Introduction
Providing realistic projections of climate change is difficult due to many
unknowns and large uncertainties that still exist
. For instance,
the recent study by suggests that the
observed increase in aerosol optical depth (AOD) over large parts of the
Middle East during the period 2001–2012 could to some extent prevail as a
result of climate change. Even in absence of growing anthropogenic aerosol
and aerosol precursor emissions, increasing temperature and decreasing
relative humidity (RH), as seen in the last decade, promote soil drying, which can
lead to increased dust (DU) emissions and hence AOD. Moreover, the discrepancies
in the geographical patterns of AOD and aerosol mass measurements can be
conclusively explained by aerosol water mass calculations
. In fact, in arid regions the water
uptake on DU aerosols also becomes important, if air pollution interacts with
DU outbreaks . The uptake of acids on
mineral DU can alter the ability of bulk DU to take up water vapor even
at a very low ambient RH – in the case of condensing
hydrochloric acid (HCl), calcium chloride (CaCl2) can be formed over
time,
which can cause water uptake at a RH as low as 28 %. While this might be the
case for arid regions all over the Earth, it is not an easy task for climate
modelers to correctly quantify the effect due to the complexity of the
underlying processes, as indicated by the studies of
. To reduce uncertainties, our latter two
studies applied the DU emissions scheme of
together with our chemical speciation
of the emissions fluxes (see Sect. ) in order to
resolve a chemical aging of mineral DU particles (see
Sect. ). Furthermore, an interaction of the emission flux
with meteorology and anthropogenic
pollutants, together with a water-mass-conserving coupling of the aerosol
hygroscopic growth into haze and clouds ,
is needed.
Proper hygroscopic growth calculations require thermodynamic models that can
calculate at least the equilibrium partitioning of aerosols and their
precursor gases from different natural sources in interaction with
anthropogenic air pollution. To calculate the gas–liquid–solid phase
partitioning, a variety of thermodynamic equilibrium models have therefore been
developed (, and references therein).
For instance, MARS is widely used in regional
modeling as the thermodynamic core of MADE/SORGAM
through
applications of the Weather Research and Forecasting model coupled to
Chemistry (WRF-Chem, https://ruc.noaa.gov/wrf/wrf-chem/, last access: 23 November 2018,
), the model of the European Monitoring and
Evaluation Programme (EMEP, http://www.emep.int/, last access: 23 November 2018
), and the European Air Pollution Dispersion model
system (EURAD, http://www.eurad.uni-koeln.de/, last access: 23 November 2018). Conversely, for
climate modeling, mainly ISORROPIA and EQSAM
are
widely used because of their computational efficiency. Both codes (among
others) were recently used for the investigation of global particulate
nitrate as part of the Aerosol Comparisons between Observations and Models
(AeroCom) phase III experiment . In addition to this
AeroCom study, different EQSAM versions have been used for various other
modeling studies, e.g., EQSAM1 (up to EQSAM_v03d):
,
, ,
, ,
, ; EQSAM2: and
; EQSAM3:
and . An overview of widely used modeling systems that
provide an option to use either EQSAM and/or ISORROPIA is given in
Table .
Modeling systems that provide an option to use EQSAM and/or
ISORROPIA. References are given for certain model versions: EQSAM_v03d (EQ1,
), EQSAM2 (EQ2,
), EQSAM3 (EQ3,
), EQSAM4clim (EQ4c,
) and/or ISORROPIA I (ISO1,
), and ISORROPIA II (ISO2,
). URL: CAMx – http://www.camx.com (last access: 23 November 2018);
CHIMERE – http://www.lmd.polytechnique.fr/chimere/ (last access: 23 November 2018); EMAC –
http://www.messy-interface.org/ (last access: 23 November 2018); EMEP – http://www.emep.int (last access: 23 November 2018);
GEOS – https://gmao.gsfc.nasa.gov/GEOS (last access: 23 November 2018); LOTOS-EUROS –
https://lotos-euros.tno.nl (last access: 23 November 2018); Meso-NH –
http://mesonh.aero.obs-mip.fr/ (last access: 23 November 2018); NASA GISS –
https://www.giss.nasa.gov (last access: 23 November 2018); POLYPHEMUS –
http://cerea.enpc.fr/polyphemus (last access: 23 November 2018); RACMO – https://www.knmi.nl/ (last access: 23 November 2018);
WRF – https://www.mmm.ucar.edu/weather-research-and-forecasting-model (last access: 23 November 2018).
