Source evaluation
In this section, the model evaluation concerning aerosol particle
microphysical and optical properties is presented. The evaluation of the
modelled meteorology for the model base run (R1, see Table )
was done by using atmospheric sounding data of nine stations in Germany. In
principle, the model simulates the meteorology well with a small negative
bias in temperature and a small positive bias in wind speed and relative
humidity. All evaluation results for R1 are summarized in Table .
BC mass concentration
The modelled BC mass concentrations were compared to Csoot mass
concentrations measured by Raman spectroscopy as described in
. Measured particle mass concentrations were
available on a daily basis. The mean normalized bias (MNB) and R2 at all
observation sites are summarized in Table . As an example, the
time series at the regional observation site Bösel and corresponding model
values are shown in Fig. a for the model run R1 (R3 and R4
refer to additional model runs with scaled emissions as described later in
detail in Sect. 3.1.5 and Table ). At Bösel site, the
measured mCsoot increased from around 0.5 to a maximum of about
1.5 µgm-3 during this time period. A similar trend can also
be seen in the modelled mBC in the base run R1, but on a lower level.
The correlation between model and observation (R2) is 0.61 and MNB is
-21 % (Table ). At the urban background site Leipzig-TROPOS,
the modelled mBC is about 70 % lower than the observation and the
correlation is R2=0.35. At the higher-altitude sites, the modelled
mBC again also correlates well with measurements, for example with R2
of 0.66 at mid-level mountain site Mt Hohenpeißenberg and R2 of 0.79 at
the Alpine mountain site Zugspitze, but the modelled mBC is also
negatively biased with MNB between -50 % and -80 %. Calculating the overall
deviation of the model, mBC is about 0.71 µgm-3 too low,
which corresponds to MNB of -53 % as can be seen in Table .
found a 69 % underestimation of BC for the regional site
Melpitz and attributed this to an emission underestimation in the southwest,
according to the main wind direction.
Comparison between measured Csoot and modelled BC mass
concentrations (mBC) (a), absorption coefficient (σap) (b),
mass absorption cross-sections (αBC) (c) and particle number
concentration (N) (d) at the regional observation site Bösel. Besides the
results for model base run R1, the time series from additional model runs R3
and R4 are also shown, which differ from R1 in the EC emissions and the
calculation of optical properties (see Table ).
Although there was a significant amount of biomass burning activities in the
Ukraine and parts of Russia in the second half of the simulation period, the
influence on the BC mass concentration at the observation sites in Germany is
very small (Fig. S1). This indicates that biomass burning activities cannot
be responsible for the overall large bias found for the BC mass
concentrations.
Summary of values of mean bias (MB), mean normalized bias (MNB),
root mean square error (RMSE) and coefficient of determination (R2)
derived from a comparison of different measurements and corresponding model
values simulated in R1. The PM10 statistics refer to the
maritime/continental split of the simulation period. The AOD statistics refer
to the sunphotometer observation technique. The results of the model
meteorology comparison are shown for the surface layer as an average over the
simulation period.
Class
Model variable
Number of sites
MB
MNB
RMSE
R2
Meteorology
T (∘C)
9
-1.27
-0.06
2.39
0.98
u (ms-1)
9
0.16
0.37
2.92
0.92
v (ms-1)
9
0.13
0.32
2.84
0.90
RH (%)
9
4.83
0.60
17.81
0.72
Gas
CO (ppmv)
2
-0.10
-0.61
0.11
0.36
Aerosol
PM10 (µgm-3)
392
-2.51/-9.73
-0.04/-0.14
9.10/18.69
0.25/0.41
BC (µgm-3)
5
-0.71
-0.53
1.07
0.35
Number (m-3)
8
-3.50
0.34
6.03×10-6
0.16
Aerosol optics
AOD
2
-0.02
-0.04
0.03
0.61
σap-wet (Mm-1)
7
-1.07
0.34
37.63
0.17
σap-dry (Mm-1)
7
-1.57
0.20
36.73
0.18
αBC-wet (m2g-1)
5
6.42
1.33
42.54
0.01
αBC-dry (m2g-1)
5
5.34
1.11
30.35
0.01
72 h back trajectories calculated from the model output (D01) for
different observation sites in the GUAN network. The trajectories are
coloured according to the MNB for individual observation sites.
