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Interactive Ensemble Coupled Modeling: Enhancing Climate Simulation and Predictability

This study explores the impact of internal atmospheric dynamics on climate variability, questioning whether ENSO is stochastically driven or self-sustained. It aims to improve climate predictions by combining multiple imperfect climate models through ensemble averaging as the coupled system evolves. Challenges such as load balancing, communication of coupling information, and computational efficiency are discussed, alongside the importance of integration with advanced data management systems. The research leverages NCAR facilities, aiming to double simulation capacity over the next five years.

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Interactive Ensemble Coupled Modeling: Enhancing Climate Simulation and Predictability

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  1. The Interactive Ensemble Coupled Modeling Strategy Ben Kirtman Center for Ocean-Land-Atmosphere Studies And George Mason University

  2. Science Questions • How Does Internal Atmospheric Dynamics Impact Climate Variability? • Is ENSO Stochastically Driven or Self-Sustained? • How Do Errors in Internal Atmospheric Dynamics Impact Climate Predictability? • Is it Possible to Combine Multiple Imperfect Climate Models to Improve Climate Simulations and Better Quantify Uncertainty?

  3. Standard Approach • A Posteriori Combinations of Ensemble Realizations (Coupled and Uncoupled) • Does Not Isolate How Internal Dynamics Impacts Evolution of the Coupled System • Want to Employ Ensemble Averaging As the Coupled System Evolves

  4. SST CAM CAM_MOM3 Heat Flux Momentum Flux MOM

  5. SST COLA COLA_MOM3 Heat Flux Momentum Flux MOM

  6. SST CAM COLA Heat Flux Momentum Flux MOM CAM_COLA_MOM3

  7. SST CAM COLA Heat Flux Momentum Flux MOM COLA_CAM_MOM3

  8. Computational Issues (I) • Load Balance Between Oceanic and Atmospheric Components • More Processors • Communication of Coupling Information • Frequency Can be a Problem • Data Management • Dataportal (little MSS)

  9. Computational Issues (II) • Use (Prospect) Blackforest, Bluesky • Conversion from Compaq to IBM Difficult • Communication: Did Not Know the Right Questions to Ask

  10. Computational Issues (II) • Use (Prospect) Blackforest, Bluesky • Conversion from Compaq to IBM Difficult • Communication: Did Not Know the Right Questions to Ask • Documentation Incomplete (e.g., loadleveler geometry) • Consulting Support Excellent • Usage (Typically 1 Node for each Component) • Blackforest: 4x75 min/simulation month (for each atmosphere) and 4x75 (for each ocean) • Bluesky: 8x20 min /simulation month (for each atmosphere) and 8x20 (for each ocean)

  11. Example of CPU (GAU) Usage • Bluesky 12 Atmospheres and 1 Ocean 100 year simulation 13 * 8 * 20 min/month * 1200 month = 41,600 CPU hours In Regular Queue Simulation Takes about 30-50 days In Practice We Get About 2-3 Experiments Completed Per Year This Should Double Every Year Over the Next 5 Years

  12. Data Management • Some Reliance on MSS • Primarily Job Recovery • Initially ftp’d Data Back to COLA • Access to dataportal Grads-Dods-Server (GDS) Has Changed My Life • Heavy usage of /ptmp

  13. Concluding Remarks • NCAR Facility Ideal for Testing Interactive Ensemble Coupling Strategy • Demand on Computing to Steadily Increase Over the Next 5 Years • Need for Local Visualization of Remote Data Large and Increasing

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