Modeling System
Model
Reference
CAMx
ISO1
, CAMx User's Guide Version 6.40
CHIMERE
EQ1/ISO1
,
EMAC/GMXe
EQ4c
, ,
EMAC/GMXe
ISO2
, ,
EMAC/MADE3
EQ1
,
EMEP
EQ1/ISO1
,
GEOS-5
EQ1
, https://bit.ly/2AgSoBl, https://go.nasa.gov/2DTzmW3 (last access: 23 November 2018)
Meso-NH
EQ1/ISO1
, General docu of Meso-NH v5.1 (https://bit.ly/2pJDe2c, last access: 23 November 2018)
NASA/GISS
EQ1/ISO2
, ,
WRF/POLYPHEMUS
EQ1/ISO2
; Polyphemus 1.6 User's Guide https://bit.ly/2DVNpG4 (last access: 23 November 2018)
RACMO-LOTOS-EUROS
EQ1
,
TM3/TM5
EQ1/ISO2
, ,
CAMx
EQ4c
Under evaluation , CAMx User's Guide Version 6.45
EMEP
EQ4c
Implementation in progress (inclusion foreseen in Report 2018)
To reduce computational costs, both EQSAM and ISORROPIA follow the MARS
approach
to determine certain domains by the degree of sulfuric acid neutralization
and then divide the RH and composition space into
subdomains to minimize the number of equations to be solved. But in contrast
to EQSAM, all other thermodynamic equilibrium models require an iterative
procedure to solve the ionic composition, which adds significantly to
computational costs.
To accurately parameterize the aerosol hygroscopic growth by also considering
the Kelvin effect as described by , the EQSAM
approach was recently extended by
. The new model version, the EQuilibrium
Simplified Aerosol Model v4 for
climate modeling, enables aerosol water uptake calculations of
concentrated nanometer-sized particles up to dilute solutions, i.e., from the
compounds RH of deliquescence (RHD) up to supersaturation
(Köhler theory). EQSAM4clim extends the single solute coefficient approach
of to multicomponent mixtures, including
semi-volatile ammonium compounds and major crustal elements. The advantage of
EQSAM4clim is that the entire gas–liquid–solid aerosol phase partitioning and
water uptake including major mineral cations (Sect. ) can now
be solved analytically without iterations, which potentially can
significantly speed up computations on climate timescales
(Appendix ). Since the thermodynamics of the few widely
used equilibrium models such as MARS are limited to either the
ammonium–sulfate–nitrate–water system or only include sodium and chloride
but no crustal compounds such as calcium, magnesium, and potassium, EQSAM4clim
has been evaluated with its introduction against ISORROPIA II at various
levels of complexity. It was shown by that the
results of EQSAM4clim and ISORROPIA II are similar for reference box-model
calculations, textbook examples, and 3-D applications on timescales of
individual years.
To scrutinize the importance of aerosol water for climate applications, we
evaluate the AOD calculations of EQSAM4clim and ISORROPIA II on climate
timescales. For this purpose we extend the model evaluation of
by using the comprehensive chemistry–climate and
Earth system model EMAC in a setup similar to that applied in our studies on
(i) the DU–air pollution dynamics over the eastern Mediterranean
, (ii) the sensitivity of transatlantic DU
transport to chemical aging and related atmospheric processes
, and (iii) the comparison of the Metop
PMAp2 AOD products using model data (EUMETSAT ITT 15/210839, Final Report,
). These studies employ a highly complex
chemistry setup, particularly with respect to the gas and aqueous phase
chemistry and the associated chemical aging of primary aerosols. Since all
three studies revealed the importance of chemical aging of primary DU
particles for the calculation of the AOD, due to the regional amplification
by the aerosol water uptake, it is important to also evaluate the aerosol water
parameterization on climate timescales. Our EMAC model setup is described
in Sect. and evaluated in Sect. for
three periods, 2005, 2000–2010, and 2000–2013, and different model setups
that are scrutinized in Sect. . Additional results are
presented in the Supplement.
Locations of selected
AERONET and EMEP stations used in this EMAC evaluation study. The
corresponding regions are shown in Fig. S1
(Supplement).
Selected AOD time series for
2000–2010 (monthly means) for the stations shown in
Fig. , representing all regions of
Fig. S1. EMAC results based on ISORROPIA II
(ISO2), EQSAM4clim (EQ4c) versus AERONET observations (black circles), and
(PO2015). Additionally, scatter plots
(Figs. S2–S4) are shown
in the Supplement for 537 AERONET stations
(Fig. S1).
Taylor diagram for
satellite and model AOD (2000–2010 mean). MODIS (1), MODIS-Aqua (2),
MODIS-Deep Blue (3), MISR (4), SeaWIFS (5), ENVISAT (6), and models (7), i.e.,
ISORROPIA II (ISO2), EQSAM4clim (EQ4c), (PO2015),
versus AERONET observations for the four seasons: spring (MAM), summer (JJA),
autumn (SON), and winter (DJF). The number of observational points used in the
seasonal analysis are shown in parentheses.
