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Cyclo-stationary inversions of d 13 C and CO 2

Cyclo-stationary inversions of d 13 C and CO 2. John Miller, Scott Denning, Wouter Peters, Neil Suits, Kevin Gurney, Jim White & T3 Modelers. Outline. Motivation: Forward modeling with T3L2 fluxes showed d 13 C data could not be fit well, even considering 13 C parameter uncertainty .

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Cyclo-stationary inversions of d 13 C and CO 2

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  1. Cyclo-stationary inversions of d13C and CO2 John Miller, Scott Denning, Wouter Peters, Neil Suits, Kevin Gurney, Jim White & T3 Modelers

  2. Outline • Motivation: Forward modeling with T3L2 fluxes showed d13C data could not be fit well, even considering 13C parameter uncertainty. • Set-up of the inversion • Results: What does d13C tell us, and is it different from using just CO2?

  3. Model Setup • Cyclo-stationary (monthly mean) response functions from Transcom3-Level 2. • Use CO2 and d13C data to optimize: • Surface Fluxes (12 months x 22 regions) • Iso-disequilibrium (~annual x 22 regions) • Terrestrial fractionation (12 months x 11 regions)

  4. 13C Mass Balance Global or 2D Calculations F=Foce + Fland Iterate until fluxes converge

  5. Model Inputs • Data: 1992-1996 Detrended Monthly Means • 55 stations: Globalview CO2 • 35 stations: CMDL d13C ( a la GV) • Model-Data Uncertainty: • MBL N 0.5 ppm 0.05 per mil • MBL S+Tropics 0.25 0.025 • Hi-Altitude 1 0.075 • Continental 2 0.25 • Priors and Uncertainty • Flux: ~T3 (CASA NEP; Tak-992); 2PgC/yr, 1PgC/yr • Disequilibrium; 5 PgC per mil/yr • Fractionation (SiB2): 2 per mil (4 per mil in mixed C3/C4 regions)

  6. Sampling and Flux Locations Green dots: CO2 and d13C dataBlack dots: only CO2data

  7. Annual Mean Disequilbrium

  8. Oceanic Disequilibrium Annual Mean Based on measurements of pCO2 and δ13C of DIC. Latitudinal gradient is caused by temperature dependent fractionation. Depending on windspeed and pCO2 data set, global integral can vary by > 20 %

  9. Terrestrial Disequilibrium Based on atmospheric history and CASA model of respiration. And, this assumes constant Δ over time.

  10. Annual Mean Flux signatures

  11. ‘Discrimination’ Map(dA) Variations dominated by C3/C4 distribution. If not accounted for, C4 uptake looks like oceanic exchange, because of its small fractionation.

  12. Fits to Data • ‘CO2-only’ fluxes tend to underestimate d13C amplitudes in NH. Black = Observations Red = Posterior (13C and CO2) Blue = Posterior (CO2 only)

  13. Annual Mean Flux Land/Ocean flux = -1.5 / -1.3 GtC/yr

  14. Annual Mean Flux: CO2 – d13C

  15. Aggregated Seasonal Fluxes anddifferences from CO2: model mean

  16. Partitioning sensitivity

  17. Annual Mean Error Reduction

  18. Annual Mean Error Reductionfor Disequilibrium and Fractionation Unc. (per mil)

  19. Questions • How to propogate uncertainty in iterative inversions? • River fluxes affect d13C and CO2 differently – how to deal with in joint inversion?

  20. Conclusions • d13C results imply that leakage across land/ocean boundaries exists. • d13C can stabilize Land/Ocean partitioning across models • Annual mean Land/Ocean partitioning is dependent upon disequilibrium, but seasonal patterns are not. Interannual patterns are also likely to be robust. • With reasonable uncertainties on 13C params, between model unc appears larger than within model uncertainty.

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