Optimized regional and interannual variability of lightning in a global chemical transport model constrained by LIS/OTD satellite data
By L. Murray, D. J. Jacob, J. Logan, R. Hudman, William J. Koshak. 2012
399 citations recorded by the source corpus.
[1] Nitrogen 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 NOxsource to the satellite lightning data in a way that preserves the coupling to deep convective transport. We show that applying monthly scaling factors over ∼37 regions globally 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 their diurnal sampling bias, we construct a monthly time series of lightning flash rates for 1998–2010 and 35°S–35°N. We find a correlation of IAV in total tropical lightning with El Nino but not with the solar cycle or the quasi-biennial oscillation. The global lightning NOxsource ± IAV standard deviation in GEOS-Chem is 6.0 ± 0.5 Tg N yr−1, compared to 5.5 ± 0.8 Tg N yr−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.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/82088984c869df9a7e62aa80fbbeca0713c6ed2b
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Citations in the corpus
- Retrieving tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects of aerosols, surface reflectance anisotropy, and vertical profile of nitrogen dioxide (2013, methodology): “(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.”
- Attribution of the accelerating increase in atmospheric methane during 2010–2018 by inverse analysis of GOSAT observations (2020, methodology): “…minor loss terms include stratospheric oxidation computed with archived monthly loss frequencies from the NASA Global Modeling Initiative model (Murray et al., 2012), tropospheric oxidation by Cl atoms computed with archived Cl concentration fields from X. Wang et al. (2019b), and monthly soil…”