1 NOVEMBER 2012

OKUMURA ET AL.

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Decadal–Interdecadal Climate Variability over Antarctica and Linkages to the Tropics:
Analysis of Ice Core, Instrumental, and Tropical Proxy Data
YUKO M. OKUMURA
Institute for Geophysics, The University of Texas at Austin, Austin, Texas, and Climate and Global Dynamics Division, NCAR,*
Boulder, Colorado

DAVID SCHNEIDER AND CLARA DESER
Climate and Global Dynamics Division, NCAR,* Boulder, Colorado

ROB WILSON
School of Geography and Geosciences, University of St Andrews, St Andrews, United Kingdom(Manuscript received 18 January 2012, in final form 3 May 2012)
ABSTRACTThe Antarctic continent contains the majority of the global ice volume and plays an important role ina changing climate. The nature and causes of Antarctic climate variability are, however, poorly understoodbeyond interannual time scales due to the paucity of long, reliable meteorological observations. This study
analyzes decadal–interdecadal climate variability over Antarctica using a network of annually resolved ice
core records and various instrumental and tropical proxy data for the nineteenth and twentieth centuries.
During the twentieth century, Antarctic ice core records indicate strong linkages to sea surface temperature(SST) variations in the tropical Pacific and Atlantic on decadal–interdecadal time scales. Antarctic surface
temperature anomalies inferred from the ice cores are consistent with the associated changes in atmosphericcirculation and thermal advection. A set of atmospheric general circulation model experiments supports theidea that decadal SST variations in the tropics force atmospheric teleconnections that affect Antarctic surface
temperatures. When coral and other proxies for tropical climate are used to extend the analysis back to 1799,a similar Antarctic–tropical Pacific linkage is found, although the relationship is weaker during the first half ofthe nineteenth century. Over the past 50 years, a change in the phase of Pacific and Atlantic interdecadal
variability may have contributed to the rapid warming of the Antarctic Peninsula and West Antarctica andrelated changes in ice sheet dynamics.

1. Introduction
The Antarctic ice sheets contain as much as 87% of
the world’s ice volume, an amount equivalent to 57 m ofsea level rise (Lythe et al. 2001). Over the past several
decades, ice shelves on the Antarctic Peninsula retreated dramatically in association with surface warming* The National Center for Atmospheric Research is sponsoredby the National Science Foundation.

Corresponding author address: Yuko M. Okumura, Institute forGeophysics, The John A. and Katherine G. Jackson School ofGeosciences, The University of Texas at Austin, J. J. Pickle Research Campus, Bldg. 196, 10100 Burnet Rd., Austin, TX 78758.
E-mail: yukoo@ig.utexas.edu
DOI: 10.1175/JCLI-D-12-00050.1
Ó 2012 American Meteorological Society(Vaughan and Doake 1996). Following the collapse ofLarsen B ice shelf in 2002, inland glaciers accelerated
between twofold and eightfold due to the removal of thebuttressing ice shelf (Scambos et al. 2004; Rignot et al.
2004). Changes in atmospheric circulation have likely
driven increased upwelling of circumpolar deep wateralong the West Antarctic coast, contributing to basal
melting of ice shelves and acceleration of inland glaciers(Payne et al. 2004; Thoma et al. 2008; Steig et al. 2012;
Pritchard et al. 2012). Estimates based on satellite observations indicate that the West Antarctic ice sheet has
been losing mass during recent decades (Zwally et al.
2005; Rignot et al. 2008; Chen et al. 2009). The associated changes in the coastal freshwater flux may also
impact Antarctic Bottom Water formation and theglobal thermohaline circulation (e.g., Rintoul 2007;

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JOURNAL OF CLIMATE

Sen Gupta et al. 2009; Weaver et al. 2003). Thus, Antarctic
climate change is not a regional issue but has important
global implications.
The surface temperature warming in the Antarctic
Peninsula region has been in part associated with an
upward trend of the southern annular mode (SAM)(e.g., Thompson and Solomon 2002; Marshall 2007). The
SAM represents variability of the westerly circumpolar
vortex and is characterized by a seesaw pattern ofpressure anomalies between the polar and midlatitude
regions (Thompson and Wallace 2000). During the positive phase of the SAM, intensified westerly winds advect relatively warm oceanic air over the peninsula while
surface temperature decreases in the continent interior
due to a suppression of the katabatic flow over the polarcap (van den Broeke and van Lipzig 2004). Although the
SAM is regarded as an internal mode of atmosphericvariability (Limpasuvan and Hartmann 2000), atmospheric and climate model studies suggest that both
stratospheric ozone depletion and greenhouse gas increases caused an upward trend in the SAM index overrecent decades (e.g., Gillett and Thompson 2003; Shindelland Schmidt 2004; Arblaster and Meehl 2006; Miller
et al. 2006; Cai and Cowan 2007; Deser and Phillips 2009;
Polvani et al. 2011; Thompson et al. 2011). The forced
SAM change is strongest in austral summer and autumn,
when this mode is coupled to the circulation of the lower
stratosphere. (Seasons refer to those in the Southern
Hemisphere hereafter.)
By reconstructing spatially complete surface temperature data based on in situ and satellite observations,
Steig et al. (2009) showed that the strong warming trend
is not confined to the Antarctic Peninsula but extends
over most of West Antarctica. The West Antarctic
warming exceeds 0.18C per decade over the past 50 years
and is strongest in winter and spring. The spatial patternand seasonality of this Antarctic warming cannot be
explained by the anthropogenic change in SAM, which
induces cooling over West Antarctica. An atmosphericmodel forced by observed history of sea surface temperature (SST) and sea ice successfully simulates theWest Antarctic warming over the past 50 years while
the same model coupled to an ocean model fails to reproduce the observed trend pattern (Steig et al. 2009).
Schneider et al. (2012a) show that the spring warming
over West Antarctica is a robust feature in many other
Antarctic temperature datasets and that the associatedchange in atmospheric circulation can be simulated in an
atmospheric model forced by observed SSTs. Ding et al.
(2011) suggest that SST warming in the central tropical
Pacific caused the West Antarctic warming during winter by forcing an atmospheric circulation change in theAmundsen Sea over the past 30 years.

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On interannual time scales, tropical Pacific SST variations associated with the El Niño–Southern Oscillation(ENSO) are known to affect Antarctica via atmosphericteleconnections [for a review, see Turner (2004)]. Achange in tropical precipitation and resultant latent heatrelease forces an atmospheric barotropic Rossby wave
into the extratropics, where it modifies circulation patterns through interactions with transient eddies along
the storm tracks (e.g., Trenberth et al. 1998). Over theSouth Pacific sector, a Rossby wave train often extends
from the subtropics poleward and eastward to SouthAmerica during ENSO evens, a configuration that is
termed the Pacific–South American (PSA) pattern (e.g.,
Mo and Ghil 1987; van Loon and Shea 1987; Karoly
1989; Kidson 1999). The ENSO teleconnections to theSouthern Hemisphere are strongest in winter and spring,
preceding the peaks of ENSO events by one season, due
to the seasonality of the background atmospheric circulation (Jin and Kirtman 2009). During El Niño events,a high pressure anomaly develops over the Amundsen–
Bellingshausen Sea as part of the PSA pattern, inducing
a zonal dipole pattern of surface temperature and precipitation anomalies over the Pacific and Atlantic sectors of Antarctica (Yuan and Martinson 2001; Kwok andComiso 2002; Ribera and Mann 2003; Bromwich et al.
2004; Lee et al. 2010; Schneider and Steig 2002; Schneider
et al. 2012b). Several studies suggest that the ENSO–
Antarctic teleconnection pattern exhibits decadal variability (Bromwich et al. 2000; Fogt and Bromwich 2006).
Tropical Pacific SST variations on decadal–interdecadal
time scales are expected to affect the Southern Hemispheric circulation through similar mechanisms (e.g.,
Garreaud and Battisti 1999; Salinger et al. 2001; Mo
2000; Villalba et al. 2011), but their impact on Antarctic
climate is not well documented. Decadal–interdecadal
climate variations in the Atlantic basin may also influence
Antarctica (Chylek et al. 2010), as suggested from studiesof millennial climate variability during the last glacial–deglacial period (e.g., Timmermann et al. 2010) althoughdetailed teleconnection mechanisms are not clear.
The pattern and amplitude of a climate trend can besignificantly influenced by the presence of natural, lowfrequency climate variability when the data record is not
sufficiently long. Most Antarctic research stations did
not start reporting surface meteorological observations
until the 1957–58 International Geophysical Year. Thesestations are mostly located near the coast with a large voidin West Antarctica, the Pacific sector of Antarctica. Thereis no year-round research station between Scott Base
(77.98S, 1668E) and Rothera Research Station (67.58S,
68.18W) and over Marie Byrd Land, where a significant
warming trend has been reported. The automatedweather station network and satellite observations fill

