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Sustained decrease in inland East Antarctic surface mass balance between 2005 and 2020

Nature Geoscience volume 18, pages 462–470 (2025) Cite this article

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Abstract

Accurate observations of surface mass balance are pivotal for assessing the Antarctic Ice Sheet mass balance and its link to climate dynamics. Studying regional changes in surface mass balance is challenging due to limited on-site observations and the susceptibility of measurements from snow pits and ice cores to localized disturbances. Satellite data and short-term localized measurements suggest no significant changes or a possible increase in surface mass balance across the East Antarctic Ice Sheet in recent decades, but these findings lack large-scale validation. Here we use observations from mass balance stakes to show a significant negative surface mass balance trend along the inland transect from Zhongshan Station to the Antarctic Ice Sheet summit (Dome A) during the period 2005–2020. The mean surface mass balance trend for the inland section over the 15-year period is −2.01 ± 0.37 kg m−2 yr–2, indicating a 35.5% decrease. This decrease is probably linked to enhanced zonal winds in the upper atmosphere and a deepened low-pressure system in the southern Indian Ocean. The former weakens meridional air transport to Antarctica, while the latter strengthens offshore winds over the study area, reducing onshore water vapour transport. These findings can be used to evaluate and improve regional climate models and refine estimates of contemporary Antarctic mass balance trends.

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Fig. 1: Spatial and temporal trends in SMB along the Zhongshan–Dome A study transect.
The alternative text for this image may have been generated using AI.
Fig. 2: Changes in the 250 hPa (jet level) circulation over high southern latitudes and teleconnection with inland transect precipitation.
The alternative text for this image may have been generated using AI.
Fig. 3: 500 hPa wind anomalies and Z500 trends over the high southern latitudes and the teleconnection between Z500 and inland transect precipitation.
The alternative text for this image may have been generated using AI.
Fig. 4: Trends and anomalies in near-surface winds in the study area from 2005 to 2020.
The alternative text for this image may have been generated using AI.
Fig. 5: Changes in normalized precipitation, snow accumulation, jet wind speed, 10 m V-wind speed and Z500 during the modern record.
The alternative text for this image may have been generated using AI.
Fig. 6: Summary of the changes in atmospheric circulation over the high southern latitudes and their impacts on precipitation in the study area.
The alternative text for this image may have been generated using AI.

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Data availability

The ERA5 atmospheric circulation data that support the findings of this study are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/. Data available via Figshare at https://doi.org/10.6084/m9.figshare.28091900. Source data are provided with this paper.

Code availability

Code used to analyse the data and prepare figures is available upon request from the corresponding author.

References

  1. Pan, X. L., Li, B. F. & Watanabe, Y. W. Intense ocean freshening from melting glacier around the Antarctica during early twenty-first century. Sci. Rep. 12, 383 (2022).

    Article  CAS  Google Scholar 

  2. Vaughan, D. G. Recent trends in melting conditions on the Antarctic Peninsula and their implications for ice-sheet mass balance and sea level. Arct. Antarct. Alp. Res. 38, 147–152 (2006).

    Article  Google Scholar 

  3. Allison, I. et al. Ice sheet mass balance and sea level. Antarct. Sci. 21, 413–426 (2009).

    Article  Google Scholar 

  4. Rignot, E. et al. Four decades of Antarctic Ice Sheet mass balance from 1979-2017. Proc. Natl Acad. Sci. USA 116, 1095–1103 (2019).

    Article  CAS  Google Scholar 

  5. Paolo, F. S., Fricker, H. A. & Padman, L. Volume loss from Antarctic ice shelves is accelerating. Science 348, 327–331 (2015).

    Article  CAS  Google Scholar 

  6. Dutrieux, P. et al. Strong sensitivity of Pine Island ice-shelf melting to climatic variability. Science 343, 174–178 (2014).

    Article  CAS  Google Scholar 

  7. Paolo, F. et al. Response of Pacific-sector Antarctic ice shelves to the El Niño/Southern oscillation. Nat. Geosci. 11, 121–126 (2018).

    Article  CAS  Google Scholar 

  8. Steig, E. J., Ding, Q., Battisti, D. S. & Jenkins, A. Tropical forcing of Circumpolar Deep Water inflow and outlet glacier thinning in the Amundsen Sea Embayment, West Antarctica. Ann. Glaciol. 53, 19–28 (2017).