Model description
Atmospheric chemistry–climate model EMAC
We use the atmospheric chemistry–climate model EMAC following
. EMAC comprises a numerical chemistry and
climate simulation system that includes sub-models describing tropospheric
and middle atmosphere processes and their interaction with oceans, land, and
human influences
.
The core atmospheric model, i.e., the fifth-generation European Centre Hamburg
general circulation model (ECHAM5, Röckner et al., 2006), is applied with
a spherical truncation of T42 and T106 (Gaussian grid of ≈2.8×2.8∘ and ≈1.1×1.1∘ in latitude and
longitude) and 31 vertical hybrid pressure levels up to 10 hPa. Our model
setup comprises several sub-models that are described below (for details see
http://www.messy-interface.org/, last access: 23 November 2018).
Dry deposition (DDEP) and sedimentation (SEDI) are described by
and are based on the big leaf approach.
Deposition fluxes are calculated as the product of the surface layer
concentration and the dry deposition velocity, which reflects the efficiency
of the transport to and destruction at the surface
. Wet deposition (SCAV) is described by
, while its impact on atmospheric composition is
detailed by and . The
offline (OFFEMIS) and online (ONEMIS) emission calculations, including tracer
nudging (TNUDGE), are described by , while
the sea–air exchange submodel (AIRSEA) calculates the transfer velocity for
certain soluble tracers (e.g., methanol, acetone, propane, propene, CO2,
and dimethylsulfide, DMS) . The atmospheric
chemistry is calculated with the chemistry submodel (MECCA), which was
introduced with .
Our chemical mechanism for the troposphere is similar to the one used in
– initially described in
(see their electronic supplement) –
although we use a reduced chemistry setup, which consists only of 40 (instead
104) gas phase species and of only 80 (instead 245) chemical reactions.
O3-related chemistry of the troposphere is included, but we exclude
decomposition of non-methane hydrocarbons (NMHCs)
. The other sub-models used in this study are
CONVECT , and LNOX as
well as CLOUD, CLOUDOPT, CVTRANS, GWAVE, H2O, JVAL, ORBIT, RAD, SURFACE, and
TROPOP . The aerosol radiative properties
(AEROPT) are
based on the scheme by . AEROPT take the width and
mean radii of the lognormal modes into account and consider the composition
to obtain the extinction coefficients (σsw,lw), single
scattering albedo (ωsw,lw), and asymmetry factors
(γsw,lw) for the shortwave (sw) and longwave (lw)
radiation. The radiative forcing is fully coupled in our EMAC version with
the primary and secondary aerosols obtained with the GMXe aerosol submodel
(Sect. ), which includes the associated water mass
thermodynamics (Sect. ), whereby the emission fluxes of primary
particles are calculated online in feedback with the EMAC model meteorology
(Sect. ).
To represent the actual day-to-day meteorology in the troposphere, the
model dynamics are weakly nudged
towards the analysis data of the European Centre for Medium-Range Weather
Forecasts (ECMWF) operational model data (up to 100 hPa). This allows a
direct comparison of our model chemistry with ground station and satellite
observations (Sect. ), by using the anthropogenic
emission inventory EDGAR Climate Change and Impact Research (CIRCE)
on a high spatial (0.1 by 0.1∘) and
moderate temporal (monthly) resolution – see
and , for example, for details.
Aerosol microphysics
Aerosol microphysics and the underlying gas–liquid–solid aerosol partitioning
is calculated with the Global Modal-aerosol eXtension (GMXe) module, which
was described by and
but originally developed as part of
. With GMXe we resolve the aerosol size
distribution in seven, i.e., four soluble (nucleation, Aitken, accumulation,
and coarse) and three insoluble (Aitken, accumulation, and coarse), log-normal
modes. Primary particles are emitted in the insoluble modes (Aitken,
accumulation, coarse) and only transferred upon chemical aging and
transport to the respective soluble modes (Aitken, accumulation, coarse). Our
description of aging depends on the amounts of available condensable
compounds that are the outcome of various emission processes (OFFEMIS,
ONEMIS) and chemistry calculations (GMXe, MECCA, SCAV). For the chemical
aging of bulk species we follow our approach introduced with
, which is discussed in
Sect. . The condensation dynamics are calculated within GMXe
such that coagulation and hygroscopic growth can alter the aerosol the
size distributions. Small particles are efficiently transferred to larger
sizes, whereby hygroscopic growth of individual aerosol compounds is
calculated from aerosol thermodynamics (Sect. ) based on a
chemical speciation of the aerosol emission fluxes
(Sect. ). Water uptake of bulk particles (OC, BC, SS,
DU), which can be optionally considered, is only treated for aged particles
in the soluble modes (Sect. ). Additionally, our EMAC version
allows us to consider the aerosol hysteresis effect
(Sect. ). To avoid an overlap with cloud formation
(especially optical thin clouds) the availability of water vapor is
dynamically determined within GMXe. This limits the aerosol hygroscopic
growth calculation by either ISORROPIA II or EQSAM4clim, described in
Sect. . Through this specific dynamical coupling, our overall
water uptake process depends on meteorology and strongly alters with
altitude, independently of the aerosol composition.
