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• The base case simulation of
the AI, including scattering
organic carbon as currently
used in GEOS-Chem, is
compared to corresponding
OMI observations.
• The simulation captures major
absorption features compared
to OMI over desert regions,
however it fails to capture
absorption by biomass
burning aerosol.
• The correlation between the
simulated and OMI AI is low in
all major biomass burning
regions with large mean
biases.
• The AI is a method of detecting absorbing aerosols from satellite measurements, and is a
product of the Ozone Monitoring Instrument (OMI) [Torres et al., 2007; 2013].
• The AI is calculated by separating the spectral contrast of radiances due to aerosol effects and
those due to Rayleigh scattering at two wavelengths (354 and 388 nm) in the near-UV region.
SimulationWe follow the method of Buchard et al., (2014) for simulating the AI using the vector radiative
transfer model VLIDORT (Spurr 2006):
Wavelength range (nm)
Mean simulated AAE values for
the four biomass burning regions
considered when including BrC
Mean AAE literature
values for biomass
burning aerosol
References for the literature values
350-400 2.7 ± 0.06 2.5-3.0 Jethva and Torres, 2011
350-700 2.1 ± 0.04 1.9 Kirchstetter and Thatcher, 2012
450-550 2.3 ± 0.1 1.7 ± 0.07 Corr et al. 2012
400-700 1.9 ± 0.03 1.6 ±0.06Schnaiter et al. 2005, Russell et al. 2010, Corr et
al. 2012, Yang et al. 2009, Clarke et al. 2007
300-1000 1.7 ± 0.02 1.5 ± 0.2Bergstrom et al. 2007, Yang et al. 2009,
Kirchstetter et al. 2004
400-1000 1.3 ± 0.005 1.4 ± 0.02Russell et al. 2010,
Rizzo et al. 2011
1. Buchard, V., A. M. da Silva, P. R. Colarco, A. Darmenov, C. A. Randles, R. Govindaraju, O. Torres, J. Campbell, and R. Spurr (2014), Using the OMI Aerosol Index and Absorption Aerosol Optical Depth to evaluate the NASA MERRA Aerosol Reanalysis, Atmos. Chem. Phys. Discuss., 14(23), 32177–32231, doi:10.5194/acpd-14-32177-2014.
2. Jethva, H., and O. Torres (2011), Satellite-based evidence of wavelength-dependent aerosol absorption in biomass burning smoke inferred from Ozone Monitoring Instrument, Atmos. Chem. Phys., 11(20), 10541–10551, doi:10.5194/acp-11-10541-2011.
3. Mao, J., S. Fan, D. J. Jacob, and K. R. Travis (2013), Radical loss in the atmosphere from Cu-Fe redox coupling in aerosols, Atmos. Chem. Phys., 13(2), 509–519, doi:10.5194/acp-13-509-2013.
4. Spurr, R. J. D. (2006), VLIDORT: A linearized pseudo-spherical vector discrete ordinate radiative transfer code for forward model and retrieval studies in multilayer multiple scattering media, J. Quant. Spectrosc. Radiat. Transf., 102(2), 316–342, doi:10.1016/j.jqsrt.2006.05.005.
5. Torres, O., A. Tanskanen, B. Veihelmann, C. Ahn, R. Braak, P. K. Bhartia, P. Veefkind, and P. Levelt (2007), Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview, J. Geophys. Res., 112(D24), D24S47, doi:10.1029/2007JD008809.
Comparing Base Case Simulation to OMI AI
Interpreting the OMI ultraviolet Aerosol Index to understand absorption by organic
carbon aerosols and implications for atmospheric oxidationMelanie S. Hammer1*, Randall V. Martin1,2, Aaron van Donkelaar1, Virginie Buchard3, Omar Torres3, Robert J.D. Spurr4
1. Dalhousie University 2. Harvard Smithsonian Center for Astrophysics 3. NASA/Goddard Space Flight Center 4. RT Solutions Inc
We develop a simulation of the ultraviolet Aerosol Index (AI) using the Vector Linearized Discrete
Ordinate Radiative Transfer model (VLIDORT) coupled with GEOS-Chem aerosol fields. The
simulation is used to interpret AI observations from the Ozone Monitoring Instrument (OMI). There
is good agreement between simulated and observed values in regions where mineral dust
dominates the AI, but a large negative bias exists in biomass burning regions. The addition of
absorbing brown carbon (BrC) to the model decreases the overall mean bias between simulated
and OMI AI values over major biomass burning regions from -0.62 to +0.004. The inclusion of BrC
in the model decreases tropospheric OH concentrations by up to 20% over major biomass burning
regions, reducing the overestimate of OH concentrations compared to observations.
