmetric about the equator.
The largest tropical SST anomalies are located along theequatorial central and eastern Pacific, with the anomalies becoming progressively stronger from MAM
through DJF. Opposite-signed SST anomalies of somewhat weaker amplitude than the central and easternequatorial Pacific anomalies occur in the western Pacificbasin. The precipitation anomalies over the Pacific basin

VOLUME 25

and Indonesia largely follow the underlying SST anomalies. In all seasons, reduced precipitation is evident along
the equator from about 1508E to 908W, while increased
precipitation is evident over Indonesia and northern
Australia. The spatial extent of the precipitation anomalies is largest in DJF.
The patterns described above provide a guide forinterpreting the linkages of the SAM with tropical variability, which are also evident in Fig. 3. The most widespread and significant anomalies in the tropics associatedwith the SAM are in DJF. Specifically, for the positive
SAM phase, tropical rainfall is generally reduced from
1508E to 908W and increased over Indonesia and theSouth Pacific convergence zone (SPCZ). The rainfall
anomalies are physically consistent with the underlying
SST patterns and also with the low-level wind convergence implied by the SLP distribution. Weaker rainfall
anomalies with the same sign as those in DJF are evident
in SON and MAM but opposite in sign during JJA.
The PSA pattern exhibits a seasonal evolution. In allseasons, there is a low pressure anomaly in the South
Pacific from approximately 1508 to 608W, with a centerat approximately 1108W, between 608 and 708S. The
strength of the center increases from 1.6 hPa in MAM
and JJA to 2 hPa in SON, and decreases to 1.2 hPa in
DJF. The weakening of the center from SON to DJF isassociated with a change in the shape of the circulation
anomaly, from a regional feature to a more hemisphericand zonally symmetric pattern. The weakening of theteleconnection occurs despite the increase of the equatorial SST and rainfall anomalies from SON to DJF.
The impacts of the teleconnection on Antarctic temperature and ice core records also show a seasonalevolution. Consistent with the cyclonic circulation anomaly in the South Pacific, positive anomalies occur from 908
to 608W and negative anomalies occur from 1308 to
1008W, in a pattern resembling the Antarctic dipole(e.g., Yuan and Martinson 2000; Renwick 2002). The
strength of the anomalies changes with the seasons, particularly in the 908–508W sector. SAT anomalies at the
Faraday and Rothera stations on the Antarctic Peninsula,for example, are only 0.28–0.48C in MAM but nearly
0.78C in SON and greater than 18C in JJA. Three ice core
records in this sector (Gomez, Siple station, and ITASE
01–5) have correlations of r . 0.4 with the SOI in SON.
The change in the nature of the teleconnection from
SON to DJF is accompanied by a change in the sign ofEast Antarctic SAT anomalies (108–1508E) with respect
to the SOI. In addition, the negative pressure anomalies
over East Antarctica increase in strength, and the adjacent SST anomalies increase in magnitude and significance. The result is that the anomalies associated with
the SOI in DJF resemble those associated with the15 JUNE 2012

SCHNEIDER ET AL.

SAM, suggesting a coupling of the SAM with ENSO
variability in the austral summer. The two spatial patterns (bottom row of Fig. 3) are consistent with SAM–
ENSO correlations found in previous work (e.g.,
Carvalho et al. 2005; L’Heureux and Thompson 2006).
Ciasto and Thompson (2008) also found a mimicking of
SAM and ENSO signatures in the austral summer forthe high-latitude SST and geopotential height fields.

b. One-point correlation maps using station data
The one-point regression maps in Figs. 4 and 5 show
the amplitude and sign of SST, SLP, and low-latitude
precipitation anomalies associated with SAT anomalies
at eight individual Antarctic stations (marked by blacksquares), including the Antarctic Peninsula region and
Scott Base in Fig. 4 and the South Pole and three East
Antarctic coastal stations in Fig. 5. The regressions in
Figs. 4 and 5 are arranged horizontally in an eastward
direction from Scott Base and vertically by seasonalmeans. This view highlights remarkable regional andseasonal variability in the teleconnection patterns, pursuant to the idea that Antarctic–tropical linkages arehighly seasonally and regionally dependent. To gain anunderstanding of the representativeness of each station,correlations of the selected individual stations withanomalies at all of the other stations and ice core records
are also shown.
From MAM to SON, SH circulation anomalies associated with the stations in Fig. 4 resemble the PSA pattern, with some shifting in the longitude of the maximum
pressure anomaly from west to east depending on thestation. The closest match of the one-point regression
maps with the SOI-related patterns (Fig. 3) occurs during
SON in association with SAT anomalies at Faraday. SST,
SLP, and rainfall anomalies over the tropical Pacific andIndian Oceans are markedly similar whether the SOI
or the Antarctic station data are used to compute theregression maps. Specifically, warm anomalies in thewestern Antarctic Peninsula region occur in association
with increased rainfall across Indonesia, eastern Australia, and the SPCZ, decreased rainfall in the central
equatorial Pacific, positive SST anomalies in the western
Pacific, and negative SST anomalies in the easternequatorial Pacific, along the western margins of Northand South America, and in the western Indian Ocean.
The overall pattern in the SH can be interpreted as indicative of a Rossby wave originating from the western
Pacific or SPCZ, impacting temperature anomalies in
the Antarctic Peninsula region through atmospheric
thermal advection.
The tropically forced teleconnection appears to abruptly
cease from SON to DJF, when Faraday no longer exhibits
a significant relationship with tropical SSTs. For Orcadas

