Reemergence of Antarctic sea ice predictability and its link to deep ocean mixing in global climate models View Full Text


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Article Info

DATE

2019-03

AUTHORS

Sylvain Marchi, Thierry Fichefet, Hugues Goosse, Violette Zunz, Steffen Tietsche, Jonathan J. Day, Ed Hawkins

ABSTRACT

Satellite observations show a small overall increase in Antarctic sea ice extent (SIE) over the period 1979–2015. However, this upward trend needs to be balanced against recent pronounced SIE fluctuations occurring there. In the space of 3 years, the SIE sank from its highest value ever reached in September 2014 to record low in February 2017. In this work, a set of six state-of-the-art global climate models is used to evaluate the potential predictability of the Antarctic sea ice at such timescales. This first multi-model study of Antarctic sea ice predictability reveals that the ice edge location can potentially be predicted up to 3 years in advance. However, the ice edge location predictability shows contrasted seasonal performances, with high predictability in winter and no predictability in summer. The reemergence of the predictability from one winter to next is provided by the ocean through its large thermal inertia. Sea surface heat anomalies are stored at depth at the end of the winter and influences the sea ice advance the following year as they resurface. The effectiveness of this mechanism across models is found to depend upon the depth of the mixed layer. One should be very cautious about these potential predictability estimates as there is evidence that the Antarctic sea ice predictability is promoted by deep Southern Ocean convection. We therefore suspect models with excessive convection to show higher sea ice potential predictability results due to an incorrect representation of the Southern Ocean. More... »

PAGES

2775-2797

References to SciGraph publications

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s00382-018-4292-2

    DOI

    http://dx.doi.org/10.1007/s00382-018-4292-2

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1104562327


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