Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Antarctic sea-ice expansion and Southern Ocean cooling linked to tropical variability

Nature Climate Change volume 12, pages 461–468 (2022)Cite this article

Subjects

Abstract

A variety of hypotheses, involving sub-ice-shelf melting, stratospheric ozone depletion and tropical teleconnections, have been proposed to explain the observed Antarctic sea-ice expansion over the period of continuous satellite monitoring and corresponding model–observation discrepancy, but the issue remains unresolved. Here, by comparing multiple large ensembles of model simulations with available observations, we show that Antarctic sea ice has expanded due to ocean surface cooling associated with multidecadal variability in the Southern Ocean that temporarily outweighs the opposing forced response. In both observations and model simulations, Southern Ocean multidecadal variability is closely linked to internal variability in the tropics, especially in the Pacific, via atmospheric teleconnections. The linkages are, however, distinctly weaker in simulations than in observations, accompanied by a marked model–observation mismatch in global warming resulting from potential model bias in the forced response and observed tropical variability. Thus, the forced response dominates in simulations, resulting in apparent model–observation discrepancy.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Observed and model-simulated changes in annual-mean SIE and SST over the SO (south of 50° S).
Fig. 2: Comparison of observed and model-simulated trends in Antarctic SIE and global-mean surface temperature.
Fig. 3: Observed and model-simulated trends in annual-mean sea ice and SST over the period 1979−2014.
Fig. 4: Multidecadal variability of SO SST and its connection to the IPO and AMV.
Fig. 5: Influence of internal variability in the Atlantic and Pacific on sea ice, SST and circulation in coupled-model simulations.

Similar content being viewed by others

Data Availability

The NSIDC data are available at https://nsidc.org, the ERSST version 5 dataset at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html, the HadISST dataset at https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html, the COBE SST2 dataset at https://psl.noaa.gov/data/gridded/data.cobe2.html, the ERA5 dataset at https://cds.climate.copernicus.eu, the HadSST4 dataset at https://www.metoffice.gov.uk/hadobs/hadsst4/, the HadCRUT5.0.1.0 dataset at https://www.metoffice.gov.uk/hadobs/hadcrut5/, GISTEMPv.4 at https://data.giss.nasa.gov/gistemp/, NOAA globaltemp v.5.0.0 at https://www.ncei.noaa.gov/products/land-based-station/noaa-global-temp, the Berkeley Earth dataset at http://berkeleyearth.org/data/, the CanESM2 Large Ensemble output at http://crd-data-donnees-rdc.ec.gc.ca/CCCMA/products/CanSISE/output/CCCma/CanESM2/, the CESM1 Large Ensemble output at https://www.cesm.ucar.edu/projects/community-projects/LENS/, the CESM2 Large Ensemble output at https://www.cesm.ucar.edu/projects/community-projects/LENS2/data-sets.html and the CMIP6 simulation output at https://esgf-node.llnl.gov/projects/cmip6/.

Code Availability

The code used to generate the figures in this study is freely available at https://doi.org/10.5281/zenodo.6330284 (ref. 84).

References

  1. Turner, J., Hosking, J. S., Bracegirdle, T. J., Marshall, G. J. & Phillips, T. Recent changes in Antarctic sea ice. Phil. Trans. R. Soc. A 373, 20140163 (2015).

    Article  Google Scholar 

  2. Parkinson, C. L. A 40-y record reveals gradual Antarctic sea ice increases followed by decreases at rates far exceeding the rates seen in the Arctic. Proc. Natl Acad. Sci. USA 116, 14414–14423 (2019).

    Article  CAS  Google Scholar 

  3. Fan, T., Deser, C. & Schneider, D. P. Recent Antarctic sea ice trends in the context of Southern Ocean surface climate variations since 1950. Geophys. Res. Lett. 41, 2419–2426 (2014).

    Article  Google Scholar 

  4. Armour, K. C. & Bitz, C. M. Observed and projected trends in Antarctic sea ice. US CLIVAR Var. 13, 12–19 (2015).

    Google Scholar 

  5. Armour, K. C., Marshall, J., Scott, J. R., Donohoe, A. & Newsom, E. R. Southern Ocean warming delayed by circumpolar upwelling and equatorward transport. Nat. Geosci. 9, 549–554 (2016).

    Article  CAS  Google Scholar 

  6. Comiso, J. C. et al. Positive trend in the Antarctic sea ice cover and associated changes in surface temperature. J. Clim. 30, 2251–2267 (2017).

