Spatiotemporal dependence of Antarctic sea ice variability to dynamic and thermodynamic forcing: a coupled ocean–sea ice model study
Satellite-derived Antarctic sea ice extent has displayed a slight upward since 1979, but with strong temporal and regional variability—the drivers of which are poorly understood. Here, we conduct numerical experiments with a circum-Antarctic ocean–sea ice–ice shelf model driven by realistic atmospheric surface boundary conditions to examine the factors responsible for the temporal and spatial patterns in observed Antarctic sea ice variability. The model successfully reproduces observed seasonal and interannual variability in total sea ice extent and the temporal/spatial patterns of sea ice concentration and seasonality (days of advance and retreat and actual ice days) for 1979–2014. Sensitivity experiments are performed, in which the interannual variability in wind stress or thermodynamic surface forcing is ignored, to delineate their contributions to Antarctic sea ice fields. The results demonstrate that: (1) thermodynamic forcing plays a key role in driving interannual variability in sea ice extent and seasonality in most Antarctic sectors; (2) only in the Ross Sea the wind stress does become the main driver of sea ice extent variability; (3) thermodynamic forcing largely regulates interannual variability in the timing of sea ice advance, while wind stress largely controls the timing of the sea ice retreat; and (4) although both wind stress and thermodynamic forcing contribute to variability in total sea ice volume, the wind stress plays a dominant role in regulating sea ice volume variability in the near-coastal zone.
KeywordsAntarctic sea ice variability An ocean–sea ice model Wind stress Thermodynamic forcing
This research was supported by the Australian Government’s Business Cooperative Research Centres Programme through the Antarctic Climate an Ecosystems Cooperative Research Centre (ACE CRC), and contributes to AAS 4116. GW was supported by the Australian Research Council’s Future Fellowship program. HH was supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number JP26247080. We thank two anonymous reviewers for their helpful and constructive comments on the manuscript.
- Hasumi H (2006) CCSR ocean component model (COCO) version 4.0. CCSR Rep. No. 25Google Scholar
- Hibler WD (1979) A dynamic thermodynamic sea ice model. J Phys Oceanogr 9:815–846. https://doi.org/10.1175/1520-0485(1979)009%3C0815:ADTSIM%3E2.0.CO;2 CrossRefGoogle Scholar
- Hunke EC, Dukowicz JK (1997) An elastic-viscous-plastic model for sea ice dynamics. J Phys Oceanogr 27:1849–1867. https://doi.org/10.1175/1520-0485(1997)027%3C1849:AEVPMF%3E2.0.CO;2 CrossRefGoogle Scholar
- Koopmans LH (1974) The spectral analysis of time series. Academic Press, New YorkGoogle Scholar
- Leppäranta M (2005) The drift of sea ice. Springer, ChichesterGoogle Scholar
- Mellor GL, Kantha L (1989) An ice–ocean coupled model. J Geophys Res 94:10937–10954Google Scholar
- Thomas D (2016) Sea ice, 3rd edn. Wiley-Blackwell, HobokenGoogle Scholar
- Thompson RORY (1979) Coherence significance levels. J Atmos Sci 36:2020–2021. https://doi.org/10.1175/1520-0469(1979)036%3C2020:CSL%3E2.0.CO;2 CrossRefGoogle Scholar
- Working Group I/IPCC (2013) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, New York. https://doi.org/10.1017/CBO9781107415324
- Yuan X, Martinson DG (2000) Antarctic sea ice extent variability and its global connectivity. J Clim 13:1697–1717. https://doi.org/10.1175/1520-0442(2000)013%3C1697:ASIEVA%3E2.0.CO;2 CrossRefGoogle Scholar