1. Breakout #2 Generic Classes of Issues Hardware –big iron (capability, not just capacity) Network –last-mile problem –computational grid Software/frameworks –interoperability –algorithms Data/information/knowledge curation –digital libraries/data grids –geo-referencing, proper metadata, and smart data services and agents Education and public outreach –disenfranchised communities –knowledge transfer –CS/IT literacy

2. Breakout #2 Question 1: Low-hanging fruit Promote interoperability in data analysis, modeling, and data assimilation –Recognize different levels of organizational coordination among various communities Atmosphere/ocean/climate modeling (high coordination, common problems) Seismological/mantle convection modeling, land-surface studies, hydrological modeling (mid-level coordination) Ecological and geological modeling (low coordination, heterogeneous collection of problems) –Initiate and sustain efforts to develop community modeling tools End-to-end simulations Leading efforts: ESMF, PRISM, IOM, PALM, SCEC –Encourage cross-disciplinary coordination Common data models Control vocabularies, semantics, ontologies

3. Breakout #2 Question 1: Low-hanging fruit Develop digital libraries and data grids –Federate data bases for community sharing –Promote geo-organized data bases (“Digital Earth”) –Develop tools for handling special data types “True 3D” data Structured vs. unstructured data –Large simulation data sets Audit trails: legacy and pedigree Visualization –Address data delivery issues Large data volumes Data foundries

4. Breakout #2 Question 1: Low-hanging fruit Establish collaboratories for coordinating activities among distributed scientific communities –Examples: EarthScope initiative, ecological science –Infrastructure for scientific collaboration –Stable organizations to sustain cyberinfrastructure –Effective mechanisms for Knowledge transfer Input to public policies Integration with social science Public education and outreach

5. Breakout #2 Question 2: Unripe fruit Develop digital libraries and data grids –Smart agents –Information services proper metadata semantically relevant access ontology translation Develop application services –Community tools –Large-scale simulations –Scalable workbenches

6. Breakout #2 Question 2: Unripe fruit Direct numerical simulation (DNS) that can resolve relevant inner scales at global outer scales and for long time series –Big iron –Operating systems and compilers –New algorithms Sustain cyberinfrastructure –New business model –Adequate expertise

7. Breakout #2 Question 3: Making it happen Staffing –Appropriate technical levels Funding –Enough –Sustained Cross-training –Innovative educational programs in computational geoscience and environmental science Industry partnerships Vigorous outreach