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Britton Stephens National Center for Atmospheric Research Atmospheric Technology Division

Carbon Model-Data Fusion. Britton Stephens National Center for Atmospheric Research Atmospheric Technology Division CDAS Team: Dave Schimel, Britt Stephens, David Baker, Steve Aulenbach, Jennifer Oxelson, Dave Brown, Roger Dargaville.

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Britton Stephens National Center for Atmospheric Research Atmospheric Technology Division

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  1. Carbon Model-Data Fusion Britton Stephens National Center for Atmospheric Research Atmospheric Technology Division CDAS Team: Dave Schimel, Britt Stephens, David Baker, Steve Aulenbach, Jennifer Oxelson, Dave Brown, Roger Dargaville

  2. Are we presently model or data limited? • Data are sparse but models can’t handle high variability • Model-data fusion • Synthesis inversion • Data assimilation • Parameter estimation • “Introduction of observations into a modeling framework, to provide: Estimates of model parameters Uncertainties on parameters and model output Ability to reject a model” (Michael Raupach)

  3. 800 m 360 m 120 m Challenges to carbon model-data fusion • Limited concentration data, far from sources • Vertical and horizontal model coarseness • “Representativeness” or model-data mismatch • Boundary-layer stable-layer height errors • Spatial flux heterogeneities •  must weight measurements appropriately S

  4. TEMPERATURE (C) (IPCC, 2001) (NRCS/USDA, 1997) Regional and smaller scales are critical for linking to underlying processes (NRCS/USDA, 1997) CHLOROPHYLL (SeaWIFS, 2002)

  5. Unresolved variance presently contains most of the information on regional- and smaller-scale fluxes

  6. Even biased high-frequency measurements do better than long-term means. . . (Rachel Law, submitted to Tellus, 2001) . . . but to use on a global scale requires a new approach.

  7. Data-assimilation • Ingests data at the time of observations • Can handle very large data streams • Used extensively in weather prediction and satellite analysis • Can assimilate multiple data types • In situ concentrations • Satellite concentrations • Satellite environmental data (e.g. standing water) • Direct flux measurements • Inventory data • Methods are relatively complex • Error statistics are not produced as easily

  8. Differences between CH4 and CO2 Assimilation of CH4 may be easier because: • Fluxes are much more unidimensional • Diurnal rectification of sources not an issue • Ocean fluxes are much less significant • Satellite measurements may be more feasible However. . . • Spatial structure of sources are highly local • In situ measurements are more challenging

  9. Carbon Data-Model Assimilation (C-DAS) http://dataportal.ucar.edu/CDAS/

  10. Carbon Data-Model Assimilation (C-DAS) Overview of CDAS Users http-Based Interface DODS Aggregation Server Simulated Observing System GrADS- DODS Server Simulated CO2 Observations Reference Global Atmospheric CO2 4D VAR Assimilation System http://dataportal.ucar.edu/CDAS/

  11. Carbon Data-Model Assimilation (C-DAS) Overview of CDAS: Production of Reference Atmospheric CO2 Users http-Based Interface Annual Land Model Fluxes (0.5o) Diurnal & Seasonal Cycle Model DODS Aggregation Server Simulated Observing System GrADS- DODS Server Ocean Model Fluxes (2o ) Atmospheric Transport Model Reference Global Atmospheric CO2 Simulated CO2 Observations Reference Global Atmospheric CO2 2.5o, resolution 25 vertical levels, 1 hour Dt, & 365 days = 2.6TB Industrial Fluxes (1o ) 4D VAR Assimilation System http://dataportal.ucar.edu/CDAS/

  12. Carbon Data-Model Assimilation (C-DAS) http://dataportal.ucar.edu/CDAS/

  13. Carbon Data-Model Assimilation (C-DAS) CDAS Application: Data Volumes Users 2.6 TB http-Based Interface DODS Aggregation Server Simulated Observing System GrADS- DODS Server Simulated CO2 Observations Reference Global Atmospheric CO2 200 MB 4D VAR Assimilation System Global Estimate, 11 North American Bioregions http://dataportal.ucar.edu/CDAS/

  14. Carbon Data-Model Assimilation (C-DAS) Overview of CDAS: retrieval of fluxes using data assimilation Users http-Based Interface 4D VAR Assimilation System Atmospheric Transport Model Estimated Annual Fluxes (Bioregional) DODS Aggregation Server Retrieved CO2 Observations Simulated Observing System GrADS- DODS Server Adjoint of Atmospheric Transport Model Compare 1st Guess fluxes Simulated CO2 Observations Reference Global Atmospheric CO2 Input Global Atmospheric CO2 fluxes Optimizer 4D VAR Assimilation System http://dataportal.ucar.edu/CDAS/

  15. Flux corrections using existing CO2 network Hour = 1 Hour = 2 Hour = 3 Hour = 4 Hour = 5 Hour = 6 Hour = 7

  16. Flux corrections constrained by regional patterns Month = 1 Month = 2 Month = 3 Month = 4 Month = 5 Month = 6 Month = 7 Month = 8 Month = 9 Month = 10 Month = 11 Month = 12

  17. Potential applications for CH4 • What is the optimal network expansion? • Continuous vs. flask measurements • Value of satellite concentrations for various sensors • Proximity of measurements to sources • Accuracy and resolution vs. density of measurements • What other types of data can we assimilate? • Satellite water distributions • Direct flux measurements • Inventory data • Can we assimilate CO2 and CH4 together?  Primary requirement is people

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