1 NOVEMBER 2012

OKUMURA ET AL.

this gap in spatial data coverage but they did not begin
until the early 1980s (e.g., Chapman and Walsh 2007;
Comiso 2000). Because of the sparseness and short duration of the observational network, the nature and causesof Antarctic climate variability beyond interannual timescales are poorly understood.
Annually resolved ice core records spanning the last
few centuries are becoming increasingly available acrossthe Antarctic continent, offering a unique opportunity
to analyze decadal–interdecadal climate variability. In
particular, a recent drilling project, the International
TransAntarctic Scientific Expedition (ITASE) (Steiget al. 2005), provided precisely dated records of water
stable isotopes at very fine depth resolution from highaccumulation rate sites over the West Antarctic ice
sheet where in situ meteorological observations are few.
Despite the complexities in the processes that control
isotopic fractionation, most isotope modeling studies
have upheld the traditional interpretation of isotope
anomalies as indicators of local temperature during
precipitation (Jouzel et al. 1997; Werner and Heimann
2002). Noone (2008) suggests that changes in large-scaleatmospheric circulation bring about synchronous changes
in local temperature and moisture transport. Although
individual ice cores are subject to noise unrelated to
local weather and climate, such as blowing of snow,
covarying signals in multiple ice cores are associatedwith dominant patterns of atmospheric circulation variability (e.g., Fisher 2002; Schmidt et al. 2007; Schneider
and Noone 2007).
This is one of the first comprehensive studies to analyze spatial patterns of Antarctic variability on decadal
and interdecadal time scales and their linkages to global
climate. We utilize a network of 17 ice core records fromthe Antarctic continent in conjunction with global instrumental datasets and tropical proxy records to address the following questions. What are the dominantpatterns of decadal–interdecadal climate variability
over Antarctica? Are they linked to climate variations inother regions of the globe? What are the potential
physical mechanisms for the linkages? Is the Antarctic
temperature trend over recent decades influenced by
low-frequency variability?
We first conduct simple correlation analysis between
ice core records and global instrumental data during thetwentieth century, without any a priori assumptions of
the patterns of variability. Next, more objective statistical analyses are conducted to extract robust patterns ofcovariability between Antarctic and tropical climate. A
set of atmospheric model simulations is also analyzed toinvestigate mechanisms of the teleconnections. We thenextend the analysis back to the nineteenth century byusing a network of coral and other proxy records from7423

the tropics to test if the knowledge obtained from theanalyses of ice core and instrumental data holds true forthe past two centuries.
The paper is organized as follows. Section 2 describes
the ice core and various observational and tropical proxy
data used in this study. Section 3 analyzes the relationships between Antarctic and tropical climate variability
during the twentieth century. The teleconnection mechanisms are investigated in section 4. Section 5 extends theanalysis presented in section 3 to the nineteenth century
by using tropical proxy records. Summary and discussion
are provided in section 6.

2. Data, model, and methods
a. Ice cores
A set of 17 Antarctic ice core records (including one
snow pit record from Vostok) is used to assess Antarctic
climate variability. We use annual-mean records ofwater isotopes (d18O or dD) that begin in 1900 or earlier
and end in 1981 or later. In this study, the annual mean
is defined for the calendar year (January–December).
There are six records that extend back to at least 1799.
Table 1 summarizes the names, locations, available periods, and types of ice core records. Figure 1 indicatesthe locations of ice cores available for 1900–81 by light
blue circles and for 1799–1981 by dark blue circles.
These ice cores are distributed across the Antarctic
continent with about half over the West Antarctic ice
sheet. All but two records were compiled by Schneider
and Noone (2007) and described in references therein.
The first additional record is from Gomez (73.68S,
70.48W), provided by E. R. Thomas and described inThomas et al. (2009); the second additional record isfrom Dolleman Island (70.38S, 60.58W), provided by
R. Mulvaney and described in Peel et al. (1988). Figure 2
overlays the locations of ice cores on annual surface
mass balance (precipitation minus sublimation andsnowmelt, van de Berg et al. 2006). The surface massbalance varies considerably from location to location,
which may affect the time scales resolved by individual
ice core records. In general, a snow accumulation rateover 100 mm yr21 is required to recover an annual timeseries. Most of the ice cores, except a few from East
Antarctica, satisfy this criterion.
To compare the time scales of variability in individual
ice core records, temporal autocorrelations at 1-yr lag
are shown for 1900–99 in Fig. 3. These values are also
compared with the autocorrelation of annual SST anomalies in the Niño-3.4 region (58S–58N, 1208–1708W), an
index for ENSO, one of the dominant forcings of interannual variability over Antarctica. The ice cores in7424

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TABLE 1. List of 17 Antarctic ice core records used in this study. The labels correspond to those in Fig. 1. Bolded names indicate records
available from 1799 or earlier.
Label

Name

Lat

Lon

Period

Type

A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q

Vostok snow pit
Law Dome 2000
Talos Dome
Siple Dome A
ITASE 00–5
ITASE 00–1
ITASE 02–4
ITASE 01–2
ITASE 01–3
ITASE 01–5
Siple Station
Gomez
Dyer Plateau
Dolleman Island
James Ross Island
Berkner Island
Dronning Maud Land78.008S
66.788S
72.808S
81.658S
77.688S
79.388S
86.508S
77.848S
78.128S
77.068S
75.928S
73.598S
70.668S
70.308S
64.208S
79.618S
75.008S

106.008E
112.828E
159.758E
148.818W
123.998W
111.238W
107.998W
102.918W
95.658W
89.148W
84.158W
70.368W
64.508W
60.508W
57.678W
45.728W
0.008W

1900–99
1900–99
1750–1999
1900–93
1750–1999
1750–1999
1753–1998
1900–99
1900–99
1799–2001
1750–1983
1857–2005
1900–89
1900–81
1900–99
1900–92
1900–97

dD
d18O stack
dD
dD
dD
dD
dD
d18O
dD
d18O
d18O
d18O
d18O
d18O
d18O
d18O
d18O stackWest Antarctica and the Antarctic Peninsula show autocorrelation values comparable to that of the Niño-3.4
index (0.15), consistent with the strong ENSO signals
evident in these cores (Schneider et al. 2012b). Autocorrelations exceed 0.4 in low snow deposition sites overEast Antarctica, indicating that these ice core records
cannot resolve interannual variability and/or that thephysical processes affecting these cores have longer timescales. The autocorrelation is as high as 0.76 in Vostok snow
pit where annual mass balance is below 100 mm yr21.
Indeed, the annual time series of the Vostok record ismuch smoother than those of the other cores and exhibits
distinct decadal–interdecadal variations (not shown).

b. Global instrumental datasets
A suite of global, monthly instrumental datasets isused to analyze patterns of large-scale climate variability
associated with isotope anomalies in the Antarctic ice
core records. For SST we use the Hadley Centre Sea Ice
and SST (HadISST) (Rayner et al. 2003) available ona 18 grid for 1870–2010. It is based on historical in situ
ship and buoy observations with satellite data blended
in after 1982. Missing grid boxes are filled by optimal
interpolation based on empirical orthogonal function(EOF) analysis. For precipitation we use the dataset
from the Climate Research Unit of the University of

FIG. 1. Locations of Antarctic ice core (light and dark blue circles) and tropical proxy (yellow and orange circles) records used in thisstudy. The labels correspond to those in Tables 1 and 2. The light blue and yellow circles indicate the records available for 1900–81, and thedark blue and orange circles indicate the records available for 1799–1981. For visual clarity, the locations of some tropical proxy records
are slightly offset.

