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“Global Carbon Project” pilot case study (CEA-LSCE)

“Global Carbon Project” pilot case study (CEA-LSCE). Philippe Peylin, Philippe Ciais, Pep Canadell, Zegbeu poussi. Perturbation of Global Carbon Budget (1850-2006). 2000-2006. fossil fuel emissions. 7.6. ?. Source. deforestation. 1.5. CO 2 flux (Pg C y -1 ). atmospheric CO 2. 4.1.

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“Global Carbon Project” pilot case study (CEA-LSCE)

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  1. “Global Carbon Project”pilot case study(CEA-LSCE) Philippe Peylin, Philippe Ciais, Pep Canadell, Zegbeu poussi QUAlity aware VIsualisation for the Global Earth Observation system of systems

  2. Perturbation of Global Carbon Budget (1850-2006) 2000-2006 fossil fuel emissions 7.6 ? Source deforestation 1.5 CO2 flux (Pg C y-1) atmospheric CO2 4.1 Sink land 2.8 ocean 2.2 Time (y) Canadell et al. 2007, PNAS Kick off meeting. February 17th, 2011

  3. Global Carbon Project ? Kick off meeting. February 17th, 2011

  4. Global Carbon Pilot case • Which products can we provide ? (based on models & data) • What are the associated Quality Data ? (still “poorly” developed) • What are the potential Metadata ? Kick off meeting. February 17th, 2011

  5. Global carbon product Direct Model simulations Data – fusion approaches Meteo forcing & surface description Land surface & ocean models Estimated surfaceC fluxes

  6. Direct estimates of Carbon fluxes Global land ecosystem model simulations Global ocean model simulations • Several Productwith different data Quality Kick off meeting. February 17th, 2011

  7. Land ecosystem model simulations Estimated surfaceC fluxes Meteorological forcing Land surface model Land surface Description (Veg. soil,..) • Several model simulations (TRENDY / RECCAP project) • For each model possibly several simulations (different forcing) • Product: - Global annual/monthly C fluxes • - Derived quantities: Trend in land C-sinks • Quality data : - on the input data (forcing , land cover) • - derived from the multi-model realization • Metadata : - information on the protocol & models Kick off meeting. February 17th, 2011

  8. Quality of product • usually arise from: • evaluation against other estimates/proxy • potentially error propagation • Assessment of model strength • How to use multimodel ?

  9. Product Evaluation as a Quality measure Diagnose Trend in Land fluxes Evaluation against other products ? (i.e. EO data) How to facilitate the link with other products ? • Climate data • land cover changes/use • forestry data • biomass burning • soil data (moisture) • crop yields

  10. Case of Data-Assimilation: atmospheric inversion… Prior flux information Transport model Atmospheric data Inverse optimization Optimized fluxes Several approaches • Flux resolution • Transport model • Level of prior inform. • Optimization algorithm key features • Combination of 2 sources of information ! • Only ~ 100 stations for many fluxes to solve for

  11. Global carbon pilot case Data availability Surface flux Maps (3D) : - weekly to monthly resolution - common grid : 1x 1 degree - several variables (Net flux, Gross fluxes, …) Spatially integrated fluxes : - Time series for a set of regions - different temporal filtering (trend, smooth curve,..) Kick off meeting. February 17th, 2011

  12. Global carbon pilot case Associated Quality Data (mainly uncertainties) Surface flux Maps (3D) : - uncertainties (in the form of std-dev) (often at lower temporal resolution) - use the spread between the different estimates Not yet completely defined ! Spatially integrated fluxes : - Uncertainties for each time step - error covariance matrix at low temp. resol. - use the spread btw estimates + individual errors Kick off meeting. February 17th, 2011

  13. Global carbon pilot case Associated Metadata Input data : Text description of the input data Model (Data Assimilation system) flow chart, little text documentation…. description of the “uncertainty calculation” Estimated fluxes & uncertainties : possibly “qualitative description of accuracy” as a function of space & time aggregation. Kick off meeting. February 17th, 2011

  14. Carbon fluxes interannual variations N. America N. Atlantic ? LSCE_var_v1. C13_MATCH CTracker_US LSCE_an_v2.1 JENA_s96_v3.2 CTracker_EU TRCOM_me RIGC_patra JMA_2010 C13_CCAM NCAM_Niwa How to associate a Quality Measure ? N. Asia Europe ? ? Kick off meeting. February 17th, 2011

  15. RMSE estimate Mean flux estimate • Estimates are not independent ? • Uncertainty on the uncertainties is very large ! Quality indications from model spread ?

  16. Summary …. • Carbon flux products : increasing number but no common visualization framework • As part of GCP  we can provide a large set of fluxes & some associated errors, few metadata... • How to use the tools from GeoViQua ? • Incorporate them in our Portal Developments • or provide the datasets to GeoViQua ? • Large volume of netcdf files… • We need tools to derive overall quality measures from different estimates (with different uncertainties) and to display them.. • We are seeking for a Post-doc….

  17. Carbon Cycle Web portals Kick off meeting. February 17th, 2011

  18. Global Carbon Project Kick off meeting. February 17th, 2011

  19. GCP: atmospheric data • In situ data • - very accurate but sparse • - spatial representativity ? • - Instrumental failure ? • - Quality Data exist..(visualisation under progress) • satellite data • - accuracy issues (biases) ! • - accuracy might vary with space • - cloud/aerosols… contamination Kick off meeting. February 17th, 2011

  20. Carbon Tracker Web site (US) N. America ? Kick off meeting. February 17th, 2011

  21. GEO carbon agenda….. • Record changes in atmospheric CO2 • Estimate fossil and land use derived emissions • Understand land and ocean carbon sinks • Measure fluxes/concentrations • Understand processes • Model time & space evolution  in order to predict future of the Earth System Kick off meeting. February 17th, 2011

  22. Model uncertainties (Baye’s theorem) Uncertainties Model – Data fusion(4D var scheme) Data uncertainties Data uncertainties Variational / Matrix / approaches Flux uncertainties difficult to estimate ! Model – Data Fusion for GCP Kick off meeting. February 17th, 2011

  23. GCP: Land ecosystem data • satellite data • - accuracy vary with space.. • - saturation signal with veg. activity • - link to vegetation function ? • In situ data • - very accurate but sparse • - spatial representativity ? • - “Quality” not well quantified.. Kick off meeting. February 17th, 2011

  24. Global Carbon Project Kick off meeting. February 17th, 2011

  25. GCP: Ocean data • Ship data • - accurate but sparse • - spatial representativity ? • - Spatial coverage ? • satellite data • - accuracy issues (biases) ! • - link to ocean biogeochemistry ? Kick off meeting. February 17th, 2011

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