Summary
Introduction
• Aerosols with spectrally independent absorption display an AAE ~ 1.
• Aerosols with spectrally dependent absorption have an AAE > 1.
• Black carbon exhibits an AAE ~ 1.
• In the base case simulation, all biomass burning regions have an AAE ~1.
• Biomass burning aerosol typically display an AAE~2 over the near-UV to visible and near-IR spectral
regions.
The AAE values calculated from the simulation including BrC are in good agreement with the literature for major
biomass burning regions.
Absorption Angstrom
Exponent (AAE)AAOD = kλ−AAE
References
Spectral Dependence of Aerosol Absorption
AI > 0 : indicates absorbing aerosolAI < 0 : indicates non-absorbing aerosolAI ~ 0 : indicates clouds or aerosol-free atmosphereDepends on: aerosol concentration, aerosol layer height, and aerosol optical properties
GEOS-Chem Simulation of Aerosol Composition Coincident with OMI
TOMS UV Surface Reflectance (from Omar Torres)
OMI Viewing Geometry
VLIDORT
AI = − 100 ∙ log10I354RAY+AER
I354RAY
R354∗
Pure Rayleigh Scattering
Including Absorbing Aerosols
Top of
Atmosphere
Radiance
Wavelength
• The two main aerosol types detected by the AI are biomass burning and mineral dust aerosol.
• Jethva and Torres (2011) first documented the need to include absorption by BrC to accurately
interpret UV radiances and the AI.
Region r Mean Bias
North Africa (January) 0.54 -0.60
South Asia (April) 0.39 -0.40
South Africa (July) 0.04 -1.0
South America(September)
0.20 -0.50
*Mean Bias = simulated AI – observed AI
Aerosol Index (AI)
Biomass burning aerosols consist mainly of black and organic carbon. Traditionally, black carbon is
assumed to be the sole absorbing carbonaceous aerosol species in models. Several recent studies
have found evidence of absorption by a subset of organic carbon known as “brown carbon” (BrC),
which is thought to contribute significantly to the overall absorption by biomass burning aerosol. We
introduce BrC to GEOS-Chem and examine its effect on aerosol absorption in biomass burning
regions.
• BrC is introduced to the simulation by constraining the imaginary part of the refractive index for
primary organic carbon (POC) between 300 and 500 nm using the spectral dependence of
absorption recommended in the literature and the OMI AI.
• All other properties of BrC are assumed to be the same as the scattering POC currently used in
GEOS-Chem.
The simulation including BrC is more consistent than the
base case simulation at reproducing the OMI AI over
major biomass burning regions.
Comparing Simulation Including BrC to OMI AI
-0.5 0 0.5 1 1.5 2 2.5 3
Simulation Including BrC
AI
January April
July September
-0.5 0 0.5 1 1.5 2 2.5 3AI
OMI Observations
Base Case Simulation
July
January April
September
January April
July September
Effect of BrC Absorption on OH Concentrations
• The figure below shows the percent changes in OH concentrations in the lower troposphere due
to the addition of absorbing brown carbon to the GEOS-Chem simulation.
• The tendency of models to overestimate OH concentrations [Mao et al., 2013] is reduced.
-20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0
ΔOH (%)
January April
July September
OH concentrations decrease by up to 20% over the major biomass burning regions.
Region r Mean Bias
North Africa (January) 0.68 -0.08
South Asia (April) 0.60 -0.003
South Africa (July) 0.55 -0.24
South America(September)
0.54 +0.34
*Mean Bias = simulated AI – observed AI