Aerosol thermodynamics
Aerosol thermodynamics is represented by EQSAM4clim
and ISORROPIA II
. Both gas–aerosol partitioning routines
calculate the gas–liquid–solid partitioning and aerosol hygroscopic growth.
They are embedded in GMXe in exactly the same way, so that a direct
comparison of the EMAC modeling results can be made. Deviations can be fully
explained by differences in the EQSAM4clim and ISORROPIA II composition
calculation approach. Both, EQSAM4clim and ISORROPIA II offer a
computationally efficient treatment of the multicomponent and multiphase
gas–liquid–solid aerosol partitioning at regional and global scales, by
dividing the RH and composition space into subdomains
that minimize the number of equations to be solved. However, the EQSAM4clim
framework is based on a single solute specific coefficient (vi), which
was introduced by to efficiently parameterize the
water uptake of concentrated nanometer-sized particles up to dilute
solutions. In contrast to ISORROPIA II, EQSAM4clim covers the mixed-solution
hygroscopic growth considering the Kelvin effect, i.e., water uptake from the
compound's RHD up to supersaturation
(Köhler theory). It was shown by that the
νi approach allows us to analytically solve the gas–liquid–solid
partitioning and the mixed-solution water uptake by eliminating the need for
numerical iterations, which can significantly speed up our EMAC computations
(Appendix ). For a consistent model intercomparison, in this study we
limit the gas–aerosol partitioning and associated hygroscopic
growth of our EMAC simulations to the inorganic compounds considered by
ISORROPIA II. Inorganic aerosol components and their thermodynamic properties
used in this study are defined in Table 1 of
(with their setup limited already to match the compounds of ISORROPIA II).
Thus, we consider the gas–liquid–solid aerosol partitioning and water uptake
of the precursor gases water vapor (H2O), sulfuric acid
(H2SO4), nitric acid (HNO3), hydrochloric acid (HCl),
and ammonia (NH3), together with the major cations sodium
(Na+), potassium (K+), calcium (Ca2+),
magnesium (Mg2+), and ammonium (NH4+) and the major
anions sulfate (SO42-), bisulfate (HSO4-), nitrate
(NO3-), and chloride (Cl-), such that nitrate, for example, can
replace chloride in sea salt (SS) aerosols (inline with our EQSAM concept
described in ;
). To enable the
full complexity of the phase partitioning with EQSAM4clim and ISORROPIA II,
we extend the default EMAC setup through ions assigned to the emission fluxes
of primary aerosol particles.
AOD and PM time series for
2000–2013 (monthly means): ISORROPIA II (ISO2) and EQSAM4clim (EQ4c) versus
AERONET and EMEP observations (a). Panel (b) shows the
corresponding climatological year for the AOD and PM (14-year average). The
two stations Harwell and Chilbolton (United Kingdom) lie well within one
model grid box (51∘ N, 1∘ W).
To calculate the aerosol water uptake of bulk species (see
Sect. ), we use Eq. (A3) of . Note
that Eq. (A3) is an inversion of Eq. (5a) of , which
can be reproduced with the parameters given in Table (with
Ke = 1, A = 1, B = 0). As detailed in Sect. 2.7 of
p. 7223, the mixed-solution aerosol water
uptake can be obtained by their Eq. (22), from tabulated single solute
molalities, or parameterized based on Eq. (5a) of
(Appendix A2, Eq. A3) in agreement with other approaches, including kappa
hygroscopicity parameters (see Figs. 3 and 4 of
, for example). The effect of the implicit assumption
(Ke = 1, A = 1, B = 0) on the overall bulk water uptake is
negligible for our sensitivity simulations presented in
Sect. (studied but not shown).
Parameters for the different chemical aging levels of
bulk species shown in Table (Sect. ).
νbulk (-) denotes the bulk water uptake coefficient,
RHDbulk (%) the bulk water uptake threshold, and
MFbulk (%) the mass fraction used for chemical aging of
the bulk aerosol species. The main reagent that is assumed to determine the
chemical aging (through implicit coating and water uptake) is included below
the bulk species. The values have been empirically determined by numerous
model applications and a very comprehensive model evaluation by the
constraint to yield the best agreement of our EMAC version with independent
model results and various observations. Key results of this evaluation cycle
are shown in Sect. ; additional results will be
presented separately.