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FIG. 2. Annual-mean surface mass balance in Antarctica (mmwater equivalent per year; shading; van de Berg et al. 2006). The
red circles indicate the locations of ice core records. The labels
correspond to those in Table 1. For reference, the black squares
indicate the locations of year-round research stations where in situ
meteorological observations are available since 1957.

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FIG. 3. Autocorrelations of unsmoothed ice core records at 1-yr lagfor 1900–99. The cross on the label bar indicates the autocorrelationof annual Niño-3.4 SST index at 1-yr lag for the same period (0.15).

East Anglia (Hulme et al. 1998) available on a 2.58
latitude 3 3.758 longitude grid for 1900–98. It is based onland station records and no interpolation has been
conducted to fill in missing grid values. Although theprecipitation data are not available over the ocean,
coastal and island stations provide some information
on changes in large-scale marine precipitation patterns. For sea level pressure (SLP) we use the Hadley
Center SLP version 2 (HadSLP2) (Allan and Ansell
2006) available on a 58 grid for 1850–2004. The HadSLP2
is a globally complete reconstruction based on terrestrial
station records and marine observations. These independent datasets show physically consistent variability over
the tropical Pacific on interannual to interdecadal timescales (Deser et al. 2004, 2010b).

1900–81, 18 of which extend back to at least 1799. The
majority of these proxies are d18O isotopic records fromcorals and the others include coral luminescence, coral
growth rate, tree ring, ocean sediment, and tropicalice core. These records represent various elements oftropical climate such as SST, salinity, river discharge, airtemperature, precipitation, and wind-induced ocean upwelling. Table 2 summarizes the information on tropicalproxy records. Figure 1 indicates the locations of tropical
proxies available for 1900–81 by yellow circles and for
1799–1981 by orange circles. All but two records werecompiled by Wilson et al. (2010) and are described inreferences therein. We added two records from the tropical Atlantic described in Black et al. (1999) and Saenger
et al. (2009). For display purposes, we reversed the signof coral isotope and ocean sediment records that are
negatively correlated with local SST anomalies.

c. Tropical proxies

d. Atmospheric modelAs will be shown in the next section, Antarctic ice core
records exhibit strong linkages to tropical climate variations during the twentieth century. To extend the analysis of such linkages back to the nineteenth century, we
utilize various proxy records of tropical climate that areavailable at annual temporal resolution and span different periods during the nineteenth and twentiethcenturies. We use 35 tropical proxy records available for

To test the hypothesized mechanism of Antarctic–
tropical linkages, we analyze ensemble atmosphericmodel simulations forced with observed monthly SSTs.
The model used for these simulations is the Community
Atmosphere Model version 3 (CAM3) (Collins et al.
2006) at a horizontal resolution of T85 (;1.48 latitude 3
1.48 longitude) with 26 vertical levels. The observed
monthly SSTs are prescribed over both the global

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TABLE 2. List of the 35 tropical proxy records used in this study. The labels correspond to those in Fig. 1. Bolded names indicate records
available from 1799 or earlier.
Label

Name

Lat

Lon

Period

Type

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35

Ras Umm Sidd
Aqaba
MalindiMadagascar (Ifaty)
Mahe, Seychelles
St. Gilles, La Réunion
Nepal
Houtman Abrolhos Island
Ningaloo Reef
Lombok Straight
Bunaken Island
Guam
Laing Island
Madang Lagoon
Havannah Island
Abraham Reef
Nauru Island
New Caledonia
TarawaMaianaFiji
Palmerston
Suwarrow Atoll
Palmyra Island
JarvisRarotonga
MooreaClipperton Atoll
Urvina Bay
Secas Island
Florida Bay
Alina’s Reef (Biscayne National Park)
BahamasQuelccaya
Cariaco Basin

27.508N
29.268N
3.008S
23.098S
4.378S
21.028S
28.508N
28.288S
21.548S
8.158S
1.308N
13.368N
4.098S5.138S18.518S
22.068S
0.308S22.298S
1.008N
1.008N
16.498S
18.038S
13.158S
5.528N
0.228S
21.008S
17.308S
10.188N
0.248S
7.598N24.568N
24.258N
25.848N
13.568S
10.768N

34.188E
34.588E
40.008E
43.348E
55.008E
55.158E
85.008E
113.468E
113.588E
115.308E
124.508E
144.508E
144.538E
145.498E
146.338E
153.008E
166.008E
166.278E
172.008E
173.008E
179.148E
163.128W
163.068W
162.088W
159.598W
159.008W
149.508W
109.138W
91.148W
82.038W
80.458W
80.108W
78.628W
70.508W
64.698W

1751–1995
1788–1992
1801–1992
1660–1995
1846–1995
1832–1995
1546–1991
1794–1994
1879–1994
1782–1989
1860–1989
1790–2000
1885–1992
1881–1992
1644–1986
1638–1983
1897–1994
1658–1992
1894–1988
1840–1994
1776–2001
1834–1998
1881–1998
1886–1996
1850–1998
1726–1996
1852–1990
1894–1993
1607–1981
1708–1983
1824–1985
1751–1986
1552–1991
1540–2002
1116–1990

Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Tree ring
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral luminescence
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral d18O
Coral growth rate
Ice core
Ocean sedimentdomain and the tropics (208S–208N) only. These simulations are referred to as the Global Ocean and Global
Atmosphere (GOGA) and Tropical Ocean and Global
Atmosphere (TOGA) runs, respectively, following convention (e.g., Lau and Nath 1994). In the TOGA run,climatological monthly SSTs are prescribed poleward of
308 latitude while monthly SSTs are linearly blended
with climatological monthly SSTs over the latitude band
208–308 in both hemispheres. The GOGA and TOGA
ensembles each consist of five integrations and areavailable for 1950–2008 and 1871–2000, respectively. We
analyze the ensemble mean fields of surface air temperature, precipitation, and SLP. See Deser and Phillips(2009) for further information on these simulations.

e. Methods
For a given analysis period, annual or monthly climatologies are removed from all data. The monthly

anomalies of instrumental data and CAM3 output arethen converted into annual means by taking an average
from January through December for each year. At least
four months of data are required to calculate annualmean anomalies of the Hulme et al. (1998) precipitationthat contains missing values. To reduce interannual
variability associated with ENSO, we apply a threepoint binominal (1–2–1) temporal filter to all annual
data. Secular trends are then removed based on leastsquares quadratic regression analysis. Using linear instead of quadratic regression to estimate the trend does
not significantly alter the main results for the twentiethcentury alone but can be problematic for the extended
period including the nineteenth century, during whichsome tropical proxy records exhibit trends departingfrom linearity. The HadISST and HadSLP2 data are also
spatially smoothed with a local nine-point filter to reduce small-scale noise. Finally, the ice core and tropical1 NOVEMBER 2012