    Article  Google Scholar 

  7. Gagné, M.-É., Gillett, N. P. & Fyfe, J. C. Observed and simulated changes in Antarctic sea ice extent over the past 50 years. Geophys. Res. Lett. 42, 90–95 (2015).

    Article  Google Scholar 

  8. Chemke, R. & Polvani, L. M. Using multiple large ensembles to elucidate the discrepancy between the 1979–2019 modeled and observed Antarctic sea ice trends. Geophys. Res. Lett. 47, e2020GL088339 (2020).

    Google Scholar 

  9. Roach, L. A. et al. Antarctic sea ice area in CMIP6. Geophys. Res. Lett. 47, e2019GL086729 (2020).

    Article  Google Scholar 

  10. Hobbs, W. R., Bindoff, N. L. & Raphael, M. N. New perspectives on observed and simulated Antarctic sea ice extent trends using optimal fingerprinting techniques. J. Clim. 28, 1543–1560 (2015).

    Article  Google Scholar 

  11. Jones, J. M. et al. Assessing recent trends in high-latitude Southern Hemisphere surface climate. Nat. Clim. Change 6, 917–926 (2016).

    Article  Google Scholar 

  12. Schneider, D. P. & Deser, C. Tropically driven and externally forced patterns of Antarctic sea ice change: reconciling observed and modeled trends. Clim. Dynam. 50, 4599–4618 (2018).

    Article  Google Scholar 

  13. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  14. Menviel, L., Timmermann, A., Timm, O. E. & Mouchet, A. Climate and biogeochemical response to a rapid melting of the West Antarctic Ice Sheet during interglacials and implications for future climate. Paleoceanography 25, PA4231 (2010).

    Article  Google Scholar 

  15. Bintanja, R., van Oldenborgh, G. J., Drijfhout, S. S., Wouters, B. & Katsman, C. A. Important role for ocean warming and increased ice-shelf melt in Antarctic sea-ice expansion. Nat. Geosci. 6, 376–379 (2013).

    Article  CAS  Google Scholar 

  16. Swart, N. C. & Fyfe, J. C. The influence of recent Antarctic ice sheet retreat on simulated sea ice area trends. Geophys. Res. Lett. 40, 4328–4332 (2013).

    Article  Google Scholar 

  17. Pauling, A. G., Bitz, C. M., Smith, I. J. & Langhorne, P. J. The response of the Southern Ocean and Antarctic sea ice to freshwater from ice shelves in an Earth system model. J. Clim. 29, 1655–1672 (2016).

    Article  Google Scholar 

  18. Bronselaer, B. et al. Change in future climate due to Antarctic meltwater. Nature 564, 53–58 (2018).

    Article  CAS  Google Scholar 

  19. Park, W. & Latif, M. Ensemble global warming simulations with idealized Antarctic meltwater input. Clim. Dyn. 52, 3223–3239 (2019).

    Article  Google Scholar 

  20. Schloesser, F. et al. Antarctic iceberg impacts on future Southern Hemisphere climate. Nat. Clim. Change 9, 672–677 (2019).

    Article  Google Scholar 

  21. Rye, C. D. et al. Antarctic glacial melt as a driver of recent Southern Ocean climate trends. Geophys. Res. Lett. 47, e2019GL086892 (2020).

    Article  Google Scholar 

  22. Goosse, H., Lefebvre, W., de Montety, A., Crespin, E. & Orsi, A. H. Consistent past half-century trends in the atmosphere, the sea ice and the ocean at high southern latitudes. Clim. Dyn. 33, 999–1016 (2009).

    Article  Google Scholar 

  23. Polvani, L. M., Waugh, D. W., Correa, G. J. P. & Son, S. W. Stratospheric ozone depletion: the main driver of twentieth-century atmospheric circulation changes in the Southern Hemisphere. J. Clim. 24, 795–812 (2011).

    Article  Google Scholar 

  24. Thompson, D. W. J. et al. Signatures of the Antarctic ozone hole in Southern Hemisphere surface climate change. Nat. Geosci. 4, 741–749 (2011).

    Article  CAS  Google Scholar 

  25. Kostov, Y. et al. Fast and slow responses of Southern Ocean sea surface temperature to SAM in coupled climate models. Clim. Dyn. 48, 1595–1609 (2017).