Bulk compound
BC
OC
DU
SS
with main reagent
NH4NO3 | NH4HSO4
(NH4)2SO4| NH4HSO4
Ca(Cl)2 | Ca(NO3)2
NaCl | NaCl
Aging case
50|90 %
50|90 %
50|90 %
50|90 %
νbulk
1.051|1.254
1.275|1.254
2.025|1.586
1.358|1.358
MFbulk
50|90
50|40
75|90
100|50
RHDbulk
60|40
80|90
28|49
50|75
Chemical speciation of aerosol emission fluxes
We extend our EMAC setup to include a basic chemical speciation of the
natural aerosol emission fluxes in terms of certain cations and/or anions.
Usually, climate models treat only bulk tracers such as SS, DU, organic carbon (OC), and black carbon (BC). Instead, we assign ions to
the bulk emission fluxes of primary aerosols by using the major cations
Na+, K+, Ca2+, and Mg2+ and anions
SO42- and Cl-. Our concept of chemical speciation was
originally developed as part of GMXe by to
extend the aerosol water uptake calculations to the so-far chemically
unresolved bulk aerosol mass. Thus, for biomass burning OC and BC aerosols,
we consider the potassium cation (K+) to be a key reagent (proxy) for
the water uptake thermodynamics (Sect. ). For DU, we
respectively consider the calcium cation
(Ca2+) to be a chemical aging proxy, while we resolve the SS emission fluxes in terms of
the seawater composition, considering the major cations Na+,
K+, Ca2+, and Mg2+ and anions Cl- and
SO42-. Our emission fluxes of primary SS and DU
particles are calculated online, in feedback with the EMAC meteorology and
radiation computations. SS is emitted in two soluble modes
(accumulation and coarse) based on the flux parameterization of
, while mineral DU particles are emitted in two
insoluble modes (accumulation and coarse), following
. The required parameters for
OC, BC, SS
and DU used in our sensitivity study (Sect. ) to
scrutinize the bulk water uptake are given in Table and
described in Sect. . Note that Table gives the
fraction of DU, for example, that is treated as Ca(Cl)2 for the
50 % (or as Ca(NO3)2 for the 90 %) aging case, though
it is relevant only for bulk water uptake calculations. The
same is true for SS, OC, and BC. But this DU fraction is not
chemically resolved and transported as Ca(Cl)2, so the overall
aerosol composition remains unchanged. This is in contrast to our normal
(default) GMXe aging, which is considered in all simulations
(Sect. ). Within GMXe, the composition of bulk DU and SS is tracked, but the fraction of chemical speciation for the bulk water
uptake is prescribed (Table ). The actual composition is
calculated online (Sect. ).
AOD (a), total (liquids and
solids) particulate matter (PM) (b), and aerosol associated water (c)
at EMEP station Cabauw for 2000–2013 (monthly means): ISORROPIA II (ISO2),
EQSAM4clim (EQ4c), and AERONET observations (black circles). Available
observations are shown. Additionally, various size-resolved aerosol
properties are shown in the Supplement
(Fig. S5).
Aerosol mass (PM) time series
for 2000–2013 (monthly means): ISORROPIA II (ISO2) and EQSAM4clim (EQ4c)
versus EMEP stations, which have long-term observations (a). The
corresponding climatological year (14-year average) is shown below each
time series.
The chemical speciation approach applied in this study was introduced by
and first applied in
. As noted in the former publication (p.
9176, line 13–16), our chemical speciation has been determined such that the
model concentrations best match the available EMEP and CASTNET measurement
data for the period 2000–2013 (to be published separately). Publication of
the comprehensive model evaluation is foreseen and in progress.
EMAC tracer statistics for the year 2005 and 189 stations based on 5-hourly model output.
Simulations based on ISORROPIA II (ISO2) and EQSAM4clim (EQ4c) (identical EMAC setup).
