1. Cyclo-stationary inversions of  13 C and CO 2 John Miller, Scott Denning, Wouter Peters, Neil Suits, Kevin Gurney, Jim White & T3 Modelers

2. Outline 1.Motivation: Forward modeling with T3L2 fluxes showed  13 C data could not be fit well, even considering 13 C parameter uncertainty. 2.Set-up of the inversion 3.Results: What does  13 C tell us, and is it different from using just CO 2 ?

3. Model Setup 1.Cyclo-stationary (monthly mean) response functions from Transcom3- Level 2. 2.Use CO 2 and  13 C data to optimize: A.Surface Fluxes (12 months x 22 regions) B.Iso-disequilibrium (~annual x 22 regions) C.Terrestrial fractionation (12 months x 11 regions)

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

5. Model Inputs 1.Data: 1992-1996 Detrended Monthly Means A.55 stations: Globalview CO 2 B.35 stations: CMDL  13 C ( a la GV) 2.Model-Data Uncertainty: A.MBL N0.5 ppm0.05 per mil B.MBL S+Tropics0.250.025 C.Hi-Altitude1 0.075 D.Continental2 0.25 3.Priors and Uncertainty A.Flux: ~T3 (CASA NEP; Tak-99 2 ); 2PgC/yr, 1PgC/yr B.Disequilibrium; 5 PgC per mil/yr C.Fractionation (SiB2): 2 per mil (4 per mil in mixed C 3 /C 4 regions)

6. Sampling and Flux Locations Green dots: CO 2 and  13 C dataBlack dots: only CO 2 data

7. Annual Mean Disequilbrium

8. Oceanic Disequilibrium Based on measurements of pCO 2 and δ 13 C of DIC. Latitudinal gradient is caused by temperature dependent fractionation. Depending on windspeed and pCO2 data set, global integral can vary by > 20 % Annual Mean

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 (  A ) Variations dominated by C 3 /C 4 distribution. If not accounted for, C 4 uptake looks like oceanic exchange, because of its small fractionation.

12. Fits to Data ‘CO 2 -only’ fluxes tend to underestimate  13 C 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: CO 2 –  13 C

15. Aggregated Seasonal Fluxes and differences from CO 2 : model mean

16. Partitioning sensitivity

17. Annual Mean Error Reduction

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

19. Questions 1.How to propogate uncertainty in iterative inversions? 2.River fluxes affect  13 C and CO 2 differently – how to deal with in joint inversion?

20. Conclusions  13 C results imply that leakage across land/ocean boundaries exists.  13 C can stabilize Land/Ocean partitioning across models 3.Annual mean Land/Ocean partitioning is dependent upon disequilibrium, but seasonal patterns are not. Interannual patterns are also likely to be robust. 4.With reasonable uncertainties on 13 C params, between model unc appears larger than within model uncertainty.