    Article  Google Scholar 

  26. Blanchard-Wrigglesworth, E., Roach, L. A., Donohoe, A. & Ding, Q. Impact of winds and Southern Ocean SSTs on Antarctic sea ice trends and variability. J. Clim. 34, 949–965 (2021).

    Article  Google Scholar 

  27. Zhang, X., Deser, C. & Sun, L. Is there a tropical response to recent observed Southern Ocean cooling? Geophys. Res. Lett. 48, e2020GL091235 (2021).

    Google Scholar 

  28. Rosenblum, E. & Eisenman, I. Sea ice trends in climate models only accurate in runs with biased global warming. J. Clim. 30, 6265–6278 (2017).

    Article  Google Scholar 

  29. Sun, S. & Eisenman, I. Observed Antarctic sea ice expansion reproduced in a climate model after correcting biases in sea ice drift velocity. Nat. Commun. 12, 1060 (2021).

    Article  CAS  Google Scholar 

  30. Polvani, L. M. & Smith, K. L. Can natural variability explain observed Antarctic sea ice trends? New modeling evidence from CMIP5. Geophys. Res. Lett. 40, 3195–3199 (2013).

    Article  Google Scholar 

  31. Zunz, V., Goosse, H. & Massonnet, F. How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent? Cryosphere 7, 451–468 (2013).

    Article  Google Scholar 

  32. 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 

  33. 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 

  34. Simpkins, G. R., McGregor, S., Taschetto, A. S., Ciasto, L. M. & England, M. H. 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 

  35. Schneider, D. P., Deser, C. & Fan, T. Comparing the impacts of tropical SST variability and polar stratospheric ozone loss on the Southern Ocean westerly winds. J. Clim. 28, 9350–9372 (2015).

    Article  Google Scholar 

  36. Meehl, G. A., Arblaster, J. M., Bitz, C. M., Chung, C. T. Y. & Teng, H. 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 

  37. Purich, A. et al. Tropical Pacific SST drivers of recent Antarctic sea ice trends. J. Clim. 29, 8931–8948 (2016).

    Article  Google Scholar 

  38. Clem, K. R. et al. Record warming at the South Pole during the past three decades. Nat. Clim. Change 10, 762–770 (2020).

    Article  Google Scholar 

  39. Li, X., Holland, D. M., Gerber, E. P. & Yoo, C. Rossby waves mediate impacts of tropical oceans on west Antarctic atmospheric circulation in austral winter. J. Clim. 28, 8151–8164 (2015).

    Article  Google Scholar 

  40. Wang, G. et al. Compounding tropical and stratospheric forcing of the record low Antarctic sea-ice in 2016. Nat. Commun. 10, 13 (2019).

    Article  CAS  Google Scholar 

  41. Stuecker, M. F., Bitz, C. M. & Armour, K. C. Conditions leading to the unprecedented low Antarctic sea ice extent during the 2016 austral spring season. Geophys. Res. Lett. 44, 9008–9019 (2017).

    Article  Google Scholar 

  42. Meehl, G. A. et al. Sustained ocean changes contributed to sudden Antarctic sea ice retreat in late 2016. Nat. Commun. 10, 14 (2019).

    Article  CAS  Google Scholar 

  43. Eayrs, C., Li, X., Raphael, M. N. & Holland, D. M. Rapid decline in Antarctic sea ice in recent years hints at future change. Nat. Geosci. 14, 460–464 (2021).

    Article  CAS  Google Scholar 

  44. Sigmond, M. & Fyfe, J. C. Has the ozone hole contributed to increased Antarctic sea ice extent? Geophys. Res. Lett. 37, L18502 (2010).

    Google Scholar 

  45. Bitz, C. M. & Polvani, L. M. Antarctic climate response to stratospheric ozone depletion in a fine resolution ocean climate model. Geophys. Res. Lett. 39, L20705 (2012).

    Article  CAS  Google Scholar 

  46. Ferreira, D., Marshall, J., Bitz, C. M., Solomon, S. & Plumb, A. Antarctic ocean and sea ice response to ozone depletion: a two-time-scale problem. J. Clim. 28, 1206–1226 (2015).

    Article  Google Scholar 

  47. Seviour, W. J. M. et al. The Southern Ocean sea surface temperature response to ozone depletion: a multimodel comparison. J. Clim. 32, 5107–5121 (2019).