Station mean
RMSE
CORR
MBE
ISO2
EQ4c
–
–
–
PM
58.05±193.45
57.23±193.03
3.64
1.00
-0.82
DU
41.91±192.84
41.75±192.34
3.41
1.00
-0.17
SS
6.83±8.47
6.37±7.78
0.93
1.00
-0.45
OC
2.32±1.94
2.33±1.94
0.07
1.00
0.01
BC
0.45±0.56
0.45±0.56
0.01
1.00
-0.00
H2O
14.48±13.71
13.53±13.07
2.32
0.99
-0.96
NO3-
1.26±1.02
1.16±0.95
0.30
0.96
-0.10
SO42-
2.25±1.53
2.40±1.66
0.32
0.99
0.15
H2SO4
0.02±0.03
0.02±0.03
0.00
1.00
-0.00
HSO4-
0.22±0.47
0.12±0.27
0.24
0.99
-0.10
Ca2+
2.25±10.28
2.24±10.26
0.18
1.00
-0.01
Mg2+
0.19±0.24
0.18±0.22
0.03
1.00
-0.01
NH4+
0.85±0.71
0.81±0.69
0.09
0.99
-0.04
Na+
0.66±0.81
0.62±0.75
0.08
1.00
-0.04
Cl-
0.64±0.90
0.56±0.83
0.15
0.99
-0.08
K+
0.19±0.12
0.19±0.12
0.01
1.00
-0.00
H+
0.02±0.02
0.02±0.02
0.01
0.93
0.00
OH-
0.06±0.09
0.06±0.09
0.02
0.97
0.00
NO
0.63±1.09
0.62±1.07
0.09
1.00
-0.00
NO2
6.00±6.70
5.98±6.66
0.18
1.00
-0.02
SO2
3.53±3.28
3.50±3.25
0.13
1.00
-0.03
HNO3
1.64±2.01
1.69±2.05
0.21
1.00
0.05
HCl
0.20±0.20
0.21±0.20
0.08
0.93
0.01
O3
56.61±19.34
56.41±19.29
0.69
1.00
-0.20
RWETAER
1.95±0.17
1.95±0.17
0.03
0.99
0.00
RDRYAER
1.75±0.07
1.75±0.06
0.01
0.98
0.00
AERNUMB
260.36±130.37
264.54±132.55
21.72
0.99
4.18
RH
69.16±20.81
69.20±20.79
0.69
1.00
0.04
T
18.94±28.84
18.95±28.83
5.11
0.98
0.01
Chemical aging and water uptake of bulk aerosols
Our chemical speciation of the primary aerosol emission fluxes is coupled to
a chemical aging of bulk species through which salt compounds and associated
water can be formed. In our model, the uptake of inorganic acids on bulk
compounds and the associated neutralization reactions and water uptake occur
during aerosol transport and thus change the (bulk) particle hygroscopicity
with time. The chemical aging process is hereby based on explicit
neutralization reactions of ions (cations, or anions), which are assigned to
the emission fluxes (e.g., K+, Ca2+; see
Sect. ). Through the reactions of these cations (anions)
with aerosol precursor gases, i.e., major oxidation products of natural and
anthropogenic air pollution (here H2SO4, HNO3, HCl,
NH3, and H2O), various neutralization (salt) compounds
can be formed, e.g., potassium sulfate (K2SO4), potassium bisulfate
(KHSO4), potassium nitrate (KNO3), potassium chloride (KCl),
calcium sulfate (CaSO4), calcium nitrate (Ca(NO3)2), calcium
chloride (CaCl2), and so on for ammonium, sodium, and magnesium; see
Table 1 of . The salts can cause an uptake of
water vapor (H2O) at different ambient humidities, with CaCl2
at RHs as low as 28 %. All salt solutions are subject to the RH
and temperature-dependent gas–liquid–solid partitioning as
described in Sect. and . For H2O
and each cation and anion, a chemical tracer is assigned such that they
undergo all aerosol microphysics and thermodynamic processes for their
respective GMXe aerosol mode(s) (Sect. ). Through this tracer
coupling, each salt compound can alter the subsequent AOD calculations in our
EMAC version, most noticeably through an associated aerosol water uptake.
Sensitivity runs with different levels of chemical aging of bulk
species as defined in Sect. and Table . Note
that the key difference between no aging and aging is the water uptake of
primary particles. This is only considered for the latter case (being based
on Sect. and ). All cases include the GMXe
coating processes (Sect. ) through condensation of gases such
as hydrochloric acid, nitric acid, sulfuric acid, and ammonia on insoluble
particles (mineral DU, black, and organic carbon). Additionally, in all cases
particles can mix through coagulation, and the formation of semi-volatile
salt compounds such as ammonium nitrate and ammonium chloride. Also, the
associated
gas–aerosol partitioning and water uptake (Sect. ) are
always applied for compounds in the soluble modes.
Case
Simulation
Option1
Option2
Option3
Application
Label
Aerosol water
Bulk aging
Hysteresis effect
Section
1
No aging
yes
no
no
Sects. + S1.3
2
No water
no
no
no
Sects. +
3
50 % aging
yes
50 %
yes
Sects. +
4
90 % aging
yes
90 %
yes
Sects. +
For the chemical aging of our bulk aerosol species (OC, BC, SS, and DU), we
assume that bulk OC behaves in terms of water uptake such that it would be
coated by ammonium sulfate with a mass fraction of 50 % OC, with the water uptake
parameters given in the first sub-column of Table . For the
90 % case, ammonium bisulfate is assumed with the water uptake parameters
given in the second sub-column (see further explanation in
Table ). To calculate the bulk water uptake, we use the
EQSAM4clim parameterizations (introduced by ) and
solve a bulk solute molality using Eq. (A3) of .
