Using Cyberinfrastructure to Study the Earth’s Climate and Air Quality Don Wuebbles Department of Atmospheric Sciences University of Illinois, Urbana-Champaign February 2005

Special Acknowledgements Donna Cox John Martirano Lorne Leonard Stuart Levy Katharine Hayhoe And the students that contributed to the visualization project

Performance Data for MOZART-3 MPI OpenMP wall-clock per NERSC IBM SP: MPI OpenMP wall-clock per GFLOPs threads CPUs model year/hr NCSA IBM p690: threads CPUs model year/hr

WACCM-3 with chemistry Global climate model from ground to 140 km Integrates a stiff system of coupled nonlinear differential equations over time Domain: 63 chemical species (or more), 66 vertical layers, 30 minute time step 96 IBM power4 cpus: full simulation year takes about 6.6 wall clock hours (72 longit. x 46 latitud.) 96 IBM power4 cpus: full simulation year takes about 27.7 wall clock hours (144 longit. x 96 latitud.)

Geosciences CI Challenges Enormously complex human-natural system –Vast temporal (sec to B yrs) and spatial (microns to 1000s of km) scales –Highly nonlinear behavior Massive data sets –physical and digital –static/legacy and dynamic/streaming –geospatially referenced –multidisciplinary and heterogeneous –open access

Geosciences CI Challenges Massive computation –weather, space weather, climate, hydrologic modeling –seismic inversion –coupled physical system models Inherently field-based, visual disciplines with the need to manage information for long periods of time Bringing advanced CI capabilities to education at all levels Connecting the last mile to operational practitioners

Atmospheric Science Research Huge growth in observational and model data volumes and data streams Huge growth in model complexity, resolution and sophistication requiring better initialization, analysis and visualization techniques Growing application of data assimilation as a tool for providing homogeneous data sets and extracting maximum information content from observations Distributed, intelligent observing systems

Atmospheric Science Education Integration of collaboration tools and distributed data capabilities into the atmospheric sciences curriculum Real-time weather data and archives of climate data now commonly available in the classroom Digital libraries – assemblies of high-quality teaching and learning resources with tools to enable exploration of large atmospheric science data sets

Cyberinfrastructure and Science Analysis Access/Delivery Archive Discovery Analysis/Visualization Information Technology Services Computing / Grid Services

Climate Data Visualization Development 2 stages Parallel Data Processing –Prepare visualization data E.g., absolute temperature change over period Relative changes (departures / anomalies) –Surface and altitude data –Various projections (e.g., globe versus map)

Climate Data Visualization Development Parallel Visualization –Partiview on tiled display wall Multigigabytes visualizaions with separate views 40 node display wall at NCSA 15 node display wall at Atmospheric Sciences –Arrange variables spatially for comparison and analysis –3-D and temporal interactivity –Can be embedded in Virtual Director Allows for collaborators to see results in real time Recording and playback of spline-based navigation

2000

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A Few Findings from our project Need to learn each others language –“Just what is a rendering anyway?” Undergraduate student help not reliable for project completion –Good workers, but did not seem concerned with whether they completed their tasks The NCSA folks were great! –Very helpful and always concerned about how they could help me