    Article  Google Scholar 

  48. Holland, P. R. & Kwok, R. Wind-driven trends in Antarctic sea-ice drift. Nat. Geosci. 5, 872–875 (2012).

    Article  CAS  Google Scholar 

  49. Chung, E.-S. et al. Reconciling opposing Walker circulation trends in observations and model projections. Nat. Clim. Change 9, 405–412 (2019).

    Article  Google Scholar 

  50. Kosaka, Y. & Xie, S.-P. Recent global-warming hiatus tied to equatorial Pacific surface cooling. Nature 501, 403–407 (2013).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  52. Handcock, M. S. & Raphael, M. N. Modeling the annual cycle of daily Antarctic sea ice extent. Cryosphere 14, 2159–2172 (2020).

    Article  Google Scholar 

  53. Chung, E.-S. & Soden, B. J. Hemispheric climate shifts driven by anthropogenic aerosol–cloud interactions. Nat. Geosci. 10, 566–571 (2017).

    Article  CAS  Google Scholar 

  54. Lee, S.-K. et al. Wind-driven ocean dynamics impact on the contrasting sea-ice trends around West Antarctica. J. Geophys. Res. Oceans 122, 4413–4430 (2017).

    Article  Google Scholar 

  55. Depoorter, M. A. et al. Calving fluxes and basal melt rates of Antarctic ice shelves. Nature 502, 89–92 (2013).

    Article  CAS  Google Scholar 

  56. Fetterer, F., Knowles, K., Meier, W. N., Savoie, M. & Windnagel, A. K. Sea Ice Index Version 3: Sea ice extent (NSIDC, 2017); https://doi.org/10.7265/N5K072F8

  57. Stroeve, J. & Meier, W. N. Sea Ice Trends and Climatologies from SMMR and SSM/I-SSMIS Version 3 (NASA National Snow and Ice Data Center Distributed Active Archive Center, 2018); https://doi.org/10.5067/IJ0T7HFHB9Y6

  58. Ropelewski, C. F. NOAA/NMC/CAC Arctic and Antarctic Monthly Sea Ice Extent, 1973–1990 Version 1: Sea ice extent (NSIDC, 1990); https://doi.org/10.7265/N5Z60KZ1

  59. Meier, W. N., Gallaher, D. & Campbell, G. G. New estimates of Arctic and Antarctic sea ice extent during September 1964 from recovered Nimbus 1 satellite imagery. Cryosphere 7, 699–705 (2013).

    Article  Google Scholar 

  60. Kukla, G. & Gavin, J. Summer ice and carbon dioxide. Science 214, 497–503 (1981).

    Article  CAS  Google Scholar 

  61. Zwally, H. J., Parkinson, C. L. & Comiso, J. C. Variability of Antarctic sea ice and changes in carbon dioxide. Science 220, 1005–1012 (1983).

    Article  CAS  Google Scholar 

  62. Curran, M. A. J. et al. Ice core evidence for Antarctic sea ice decline since the 1950s. Science 302, 1203–1206 (2003).

    Article  CAS  Google Scholar 

  63. Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. 108, 4407 (2003).

    Article  Google Scholar 

  64. Huang, B. et al. Extended Reconstructed Sea Surface Temperature Version 5 (ERSST v.5): Upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Article  Google Scholar 

  65. Hirahara, S., Ishii, M. & Fukuda, Y. Centennial-scale sea surface temperature analysis and its uncertainty. J. Clim. 27, 57–75 (2014).

    Article  Google Scholar 

  66. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  67. Kennedy, J. J., Rayner, N. A., Atkinson, C. P. & Killick, R. E. An ensemble data set of sea surface temperature change from 1850: the Met Office Hadley Centre HadSST.4.0.0.0 data set. J. Geophys. Res. Atmos. 124, 7719–7763 (2019).

    Article  Google Scholar 

  68. Morice, C. P., Kennedy, J. J., Rayner, N. A. & Jones, P. D. Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. J. Geophys. Res. 117, D08101 (2012).

    Google Scholar 

  69. GISTEMP Team GISS Surface Temperature Analysis (GISTEMP) Version 4 (NASA Goddard Institute for Space Studies, 2022); https://data.giss.nasa.gov/gistemp/

  70. Lenssen, N. J. L. et al. Improvements in the GISTEMP uncertainty model. J. Geophys. Res. Atmos. 124, 6307–6326 (2019).

    Article  Google Scholar 

  71. Rohde, R. et al. Berkeley Earth temperature averaging process. Geoinform. Geostat. Overv. https://doi.org/10.4172/2327-4581.1000103 (2013).