4055

station, however, associated tropical precipitation and SST
anomalies strengthen from SON to DJF, in association
with a shift in the circulation pattern from a cyclonic toa more zonal pattern. Temperature anomalies at Orcadas station tend to be anticorrelated with those at East
Antarctic stations in all seasons of the year, indicative ofa relationship between Orcadas SAT and the SAM. By
contrast, SAT anomalies at Faraday do not always show
significant relationships with the SAM (e.g., Marshallet al. 2006).
One-point regression maps using the South Pole andcoastal East Antarctic station data (Fig. 5) exhibit
markedly different patterns than the Antarctic Peninsula
maps. In all seasons, the patterns using these stations resemble the SAM patterns with opposite sign, and thestrongest zonal symmetry of the SLP patterns occurs
during DJF. SAT anomalies at East Antarctic stations arehighly coherent with each other; for example in DJF
anomalies at Davis are correlated at r . 0.7 with anomalies at the seven other stations from 108 to 1508E. In
MAM and JJA, the contrasting pattern of opposite-signed
anomalies between the western Antarctic Peninsulaand coastal East Antarctica is evident, with positive
anomalies at Novolazarevskaya, Davis and Dumont
D’Urville opposing anomalies at Rothera, Faraday,
Esperanza and Orcadas. The East Antarctic stations
have relatively weak relationships with anomalies in
the ice cores scattered across West Antarctica.
In contrast to the lack of tropical linkages aroundthe western Antarctic Peninsula in DJF, SAT anomalies
at East Antarctic coastal stations are related to positive
SST and rainfall anomalies in the central and eastern
tropical Pacific, and negative SST and rainfall anomalies
in the western Pacific, as well as an anomalously deep
Aleutian low (Fig. 5). Remarkably, the tropical rainfall
anomalies associated with positive SAT anomalies in
East Antarctica are nearly opposite those associatedwith positive SAT anomalies at Orcadas, suggestive ofa systematic alteration of the SAM associated with
ENSO variability.

c. Zonal symmetry of tropical linkages in DJF
Important to the interpretation of the coupling behavior of ENSO and the SAM is the vertical structure oftemperature and zonal wind anomalies associated withthese two phenomena. L’Heureux and Thompson (2006)
showed that SH tropospheric zonal wind anomalies associated with SSTs in the eastern equatorial Pacific (thecold tongue region) resemble those associated with the
SAM index, suggesting that the SAM captures most of
the extratropical response to ENSO in the austral summer. Does the SAM response to ENSO, which involves
the zonal mean circulation, impact the surface climate of

JOURNAL OF CLIMATE

FIG. 4. As in Fig. 3, but for seasonal mean anomalies of SST, SLP, and precipitation regressed upon Antarctic station SATs at (left to right) Scott Base and three locations in the
Antarctic Peninsula region (locations marked by solid black square). Also shown are correlations (values according to bottom scale) of the single station with all other Antarctic stations(generally, 1958–2008; colored squares) and with the annual ice core records (generally 1958–99; colored circles). All time series were linearly detrended before computing the regression
or correlation coefficients.

4056
VOLUME 25

FIG. 5. As in Fig. 4, but for four station locations in the interior of Antarctica and coastal East Antarctica.

15 JUNE 2012
SCHNEIDER ET AL.

4057

4058

JOURNAL OF CLIMATEVOLUME 25

FIG. 6. Latitude–height cross sections showing (left) regressions of the austral summer (DJF) anomalies of NCEP2 zonal mean zonal wind(black contours, m s21 per standard deviation; positive values solid and negative values dashed, interval of 0.2 m s21) and NCEP2 zonal mean
temperature (colors; white stippling indicates values of at least the 95% significance level) upon the Davis station (top) temperature record,(middle) the Niño-3.4 index, and (bottom) the SAM index (sign inverted). (right) Regressions use the same indices but the Niño-3.4 variancehas been removed from the NCEP2 anomalies for the Davis and SAM regressions, while the SAM variance has been removed for the Niño3.4 regression. All time series were linearly detrended before computing the regression coefficients, and the period is 1979–2008.

Antarctica? We explore this question in Fig. 6, which
compares the regressions of zonal mean zonal wind andtemperature anomalies upon the Davis station record(representative of coastal East Antarctica) with thoseusing the Niño-3.4 SST index (chosen instead of the SOI
to more directly represent tropical heating–cooling) andthe inverted SAM index. An immediate impression is theclose resemblance of the three patterns. In the SH, allshow a strengthening of zonal winds on the equatorward
side of the subtropical jet, weakening of zonal winds on
the poleward side of the jet, warming in the tropics andpoleward of ;608S, and cooling in the lower tropospherecentered at ;508S. These anomalies are almost exactly as
described by Seager et al. (2003) and L’Heureux andThompson (2006). As in L’Heureux and Thompson(2006), we find that this pattern has a distinct seasonaloccurrence confined to DJF.
To further explore the austral summer pattern, weshow regressions with the Niño-3.4 variance linearlyremoved from the Davis and SAM records and the SAM
variance removed from the Niño-3.4 index, in the righthand column on Fig. 6. If the SAM variance is removed15 JUNE 2012

SCHNEIDER ET AL.

