The Model for Prediction Across Scales (MPAS) is a novel set of earth-system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This makes MPAS a promising tool for conducting climate-related impact studies of, for example, land use changes in a consistent approach.
Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different High Performance Computing sites with different architectures and compilers. We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes. We further demonstrate the model performance of MPAS in terms of its capability to reproduce the dynamics of the West African Monsoon and its associated precipitation. ... mehrComparing 11 month runs for two meshes with observations and a Weather Research & Forecasting tool (WRF) reference model, we show that MPAS can reproduce the atmospheric dynamics on global and local scales, but that further optimisation is required to address a precipitation excess for this region.
Finally, we conduct extreme scaling tests on a global 3 km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70 % parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3 km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities.