A new aerosol-optics model is implemented in which realistic morphologies and
mixing states are assumed, especially for black carbon particles. The model
includes both external and internal mixing of all chemical species, it treats
externally mixed black carbon as fractal aggregates, and it accounts for
inhomogeneous internal mixing of black carbon by use of a novel
“core-grey-shell” model. Simulated results of aerosol optical properties,
such as aerosol optical depth, backscattering coefficients and the
Ångström exponent, as well as radiative fluxes are computed with the
new optics model and compared with results from an older optics-model version
that treats all particles as externally mixed homogeneous spheres. The
results show that using a more detailed description of particle morphology
and mixing state impacts the aerosol optical properties to a degree of the
same order of magnitude as the effects of aerosol-microphysical processes.
For instance, the aerosol optical depth computed for two cases in 2007 shows
a relative difference between the two optics models that varies over the
European region between
Aerosol-optics models are employed in large-scale chemical transport models (CTMs) in mainly two contexts, namely, in chemistry-climate modelling (CCM), and in conjunction with remote sensing observations. In a CCM one couples a CTM to an atmosphere–ocean general circulation model (GCM). One purpose is to account for the dynamic effects of aerosol particles on cloud microphysics. Another is to obtain a better description of the direct effect of aerosol particles and radiatively active trace gases on the radiative balance. The aerosol-optics model provides a link that converts the aerosol fields delivered by the CTM to the aerosol optical properties that are required as input to the radiative transfer model, with which one computes the radiative energy budget. In remote sensing applications one is faced with the obstacle that the aerosol concentration fields computed with a CTM are not directly comparable to the radiometric quantities that are observed with remote sensing instruments. The aerosol-optics model provides the observation operator that maps the CTM output to radiometric variables that can be compared to satellite observations or satellite retrieval products. This allows us to either employ satellite observations for evaluating CTM model results, or to assimilate satellite data into a CTM-based air-quality forecasting system. It is clear that the aerosol-optics model has a pivotal role in these kinds of applications. It may constitute an additional source of error that could compromise the reliability of CCMs, impair the reliability of CTM evaluations, or degrade chemical data assimilation results. It is, therefore, important to better understand this potential source of error, quantify its possible impact on model predictions of aerosol radiometric quantities, and assess the level of morphological detail that might be required in aerosol-optics models coupled to CTMs.
A main difficulty is that aerosol particles in nature can have a high
degree of morphological complexity. For instance, mineral dust
particles can have irregular shapes, small-scale surface roughness,
and inhomogeneous mineralogical composition
(e.g.
In environmental modelling practical and computational constraints
often force us to invoke drastically simplifying assumptions about
aerosol morphology. For instance, one frequently computes aerosol
optical properties based on the assumption that all chemical
aerosol components are contained in separate particles (externally
mixed), and that each such particle can be approximated as
a homogeneous sphere. As pointed out in
This study describes the coupling of two different aerosol-optics
models to a regional CTM. One optics model is based on the simple
external-mixture and homogeneous-sphere approximations. The second
model takes both external and internal mixing of aerosol
components into account. Also, it employs morphologically more
realistic models for black carbon particles. Although black carbon
contributes, on average, only some 5
The main goal of this study is to assess the impact of aerosol morphology
and mixing state on radiometric quantities and radiative forcing
simulated with a chemical transport model. To this end we
compare the two optics models, and we gauge the significance of
morphology by comparing the differences in the optics model output to
other sources of error. As a gauge we use the impact of including
or omitting aerosol microphysical processes; this provides us with
a reference which is generally agreed to have a significant effect
on aerosol transport models
The CTM, its aerosol microphysic and mass transport set-ups, and the
aerosol-optics models are described in Sect.
Aerosol particles typically originate from different emission
sources, such as sea-salt particles coming from marine sources,
wind-blown dust from dry land surfaces, volcanic ash from magmatic
or phreatomagmatic eruptions, or black carbon produced during
combustion of fossil fuel, biofuel, or biomass. During atmospheric
transport, particles from different sources can be mixed, resulting
in heterogeneous aerosol populations consisting of particles of
different morphologies, sizes, and chemical composition. A mixture
in which different chemical species are contained in separate
particles is referred to as an
Aerosol optical properties are strongly dependent on not only the size and chemical composition, but also on the mixing state, shape, and internal structure of particles. Therefore, before explaining the aerosol-optics model, we first need to briefly describe the kind of information that can be provided by the aerosol transport model. In particular, we need to understand the level of detail with which the size distribution, size-dependent chemical composition, and the mixing state of the aerosol particles can be computed in a large-scale model.
