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Arctic sea-ice change tied to its mean state through thermodynamic processes
Massonnet, F.; Vancoppenolle, M.; Goosse, H.; Docquier, D.; Fichefet, T.; Blanchard-Wrigglesworth, E. (2018). Arctic sea-ice change tied to its mean state through thermodynamic processes. Nat. Clim. Chang. 8(7): 599-603. https://dx.doi.org/10.1038/s41558-018-0204-z
In: Nature Climate Change. Nature Publishing Group: London. ISSN 1758-678X; e-ISSN 1758-6798, more
Peer reviewed article  

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Keyword
    Marine/Coastal
Author keywords
    Climate and Earth system modelling; Cryospheric science; Projection and prediction

Authors  Top 
  • Massonnet, F., more
  • Vancoppenolle, M., more
  • Goosse, H., more
  • Docquier, D., more
  • Fichefet, T., more
  • Blanchard-Wrigglesworth, E.

Abstract
    One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state-of-the-art climate models consistently reproduce the sign of these changes, they largely disagree on their magnitude, the reasons for which remain contentious. As such, consensual methods to reduce uncertainty in projections are lacking7. Here, using the CMIP5 ensemble, we propose a process-oriented approach to revisit this issue. We show that intermodel differences in sea-ice loss and, more generally, in simulated sea-ice variability, can be traced to differences in the simulation of seasonal growth and melt. The way these processes are simulated is relatively independent of the complexity of the sea-ice model used, but rather a strong function of the background thickness. The larger role played by thermodynamic processes as sea ice thins8,9 further suggests that the recent and projected reductions in sea-ice thickness induce a transition of the Arctic towards a state with enhanced volume seasonality but reduced interannual volume variability and persistence, before summer ice-free conditions eventually occur. These results prompt modelling groups to focus their priorities on the reduction of sea-ice thickness biases.

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