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Amospheric inversions : Investigating the recent inter-annual flux variations !

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

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Amospheric inversions : Investigating the recent inter-annual flux variations !

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

  2. “CSIRO” Inverse approach : Observations : Time – independent140 regions Montlhy CO2+ 13CO2 transport model : Match(4° x 4°) Prior information “CASA model” No IAV prior 2 independent inversions (Similarities / differences) : LSCE MPI Time-dependent Bayesian Inversion / Solve for “pixel” fluxes Monthly mean conc. Individual flask / hourly data-> Monthly fluxes -> ~ Weekly fluxes LMDz (2.5° x 3.7°) TM3 (4° x 5°) ORCHIDEE mean fluxes& GFED priorsDistance / Biome correlation “generic” No IAV prior& GFED priorsdistance correlation

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

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

  5. European scale: raw fluxes JENA_refLSCE_refLSCE_ObsJena bottum-up range Pixgro Mod17 ANN ORCHIDEE LPJ JULES BIOME-BGC

  6. European scale: raw fluxes JENA_refLSCE_refLSCE_ObsJena “CSIRO”_ Peter T3 mean bottum-up range Pixgro Mod17 ANN ORCHIDEE LPJ JULES BIOME-BGC

  7. Annual land fluxes : In GtC / year Mean over1997-2003 2003 -> 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 JENA_ref JENA_s99 LSCE_refLSCE_ObsJena • Agreementfor the majoranomalies !

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

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

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

  13. European sub-region: (120 days filtering) North Europe bottum-up range MPI_ref MPI_s99LSCE_ObsJenaLSCE_new West Europe Central Europe

  14. European sub-region: summer anomalies(Jul-Aug-Sep) North Europe bottum-up range MPI_ref MPI_s99LSCE_ObsJenaLSCE_new West Europe Central Europe

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

  16. Annual anomalies 2001 2002 2003 LSCE MPI BIOME

  17. Annual anomalies 2001 2002 2003 ORCHIDEE LPJ JULES

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

  19. Potential of the current network : perfect transport experiment ! • 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

  20. Correlation prior NSD prior • “Correlation” & • “Normalized standard deviation” • between • “True fluxes” • and “Estimated fluxes” Spatial aggregation (km) Correlation posterior NSD posterior temporal aggregation (days)

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

  22. Error reduction on estimated CO2 fluxes 2001 surface network Future 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. Atmospheric data : Deviation from a linear fit of summer (JAS) mean values Raw data / Fit (use in LSCE inversion) Monte Cimone (Ppm) Schauinsland

  25. Annual land fluxes : In GtC / year Mean over1997-2003 2003 -> 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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