As a regional model we employ the Multiple-scale Atmospheric
Transport and CHemistry modelling system (MATCH), an offline Eulerian
model developed by the Swedish Meteorological and Hydrological Institute
The MATCH model allows us to choose between two aerosol model versions, a simpler mass-transport model, and a more sophisticated aerosol dynamic transport model.
A simple version of the MATCH CTM, which we refer to as the “mass-transport
model”, neglects all aerosol dynamic processes. It contains
a photochemistry model that computes mass concentrations of
secondary inorganic aerosols (SIAs), which are formed from precursor gases.
The SIA fraction of aerosol particles consists of ammonium sulfate
((
Size bins (characterized by the radius
A more realistic description of particles can be achieved by
accounting for aerosol microphysical processes. To this end the Sectional
Aerosol module for Large Scale Applications (SALSA)
Table
Size bins and chemical species in the MATCH-SALSA aerosol microphysical transport model. An “x” marks that the species is present in a particular size bin.
As in the mass-transport model, “other PPM”,
i.e. primary particles other than BC and OC, are interpreted as
dust particles.
Note that water is not directly calculated as a prognostic variable
in MATCH-SALSA. Rather, it is a diagnostic variable computed in the
MATCH-optics model as explained in Sect.
Aerosol-optics models coupled to a CTM have to make consistent use
of the information provided by the CTM, while invoking assumptions
on optically relevant parameters that are not provided by the CTM.
The parameters that influence the particles' optical properties are
the aerosol size distribution; the refractive index of the materials of which the aerosol particles are
composed; and the morphology of the particles.
The information provided by the CTM depends on the level of detail
in the process descriptions. In the MATCH mass transport model, we
have size information for the primary particles, but only the total
mass for secondary inorganic aerosols. Thus we have to invoke
assumptions about the size distribution of these particles. The
MATCH optics models in conjunction with the MATCH mass transport
model assume that 10
The simplest conceivable optics model assumes that all particles
are homogeneous spheres, and that all chemical species are each in
separate particles, i.e. externally mixed. As explained in
The external-mixture model is implemented in the MATCH mass
transport model, where it is primarily being used in the MATCH
3DVAR data assimilation system
As explained in
Examples of fractal aggregate model particles for computing optical properties of externally mixed black carbon. The aggregates consist of 1000, 1500, 2000, and 2744 monomers (in clockwise order, starting from upper left).
Absorption cross section
The new MATCH-optics model accounts for both internally and
externally mixed aerosol particles, and it contains both homogeneously and
inhomogeneously mixed aerosol particles. Different shapes and morphologies
are assumed for different types of particles.
Pure, externally mixed black carbon particles are assumed to have
a fractal aggregate morphology as shown in
Fig. The calculations in Black carbon particles that are internally mixed with other
aerosol components are morphologically very complex. It is
technically beyond the reach of our present capabilities to build an
aerosol-optics database with the use of morphologically realistic
model particles. However, it is possible to employ realistic model
particles in reference computations for some selected cases. This
has recently been done in different studies
In Note that in earlier studies (e.g. The CGS model has been employed in generating the new MATCH-optics look-up
table. The shell material can be any mixture of water-soluble
components. We use the same values of In the mass transport model, we assume that all SIA
components and all sea salt are internally mixed. We furthermore assume
that in size bins 1–4, 0, 70, 70, and 100 All other externally mixed particles not containing black carbon
are assumed to be homogeneous spheres in the present version of the
look-up table.
Core fraction
Morphologically realistic encapsulated aggregate model for internally mixed black carbon (left), and core-grey-shell model (right).
The look-up tables contain results for
The MATCH-optics model computes in each grid cell and for each
size bin the effective refractive index of the internally mixed
material by use of EMT. The corresponding optical properties are
obtained by linearly interpolating the closest pre-computed results in the
look-up table. Size-averaging is performed by weighing the optical
cross sections as well as
The new internal-mixture optics model with its BC fractal aggregate
and core-grey-shell model particles accounts for significant
morphological details in aerosol particles. The main question we want to
address is whether or not this high level of detail is really
necessary, i.e. whether it has any with the MATCH mass-transport model (i.e. with aerosol microphysics
switched off), in conjunction with the old optics model
(abbreviated by MT-EXT,
“mass-transport external mixture”); with the MATCH mass-transport model in conjunction with the new optics
model (MT-CGS, “mass-transport core-grey-shell”); with the MATCH-SALSA model (i.e. with aerosol microphysics switched
on), in conjunction with the old optics model (abbreviated SALSA-EXT,
“MATCH-SALSA external mixture”); and with the MATCH-SALSA model, in conjunction with the new optics model
(SALSA-CGS, “MATCH-SALSA core-grey-shell”).
