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Earth Sciences


Computational Earth Sciences research at ORNL encompasses many important aspects of global and regional Earth system model development and analysis. We focus on numerical methods development and implementation, data analytics, verification and validation of Earth system components, and the development of methods to characterize stochastic behavior. Significant progress is underway in the areas of scalable time stepping algorithms, utilization of hybrid architectures to enable efficient and effective use of leadership-class computing architectures, and algorithms to transport of large sets of aerosol and chemical species. Data analytics research includes involvement with an early warning detection system to monitor forest health, development of clustering algorithms to identify geographic ecoregions, and coordinated efforts to link observational networks with simulation through model-informed data collection. Global Earth system model evaluation is becoming more crucial as models become increasingly complex, and advancements in tools and methods for component, fully coupled, and intermodel comparison are underway. Moving forward, Earth system models that imbed a stochastic representation of variable Earth system behavior such as cloud physics are of interest, and increasingly, Earth science research at ORNL is addressing this for the model representation of features, sensitivity analysis, and uncertainty quantification. Computational Earth sciences research at ORNL is driven by the large scale science questions that scalable, efficient, and validated simulation efforts can address, and recent high-impact experiments to characterize and compare regional models, intermodel comparisons, and the investigation of aerosol sensitivities are some fruits of these model development foci. See the project summaries below for details of this research.

Related Highlights

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Mapping dielectric response and charge distribution on the atomic scale reveals the origin of conductivity at the LAO/STO interface
— A combination of scanning transmission electron microscopy and energy loss spectroscopy at the conducting interface between the insulators LaAlO3 (LAO) and SrTiO3 (STO) provides the first direct experimental evidence of a transfer of electrons from the LAO film to interfacial Ti atoms1.

Local detection of activation energy for ionic transport in lithium cobalt oxide
— A new scanning probe technique has led to the first measurements of the activation energy for Li-ion transport with nanometer resolution in the battery electrode material LiCoO2. Understanding ionic transport at the level of individual grains and grain facets is of great importance in improving future energy storage (battery) energy and conversion (fuel cell) devices.

Hydration of NaxCoO2 yields proximity to ferromagnetism and triplet superconductivity
— Hydrated sodium cobalt oxide was shown to be near a ferromagnetic quantum critical point implying that the superconductivity exhibited is of triplet nature. This was possible by performing transport and magnetic measurements of susceptibility and scattering on highly perfect single crystals, and combining the results with MSED-funded theory.

 
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Related Projects

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eSiMon
— eSiMon (electronic Simulation and Monitoring) is a one-point-access to the simulation. It is a one-stop-shop for collaborating scientists to monitor and access results. It is a light-weight in order to allow easy access by team members from any browser and platform.

SDAV (Data Management/Scientific Software Tools)
— Scalable Data Management, Analysis, and Visualization (SDAV) provides comprehensive expertise in scientific data management, analysis, and visualization aimed at transferring state of the art techniques into operational use by application scientists on leadership-class computing facilities.

ADIOS
— The Adaptable IO System (ADIOS) is a parallel IO middleware designed for large-scale scientific simulations. The goal of this project is to provide fast, adaptable and scalable IO interfaces so that scientific codes can run highly efficiently across all computing platforms.

 
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