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Amospheric inversions: Investigating the recent inter-annual flux variations ! P. Peylin, C. Rödenbeck, P. Rayner, experimentalists, … Inverse models Data.

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Presentation on theme: "Amospheric inversions: Investigating the recent inter-annual flux variations ! P. Peylin, C. Rödenbeck, P. Rayner, experimentalists, … Inverse models Data."— Presentation transcript:

1 Amospheric inversions: Investigating the recent inter-annual flux variations ! P. Peylin, C. Rödenbeck, P. Rayner, experimentalists, … Inverse models Data signal Net carbon fluxes ? inter-annual flux time series 2003 summer flux annomaly

2 2 independent inversions ( Similarities / differences ) : LSCEMPI Time-dependent Bayesian Inversion / Solve for pixel fluxes LMDz (2.5° x 3.7°) TM3 (4° x 5°) ORCHIDEE mean fluxes & GFED priors Distance / Biome correlation generic No IAV prior & GFED priors distance correlation Observations : transport model : Prior information Inverse approach : Monthly mean conc. Individual flask / hourly data -> Monthly fluxes -> ~ Weekly fluxes CSIRO Time – independent 140 regions Montlhy CO2 + 13CO2 Match (4° x 4°) CASA model No IAV prior

3 Raw data / Fit (use in LSCE inversion) Deviation from a linear fit of winter (DJF) / summer (JAS) mean values CMN SCH CMN SCH (Ppm)

4 European scale: raw fluxes Pixgro Mod17 ANN JENA_ref ORCHIDEE LPJ JULES BIOME-BGC bottum-up range

5 European scale: raw fluxes Pixgro Mod17 ANN JENA_ref LSCE_ref LSCE_ObsJena ORCHIDEE LPJ JULES BIOME-BGC bottum-up range

6 European scale: raw fluxes Pixgro Mod17 ANN JENA_ref LSCE_ref LSCE_ObsJena CSIRO_ Peter T3 mean ORCHIDEE LPJ JULES BIOME-BGC bottum-up range

7 Annual land fluxes : LSCELSCE (Jena obs) JENACSIRO Europe N. Asia ,27 -0, Mean over In GtC / year -> Impact of fossil fuel emissions : Differences between Edgar and IER up to ~ 0.2 Gt / year Net annual fluxes not robust yet !

8 Flux anomalies filtered fluxes : 120 days

9 Continental scale: 120 days filtered Agreement for the major anomalies ! JENA_ref JENA_s99 LSCE_ref LSCE_ObsJena

10 European scale: « flux anomalies » Pixgro Mod17 ANN ORCHIDEE LPJ JULES BIOME-BGC bottum-up range De-seasonnalised + zero mean + filtering high freq. (< 120 days) JENA_ref

11 European scale: « flux anomalies » Pixgro Mod17 ANN ORCHIDEE LPJ JULES BIOME-BGC bottum-up range De-seasonnalised + zero mean + filtering high freq. (< 120 days) JENA_ref LSCE_ref LSCE_ObsJena

12 European scale: « flux anomalies » Pixgro Mod17 ANN ORCHIDEE LPJ JULES BIOME-BGC bottum-up range De-seasonnalised + zero mean + filtering high freq. (< 120 days) JENA_ref LSCE_ref LSCE_ObsJena CSIRO_ Peter T3 mean

13 European sub-region: (120 days filtering) North Europe West Europe Central Europe MPI_ref MPI_s99 LSCE_ObsJena LSCE_new bottum-up range

14 MPI_ref MPI_s99 LSCE_ObsJena LSCE_new bottum-up range European sub-region: summer anomalies (Jul-Aug-Sep) North Europe West Europe Central Europe

15 LSCE ref ORCHIDEE gC/m2/mth Biome BGC June – July – August anomalies LPJ MPI Ref JULES

16 MPI Annual anomalies BIOME LSCE

17 LPJ ORCHIDEE JULES Annual anomalies

18 Robustness is scale dependant Uncertainties increase with decreasing spatial scale Prior fluxes & errors / correlations are critical ! Summary Major flux anomalies are seen by two completely independent inverse approaches Net annual fluxes : remain uncertain at European scale But Future Synthesis under preparation ! Use additional data CCDAS approach ! Using regional/better Models & more data will reduce the uncertainties

19 Pixel based inversion LMDz zoomed over Europe (0.5 x 0.5 degres over Europe) Daily fluxes Using Pseudo-data 10 sites (continuous) Prior fluxes from TURC model + random noise TRUE fluxes from ORCHIDEE + random noise Potential of the current network : perfect transport experiment !

20 Correlation & Normalized standard deviation between True fluxes and Estimated fluxes Spatial aggregation (km) temporal aggregation (days) Correlation priorNSD prior Correlation posteriorNSD posterior

21 LSCE ref LSCE (ObsMPI ) MPI Ref gC/m2/mth MPI old case June – July – August anomalies

22 Error reduction on estimated CO 2 fluxes 2001 surface networkFuture surface network % of error reduction Carouge, phd, 2006.

23 Case with large noise (equivalent to real data inversion) compute Correlation & Normalized standard deviation between True fluxes & Estimated fluxes

24 Raw data / Fit (use in LSCE inversion) Deviation from a linear fit of summer (JAS) mean values Monte Cimone Schauinsland (Ppm) Atmospheric data :

25 Annual land fluxes : LSCELSCE (Jena obs) JENA (old ref) Europe N. Asia ,27 -0,18 Mean over In GtC / year -> Impact of fossil fuel emissions : Differences between Edgar and IER up to ~ 0.2 Gt / year Net annual fluxes not robust yet !

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