We first perform a comparison of monthly and geographically averaged
differences in aerosol optical properties. More specifically,
comparison of model set-ups MT-EXT with MT-CGS, or SALSA-EXT with SALSA-CGS
allows us to assess the impact of the morphological assumptions
in the optics model. Comparison of MT-EXT with SALSA-EXT, or MT-CGS with
SALSA-CGS will give us an estimate of how much the inclusion
or omission of microphysical processes impacts modelling results of
aerosol radiometric properties.
While statistical analyses can uncover general trends, it is difficult
to understand the underlying physical reasons for model differences from an
analysis of temporally and geographically averaged model results. Therefore,
we also consider a few case studies in some more detail. We take the optical
properties modelled with different MATCH versions as input to a radiative
transfer model and analyse the total aerosol radiative forcing and the
black carbon radiative forcing. The main goal is to understand how
differences
in single-scattering optical properties between the two optics models impact
the outcome of the radiative transfer simulations. To keep the case studies
within manageable bounds, we restrict ourselves to four geographic locations
(two over land, two over the ocean), two instances (one representing
low-BC summer concentrations, one representing high-BC winter conditions),
and we limit ourselves to comparing model set-ups MT-EXT, MT-CGS, and
SALSA-CGS. More specifically, we consider one site over northern Italy (45.0
To further investigate the significance of the optics model for
radiometric properties, we also look at optical properties relevant
for remote sensing, namely, backscattering coefficient and Ångström
exponent. These results are discussed in
Sects.
Figure
Aerosol optical depth over Europe on 22 December 2007,
12:00
Averaged relative difference in aerosol optical depth (AOD),
backscattering coefficient (BSCA), single scattering albedo (SSA) and
asymmetry parameter (
Aerosol forcing and optical properties at 532(CGS)/500(EXT) nm over
northern Italy in June. Results are shown for the three model versions MT-EXT
(blue), MT-CGS (red), and SALSA-CGS (green). The aerosol forcing is derived
by taking the difference in radiative fluxes between an aerosol-laden and
clear sky. The difference in direct (
This is also evident from a comparative analysis of geographically
and temporally averaged aerosol optical properties. Table
Comparison of the columns “MT(EXT,CGS)” and “SALSA(EXT,CGS)”
illustrates the differences
between the optics models in the absence and presence of
aerosol-microphysical processes. As we already suspected from inspection
of Fig.
The “CGS(MT,SALSA)” column shows differences between optical properties computed in the absence and presence of aerosol-microphysical processes, where the optics computations have been performed with the CGS model. The “EXT(MT,SALSA)” column shows an analogous comparison, where the optics computations have been performed with the EXT model. If we compare the magnitudes of the entries in columns “MT(EXT,CGS)” and “SALSA(EXT,CGS)” with the corresponding magnitudes of the entries in columns “CGS(MT,SALSA)” and “EXT(MT,SALSA)”, then we see that the differences between the two optics models (EXT,CGS) are roughly of the same order as the differences between the two aerosol models (MT,SALSA). Thus, the main observation is that the choice of aerosol-optics model can have an effect on modelled optical properties that is of comparable magnitude to the level of detail in the description of aerosol-microphysical processes.
While spatio-temporally averaged model results allow us to draw some general conclusions, it is difficult to understand the reasons for the observed differences from such an analysis. We will, therefore, complement this investigation in the following sections with a more detailed analysis of some selected case studies.
In Sect.
The result for the optical properties obtained with the three model
versions (AOD per layer, SSA, and
Same as Fig.
We start by comparing radiative fluxes in the presence and absence
of all aerosol components, which we refer to as the “aerosol radiative
effects”. Figures
If we focus now on differences in the radiative net flux
At both locations the diffuse upwelling flux is smaller for SALSA
then for MT, but for different reasons. Over the Mediterranean
(Fig.
Effective radius,
Over northern Italy (Fig.
To further analyse the difference in optical properties between MT
and SALSA, we look at the aerosol masses and the relative sizes of
the particles. Figure
For the other two geographical locations and the second time event, the TOA
results are summarized in Table
Vertical distribution of aerosol particles in northern Italy and over the Mediterranean on 22 June 2007 at 12:00.
We now compare radiative fluxes in the presence and absence of
black carbon, which we refer to as the “black carbon radiative
effect”. Figures
Figure
Table
The comparison between SALSA and the MT model in the previous section served two purposes. First, it helped us to develop a basic understanding of the effects of aerosol particles and black carbon on radiative fluxes. Second, it provided us with a gauge for assessing the importance of the aerosol-optics model, which will be the subject of this section.
