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| MPAS-O | |
|---|---|
| Name | MPAS-O |
| Title | Model for Prediction Across Scales – Ocean (MPAS-O) |
| Developer | National Center for Atmospheric Research; Los Alamos National Laboratory; University Corporation for Atmospheric Research |
| Initial release | 2013 |
| Latest release | 2024 |
| Programming language | C, Fortran, Python |
| Operating system | Linux, macOS |
| License | BSD |
MPAS-O The Model for Prediction Across Scales – Ocean (MPAS-O) is a numerical ocean circulation model designed for multiscale, nonhydrostatic and hydrostatic simulations on unstructured polygonal meshes. It supports variable-resolution modeling for regional refinement and global simulations, enabling studies linking mesoscale eddies to basin-scale circulation. MPAS-O is developed by a consortium including National Center for Atmospheric Research, Los Alamos National Laboratory, and partners in the academic and national laboratory communities, and is used in operational, research, and coupled Earth system settings.
MPAS-O provides an ocean component built to interoperate with climate and weather systems such as Community Earth System Model, Weather Research and Forecasting Model, and regional systems developed at NOAA research centers. The framework emphasizes conservation properties, energetic consistency, and compatibility with unstructured Voronoi meshes employed by projects originating at Los Alamos National Laboratory and collaborators at University of Washington and Oregon State University. MPAS-O supports barotropic and baroclinic dynamics, passive tracers, and biogeochemical modules contributed by partners like Geophysical Fluid Dynamics Laboratory researchers and university groups.
Development of MPAS-O traces to efforts at Los Alamos National Laboratory to create models on quasi-uniform and variable-resolution meshes and to the National Science Foundation-funded collaborations linking University of Colorado Boulder, University of California, San Diego, and Princeton University. Architectural choices favor modular Fortran cores with C-based mesh utilities and Python tooling for pre- and post-processing; this leverages community software such as ESMF and MPI implementations like Open MPI and MPICH. The open-source BSD license encourages contributions from institutions including Scripps Institution of Oceanography, Woods Hole Oceanographic Institution, and University of Miami.
MPAS-O implements primitive equation dynamics on centroidal Voronoi tessellations, with options for hydrostatic and nonhydrostatic solvers developed in collaboration with researchers at California Institute of Technology and Massachusetts Institute of Technology. Physical parameterizations include vertical mixing schemes influenced by formulations from Geophysical Fluid Dynamics Laboratory and boundary layer closures similar to those used at National Oceanic and Atmospheric Administration laboratories. Surface flux coupling interfaces allow exchange with atmospheric components such as Community Atmosphere Model and wave components like WaveWatch III derivatives. Tracer advection schemes, subgrid eddy parameterizations (e.g., variants of Gent–McWilliams), and tidal forcing modules have been implemented with input from teams at University of Oxford and Imperial College London.
MPAS-O is optimized for parallel execution on distributed-memory architectures using domain decomposition guided by mesh partitioners created at Los Alamos National Laboratory and tools from Sandia National Laboratories. Computational kernels exploit vectorization and cache-aware data layouts; performance tuning has been conducted on systems at Oak Ridge National Laboratory, Argonne National Laboratory, and European centers such as European Centre for Medium-Range Weather Forecasts collaborations. Scalability studies report strong scaling to tens of thousands of cores for global meshes and efficient weak scaling for nested regional refinement, enabling community use on leadership-class systems like Summit (supercomputer) and cloud clusters managed by Amazon Web Services in research contexts.
MPAS-O has been applied to global climate studies linking mesoscale eddies to heat transport in basins investigated by teams at University of Bristol and University of Tasmania, coastal prediction systems developed with NOAA Pacific Marine Environmental Laboratory, and coupled ocean–ice simulations with Alfred Wegener Institute collaborators. Regional applications include high-resolution studies of upwelling near Peru and California, tsunami propagation scenarios analyzed by researchers at Harvard University and University of Hawaii at Manoa, and studies of Arctic circulation with contributions from Norwegian Polar Institute and Scott Polar Research Institute groups. The model is also used in educational settings at institutions like Stanford University and University of Washington.
Validation efforts for MPAS-O involve comparisons with observational datasets from observing systems such as ARGO (oceanography), satellite altimetry missions like TOPEX/Poseidon and Jason (satellite), and in situ programs coordinated by International CLIVAR Project Office. Model evaluation employs standardized protocols used by intercomparison projects including CMIP-like frameworks adapted for regional ensembles and tests against canonical benchmarks developed by Geophysical Fluid Dynamics Laboratory and community workshops hosted by National Center for Atmospheric Research. Studies assessing energy-conserving properties, tracer fidelity, and eddy-resolving skill have been published by researchers affiliated with University of Cambridge, Plymouth Marine Laboratory, and University of Bergen.
MPAS-O development and maintenance are coordinated through working groups involving National Center for Atmospheric Research, Los Alamos National Laboratory, and academic partners, with community governance shaped by contributor agreements similar to those used in other Earth system projects at University Corporation for Atmospheric Research. User support includes documentation portals curated by developers, community forums, and training workshops held at venues such as AGU and EGU meetings, and code hosting and issue tracking integrated with platforms used by groups at GitHub-based organizations. Funding and collaborations span agencies including National Science Foundation, Department of Energy, NOAA, and international research councils.
Category:Ocean modeling software