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GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L16707, doi:10.1029/2006GL027057, 2006

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ArticleAntarctic temperatures over the past two centuries from ice cores
David P. Schneider,1 Eric J. Steig,1 Tas D. van Ommen,2 Daniel A. Dixon,3Paul A. Mayewski,3 Julie M. Jones,4 and Cecilia M. Bitz5
Received 30 May 2006; revised 26 June 2006; accepted 24 July 2006; published 30 August 2006.

[1] We present a reconstruction of Antarctic mean surface
temperatures over the past two centuries based on water
stable isotope records from high-resolution, precisely dated
ice cores. Both instrumental and reconstructed temperaturesindicate large interannual to decadal scale variability, withthe dominant pattern being anti-phase anomalies between
the main Antarctic continent and the Antarctic Peninsula
region. Comparative analysis of the instrumental Southern
Hemisphere (SH) mean temperature record and thereconstruction suggests that at longer timescales,
temperatures over the Antarctic continent vary in phasewith the SH mean. Our reconstruction suggests that
Antarctic temperatures have increased by about 0.2Csince the late nineteenth century. The variability and thelong-term trends are strongly modulated by the SH Annular
Mode in the atmospheric circulation. Citation: Schneider,
D. P., E. J. Steig, T. D. van Ommen, D. A. Dixon, P. A. Mayewski,
J. M. Jones, and C. M. Bitz (2006), Antarctic temperatures overthe past two centuries from ice cores, Geophys. Res. Lett., 33,
L16707, doi:10.1029/2006GL027057.

1. Introduction[2] Given the enormous amount of freshwater stored in
the Antarctic ice sheet and the impact that temperaturechanges may have on the ice sheet mass balance, it is important to understand how and why Antarctic temperatures
have changed. Several studies have presented summaries
of Antarctic temperature change based on instrumental records [e.g., Turner et al., 2005; Jacka et al., 2004; Vaughan
et al., 2003]. The Antarctic Peninsula region has experienced some of the strongest surface warming on Earthduring the past 50 years, while temperature trends acrossthe continent differ in sign and magnitude among different
time periods and seasons [Turner et al., 2005]. Recent cooling across the continent in the summer and autumn has been
linked to persistence of the positive index phase of theSouthern Hemisphere Annular Mode (SAM) [Thompson
and Solomon, 2002]. The recent trend in the SAM has been
attributed to various combinations of stratospheric ozone
1
Department of Earth and Space Sciences, University of Washington,
Seattle, Washington, USA.
2
Department of the Environment and Heritage, Australian Antarctic
Division, Antarctic Climate and Ecosystems Cooperative Research Centre,
Hobart, Tasmania, Australia.
3
Climate Change Institute, University of Maine, Orono, Maine, USA.
4
Institute for Coastal Research, GKSS Research Centre, Geesthacht,
Germany.
5
Department of Atmospheric Sciences, University of Washington,
Seattle, Washington, USA.

Copyright 2006 by the American Geophysical Union.
0094-8276/06/2006GL027057$05.00

depletion and rising atmospheric CO2 concentrations [e.g.,Thompson and Solomon, 2002; Shindell and Schmidt,
2004], implying anthropogenic influences on Antarctic
climate. However, the temporal variability of Antarctic
climate is not well known, as continuous meteorologicalobservations in the Antarctic began only in the late 1950s.
Quantitative reconstruction of Antarctic temperatures has
been faced with several challenges. First, the short andsparse instrumental observations make it difficult to determine how well variations in temperatures at research stations represent regional temperature variations across thecontinent. Second, while stable isotope time series from
Antarctic ice cores are well-known, reaching up to eightglacial cycles into the past [EPICA Community Members,
2004], such records have generally not been available atthe high-resolution required for reconstructing the instrumental record. Finally, while statistical reconstruction
approaches have been applied in regional to hemisphericscale reconstructions [e.g., Jones and Mann, 2004], suchapproaches have not been applied to Antarctic data.
[3] New data and improved understanding of the mechanisms explaining Antarctic temperature and stable isotope
variations enable the quantitative reconstruction of Antarctic temperatures for the first time. Targeted ice-coring
projects, such as the International Trans-Antarctic Scientific
Expedition (ITASE), which has the specific objective ofcollecting numerous high-resolution records, have greatly
expanded the availability of proxy indicators of Antarctic
climate [Mayewski et al., 2006; Steig et al., 2006]. The aimof this paper is to utilize these new data in a 200-year-long
Antarctic temperature reconstruction (representing the mainpart of the continent) methodologically similar to temperature reconstructions covering other geographic regions.

