Snow and Sea Ice in the ACME Global Climate Model

Kelly Kochanski, University of Colorado

Global climate models run on the fastest supercomputers with resolutions on the order of 25 km. Yet the Earth's climate is controlled by thousands of physical and chemical processes that vary non-linearly over scales of meters and less. The accuracy of climate models is limited by the accuracy with which we can identify and parameterize the processes – such as the growth and melt of sea ice – that have the greatest influence on the Earth's energy balance. When the sea is bare, it absorbs as much as six times more sunlight than it absorbs when it is covered by ice and nine times more than the same ice covered by fresh snow.

We present a new model of snow depth, distribution and density in MPAS-seaice. This is the sea-ice component of the Department of Energy's premier climate model, the Accelerated Climate Model for Energy, now under development. The MPAS framework traces climate variables across an unstructured variable-resolution Voronoi mesh and improves climate model resolution in critical regions. We parameterize the density, thickness and albedo of the snow to account for variation within each grid cell and compare the model output to control runs using past climate data.

Abstract Author(s): K. Kochanski, N. Jeffery, E. Hunke