Development of an Atmospheric Climate Model with Self-Adapting Grid and Physics

This project was targeting the development of a computational approach that would allow resolving cloud processes on small-scales within the framework of the most recent version of the NASA/NCAR Finite-Volume Community Atmospheric Model (FVCAM). The FVCAM is based on the multidimensional Flux-Form Semi-Lagrangian (FFSL) dynamical core and uses a ?vertically Lagrangian? finite-volume (FV) representation of the model equations with a mass-conserving re-mapping algorithm. The Lagrangian coordinate requires a remapping of the Lagrangian volume back to Eulerian coordinates to restore the original resolution and keep the mesh from developing distortions such as layers with overlapping interfaces. The main objectives of the project were, first, to develop the 3D library which allows refinement and coarsening of the model domain in spherical coordinates, and second, to develop a non-hydrostatic code for calculation of the model variables within the refined areas that could be seamlessly incorporated with the hydrostatic finite volume dynamical core when higher resolution is wanted. We also updated the aerosol simulation model in CAM in order to ready the model for the treatment of aerosol/cloud interactions