Cyclo-stationary inversions of 13C and CO2

John Miller, Scott Denning, Wouter Peters, Neil Suits, Kevin

Gurney, Jim White & T3 Modelers

Outline

1. Motivation: Forward modeling with T3L2 fluxes showed 13C data could not be fit well, even considering 13C parameter uncertainty.

2. Set-up of the inversion

3. Results: What does 13C tell us, and is it different from using just CO2?

Model Setup

1. Cyclo-stationary (monthly mean) response functions from Transcom3-Level 2.

2. Use CO2 and 13C 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)

13C Mass Balance

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Global or 2D CalculationsF=Foce + Fland

Iterate until fluxes converge

Model Inputs

1. Data: 1992-1996 Detrended Monthly MeansA. 55 stations: Globalview CO2 B. 35 stations: CMDL 13C ( a la GV)

2. Model-Data Uncertainty:A. MBL N 0.5 ppm 0.05 per milB. MBL S+Tropics 0.25 0.025C. Hi-Altitude 1 0.075D. Continental 2 0.25

3. Priors and UncertaintyA. Flux: ~T3 (CASA NEP; Tak-992); 2PgC/yr, 1PgC/yrB. Disequilibrium; 5 PgC per mil/yr

C. Fractionation (SiB2): 2 per mil (4 per mil in mixed C3/C4 regions)

Sampling and Flux Locations

Green dots: CO2 and 13C data Black dots: only CO2 data

Annual Mean Disequilbrium

Oceanic DisequilibriumBased 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 %

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 C3/C4 distribution.

If not accounted for, C4 uptake looks like oceanic exchange, because of its small fractionation.

Fits to Data

• ‘CO2-only’ fluxes tend to underestimate 13C amplitudes in NH.

Black = ObservationsRed = Posterior (13C and CO2)Blue = Posterior (CO2 only)

Annual Mean FluxLand/Ocean flux = -1.5 / -1.3 GtC/yr

Annual Mean Flux: CO2 – 13C

Aggregated Seasonal Fluxes anddifferences from CO2: model mean

Partitioning sensitivity

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Annual Mean Error Reduction

Annual Mean Error Reductionfor Disequilibrium and Fractionation

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Questions

1. How to propogate uncertainty in iterative inversions?

2. River fluxes affect 13C and CO2 differently – how to deal with in joint inversion?

Conclusions

1. 13C results imply that leakage across land/ocean boundaries exists.

2. 13C 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 13C params, between model unc appears larger than within model uncertainty.