OKUMURA ET AL.

proxy records are standardized to have a mean of zeroand standard deviation of one. The temporal filteringand standardization serve to reduce the inhomogeneity
in types and effective temporal resolutions of these proxyrecords. Although climate reconstruction studies oftenprescreen proxy records for correlation with local climate parameters (e.g., Wilson et al. 2010), we use all theavailable proxy records and attempt to extract the patterns of covarying signals.
To quantify the relationships between Antarctic ice
core records and global climatic fields, we conduct linear
temporal correlation analysis. The statistical significance of the resulting correlation coefficients is assessedby a one-tailed Student’s t test. For simplicity, the effective sample size (Neff) is estimated by dividing thenumber of years by the mean characteristic time scale (t)of annual anomalies smoothed with a three-point binomial filter. We define t as a minimum lag for which the
autocorrelation falls below 0.2. On average, t is largest
for SST (5.4 yr) compared to 2.9 yr for the ice cores and3.4 yr for precipitation. A correlation coefficient thatexceeds 0.37 and 0.27 in absolute value is significantly
different from 0 at the 95% confidence level for record
lengths of 100 years (Neff 5 19) and 200 years (Neff 537), respectively. Besides the simple significance test, werely on physical consistency among variables from independent sources and robustness of the results in various analyses. To more objectively analyze the dominantpatterns of covariability between ice cores and tropical SSTs or proxies, we also conduct singular value
decomposition (SVD) analysis (Bretherton et al. 1992;
Wallace et al. 1992). The SVD analysis is a multivariate
statistical technique widely used in climate research toisolate linearly related spatial patterns in two fields bydecomposing the cross-covariance matrix into singular
vectors.

3. Antarctic–tropical linkages during the twentiethcenturya. Correlation analysis
What are the patterns of SST anomalies associatedwith decadal–interdecadal climate variations over Antarctica? Figure 4 shows one-point correlation maps
between a given ice core record and the remaining
16 ice cores and global SSTs during 1900–99. All of theice core records exhibit distinct large-scale SST correlation patterns, especially over the tropical oceans.
Talos Dome and several cores in central West Antarctica (ITASE 00–5, 00–1, 01–2, and 01–3) show significant positive SST correlations in the tropical Pacificand Indian Ocean and negative correlations in the central

7427

North Pacific. The area of positive SST correlations
extends into the subtropics over the eastern Pacific,
along the west coast of North America, and as far as theGulf of Alaska. This horseshoe pattern of SST correlations in the North Pacific is reminiscent of the anomaly
pattern associated with the Pacific decadal oscillation(PDO) or interdecadal Pacific oscillation (e.g., Mantua
et al. 1997; Zhang et al. 1997; Garreaud and Battisti
1999; Power et al. 1999; Salinger et al. 2001; Deser et al.
2004, 2010a). These ice core records that exhibit similar
SST correlation patterns are, in general, positively correlated with each other. ITASE 02–4 also shows a SST
correlation pattern suggestive of the PDO with the sign
reversed.
Many ice cores in the peninsula and eastern West
Antarctica exhibit organized patterns of SST correlationin the Atlantic basin (Fig. 4). In particular, ITASE 01–5,
Siple Station, Gomez, Dolleman Island, and James Ross
Island show a meridional dipole pattern of SST correlation between the North and South Atlantic, indicatingthat positive isotope anomalies in these cores tend tooccur with positive SST anomalies in the tropical South
Atlantic and negative SST anomalies over most of theNorth Atlantic and vice versa. The ITASE 01–5 andDolleman Island show significant negative SST correlations to the south of Iceland. This basin-scale patternof SST correlation bears some resemblance to theanomaly pattern associated with the Atlantic multidecadal oscillation (AMO) (e.g., Delworth and Mann
2000, Enfield et al. 2001; Guan and Nigam 2009; Deseret al. 2010a) although all of these ice core records exhibit
stronger correlation with SSTs in the tropical South
Atlantic than in the North Atlantic. The dipole patternof SST correlation in the tropics is also reminiscent of
the meridional mode of tropical Atlantic variability on
interannual–decadal time scales (e.g., Nobre and Shukla
1996; Chang et al. 1997; Okumura et al. 2001; Xie andCarton 2004). Among the remaining cores, Vostok snow
pit shows significant negative correlations with North
Atlantic SSTs and positive correlations with tropicalIndian Ocean SSTs. The SST correlation map for Dronning Maud Land projects onto the positive phases ofboth the PDO and AMO although the correlations are
weak overall. Dyer Plateau and Berkner Island do notexhibit a coherent pattern of SST correlation that resembles a known pattern of climate variability. Someice core records (e.g., Gomez and Dolleman Island)
are strongly correlated with SSTs in the high-latitude
Southern Ocean. However, we note that in situ SSTs
are very poorly sampled in this region throughout thetwentieth century (e.g., Deser et al. 2010a).
The simple correlation analysis suggests that decadal–
interdecadal variations in Antarctic ice core records are

7428

JOURNAL OF CLIMATEVOLUME 25

FIG. 4. One-point correlation maps between a given ice core record (identified by name in the upper left cornerand marked by a green circle) and the remaining 16 ice cores (circles) and global SSTs (shading) during 1900–99.
The color bar applies to both SST and ice core correlation coefficients. The panels are arranged by increasing
longitude.

linked with basin-scale SST variations, particularly inthe tropical Pacific and Atlantic. We ask, in turn, what
are the dominant patterns of tropical SST variability andhow are they related to the ice core records? Figure 5ashows the leading EOF and associated principal component (PC) time series of tropical Indo-Pacific
SSTs (308S–308N, 308E–808W) for 1900–81. This mode

represents the tropical extension of the PDO. The PC
time series indicates decadal–interdecadal variations
with predominantly positive and negative phases alternating every two to three decades. Figure 5b shows theleading EOF and associated PC time series of tropicalAtlantic SSTs (308S–308N, 808W–308E) for 1900–81. To
isolate tropical Atlantic variability that is independent1 NOVEMBER 2012

OKUMURA ET AL.

7429

FIG. 4. (Continued)of the tropical Pacific, linear regressions onto the leading
PC of tropical Indo-Pacific SSTs are removed from Atlantic SSTs prior to the EOF analysis. This mode
exhibits a dipole pattern between the North and South
Atlantic, similar to the meridional mode of tropicalAtlantic variability. The PC time series is dominated bydecadal variability during the first 40 yr, followed bya prolonged positive phase and a rapid transition toa negative phase in the early 1970s.
Based on the leading EOF patterns of tropical IndoPacific and Atlantic SSTs, we define simple indices torepresent Pacific decadal–interdecadal variability as
SST anomalies averaged in the southeast tropical Pacific(58–208S, 1408–908W; rectangle in Fig. 5a) and Atlantic
decadal–interdecadal variability as differences in zonal
mean SST anomalies in the northern and southern
tropical Atlantic (58–258N, 658–158W minus 08–208S,

408W–108E; rectangles in Fig. 5b; note that the nodeof the meridional SST dipole is located north of theequator). These SST indices are highly correlated withthe leading PCs at coefficients of 0.90 for Pacific variability and 0.86 for Atlantic variability. The advantage
of using indices instead of PCs is that they may be easily
defined for different analysis periods and can be extended to the present.
Figure 6 shows correlation maps of global SSTs, land
station precipitation, and ice core records with the Pacific and Atlantic SST indices for 1900–99. The SST
correlation map based on the Pacific SST index exhibitsa familiar pattern of the PDO, which is similar to the
one-point correlation maps based on Talos Dome andseveral ice core records in central West Antarctica(Fig. 4). Across the margin of positive and negative SST
correlations in the southwestern tropical Pacific, island

