Representation of subgrid-scale sea ice-ocean interactions in large-scale models

• Main Author: Barthélemy, Antoine
• Other Authors: UCL - SST/ELI/ELIC - Earth & Climate, UCL - Faculté des Sciences, Fichefet, Thierry, Goosse, Hugues, De Keersmaecker, Marie-Laurence, Deleersnijder, Eric, Le Sommer, Julien, Vancoppenolle, Martin, Madec, Gurvan
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2016
• Subjects: Model; Ice thickness distribution; Subgrid-scale; Ocean vertical mixing; Arctic; Antarctic; Antarc*; Sea ice
• Online Access: http://hdl.handle.net/2078.1/171915

Polar oceans are partly covered with sea ice that results from the freezing of seawater. Sea ice is a leading player both in the climate of polar regions and for the Earth climate system as a whole. It acts as an insulating layer between the ocean and the atmosphere and it strongly influences the upper ocean physics. Because it is moved and deformed by winds and currents, the sea ice cover turns out to be an extremely heterogeneous medium. Sea ice models have been developed for decades, reaching nowadays a high level of complexity. In particular, the most advanced of them include a representation of the small-scale spatial variability in ice properties. The heterogeneous nature of sea ice-ocean exchanges has however been so far overlooked. This hampers the simulation of potentially important processes and introduces uncertainties in the predictions based on these models. The objective of this doctoral thesis was to investigate how subgrid-scale sea ice-ocean interactions can be represented in climate models, and to assess their impacts on the oceanography of polar regions. To this end, schemes of increasing complexity have been introduced in a state-of-the-art ocean and sea ice model. Our results show that the spatial heterogeneity of ice-ocean processes has a large-scale influence on the upper layer of the ocean as well as on the sea ice cover itself. The tools developed in this study provide concrete ways of improving the physical accuracy of current models. (SC - Sciences) -- UCL, 2016