For the sake of simplicity, we neglect the Kelvin term (Ke=1, A=1,
B=0) and further assume that the water uptake of the bulk compounds can be
described by a mean value, for which we can use our single coefficient
νi. We further assume a single chemical reagent to be representative for
the bulk water uptake due to chemical aging of the bulk aerosol mass, but we
only calculate bulk water uptake if the RH exceeds a certain threshold. This
aging proxy is given in Table together with the required
parameters for our aging setup used in Sect. . For instance,
for the 50 % aging case of bulk SS mass, we assume
50% of the mass to be subject to
water uptake if the RH exceeds a threshold of 50%. And for this case
we assume NaCl as the proxy with νi=1.358 (Table 1 of
). Accordingly, we assume for DU that
75% of the mass is subject to water uptake if the RH exceeds the
threshold of 28%, due to a predominant coating by CaCl2 (with
νi=2.025).
CPU times. EMAC @ 96 CPU cores, Cy-Tera
(http://web.cytera.cyi.ac.cy/, last access: 23 November 2018).
Simulation
Memory (Gb node-1)
CPU time (h node-1)
Wall time (h)
A – ISORROPIA II
5.713064
173:49:49
14:31:26
B – EQSAM4clim
5.751476
158:53:35
13:16:42
C – EQSAM4clim
5.756064
158:08:04
13:12:58
D – none of both
5.738376
153:11:01
12:48:10
A0 – ISORROPIA II
5.748988
172:33:56
14:25:05
B0 – EQSAM4clim
5.744580
152:24:34
12:44:16
To distinguish between our EMAC setup that considers the water uptake of
normally chemically unresolved particles (SS, DU, OC, BC), in our
study we use the label “aging”, referring to a chemical aging that is used in
Sect. . In contrast, our EMAC setup that omits the chemical
aging and associated water uptake of bulk aerosols is labeled “no
aging” (Sect. ).
Global aerosol distributions of the
total (liquids and solids) particulate matter: meridional mean (a, d, g),
zonal mean (b, e, h), and atmospheric burden (c, f, i). The EMAC
results shown are based on ISORROPIA II (ISO2, a, b, c), EQSAM4clim (EQ4c,
d, e, f), and the corresponding difference between both simulations (EQ4c
minus ISO2, g, h, i).
Figure continued for EMAC
aerosol-associated water (2000–2013 mean). Please note the inversion of the
color scale (compared to Fig. 7).
Independent of this aging label, all our EMAC simulations consider a
comprehensive treatment of the chemical aging of the non-bulk aerosol
emission fluxes such that particles can deliquesce or effloresce with age,
which is part of our GMXe aerosol dynamical and thermodynamical treatment
(Sect. ). The chemical aging includes the dynamically limited
condensation of aerosol precursor gases on primary aerosol particles. Our
primary aerosol particles are emitted in the insoluble modes and, depending
on the coating level (i.e., the amount of gases condensed on the insoluble
particles), they are transferred to the soluble modes. But only the
chemically identified compounds of the soluble modes (Aitken, accumulation,
and coarse mode) are subject to the water uptake calculations by either
EQSAM4clim or ISORROPIA II by our no aging setup. Since the inorganic
aerosol composition usually explains only a fraction of the emission fluxes,
and since the coating process may involve complicated and largely unknown
chemical reactions that alter (age) the aerosol surfaces, for
our sensitivity study in Sect. we consider the water uptake of the
bulk aerosol mass (as described above). Normally, the bulk aerosol mass would
be otherwise considered to be dry only. And it was shown by our recent studies
by and that
the results of our EMAC aging setup agree better with various ground
station observations and satellite measurements.
Aerosol water mass – hysteresis effect
Our EMAC version further allows us to consider the so-called hysteresis effect.
That is, we can obtain the aerosol water mass for two cases, i.e, (1) the dry
case, in which RH increases and exceeds the compound's RHD or mixed-solution RHD
(Sect. 2.6 of ), and (2) the wet case, in which the RH
decreases until crystallization (efflorescence) point of the dissolved
compound(s) is reached. Below these thresholds no aerosol water is
calculated. The hysteresis effect can become regionally important since many
inorganic salt compounds, which take up water at a given RHD threshold, do
not crystallize at the same threshold. The efflorescence thresholds are often
observed to be much lower. Although the hysteresis effect might be less
pronounced in ambient observations (simply because the aerosol composition
usually changes over time due to transport and chemical reactions), the
instantaneous effect on radiation can locally become important.