7430

JOURNAL OF CLIMATE

FIG. 5. (a) Leading EOF and associated time series of tropicalIndo-Pacific SSTs (308S–308N, 308E–808W) for 1900–81. This mode
explains 38% of the total variance. The EOF is computed froma correlation matrix. Standardized time series of the PC and Pacific
SST index are shown by the color bars and black curve, respectively. The Pacific SST index is defined in the region indicated by
the rectangle (58–208S, 1408–908W) and shown for 1900–99. Thesetime series are correlated at a coefficient of 0.90. (b) As in (a) but
for the leading EOF and associated time series of tropical Atlantic
SST (308S–308N, 808W–308E). This mode explains 29% of the total
variance. Prior to the EOF analysis, linear regressions onto theleading PC of tropical Indo-Pacific SSTs are removed from Atlantic SSTs. The PC and Atlantic SST index (58–258N, 658–158W
minus 08–208S, 408W–108E) are correlated at a coefficient of 0.86.

station precipitation shows a sharp change in the sign ofcorrelations, indicative of a meridional shift of the South
Pacific convergence zone (SPCZ). Consistent with the
one-point correlation analysis, the Pacific SST index is
positively correlated with Talos Dome and several ice
core records in central West Antarctica and negatively
with the ITASE 02–4 record. The SST correlation map
based on the Atlantic SST index exhibits a dipole pattern between the North and South Atlantic, similar tothe one-point correlation maps based on the ice core
records from the Antarctic Peninsula and eastern West
Antarctica (Fig. 4). The land station precipitation shows
positive correlations in the Sahel region and negative
correlations in northeast Brazil, indicative of a meridional
shift of the Atlantic intertropical convergence zone
(ITCZ) toward the warmer hemisphere. In agreementwith the one-point correlation analysis, the Atlantic SST

VOLUME 25

FIG. 6. (a) Pattern and time series associated with Pacific interdecadal variability. Map shows correlations of SSTs (shading), land
station precipitation (brown open circles ,20.3; green open circles.0.3), and ice core records (filled circles) upon the Pacific SST
index. Standardized time series of the Pacific SST and ice core
indices are shown by the black curve and color bars, respectively.
The ice core index is defined as a weighted average of those ice core
records that exhibit correlations with the Pacific SST index greaterthan 0.2 in absolute value (indicated by thick black circles) using
the correlation coefficients as weights. These time series are correlated at a coefficient of 0.62. (b) As in (a) but for Atlantic interdecadal variability. The Atlantic SST and ice core indices arecorrelated at a coefficient of 0.58.

index is negatively correlated with the ice core records
in the peninsula and eastern West Antarctica. For both
Pacific and Atlantic patterns, the sign of ice core correlations along the coast of Antarctica is in generalconsistent with the sign of SST correlations to the northalthough SST observations are sparse in this region before the satellite era.
We construct proxy indices of Pacific and Atlanticinterdecadal variability from ice core records by computing a weighted average of those cores that exhibit
correlations with the SST indices greater than 0.2 in1 NOVEMBER 2012

OKUMURA ET AL.

7431

absolute value, using the correlation coefficients as
weights. The resulting ice-core-based indices of Pacificand Atlantic interdecadal variability are significantly
correlated with the SST indices at 0.62 and 0.58, respectively. For Pacific variability the prolonged negative phase from the late 1940s and rapid shift to a
positive phase in the late 1970s seen in the SST index
are well captured in the ice core index, although thephase shift in the mid-1920s is not very clear. For Atlantic variability the persistent positive phase from the1940s and very rapid shift to a negative phase in theearly 1970s in the SST index are also evident in the icecore index.

b. SVD analysis of ice core and tropical SST
To more objectively analyze the linkages between
Antarctic and tropical climate variability, we conduct
SVD analysis between the set of 17 ice core records and
SSTs over the tropics (308S–308N) for 1900–81. Prior to
conducting the SVD analysis, the SST data are aggregated onto a coarse 48 latitude 3 108 longitude grid andthe time series at each grid point is normalized. We
obtain similar results by using fewer (9–16) ice core
records to extend the analysis period up to 1983–99(not shown). Figure 7 shows the time series of expansion coefficients and the correlation maps based onthese expansion coefficients for the first and second
SVD modes. These two leading modes explain 50%
and 25% of the total squared covariance, well separated from the higher modes that explain less than 8%.
The expansion coefficients of ice core and tropical SST
are highly correlated to each other for both modes (r 50.73 for the first mode and r 5 0.75 for the secondmode), indicating strong linkages between these twodifferent fields.
To examine the global patterns associated with thetwo leading SVD modes, the ice core expansion coefficients are correlated with full-resolution global SSTs
and land station precipitation and the expansion coefficients of tropical SST are correlated with ice core
records in Fig. 7 (i.e., the heterogeneous correlationmaps). The correlation map for the first mode bears
a striking resemblance to the correlation map based on
the Pacific SST index (cf. Figs. 6a and 7a). Indeed, theexpansion coefficients of ice core and tropical SST for
this mode are significantly correlated with the Pacific
SST index at 0.57 and 0.90, respectively. Positive isotope
anomalies at Talos Dome and in central West Antarctica are associated with positive SST anomalies in thetropical oceans. It is interesting to note that the ice core
expansion coefficient shows a larger positive correlation
with SSTs in the northwestern Indian Ocean than in
the tropical Pacific. The correlation map for the second

FIG. 7. (a) Pattern and time series associated with the first SVD
mode of ice core and tropical SST (308S–308N) for 1900–81. Thismode explains 50% of the total squared covariance. Map shows
correlations of SSTs (shading) and land station precipitation(brown open circles ,20.3; green open circles .0.3) upon the icecore expansion coefficient and correlations of ice core records(filled circles) upon the tropical SST expansion coefficient. Although the SVD calculation is based on SSTs only in the tropics,
the correlation pattern is displayed globally. Standardized timeseries of the ice core and tropical SST expansion coefficients areshown by the color bars and black curve, respectively. These timeseries are correlated at a coefficient of 0.73. (b) As in (a) but for thesecond SVD mode, which explains 25% of the total squared covariance. Time series of the expansion coefficients are correlated ata coefficient of 0.75.

SVD mode is very similar to the correlation map basedon the Atlantic SST index (cf. Figs. 6b and 7b). The
expansion coefficients of ice core and tropical SST for
this mode are correlated with the Atlantic SST index at
0.55 and 0.65, respectively. Negative isotope anomalies
in the peninsula and eastern West Antarctica are again
associated with positive SST anomalies in the North
Atlantic and negative SST anomalies in the South Atlantic. Compared to Fig. 6b, positive SST correlations in7432

JOURNAL OF CLIMATEVOLUME 25

FIG. 8. Regression and correlation maps of observed and simulated anomalies upon the (left) Pacific and (right)
Atlantic SST indices. (top) Regressions of observed SLP (black contours at intervals of 0.2 hPa; zero contours
thickened) and SST (8C, shading) and correlations of land station precipitation (brown open circles ,20.3, green
open circles .0.3) and ice core (filled circles) for 1957–99. (top middle) Regressions of SLP (black contours at
intervals of 0.2 hPa; zero contours thickened), surface air temperature (8C, shading), and precipitation (negative/
positive contours in brown/green at intervals of 0.3 mm day21 starting from 60.2) simulated in the CAM3 GOGA
run for 1957–99. (bottom middle) As in (top middle) but for the CAM3 TOGA run. (bottom) As in (bottom middle)
but for 1900–99. In the top three rows the data are not detrended.

the northern tropical Pacific and negative SST correlations in the South Pacific and Indian Ocean are more
pronounced in Fig. 7b while positive correlations in thenorthern tropical Atlantic weaken. The SVD analysis
thus confirms the results of simple correlation analyses
that decadal–interdecadal climate variability over Antarctica is strongly tied to SST variations in the tropical
Pacific and Atlantic.

