Abstract
We use formaldehyde (HCHO) vertical column measurements from the Scanning Imaging Absorption spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI), and a nested‐grid version of the GEOS‐Chem chemistry transport model, to infer an ensemble of top‐down isoprene emission estimates from tropical South America during 2006, using different model configurations and assumptions in the HCHO air‐mass factor (AMF) calculation. Scenes affected by biomass burning are removed on a daily basis using fire count observations, and we use the local model sensitivity to identify locations where the impact of spatial smearing is small, though this comprises spatial coverage over the region. We find that the use of the HCHO column data more tightly constrains the ensemble isoprene emission range from 27–61 Tg C to 31–38 Tg C for SCIAMACHY, and 45–104 Tg C to 28–38 Tg C for OMI. Median uncertainties of the top‐down emissions are about 60–260% for SCIAMACHY, and 10–90% for OMI. We find that the inferred emissions are most sensitive to uncertainties in cloud fraction and cloud top pressure (differences of ±10%), the a priori isoprene emissions (±20%), and the HCHO vertical column retrieval (±30%). Construction of continuous top‐down emission maps generally improves GEOS‐Chem's simulation of HCHO columns over the region, with respect to both the SCIAMACHY and OMI data. However, if local time top‐down emissions are scaled to monthly mean values, the annual emission inferred from SCIAMACHY are nearly twice those from OMI. This difference cannot be explained by the different sampling of the sensors or uncertainties in the AMF calculation.
Highlights
Additional supporting information may be found in the online version of this article.[2] It is well established that terrestrial vegetation emit a diverse range of reactive biogenic volatile organic compounds (BVOCs) into the atmosphere, which serve important roles in the biosphere and which influence atmospheric chemistry and climate [Kesselmeier and Staudt, 1999; Laothawornkitkul et al, 2009]
AThe emission estimates are based on the daily screening of fires and the use of grid cells where S is within 1300–1800 s. bRegression between the model isoprene emissions and HCHO vertical columns (equation (3)); S and B are the gradient and intercept of the reduced major axis fit which allows for errors in both parameters [Hirsch and Gilroy, 1984], and r is the Pearson correlation coefficient. cThe percent differences of the monthly emission totals, i.e., 100%/prior (see equation (5)). dSummary over the ensemble
We find that the SCIAMACHY top-down estimates are nearly twice those derived from OMI, even though the HCHO columns from the two instruments differ on average by 25%; the top-down isoprene emissions derived
Summary
Additional supporting information may be found in the online version of this article. Calculation of HCHO Vertical Columns [17] We compute AMF look-up tables for SCIAMACHY and OMI using monthly averaged HCHO profiles and aerosol optical depths (AOD) from each GEOS-Chem simulation, appropriate to each instrument’s overpass time. Sources of uncertainty in the AMF include uncertainties in the aerosol loading, cloud fraction, cloud-top pressure, surface reflectance, and the HCHO vertical distribution, which is influenced by the a priori isoprene emissions and other model uncertainties (e.g., chemical mechanism, boundary-layer mixing). 50% ( 2 1015 molecules cm–2) based on the work of De Smedt et al [2008] By applying these upper limit uncertainties and taking into account that on average there are six SCIAMACHY and 134 OMI observations per grid cell per month, we find monthly averaging onto the GEOS-Chem grid results in median HCHO vertical column errors of 40–60% for SCIAMACHY and 5–15% for OMI, assuming the errors are truly random. Unknown systematic biases in the HCHO data may exist
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