The authors wish to thank the participants at the 2014 Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference, whose presentations and conference report input helped considerably in the development of this article (Williams et al. 2015). This work was supported by the U.S. Department of Energy Office of Science/Office of Biological and Environmental Research under Contract DE-AC52-07NA27344 at Lawrence Livermore National Laboratory. VB is supported by the Cooperative Institute for Climate Science, Princeton University, under Award NA08OAR4320752 from the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. Part of this work was undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. Part of this activity was performed on behalf of the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Part of this activity was performed on behalf of the Goddard Space Flight Center, under a contract with NASA. This work was supported by ANR Convergence project (Grant Agreement ANR-13-MONU-0008). This work was supported by FP7 IS-ENES2 project (Grant Agreement 312979). The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of Princeton University, the National Oceanic and Atmospheric Administration, or the U.S. Department of Commerce.
ACME Council, 2014: Accelerated Climate Modeling for Energy: Project strategy and initial implementation plan. ACME Council, Department of Energy, 25 pp. [Available online at http://climatemodeling.science.energy.gov/sites/default/files/publications/acme-project-strategy-plan.pdf.]
Advanced Scientific Computing Advisory Committee Subcommittee, 2010: The opportunities and challenges of exascale computing. ASCAC Subcommittee Rep. on Exascale Computing, ASCAC, Department of Energy, 71 pp. [Available online at http://science.energy.gov/∼/media/ascr/ascac/pdf/reports/Exascale_subcommittee_report.pdf.]
Cinquini, L., and Coauthors, 2014: The Earth System Grid Federation: An open infrastructure for access to distributed geospatial data. Future Gener. Comput. Syst., 36, 400–417, doi:10.1016/j.future.2013.07.002.
IPCC, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp., doi:10.1017/CBO9781107415324.
Knutti, R., , and J. Sedláček, 2013: Robustness and uncertainties in the new CMIP5 climate model projections. Nat. Climate Change, 3, 369–373, doi:10.1038/nclimate1716.
Meehl, G. A., , R. Moss, , K. E. Taylor, , V. Eyring, , R. J. Stouffer, , S. Bony, and B. Stevens, 2014: Climate Model Intercomparison: Preparing for the next phase. Eos, Trans. Amer. Geophys. Union, 95 (9), 77–78, doi:10.1002/2014EO090001.
Overpeck, J. T., , G. A. Meehl, , S. Bony, , and D. R. Easterling, 2011: Climate data challenges in the 21st century. Science, 331, 700–702, doi:10.1126/science.1197869.
Williams, D. N., 2014: Visualization and analysis tools for ultrascale climate data. Eos, Trans. Amer. Geophys. Union, 95 (42), 377–378, doi:10.1002/2014EO420002.
Williams, D. N., , G. Palanisamy, , G. Shipman, , T. A. Boden, , and J. W. Voyles, 2014: Department of Energy strategic roadmap for Earth system science data integration. Proc. Conf. on Big Data, Washington, DC, IEEE, 772–777, doi:10.1109/BigData.2014.7004304.
Williams, D. N., and Coauthors, 2015: Fourth Annual Earth System Grid Federation and Ultrascale Visualization Climate Data Analysis Tools Conference report. Lawrence Livermore National Laboratory Tech. Rep. LLNL-TR-666753, 73 pp. [Available online at http://aims-group.github.io/pdf/2014-ESGF_UV-CDAT_Conference_Report.pdf.]