Implementing Online Marine Organic Aerosol Emissions into
GEOS-Chem
B. Gantt1, M. S. Johnson2, M. Crippa3, A. S. H. Prévôt3, and N. Meskhidze1
Funding: Office of Science (BER),
US Department of Energy Grant
No. DE-FG0208ER64508, and the
NASA Ames Research Center
Earth Science Division
1
North Carolina State University
NASA Ames Research Center
3 Laboratory of Atmospheric
Chemistry, Paul Scherrer
Institute
2
NASA Ames Research Center
7th International GEOS-Chem Meeting
May 5, 2015
Importance of Marine Organic Aerosols (MOA)
Rinaldi et al. (2010)
 Need for improved climate assessments has led to increased emphasis on understanding emission
sources and concentrations of natural aerosols
 The majority of the Earth’s surface is covered by oceans
 Oceanic emissions of sea salt and organic matter, in particulate form, and of sulfur, halogens, and
volatile organic compounds, in gaseous form, affect the formation, number concentration, and
composition of atmospheric cloud condensation nuclei (CCN) and ice nuclei (IN)
Previous GEOS-Chem MOA Emission Modeling
Gantt et al. (2012)
Annual Average Emission Rates
 Using GEOS-Chem v8-01-01
 Presented at the 6th Annual
GC Meeting
 Evaluated 5 different organic
sea spray emission schemes
against hourly to monthly
observations
 Global MOA emission rates
ranged from 0.1 to 11.9 Tg yr -1
Gantt et al. (2012)
GEOS-Chem-predicted Global MOA Emissions
 Applying top-down emission
scheme from Gantt et al.
(2012)
 Annual submicron MOA
emissions of ~9.0 Tg was
predicted for 2009
 Falls within the range of
previously predicted totals of
MOA emissions
 Emissions range from < 0.1 to
> 10 ng m-2 s-1
 Largest emission rates in highlatitude waters during the
respective spring/summer
seasons
Gantt et al. (2015)
GEOS-Chem-predicted Global MOA Concentrations
 MOA surface concentrations range from
< 0.1 µg m-3 to > 1.0 µg m-3
 MOA concentrations are largest over
regions of highest emission sources
which are correlated with [chl-a] spatial
distribution
 The fraction of total submicron OA made
up by primary MOA are largest (>80%)
over marine regions and decreases
rapidly over terrestrial regions
Gantt et al. (2015)
Improved Prediction of Global Total Organic Aerosol
Concentrations in Clean Marine Regions
*Data is considered
“clean marine” when
[BC] < 50 ng m-3 and
upwind fetch over
the ocean
With online MOA
emissions
Without
online
MOA emissions
Gantt et al. (2015)
 GEOS-Chem without MOA emissions tends to under-predict (normalized mean bias -79%) in situ
measurements and displays poor correlation (0.16) when compared to observations
 Model simulations with MOA emissions included in the comparison had substantially lower model bias
(normalized mean bias -12%) and improved correlation (0.28)
Conclusions
 Online emission parameterization of submicron primary MOA was implemented into the GEOS-Chem
model (v9-02)
 This model development was designed to be used in the default setting of GEOS-Chem with the following
characteristics: (1) adds minimal computational expense, (2) capable of being used for all GEOS-Chem
model domains/simulation periods, and (3) treated with unique tracers for explicit atmospheric aging and
tracking
 GEOS-Chem predicts an annual submicron MOA total of ~9.0 Tg which is comparable to past predictions
 Emission rates range from < 0.1 ng m-2 s-1 to > 10 ng m-2 s-1, with largest values in high-latitude oceans
during the summer season
 Model-predicted MOA concentrations range from < 0.1 µg m-3 to > 1.0 µg m-3 and make up the majority of
total submicron OA over oceanic regions
 Model results are comparable with existing data sets and have been extensively discussed in scientific
literature; therefore proposed to be implemented in the default code
 Please see our publication in Geosci. Model Dev.: http://www.geosci-modeldev.net/8/619/2015/gmd-8-619-2015.pdf
Additional Slides
Gantt et al. (2011) Emission Parameterization
Gantt et al. (2011) Atmos. Chem. Phys.
Marine Primary Organic Aerosol Emission Rate (EPOA)
1
1+exp(3(−2.63[chl a])+3(0.18(U10))
0.03
OMSSA(chl a, U10, Dp) =
+
1+0.03exp(6.81Dp )
1+exp(3(−2.63[chl a])+3(0.18(U10 ))
Gantt et al. (2012)
EPOA (chl a, U10 , Dp ) = 6 ×VSSA ×OMSSA ×ρSSA
sea-salt emissions based on Jaeglé et al. (2011)
10m winds (U10)
[chl-a]
GEOS-Chem (v9-02) Model
 3-D global chemical transport model (v9-02)
 Online sea-salt emissions
 Developed at Harvard University and other
Power relationship with 10m winds speeds (Gong
institutions around the world
2003) and 3rd order polynomial dependence on sea
 Full chemistry configuration
surface temperature (Jaeglé et al., 2011)
 SMVGEAR II chemistry solver package w/ SOA
Two bin sizes: fine mode (0.02 to 1.0 µm diameter)
formation (Pye et al., 2010)
and coarse mode (1.0 to 16.0 µm diameter)
 GEOS-5 meteorology
 Online MOA emission scheme
 Goddard Earth Observing System (GEOS) of
Top-down emission parameterization developed
the NASA Global Modeling Assimilation Office
from Gantt et al. (2012) applying in situ data at
 Detailed emission inventories
Mace Head, Ireland
 Fossil fuel, biomass burning, biofuel burning,
 Dependence on:
biogenic and anthropogenic aerosols
Monthly-averaged Aqua MODIS [chl-a] at 1/12°
 State-of-the-art
transport
(TPCORE)
and
which is spatially averaged online
deposition routines
 GEOS-5 10m wind speeds
 2⁰ x 2.5⁰ global grid resolution
 2 additional tracers: 1) hydrophobic and 2)
 0.5⁰ x 0.67⁰ nested regional grid resolution
hydrophilic which is formed with an e-folding time
 47 vertical grids
of 1.15 days (identical to terrestrial OA)
GEOS-Chem-predicted Nested MOA Concentrations
 Nested-grid simulations (0.5° x 0.67°) for July
2009 demonstrate a sharp concentration
gradient over Europe
 Data from Paris (Crippa et al., 2013; AMSderived MOA concentrations) was used to
evaluate
high-resolution
GEOS-Chem
simulations
 The model demonstrates the ability to
capture the temporal pattern and magnitude
of observed inland MOA concentrations
 Correlation of 0.62
 Mean bias of -120 ng m-3
 Normalized mean bias of -36%
Gantt et al. (2015)