CPL6: The New Extensible, High Performance Parallel Coupler for the Community Climate System Model

  • Authors:

  • Anthony P. Craig;Robert Jacob;Brian Kauffman;Tom Bettge;Jay Larson;Everest Ong;Chris Ding;Yun He

  • Affiliations:

  • Climate and Global Dynamics Division National Center for Atmospheric Research Boulder, CO 80305, USA;Mathematics and Computer Science Division Argonne National Laboratory Argonne, IL 60439, USA;Climate and Global Dynamics Division National Center for Atmospheric Research Boulder, CO 80305, USA;Scientific Computing Division National Center for Atmospheric Research Boulder, CO, 80305, USA;Mathematics and Computer Science Division Argonne National Laboratory Argonne, IL 60439, USA;Mathematics and Computer Science Division Argonne National Laboratory Argonne, IL 60439, USA;Computational Research Division Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA;Computational Research Division Lawrence Berkeley National Laboratory Berkeley, CA 94720, USA

  • Venue:
  • International Journal of High Performance Computing Applications
  • Year:
  • 2005

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Abstract

Coupled climate models are large, multiphysics applications designed to simulate the Earth's climate and predict the response of the climate to any changes in the forcing or boundary conditions. The Community Climate System Model (CCSM) is a widely used state-of-the-art climate model that has released several versions to the climate community over the past ten years. Like many climate models, CCSM employs a coupler, a functional unit that coordinates the exchange of data between parts of the climate system such as the atmosphere and ocean. In this paper we describe the new coupler, cpl6, contained in the latest version of CCSM, CCSM3. Cpl6 introduces distributed-memory parallelism to the coupler, a class library for important coupler functions, and a standardized interface for component models. Cpl6 is implemented entirely in Fortran90 and uses the Model Coupling Toolkit as the base for most of its classes. Cpl6 gives improved performance over previous versions and scales well on multiple platforms.