We compare the old EXT (blue line) and the new CGS (red line)
optics models in Figs.
The aerosol forcing at the top of the atmosphere (TOA),
Over the Mediterranean (Fig.
Table
The black carbon forcing in Figs.
Black carbon forcing and optical properties at 532(CGS)/500(EXT) nm
over northern Italy in June. Results are shown for the three model versions
MT-EXT (blue), MT-CGS (red), and SALSA-CGS (green). The black carbon forcing
is derived by taking the difference in radiative fluxes between an
aerosol-laden sky including black carbon and an aerosol-laden sky omitting
black carbon. The difference in direct (
Same as Fig.
Same as Table
Let us now return to the main
question of this study, namely, whether or not the level of detail in aerosol
optics modelling has a significant impact on observable
radiometric properties. We already saw in Table
In the following two subsections, we will focus on the selected case studies and discuss the significance of the optics model for radiometric quantities that are relevant for remote sensing applications.
From ground-based and space-borne lidar measurements one can
obtain the aerosol backscattering coefficient
Backscattering coefficient at a wavelength of 532
We saw in Fig.
A similar comparison of the two optics models (red and blue lines
in Fig.
The Ångström exponent
Ångström exponent in the wavelength region
532–1064
We have implemented a new aerosol-optics model in a regional
chemical transport model. The new model differs from an earlier
optics model described in
We first performed a comparison of optical properties averaged over
the entire model domain and over 1 month. To gauge the differences between
the new and old optics models, we furthermore compare two model versions of
the CTM with different levels
of detail in the aerosol process descriptions, namely, one version
that includes aerosol-dynamic processes, and a simpler
mass-transport model, in which aerosol microphysics is switched
off. The importance of aerosol microphysics is well understood and can
therefore serve as a reference. We found that the differences in optical
properties between the two optics models are of the same order as those
between the versions that include and exclude microphysical processes.
For example, the aerosol optical
depth computed with the two optics models differs
by
We furthermore wanted to understand how the differences in optical properties impact radiative transfer processes in an aerosol-laden atmosphere. To this end we compare radiative fluxes modelled with the old and new optics models. The comparison showed that the differences in radiative net fluxes between the two optics models are of similar magnitude as corresponding differences between the aerosol-microphysics and mass-transport models.
These results strongly suggest that simplifications in the assumptions on aerosol morphology in the optics model can introduce substantial errors in modelled radiative fluxes and observables relevant to remote sensing. In chemistry-climate models such errors would enter into the simulation of the direct aerosol radiative forcing effect and add to all other sources of error in the model. In model evaluations that make use of remote sensing observations these errors would complicate the comparison between model results and observations.
The modifications to the morphology assumptions in the optics model were
limited to black carbon particles. There are many other aerosol particles
with complex
morphological properties, such as mineral dust, which our optics
model still treats by a simple homogeneous-sphere
model. The findings of our study should be an incentive for
improving the description of dust and volcanic-ash optical
modelling in CTMs. A recent review of our current state of knowledge on
aerosol morphology and aerosol optics for a variety of different
aerosol particles can be found in
The findings of this study are likely to have implications for
chemical data assimilation. In data assimilation one employs an
The SALSA aerosol microphysics code is distributed under the Apache 2.0 license, while the MATCH chemistry-transport model and the aerosol-optics database are available upon request from SMHI.
The external-mixture optics model is based on using four size bins that cover
the dry-radius intervals
In the external-mixture optics database, optical properties are pre-computed
by integrating optical properties at discrete sizes over the truncated
log-normal size distribution. This integration is done numerically with a
high size resolution. The computation is performed for different refractive
indices
Secondary inorganic aerosols as well as organic aerosols and sea salt are
assumed to be hydrophilic. We use the parameterization by
Once the ensemble-averaged optical properties in each grid cell of the
model domain have been computed, one can compute radiometric observables,
such as the extinction aerosol optical depth
In SALSA the number density as a function of particle radius,
In effective-medium theory (EMT) one considers a composite material
consisting of two materials with refractive indices
The volume fraction is obtained from the mass concentrations
The refractive indices that are used in the new optics model (and in the
effective-medium calculations) are listed in Table
Refractive index
E. Andersson acknowledges funding from the Swedish National Space Board within the OSCES project (no. 101/13). M. Kahnert has been funded by the Swedish Research Council (Vetenskapsrådet) within the AGES project (project 621-2011-3346). Edited by: S. Unterstrasser