1. Department of Earth and Space Sciences and Quaternary Research Center, University of Washington, Seattle, Washington 98195, USA
2. National Center for Atmospheric Research, Boulder, Colorado 80307, USA
3. Department of Environmental Science, Roger Williams University, Bristol, Rhode Island, USA
4. Department of Meteorology, and Earth and Environmental Systems Institute, Pennsylvania State University, University Park, Pennsylvania 16802, USA
5. NASA Laboratory for Hydrospheric and Biospheric Sciences, NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
6. NASA Goddard Institute for Space Studies and Center for Climate Systems Research, Columbia University, New York, New York 10025, USA

Correspondence to: Eric J. Steig¹ Correspondence and requests for materials should be addressed to E.J.S. (Email: steig@ess.washington.edu).

Recent changes in Antarctic ice-sheet surface temperatures appear enigmatic when compared with global average temperature trends. Although the Antarctic Peninsula is one of the most rapidly warming locations on Earth, weather stations on the Antarctic continent generally show insignificant trends in recent decades¹. However, all but two of the continuous records from weather stations are near the coast, providing little direct information on conditions in the continental interior. The widely used weather forecast reanalysis data are known to have errors owing to inconsistent assimilation skill in the satellite and pre-satellite eras³.

In this Letter, we use statistical climate-field-reconstruction techniques to obtain a 50-year-long, spatially complete estimate of monthly Antarctic temperature anomalies. In essence, we use the spatial covariance structure of the surface temperature field to guide interpolation of the sparse but reliable 50-year-long records of 2-m temperature from occupied weather stations. Although it has been suggested that such interpolation is unreliable owing to the distances involved¹, large spatial scales are not inherently problematic if there is high spatial coherence, as is the case in continental Antarctica⁴.

Previous reconstructions of Antarctic near-surface temperatures have yielded inconsistent results, particularly over West Antarctica, where records are few and discontinuous^{5, 6, 7}. We improve upon this earlier work in several ways. We use two independent estimates of the spatial covariance of temperature across the Antarctic ice sheet: surface temperature measurements from satellite thermal infrared (T_IR) observations⁸, and up-to-date automatic weather station (AWS) measurements of near-surface air temperature. We use a method^{9, 10} adapted from the regularized expectation maximization algorithm¹¹ (RegEM) for estimating missing data points in climate fields. RegEM is an iterative algorithm similar to principal-component analysis, used as a data-adaptive optimization of statistical weights for the weather station data. Unlike simple distance-weighting^{5, 6} or similar⁷ calculations, application of RegEM takes into account temporal changes in the spatial covariance pattern, which depend on the relative importance of differing influences on Antarctic temperature at a given time. Furthermore, the iterative nature of RegEM allows it to be used with discontinuous time series, permitting us to take full advantage of the data available from occupied weather stations. We assess reconstruction skill using reduction-of-error (RE) and coefficient-of-efficiency (CE) scores as well as conventional correlation (r) scores. Such verification metrics are lacking in previous Antarctic temperature reconstructions^{5, 6, 7}, but are required for demonstrating skill relative to the climatological mean and are therefore critical for confidence in the calculation of temporal trends¹⁰. Skill metrics for our T_IR-based reconstruction from split calibration and verification experiments are significant (>99% confidence) at all grid points except in some restricted areas, mostly on the eastern side of the Antarctic Peninsula (Fig. 1).

Figure 1: Verification and upper-limit calibration statistics calculated for each grid point from the comparison of reconstructed and original satellite-derived monthly temperature anomalies.

a, Calibration r, 1982–1994.5; b, calibration r, 1994.5–2006; c, verification r, 1994.5–2006; d, verification r, 1982–1994.5; e, verification RE, 1994.5–2006; f, verification RE, 1982–1994.5. Warm colours in e and f (RE scores greater than zero) show where results are more accurate than the climatological mean temperature. Mean grid-point verification results are RE = 0.11, CE = 0.09 and r = 0.46. Crosses show locations of occupied weather stations.

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Results from our AWS-based reconstruction agree well with those from the T_IR data (Fig. 2). This is important because the infrared data are strictly a measure of clear-sky temperature⁸ and because surface temperature differs from air temperature 2–3 m above the surface, as measured at occupied stations or at AWSs. Trends in cloudiness or in the strength of the near-surface inversion could both produce spurious trends in the temperature reconstruction. The agreement between the reconstructions, however, rules out either potential bias as significant. Furthermore, detrending of the T_IR data before reconstruction demonstrates that the results do not depend strongly on trends in said data (Supplementary Information).

Figure 2: Reconstructed annual mean Antarctic temperature anomalies, January 1957 to December 2006.

a, East Antarctica; b, West Antarctica. Solid black lines show results from reconstruction using infrared satellite data, averaged over all grid points for each region. Dashed lines show the average of reconstructed AWS data in each region. Straight red lines show average trends of the T_IR reconstruction. Verification results for the continental mean of the T_IR reconstruction are RE = 0.34, CE = 0.31 and r = 0.73. Grey shading, 95% confidence limits.

