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.

Research Highlights

Regional Modeling Frameworks

Predicting the regional hydrologic cycle at time scales from seasons to centuries is one of the most challenging goals of climate modeling. Because hydrologic cycle processes are inherently multi-scale, increasing model resolution to more explicitly represent finer scale processes...

Ultra High-resolution modeling

The problem of predicting climate change and its consequences is motivated by the increasingly urgent need to adapt to near term trends in climate change and the potential changes in the frequency and intensity of extreme events. This project is developing the scientific framework...