2. Data[4] For Antarctic surface temperature observations, we
use the quality-controlled station records from the Antarctic
READER project, which archives continuous monthly
observations covering generally the late 1950s to present[Turner et al., 2004]. Time series from eight stations on the
coast and the continental interior are included in our
analysis, while data from the Peninsula region are excluded
due to their location with respect to the main climatic
pattern that dominates the continent (see Methods, below).
[5] Subannually-resolved d18O and dD (hereafter denoted‘‘d’’) ice core records are compiled from Law Dome[van Ommen et al., 2004], Siple Station [Mosley-Thompson
et al., 1990], Dronning Maud Land (DML) [Graf et al.,2002], and two West Antarctic sites of the United Statescomponent of ITASE [Steig et al., 2006] (Table 1). The
Law Dome and DML records are stacks of several records

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Table 1. Ice Core Site Characteristics
Core Name

LatitudeLongitude

Elevation, mTime span

IsotopeLaw Dome 2000 (stack)
Siple Station
Dronning Maud Land (several sites)a
US ITASE 2000-1
US ITASE 2000-5

66.78 S
75.92 S
75 S
79.38 S77.68 S112.82 E
84.1 W
0 E
111.23 W
123.99 W

1370
1054
2900
1791
1828

1800 – 1999
1800 – 1983
1800 – 1997
1800 – 1999
1800 – 1999

d18O
d18O
d18O
d18O
dD

a
DML cores (documented by Graf et al. [2002]) in the DML stack: DML18(FB9804); DML19(FB806); DML05(FB9807); DML20(FB9808);
DML21(FB9810); DML22(FB9811); DML16(FB9813); DML14(FB9815); DML13(FB9816); DML12(FB9817); DML07(B31); DML05(B32);
DML17(B33); DML03(FB9809).

from closely-spaced sites in the region. These records havebeen well-dated through counting of annual layers in ionchemistry and d concentrations, and by the identification of
volcanic eruption marker horizons. All isotopic data werereferenced to the VSMOW and SLAP (Standard Light
Antarctic Precipitation) standards from the International
Atomic Energy Agency.
[6] We also utilize gridded 2-m temperature (2-m T) datafrom a polar-adapted mesoscale climate model driven at thelateral boundaries by the ERA-40 Reanalysis over 1980 –2001 [Monaghan et al., 2006] to characterize the spatial
variability of Antarctic temperatures.

3. Methods[7] The gridded 2-m T data are too short in duration touse directly in the reconstruction, but they are used to assess
the skill of the station network in representing the varianceof Antarctic temperatures. The leading empirical orthogonal
function (EOF) of annual mean 2-m T over the continent
captures 46% of the variance (Figure 1a). This ‘‘coldcontinent – warm Peninsula’’ pattern can be interpreted as
the expression of the SAM, as illustrated by correlation of
the station-based SAM index of Marshall [2003] with the
2-m T field (Figure 1b), or with satellite-derived temperatures [Schneider et al., 2004]. The SAM is a roughly zonallysymmetric seesaw in atmospheric mass between the Antarctic and the mid-latitudes, characterized by fluctuations inthe strength of the westerly polar vortex [Thompson andWallace, 2000]. Discussion of the physical mechanisms
relating the SAM to temperature anomalies over the ice
sheet is provided by van den Broeke and van Lipzig [2004].
[8] EOF analysis of annual station data spanning 1961 –2003 indicates that the first EOF explains 43% of the total
variance, with all stations having positive weights. These
results are very similar to the gridded 2-m T data, indicating
the robustness of the leading temperature pattern. The first
PC is correlated at r = 0.98 with the simple average of thestation temperature records. This average, which we call
A8, is significantly correlated at r = 0.76 (p < 0.01) (degrees
of freedom are reduced to account for autocorrelation for thesignificance of all correlations and trends discussed herein)with the mean of the 2-m T data over 1980 – 2001. In
addition, the cold continent - warm Peninsula pattern isevident in the correlation of A8 with the 2-m T field(Figure 1c), showing that A8 provides a meaningful estimate of temperature variability on a continent-wide scale.
While there is not a significant linear trend in A8, A8 depicts temperature increases in roughly the first half of theseries and decreases in the second half (Figure 2), inagreement with other studies of recent Antarctic tempera-ture trends [Jacka et al., 2004; Turner et al., 2005]. A8 isthus our target temperature index for reconstruction; it
provides as long and homogeneous a record as is possible
from existing Antarctic data.
[9] An advantage of isotopic records over many otherclimate proxy indicators is that the physics governing theirvariability is well understood. The relevant processes can bedepicted in models ranging in completeness from simple
Rayleigh models [e.g., Dansgaard, 1964] and intermediate
complexity models [e.g., Kavanaugh and Cuffey, 2003], tofull general circulation models (GCM) [e.g., Werner andHeimann, 2002; Noone and Simmonds, 2002]. In Antarctica, most isotope modeling studies have upheld the traditional interpretation of isotope anomalies as indicators of
local temperature during precipitation, despite complexities
in the processes that control isotope fractionation [Jouzel etal., 1997; Werner and Heimann, 2002]. However, while

