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Evaluation of global AEROCOM aerosol optical properties
against satellite MODIS aerosol products
V. Pappas1, N. Hatzianastassiou1, C. Papadimas1, C. Matsoukas2, S. Kinne3, I. Vardavas4
(1) Laboratory of Meteorology, Physics Department, University of Ioannina, Greece,
(2) Department of Environment, University of the Aegean, Mytilene, Greece,
(3) Max-Planck Institute for Meteorology, Hamburg, Germany
(4) Physics Department, University of Crete, Crete, Greece
AEROCOM
MODIS
Zonal variation
0.2
0.15
0.1
0.05
0
85S 75S 65S 55S 45S 35S 25S 15S
5S
5N
15N 25N 35N 45N 55N 65N 75N 85N
Latitude (degrees)
AOD
Global
North Hem.
ω
MODIS
0.130
0.155
0.163
0.2
0.182
South Hem.
0.097
0.125
Global
0.962
0.925
North Hem.
0.952
0.933
South Hem.
gaer
AEROCO
M
Global
0.972
0.676
0.916
0.580
MODIS
AEROCOM
0.18
MODIS-AEROCOM
0.16
0.14
0.12
AOD
Values for 550nm
0.1
0.08
0.06
0.04
North Hem.
0.663
0.534
0.02
South Hem.
0.689
0.631
0
1
2
3
4
5
6
7
8
9
10
Month (January-December)
11
AEROCOM AODs are systematically smaller than MODIS, with
slightly larger/smaller differences in winter/summer.
12
AEROCOMAOD: 0.137
MODISAOD: 0.164
AEROCOMAOD: 0.144
MODISAOD: 0.162
AEROCOM
RFTOA
RFATM
RFSURF
AEROCOM
-2.85
2.57
-6.33
20.18
MODIS
-2.94
4.00
-8.24
20.88
(no units)
Absolute
difference
0.09
-1.43
1.91
-0.7
Relative
difference
3.1 %
35.8 %
23.2 %
-3.5 %
•For the AEROCOM RF computations, AEROCOM
AOD/SSA/gaer have been used, while for MODIS RF,
MODIS AOD and GADS SSA/gaer have been used.
•Very good agreement of global mean TOA forcing,
but large difference in S. Hemisphere oceans with
AEROCOM underestimating it.
•Overestimation of normalised RF (radiative forcing
efficiency) up to 50 Wm-2 in areas with industrial
type of aerosol . Underestimation in the largest part
of oceans.
AEROCOM
6. Vertical distribution
AEROCOM
Accumulated fraction of columnar AOD
16
CALIOP (Mediterranean)
14
AEROCOM (Mediterranean)
12
AEROCOMAOD: 0.123
MODISAOD: 0.151
AEROCOMAOD: 0.117
MODISAOD: 0.144
4 sites characteristic of different
types of aerosols (pollutants, sea
salt spray, biomass, dust) have
been selected.
•Generally AOD compares well with
AEROCOM underestimating.
•ω is available by MODIS only over
land
•g is available by MODIS only over
oceans
Aerosol Optical Depth
RFTOA/AOD
Global mean
values (Wm-2).
4. Seasonal variability
•For both
datasets, the
season with the
largest AOD
values is spring
and summer.
However, while
for MODIS the
highest values
appear during
spring with
summer coming
second , for
AEROCOM this is
reversed.
•The differences
between the two
datasets are
largest in
autumn.
7. Spectral variation of AOD, g, ω
10
8
6
4
2
0
0
10
20
30
40
50
60
Accumulated fraction (%)
70
80
90
100
•50 % of total columnar
AOD is due to aerosols
below 1.50 / 1.44 km
for AEROCOM /CALIOP.
•85 % of total columnar
AOD is due to aerosols
below 2.73 / 3.2 km for
AEROCOM/CALIOP.
Wavelength (nm)
Aerosol Optical Depth
•AEROCOM overestimates AOD in areas with large
aerosol load.
•Most grids of larger AEROCOM AOD are over land,
implying an overestimation of AOD in AEROCOM or an
underestimation of MODIS land values.
•AEROCOM overestimates sea salt AOD at the latitude
band between 45 ° and 55° S, probably due to large
value of prescribed effective radius (Kinne et al., 2006).
Altitude (km)
Aerosol Optical Depth (AOD)
0.25
Atmospheric forcing relative
difference (%)
MODIS
2. Global and hemispherical view of AOD
(TOTAL)
MODIS
Aerosol optical properties are difficult to measure
globally using ground-based instruments, due to limited
number of stations. At the same time, their retrieval
through satellites is subject to certain uncertainties and
limitations.
AEROCOM (Kinne et al., 2006) is a blend of groundbased observations and global modeling output, offering
aerosol optical properties globally at 1°x1° resolution. In
the present study AEROCOM is evaluated against MODIS
retrievals, by comparing aerosol optical depth (AOD),
single scattering albedo (ω) and asymmetry parameter
(gaer), as well as radiative forcing simulations. For the
simulations, a spectral radiation transfer model has been
used (Hatzianastassiou &Vardavas, 1999). The vertical
distribution of AEROCOM (model ECHAM output) is also
compared with CALIOP version 2 data.
AEROCOM data include AERONET quality observations
from 1998 to 2007. These data are available as a
climatological set and not as a time series. Hence, for the
comparison, the mean value of MODIS data from 2000 to
2007 has been used.
All AEROCOM data are freely available at the ftp site :
ftp://ftp-projects.zmaw.de/aerocom/climatology/
5. Radiative forcing (RF)
(plots for [RFAEROCOM-RFMODIS/RFMODIS])
Aerosol Optical Depth
3. Spatial variability
MODIS
1. Introduction-Motivation
Wavelength (nm)
8. Conclusions
Summer and spring are the seasons with the best agreement for
AOD, while the worst is during autumn.
Radiative forcings using AEROCOM data have smaller magnitude
than those based on MODIS, mainly because of smaller
AEROCOM AODs. However, differences at surface and in
atmosphere cancel out so that the difference at TOA is small (3%).
In places where AEROCOM AOD is larger than MODIS, Net
Radiative forcing estimated by AEROCOM is larger.
In the global perspective, RFTOA only differs by 3.1%.
On a global annual mean basis, AEROCOM overestimates g and
ω by 16.6% and 4.1%, respectively.
Vertical distribution of AEROCOM aerosols is similar to the one
of CALIOP version 2 data. This is expected to be improved with
the implementation of CALIOP version 3 data by the AEROCOM
team.
In the total, AEROCOM is a valuable tool for studies using
aerosol optical properties, given the relatively small differences in
magnitude and spectral profiles of AOD, SSA and gaer with MODIS.
The good performance of AEROCOM maximizes its usefulness
based on advantages like the separation between natural and
anthropogenic aerosols and the spectral resolution of the
1°x1°aerosol optical properties.
References
•Kinne et al., 2006: An AeroCom initial assessment optical properties in aerosol
component modules of global models. Atmos. Chem. Phys., 6, 1-20
•Hatzianastassiou, N., and I. Vardavas, 1999: Shortwave radiation budget of the
Northern Hemisphere using International Satellite Cloud Climatology Project and
NCEP/NCAR climatological data. J. Geophys. Res., 104, 24401-24421.