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Earth System Grid Federation: Federated and Integrated Climate Data from Multiple Sources. IN: Earth System Modeling, Volume 6

Book ·
 [1];  [1];  [2];  [3];  [4];  [5]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Program for Climate Model Diagnosis and Intercomparison
  2. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL)
  3. Rensselaer Polytechnic Inst., Troy, NY (United States)
  4. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  5. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
+ Show Author Affiliations
In climate science, the quantity of data in use by 2020 is expected to be in the hundreds of exabytes (EB, where 1 exabyte = 1018 bytes). Current and future heterogeneous climate data will be distributed around the globe and must be harnessed to find solutions to mission-critical problems. Additionally, more requirements and more constraints are needed to expand and integrate new modeling capabilities and tasks, such as climate prediction, uncertainty quantification (UQ) of model performance, test-bed development, and assimilation of more diverse data sets. These data exploration tasks can be complex and time-consuming, and frequently involve numerous resources spread throughout the modeling and observational climate communities. Staff expertise and core competencies must therefore be flexibly applied to multiple projects and programs to accommodate more complex applications and state-of-the-art science analysis, while allowing resources to be adapted to address future areas of interest in climate research.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Organization:
USDOE Office of Science (SC)
DOE Contract Number:
AC52-07NA27344
OSTI ID:
1567529
Country of Publication:
United States
Language:
English

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Related Subjects

Climate Project
Climate Science
Couple Model Intercomparison Project
Lawrence Livermore National Laboratory
Uncertainty Quantification