  72. Vose, R. S. et al. NOAA’s merged land–ocean surface temperature analysis. Bull. Am. Meteorol. Soc. 93, 1677–1685 (2012).

    Article  Google Scholar 

  73. Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Article  Google Scholar 

  74. Kirchmeier-Young, M. C., Zwiers, F. W., Gillett, N. P. & Cannon, A. J. Attributing extreme fire risk in western Canada to human emissions. Climatic Change 144, 365–379 (2017).

    Article  Google Scholar 

  75. Kay, J. E. et al. The Community Earth System Model (CESM) Large Ensemble Project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).

    Article  Google Scholar 

  76. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  77. Rodgers, K. B. et al. Ubiquity of human-induced changes in climate variability. Earth Syst. Dyn. 12, 1393–1411 (2021).

    Article  Google Scholar 

  78. Boer, G. J. et al. The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geosci. Model Dev. 9, 3751–3777 (2016).

    Article  Google Scholar 

  79. Trenberth, K. E. & Shea, D. J. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. 33, L12704 (2006).

    Article  Google Scholar 

  80. Delworth, T. L. & Mann, M. E. Observed and simulated multidecadal variability in the Northern Hemisphere. Clim. Dynam. 16, 661–676 (2000).

    Article  Google Scholar 

  81. Lapointe, F. et al. Annually resolved Atlantic sea surface temperature variability over the past 2,900 y. Proc. Natl Acad. Sci. USA 117, 27171–27178 (2020).

    Article  CAS  Google Scholar 

  82. Booth, B. B. B., Dunstone, N. J., Halloran, P. R., Andrews, T. & Bellouin, N. Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484, 228–232 (2012).

    Article  CAS  Google Scholar 

  83. Henley, B. J. et al. A tripole index for the Interdecadal Pacific Oscillation. Clim. Dyn. 45, 3077–3090 (2015).

    Article  Google Scholar 

  84. Chung, E.-S. et al. Code for Antarctic sea ice expansion and Southern Ocean cooling linked to tropical variability. Zenodo https://doi.org/10.5281/zenodo.6330284 (2022).

Download references

Acknowledgements

We are grateful to the National Snow and Ice Data Center, the National Oceanic and Atmospheric Administration Physical Sciences Laboratory, the European Centre for Medium-Range Weather Forecasts, the Met Office Hadley Centre, Japan Meteorological Agency, the GISTEMP Team, the National Centers for Environmental Information, Berkeley Earth, the National Center for Atmospheric Research, Environment and Climate Change Canada and modelling centres participating in CMIP6 for providing their respective datasets. E.-S.C. and S.-J.K. were supported by the project PE22030 of the Korea Polar Research Institute. A.T., K.-J.H., K.B.R., S.-S.L. and L.H. were supported by the Institute for Basic Science (IBS) under IBS-R028-D1. M.F.S. was supported by NOAA’s Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) programme grant NA20OAR4310445. This is IPRC publication 1559 and SOEST contribution 11482. The CESM2 Large Ensemble simulations were conducted on the IBS/ICCP supercomputer ‘Aleph’ through a partnership between the ICCP in South Korea and the CESM Project at the National Center for Atmospheric Research (NCAR) in the United States. We thank N. Rosenbloom and J. Edwards for their important contributions to the CESM2 Large Ensemble project.

Author information

Authors and Affiliations

  1. Division of Atmospheric Sciences, Korea Polar Research Institute, Incheon, South Korea

    Eui-Seok Chung & Seong-Joong Kim

  2. Center for Climate Physics, Institute for Basic Science, Busan, South Korea

    Axel Timmermann, Kyung-Ja Ha, Keith B. Rodgers, Sun-Seon Lee & Lei Huang

  3. Pusan National University, Busan, South Korea

    Axel Timmermann, Keith B. Rodgers, Sun-Seon Lee & Lei Huang

  4. Department of Climate System, Pusan National University, Busan, South Korea

    Kyung-Ja Ha

  5. BK21 School of Earth and Environmental Systems, Pusan National University, Busan, South Korea

    Kyung-Ja Ha

  6. Atlantic Oceanographic and Meteorological Laboratory, NOAA, Miami, FL, USA

    Sang-Ki Lee

  7. Department of Oceanography and International Pacific Research Center, School of Ocean and Earth Science and Technology, University of Hawaiʻi at Mānoa, Honolulu, HI, USA

    Malte F. Stuecker

Authors
  1. Eui-Seok Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seong-Joong Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Axel Timmermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kyung-Ja Ha
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sang-Ki Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Malte F. Stuecker
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Keith B. Rodgers
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sun-Seon Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lei Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

E.-S.C. and S.-J.K. designed the study. E.-S.C. performed the analysis and produced figures. S.-J.K., A.T., K.-J.H., S.-K.L., M.F.S., K.B.R., S.-S.L. and L.H. provided feedback on the analyses, the interpretation of the results, and the figures. All authors contributed to the writing of the manuscript and the improvement of the manuscript.

