Comparison of different calving laws using a level set method
Authors: Garcia-Molina Cruz , Gillet-Chaulet Fabien , Chekki Mondher , Durand Gael , Gagliardini Olivier 
Published: March 2022

&lt;p&gt;Calving is one of the most important processes that induce mass loss in Greenland and Antarctic ice shelves.&amp;#160;These major ice discharges modify the calving front position with an impact over the whole stress regime of these glaciers. Because the calving rate depends on several physical parameters, having an empirical parameterization for simulations over long periods is a big challenge. We study the calving front position using the open-source finite element, Elmer/Ice (&lt;span&gt;http://elmerice.elmerfem.org/&lt;/span&gt;) code. We use a time constant mesh coupled with a time-evolving signed distance to the front (level-set function &amp;#934;) that activates or masks nodes as needed. We study the front position (given as the 0 level set value) evolution by solving&lt;/p&gt;&lt;p&gt;&lt;img src=&quot;data:image/png;base64, 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&quot; alt=&quot;&quot; width=&quot;99&quot; height=&quot;32&quot; /&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;with w=c+v, where &lt;em&gt;c&lt;/em&gt; is the calving rate and v is the velocity of the ice normal to the front. By using a realistic synthetic configuration, based on the intercomparison models (MISMIP), we validate our level-set method, we study the numerical sensibility, and the impact of different calving laws reported in the literature.&lt;/p&gt;