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Liang Feng, Paul Palmer geos.ed.ac.uk/eochem Hartmut B ösch and Sarah Dance

Estimating Terrestrial CO 2 Fluxes from X CO2 Data using an EnKF: Sensitivity to Glint-view Measurements & Spatial Resolution of Control Variables. Liang Feng, Paul Palmer http://www.geos.ed.ac.uk/eochem Hartmut B ösch and Sarah Dance. Observing System Simulation Experiments.

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Liang Feng, Paul Palmer geos.ed.ac.uk/eochem Hartmut B ösch and Sarah Dance

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  1. Estimating Terrestrial CO2 Fluxes from XCO2 Data using an EnKF: Sensitivity to Glint-view Measurements & Spatial Resolution of Control Variables Liang Feng, Paul Palmer http://www.geos.ed.ac.uk/eochem Hartmut Bösch and Sarah Dance

  2. Observing System Simulation Experiments Overall Aim: Determine the potential of space-borne XCO2 data to improve 8-day surface CO2 flux estimates over tropical continental regions of size ~12º×15º. How sensitive are these estimates to changes in alternative measurement and model configurations?

  3. Surface CO2 Ensemble GEOS-Chem 8-day forecasts (3-D CO2, T & H2O etc) Obs operator Model XCO2 Ensemble XCO2 Data Model XCO2 8-day Flux Forecasts (climatology) GEOS-Chem 8-day forecast (3-D CO2, T & H2O etc) Prior + error Posteriori + error Obs operator 8-day OCO XCO2 ETKF (Living and Dance, 2008)

  4. XCO2 Data Model XCO2 8-day Flux Forecasts (climatology) (+Perturbations) Surface CO2 Ensemble GEOS-Chem GEOS-Chem 8-day forecast (3-D CO2, T & H2O etc) 8-day forecasts (3-D CO2, T & H2O etc) Prior + error Posteriori + error Obs operator Obs operator 8-day OCO XCO2 ETKF (Living and Dance, 2008) Model XCO2 Ensemble

  5. XCO2 Data Model XCO2 8-day Flux Forecasts (climatology) Surface CO2 Ensemble GEOS-Chem GEOS-Chem 8-day forecast (3-D CO2, T & H2O etc) 8-day forecasts (3-D CO2, T & H2O etc) Prior + error Posteriori + error Obs operator Obs operator 8-day OCO XCO2 ETKF (Living and Dance, 2008) Model XCO2 Ensemble

  6. 1) Sampled along Aqua orbits GEOS-Chem transport model (4x5 degree resolution): Biosphere (CASA), Biomass (GFED), Fossil fuel (NDIAC), Ocean (Takahashi) Realistic XCO2 observation operator 1-day 3) Averaging kernels applied 2) Scenes with cloud or AOD > 0.3 removed Glint mode Pressure [hPa] Jan Averaging kernels

  7. Ensemble Kalman Filter Approach Forecast: Analysis: Based on Kalman Filter: K=PfHT(HPfHT+R)-1 is the Kalman gain matrix H is the Jacobian (adjoint) matrix. EnKF samples the forecast error covariance of the forecast using an ensemble of forecasts. Advantages: no adjoint; provides error characterization; can sample non-Gaussian PDF (e.g., CO2-CO-CH4 inversion). Disadvantages: the size of the ensemble can be large (12x144+1).

  8. Control calculation: 9×11 land regions, 4×11 ocean regions and 1 snow region (cf T3: 11 land and 11 ocean regions) Regional flux definitions based on TransCom 3 regions • Uncertainties based on TransCom 3 • We assume NO correlation in prior estimates • Assume model error of 2.5 (1.5) ppm over land (ocean)

  9. Small Large Mean Error Reduction from 2-Month Control Inversion of 8-Day Surface Fluxes Jan - Feb Example: South American Tropical: A priori err ~ 3.2 Gt C/y; A posteriori err ~1.9-0.5 Gt C/y ; Error reduction~0.45-0.85.

  10. Error reductions are obviously sensitive to number of clean (aerosol and cloud free) observations 

  11. Because of large assumed model error results are insensitive to observation error of single OCO retrieval 

  12. [Results for 8-day mean flux estimates during May to June] Glint observations over ocean are more effective at constraining continental fluxes than nadir measurements 

  13. Sensitivity to the spatial resolution of control variables: from TransCom3 to Model Grid South American Tropical Region 1 Avg Error Reduction 0.3 9x1/9 Transcom3 4x5 degree model grid 4x1/4 Transcom3 Transcom3 Correlations between neighbouring regions get progressively larger using regions smaller than 1000x1000 km2.

  14. Sensitivity to the spatial resolution of control variables: from TransCom3 to Model Grid Inversions at high spatial resolutions are under-determined, and usually show strong negative spatial correlation in the resulting error covariances:

  15. Concluding Remarks • We have an EnKF assimilation tool for interpreting XCO2 data • Realistic XCO2 distributions and associated errors will significantly reduce the uncertainty of continental CO2 fluxes on 8-day timescales BUT some consideration must be given to the lag window (not shown) • Perturbing random and systematic components of measurement error lead to results consistent with 4DVAR studies (not shown) • Results are sensitive to assumed model error • The number of clean observations impacts the quality of the flux estimates • Glint observations offer the most leverage to reduce uncertainty in estimated continental CO2 fluxes – implications for 16-16 duty cycle? • The spatial resolution of independently estimated CO2 fluxes from realistic XCO2 distributions is close to 1000x1000 km2

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