skip to main content

Title: Algorithmically scalable block preconditioner for fully implicit shallow-water equations in CAM-SE

Performing accurate and efficient numerical simulation of global atmospheric climate models is challenging due to the disparate length and time scales over which physical processes interact. Implicit solvers enable the physical system to be integrated with a time step commensurate with processes being studied. The dominant cost of an implicit time step is the ancillary linear system solves, so we have developed a preconditioner aimed at improving the efficiency of these linear system solves. Our preconditioner is based on an approximate block factorization of the linearized shallow-water equations and has been implemented within the spectral element dynamical core within the Community Atmospheric Model (CAM-SE). Furthermore, in this paper we discuss the development and scalability of the preconditioner for a suite of test cases with the implicit shallow-water solver within CAM-SE.
 [1] ;  [1] ;  [2]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
OSTI Identifier:
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Computational Geosciences
Additional Journal Information:
Journal Volume: 19; Journal Issue: 1; Journal ID: ISSN 1420-0597
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; CAM-SE; implicit solvers; preconditioning algorithmic scalability; shallow-water equations; atmospheric climate; community atmospheric model; spectral element method