develops methods and tools for the diagnosis and intercomparison of general circulation models (GCMs) that simulate the global climate. The need for innovative analysis of GCM climate simulations is apparent, as increasingly more complex models are developed, while the disagreements among these simulations and relative to climate observations remain significant and poorly understood. The nature and causes of these disagreements must be accounted for in a systematic fashion in order to confidently use GCMs for simulation of putative global climate change. Variability (rate of Data is produced by codes running at change) supercomputer centers. During runtime, intense periods of data i/O occur regularly, but typically consume only a few percent of the total run time. Runs are carried out routinely, but spike as deadlines for reports approach. Big Data Science Veracity (Robustness Data produced by climate simulations is plays a (collection, Issues) and Quality large role in informing discussion of climate change curation, simulations. Therefore it must be robust, both from analysis, the standpoint of providing a scientifically valid action) representation of processes that influence climate, but also as that data is stored long and transferred world-wide to collaborators and other scientists. Visualization Visualization is crucial to understanding a system as complex as the Earth ecosystem. Data Types Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Data Analytics There is a need to provide data reduction and analysis web services through the Earth System Grid (ESG). A pressing need is emerging for data analysis capabilities closely linked to data archives. Big Data Specific The rapidly growing size of datasets makes scientific analysis a challenge. Challenges (Gaps) The need to write data from simulations is outpacing supercomputers’ ability to accommodate this need. Big Data Specific Challenges in Mobility Data from simulations and observations must be shared among a large widely distributed community. Security & Privacy Requirements Highlight issues for ESGF is in the early stages of being adapted for use in two additional generalizing this use domains: biology (to accelerate drug design and development) and case (e.g. for ref. energy (infrastructure for California Energy Systems for the 21st Century architecture) (CES21)). More Information (URLs)
develops methods and tools for the diagnosis and intercomparison of general circulation models (GCMs) that simulate the global climate. The need for innovative analysis of GCM climate simulations is apparent, as increasingly more complex models are developed, while the disagreements among these simulations and relative to climate observations remain significant and poorly understood. The nature and causes of these disagreements must be accounted for in a systematic fashion in order to confidently use GCMs for simulation of putative global climate change. Variability (rate of change) Data is produced by codes running at supercomputer centers. During runtime, intense periods of data i/O occur regularly, but typically consume only a few percent of the total run time. Runs are carried out routinely, but spike as deadlines for reports approach. Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) and Quality Data produced by climate simulations is plays a large role in informing discussion of climate change simulations. Therefore it must be robust, both from the standpoint of providing a scientifically valid representation of processes that influence climate, but also as that data is stored long and transferred world-wide to collaborators and other scientists. Visualization Visualization is crucial to understanding a system as complex as the Earth ecosystem. Data Types Earth system scientists are being inundated by an explosion of data generated by ever-increasing resolution in both global models and remote sensors. Data Analytics There is a need to provide data reduction and analysis web services through the Earth System Grid (ESG). A pressing need is emerging for data analysis capabilities closely linked to data archives. Big Data Specific Challenges (Gaps) The rapidly growing size of datasets makes scientific analysis a challenge. The need to write data from simulations is outpacing supercomputers’ ability to accommodate this need. Big Data Specific Challenges in Mobility Data from simulations and observations must be shared among a large widely distributed community. Security & Privacy Requirements Highlight issues for generalizing this use case (e.g. for ref. architecture) ESGF is in the early stages of being adapted for use in two additional domains: biology (to accelerate drug design and development) and energy (infrastructure for California Energy Systems for the 21st Century (CES21)). More Information (URLs)