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

Marchi, S., Fichefet, T., Goosse, H., Zunz, V., Tietsche, S., Day, J. and Hawkins, E. (2019) Reemergence of Antarctic sea ice predictability and its link to deep ocean mixing in global climate models. Climate Dynamics, 52 (5-6). pp. 2775-2797. ISSN 0930-7575

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To link to this item DOI: 10.1007/s00382-018-4292-2

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > NCAS
Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
ID Code:80787
Publisher:Springer

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