1. Constraining the lightning-NO x (LiNOx) source using TES O 3 observations N. Bousserez, R.V. Martin, K.W. Bowman, D.K. Henze 5th International GEOS–Chem Meeting 2-5 May, 2011

2. Problem  Lightning-NO x has large impact on tropical tropospheric O 3 (> 28% of the annual O 3 burden)  Large uncertainties in CTM:  Cloud-Top-Height (CTH) parameterization (Price and Rind, 1992):  Profile shapes used: ELiNOx(z) = 3.44x10 -5 CTH 4.90 NO yield/flash frac(z) Flash rate Fraction at altitude z  Before: “C-shape” (2D-cloud model)  Now: “backward C-shape” (3D-cloud model)  LiNOx remain in mid/upper troposphere (Courtesy Lee Murray) Tropical land profile hypothetical (not from 3D-cloud simulation)

3.  Sensitive to injection height  Not sensitive to NO yield/flash If we assume a linear relationship between O 3 production and LiNOx emissions, the ratio is: Assuming a gaussian profile shape, consider : Methodology 4D-var analysis GC adjoint v8-02-01 Sensitivities w.r.t. NO yield/flash, injection height: Minimize the cost function: Invert σ NOyield and σ injh simultaneously, globally, what time window? Non-linearity leads to multiple minima  Optimizing NO yield/flash and injection height:  Constraint: TES O 3 Nadir profiles (V003), winter 2006  Problem: need to uncouple impacts of NO yield/flash and injection height on O 3  Metric for injection height: (σ NOyield, injh scaling factors) If we assume a linear relationship between O 3 production and LiNOx emissions, r O3 is:  Sensitive to change in injection height  Not sensitive to change in NO yield/flash (scaling) Need to check using sensitivity tests

4. GEOS-Chem sensitivity tests Biomass burning (+30%) O 3 ratio most sensitive to injection height O 3 (195hPa)/O 3 (562hPa) TESGEOS-Chem w/ AKs NO yield/flash (+20%) Injection height (7  10 km) Δ ( O 3 (195hPa)/O 3 (562hPa ) ) O 3 (195hPa)/O 3 (562hPa) sensitivity to:

5. 4D-Var inversion using the adjoint of GEOS-Chem Best Linear Unbiased Estimator is the minimum of: σ injh scaling factor, H TES observational operator, S obs, σ error covariance matrices, Ω domain of observations (distributed in space and time) Iterative solution  need for  adjoint of GEOS-Chem (v8-02-01) Minimization over 3 tropical continental areas: Africa, Indonesia, South America A priori error for injection height set to 30%

6. Pseudo-observations inversion tests  Generate pseudo-observations from GC simulations with perturbed injection heights:  A priori injection height x 1.2  A priori injection height x 0.8  Starting from the a priori (σ = 1) we assimilate the pseudo-observations  The inversion allows to recover reasonably well the perturbed injection heights A priori injection height *1.2 A priori injection height *0.8 σ σ 2 weeks-assimilation of pseudo-observations 1.00 1.2 1.40 0.60 0.80 1.00

7. Preliminary results 2 weeks-assimilation of TES O 3 observations (12/01/06  12/15/06)(GEOS-4) 0 4 8 12 [km] -13% -23% -7%  Optimized injection heights lower than a priori  Tropical continental profile shape ~ mid-latitude continental?  Remark: we implicitly correct for bias in Cloud-Top-Height bias in Cloud-Top-Height => bias in optimized profile shape  Next steps: NO yield/flash inversion using optimized injection height Evaluate the new LiNOx profiles using SHADOZ/MOZAIC in situ ozone profiles Original Optimized GC injection height Alt