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Our reconstructions show more significant temperature change in Antarctica (Fig. 2), and a different pattern for that change than reported in some previous reconstructions^{5, 7} (Fig. 3). We find that West Antarctica warmed between 1957 and 2006 at a rate of 0.17  0.06 °C per decade (95% confidence interval). Thus, the area of warming is much larger than the region of the Antarctic Peninsula. The peninsula warming averages 0.11  0.04 °C per decade. We also find significant warming in East Antarctica at 0.10  0.07 °C per decade (1957–2006). The continent-wide trend is 0.12  0.07 °C per decade. In the reconstruction based on detrended T_IR data, warming in West Antarctica remains significant at greater than 99% confidence, and the continent-wide mean trend remains at 0.08 °C per decade, although it is no longer demonstrably different from zero (95% confidence). This is in good agreement with ref. 6, which reported average continent-wide warming of 0.082 °C per decade (1962–2003) and shows overall warming in West Antarctica, although statistical significance could not be demonstrated owing to the shorter length and greater variance of the reconstruction. We emphasize that, in general, detrending of predictand data lowers the quality of reconstructions by removing spatial covariance information¹⁰. The detrended reconstruction therefore represents a conservative lower bound on trend magnitude. Although ref. 7 concluded that recent temperature trends in West Antarctica are statistically insignificant, the results were strongly influenced by the paucity of data from that region. When the complete set of West Antarctic AWS data is included, the trends become positive and statistically significant, in excellent agreement with our results¹².

Figure 3: Spatial pattern of temperature trends (degrees Celsius per decade) from reconstruction using infrared (T_IR) satellite data.

a, Mean annual trends for 1957–2006; b, Mean annual trends for 1969–2000, to facilitate comparison with ref. 2. c–f, Seasonal trends for 1957–2006: winter (June, July, August; c); spring (September, October, November; d); summer (December, January, February; e); autumn (March, April, May; f). Black lines enclose those areas that have statistically significant trends at 95% confidence (two-tailed t-test). Where it would otherwise be unclear, NS (not significant) refers to areas of insignificant trends. Red circles and adjacent numbers in a show the locations of the South Pole and Vostok weather stations and their respective trends (degrees Celsius per decade) during the same time interval as the reconstruction (1957–2006). Black circles in b show the locations of Siple and Byrd Stations, and the adjacent numbers show their respective trends¹³ for 1979–1997.

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Independent data provide additional evidence that warming has been significant in West Antarctica. At Siple Station (76° S, 84° W) and Byrd Station (80° S, 120° W), short intervals of data from AWSs were spliced with 37-GHz (microwave) satellite observations, which are not affected by clouds, to obtain continuous records from 1979 to 1997 (ref. 13). The results show mean trends of 1.1  0.8 °C per decade and 0.45  1.3 °C per decade at Siple and Byrd, respectively¹³. Our reconstruction yields 0.29  0.26 °C per decade and 0.36  0.37 °C per decade over the same interval. In our full 50-year reconstruction, the trends are significant, although smaller, at both Byrd (0.23  0.09 °C per decade) and Siple (0.18  0.06 °C per decade). Furthermore, the seasonal characteristics of these data¹³ agree well with those from our reconstructions, with the greatest amount of warming in austral spring and winter (Fig. 3). Independent analyses of tropospheric temperature trends have also found spring and winter warming to be greatest in West Antarctica^{14, 15}.

The spatial and seasonal characteristics of our temperature reconstruction have important implications for understanding recent Antarctic climate change. Several studies have emphasized a warming-peninsula, cooling-continent pattern that is attributed to changes in atmospheric circulation associated with the southern annular mode (SAM)^{2, 16}. Cooling over much of East Antarctica did occur in recent decades, but was strongest during the short time interval considered in earlier studies (1969–2000; Fig. 3b). Virtually all areas warmed between 1957 and 1980. Our reconstruction differs from the results of modelling experiments that tie Antarctic surface temperature change to stratospheric ozone loss through changes in the SAM^{16, 17, 18}. In such simulations, the largest negative temperature anomalies in East Antarctica occur in summer, whereas in our reconstruction, East Antarctic cooling is restricted to autumn (Fig. 3). The simulations show warming in austral summer and autumn, restricted to the peninsula, whereas in our reconstruction the greatest warming is in winter and spring, and in continental West Antarctica as well as on the peninsula.