    Article  Google Scholar 

  9. Holland, P. R. et al. West Antarctic ice loss influenced by internal climate variability and anthropogenic forcing. Nat. Geosci. 12, 718–724 (2019).

    Article  CAS  Google Scholar 

  10. Simpkins, G. R. et al. Tropical connections to climatic change in the extratropical Southern Hemisphere: the role of Atlantic SST trends. J. Clim. 27, 4923–4936 (2014).

    Article  Google Scholar 

  11. Li, X., Holland, D. M., Gerber, E. P. & Yoo, C. Impacts of the north and tropical Atlantic Ocean on the Antarctic Peninsula and sea ice. Nature 505, 538–542 (2014).

    Article  CAS  Google Scholar 

  12. Shepherd, A. et al. Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature 558, 219–222 (2018).

    Article  Google Scholar 

  13. Li, X. et al. Tropical teleconnection impacts on Antarctic climate changes. Nat. Rev. Earth Environ. 2, 680–698 (2021).

    Article  Google Scholar 

  14. Thiery, W. et al. Surface and snowdrift sublimation at Princess Elisabeth station, East Antarctica. Cryosphere 6, 841–857 (2012).

    Article  Google Scholar 

  15. Das, I. et al. Influence of persistent wind scour on the surface mass balance of Antarctica. Nat. Geosci. 6, 367–371 (2013).

    Article  CAS  Google Scholar 

  16. Van Wessem, J. M. et al. Improved representation of East Antarctic surface mass balance in a regional atmospheric climate model. J. Glaciol. 60, 761–770 (2017).

    Article  Google Scholar 

  17. Lenaerts, J. T. M. et al. A new, high-resolution surface mass balance map of Antarctica (1979-2010) based on regional atmospheric climate modeling. Geophys. Res. Lett. 39, L04501 (2012).

    Article  Google Scholar 

  18. Baumhoer, C., Dietz, A., Dech, S. & Kuenzer, C. Remote sensing of Antarctic glacier and ice-shelf front dynamics—a review. Remote Sens. 10, 1445 (2018).

    Article  Google Scholar 

  19. Monaghan, A. J. et al. Insignificant change in Antarctic snowfall since the International Geophysical Year. Science 313, 827–831 (2006).

    Article  CAS  Google Scholar 

  20. Parrenin, F. et al. Climate dependent contrast in surface mass balance in East Antarctica over the past 216 ka. J. Glaciol. 62, 1037–1048 (2016).

    Article  Google Scholar 

  21. Scott, R. C. et al. Meteorological drivers and large-scale climate forcing of West Antarctic Surface Melt. J. Clim. 32, 665–684 (2019).

    Article  Google Scholar 

  22. The IMBIE team. Mass balance of the Antarctic Ice Sheet from 1992 to 2017. Nature 558, 219–222 (2018).

  23. Wouters, B. et al. Dynamic thinning of glaciers on the Southern Antarctic Peninsula. Science 348, 899–903 (2015).

    Article  CAS  Google Scholar 

  24. Martin‐Español, A., Bamber, J. L. & Zammit‐Mangion, A. Constraining the mass balance of East Antarctica. Geophys. Res. Lett. 44, 4168–4175 (2017).

    Article  Google Scholar 

  25. Philippe, M. et al. Ice core evidence for a 20th century increase in surface mass balance in coastal Dronning Maud Land, East Antarctica. Cryosphere 10, 2501–2516 (2016).

    Article  Google Scholar 

  26. Frezzotti, M. et al. Spatial and temporal variability of surface mass balance near Talos Dome, East Antarctica. J. Geophys. Res. 112, F02032 (2007).

    Google Scholar 

  27. Furukawa, T., Kamiyama, K. & Maeno, H. Snow surface features along the traverse route from the coast to Dome Fuji Station, Queen Maud Land, Antarctica. Proc. NIPR Symp. Polar Meteorol. Glaciol. 10, 13–24 (1996).

    Google Scholar 

  28. Wang, Y. et al. The AntSMB dataset: a comprehensive compilation of surface mass balance field observations over the Antarctic Ice Sheet. Earth Syst. Sci. Data 13, 3057–3074 (2021).

    Article  Google Scholar 

  29. Ding, M. et al. Surface mass balance and its climate significance from the coast to Dome A, East Antarctica. Sci. China Earth Sci. 58, 1787–1797 (2015).