In Fig. the 72 h back trajectories coloured according to the
MNB values are shown. Especially in the southern and the eastern part of
Germany several trajectories indicate that air masses arrived from
continental origins to the east of Germany. As described in
this was mainly the case in the second half of the
simulation period. However, the trajectories indicate that the negative
model bias is increased for continental air masses. In order to evaluate whether
the underestimation of BC concentration is mainly due to uncertainties in the
model or in the emission sources, we will compare other modelled aerosol and
gas phase species with observations in the following sections, e.g. aerosol
mass and number concentration, aerosol optical depth and CO.
Comparison between modelled and measured daily PM10 mass
concentrations for the more maritime time period (24 March–31 March 2009)
and the more continental time period (1 April–9 April 2009) in terms of
the mean normalized bias (MNB) is shown. The right figure depicts the
correlation coefficient. Measurements performed by the German Federal
Environmental Agency (UBA).
Aerosol mass and number concentrations
The simulated PM10 mass concentrations from the base run R1 were
compared to UBA measurements. For this purpose, the daily
averaged PM10 mass concentrations were considered. The results of
this comparison in terms of MNB are shown for the more maritime and more
continental time periods in Fig. . Among the 392 monitoring
stations over Germany, the overall correlation between modelled and observed
PM10 is quite good with R2 of 0.60. Compared to the BC simulation, the
model bias in PM10 is much smaller with an overall average MNB of
about -9 %. However, we could still clearly see the pattern that the model
slightly overestimates the PM10 mass in the western part and
underestimates in the eastern part of Germany. The overprediction of
PM10 by the model, especially in the maritime air mass and for
locations not far away from the sea, may be attributed to an overestimation
of the sea salt emission calculated in the WRF-Chem. also
found an overestimation of PM10 for several European observation
sites and attributed this to an overestimation of the sea salt emission by
WRF-Chem. The underestimation is especially true for the continental time
period during which the average MNB is about -14%, while it is only about
-4 % during the marine time period.
The total aerosol number concentration from mobility particle size
spectrometer measurements with a temporal resolution of 1 h in GUAN was
compared to model values. The modelled values in the first three Mosaic bins were
summed in order to match the measured particle sizes (<1 µm).
The time series for a regional observation site is shown in Fig. d. In general, the modelled total aerosol number concentration
follows the trend of the observations. Taking all observation sites into
account, a positive MNB of 34 % was derived (see Table ), with
the best correlation for the comparison with regional observation sites, for
example R2 = 0.27 at Bösel.
Aerosol optical depth
The aerosol optical depth (AOD) is defined as the particle-induced light
extinction along a specific path. Therefore, an AOD comparison between model
and measurement gives some evidence about how well the model simulates the
vertical distribution of aerosol particles.
Mean normalized bias in AOD (dimensionless) from comparison with
MODIS measurements. Two different products were used, Level 2 (left) and data
with additional quality assurance filters according to
(right). Simulated values of AOD were considered for times and locations with
available MODIS data.
Because WRF-Chem calculates aerosol particle optical properties only for four
wavelengths, linear interpolation between AOD at 400 and at 600 nm
was used to derive the AOD at 550 nm. The WRF data were masked
according to the availability of MODIS data. The average MODIS AOD in the
modelling period ranged between 0.05 and 0.6. The model simulated a broader
range with 0.01 to 0.8. In Fig. , the horizontal distribution
of MNB is shown. Over most regions in central Germany there is a negative
model bias around -40 % for the Level 2 MODIS products (left panel in Fig. ). On the other hand, especially in the southwestern and
northeastern part, the model overestimates AOD up to 200 % for the Level 2
MODIS AOD (left panel in Fig. ). However, when additional
filtering processes were applied to the MODIS AOD data following
, the model bias in this region become significantly
smaller (right panel in Fig. ). A possible explanation for
the overestimation of AOD in the southwestern part of Germany may be the
lack of detailed information of the stack height of point source emissions.
