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Volume 11, issue 7 | Copyright
Geosci. Model Dev., 11, 2941-2953, 2018
https://doi.org/10.5194/gmd-11-2941-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model description paper 24 Jul 2018

Model description paper | 24 Jul 2018

GEOS-Chem High Performance (GCHP v11-02c): a next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications

Sebastian D. Eastham1,2, Michael S. Long2, Christoph A. Keller3,4, Elizabeth Lundgren2, Robert M. Yantosca2, Jiawei Zhuang2, Chi Li5, Colin J. Lee5, Matthew Yannetti2, Benjamin M. Auer3,6, Thomas L. Clune3, Jules Kouatchou3,6, William M. Putman3, Matthew A. Thompson3,6, Atanas L. Trayanov3,6, Andrea M. Molod3, Randall V. Martin5,7, and Daniel J. Jacob2 Sebastian D. Eastham et al.
  • 1Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
  • 2John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
  • 3NASA Global Modeling and Assimilation Office, Greenbelt, Maryland, USA
  • 4Universities Space Research Association, Columbia, Maryland, USA
  • 5Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
  • 6Science Systems and Applications, Inc., Lanham, Maryland, USA
  • 7Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA

Abstract. Global modeling of atmospheric chemistry is a grand computational challenge because of the need to simulate large coupled systems of  ∼ 100–1000 chemical species interacting with transport on all scales. Offline chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have usability advantages and are important vehicles for developing atmospheric chemistry knowledge that can then be transferred to Earth system models. However, they have generally not been designed to take advantage of massively parallel computing architectures. Here, we develop such a high-performance capability for GEOS-Chem (GCHP), a CTM driven by meteorological data from the NASA Goddard Earth Observation System (GEOS) and used by hundreds of research groups worldwide. GCHP is a grid-independent implementation of GEOS-Chem using the Earth System Modeling Framework (ESMF) that permits the same standard model to operate in a distributed-memory framework for massive parallelization. GCHP also allows GEOS-Chem to take advantage of the native GEOS cubed-sphere grid for greater accuracy and computational efficiency in simulating transport. GCHP enables GEOS-Chem simulations to be conducted with high computational scalability up to at least 500 cores, so that global simulations of stratosphere–troposphere oxidant–aerosol chemistry at C180 spatial resolution ( ∼ 0.5° × 0.625°) or finer become routinely feasible.

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Global atmospheric chemical transport models are crucial tools in atmospheric science, used to address problems ranging from climate change to acid rain. GEOS-Chem High Performance (GCHP) is a new implementation of the widely used GEOS-Chem model, designed for massively parallel architectures. GCHP v11-02c is shown to be highly scalable from 6 to over 500 cores, enabling the routine simulation of global atmospheric chemistry from the surface to the stratopause at resolutions of ~50 km or finer.
Global atmospheric chemical transport models are crucial tools in atmospheric science, used to...
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