Net ecosystem fluxes of isoprene over tropical South America inferred from Global Ozone Monitoring Experiment (GOME) observations of HCHO columns
Barkley, Michael P.
Palmer, Paul I.
Kurosu, Thomas P.
Martin, Randall V.
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CitationBarkley, Michael P., Paul I. Palmer, Uwe Kuhn, Juergen Kesselmeier, Kelly Chance, Thomas P. Kurosu, Randall V. Martin, Detlev Helmig, and Alex Guenther. 2008. “Net Ecosystem Fluxes of Isoprene over Tropical South America Inferred from Global Ozone Monitoring Experiment (GOME) Observations of HCHO Columns.” Journal of Geophysical Research 113 (D20). https://doi.org/10.1029/2008jd009863.
AbstractWe estimate isoprene emissions over tropical South America during 1997-2001 using column measurements of formaldehyde (HCHO) from the Global Ozone Monitoring Experiment (GOME) satellite instrument, the GEOS-Chem chemistry transport model, and the MEGAN (Model of Emissions of Gases and Aerosols from Nature) bottom-up isoprene inventory. GEOS-Chem is qualitatively consistent with in situ ground-based and aircraft concentration profiles of isoprene and HCHO, and GOME HCHO column data (r = 0.41; bias = +35%), but has less skill in reproducing wet season observations. Observed variability of GOME HCHO columns over South America is determined largely by isoprene and biomass burning. We find that the column contributions from other biogenic volatile organic compounds (VOC) are typically smaller than the column fitting uncertainty. HCHO columns influenced by biomass burning are removed using Along Track Scanning Radiometer (ATSR) firecounts and GOME NO2 columns. We find that South America can be split into eastern and western regions, with fires concentrated over the eastern region. A monthly mean linear transfer function, determined by GEOS-Chem, is used to infer isoprene emissions from observed HCHO columns. The seasonal variation of GOME isoprene emissions over the western region is broadly consistent with MEGAN (r = 0.41; bias = -25%), with largest isoprene emissions during the dry season when the observed variability is consistent with knowledge of temperature dependence. During the wet season, other unknown factors play a significant role in determining observed variability.
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