Remote sensed and in situ constraints on processes affecting tropical tropospheric ozone

By B. Sauvage, Randall V. Martin, A. Donkelaar, Xiong Liu, Kelly Chance, L. Jaeglé, Paul I. Palmer, Shiliang Wu, T. Fu. 2006

159 citations recorded by the source corpus.

We use a global chemical transport model (GEOS-Chem) to evaluate the consistency of satellite measurements of lightning flashes and ozone precursors with in situ measurements of tropical tropospheric ozone. The measurements are tropospheric O 3 , NO 2 , and HCHO columns from the GOME satellite instrument, lightning flashes from the OTD and LIS satellite instruments, profiles of O 3 , CO, and relative humidity from the MOZAIC aircraft program, and profiles of O 3 from the SHADOZ ozonesonde network. We interpret these multiple data sources with our model to better understand what controls tropical tropospheric ozone. Tropical tropospheric ozone is mainly affected by lightning NO x and convection in the upper troposphere and by surface emissions in the lower troposphere. Scaling the spatial distribution of lightning in the model to the observed flashes improves the simulation of O 3 in the upper troposphere by 5–20 ppbv versus in situ observations and by 1–4 Dobson Units versus GOME retrievals of tropospheric O 3 columns. A lightning source strength of 6±2 Tg N/yr best represents in situ observations from aircraft and ozonesonde. Tropospheric NO 2 and HCHO columns from GOME are applied to provide top-down constraints on emission inventories of NO x (biomass burning and soils) and VOCs (biomass burning). The top-down biomass burning inventory is larger than the bottom-up inventory by a factor of 2 for HCHO and alkenes, and by a factor of 2.6 for NO x over northern equatorial Africa. These emissions increase lower tropospheric O 3 by 5–20 ppbv, improving the simulation versus aircraft observations, and by 4 Dobson Units versus GOME observations of tropospheric O 3 columns. Emission factors in the a posteriori inventory are more consistent with a recent compilation from in situ measurements. The ozone simulation using two different dynamical schemes (GEOS-3 and GEOS-4) is evaluated versus observations; GEOS-4 better represents O 3 observations by 5–15 ppbv, reflecting enhanced convective detrainment in the upper troposphere. Heterogeneous uptake of HNO 3 on aerosols reduces simulated O 3 by 5–7 ppbv, reducing a model bias versus in situ observations over and downwind of deserts. Exclusion of HO 2 uptake on aerosols increases O 3 by 5 ppbv in biomass burning regions, reducing a model bias versus MOZAIC aircraft measurements.

Subfields: Atmospheric Science, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/f20461373a3d169e16580acbaf1699feff727ada

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Citations in the corpus

Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data (2012, unknown): “Because of the sparseness of the satellite lightning data, past studies have limited themselves to climatological scaling using multiyear data [e.g., Sauvage et al. , 2007b; Stajner et al. , 2008; Allen et al. , 2010; Jourdain et al. , 2010].”
Space‐based constraints on the production of nitric oxide by lightning (2007, methodology): “Sauvage et al. [2006a] further discuss the implications of both sensitivity simulations on tropospheric O3.”
Retrieving tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen dioxide (2013, methodology): “Lightning emissions of NOx follow Price et al. (1997) with a local correction based on the OTD/LIS satellite measurements (Sauvage et al., 2007; Murray et al., 2012), and are distributed vertically with a backward “C-shape” profile (Ott et al., 2010).”
The global lightning-induced nitrogen oxides source (2007, background): “98) was obtained 25 after scaling the model results locally to fit observed seasonal mean values (Martin et al., 2007; Sauvage et al., 2007).”