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When data and model are in isolation. Data. Model. We are getting …. Soil carbon modeled in CMIP5 vs. HWSD. Integrated Data-Model Approaches to Carbon Cycle Research. Data. Model. Prediction. Panelists. Mike Kuperberg , DOE program officer: DOE's

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Data

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  1. When data and model are in isolation Data Model We are getting …

  2. Soil carbon modeled in CMIP5 vs. HWSD

  3. Integrated Data-Model Approaches to Carbon Cycle Research Data Model Prediction

  4. Panelists Mike Kuperberg, DOE program officer: DOE's perspective on the data-model integration  Yiqi Luo, University of Oklahoma: Challenges and opportunities in data-model integration Anthony Walker, ORNL: Benchmark analysis of models against data from FACE experiments Sasha Hararuk, University of Oklahoma: Evaluation and improvement of global land models against soil carbon data.  Robert Cook, ORNL: Ecoinformatics and cyberinfrastructureto promote data-model integration

  5. A new philosophy of research Modeling activities guide field research activities, which in turn informs modeling activities.  This cyclical processing of information should maximize the financial and scientific investments and result in high quality predictive models

  6. Scientific inquiry Field research Modeling Data Process thinking ? Gain best knowledge from imperfect data and imperfect models

  7. Experiment results  model • Benchmarking: Data used to evaluate model performance • Data assimilation: Multiple streams of data ingested into model to improve its performance • Parameterization: Data used to parameterize models • Process representation: New algorithms to represent processes instead of a black-box approach

  8. Benchmarking

  9. Problem: If an incomplete set of variables are used for benchmarking, …benchmarking can give false confidence when models predict with some accuracy but for the wrong reasons

  10. How do CLM-CASA’ and CABLE simulate Soil C? IGBP-DIS data

  11. Data assimilation to improve soil C simulation by two global models: IGBP-DIS data

  12. Changes in temporal dynamics: CLM-CASA’ 5,270 Pg 5,780 Pg 11,100 Pg

  13. 5th NSF RCN FORECAST meeting Forecast of Resource and Environmental Change using data Assimilation Science and Technology

  14. Strategies to promote experiment-model interactions • Model strengths and deficiencies: Effective communication from modeling to experimental communities? • Data model products: What are the data model products directly useful for model improvement? • Infrastructure: Data assimilation techniques, data model products, cyberinfrastructure, visualization, and analytic tools. • Possible national center(s): Infrastructure development and coordination of activities

  15. Scientific inquiry Field research Modeling Data Process thinking DAAC, CDIAC NCAR CLM

  16. Pilot Study: Integrate Observations with Models using “Access Broker” Model-Data Comparison Framework Stefano Nativi et al. Customized Observational Data Request for Data Data Assimilation Framework Data MODIS Web Service SCRIP (regrid) Original MODIS Data Process Original Observational Data Users can access observational data and convert to their specified format, spatial resolution, spatial extent, and temporal extent. FTP/HTTP/…

  17. Scientific inquiry Field research Modeling Data Process thinking National center for experiment-model integration (NCEMI)

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