4059

from the Niño-3.4 index, then the weakening of the SH
subtropical jet is much less pronounced than for the originalNiño-3.4 index. Removing the Niño-3.4 variance from thedata, the tropical anomalies associated with the Davis andSAM records weaken considerably. For the SAM, theDJF regression with the Niño-3.4 variance removed
resembles the SAM regressions in other seasons (notshown). This suggests that the SAM response to ENSO
variability is only significant in DJF and supports thearguments of L’Heureux and Thompson (2006), thatmost of the extratropical SH response to ENSO projects
onto the SAM. As a corollary, temperature anomalies atDavis are largely explained by the response of the SAM
to ENSO. This explains the one-point regression maps in
Fig. 5, which show correlations of temperature anomalies
at East Antarctic coastal stations with SST and rainfall
anomalies in the tropics, but no obvious signature of
a Rossby wave train in the atmospheric circulation.

Davis and Faraday records. This summarizes the key
regional and seasonal aspects of the Antarctic–tropicallinkages in the observational records, and suggests a fit
to the mechanisms discussed in recent work. Essentially,
the Faraday record exhibits a clear correspondence with
ENSO variability in the austral spring. The rainfall, SST,and SLP patterns associated with anomalies at Faraday
closely resemble those associated with the SOI, especially south of about 308S. The prevalence of this linkage
in spring is consistent with the seasonality of the SH
Rossby wave source as defined by Jin and Kirtman (2009)
and with the seasonality of the strength of the PSA wave
train (Mo 2000; Trenberth and Caron 2000).
The spring ENSO teleconnection exhibits a modest
impact in East Antarctica, as suggested by the resemblance of the regression patterns based on the SOI and
Davis records (Fig. 7). However, the correlation of theDavis record with the SOI in spring (r 5 0.22) is low
and not statistically significant. In austral summer, Faraday SAT anomalies exhibit relationships with SST
anomalies near the Antarctic Peninsula but not withremote SST anomalies in the tropics. At the same time,
significant correlations develop between ENSO variability, the SAM, and East Antarctic temperature
anomalies. This summer linkage cannot be explained by
Rossby waves, but instead is consistent with the projection of ENSO variability onto the SAM.

d. Timescale characteristics

e. Tropical linkages in ice cores

To highlight the interannual time scales and seasons
for which the Antarctic–tropical linkages are most pronounced, we apply an 8-yr high-pass filter to the seasonally stratified data. The high-pass filter is the Lanczos
type using nine weights (Duchon 1979). Table 2 displays
correlations of the filtered time series, using Faraday
and Davis as representative records for the Antarctic
Peninsula and coastal East Antarctica, respectively. In
both SON and DJF, the Davis record is significantly
correlated ( p , 0.05) with the SAM (r 5 20.48 and20.74, respectively). However, in DJF the Davis record
also exhibits significant correlation with the SOI (r 520.43). Consistently, the SAM and the SOI have a significant positive correlation in DJF (r 5 0.38). The
Faraday record has a significant correlation with the
SOI in SON only (r 5 0.43). In summary, in the austral
spring, about 20% of the temporal variance in the Antarctic Peninsula region is linearly related to variations in
the SOI, and in the austral summer, about 20% of thevariance at East Antarctic coastal stations is linearly related to variations in the SOI.
We present the spatial patterns corresponding to thefiltered time series in Fig. 7, comparing regressions using
the SOI and the SAM index with regressions using the

To provide a direct comparison to our analyses basedon the Antarctic station data, we select eight of the icecore records described previously and compute onepoint regression maps analogous to the maps using station data. Results are displayed in Fig. 8 for Gomez andthree records from East Antarctica, and in Fig. 9 for four
records from the West Antarctic Ice Sheet. To emphasize the interannual ENSO frequencies, the records arefiltered with the 8-yr high-pass filter described above forthe 1958–99 period.
Despite the annual resolution of the ice core records,
the seasonal one-point regression maps based on thesedata tell a similar story to those based on the seasonallystratified station data. In Fig. 8, Gomez and Talos Dome
records are associated with SLP anomalies in the South
Pacific of the opposite sign. The amplitudes are largest in
SON, consistent with the station data. The tropical SST
pattern linearly related to positive isotopic anomalies at
Gomez closely resembles the pattern related to therelatively close Faraday SAT record in SON. Rainfall
anomalies are less evident because of the limited temporal overlap between the rainfall records and theGomez record, which ends in 1980. Increased isotopic
values at Talos Dome occur in association withTABLE 2. Correlations of 8-yr high-passed time series associatedwith the regressions in Fig. 7. Boldface values are statistically significant at the 95% confidence level or above.

FaradayDavis
SAM

SOI SONSOI DJFSAM SONSAM DJF

0.43
0.22
0.10

20.04
20.43
0.38

20.18
20.48

20.42
20.74

4060

JOURNAL OF CLIMATEVOLUME 25

FIG. 7. Regressions of (left) SON and (right) DJF seasonal mean anomalies of SST (1958–2008; gridded colors, 8C per standard deviation;
white stippling indicates values of at least the 95% significance level) and SLP (1958–2008; black contours, hPa per standard deviation; positive
values solid and negative values dashed, interval of 0.4 hPa) on corresponding seasonal mean (top to bottom) SOI, Faraday, Davis, and SAM
time series. Also shown are correlations of the indices with Hulme et al. (1998) precipitation (generally, 1958–98; green diamonds positive values
and orange diamonds negative values, scaled to the size of the correlation coefficient. Smallest size corresponds to values of 20.3 or 0.3. Largest
negative value is 20.93; largest positive value is 0.79. No values between 20.30 and 0.30 are shown. A minimum overlap of 20 yr is required toshow the precipitation correlations). In addition, the correlations of the index or single station with all other Antarctic stations (generally, 1958–
2008; colored squares) and with the annual ice core records (generally 1958–99; colored circles) are shown. An 8-yr high-pass filter was applied tothe seasonal mean Faraday, Davis, SAM, and SOI records as well as the Antarctic station and ice core records. All time series were linearly
detrended before computing the regression or correlation coefficients.