This may lead to an underestimation of pollutant emissions to higher
atmospheric levels in this densely populated region. On the other hand, the
overestimation in the northeastern part may be attributed to overestimated
sea salt concentrations in lower atmospheric levels as previously mentioned
for the PM10 model comparison. Contrastingly, the largest negative
model bias in domain 2 can be seen over the large area of Poland and the
Czech Republic (right panel in Fig. ). It also extends to
most of the regions in the countries to the east of Germany. This implies that
the emissions are underestimated in the eastern part of the modelling
domain.
At least, it has to be pointed out that the uncertainties in the AOD itself
may also contribute to these discrepancies in the comparison. In previous
studies, the MODIS AOD product was evaluated with AERONET sun photometer
measurements and it was found that MODIS AOD is often positively biased by up
to 50 % and 48 % over the
European land surface. This bias was attributed to uncertainties in land
surface reflectance in the MODIS AOD retrieval and possible cloud
contamination . Considering these findings, we
also compared the simulated AOD to AERONET sunphotometer measurements
performed at two observation sites in Germany (Fig. ).
Regarding only the AOD of those two AERONET sites, the model follows nicely the
measured AOD (R2=0.61) and the MNB was found to be only -4 % (see Table ). Consequently, if the 50 % positive bias of the MODIS AOD in
comparison to the “true AOD” (AERONET AOD) is considered, our model results
are actually closer to the “true values”, indicating a general good
model performance in the simulation of AOD.
Site-dependent statistical evaluation of model simulation of BC mass
concentration and absorption coefficient in terms of mean bias (MB), mean
normalized bias (MNB), root mean square error (RMSE) and coefficient of
determination (R2).
Site
location
BC mass concentration
Absorption coefficient
MB
MNB
RMSE
R2
MB
MNB
RMSE
R2
Augsburg
48.36∘ N, 10.91∘ E
-6.69
-0.52
11.09
0.13
Leipzig-TROPOS
51.35∘ N, 12.43∘ E
-1.57
-0.70
1.82
0.35
-3.30
-0.12
7.67
0.18
Melpitz
51.54∘ N, 12.93∘ E
-0.59
0.31
4.23
0.41
Bösel
53.00∘ N, 7.96∘ E
-0.31
-0.21
0.46
0.61
1.93
1.07
5.12
0.27
Hohenpeißenberg
47.80∘ N, 11.00∘ E
-0.59
-0.72
0.72
0.66
-1.54
-0.26
2.57
0.42
Schauinsland∗
47.91∘ N, 7.91∘ E
-0.87
-0.55
1.05
0.01
1.00
0.51
3.47
0.04
Zugspitze
47.42∘ N, 10.98∘ E
-0.26
-0.46
0.37
0.79
-0.46
0.58
1.24
0.59
Carbon monoxide
Because of the large negative bias found for the simulation of mBC, the
model performance of CO, as the most related gas phase species to the
combustion process, was also checked. The volume mixing ratios of CO
from hourly measurements at three observation sites of the UBA network were
compared to the corresponding model values from the base run R1. At both, the
regional and the mountain observation site, the CO concentrations show
a similar temporal behaviour with an overall R2 of 0.36. A substantial
negative bias of 61 % was calculated, which is very similar to the bias found
for mBC, therefore suggesting that emissions from combustion processes
may be significantly underestimated.
BC source adjustment
As discussed before, the modelled BC mass concentrations were found generally
too low in comparison to the ground-based measurements. Trajectory analysis
of the model bias indicates that the largest model bias in the BC mass
concentration was found when the air masses came from the eastern and
southeastern directions. Considering that the meteorology was well simulated
by the model and taking also the results for PM10 and AOD into
account, there are strong indications that emission rates in regions to the
east of Germany may be underestimated for the period evaluated.
