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Impacts of the north and tropical Atlantic Ocean on the Antarctic Peninsula and sea ice

Abstract

In recent decades, Antarctica has experienced pronounced climate changes. The Antarctic Peninsula exhibited the strongest warming1,2 of any region on the planet, causing rapid changes in land ice3,4. Additionally, in contrast to the sea-ice decline over the Arctic, Antarctic sea ice has not declined, but has instead undergone a perplexing redistribution5,6. Antarctic climate is influenced by, among other factors, changes in radiative forcing7 and remote Pacific climate variability8,9, but none explains the observed Antarctic Peninsula warming or the sea-ice redistribution in austral winter. However, in the north and tropical Atlantic Ocean, the Atlantic Multidecadal Oscillation10,11 (a leading mode of sea surface temperature variability) has been overlooked in this context. Here we show that sea surface warming related to the Atlantic Multidecadal Oscillation reduces the surface pressure in the Amundsen Sea and contributes to the observed dipole-like sea-ice redistribution between the Ross and Amundsen–Bellingshausen–Weddell seas and to the Antarctic Peninsula warming. Support for these findings comes from analysis of observational and reanalysis data, and independently from both comprehensive and idealized atmospheric model simulations. We suggest that the north and tropical Atlantic is important for projections of future climate change in Antarctica, and has the potential to affect the global thermohaline circulation6 and sea-level change3,12.

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Figure 1: Temporal variability of north and tropical Atlantic sea surface temperature anomalies at different timescales, and the spatial pattern of the Atlantic Multidecadal Oscillation.
Figure 2: North and tropical Atlantic variability projected onto austral winter Southern Hemisphere SLP by three independent analyses.
Figure 3: The austral winter patterns of Antarctic SLP, SAT and SIC related to tropical Atlantic SST warming.

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Acknowledgements

X.L., D.M.H. and C.Y. were supported by the NSF Office of Polar Programs (grant number ANT-0732869), the NASA Polar Programs (grant number NNX12AB69G), and New York University Abu Dhabi (grant number G1204). E.P.G. was supported by the NSF Office of Atmospheric and Geospace Sciences (grant number AGS-1264195). The HadISST SST and SIC data was provided by the British Met Office, Hadley Centre. The Antarctic weather station data was made available by the British Antarctic Survey. The MERRA atmospheric reanalysis data was provided by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center (GSFC) through the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC) online archive (http://disc.sci.gsfc.nasa.gov/mdisc/data-holdings/merra/merra_products_nonjs.shtml). The ERA-Interim atmospheric reanalysis was provided by the ECMWF. The comprehensive atmospheric model (CAM4) was made available by the National Center for Atmospheric Research (NCAR), supported by the National Science Foundation (NSF) and the Office of Science (BER) of the US Department of Energy (DOE). The idealized atmospheric model (the GFDL dry dynamical core) was developed by the National Oceanic and Atmospheric Administration (NOAA) at the GFDL. Computing resources were provided by the National Energy Research Scientific Computing Center (NERSC) and High Performance Computing (HPC) at New York University (NYU).

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X.L., D.M.H. and E.P.G. designed the experiments; X.L. performed the data analysis and CAM4 numerical simulations, and prepared all figures; C.Y. ran the initial value calculations; C.Y. and X.L. created Extended Data Fig. 1 and all authors wrote and reviewed the main manuscript text.

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Correspondence to Xichen Li.

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Extended data figures and tables

Extended Data Figure 1 Simulated 200-hPa geopotential height response to tropical Atlantic SST warming in the idealized atmospheric model and comprehensive climate model.