SCHNEIDER ET AL.

FIG. 8. Seasonal mean anomalies of SST, SLP, and precipitation regressed on annual mean anomalies of four Antarctic ice core records (locations indicated by solid black circles andnames at the top of each column). SST, SLP, and precipitation fields are contoured as in Fig. 7. For the precipitation correlations, the largest positive value is r 5 0.74 and the largest
negative values is r 5 20.73. Also shown are correlations of the ice core record with the Antarctic stations (generally, 1958–99; colored squares) and with the entire annual ice core records(generally 1958–99; colored circles). All time series were linearly detrended before computing the regression or correlation coefficients. Additionally, the ice core records were filteredwith an 8-yr high-pass filter and then restandardized.

15 JUNE 2012

4061

JOURNAL OF CLIMATE

FIG. 9. As in Fig. 8, but for four ice core records from the West Antarctic Ice Sheet.

4062
VOLUME 25

15 JUNE 2012

SCHNEIDER ET AL.

anticyclonic circulation anomalies in the Amundsen,
Bellingshausen, and Ross Seas, which are consistent witha Rossby wave response to increased SSTs and rainfall
in the central equatorial Pacific. This is also consistent
with back-trajectory analysis for Talos Dome, which
reveals that a large component of the annual accumulation comes from moisture transported from the
Pacific sector during blocking events (Scarchilli et al.
2011; Masson-Delmotte et al. 2011).
Unlike Talos Dome, other records from East Antarctica, Dronning Maud Land, and Law Dome do not show
clear teleconnection patterns. This suggests limited prospects for resolving the SAM component of the ENSO
response in the high-latitude SH in DJF. This may be dueto the short seasonal window during which this mechanism
operates. As illustrated in Fig. 3, the PSA pattern associated with the SOI exists through much of the year, while
the SAM’s association with tropical variability is only
pronounced in DJF. An ice core record, which integrates
climatic conditions throughout the year and naturallysmoothes short-lived, high-frequency signals, would nothave fidelity in resolving a seasonal teleconnection likethe SAM response in austral summer. Consideration of
the seasonality of snow deposition rates, as well as theseasonality and locations of moisture sources (e.g.,
Scarchilli et al. 2011), further compounds the problem.
In Fig. 9, which shows results from West Antarctica,
ice cores from Siple Dome and ITASE 01–5 show similar
teleconnection patterns to Gomez, while the ITASE 00–1
and ITASE 01–3 patterns are rather noisy. The ITASE
01–5 site is closest to Gomez and sits in a relatively highdeposition area (Fig. 1), while the ITASE 01–3 and 00–1
sites are farther away and have lower deposition rates.
They are also located approximately in the middle of
the Antarctic dipole region (e.g., Bromwich et al. 2004),
so there is a high likelihood of nonstationarity in thestrength and sign of ENSO signals in this region, a point
explored by Gregory and Noone (2008).
Why do the cores from sites ITASE 01–5, Gomez, andSiple station (not shown) show such clear SLP patterns?
Not only are the sites in relatively high snow accumulation
areas and well located with respect to the PSA wave train
but there is additional evidence that the seasonality ofsnow accumulation may align with the seasonality of the
ENSO signal in this region. Studies of circulation andprecipitation regimes in atmospheric models and reanalysis fields lend credibility to this idea. Specifically, as
noted by Marshall (2009), the wave-3 nature of the annual surface mass balance coincides with the climatological wave-3 pattern of low pressure centers in thecircumpolar trough (Bromwich and Wang 2008). The
deepest low pressure center occurs near 1308W, with thelargest surface mass balance to the east. Secondary closed

4063

lows are evident near 258 and 1108E, consistent with surface mass balance maxima at ;508 and ;1308E. From
austral winter to summer, the low pressure centers
migrate: the ‘‘Amundsen Sea’’ low center moves from;1508W off the coast of Marie Byrd Land to the Bellingshausen Sea (see Fig. 2 in Bromwich and Wang 2008).
This implies more accumulation in West Antarctica fromroughly 908 to 708W in austral spring and summer than atother times of the year. Nicolas and Bromwich (2011)
presented a limited-period dataset that shows the greatest
accumulation in Ellsworth Land in the austral spring,consistent with the seasonality of the regression patternsof cores Gomez and ITASE 01–5. High-quality, seasonally resolved, long-term accumulation datasets areneeded to develop this idea further.