Corresponding author

Correspondence to Seong-Joong Kim.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Clare Eayrs and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Comparison of annual-mean sea ice extent (SIE) and SST trends between observations and model simulations.

a, Boxplots of model-simulated Antarctic SIE trends over 1950−1978 (yellow green) and 1979−2014 (dark blue) under historical forcing (and RCP8.5 forcing over 2006−2014 for CanESM2 and CESM1) with the line inside the box representing the median value across ensemble members of a given model (M1: CanESM2 Large Ensemble, M2: CESM1 Large Ensemble, M3: ACCESS-ESM1-5, M4_1: CanESM5 physics 1, M4_2: CanESM5 physics 2, M5: CESM2 Large Ensemble, M6: EC-Earth3, M7: IPSL-CM6A-LR, M8: NorCPM1, M9: UKESM1-0-LL). The box covers the inter-quartile range and whiskers denote the maximum and minimum values. b, Same as in a, but for all possible overlapping 29-year (yellow green) and 36-year (dark blue) segments of related pre-industrial control run. c, Same as in b, but with the corresponding ensemble-mean value for the period 1950−1978 and 1979−2014 added. d-f, Same as in a-c, but for Southern Ocean-mean SST trends. In a-c, the solid line in red denotes the satellite-observed 1979−2014 SIE trend with the accompanying dashed lines representing the standard error of the trend. In d-f, the solid line in orange denotes the observed 1950−1978 SST trend averaged over four SST datasets (ERSST, HadISST, COBE, and ERA5) with the accompanying dashed lines representing minimum and maximum trends. The solid and dashed lines in red denote the corresponding observed SST trends over 1979−2014. Note that the y-axis is reversed in d-f to facilitate comparison with SIE changes.

Extended Data Fig. 2 Annual mean evolution of global-mean surface temperature over 1950−2020.

a, Timeseries of the observed (red) and model-simulated ensemble-mean global-mean surface temperature anomaly relative to the 1979−2020 mean. b, Timeseries of the observational minus model-simulated ensemble-mean surface temperature anomalies. c, Timeseries of the ensemble-mean temperature anomalies resulting from observed eastern equatorial Pacific SST variability in IPSL-CM6A-LR. The response is estimated by subtracting temperature changes in coupled historical experiments from those obtained from pacemaker experiments where the observed SST anomalies in the eastern equatorial Pacific were assimilated under the same forcing as in the historical experiments. In c, the dashed line denotes the linear trend over 1979−2014.

Extended Data Fig. 3 Connection of the unforced component of SST changes to both Atlantic Multidecadal Variability and the Interdecadal Pacific Oscillation.

a, Regression slope of detrended ERSST annual-mean SST changes at each grid point against the Atlantic Multidecadal Variability index. b, Same as in a, but against the Interdecadal Pacific Oscillation. c,d, Same as in a and b, but for ERSST minus CESM2 ensemble mean. e,f, Same as in a and b, but for ERSST minus CanESM5 ensemble mean. g,h, Same as in a and b, but for ERSST minus NorCPM1 ensemble mean. Stippling indicates statistical significance of the regression slopes at the 95% confidence level.

Extended Data Fig. 4 Connection of the unforced component of SST changes to both Atlantic Multidecadal Variability and the Interdecadal Pacific Oscillation.

a, Temporal correlation of detrended HadISST annual-mean SST changes at each grid point with corresponding Southern Ocean-mean SST changes over the period 1950−2020. b, Same as in a, but regression slope against the Atlantic Multidecadal Variability index. c, Same as in a, but regression slope against the Interdecadal Pacific Oscillation index. d-f, Same as in a-c, except for COBE SST over the period 1950−2019. g-i, Same as in a-c, except for ERA5 SST. Stippling indicates statistical significance of the correlation coefficients or regression slopes at the 95% confidence level.