    Article  Google Scholar 

  30. Liu, J., Du, Z., Zhang, D. & Wang, S. Diagnoses of Antarctic inland water cycle regime: perspectives from atmospheric water vapor isotope observations along the transect from Zhongshan Station to Dome A. Front. Earth Sci. 10, 823515 (2022).

    Article  Google Scholar 

  31. Stefanini, C. et al. Extreme events of snow grain size increase in East Antarctica and their relationship with meteorological conditions. Cryosphere 18, 593–608 (2024).

    Article  Google Scholar 

  32. Wang, S. et al. Potential mechanisms governing the variation in rain/snow frequency over the northern Antarctic Peninsula during austral summer. Atmos. Res. 263, 105811 (2021).

    Article  Google Scholar 

  33. Ding, Q., Steig, E. J., Battisti, D. S. & Wallace, J. M. Influence of the tropics on the southern annular mode. J. Clim. 25, 6330–6348 (2012).

    Article  Google Scholar 

  34. Marshall, G. J., Thompson, D. W. & van den Broeke, M. R. The signature of Southern Hemisphere atmospheric circulation patterns in Antarctic precipitation. Geophys. Res. Lett. 44, 11,580–511,589 (2017).

    Article  Google Scholar 

  35. Wang, W. et al. Spatiotemporal mass change rate analysis from 2002 to 2023 over the Antarctic Ice Sheet and four glacier basins in Wilkes-Queen Mary Land. Sci. China Earth Sci. 68, 1086–1099 (2025).

    Article  Google Scholar 

  36. Turner, J. et al. Absence of 21st century warming on Antarctic Peninsula consistent with natural variability. Nature 535, 411–415 (2016).

    Article  CAS  Google Scholar 

  37. Clem, K. R., Lintner, B. R., Broccoli, A. J. & Miller, J. R. Role of the South Pacific Convergence Zone in West Antarctic decadal climate variability. Geophys. Res. Lett. 46, 6900–6909 (2019).

    Article  Google Scholar 

  38. Trenberth, K. E., Fasullo, J. T., Branstator, G. & Phillips, A. S. Seasonal aspects of the recent pause in surface warming. Nat. Clim. Change 4, 911–916 (2014).

    Article  Google Scholar 

  39. Meehl, G. A. et al. Antarctic sea-ice expansion between 2000 and 2014 driven by tropical Pacific decadal climate variability. Nat. Geosci. 9, 590–595 (2016).

    Article  CAS  Google Scholar 

  40. Zhang, S. et al. Ice velocity from static GPS observations along the transect from Zhongshan station to Dome A, East Antarctica. Ann. Glaciol. 48, 113–118 (2008).

    Article  CAS  Google Scholar 

  41. Bretherton, C. S. et al. The effective number of spatial degrees of freedom of a time-varying field. J. Clim. 12, 1990–2009 (1999).

    Article  Google Scholar 

  42. Björnsson, H. & Venegas, S. A Manual for EOF and SVD Analyses of Climatic Data Report No. 97, 112–134 (CCGCR, 1997).

  43. Ding, Q., Steig, E. J., Battisti, D. S. & Küttel, M. Winter warming in West Antarctica caused by central tropical Pacific warming. Nat. Geosci. 4, 398–403 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers 42276243 and 41922046), the National Key Research and Development Program of China (grant numbers 2023YFC2812601 and 2021YFC2801405) and the Fundamental Research Funds for the Central Universities. We thank CHINARE members for their support and assistance with SMB observations.

Author information

Author notes

  1. These authors contributed equally: Danhe Wang, Hongmei Ma.

Authors and Affiliations

  1. Key Laboratory of Geographic Information Science, School of Geographic Sciences and State Key Lab of Estuarine and Coastal Research, East China Normal University, Shanghai, China

    Danhe Wang, Ye Hu, Yuansheng Li & Guitao Shi

  2. Polar Research Institute of China, Shanghai, China

    Danhe Wang, Hongmei Ma, Zhengyi Hu, Chunlei An, Chuanjin Li, Su Jiang, Yuansheng Li, Siyu Lu & Bo Sun

  3. Institute of Agro-food Standards and Testing Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China

    Danhe Wang

  4. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Xichen Li

  5. Institute of Tibetan Plateau and Polar Regions Meteorology, Chinese Academy of Meteorological Sciences, Beijing, China

    Minghu Ding

  6. Key Laboratory of Meteorological Disaster of Ministry of Education (KLME), Nanjing University of Information Science and Technology, Nanjing, China

    Gang Zeng

  7. Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the Netherlands

    Michiel van den Broeke

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Contributions

D.W. and G.S. contributed to the data collection, data analysis, writing the original draft and reviewing and editing. G.S. and H.M. contributed to the research design, data collection and supervision. X.L. contributed to the research design and reviewing and editing. Y.H. contributed to the data analysis. Z.H., C.A., M.D., C.L., S.J., Y.L., S.L. and B.S. contributed to data collection and reviewing and editing. G.Z. and M.v.d.B. contributed to the reviewing and editing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Guitao Shi.