4. Summary and discussion
Our results detail a variety of physical evidence thatcollectively illustrates the impacts of tropical climate variability on the climate of the Antarctic. Our results also
confirm and extend evidence for two mechanisms whereby
tropical Pacific ENSO signals reach the Antarctic. The
first mechanism, involving the PSA pattern and its interpretation as a Rossby wave train propagating from thetropics to the high latitudes, has been discussed in numerous studies (e.g., Karoly 1989; Mo and Higgins 1998;
Mo 2000; Jin and Kirtman 2009). Impacts of the PSA havebeen previously summarized by Turner (2004) and Russell
and McGregor (2010). However, these studies have paid
little attention to seasonality or have emphasized onlya single season, for example, winter in Mo and Higgins(1998). Regressions using the SOI demonstrate that the
maximum amplitude of the circulation anomaly in thesoutheastern South Pacific occurs during austral spring.
Supporting evidence includes regressions using Faraday station data from the western Antarctic Peninsula,
as well as nearby ice cores Gomez and ITASE 01–5. Allof these results illustrate the strong seasonality of the
PSA teleconnection, and significant linkages to tropical
SST and precipitation anomalies. These linkages,
however, disappear in the austral summer at the peakof the ENSO cycle, and are relatively weak during the
austral autumn and winter preceding the ENSO peak.
Physical explanations for this seasonality have been
provided only recently (Jin and Kirtman 2009, 2010).
The second mechanism involving the projection of
ENSO variability onto the SAM is a relatively new areaof study (Seager et al. 2003; L’Heureux and Thompson
2006; Fogt and Bromwich 2006; Fogt et al. 2011). Thismechanism also has a distinct seasonality, evident primarily in the austral summer, for reasons that have notbeen fully explored. The seasonality may simply reflect the4064

JOURNAL OF CLIMATE

maximum amplitude of tropical heating associated with the
ENSO cycle (L’Heureux and Thompson 2006), but it likely
also involves seasonal characteristics of the extratropicalatmosphere (Jin and Kirtman 2009, 2010; Trenberth et al.
1998). In the austral summer, our results show statisticallysignificant correlations between ENSO variability andtemperature anomalies at East Antarctic coastal stations. One-point regression maps show clear ENSO
signatures in the tropics: Associated with positive temperature anomalies at Davis, there are increased rainfall
and positive SST anomalies in the eastern Pacific, anddecreased rainfall and negative SST anomalies in thewestern Pacific, consistent with El Niño conditions. As
discussed in other studies, the mechanism involves the
combination of thermally driven zonal wind anomalies
in the tropics and subtropics, and eddy-driven zonal
wind anomalies in the mid- and high latitudes (Seager
et al. 2003; L’Heureux and Thompson 2006; Fogt et al.
2011; Gong et al. 2010). Our regressions using the Davis
station data (Fig. 6) illustrate the impacts of this mechanism and confirm the seasonality suggested by
L’Heureux and Thompson (2006).
The disappearance of the tropical teleconnection at
Faraday from austral spring to summer is an apparent
paradox, as it might be expected that in spring SAT
anomalies at Faraday are associated with the PSA pattern and in summer they are associated with the SAM.
However, a number of observational and modelingstudies have shown that anomalous northerly or northwesterly winds (associated with the PSA) usually mean
relatively warm conditions at Faraday, but anomalous
westerly winds do not always show a significant SAT
response. Marshall et al. (2006) observed that in DJF,
SAT anomalies are weakly negatively correlated with
the SAM, partly due to a northerly component to thewinds. The authors also found that anomalous westerlies
during positive SAM phases, which would also be expected to favor warm air advection, tend to be focusedfarther north on the Antarctic Peninsula than Faraday
station. In addition, the positive SAM phase is associated with less blocking on the western side of the Antarctic Peninsula and more flow-over conditions to theeastern side, where adiabatic compression leads towarming. The observational analyses of Marshall et al.
(2006) was followed up and generally confirmed by thehigh-resolution modeling study of van Lipzig et al.
(2008). These studies, which highlight the role of thecomplex orography around the Antarctic Peninsula indetermining the SAT response to atmospheric circulation anomalies, help to explain the association of SAT
anomalies at Faraday with the tropical teleconnection
patterns. The same studies also point out that the positive SAM phase (increased westerlies) is associated withVOLUME 25

warm anomalies at stations to the north and east of
Faraday, Esperanza, and Orcadas, consistent with ourregression maps (e.g., Fig. 4).
The characterization of Antarctic climate variability andits tropical linkages on the interannual time scale likely
relates as well to the mechanisms that drive decadal climate variability in Antarctica. It has long been recognizedthat large decadal variability exists in Antarctic climate(e.g., Chapman and Walsh 2007), but what controls thisvariability is not well known. Using extended versions of
the ice core records discussed here, we are currently
evaluating Antarctic decadal climate variability and its
tropical linkages (Okumura et al. 2012). Better understanding of these linkages could inform both seasonal andlonger-term predictions of Antarctic climate. The prevalence of ENSO signals in the austral spring and summer
suggests a potential for seasonal predictability, at the timeof year when seasonal forecasts would be very useful to
Antarctic logistical operations. On the longer time-scale
perspective, Schneider et al. (2012) assessed Antarctic
temperature trends, finding the most significant trend on
the continent to be the warming of West Antarctica in the
austral spring for 1979–2009. The warming trend is related to a strong cyclonic atmospheric circulation trend in
the South Pacific, which is consistent with intensified
Rossby wave activity driven by trends in low-latitude
SSTs. If tropical SST trends and low-frequency phenomena, such as the Pacific decadal oscillation, (Mantua
et al. 1997) could be predicted, then this implies a new
potential for Antarctic climate predictability, in addition
to ozone hole trajectories (e.g., Perlwitz et al. 2008; Sonet al. 2009). More detailed statistical and modeling studies will be necessary to test these ideas further.
Acknowledgments. D. P. Schneider and Y. Okumura
were supported the National Science Foundation, Officeof Polar Programs (Grant 0838871). We thank threeanonymous reviewers for their constructive comments,
which led to numerous improvements to the paper.
REFERENCES
Adler, R. F., and Coauthors, 2003: The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation
analysis (1979–present). J. Hydrometeor., 4, 1147–1167.
Allan, R., and T. Ansell, 2006: A new globally complete monthly
historical gridded mean sea level pressure dataset (HadSLP2):
1850–2004. J. Climate, 19, 5816–5842.
Bals-Elsholz, T., E. Atallah, L. Bosart, T. Wasula, M. Cempa, and
A. Lupo, 2001: The wintertime Southern Hemisphere split jet:
Structure, variability, and evolution. J. Climate, 14, 4191–4215.
Bromwich, D. H., and S. H. Wang, 2008: A review of the temporaland spatial variability of the Arctic and Antarctic atmosphericcirculations based upon ERA-40. Dyn. Atmos. Oceans, 44,
213–243.