4. Atmospheric teleconnections
What causes the linkages between Antarctic and tropical climate variability on decadal–interdecadal timescales? Does the atmospheric Rossby wave forced bytropical precipitation changes induce surface air temperature anomalies over Antarctica, in analogy with interannual ENSO events? Are the surface air temperature

1 NOVEMBER 2012

OKUMURA ET AL.

anomalies induced by the atmospheric Rossby wave
consistent with isotope anomalies in Antarctic ice cores?
To address these questions, SLP data are regressed onto
the Pacific and Atlantic SST indices along with SSTs,
land station precipitation, and ice core records in the top
panels of Fig. 8. We limit the regression analysis to theperiod after 1957 because there are insufficient SLP
observations in the high-latitude Southern Hemisphere todefine large-scale patterns of variability prior to this year.
We do not detrend the data before the analysis because
both Pacific and Atlantic SST indices show shifts in thephase of interdecadal variability in the 1970s (Fig. 6),
which would be largely effaced by detrending the data.
The regression map based on the Pacific SST index
indicates that SLP increases over Indonesia and Australia and decreases over the central–eastern tropical
Pacific in association with an equatorward displacement of the SPCZ during the positive phase of Pacificvariability (Fig. 8, top left). This high pressure anomaly
over Australia is connected with a low pressure anomaly to the south and a high pressure anomaly over theAmundsen–Bellingshausen Sea. The anomalous northerly wind to the east of this high pressure center over
the Amundsen–Bellingshausen Sea advects warmer air
into central West Antarctica, consistent with the positive isotope anomalies there. The regression map basedon the Atlantic SST index also shows that SLP increases
over the Amundsen–Bellingshausen Sea during thepositive phase of Atlantic variability (Fig. 8, top right).
Compared to the regression map based on the Pacificindex, the center of high pressure anomaly is displacedsouthwestward and the cold advection is more pronounced in the peninsula and eastern West Antarctica,
consistent with the negative isotope anomalies in theseregions. This high pressure anomaly associated with
Atlantic variability appears to be part of the wave train
emanating from the western tropical Pacific analogous
to the PSA pattern, although the island stations in thewestern tropical Pacific do not exhibit a very coherent
pattern of precipitation anomalies.
To test the hypothesis of tropical forcing of Antarctic
climate variability on decadal–interdecadal time scales,
we regressed the global SLP, surface air temperature,and precipitation fields simulated in the CAM3 GOGA
and TOGA runs onto the Pacific and Atlantic SST indicesfor the same period after 1957 (Fig. 8, top and bottom
middle). The CAM3 successfully simulates the positive
SLP anomalies over the Amundsen–Bellingshausen Sea
during the positive phase of Pacific and Atlantic interdecadal variability when forced with the observed monthly
SSTs over the global domain (GOGA run; Fig. 8, top
middle). As in the observations, the center of high pressureanomaly is displaced southwestward in the regression

7433

map based on the Atlantic SST index compared to thatbased on the Pacific index. The simulated high pressureanomalies are too zonally elongated compared to observations, but the large-scale pattern of SLP anomalies
is well reproduced in the Southern Hemisphere. Similar
to the observations, negative SLP anomalies are foundto the west and northwest of these high pressure anomalies in the regression maps based on the Pacific andAtlantic indices, respectively. The surface air temperature
anomalies are consistent with the isotope anomalies over
Antarctica. Note that the large surface air temperature
anomalies off the Antarctic coast is due to the fact thatsea ice anomalies are prescribed in the GOGA run.
The extratropical atmospheric anomalies in the GOGA
run are linked to large-scale patterns of precipitation
anomalies in the tropics (Fig. 8, top middle). During thepositive phase of Pacific interdecadal variability, precipitation increases in the equatorial Pacific and decreases in the northwest and southwest tropical Pacific,
consistent with observational results based on limited
island station data. During the positive phase of Atlanticinterdecadal variability, precipitation increases in thenorthern tropical Atlantic and decreases in the southern
tropical Atlantic in association with the meridional dipole pattern of SST anomalies. The positive precipitation
anomaly extends westward into the northern tropical
Pacific. Precipitation also increases in the western-central
equatorial Pacific and decreases in the southeast tropical
Pacific in response to weak SST warming and cooling inthese regions. Compared to the regression map based on
the Pacific SST index, positive precipitation anomalies
are confined north of the equator in the Pacific. The island
station precipitation data are too sparse to verify the simulated pattern of tropical Pacific precipitation anomalies
associated with Atlantic variability, but cloudiness anomalies based on shipboard observations (Woodruff et al.
2011) provide some support (not shown).
When the observed SST is prescribed only in thetropics (TOGA run; Fig. 8, bottom middle), the CAM3
simulates SLP anomaly patterns similar to those in the
GOGA run, suggesting that a large part of the extratropical atmospheric circulation changes in the GOGA
run are forced by tropical SST anomalies. The patternsof surface air temperature anomalies are also similar
over West Antarctica between the GOGA and TOGA
runs. There are, however, some differences in the location and amplitude of SLP anomalies in the Southern
Hemisphere between the GOGA and TOGA runs. In
particular, the shift of the high pressure anomaly centersover the Amundsen–Bellingshausen Sea between the
Pacific and Atlantic variability is less pronounced in
the TOGA run compared to the GOGA run. These
differences may arise from weaker SST anomalies in7434

JOURNAL OF CLIMATEthe southwest tropical Pacific between 208 and 308S in
TOGA compared to GOGA (recall section 2d). Overthe southwestern tropical Pacific, the negative precipitation anomalies associated with negative SST anomalies do not extend as far south, and the high pressureanomalies are weaker in the TOGA run than in the
GOGA run. Schneider et al. (2012a) suggest that theextratropical atmospheric circulation in the Southern
Hemisphere is sensitive to the precipitation anomalies inthis region. This hypothesis needs to be tested by widening the tropical band in the TOGA experiment.
The regression analysis of the TOGA run is extended
for the entire twentieth century to validate that the sameteleconnection mechanism is at work prior to 1957 (Fig.
8, bottom row). The regression pattern based on the
Pacific SST index is very similar to that for the period
after 1957. However, in the regression map based on the
Atlantic SST index, the precipitation anomalies in thetropical Pacific and the SLP anomalies in the Southern
Hemisphere are much smaller compared to those for the
recent period and the high pressure anomaly is centeredeast of the Antarctic Peninsula. This discrepancy may be
due to smaller ocean–atmosphere anomalies in thetropical Pacific associated with Atlantic interdecadal
variability before 1957 (not shown) in addition to theissue with the experimental setting of the TOGA run.
The regression analysis presented in Fig. 8 has been
repeated for seasonal-mean data. In the observations,
the Southern Hemispheric circulation anomalies associated with Pacific and Atlantic interdecadal variability
are evident from fall through spring and similar to those
based on the annual-mean data (not shown), consistent
with the analysis of interannual variability (e.g., Jin andKirtman 2009; Schneider et al. 2012b). CAM3 simulates
this observed seasonality for Atlantic interdecadal variability, but the seasonal dependence is less clear for
Pacific variability.
We have also analyzed the GOGA and TOGA ensemble simulations with the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric GCM (AM2),
which are available for 1870–2004 and 1950–2000, respectively. The results are very similar to those of CAM3
(not shown). In the AM2 GOGA run, the atmosphericteleconnection patterns are similar between 1900–99
and 1957–99 for both Pacific and Atlantic oscillations.