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The authors declare no competing interests.

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Nature Geoscience thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Thomas Richardson, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Map of the Zhongshan-Dome A traverse route and ERA5 precipitation trends (2005-2020).

a, A total of about 600 surface mass balance stakes were placed at 2 km intervals along the route, with annual surface snow accumulation measured from 2005-2020. Black scatter indicates areas of significant decreasing precipitation trends from linear regression (P < 0.05, two-sided), highlighted by yellow contours. b, Spatially varying annual-mean precipitation trends along the study transect (76°E-78°E, 70°S- 80°S), quantified using linear regression. The left axis shows the starting position, and the lower axis shows the ending position. The x = y axis represents the exact precipitation trend at each point. Stippling denotes trends significant at P < 0.05 (two-sided). c, 200 km annual-mean precipitation trends along the study transect. d, Significance levels of the 200 km running precipitation trends, with blue dashed line indicating P = 0.05.

Extended Data Fig. 2 The field operation scene for recording surface mass balance (SMB) stake heights.

Two personnel are required to measure the bamboo stakes’ height above the snow surface. One aligns a measuring rod vertically and parallel to the ice sheet, while the other, standing 5 meters away, records the height. Each stake is uniquely labeled with a metal plate showing its name and location, and GPS positions are recorded during each campaign. Credit: J. Deng.

Extended Data Fig. 3 Seasonal Maximum Covariance Analysis (MCA) between the 250 hPa zonal wind (U) and precipitation of the study area, with the spatial patterns of the first mode of Z1000 and precipitation in the MCA for the study period (2005-2020).

a, SON b, DJF c, MAM and d, JJA. Areas of significance heterogeneous Pearson correlation (P < 0.05, two-sided) are shown within the yellow contours and marked with black dots. The red line indicates the study transect.

Extended Data Fig. 4 Seasonal Maximum Covariance Analysis (MCA) between the 500 hPa geopotential height (GPH) (Z500) and precipitation of the study area, with the spatial patterns of the first mode of Z500 and precipitation in the MCA for the study period (2005-2020).

a, SON b, DJF c, MAM and d, JJA. Areas of significance heterogeneous Pearson correlation (P < 0.05, two-sided) are shown within the yellow contours and marked with black dots. The red line indicates the study transect.

Extended Data Fig. 5 Tele-correlation and Maximum Covariance Analysis (MCA) between the 1000 hPa geopotential height (GPH) (Z1000) and precipitation of the study area.

a, Pearson correlation between monthly precipitation and Z1000 in the inland area (70°E-85°E, 75°S-82°S). The areas enclosed by the blue and green dashed boxes correspond to the regions depicted in panels b and c, respectively. b, Spatial patterns of Z1000 from the first MCA mode. c, Spatial patterns of precipitation from the first MCA mode. The areas of significant heterogeneous Pearson correlation areas (P < 0.05, two-sided) are shown within the yellow contours and marked with black dots.

Extended Data Fig. 6 Seasonal Maximum Covariance Analysis (MCA) between the 1000 hPa geopotential height (GPH) (Z1000) and precipitation of the study area, with the spatial patterns of the first mode of Z1000 and precipitation in the MCA for the study period (2005-2020).

a, SON b, DJF c, MAM and d, JJA. Areas of significance heterogeneous Pearson correlation (P < 0.05, two-sided) are shown within the yellow contours and marked with black dots. The red line indicates the study transect.

Source data

Source Data Figs. 1 and 5 (download XLSX )

Field measurement source data from surface mass balance stakes along the Zhongshan Station to Dome A study transect.

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Wang, D., Ma, H., Li, X. et al. Sustained decrease in inland East Antarctic surface mass balance between 2005 and 2020. Nat. Geosci. 18, 462–470 (2025). https://doi.org/10.1038/s41561-025-01699-z

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