Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data
Hudman, Rynda C.
Koshak, William J.
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CitationMurray, Lee T., Daniel J. Jacob, Jennifer A. Logan, Rynda C. Hudman, and William J. Koshak. 2012. “Optimized Regional and Interannual Variability of Lightning in a Global Chemical Transport Model Constrained by LIS/OTD Satellite Data.” Journal of Geophysical Research: Atmospheres 117 (D20) (October 27).
AbstractNitrogen oxides (NOx≡ NO + NO2) produced by lightning make a major contribution to the global production of tropospheric ozone and OH. Lightning distributions inferred from standard convective parameterizations in global chemical transport models (CTMs) fail to reproduce observations from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) satellite instruments. We present an optimal regional scaling algorithm for CTMs to fit the lightning NOx source to the satellite lightning data in a way that preserves the coupling to deep convective transport. We show that monthly scaling using ~35 global regions significantly improves the tropical ozone simulation in the GEOS-Chem CTM as compared to a simulation unconstrained by the satellite data, and performs equally well to a simulation with local scaling. The coarse regional scaling preserves sufficient statistics in the satellite data to constrain the interannual variability (IAV) of lightning. After processing the LIS data to remove its diurnal sampling bias, we construct a monthly time series of lightning flash rates for 1998-2010 and 35ºS-35ºN. We find a correlation of IAV in tropical lightning with El Niño but not with the solar cycle or the quasi-biennial oscillation. The resulting global lightning NOx source in GEOS-Chem is 6.0 ± 0.5 Tg N a-1, compared to 5.5 ± 0.8 Tg N a-1 for the biomass burning source. Lightning NOx could have a large influence on the IAV of tropospheric ozone because it is released in the upper troposphere where ozone production is most efficient.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11828626
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