5. Extending the analysis of Antarctic–tropicallinkages to the nineteenth century
The analysis of ice core and various instrumental data
reveals strong linkages between Antarctic and tropicalclimate variability on decadal–interdecadal time scales
during the twentieth century. In this final results section,

VOLUME 25

FIG. 9. (a) Pattern and time series associated with the first SVD
mode of ice core and tropical proxy records for 1900–81. This mode
explains 30% of the total squared covariance. Map shows correlations of tropical proxy records (circles in the tropics) and SSTs
(shading) upon the ice core expansion coefficient and correlationsof ice core records (circles in Antarctica) upon the tropical proxy
expansion coefficient. Standardized time series of the ice core andtropical proxy expansion coefficients are shown by the color bars
and black curve, respectively. These time series are correlated ata coefficient of 0.79. (b) As in (a) but for the second SVD mode,
which explains 22% of the total squared covariance. Time series ofthe expansion coefficients are correlated at a coefficient of 0.71.

we extend the analysis further back in time using a set ofice core and various tropical proxy records available
from 1799 to examine if the same Antarctic–tropicallinkages exist during the nineteenth century.
Before extending the analysis to the nineteenth century, we first verify that a set of tropical proxy records
can capture Pacific and Atlantic interdecadal variability and their linkages to Antarctic climate during thetwentieth century. As in section 3b, the SVD analysis isconducted for 1900–81 using 17 ice core records but
replacing tropical SSTs with 35 tropical proxy records.
Figure 9 shows the time series of expansion coefficients

1 NOVEMBER 2012

OKUMURA ET AL.

and the heterogeneous correlation maps based on these
expansion coefficients for the first and second SVD
modes. In the correlation maps, the ice core expansion
coefficients are also correlated with global SSTs. These
two leading modes explain 30% and 22% of the totalsquared covariance and the expansion coefficients of ice
core and tropical proxy data are highly correlated (r 50.79 for the first mode and r 5 0.71 for the second mode).
The time series of ice core coefficients and the SST
correlation patterns bear a striking resemblance to thosefor the leading SVD modes of ice core and tropical SST(Fig. 7). The first and second SVD modes again represent Pacific and Atlantic interdecadal variability, respectively. Compared to the SVD analysis of ice core
and tropical SST, the first SVD mode shows weaker
positive SST correlation in the northern tropical Pacificand the phase shift in the late 1970s is less clear in the icecore coefficient. For the second SVD mode, the expansion coefficient of tropical proxy data has a strong positive peak around 1941–42, out of phase with that of theice core data.
The correlation patterns of tropical proxy data areconsistent with the correlation patterns of SST over theocean except in the western Indian Ocean for both firstand second SVD modes (Fig. 9). For the first SVD modethe coral records in the western tropical Pacific exhibit
a distinct meridional dipole pattern of correlations inassociation with the anomalous meridional SST gradientand resultant shift of the SPCZ. The coral records fromthe northern Red Sea (Ras Umm Sidd) and the westernequatorial Indian Ocean (Malindi), which are known toshow strong signals of Pacific interdecadal variability(Felis et al. 2000; Cole et al. 2000), are also significantly
correlated with the ice core coefficient of the first SVD
mode. For the second SVD mode coral records show
a coherent pattern of positive correlation in the central
tropical Pacific. Most coral records in the southwest
tropical Pacific exhibit negative correlations similar to
but weaker than in the correlation map for Pacific variability. The tropical ice core record from the Andes(Quelccaya) and tree ring record from Nepal also havesignificant correlations with the ice core coefficient of
the second SVD mode. Among the four proxy records
from the tropical Atlantic, only the coral record from
Bahamas exhibits significant correlation with the icecore coefficient of the second SVD mode.
We now extend the SVD analysis of ice core andtropical proxy for 1799–1981 using 6 ice core and 18
tropical proxy records. The first and second modes arepresented in Fig. 10. In the heterogeneous correlation
maps, the ice core expansion coefficients are correlatedwith global SSTs only during the overlapping period of
1870–1981. The first SVD mode explains 52% of the7435

total squared covariance and again represents Pacificinterdecadal variability. The correlation map is very
similar to that of the first SVD mode for 1900–81 (Fig. 9),
although the correlations are generally weaker. As in
Fig. 9, positive isotope anomalies in the central West
Antarctic ice cores are associated with negative anomalies in the southwest tropical Pacific coral records.
There are no coral records available for the entire
nineteenth century over the western equatorial Pacificwhere a coherent pattern of positive correlations isfound in the SVD analysis for 1900–81. The expansion
coefficients of ice core and tropical proxy data are correlated at a coefficient of 0.5, significant but much
weaker than in the SVD analysis for 1900–81 (r 5 0.79).
Potential dating errors that increase in the coral records
going back in time (Wilson et al. 2010) may partly contribute to the weaker correlations. The time series of
expansion coefficients suggest that the Pacific interdecadal variability was weaker and more variable during
the first half of the nineteenth century, consistent withprevious studies of Pacific climate reconstruction (e.g.,
Biondi et al. 2001; D’Arrigo et al. 2005; D’Arrigo andWilson 2006).
The second SVD mode explains 21% of the totalsquared covariance, and the expansion coefficients ofice core and tropical proxy data are correlated at a coefficient of 0.47 (Fig. 10). The correlation map bears
some resemblance to that of the second SVD mode for
1900–81 (e.g., Atlantic interdecadal variability; Fig. 9).
However, the correlations are insignificant except fora few proxy records and SSTs in very limited regions.
The expansion coefficients are positively correlated withthose of the second SVD mode for 1900–81 during theoverlapping period, but the rapid shift from a positive tonegative phase in the early 1970s is absent. As shown in
the previous sections and in the SVD analysis of ice core
and tropical proxy for 1900–81, the largest signals of
Atlantic interdecadal variability are found in the ice
cores from the Antarctic Peninsula and eastern West
Antarctica and the coral records from the central tropical Pacific. Most of these ice core and coral records areunavailable for the nineteenth century, and thus Atlantic interdecadal variability is poorly represented in
the SVD analysis of ice core and tropical proxy records
for 1799–1981.

6. Summary and discussion
Due to the sparseness and short duration of meteorological observations, Antarctic climate variability ispoorly understood beyond interannual time scales. In
this study, we utilized a network of annually resolved
ice core records and analyzed patterns and causes of7436

JOURNAL OF CLIMATEVOLUME 25

FIG. 10. As in Fig. 9 but for the first and second SVD modes of ice core and tropical proxyrecords for 1799–1981, which explain 52% and 21% of the total squared covariance, respectively. SSTs are correlated with the ice core expansion coefficient only for 1870–1981. Time
series of the expansion coefficients are correlated at coefficients of 0.50 and 0.47 for the first andsecond SVD modes, respectively.

decadal–interdecadal climate variability over Antarctica for the nineteenth and twentieth centuries. The
analysis of ice core and various instrumental data indicates strong linkages between Antarctic and tropicalclimate variability during the twentieth century. In particular, the ice core records in central West Antarctica
are positively correlated with Pacific interdecadal variability, and the ice cores in the Antarctic Peninsula andeastern West Antarctica are negatively correlated with
Atlantic interdecadal variability. These Antarctic–tropicallinkages are robust and are recovered in various statisticalmethodologies such as simple correlation analysis andobjective SVD analysis.
The isotope anomalies in the Antarctic Peninsula andWest Antarctic ice cores are consistent with the associated changes in atmospheric circulation and implied
thermal advection. The analysis of atmospheric GCM

experiments suggests that tropical precipitation changes
accompanying Pacific and Atlantic interdecadal variability forces atmospheric Rossby waves similar to the
PSA pattern into the extratropical Southern Hemisphere, inducing surface air temperature anomalies
in the peninsula and West Antarctica. A high pressureanomaly develops over the Amundsen–Bellingshausen
Sea during the positive phase of both Pacific and Atlantic interdecadal variability. Because of a slight shift
in the center of this high pressure anomaly, anomalous
warm advection is pronounced in central West Antarctica
during the positive phase of Pacific interdecadal variability while anomalous cold advection is prominent in
the Antarctic Peninsula and eastern West Antarctica
during the positive phase of Atlantic interdecadal variability. More diagnostic studies are needed to understand what causes the different patterns of atmospheric1 NOVEMBER 2012

