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Impact of stratospheric polar vortex variability on Antarctic sea ice in CMIP6 models
Mezzina, B.; Palmeiro, F.M.; Goosse, H.; Anstey, J. (2025). Impact of stratospheric polar vortex variability on Antarctic sea ice in CMIP6 models. Clim. Dyn. 63(7): 278. https://dx.doi.org/10.1007/s00382-025-07765-x
In: Climate Dynamics. Springer: Berlin; Heidelberg. ISSN 0930-7575; e-ISSN 1432-0894, meer
Peer reviewed article  

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Author keywords
    Sea ice;Polar vortex;Southern Ocean;Antarctica;Climate variability;surface climate;southern

Auteurs  Top 
  • Mezzina, B., meer
  • Palmeiro, F.M.
  • Goosse, H.
  • Anstey, J.

Abstract
    The stratospheric polar vortex (SPV) is a potential source of variability for Antarctic sea ice at interannual timescales. While the downward influence of the SPV on the lower troposphere is relatively well-documented, its impact on sea ice is still uncertain. Here, we evaluate how this link between the SPV, the troposphere and sea ice is simulated by 23 CMIP6 models, highlighting the robust features and examining the related processes. We define years in which the springtime SPV is relatively weak, average or strong. We then build composites of atmospheric and sea ice anomalies with respect to neutral conditions (average strength) and present results from the multi-model mean. When the SPV is weakened, we find an overall decrease of sea ice extent in late spring, followed by an increase in summer. We suggest that this switch in the integrated response over the Southern Ocean is related to different regional characteristics. The initial decrease in spring is dominated by anomalous melting in the Ross sector driven by meridional winds, with potential contributions from temperature perturbations descending from the stratosphere in this specific region. In turn, the summer increase is mostly associated with surface winds in the Weddell Sea acting during both spring and summer. The response to a strengthened SPV is largely symmetric, namely with opposite-signed anomalies, though the atmospheric response is lagged by about one month. While the modelled signal is robust, some inconsistencies emerge in the observations, possibly due to the different sample size and period.

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