15 JUNE 2012

SCHNEIDER ET AL.

——, A. N. Rogers, P. Kållberg, R. I. Cullather, J. W. C. White, andK. J. Kreutz, 2000: ECMWF analyses and reanalyses depictionof ENSO signal in Antarctic precipitation. J. Climate, 13,
1406–1420.
——, A. J. Monaghan, and Z. C. Guo, 2004: Modeling the ENSO
modulation of Antarctic climate in the late 1990s with thepolar MM5. J. Climate, 17, 109–132.
Cai, W. J., and I. G. Watterson, 2002: Modes of interannual variability of the Southern Hemisphere circulation simulated by
the CSIRO climate model. J. Climate, 15, 1159–1174.
Carvalho, L. M. V., C. Jones, and T. Ambrizzi, 2005: Opposite
phases of the Antarctic Oscillation and relationships with intraseasonal to interannual activity in the tropics during the
austral summer. J. Climate, 18, 702–718.
Chapman, W. L., and J. E. Walsh, 2007: A synthesis of Antarctic
temperatures. J. Climate, 20, 4096–4117.
Ciasto, L. M., and D. W. J. Thompson, 2008: Observations of largescale ocean–atmosphere interaction in the Southern Hemisphere. J. Climate, 21, 1244–1259.
Cullather, R. I., D. H. Bromwich, and M. L. VanWoert, 1996: Interannual variations in Antarctic precipitation related to El
Niño-Southern Oscillation. J. Geophys. Res., 101, 19 109–19 118.
Deser, C., A. Capotondi, R. Saravanan, and A. S. Phillips, 2006:
Tropical Pacific and Atlantic climate variability in CCSM3.
J. Climate, 19, 2451–2481.
——, M. A. Alexander, S.-P. Xie, and A. S. Phillips, 2010: Sea
surface temperature variability: Patterns and mechanisms.
Annu. Rev. Mar. Sci., 2, 115–143, doi:10.1146/annurev-marine120408-151453.
Ding, Q., E. J. Steig, D. S. Battisti, and M. Kuttel, 2011: Winterwarming in West Antarctica caused by central tropical Pacificwarming. Nat. Geosci., 4, 398–403, doi:10.1038/ngeo1129.
Divine, D. V., and Coauthors, 2009: Tropical Pacific–high latitude
South Atlantic teleconnections as seen in d18O variability in
Antarctic coastal ice cores. J. Geophys. Res., 114, D11112,
doi:10.1029/2008JD010475.
Duchon, C. E., 1979: Lanczos filtering in one and two dimensions.
J. Appl. Meteor., 18, 1016–1102.
Fogt, R. L., and D. H. Bromwich, 2006: Decadal variability of the
ENSO teleconnection to the high-latitude South Pacific governed by coupling with the southern annular mode. J. Climate,19, 979–997.
——, ——, K. M. Hines, 2011: Understanding the SAM influenceon the South Pacific ENSO teleconnection. Climate Dyn., 36,
1555–1576, doi:10.1007/s00382-010-0905-0.
Ghil, M., and K. Mo, 1991: Intraseasonal oscillations in the global
atmosphere. Part I: Northern Hemisphere and tropics. J. Atmos. Sci., 48, 752–779.
Gong, T., S. B. Feldstein, and D. Luo, 2010: The impact of ENSO on
wave breaking and southern annular mode events. J. Atmos.
Sci., 67, 2854–2870.
Gregory, S., and D. Noone, 2008: Variability in the teleconnection between the El Niño–Southern Oscillation andWest Antarctic climate deduced from West Antarctic ice coreisotope records. J. Geophys. Res., 113, D17110, doi:10.1029/
2007JD009107.
Hulme, M., T. J. Osborn, and T. C. Johns, 1998: Precipitation
sensitivity to global warming: Comparison of observations
with HadCM2 simulations. Geophys. Res. Lett., 25, 3379–3382.
Ichiyanagi, K., A. Numaguti, and K. Kato, 2002: Interannual variation of stable isotopes in Antarctic precipitation in response
to El Niño-Southern Oscillation. Geophys. Res. Lett., 29, 1001,
doi:10.1029/2000GL012815.

