Hauglustaine et al. - HYMN KO Meeting th October 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

Hauglustaine et al. - HYMN KO Meeting th October LMDzINCA Model Atmospheric chemistry in the troposphere and stratosphere; Long-lived greenhouse gases (CO 2,CH 4, N 2 O, CFC); Aerosols (sulfur, carbon, natural); Data assimilation and inverse modelling (CO 2, CH 4, CO, CH 2 O, O 3, NO 2 ); Global-regional coupling LMDzINCA-CHIMERE.

Hauglustaine et al. - HYMN KO Meeting th October Forward modelling of nitrous oxide Hauglustaine et al., JGR, 2004.

Hauglustaine et al. - HYMN KO Meeting th October Forward modelling of molecular hydrogen Hauglustaine and Ehhalt, JGR, 2002.

Hauglustaine et al. - HYMN KO Meeting th October Forward modelling of molecular hydrogen Hauglustaine et al., JGR, 2004.

CEA, 10 Octobre 2006 F. Chevallier/ LSCE slide 6 Bayesian LSCE  To estimate sources and sinks of CO 2 and CH 4 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

Hauglustaine et al. - HYMN KO Meeting th October  Wetlands contribute the most to the methane inter-annual variability. Biomass burning contributes less except during specific events as (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 Methane inverse modelling (1/2)

Hauglustaine et al. - HYMN KO Meeting th October  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 Methane inverse modelling (1/2)

Hauglustaine et al. - HYMN KO Meeting th October Long-term simulations : oxidizing capacity - hydroxyl radical OH Consistent with top-down estimates ? Bousquet et al., ACP, 2005

CEA, 10 Octobre 2006 F. Chevallier/ LSCE slide 10 Variational 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

CEA, 10 Octobre 2006 F. Chevallier/ LSCE slide 11 LMDZ vs. ECMWF for tracer transport LMDZ 3.75 o x2.5 o 19L nudged to ECMWF winds ECMWF T159 (125km) 60L (thanks to S. Serrar and R. Engelen)  1 Nov. 2003, 0 UTC  3-month simulation  CO2 550 hPa (ppm)

CEA, 10 Octobre 2006 F. Chevallier/ LSCE slide 12 Application NASA’s OCO (2008)  Estimated uncertainty reduction [0-1] on weekly CO 2 fluxes brought by OCO measurements 0 = no reduction1 = certainty

CEA, 10 Octobre 2006 F. Chevallier/ LSCE slide 13 Simplified chemistry model

CO - MOPITT Remote sensing of CH4 using the thermal infrared spectral Service d’Aéronomie IMG ( ) IASI ( )

Ts CO CH 4 O3O3 Clerbaux et al., IEEE 1999; JGR 2001 Hadji-Lazaro et al., JGR 1999 IMG distributions using IASI processing tools Turquety et al. GRL 2002 Clerbaux et al., ACP 2003 Hadji-Lazaro et al., GRL 2001 SA-NN [Turquety et al, JGR 2004]

+ GOME-2 Launch scheduled on October 17