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Forward modelling with the LMDz-INCA coupled climate-chemistry model;

Forward modelling with the LMDz-INCA coupled climate-chemistry model; Inverse modelling and data assimilation; IASI/METOP instrument CH4 distribution. D. Hauglustaine, P. Bousquet, F. Chevallier LSCE, Gif-sur-Yvette, France C. Clerbaux Service d’Aéronomie, Paris, France. LMDzINCA Model

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Forward modelling with the LMDz-INCA coupled climate-chemistry model;

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  1. Forward modelling with the LMDz-INCA coupled climate-chemistry model; Inverse modelling and data assimilation; IASI/METOP instrument CH4 distribution D. Hauglustaine, P. Bousquet, F. Chevallier LSCE, Gif-sur-Yvette, France C. Clerbaux Service d’Aéronomie, Paris, France

  2. LMDzINCA Model Atmospheric chemistry in the troposphere and stratosphere; Long-lived greenhouse gases (CO2,CH4, N2O, CFC); Aerosols (sulfur, carbon, natural); Data assimilation and inverse modelling (CO2, CH4, CO, CH2O, O3, NO2); Global-regional coupling LMDzINCA-CHIMERE.

  3. Forward modelling of nitrous oxide Hauglustaine et al., JGR, 2004.

  4. Forward modelling of molecular hydrogen Hauglustaine and Ehhalt, JGR, 2002.

  5. Forward modelling of molecular hydrogen Hauglustaine et al., JGR, 2004.

  6. Bayesian inference @ LSCE • To estimate sources and sinks of CO2 and CH4 using measurements of atmospheric concentrations • Bousquet et al., Science, 2000 • Peylin et al., 2002, 2005 • Bousquet et al., ACP, 2005 • Bousquet et al., Nature, 2006 • Bayes’ theorem applied with • Gaussian pdfs • Linear framework • Matrix formulation • Use LMDZ model of atmospheric tracer transport F. Chevallier/ LSCE slide 6

  7. Methane inverse modelling (1/2) • Wetlands contribute the most to the methane inter-annual variability. Biomass burning contributes less except during specific events as 1997-98 (El Niño). • Since 1999, compensation between rising fossil fuel emissions and decreasing wetland emissions associated to general dryness of the northern hemisphere. Explain the decrease in CH4 growth rate. • Inverse modelling results in good agreement with satellite data derived emissions for biomass burning and wetlands. Bousquet et al., Nature, 2006

  8. Methane inverse modelling (1/2) • Variability in global OH concentration contributes to methane inter-annual variability. • Tropical methane sources contribute the most to the inter-annual variability. • Northern hemisphere sources contribute the most to long-term methane variability. Bousquet et al., Nature, 2006

  9. Long-term simulations :oxidizing capacity - hydroxyl radical OH 1960 2000 Consistent with top-down estimates ? Bousquet et al., ACP, 2005

  10. Variational formulation @ LSCE • To process many more observations and estimate many more variables • Chevallier et al., 2005, 2006 • Inference problem solved by minimizing a cost function • TL and AD of LMDZ tracer transport manually coded for an efficient computation of the gradient of the cost function F. Chevallier/ LSCE slide 10

  11. LMDZ vs. ECMWF for tracer transport • 1 Nov. 2003, 0 UTC • 3-month simulation • CO2 conc. @ 550 hPa (ppm) LMDZ 3.75ox2.5o 19L nudged to ECMWF winds ECMWF T159 (125km) 60L (thanks to S. Serrar and R. Engelen) F. Chevallier/ LSCE slide 11

  12. Application NASA’s OCO (2008) • Estimated uncertainty reduction [0-1] on weekly CO2 fluxes brought by OCO measurements • 0 = no reduction 1 = certainty F. Chevallier/ LSCE slide 12

  13. Simplified chemistry model F. Chevallier/ LSCE slide 13

  14. Remote sensing of CH4 using the thermal infrared spectral range @ Service d’Aéronomie IMG (1996-1997) IASI (2006-2020) CO - MOPITT

  15. IMG distributions using IASI processing tools Ts O3 CH4 CO Hadji-Lazaro et al., GRL 2001 Turquety et al. GRL 2002 Clerbaux et al., IEEE 1999; JGR 2001 Hadji-Lazaro et al., JGR 1999 Clerbaux et al., ACP 2003 SA-NN [Turquety et al, JGR 2004]

  16. + GOME-2 Launch scheduled on October 17

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