Blue (or red) contours at 10-m intervals show negative (or positive) anomalies. Colour shading shows the model’s climatological zonal wind on 200 hPa. The strong westerly winds (red regions) mark the sub-tropical jet. In the idealized model, geopotential height (z) anomalies are shown at six different snapshots, from day 3 to day 18 in a–f (note that the longitude mapping has been changed to make the tropical Atlantic appear in the upper left corner of each panel). Heating over the tropical Atlantic initially leads to a local increase in geopotential height (a, red contour), generating Rossby wave trains that extend poleward (b), and then eastward (c, d) along the southern edge of the sub-tropical jet. Downstream anomalies appear one by one (be) and converge on the Amundsen Sea region within two weeks (e, f), enhancing the Amundsen Sea Low. The 100-year integration of CAM4 (g) shows a similar wave pattern, albeit smoother and with an eastward shift. The resemblance between the initial condition integration (f, by GFDL model) and the boundary condition sensitive experiments (g, by CAM4) provides evidence that the propagation of Rossby wave trains directly link the tropical Atlantic and Antarctica, in particular, the Amundsen Sea Low area.

Extended Data Figure 2 North Atlantic SST projected onto Southern Hemisphere SLP and geopotential height by linear regression, using both MERRA and ERA-Interim data sets.

ac, The regression using MERRA data, from 200 hPa (a) to the surface (c). The main low pressure anomaly centre emerges around Antarctica over the Amundsen–Bellingshausen seas. It is barotropic, from the surface to 200 hPa. d–f, The same regressions using ERA-interim data, which show exactly the same phenomena as MERRA. Thick black contours indicate areas with >95% significance.

Extended Data Figure 3 Regression of MERRA sea-level pressure onto detrended Tropical Atlantic sea-surface temperature time series, in all four seasons.

a, December, January and February; b, June, July and August; c, March, April and May; and d, September, October and November. The Amundsen Sea Low has been deepened in all seasons except in austral summer (December, January and February), and the pattern is most robust in austral winter (June, July and August). Thick black contours indicate areas with >95% significance.

Extended Data Figure 4 CAM4-simulated Antarctic SLP and SAT with three different SST forcings.

a, North Atlantic warming (20° S–70° N); b, tropical Atlantic warming (20° S–20° N); and c, mid-latitude north Atlantic warming (20° N–70° N). a and b show a similar SLP response, whereas c exhibits a much weaker low-pressure response over west Antarctica, which implies that the tropical Atlantic plays the key part in the teleconnection. The response of SLP in these three experiments show some linearity, whereas the response of SAT is nonlinear. All three experiments, despite having different amplitudes of SLP response, show similar amplitudes of SAT warming/cooling over west Antarctica and the Amundsen–Bellingshausen–Weddell seas. This nonlinearity in SAT response strongly suggests that the impact of mid-latitude north Atlantic SST warming should not be neglected.

Extended Data Figure 5 Observed Southern Annular Mode time series and trend in June, July and August.

a, The Southern Annular Mode time series in June, July and August since 1957. The data comes from 12 weather stations, compiled by ref. 18. b, The linear trend during different periods, all exhibiting a statistically insignificant positive trend. Error bars in b indicate the 95% confidence interval (Student’s t-test).

Extended Data Figure 6 Three tropical regions and their SST trends and variability.

a, The blue box indicates the central Pacific area, the red box indicates the Niño 3 region, and the black box indicates the tropical Atlantic region. b, SST trend averaged over the three tropical regions in a, in June, July and August from 1979 to 2012. The error bars indicate the 95% confidence interval. c, The SST time series in June, July and August over these three regions.

Extended Data Figure 7 Epochal difference and ‘with-trend’ regression of sea-level pressure, surface air temperature and sea-ice concentration.

a, This panel is a duplicate of Fig. 3b and serves as a reference. b, c, Regression of SLP, SAT and SIC onto ‘with-trend’ time series of the north (b) and tropical (c) Atlantic sub-decadal variability. Both of these show better agreement with the spatial pattern of the epochal difference in a; compare with the ‘detrended’ regression results (Fig. 3a).

Extended Data Figure 8 Three areas selected to test the significant level of SIC and surface SAT regression.

The red shading indicates the area with SIC regression >1% (Fig. 3a), and the blue area shows the SIC regression <−1%. The land area inside the grey box was selected to calculate the spatial-mean SAT over the Antarctic Peninsula. The significance level of the regression is tested over these three areas.

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Li, X., Holland, D., Gerber, E. et al. Impacts of the north and tropical Atlantic Ocean on the Antarctic Peninsula and sea ice. Nature 505, 538–542 (2014). https://doi.org/10.1038/nature12945

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