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

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

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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.

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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.

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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).

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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.

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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

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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.

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Correspondence to Seong-Joong Kim.

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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.

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

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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

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