HR: 08:15h
AN: A11D-02 [PDF]
TI: Adaptive Grids in Climate Modeling: Tests of the Dynamical Core
AU: * Jablonowski, C
EM: cjablono@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48108 United States
AU: Herzog, M
EM: herzogm@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48108 United States
AU: Oehmke, R C
EM: oehmke@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48108 United States
AU: Penner, J E
EM: penner@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48108 United States
AU: Stout, Q F
EM: qstout@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48108 United States
AU: van Leer, B
EM: bram@umich.edu
AF: University of Michigan, 2455 Hayward, Ann Arbor, MI 48108 United States
AB:
Adaptive grids in climate modeling offer unique opportunities for future climate model predictions. They allow the use of
static and dynamic refinements depending on terrain features or flow characteristics.
For the first time, an adaptive hydrostatic dynamical core for global atmospheric General Circulation Models has been built.
It is based on the NASA/NCAR finite volume dynamical core and utilizes a parallel, spherical adaptive grid library with a
block-structured data layout. The model can
adapt its resolution statically and dynamically during a model run. Static adaptations resolve predetermined regions of
interests, like mountain ranges, at high resolutions and allow the efficient use of reduced grids in polar regions.
Dynamic adaptations are guided by user defined refinement/coarsening criteria and are capable of tracking a wide variety of
features of interest. Examples include vorticity and pressure-based adaptation criteria that track a cyclone path.
The presentation shows the highlights of the adaptive dynamical core experiments using both 2D shallow water tests and
idealized 3D test cases. The results emphasize the pros and cons of different refinement criteria and suggest that
adaptive grids can be considered an alternative to today's nested grid approaches.
DE: 3319 General circulation
DE: 3337 Numerical modeling and data assimilation
SC: Atmospheric Sciences [A]
MN: 2003 Fall Meeting