Ocean Dynamics

, Volume 58, Issue 5–6, pp 475–498 | Cite as

A multiresolution method for climate system modeling: application of spherical centroidal Voronoi tessellations

  • Todd RinglerEmail author
  • Lili Ju
  • Max Gunzburger


During the next decade and beyond, climate system models will be challenged to resolve scales and processes that are far beyond their current scope. Each climate system component has its prototypical example of an unresolved process that may strongly influence the global climate system, ranging from eddy activity within ocean models, to ice streams within ice sheet models, to surface hydrological processes within land system models, to cloud processes within atmosphere models. These new demands will almost certainly result in the develop of multiresolution schemes that are able, at least regionally, to faithfully simulate these fine-scale processes. Spherical centroidal Voronoi tessellations (SCVTs) offer one potential path toward the development of a robust, multiresolution climate system model components. SCVTs allow for the generation of high-quality Voronoi diagrams and Delaunay triangulations through the use of an intuitive, user-defined density function. In each of the examples provided, this method results in high-quality meshes where the quality measures are guaranteed to improve as the number of nodes is increased. Real-world examples are developed for the Greenland ice sheet and the North Atlantic ocean. Idealized examples are developed for ocean–ice shelf interaction and for regional atmospheric modeling. In addition to defining, developing, and exhibiting SCVTs, we pair this mesh generation technique with a previously developed finite-volume method. Our numerical example is based on the nonlinear, shallow-water equations spanning the entire surface of the sphere. This example is used to elucidate both the po tential benefits of this multiresolution method and the challenges ahead.


Voronoi diagram Delaunay triangulation Climate modeling Multiresolution 



This work was supported by the DOE Office of Science Climate Change Prediction Program through DE-FG02-07ER64431, DE-FG02-07ER64432, and DOE 07SCPF152. The authors would like to thank Dr. Sebastien Legrand and two anonymous reviewers for their constructive comments.


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Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.T-3 Fluid Dynamics Group, Theoretical DivisionLos Alamos National LaboratoryLos AlamosUSA
  2. 2.Department of MathematicsUniversity of South CarolinaColumbiaUSA
  3. 3.School of Computational Science, 400 Dirac Science LibraryFlorida State UniversityTallahasseeUSA

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