Figure 1. Correlation maps of several time series with 2-m
T from the climate model simulation of Monaghan et al.
[2006]. Negative correlations (blue) and positive correlations (red) imply negative and positive temperature
anomalies, respectively: (a) The first principal component(PC) of 2-m T over the continent; (b) The SAM index;(c) The A8 composite temperature index (inverted) from thestations indicated by black dots; (d) The stacked ice coreisotopic record (inverted), with core sites indicated by ablack ‘‘x’’. At each site, the numbers indicate the correlation
coefficient of the annual ice core data with the nearest gridpoint 2-m T, PC1 of 2-m T, and A8, respectively.

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Figure 2. The gray line shows A8. Calibrated stacked ice
core record (black line). Mean of 2-m T anomalies over theice sheet (dashed line).
observed event-scale and seasonal correlations between dand local temperature are very high [e.g., Schneider et al.,2006; van Ommen and Morgan, 1997], interannual correlations, while positive, are generally low (e.g., see Figure 1d).
A GCM study has suggested that the SAM leads to an
observable, organized signature in stable isotopes, with atypical positive phase SAM anomaly ranging from 0.2
to 0.8 % in d18O across the continent [Noone andSimmonds, 2002]. A number of mechanisms are involved;
the primary contribution to the depletion of d18O is increased distillation along the path of moisture transport. The
spatial pattern of isotope anomalies is analogous to and also
physically linked with (via the local condensation temperature of precipitation [Noone and Simmonds, 2002; Jouzel etal., 1997]) the SAM’s expression in the temperature field.
At three of our widely separated sites (DML, Law Dome,and ITASE 00-5), the covariance of the SAM index andd18O is significant, with a coefficient ranging from 0.30 to
0.90 % per standard deviation of the SAM index, within
the range of the model calculations. Thus, the positive phase
SAM is associated both with lower temperatures anddepleted stable isotopes on the scale of the continent.
[ 10 ] As opposed to the traditional use of local
d-temperature calibrations, our approach to temperature reconstruction is to combine multiple, annually-resolved ice
core records into a single record, calibrated at the continental scale. This is supported on physical grounds both by thelarge-scale covariance of temperature and isotope anomalies
and by the reduction of non-climate related noise whenproxy records are combined. We first normalize the individual records with respect to their common 1800 – 1983
period of coverage. They are then combined through aweighted average on the basis of each record’s detrended
correlation with the A8 index (correlations indicated in
Figure 1d.). This stack is significantly correlated with the
A8 index at r = 0.63 (p < 0.01), and correlation with 2-m T
shows that it corresponds to the cold continent - warm
Peninsula pattern (Figure 1d), demonstrating that our
stacked record captures the dominant pattern of Antarctic
temperature variability. To calibrate the reconstruction, theice core stack is adjusted to the variance and mean of thetarget A8 index over the 1961 – 1999 overlap interval.
Despite local differences in the variance of temperatures,
we find that A8 provides a good approximation of thevariance of temperatures on the continental scale. The
2-m T data averaged over the ice sheet (excluding areas
with negative weights in the first EOF), have a variance of