To verify the assumption of underestimated EC emissions, three additional
model runs were performed in order to improve the simulation of BC mass
concentrations, as summarized in Table . For the second model
run (R2), the EUCAARI EC emissions were spatially scaled to the ARCTAS BC
inventory level to account for possible emission changes from 2005 to 2008.
The scaling was done by calculating the EC emissions in both inventories on a
1∘ × 1∘ grid. Dividing each ARCTAS emission rate in individual 1∘ grid cell by the corresponding EUCAARI EC emission rate in the same grid
cell, a scaling map was derived. This scaling map was then applied on the
original EUCAARI emission inventory to conserve its high spatial resolution.
The scaling leads to an increase of emissions especially in the western and
southwestern part of Germany and in Poland (Fig. S2). However, it is worth
noticing that different methodologies were used for developing both
inventories, which may also result in differences in emission numbers. This
scaling procedure results in a slightly lower total emission rate in the
modelling domain of 0.62 tons day-1 in comparison to the emission from
the original EUCAARI inventory (Table ). For model run R3, the
EUCAARI EC emissions were simply multiplied by a factor of 2, which is
justified by the fact that global BC emission estimates may vary by a factor
of 0.5–2 . For model run R4, another scaling
procedure was applied, in which the inventory was scaled by a factor of 5 but
only for locations to the east of 15∘ E longitude. This scaling strategy is
supported by the fact that the BC model bias is higher when the continental
air masses were originated from the east and southeast. It is also justified
by the fact that the BC emission may vary by a factor of 2–5 on a regional
scale . On a related note, also
reported that by using EMEP CO emissions in a simple Lagrange disperse
model, the simulated CO concentration was by a factor of 5 lower than
the observation at a remote boreal forest site in Russia. This scaling method
increases the total emission in the domain to 2.07 tons day-1. For R5
in Table the EC emissions are set to 0 for calculating the
radiative forcing, which will be described later.
Time series of AOD from AERONET sun photometer measurements with
model values shown as straight lines.
In general, an increase of simulated mBC towards the measured values can
be seen for the three additional model runs (Table ). The
simulation of BC is improved with MNB -36 % for R2, -6 % for R3 and -40 % for
R4. The simulation of the temporal pattern remains nearly unchanged for R2
(R2=0.35) and R3 (R2=0.37). This is because although the overall model
bias improved significantly in R3, as shown in Fig. , the
modelled value matches the observation during the polluted continental time
period, while the simulations during the clean marine time period degraded
with pronounced overestimation by a factor of 2. This indicates that during the
clean marine time period the EC emissions in Germany and in the countries to the west are not necessarily underestimated. However, the run
R4 improved the correlation between model and observation to R2=0.45,
which captures the observed temporal pattern during both marine and
continental polluted periods. For 1–2 April and 6–7 April 2009, the BC mass
concentration is still underestimated by R4. This may be due to slight underestimation of EC emissions to the west of
15∘ E or the emissions to the east of 15∘ E being underestimated by more
than a factor of 5.
It is worth noticing that there might also be interannual/monthly variation in the EC emission, which we kept constant.
However, if monthly variation were taken into account,
spring time (April) would be the relatively lower emission time period in
comparison to the heating time period in winter. This means that we actually already
have an overfeed in emission into the model run. It further justifies
that the annual emission of BC that we are using now is even more strongly
underestimated.
Summary of values of mean bias (MB), mean normalized bias (MNB),
root mean square error (RMSE) and coefficient of determination (R2)
derived from a comparison of different measurements and corresponding model
values simulated in the runs R2, R3 and R4.