Extended Data Fig. 5 Seasonality of the connection between multidecadal variability of SST and both Atlantic Multidecadal Variability and the Interdecadal Pacific Oscillation.

a, Regression slope of detrended SST changes at each grid point from ERSST against the Atlantic Multidecadal Variability index for austral summer (December-January-February) over the period 1950−2020. b, Same as in a, but for austral autumn (March-April-May). c, Same as in a, but for austral winter (June-July-August). d, Same as in a, but for austral spring (September-October-November). e-h, Same as in a-d, but for regression slopes against the Interdecadal Pacific Oscillation index. Stippling indicates statistical significance of the regression slopes at the 95% confidence level.

Extended Data Fig. 6 Relations between the unforced components of SST changes with climate variability in the Atlantic and Pacific in pre-industrial control simulations.

a,b, Regression slopes of annual-mean SST changes against Atlantic Multidecadal Variability (a) and the Interdecadal Pacific Oscillation (b) in ACCESS-ESM1-5. c,d, Same as in a and b, but for CanESM5 (physics 1). e,f, Same as in a and b, but for CESM2. g,h, Same as in a and b, but for EC-Earth3. i,j, Same as in a and b, but for IPSL-CM6A-LR. k,l, Same as in a and b, but for NorCPM1. m,n, Same as in a and b, but for UKESM1-0-LL.

Extended Data Fig. 7 Relations between the unforced components of Antarctic sea ice changes with climate variability in the Atlantic and Pacific in pre-industrial control simulations.

a,b, Regression slopes of annual-mean sea ice concentration changes against Atlantic Multidecadal Variability (a) and the Interdecadal Pacific Oscillation (b) in ACCESS-ESM1-5. c,d, Same as in a and b, but for CanESM5 (physics 1). e,f, Same as in a and b, but for CESM2. g,h, Same as in a and b, but for EC-Earth3. i,j, Same as in a and b, but for IPSL-CM6A-LR. k,l, Same as in a and b, but for NorCPM1. m,n, Same as in a and b, but for UKESM1-0-LL.

Extended Data Fig. 8 Southern Ocean-mean SST trend congruent to the observed Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Variability (AMV) trend.

For observations, the congruent trends over 1979–2014 are estimated by summing over the multiplicative products of the observed IPO trend and the regression coefficient for the IPO at each grid point in the multiple linear regression of the unforced component of SST changes against the IPO and AMV indices and that for the AMV. In the case of model simulations, the regression coefficients derived from each model’s pre-industrial control runs are used to estimate the congruent trends.

Extended Data Fig. 9 Connection of Antarctic sea ice changes to the Interdecadal Pacific Oscillation (IPO) and Atlantic Multidecadal Variability (AMV).

a, Sea ice concentration (SIC) trends over 1979−2014, which are linearly congruent with the observed IPO and AMV trends over 1979−2014. The congruent trends are estimated by summing over the multiplicative products of the observed IPO trend and the regression coefficient for the IPO at each grid point in the multiple linear regression of the unforced component of SIC changes against the IPO and AMV indices and that for the AMV. b, Same as in a, but with the regression coefficients derived from the multi-model pre-industrial control runs. In b, stippling indicates regions where the multi-model mean congruent SIC trend exceeds two standard deviations of the trend across the models.

Extended Data Fig. 10 Influence of observed eastern equatorial Pacific SST variability on sea ice and SST changes over the Southern Ocean in coupled model simulations.

a, Time series of the ensemble-mean annual-mean total sea ice extent anomaly resulting from observed eastern equatorial Pacific SST variability in IPSL-CM6A-LR. The response is estimated by subtracting sea ice extent changes from coupled historical experiments from those obtained from pacemaker experiments where the observed SST anomalies in the eastern equatorial Pacific were assimilated under the same forcing as in the historical experiments. b, Same as in a, but for Southern Ocean-mean SST time series. In b, the y-axis is reversed to facilitate comparison with sea ice extent changes.

Supplementary information

Supplementary Information

Supplementary Text 1, Figs. 1–4 and Tables 1 and 2.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chung, ES., Kim, SJ., Timmermann, A. et al. Antarctic sea-ice expansion and Southern Ocean cooling linked to tropical variability. Nat. Clim. Chang. 12, 461–468 (2022). https://doi.org/10.1038/s41558-022-01339-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-022-01339-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing
pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy