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Model for Prediction Across Scales ( MPAS): Ocean Component

Model for Prediction Across Scales ( MPAS): Ocean Component. Xylar Asay -Davis Matthew Hecht, Philip Jones, Mathew Maltrud , Christopher Newman, Mark Petersen, Todd Ringler Los Alamos National Lab PDEs on the Sphere 2010 August 2010. Contributors.

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Model for Prediction Across Scales ( MPAS): Ocean Component

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  1. Model for Prediction Across Scales (MPAS): Ocean Component XylarAsay-Davis Matthew Hecht, Philip Jones, Mathew Maltrud, Christopher Newman, Mark Petersen, Todd Ringler Los Alamos National Lab PDEs on the Sphere 2010 August 2010

  2. Contributors LANL: XylarAsay-Davis, Matthew Hecht, Phil Jones, Mark Petersen, Mat Maltrud, Chris Newman, Todd Ringler NCAR: Michael Duda, Joe Klemp, Bill Skamarock LLNL: Dan Bergmann, Art Mirin USC: LiliJu FSU: Max Gunzburger, Doug Jacobsen

  3. What is MPAS? 1. MPAS is an unstructured-grid approach to climate system modeling. 2. MPAS supports both quasi-uniform and variable resolution meshing of the sphere using quadrilaterals, triangles or Voronoi tessellations. 3. MPAS is a software framework for the rapid prototyping of single-components of climate system models (atmosphere, ocean, land ice, etc.) 4. MPAS offers the potential to explore regional-scale climate change within the context of global climate system After testing, models will be distributed through SourceForge at http://mpas.sourceforge.net/.

  4. MPAS is built on top of a finite-volume technique. The finite-volume approach is a generalization of the Arakawa and Lamb (1981) energy and potential enstrophy conserving scheme. In the shallow-water system, our approach conserves energy and potential vorticity while dissipating potential enstrophy (without dissipating energy).

  5. MPAS will have access to several different high-order tracer transport algorithms, each with their appropriate flux-limiter.

  6. Results from one high-order transport scheme. 2nd-order Initial Condition Snapshot at Day 5. Limiter turned off. Conservative Transport Schemes for Spherical Geodesic Grid. See Bill Skamarock’s talk tomorrow. 3rd-order 4th-order

  7. MPAS will have access to a high-order extension of Prather’s method-of-moments transport algorithm. The Characteristic Discontinuous Galerkin (CGD) method takes advantage of the Lagrangian nature of transport while maintaining a static mesh. (Lowrie, 2010) The method has been extended to arbitrary convex polygons and to arbitrary order of accuracy. (Buonoi, Lowrie and Ringler 2010). The method has also illuminated the connection between CDG and Prather’s Method-of-Moments.

  8. MPAS Project Status: Ocean Summary • Running MPAS with stretched lat-lon meshes with real-world topography with a z-level vertical coordinate. • We have started the (2nd) evaluation of the barotropic-baroclinic splitting. • Code runs on 1 to 4096+ processors on a variety of platforms (MacBook Air, Lobo, Coyote, Ranger, Jaguar) • Ocean model can run in pure isopycnalmode as well as z-level mode. • We have tested and are implementing a high-order advection scheme (Skamarock) • Running ocean model with quasi-uniform and variable resolution Voronoi tessellations

  9. POP to MPAS comparison Subtropical gyres are weaker in MPAS by 30%. We are checking parameter settings in each model to determine the source of this difference. MPAS will not have checker-boarding. day 150 year 6 POP MPAS

  10. MPAS: Ocean Voronoi Tessellations Uniform and variable resolution Voronoi tessellations have been created at resolutions between 120 and 15 km. Right: Global 15km mesh, PV shown, single-layer, flat bottom. The meshes use nearest-neighbor search from ETOPO2 to define land/sea mask and ocean depth.

  11. Variable Resolution Voronoi Tessellations Movie

  12. Variable Resolution Voronoi Tessellations Mesh grid spacing varies by a factor of 8, ranging from 25 km to 200 km. This mesh has about 1/10 the number of nodes as the globally uniform 15 km mesh.

  13. Variable Resolution Voronoi Tessellations variable resolution mesh 120,000 nodes uniform mesh 1,800,000 nodes

  14. My Work: Immersed Boundary Method Ice Shelf/Ocean interface, coast lines and bathymetry Used to represent complex and/or moving boundaries Boundaries can intersect computational grid cells Requires minimal modification of ocean model Ice Shelf Ocean

  15. Radial Basis Functions (RBFs) Reconstruction of scalar and vector fields Vector reconstruction: • Based on Baudisch et al. (2006). • Only fromnormal components at edges • Exactly reproduces a constant vector field

  16. MPAS Ocean: Challenges Looking Forward • Porting POP physics to MPAS • Obtaining quality simulations • Implementing barotropic-baroclinic splitting • Implementing high-order vertical remapping • Designing computationally efficient kernels

  17. Thanks!

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