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

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Presentation transcript:

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

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 ?

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)

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

Model Inputs 1.Data: 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+Tropics C.Hi-Altitude D.Continental 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)

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

Annual Mean Disequilbrium

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

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

Annual Mean Flux signatures

‘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.

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)

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

Annual Mean Flux: CO 2 –  13 C

Aggregated Seasonal Fluxes and differences from CO 2 : model mean

Partitioning sensitivity

Annual Mean Error Reduction

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

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?

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.