Class
Model variable
Number of sites
Run
MB
MNB
RMSE
R2
Aerosol
PM10 (µgm-3)
392
R2
-2.40
-0.07
13.71
0.59
R3
-5.30
-0.06
14.24
0.61
R4
-5.77
-0.08
14.37
0.61
BC (µgm-3)
5
R2
-0.56
-0.36
0.83
0.35
R3
-0.30
-0.06
0.54
0.37
R4
-0.55
-0.4
0.73
0.45
Aerosol optics
σap-dry (Mm-1)
7
R2
-3.23
-0.23
40.89
0.22
R3
-2.51
-0.04
38.61
0.20
R4
-3.31
-0.33
42.54
0.20
αBC-dry (m2g-1)
5
R2
-0.15
-0.01
0.58
0.00
R3
-0.87
0.16
1.39
0.00
R4
-0.44
-0.07
0.76
0.00
Evaluation of particle light absorption and warming effects
Evaluation of particle light absorption
Aerosol particle optical properties such as the particle light extinction
coefficient, single scattering albedo and asymmetry factor are determined in
WRF using Mie theory as described before. Using output variables of the
particle light extinction coefficient and single scattering albedo at
wavelengths of 600 and 1000 nm, the particle light absorption
coefficient at the MAAP wavelength of 637 nm can be derived from the
model output by linear interpolation. All measurements were performed for dry
aerosol particles, which is a problem when comparing with the modelled light
absorption coefficient, because simulated values are derived for particles at
ambient conditions. In several studies, it was shown that BC, internally
mixed with hydrophilic substances (e.g. sulfate) is able to take up water,
which then amplifies the absorption of solar radiation
. For that
reason, the particle optical properties were calculated again, after the
model run was finished, using an offline version of the module
“optical_averaging.F” in WRF-Chem and the simulated concentrations of
the chemical constituents. For this offline run of the optical module, the
aerosol water content was removed.
In Fig. b, the modelled and measured hourly values of the dry
absorption coefficients are shown for one regional observation site.
Corresponding statistics are shown in Table . An increase of
σap occurred in association with an increase in BC mass
concentration in the continental air mass. For the base case model run R1,
regarding the urban sites, the increase is clearly visible in model values,
at least for Leipzig-TROPOS. The model underestimates σap,
especially for the station in Augsburg. MNB at urban sites is between -12 %
and -52 % with values of R2 between 0.13 and 0.18. At the regional sites,
the model simulates the absorption coefficient better, so that even some peak
values are reflected in the model output. In addition, the correlation is
better than at urban sites with values of R2 between 0.27 and 0.41, but in
the entire period the model is positively biased with MNB between 31 % and
107 %. At the mid-level mountain sites the model is again on the level of
measured σap, except for some shorter time periods at the beginning of
April at Mt Hohenpeißenberg. The values of MNB are between -26 % and 51 % with coefficients of determination between 0.04 and 0.42. For the observation
site Schauinsland, only data from the continental air mass were available. The
best correlation between model and measurement is found for the Alpine
mountain site Zugspitze, with R2=0.59 and a positive bias around 58 %. In
summary, a value of MNB = 20 % was found as an average over all sites
(see Table ). If the water is not eliminated before the optical
calculation, the MNB is nearly doubled (34 %), whereas the correlation
remains unchanged.
In summary, for the base case R1, while the modelled BC
mass concentration is too low (by a factor of 2), the modelled particle
light absorption coefficient can still match the observations with even a
positive bias of about 20 %. Calculating the quotient of both, the dry
αBC at 637 nm can be derived. This can be then compared to
αCsoot from measurements. In Fig. c the time series
of daily averaged αBC for the R1 model run and the measurements of
αCsoot are shown. As determined from measurements, αCsoot
shows only little variation during this time period with values between 3 and
6 m2g-1, which is on the same level at all observation sites
ranging from urban to mountain characteristics. When looking at the model
values a similar behaviour can be seen. It has to be pointed out that
αBC is higher with values around 9 and 12 m2g-1. The
overall MNB is 111 %, which is equivalent to a mean bias of
5.34 m2g-1. Regarding humidified particles, MNB is even higher
(133 %) (see Table ). This means that in terms of simulating
aerosol light absorption, the model error in the simulation of mBC is
somehow overcompensated by a too large αBC.
