Remote sensed and in situ constraints on processes affecting tropical tropospheric ozone
- 1Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia, Canada
- 2Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, Cambridge, Massachusetts, USA
- 3Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
- 4School of GeoSciences, University of Edinburgh, UK
- 5Department of Earth and Planetary Sciences and Division of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
Abstract. 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 O3, NO2, and HCHO columns from the GOME satellite instrument, lightning flashes from the OTD and LIS satellite instruments, profiles of O3, CO, and relative humidity from the MOZAIC aircraft program, and profiles of O3 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 NOx 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 O3 in the upper troposphere by 5–20 ppbv versus in situ observations and by 1–4 Dobson Units versus GOME retrievals of tropospheric O3 columns. A lightning source strength of 6±2 Tg N/yr best represents in situ observations from aircraft and ozonesonde. Tropospheric NO2 and HCHO columns from GOME are applied to provide top-down constraints on emission inventories of NOx (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 NOx over northern equatorial Africa. These emissions increase lower tropospheric O3 by 5–20 ppbv, improving the simulation versus aircraft observations, and by 4 Dobson Units versus GOME observations of tropospheric O3 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 O3 observations by 5–15 ppbv, reflecting enhanced convective detrainment in the upper troposphere. Heterogeneous uptake of HNO3 on aerosols reduces simulated O3 by 5–7 ppbv, reducing a model bias versus in situ observations over and downwind of deserts. Exclusion of HO2 uptake on aerosols increases O3 by 5 ppbv in biomass burning regions, reducing a model bias versus MOZAIC aircraft measurements.