L167070.28C, which is similar to the variance of A8 over 1961–
1999 (Figure 2).
[11] In addition to the correlation coefficient, the reduction of error (RE) statistic provides a measure of reconstruction resolved variance [e.g., Jones and Widmann,2003]. For the 1961 – 1999 calibration interval, the RE is
0.26, which indicates skill relative to climatology. Given thepaucity of instrumental data, we do not have the luxury of aperiod of data to withhold for cross-verification. However,
if we consider the two halves of the calibration interval as
independent calibration/verification periods, we find thatthe calibration statistics and verification RE scores are quite
similar (Table 2), supporting confidence in the reconstruction. The 2s (i.e. 95%) confidence intervals of the reconstruction are estimated as twice the root-mean-square errorof the calibration period correlation times the standard
deviation of A8.
[12] We note potential sources of uncertainty, including
possible changes in accumulation rates that could alter the
relation of stable isotopes to annual mean temperature, andchanges in sea surface temperatures (at the moisture source).
With regard to accumulation rates, we note that a recent
study of accumulation trends finds them to be highly
spatially and seasonally variable, largely canceling out overthe ice sheet in the annual mean [Monaghan et al., 2006].
Seasonal accumulation trends could nonetheless alter theseasonal representativeness of the reconstruction. However,
over the calibration period, the ice core stack is better
correlated with the annual mean temperature than withany particular seasonal mean temperature, and the lowfrequency behavior of the stacked record is consistent withthat of A8. With regard to source conditions, available
deuterium excess measurements (a proxy for moisturesource conditions) show no evidence of long term trends.

4. Results and Discussion[13] Our reconstruction (Figure 3a) depicts significant
interannual to decadal scale variability, which may be
traceable to the SAM. Prior to the late 1950s, there is only
indirect evidence for the strength of the SAM. The only
long instrumental polar data are from Orcadas station,
situated to the northeast of the Peninsula, where the positive
phase of the SAM results in higher than normal temperatures (r (SAM index, Orcadas temperature) = 0.33 (p <0.05) see also Figure 1c.). The anti-phase temperature
anomaly relationship between the Peninsula region andthe continent can be represented by the significant correlation of (detrended) A8 and Orcadas temperature over 1961–
2002 (r =0.47, p < 0.01). The correlation of ourtemperature reconstruction with Orcadas temperature isnearly as high as the instrumental correlation (r = 0.44,

Table 2. Reconstruction Statistics for Different Calibration
Periods
Periodr (A8, ice)a

Reduction of Error

1961 – 1999
1961 – 1980
1981 – 1999

0.63 p < 0.01
0.62 p < 0.01
0.63 p < 0.01

0.26 calibration
0.24 verification
0.25 verification

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Ice refers to the stacked ice core record.

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Figure 3. Antarctic temperature reconstruction: (a) Fullannual resolution reconstruction (black line) with ±2s
uncertainty shaded in gray and multi-decadal smoothedversion (heavy line). Instrumental calibration record, A8(gray line); (b) Reconstruction (black line) compared with
Orcadas temperature (gray line, inverted) after the linear
trends have been removed; (c) Reconstruction (black line)
compared with SH mean instrumental record (gray line)
after a multi-decadal (0.05 cycles/yr cut-off) low-pass filter
has been applied. In all plots, the zero line represents the1961 – 1990 climatological mean.
p < 0.01 over 1904 –1999), even in the pre-calibration period (r = 0.44, p < 0.01 over 1904 – 1960) (Figure 3b).
This result is consistent with the observed cold continent warm Peninsula pattern being a dominant feature of Antarctic climate variability. On a monthly timescale, analysis
of gridded data indicates that about 17% of the spatialtemporal variance of temperature anomalies over the continent is explained by the SAM [Schneider et al., 2004], andour results suggest that on interannual to decadal timescales,
a similar amount of variance is explained by the SAM overat least the last century. Large variability associated with the
SAM at these timescales has also been inferred fromindependent reconstructions of the SAM index using station
sea-level pressure [Jones and Widmann, 2004, 2003].
[14] In addition to the significant high frequency variability, our reconstruction shows an extended period ofcooler than present conditions from the mid-19th to early
20th-century. Existing evidence suggests that there was not
an extended positive phase SAM over this time period that
could explain the lower temperatures [Jones and Widmann,