The well-known increases in temperature on the Antarctic Peninsula are strongly associated with changes in sea ice¹⁹. Similarly, negative anomalies in sea-ice extent²⁰ and the length of the sea-ice season²¹ in the Amundsen–Bellingshausen Sea may be related to the warming trends we observe in adjacent West Antarctica. To explore this, we examined model output from the NASA Goddard Institute for Space Studies (GISS) ModelE atmosphere-only and coupled general circulation models, which were run with multiple oceanic and atmospheric boundary conditions until the end of 2003 (ref. 22). A slightly earlier atmospheric version of GISS ModelE has been used in simulations of circulation anomalies associated with polar stratospheric ozone depletion¹⁷. When driven by observed sea-surface-temperature (SST) and sea-ice boundary conditions²³, the model reproduces many of the basic features of our reconstruction, with warming over most of the continent and persistent in West Antarctica (Fig. 4). SST and sea-ice changes alone produced weak cooling over parts of East Antarctica during the 1980s and 1990s. The details of the comparisons obviously depend on the accuracy of the SST and sea-ice observations (the latter are not generally considered reliable before 1979), and multi-decadal internal variability in the model is substantial. However, it is noteworthy that both in the reconstruction and in the model results, the rate of warming is greater in continental West Antarctica, particularly in spring and winter, than either on the peninsula or in East Antarctica. In GISS ModelE, this is related to SST changes and the location of sea-ice anomalies, particularly during the latter period (1979–2003), when they are strongly zonally asymmetric, with significant losses in the West Antarctic sector but small gains around the rest of the continent (Fig. 4e). Radiative forcings alone are inadequate to account for the observations (Supplementary Information).

Figure 4: Comparison of reconstructed and modelled mean annual temperature trends (degrees Celsius per decade) for the periods 1957–1981 and 1979–2003.

a–d, 1957–1981; e–h, 1979–2003. a, e, Reconstructed surface temperature and observed 25-year change in sea-ice fractional area²³. b, f, Surface air temperature from five-member GISS ModelE atmosphere-only ensemble simulations with observed sea-surface-temperature and sea-ice boundary conditions. c, g, Four-member ensemble with the same boundary conditions plus atmospheric forcings (changes in atmospheric concentrations of radiatively active species, including ozone). d, h, Difference between simulations with the same forcings but observed versus climatological sea ice, to isolate the effect of sea ice alone.

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The net impact of SST, sea ice, and radiative forcings on Antarctic temperatures in GISS ModelE is in general agreement with our reconstruction. The same model, when run in coupled mode (that is, with a dynamic ocean) fails to reproduce the strong trends observed in West Antarctica and the peninsula. The probable cause of this discrepancy, common to other coupled models²⁴, is inadequate representation of sea-ice anomalies and their associated higher-order modes of atmospheric circulation. In this context, it is important that the pattern of observed temperature trends closely resembles the pattern of temperature anomalies associated with the zonal wave-3 pattern in atmospheric circulation⁴. This circulation regime is efficient for the exchange of air between the ocean and the Antarctic continental interior, and is associated with atmospheric circulation anomalies in the Amundsen–Bellingshausen Sea, known to precede winter sea-ice anomalies²⁵. Forced coupled models, including GISS ModelE, generally show a positive shift in the SAM and an associated increase in the circumpolar westerlies over recent decades, in good agreement with observations²⁶. Observations also suggest a bias towards the positive phase in the wave-3 pattern²⁵ since about 1979, which is not reproduced in the coupled models. Using observed SST and sea ice, GISS ModelE does produce substantial shifts in the wave-3 circulation. Under those model conditions, greater cyclonic flow in the Amundsen Sea region brings warm, moist air to West Antarctica, countering the effect of the enhanced circumpolar westerlies.

An outstanding question in Antarctic climatology has been whether the strong warming of the peninsula has also occurred in continental West Antarctica¹⁹. Our results indicate that this is indeed the case, at least over the last 50 years. Moreover, ice-core analyses indicate average warming of West Antarctica over the entire twentieth century²⁷. Although the influence of ozone-related changes in the SAM has been emphasized in recent studies of Antarctic temperature trends, the spatial and seasonal patterns of the observed temperature trends indicate that higher-order modes of atmospheric circulation, associated with regional sea-ice changes, have had a larger role in West Antarctica.

Mean surface temperature trends in both West and East Antarctica are positive for 1957–2006, and the mean continental warming is comparable to that for the Southern Hemisphere as a whole²⁸. This warming trend is difficult to explain without the radiative forcing associated with increasing greenhouse-gas concentrations. However, the future trajectory of Antarctic temperature change also depends on the extent to which changes in atmospheric composition (whether from greenhouse gases or stratospheric ozone) affect Southern Hemisphere sea ice and regional atmospheric circulation patterns. Improved representation in models of coupled atmosphere/sea-ice dynamics will be critical for forecasting Antarctic temperature change.

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Supplementary Information

Supplementary information accompanies this paper.

Acknowledgements

E.J.S. and D.P.S. were supported by the US National Science Foundation, grant numbers OPP-0440414 and OPP-0126161, as part of the US ITASE programme. M.E.M. was supported by the US National Science Foundation, grant number OPP-0125670. We thank D. Winebrenner, A. Monaghan, D. Bromwich, J. Turner, P. Mayewski, T. Scambos, E. Bard and O. Bellier.

Author Contributions E.J.S., D.P.S., S.D.R. and M.E.M. made the reconstruction and statistical calculations. J.C.C. performed the cloud-masking calculations and provided the updated satellite data set. D.T.S. provided the general circulation model output and guided its interpretation. E.J.S. wrote the paper. All authors discussed the results and commented on the manuscript.

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