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Jiawei Zhuang, Daniel J. Jacob, Judith Flo Gaya, Robert M. Yantosca, Elizabeth W. Lundgren, Melissa P. Sulprizio, and Sebastian D. Eastham


Cloud computing platforms can provide fast and easy access to complex Earth science models and large datasets. This article presents a mature capability for running the GEOS-Chem global 3D model of atmospheric chemistry on the Amazon Web Services (AWS) cloud. GEOS-Chem users at any experience level can get immediate access to the latest, standard version of the model in a preconfigured software environment with all needed meteorological and other input data, and they can analyze model output data easily within the cloud using Python tools in Jupyter notebooks. Users with no prior knowledge of cloud computing are provided with easy-to-follow, step-by-step instructions. They can learn how to complete a demo project in less than one hour, and from there they can configure and submit their own simulations. The cloud is particularly attractive for beginning and occasional users who otherwise may need to spend substantial time configuring a local computing environment. Heavy users with their own local clusters can also benefit from the cloud to access the latest standard model and datasets, share simulation configurations and results, benchmark local simulations, and respond to surges in computing demand. Software containers allow GEOS-Chem and its software environment to be moved smoothly between cloud platforms and local clusters, so that the exact same simulation can be reproduced everywhere. Because the software requirements and workflows tend to be similar across Earth science models, the work presented here provides general guidance for porting models to cloud computing platforms in a user-accessible way.

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Gabriele G. Pfister, Sebastian D. Eastham, Avelino F. Arellano, Bernard Aumont, Kelley C. Barsanti, Mary C. Barth, Andrew Conley, Nicholas A. Davis, Louisa K. Emmons, Jerome D. Fast, Arlene M. Fiore, Benjamin Gaubert, Steve Goldhaber, Claire Granier, Georg A. Grell, Marc Guevara, Daven K. Henze, Alma Hodzic, Xiaohong Liu, Daniel R. Marsh, John J. Orlando, John M. C. Plane, Lorenzo M. Polvani, Karen H. Rosenlof, Allison L. Steiner, Daniel J. Jacob, and Guy P. Brasseur


To explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.

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