Adjustment of the BC mass absorption cross-section
The particle light absorption coefficient is a
complex function depending on the particle number size distribution, the
refractive index and the mixing state of BC. While the particle number size
distributions and particle mass concentrations are relatively easy to adjust,
the treatment of the mixing state is a rather difficult task. An evaluation
would also be difficult, because the mixing state of BC can only be directly
measured by single particle analysis such as electron microscopy
, or the SP2 instrument
or indirectly by using VTDMA
measurements . In many models, the
change of BC from hydrophobic to hydrophilic is simply parameterized by
applying a fixed ageing time . In the version of
WRF-Chem applied in this study, BC is assumed to be internally mixed with all
other chemical compounds, which implies an impact on the light
absorption properties of these particles by a factor up to 2-3, which may
lead to the overestimation in aerosol mass absorption cross-section. To
change the mixing state treatment itself in WRF-Chem is very difficult. This
is because the core-shell coated treatment in the optical calculation module
of WRF-Chem may result in a smaller mass absorption cross-section, but the
model turns out to be non-robust and crashes during the model simulation. The
treatment of external mixture of BC is inherently limited by the MOSAIC-bin
model treatment of aerosol compositions; that is, there are no explicit
different bins for different aerosol compositions.
So, in this study, we chose a compromise by adjusting the imaginary
part of the BC refractive index so that the modelled mass absorption
cross-section can match the measurements available for the simulation period
from . It has to be emphasized that αBC
is not constant and may vary during the simulation period, because Mie theory
is applied in the model as described in Sect. .
For the adjustment procedure, the particle mass concentrations of all simulated
chemical constituents are read in from the model output. Using bilinear
interpolation, the model particle mass concentrations are calculated for five
observation sites. Passing the interpolated particle mass concentrations of
all chemical constituents to the subroutine “optical_prep_sectional.F”,
particle diameters and corresponding refractive indices are derived, which in
turn are passed to the Mie subroutine to calculate absorption coefficients of
the modelled particle population at individual measurement sites. It is
important to mention that the particle light absorption coefficients are
calculated for dry particles by setting the aerosol water content to zero
before passing the particle mass concentrations to
“optical_prep_sectional.F”, because measurements were also performed for
dry aerosol. Values of αBC are derived at each of the measurement
locations by dividing the modelled dry absorption coefficients by the
modelled BC mass concentrations. This procedure can be summarized by the
following scheme:
Obtain particle mass concentrations from the full model run.
Interpolate particle mass concentrations to measurement site coordinates.
Set aerosol water to 0.
Calculate volume equivalent particle diameter and refractive index in subroutine “optical_prep_sectional.F”.
Calculate aerosol particle optical properties in the subroutine “mieaer”.
Calculate the mass absorption cross-section (αBC) from model output.
The overall deviation between measured and modelled mass absorption
cross-sections is minimized in terms of the root mean square deviation
(χ2) by using a Newton–Raphson method . For
this approach, the imaginary part of the complex refractive index of BC is
chosen to be the independent variable. Repeating steps 4–6
several times with modified refractive index, a new imaginary part of the
complex refractive index can be found iteratively. The new imaginary part was
used in WRF-Chem by modifying the default value in
“optical_prep_sectional.F”.
Particle light absorption coefficient and implication for the mixing state
In Table it can be seen that the adjustment of the modified BC
imaginary part leads to a slight decrease of modelled absorption coefficients
at all seven sites under consideration. Especially for R3, the overall MNB is
improved with a value of -2 %. The overall pattern remains almost the same
since the correlation coefficient only slightly increased to 0.22 for R2 and
0.20 for R3 and R4, respectively.
The simulation of the absorption coefficient could be improved by the adjustment of αBC to observed
αCsoot by varying the imaginary part of the BC refractive index.
