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A new continuum rheological model for the deformation and drift of sea ice

Presented by: 
Veronique Dansereau
Monday 11th September 2017 - 14:00 to 14:30
INI Seminar Room 1
Co-authors: Pierre Saramito (CNRS-LJK), Jérôme Weiss (CNRS-ISTerre), Philippe Lattes (Total S.A. E&P)

Axel Roy (1)
Véronique Dansereau (2)*
Jérôme Weiss (2)  
Christian Haas (3)
Matthieu Chevalier (4)

1 École Nationale de la Météorologie, Météo France, Toulouse, France
2 Institut des Sciences de la Terre, CNRS UMR 5275, Université de Grenoble, Grenoble, France
3 Alfred Wegener Institute, Bremerhaven, Germany
4 CNRM/GMGEC/IOGA Météo France, Toulouse, France

Sea ice models are most often compared to each other and to observations in terms of the spatial distribution of the simulated ice thickness. An equally important, and perhaps more appropriate, metric to investigate the mechanical behaviour of the sea ice cover is the ice thickness distribution, i.e., the probability density function, of which some valuable information have been available for some time from drill-hole, upward looking submarine-mounted sonar (USL) and airborne electromagnetic (EM) sounding measurements.

An important issue naturally arises when comparing sea ice thickness distributions based on measurements made at the meter scale with that estimated from regional and global sea ice model simulations, with a typical resolution of a few kilometres; the issue of scale dependance. Using USL sea ice draft profiles and EM thickness measurements, we investigate the scaling properties of the sea ice thickness over the Arctic to address the following question: how does the sea ice thickness distribution evolve with the scale of observation?

The autocorrelation calculations performed here allow extending previous analyses based on single USL transects (up to 50 km-long) and point to long-range correlations in the thickness of the sea ice cover reaching as far as a few hundreds of kilometres. Multi-fractal analyses are conducted to investigate the variability of the the ice thickness distribution with the spatial scale of observation up to these scales.
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University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons