Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
https://doi.org/10.5194/gmd-2018-55
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
08 Mar 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).
GEOS-Chem High Performance (GCHP): 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, William M. Putman3, Matthew A. Thompson3, Atanas L. Trayanov3, Andrea M. Molod3, Randall V. Martin5,7, and Daniel J. Jacob2 1Laboratory for Aviation and the Environment, Massachusetts Institute of Technology
2John A. Paulson School of Engineering and Applied Sciences, Harvard University
3NASA Global Modeling and Assimilation Office
4Universities Space Research Association, Columbia, MD, USA
5Department of Physics and Atmospheric Science, Dalhousie University
6Science Systems and Applications, Inc., Lanham, MD, USA
7Smithsonian Astrophysical Observatory, Harvard-Smithsonian Center for Astrophysics
Abstract. Global modeling of atmospheric composition is a grand computational challenge because of the need to simulate large coupled systems of chemical species interacting with transport on all scales. Off-line chemical transport models (CTMs), where the chemical continuity equations are solved using meteorological data as input, have the advantages of simplicity and reproducibility, and are important vehicles for developing 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 (GCHP) for GEOS-Chem, a CTM driven by GEOS 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 be run in a distributed-memory framework, scalable from six cores on a single node up to hundreds of cores distributed across a network. 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.
Citation: Eastham, S. D., Long, M. S., Keller, C. A., Lundgren, E., Yantosca, R. M., Zhuang, J., Li, C., Lee, C. J., Yannetti, M., Auer, B. M., Clune, T. L., Kouatchou, J., Putman, W. M., Thompson, M. A., Trayanov, A. L., Molod, A. M., Martin, R. V., and Jacob, D. J.: GEOS-Chem High Performance (GCHP): A next-generation implementation of the GEOS-Chem chemical transport model for massively parallel applications, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-55, in review, 2018.
Sebastian D. Eastham et al.
Sebastian D. Eastham et al.
Sebastian D. Eastham et al.

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Short summary
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 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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