L167072003]. From our reconstruction, we estimate that Antarctic
temperatures over the 1961 – 1999 calibration period wereabout 0.2C higher than in the period 1861– 1899. Other
evidence for long-term warming includes ice boreholetemperature inversions at Siple Dome [Engelhardt, 2004]
in West Antarctica and Law Dome [Dahl-Jensen et al.,1999] in East Antarctica. At Law Dome, the minimum(1850) to maximum (1990) temperature difference wasquantified as 0.7C, which agrees with the difference of
0.6C that is evident in a smoothed version of our reconstruction between the low in the 1800s to the maximum
around 1990 (Figure 3c). Some lower resolution isotopic
records, at Siple Dome [Mayewski et al., 2004] and Dronning Maud Land [Isaksson et al., 1996], also suggest 20thcentury warming.
[15] It is notable that the reconstructed Antarctic temperature record is in phase with the SH mean instrumental
temperature record [Folland et al., 2001], especially over
the two well-known global warming periods, from 1910
to 1945 and from the 1970s to present (Figure 3c). This
phasing can be explored with cross-spectral analyses of the
Antarctic versus the SH mean and the Orcadas records.
Although no significant spectral peaks are present in comparison with a simple red noise continuum, the phasing andcoherency relationships are informative (Figure 4). The cold
continent-warm Peninsula pattern is evident in the antiphasing of the Orcadas record and the reconstruction in the
broad band from about three to 10 years. The phasing with
the SH mean becomes significantly coherent at the longest
periods resolved, about 20 years.
[16] Since the SAM had a strong positive trend from themid-1970s to the 1990s [Marshall, 2003], we separate
temperature trends before and after 1975. From 1856 to
1975, trends (C per 100 yr) in the SH mean (0.32 ± .10,
p < 0.01) and Antarctic record (0.22 ± .26, p < 0.10) aresimilar, while they are markedly different from 1975 – 1999
(1.40 ± .59, p < 0.01 and 2.2 ± 3.8, not significant,Figure 4. (a) Coherency and (b) phase estimates of the
Antarctic temperature reconstruction versus the SH mean
temperature (black dashed line) and the Orcadas temperature (gray solid line). Annual data were used, and crossspectra were estimated with a Hanning Window width of
32 years using a fast Fourier transform. 95% confidence
levels are indicated. Results are independent of linear trends
in the records.

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respectively). As discussed above, the reconstruction provides evidence for long-term Antarctic warming, and thelinear trend for the full period of overlap with the SH mean
(1856 – 1999) is an increase of 0.18 ±. 21C per 100 yr(p < 0.10), which has greater mean and statistical significance (0.23 ± .21C per 100 yr, p < 0.05) if the end date,1999, is excluded because it is an outlier in comparison with
the instrumental record. Thus, recent Antarctic cooling is
superimposed on longer-term warming. These results support attribution of the cooling trend to the trend in the SAM[Thompson and Solomon, 2002], however, strong variability
precludes unambiguous attribution of the SAM trend toanthropogenic forcing, such as ozone depletion.

5. Conclusions[17] Our reconstruction based on high resolution stable
isotope time series emphasizes that the short Antarctic
instrumental records provide very limited context for assessing Antarctic temperature change, as there is large internannual to decadal scale variability, attributable in part to
the SAM. In the longer-term context, our reconstruction,
combined with other evidence, suggests that the Antarctic
continent has experienced modest warming over the last
150 years. Similar phasing as the SH mean temperature record suggests that this warming may be linked to globalchanges and suggests that Antarctica will warm in parallelwith the SH, in general accordance with model-based predictions [e.g., Shindell and Schmidt, 2004].
[18] Acknowledgments. The authors thank G. Marshall for providing
the SAM index data, E. Mosley-Thompson for Siple Station ice core data,and H. Oerter for DML ice core data. READER data are from http://
www.antarctica.ac.uk/met/READER, Polar MM5 output is from http://polarmet.mps.ohio-state.edu/PolarMet/ant_hindcast.html, and SH mean
temperature data are from http://www.cru.uea.ac.uk/cru/data/temperature/.
Primary support for this work, to D.P.S., E.J.S., D.A.D., and P.A.M. is fromthe U.S. National Science Foundation, Office of Polar Programs (grants
0229416 and 0196105). D.P.S. acknowledges additional support from the
Scientific Committee on Antarctic Research. T.v O. acknowledges support
from the Australian Government’s Cooperative Research Centre Program.
The authors thank anonymous reviewers for their constructive comments.

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C. M. Bitz, Department of Atmospheric Sciences, University ofWashington, Box 351640, Seattle, WA 98195, USA.
D. A. Dixon and P. A. Mayewski, Climate Change Institute, University ofMaine, 303 Edward T. Bryand Global Sciences Center, Orono, ME 044695790, USA.
J. M. Jones, Institute for Coastal Research, GKSS Research Centre, MaxPlanck-Strasse 1, D-21502 Geesthacht, Germany.
D. P. Schneider and E. J. Steig, Department of Earth and Space Sciences,
University of Washington, Box 351310, Seattle, WA 98195, USA.
(schneidd@u.washington.edu)
T. D. van Ommen, Department of the Environment and Heritage,
Australian Antarctic Division, Antarctic Climate and Ecosystems CRC,
Private Bag 80, Hobart, Tas 7001, Australia.

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