From the procedure described in Sect. , an average
value of 0.263 with a standard deviation of 0.02 was found, which is much
smaller than the default value in WRF-Chem of 0.71 and also the summarized
range of imaginary part of BC refractive index in the literature
. An explanation for this result is given by Fig. . Theoretical values of the mass absorption cross-section were
derived using the Mie code for spherical particles
and the volume-averaging method, which is also
applied in WRF-Chem. For simplicity, aerosol particles are assumed to be
decomposed of an absorbing (BC) and a non-absorbing fraction. For internally
mixed particles, the mass absorption cross-section was around
4.4 m2g-1 for the adjusted imaginary part of 0.263, which is in
the size range of the measurements and the values in R2, R3 and R4. Repeating
the calculation for the internally mixed case with the default imaginary part
a mass absorption cross-section of 10.7 m2g-1 is calculated,
which is in the range of the values for R1. Assuming further that all BC
particles are externally mixed and taking the default imaginary part, the
mass absorption cross-section is 3.6 m2g-1, which is close to
the value for internal mixture and refractive index of 0.263. Assuming an
externally mixed fraction of 90 % and the default imaginary part, the mass
absorption cross-section is in the size range of the measurements. This
strongly suggests that the discrepancy between modelled and measured mass
absorption cross-section is due to the fact that a large portion of
externally mixed BC is not considered in WRF-Chem. This can be compensated by
lowering the imaginary part of BC refractive index from adjustment in the
present study.
Mass absorption cross-section for externally and internally mixed BC
particles in dependence on the imaginary part (k) of the refractive index
(m=n+ik) from Mie calculations. Values are derived using spherical log
normally distributed BC particles (N=800, Dpg=120 nm,
σg=1.9, ρ=1.8 gcm-3, m=1.85+ik) and
non-absorbing particles (N=2500, Dpg=200 nm, σg=2,
m=1.55+i1×10-7)
Effect on radiative forcing
The effect of BC on the radiation balance over Germany at the surface and at
TOA was examined by comparing the net irradiances from R3 and an additional
unperturbed model run R5 with no anthropogenic and natural EC emissions. The
radiative forcing can be calculated by subtracting the radiant fluxes of the
model runs with and without emissions on either the surface or at the top of the
atmosphere. These calculations were performed for R3, by using the default
and adjusted imaginary part of the BC refractive index. This was done in
order to estimate the effect of the adjusted particle light absorption on the
radiative forcing.
For the evaluation of the direct radiative forcing, grid cells containing
cloud ice or water were not considered. A relatively short time period was
identified, which was characterized by cloud-free conditions. The radiative
forcing at the surface and at TOA for 3 April 2009 at 12:00 in the nested
model domain is shown in Fig. for both model runs. In
general, the BC radiative forcing is negative at the surface and positive at
TOA. The absolute value is higher at the surface than at TOA, which is in
agreement with previous studies
. For run R3, it can be seen
that the BC radiative forcing at the surface is mostly between -2 and
-10 Wm-2 in large parts of the model domain. For the same BC
concentrations, the radiative forcing at the surface using the default
imaginary part of BC refractive index is higher with values between -4 and
-16 Wm-2. In some grid cells the values may be even higher
especially when large point sources are situated in immediate vicinity as can
be seen for example in combination with Fig. in the
northwestern part of the Czech Republic.
The radiative forcing for R3 at TOA is comparably small with values mostly
between 0 and 4 Wm-2. Using the default BC refractive index,
values between 2 and 6 Wm-2 were determined. Calculating the
quotient between the radiative forcing for the runs with modified and
unmodified imaginary part of the BC refractive index it was found that the
decrease in radiative forcing at TOA and the surface is mostly between 30 % and 70 %.
The dependence of the radiative forcing on the vertical profile of
the simulated BC mass concentration is shown in Fig. S3. It can be seen
that the radiative forcing increases with higher concentration near the
surface in the morning hours. In the afternoon the concentration near the
surface decreases because more particles are transported to higher altitudes.
This leads to a second maximum. Accordingly, model validation of the vertical
profile of BC is expected to provide more information on the evaluation of BC
warming effects in the future.
Direct radiative forcing in Wm-2 for 3 April 2009
12:00 of BC at (a) the surface and (b) the top of the atmosphere, derived
from the comparison of the runs with and without EC emissions. In (a) and (b)
the upper panels refer to the run R3 using the unmodified imaginary part of
BC in each case, whereas for the lower panels the adjusted value of 0.26 was
used.