4065

Jin, D., and B. P. Kirtman, 2009: Why the Southern Hemisphere
ENSO responses lead ENSO. J. Geophys. Res., 114, D23101,
doi:10.1029/2009JD012657.
——, and ——, 2010: How the annual cycle affects the extratropicalresponse to ENSO. J. Geophys. Res., 115, D06102, doi:10.1029/
2009JD012660.
Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo,
M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II
Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1643.
Karoly, D. J., 1989: Southern Hemisphere circulation features associated with El Niño–Southern Oscillation events. J. Climate,2, 1239–1252.
Kwok, R., and J. C. Comiso, 2002: Spatial patterns of variability in
Antarctic surface temperature: Connections to the Southern
Hemisphere annular mode and the Southern Oscillation.
Geophys. Res. Lett., 29, 1705, doi:10.1029/2002GL015415.
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.
Limpasuvan, V., and D. L. Hartmann, 2000: Wave-maintained
annular modes of climate variability. J. Climate, 13, 4414–
4429.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C.
Francis, 1997: A Pacific interdecadal climate oscillation with
impacts on salmon production. Bull. Amer. Meteor. Soc., 78,
1069–1079.
Marshall, G. J., 2003: Trends in the southern annular mode fromobservations and reanalyses. J. Climate, 16, 4134–4143.
——, 2009: On the annual and semiannual cycles of precipitation
across Antarctica. Int. J. Climatol., 29, 2298–2308.
——, A. Orr, N. P. M. van Lipzig, and J. C. King, 2006: The impactof a changing Southern Hemisphere annular mode on Antarctic Peninsula summer temperatures. J. Climate, 19, 5388–
5404.
Masson-Delmotte, V., and Coauthors, 2008: A review of Antarctic
surface snow isotopic composition: Observations, atmosphericcirculation, and isotopic modeling. J. Climate, 21, 3359–3387.
——, and Coauthors, 2011: A comparison of the present and lastinterglacial periods in six Antarctic ice cores. Climate Past, 7,
397–423.
Mo, K. C., 2000: Relationships between low-frequency variability
in the Southern Hemisphere and sea surface temperature
anomalies. J. Climate, 13, 3599–3610.
——, and G. H. White, 1985: Teleconnections in the Southern
Hemisphere. Mon. Wea. Rev., 113, 22–37.
——, and R. W. Higgins, 1998: The Pacific–South American modes
and tropical convection during the Southern Hemisphere
winter. Mon. Wea. Rev., 126, 1581–1596.
——, and J. N. Paegle, 2001: The Pacific–South American modes
and their downstream effects. Int. J. Climatol., 21, 1211–1229.
Naik, S. S., M. Thamban, C. M. Laluraj, B. L. Redkar, and
A. Chaturvedi, 2010: A century of climate variability in central
Dronning Maud Land, East Antarctica, and its relation tosouthern annular mode and El Niño-Southern Oscillation.
J. Geophys. Res., 115, D16102, doi:10.1029/2009JD013268.
Neelin, J. D., D. S. Battisti, A. C. Hirst, F.-F. Jin, Y. Wakata,
T. Yamagata, and S. E. Zebiak, 1998: ENSO theory. J. Geophys. Res., 103, 14 261–14 290.
Nicolas, J. P., and D. H. Bromwich, 2011: Climate of West Antarctica and influence of marine air intrusions. J. Climate, 24,
49–67.
Okumura, Y. M., D. Schneider, C. Deser, and R. Wilson, 2012:
Decadal–interdecadal climate variability over Antarctica and4066