OKUMURA ET AL.

teleconnections between Pacific and Atlantic interdecadal variability. Although we attempted to explain
the decadal atmospheric teleconnections in terms ofsteady response to decadal–interdecadal tropical SST
changes in this study, they may also reflect slow modulations of interannual ENSO and associated atmosphericteleconnection for Pacific variability (e.g., Bromwich et al.
2000; Turner 2004; Fogt and Bromwich 2006).
The tropical Pacific precipitation anomalies appear
to be important in forcing the atmospheric circulation
anomalies over the Amundsen–Bellingshausen Sea not
only for Pacific variability but also for Atlantic variability. It is not clear what causes these Pacific anomalies
associated with Atlantic interdecadal variability. Kushnir
et al. (2010) show that a change in tropical Atlantic
precipitation affects atmospheric deep convection in thetropical Pacific through adjustments of the Walker circulation. Modeling studies of paleoclimate variations
during the last glacial–deglacial period also suggestthat both atmospheric and oceanic teleconnections canconvey the Atlantic changes to the Pacific (e.g., Dongand Sutton 2002; Vellinga and Wood 2002; Zhang andDelworth 2005; Timmermann et al. 2007; Wu et al. 2008;
Xie et al. 2008; Okumura et al. 2009). Timmermann et al.
(2010) show that many climate models simulate a low
pressure anomaly over the Amundsen–Bellingshausen
Sea in response to a slowdown of the Atlantic thermohaline circulation and associated negative dipole patternof Atlantic SST anomalies, consistent with our analysis
of interdecadal variability. This low pressure anomaly
cannot be explained without small changes in tropical
Pacific SSTs in their atmospheric model experiments.
Thus, weak SST anomalies in the central tropical Pacificaccompanying Atlantic interdecadal variability may
play a crucial role in forcing an atmospheric Rossby wave
to Antarctica. Recent studies suggest a strong linkage
between the central tropical Pacific SST and Southern
Hemispheric atmospheric circulation (Lee et al. 2010;
Ding et al. 2011). The relative importance of tropicalAtlantic and Pacific SSTs and the mechanism of theirlinkages on decadal–interdecadal time scales need to be
explored further in future.
The set of ice core and tropical proxy records used inthis study can capture the climate variations associatedwith Pacific and Atlantic decadal–interdecadal variability during the twentieth century surprisingly well.
The subset of records that extend back to the nineteenth
century also exists in the regions where climate signals
associated with Pacific interdecadal variability are strong.
These ice core and tropical proxy records can be used toreconstruct the Pacific climate variations before the instrumental period. However, the nineteenth-century
subset of ice core and tropical proxy records is too sparse7437

to fully resolve climate variations associated with Atlantic interdecadal variability. Based on the twentiethcentury analysis, we suggest that more ice core records
from the peninsula and eastern West Antarctica andcoral and other proxy records from the central tropical
Pacific as well as from the tropical Atlantic (see alsosimilar recommendations in Wilson et al. 2010) may help
to reconstruct the Southern Hemispheric climate signals
associated with Atlantic interdecadal variability.
We note that the set of ice core records used in thisstudy is primarily located over the Antarctic Peninsula
and West Antarctica and thus may focus on the linkages
to tropical Pacific SSTs and associated PSA teleconnection
pattern, dismissing possible teleconnections between
the tropics and East Antarctica. Based on a detailed
analysis of Antarctic station temperature and ice core
records, Schneider et al. (2012b) suggest that temperature
variations over East Antarctica are largely controlled by
the SAM, which is in turn significantly influenced bytropical SSTs only during summer. Due to this shortseasonal window, tropical signals may not be preserved
well in East Antarctic ice core records. On the other hand,
the PSA teleconnections are evident from fall throughspring, clearly imprinted in many ice core records from thepeninsula and West Antarctica (Schneider et al. 2012b).
Both Pacific and Atlantic interdecadal variability
underwent major shifts in their phases during the second
half of the twentieth century (Fig. 6). The shift towarda positive phase of Pacific variability in the late 1970s is
likely to have contributed to the warming of central
West Antarctica and the shift toward a negative phase of
Atlantic variability in the early 1970s to the warming of
the Antarctic Peninsula and eastern West Antarctica via
atmospheric Rossby waves and associated thermal advection as indicated by the results shown in Fig. 8. Indeed, the ice core records from these regions exhibit
strong positive trends during the second half of thetwentieth century (Fig. 6), whereas the trends are weakerand show less coherent pattern for the entire twentiethcentury (not shown). Thus, interdecadal climate variability might have contributed to the significant warming
trend over the peninsula and West Antarctica suggested
from the analysis of instrumental data during the past 50
years (Steig et al. 2009; Schneider et al. 2012a).
Our results point to the difficulty in separating forced
climate signals from low-frequency climate variations inshort Antarctic observational records. Another difficulty comes from the possibility that the tropical climate
response to anthropogenic forcing may project onto thepatterns of natural climate variability. For example, it issuggested that stronger anthropogenic aerosol cooling in
the Northern Hemisphere than in the Southern Hemisphere resulted in asymmetric warming between the7438

JOURNAL OF CLIMATE

North and South Atlantic and southward displacementof the Atlantic ITCZ during the twentieth century (e.g.,
Rotstayn and Lohmann 2002; Biasutti and Giannini
2006; Chang et al. 2011). In the tropical Pacific, observational and modeling studies suggest that the Walker
circulation weakens in response to greenhouse gas increases, although it remains controversial whether theatmospheric circulation change is accompanied by achange in the zonal SST gradient (e.g., Vecchi et al. 2006,
2008; DiNezio et al. 2009; Deser et al. 2010b; Tokinaga
et al. 2012a,b, manuscript submitted to Nature; Ma et al.
2012). The associated changes in the tropical precipitation pattern are, in turn, likely to force atmospheric
Rossby waves into the extratropics (Deser et al. 2012),
affecting the regional pattern of climate change over
Antarctica. Climate proxy records can be powerful tools
to analyze climate variability and change where long
instrumental records are not available. The analysis of
Antarctic ice core records presented in this study willserve as a basis for continued assessment of Antarctic
climate change in the past and future.
Acknowledgments. We thank Alexander Tudhope forgenerously allowing us to use several unpublished coral
records and Jian Lu for providing the GFDL AM2
simulations. We would also like to thank Mike Wallace,
Qinghua Ding, Laurent Terray, Paul Kushner, and Axel
Timmermann for useful discussions and Dennis Sheafor technical assistance during the course of this study.
Comments and suggestions by Qinghua Ding and two
anonymous reviewers helped to improve the manuscript. The CAM3 simulations were conducted by Adam
Phillips at NCAR and made available on the CESM Climate Variability and Change Working Group’s webpage(http://www.cesm.ucar.edu/working_groups/Climate/).
The following datasets were obtained online: HadISST
and HadSLP2 from the Met Office Hadley Center(http://www.metoffice.gov.uk/hadobs/); land station precipitation from the Climate Research Unit of the University of East Anglia (http://www.cru.uea.ac.uk/); andCariaco Basin G. bulloides abundance data and Bahamas coral SST reconstruction from the NOAA National
Climatic Data Center Paleoclimatology Branch (http://
www.ncdc.noaa.gov/paleo/paleo.html). Y. M. Okumura
and D. P. Schneider were supported by a grant from the
National Science Foundation Office of Polar Programs(ANT-0838871).
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