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The Unknown And The Unexpected: Climate Change And The Pacific Northwest. Mark R. Abbott College of Oceanic and Atmospheric Sciences Oregon State University. Overview. Global scale climate Modeling and climate Climate and the Northwest New ways to approach the unknowable.
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The Unknown And The Unexpected:Climate Change And The Pacific Northwest Mark R. Abbott College of Oceanic and Atmospheric Sciences Oregon State University
Overview • Global scale climate • Modeling and climate • Climate and the Northwest • New ways to approach the unknowable
Past And Future Rise In Sea Level 20,000 years ago 2200? ( + 5 meters)
Timeline Of Climate Model Development Figure courtesy W. Washington, NCAR
Impacts Of Spatial Resolution On The Types Of Terrain That Are Included In Models Figure courtesy S. Hostetler, USGS/OSU
Climate Change Impacts Depend On Variability • Variability may have more human impact than mean state • Examples • European heat wave • Niger drought Figure courtesy A. Mix, OSU
The Five “Currencies” Of The Earth System • Energy • Water • Carbon • Money • Information • Next-generation Earth system models must account for all of these currencies
Innovation In Earth System Models The “Old” Way • Increased temporal and spatial resolution • Data assimilation and near real-time observatories and sensor networks • Increasing coupling of components and increasing richness of models • Impact on HPC requirements has been a focus on increased capacities for computation as well as on post-production analyses • Can the community obtain the HPC resources it needs in a rapidly-changing market?
New Demands On Climate Models • Not just increased resolution and more physics • Risk assessments and scenarios • Impacts of climate change on human migration patterns • Rare but high impact scenarios • Feedbacks between climate and socioeconomic processes • Past as a poor predictor of the future • Need to create diverse and resilient knowledge networks to develop and test scenarios • Adaptive management rather than rigid protocols
CPU CPU MPI System Architectures • The Intel and AMD Approach • General purpose systems for a wide array of applications • 6 month technology cycle • Science community started rolling their own clusters because of lower costs
Core 1 Core 2 Core 3 Core 4 Core 1 Core 2 Core 3 Core 4 OpenMP OpenMP MPI Increasingly Complex Architectures • Intel and AMD stalled at 3 GHz because of packaging issues (i.e., heat dissipation) • Now going multi-core at lower frequency • Increasingly complex programming model • Low efficiencies because fundamental models require lots of shared memory (and hence message passing) • Focus on communications infrastructure • Hard to take generalized commodity systems and support innovation • New architectures (CPU/GPU) now entering the fray
Is This Innovation? • Community models • Good for production runs and post-production analyses • Good for education • Not so good for adding new components and new physics and biogeochemistry • Supercomputer centers • Good for production runs of very large models • Not especially efficient for exploration of new models and new approaches • New architectures make it increasingly difficult to code • We need more than a big data center with VT 100 terminals
Shifting The Context For IT • Transforming our workflows • Real-time, continuous, adaptive • Shifting the balance of data and knowledge • Collaborative, networked, interdisciplinary • Not just bigger and faster • A collection of adaptable, dynamic services • Increased customization in a commodity world • HPC-enabled components live everywhere, not just data centers • End-to-end, user experience is critical • Not just in the office or lab
A New Approach? • Overcoming language and cultural barriers to enable collaboration • Merging data streams and sensors • Developing and testing models • Seeking out underlying and emergent rules to make projections and predictions • This looks more like a network gaming metaphor • New capabilities include • Preservation, provenance, collaboration, accountability, and reputation
Shifting The Community • From data delivery to knowledge services • The Wikipedia model for smart mobs • Networks of potential resources • Innovation will come from the fringes • More “democracy” in the system • What used to be expensive and complex is now available to everyone • Need for standard, open frameworks • Simple and extensible • Not driven solely by science requirements • But still require enormous amount of work
Convergences • New IT capabilities will bring more integration among diverse communities, data sets, and models • New demands for climate models and climate services will require these capabilities • Need a resilient approach to the complexities and uncertainties of the future
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