Climate change research relies on models to better understand and predict the complex, interdependent processes that affect the atmosphere, ocean, and land. These models are computationally intensive and produce terabytes to petabytes of data. Visualization and analysis is increasingly difficult, yet is critical to gain scientific insights from large simulations. The recently-developed Model for Prediction Across Scales-Ocean (MPAS-Ocean) is designed to investigate climate change at global high-resolution (5 to 10 km grid-cells) on high performance computing platforms. In the accompanying video, we use state-of-the-art visualization techniques to explore the physical processes in the ocean relevant to climate change. This project exemplifies the benefits of tight collaboration among scientists, artists, computer scientists, and visualization specialists.
Mark Petersen, Climate Ocean Sea-Ice Modeling, Los Alamos National Laboratory
Francesca Samsel, Center for Agile Technology, University of Texas at Austin
Gregory Abram, Texas Advanced Computing Center, University of Texas at Austin
James Ahrens, Data Science at Scale, Los Alamos National Laboratory