JOURNAL OF CLIMATE

linkages to the tropics: Analysis of ice core, instrumental, andtropical proxy data. J. Climate, in press.
Peel, D. A., R. Mulvaney, and B. M. Davison, 1988: Stable-isotope /air-temperature relationships in ice cores from Dolleman Island and the Palmer Land Plateau, Antarctic Peninsula. Ann.
Glaciol., 10, 130–136.
Perlwitz, J., S. Pawson, R. L. Fogt, J. E. Nielsen, and W. D. Neff,
2008: Impact of stratospheric ozone hole recovery on Antarctic climate. Geophys. Res. Lett., 35, L08714, doi:10.1029/
2008GL033317.
Renwick, J. A., 2002: Southern Hemisphere circulation and relations with sea ice and sea surface temperature. J. Climate, 15,3058–3068.
Robertson, A. W., and C. R. Mechoso, 2003: Circulation regimes
and low-frequency oscillations in the South Pacific sector.
Mon. Wea. Rev., 131, 1566–1576.
Russell, A., and G. R. McGregor, 2010: Southern Hemisphere atmospheric circulation: Impacts on Antarctic climate and reconstructions from Antarctic ice core data. Climatic Change,
99, 155–192, doi:10.1007/s10584-009-9673-4.
Savage, M. L., C. R. Stearns, and G. A. Weidner, 1988: The
Southern Oscillation signal in Antarctica. Preprints, Second
Conf. on Polar Meteorology and Oceanography, Madison, WI,
Amer. Meteor. Soc., 141–144.
Scarchilli, C., M. Frezzottu, and P. M. Ruti, 2011: Snow precipitation at four ice core sites in East Antarctica: Provenance,seasonality and blocking factors. Climate Dyn., 37, 2107–2125,
doi:10.1007/s00382-010-0946-4.
Schneider, D. P., and D. C. Noone, 2007: Spatial covariance ofwater isotope records in a global network of ice cores spanning
twentieth-century climate change. J. Geophys. Res., 112,
D18105, doi:10.1029/2007JD008652.
——, and E. J. Steig, 2008: Ice cores record significant 1940s
Antarctic warmth related to tropical climate variability.
Proc. Natl. Acad. Sci. USA, 105, 12 154–12 158, doi:10.1073/
pnas.0803627105.
——, ——, and J. C. Comiso, 2004: Recent climate variability in
Antarctica from satellite-derived temperature data. J. Climate, 17, 1569–1583.
——, ——, T. D. van Ommen, D. A. Dixon, P. A. Mayewski, J. M.
Jones, and C. M. Bitz, 2006: Antarctic temperatures over the
past two centuries from ice cores. Geophys. Res. Lett., 33,
L16707, doi:10.1029/2006GL027057.
——, C. Deser, Y. M. Okumura, 2012: An assessment and interpretation of the observed warming of West Antarctica in
the austral spring, Climate Dyn., 38, 332–347, doi: 10.1007/
s00382-010-0985-x.
Seager, R., N. Harnik, Y. Kushnir, W. Robinson, and J. Miller,
2003: Mechanisms of hemispherically symmetric climate variability. J. Climate, 16, 2960–2978.
——, ——, W. Robinson, Y. Kushnir, M. Ting, H.-P. Huang, and
J. Velez, 2005: Mechanisms of ENSO-forcing of hemispherically symmetric precipitation variability. Quart. J. Roy. Meteor. Soc., 131, 1501–1527, doi:10.1256/qj.04.96.
——, N. Naik, M. Ting, M. A. Cane, N. Harnik, and Y. Kushnir,
2010: Adjustment of the atmospheric circulation to tropical
Pacific SST anomalies: Variability of transient eddy propagation in the Pacific–North America sector. Quart. J. Roy.
Meteor. Soc., 136, 277–296, doi:10.1002/qj.588.

VOLUME 25

Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore,
2008: Improvements to NOAA’s historical merged land–
ocean surface temperature analysis (1880–2006). J. Climate,21, 2283–2296.
Son, S.-W., N. F. Tandon, L. M. Polvani, and D. W. Waugh, 2009:
Ozone hole and Southern Hemisphere climate change. Geophys. Res. Lett., 36, L15705, doi:10.1029/2009GL038671.
Steig, E. J., and Coauthors, 2005: High-resolution ice cores fromUSITASE (West Antarctica): Development and validation ofchronologies and determination of precision and accuracy.
Ann. Glaciol., 41, 77–84.
——, Q. Ding, D. S. Battisti, A. Jenkins, 2012: Tropical forcing of
circumpolar deep water inflow and outlet glacier thinning in
the Amundsen Sea Embayment, West Antarctica. Ann. Glaciol., 53, 19–28.
Thompson, D. W. J., J. M. Wallace, and G. C. Hegerl, 2000: Annular modes in the extratropical circulation. Part I: Month-tomonth variability. J. Climate, 13, 1000–1016.
Trenberth, K. E., 1975: A quasi-biennial standing wave in Southern
Hemisphere and interrelations with sea surface temperature.
Quart. J. Roy. Meteor. Soc., 101, 55–74.
——, 1984: Signal versus noise in the Southern Oscillation. Mon.
Wea. Rev., 112, 326–332.
——, and J. Caron, 2000: The Southern Oscillation revisited: Sealevel pressures, surface temperatures, and precipitation.
J. Climate, 13, 4358–4365.
——, G. W. Branstator, D. Karoly, A. Kumar, N.-C. Lau, and
C. Ropelewski, 1998: Progress during TOGA in understanding
and modeling global teleconnections associated with tropicalsea surface temperatures. J. Geophys. Res., 103, 14 291–
14 324.
Turner, J., 2004: The El Niño-Southern Oscillation and Antarctica.
Int. J. Climatol., 24, 1–31.
——, and Coauthors, 2004: The SCAR READER project: Towarda high-quality database of mean Antarctic meteorologicalobservations. J. Climate, 17, 2890–2898.
van de Berg, W. J., M. R. van den Broeke, C. H. Reijmer, andE. van Meijgaard, 2006: Reassessment of the Antarctic surface
mass balance using calibrated output of a regional atmosphericclimate model. J. Geophys. Res., 111, D11104, doi:10.1029/
2005JD006495.
van Lipzig, N. P. M., G. J. Marshall, A. Orr, and J. C. King, 2008:
The relationship between the Southern Hemisphere annularmode and Antarctic Peninsula summer temperatures: Analysis of a high-resolution model climatology. J. Climate, 21,
1649–1668.
Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in thegeopotential height field during the Northern Hemisphere
winter. Mon. Wea. Rev., 109, 784–812.
Williams, L. N., S. Lee, and S. Son, 2007: Dynamics of the Southern
Hemisphere spiral jet. J. Atmos. Sci., 64, 548–563.
Yuan, X., and C. Li, 2008: Climate modes in southern high latitudes
and their impacts on Antarctic sea ice. J. Geophys. Res., 113,
C06S91, doi:10.1029/2006JC004067.
——, and D. G. Martinson, 2000: Antarctic sea ice extent variability and its global connectivity. J. Climate, 13, 1697–
1717.
Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability: 1900–93. J. Climate, 10, 1004–1020.