EMAC AOD versus AERONET observations for the period 2000–2013 (a)
and the year 2005 (b). Different time averages are shown for the results
of ISORROPIA II (ISO2) and EQSAM4clim (EQ4c) based on 537 AERONET station
locations (Fig. S1).
EMAC AOD results versus AERONET observation (Obs) at Cabo Verde
(year 2005) with (a) 5-hourly means and (b) monthly means
for EQSAM4clim (EQ4c) and ISORROPIA II (ISO2).
Total (liquids and
solids) particulate ammonium (NH4+) at the EMEP site Vredepeel for the
year 2005. Daily means (a), monthly means (b), climatological year
based on the 14-year monthly mean (c). ISORROPIA II (ISO2) and EQSAM4clim
(EQ4c) versus observations (EMEP).
EMAC model AOD results for
the year 2005 (annual mean) based on ISORROPIA II (a, c) and
EQSAM4clim (b, d). (a, b) No aging and (c, d)
aging cases. AERONET ground station observations are included as squares
(same color scale). (e, f) Satellite observations by MODIS
(e) and MISR (f) (550 nm, annual mean 2005).
MODIS monitors the ambient AOD from space and provides data over the oceans
and, except deserts, also over continents
(http://modis-atmos.gsfc.nasa.gov/, last access: 23 November 2018). The
MISR aerosol product is available globally (products can be obtained from
http://disc.sci.gsfc.nasa.gov/giovanni, last access:
23 November 2018).
To consider the hysteresis effect in a climate model, we assume for the sake
of simplicity (and because of missing measurements) no single compound
efflorescence thresholds. Our criteria that determine a wet case or
dry case instead depend on two factors: (i) a RH threshold and (ii) the
existence of aerosol water mass from the previous time step. In case aerosol
water mass from the previous time step is nonzero for the given time step
(and model grid box), and, if additionally the RH is above 40% (fixed
efflorescence value), we consider the upper hysteresis loop and only
calculate the gas–liquid partitioning with either EQSAM4clim or ISORROPIA II.
Otherwise, we account for the full gas–liquid–solid partitioning (lower
hysteresis loop). The water uptake is then based on deliquescence of single
or mixed solutions as described in . Note that
the aerosol water mass is treated prognostically in our EMAC version
Sect. . That is, we assign a model tracer for water vapor and
for each aerosol mode to transport the different water masses. This allows us to
retrieve the required time information for a certain location on Earth,
although we are only approximately able to distinguish between the upper or
lower hysteresis loop. Results of our EMAC setup that include the hysteresis
effect are shown in Sects. and .
Climate applications
To evaluate the hygroscopic growth calculations of EQSAM4clim and
ISORROPIA II and to evaluate our EMAC version, we focus on the AOD since
long-term observations are available for many regions of the Earth. The AOD,
or extinction coefficient, is a measure of radiation scattering and
absorption at different wavelengths and sensitive to gas–liquid–solid
partitioning and aerosol hygroscopic growth. We use ground station
observations from the AErosol RObotic NETwork (AERONET,
http://aeronet.gsfc.nasa.gov, last access: 23 November 2018). Complementarily, we use independent satellite observations from
MODIS and MISR (both available from
http://disc.sci.gsfc.nasa.gov/giovanni, last access: 23 November 2018). The comparison of model results against
measurements includes the in situ observations of the Clean Air Status and
Trends NETwork (CASTNET, http://www.epa.gov/castnet, last
access: 23 November 2018). CASTNET is a national
air quality monitoring network of the United States of America designed to
provide data to assess trends in air quality, atmospheric deposition, and
ecological effects due to changes in air pollutant emissions. For Europe, we
use data of the European Monitoring and Evaluation Programme (EMEP)
(http://www.emep.int/, last access: 23 November 2018). EMEP is a scientifically based and policy-driven program under the
Convention on Long-range Transboundary Air Pollution (CLRTAP) for
international cooperation to solve transboundary air pollution problems
. Our EMAC model evaluation is based on two
model resolutions, i.e., T42 and T106 (Sect. ). Most of our
model output is based on 5-hourly averages, such that any full hour serves as
an
averaging-interval center once within 5 days. An extension of our study to a
more in-depth evaluation of the underlying aerosol composition and
neutralization levels will be presented separately, while the sensitivity of
the inorganic aerosol composition to model assumptions (e.g., ISORROPIA II
vs.
EQSAM4clim) is presented in the Supplement of this work (see Sect. S1.3, Figs. S6–S20).
EMAC AOD results versus AERONET observations at Lampedusa and
Beijing (shown in Fig. ) for the year 2005.
The first and third rows show 5-hourly means; the second and third rows show monthly means. Panels (a) and (b)
show EQSAM4clim (EQ4c) and ISORROPIA II (ISO2). Panels
(g) and (h) show sensitivity of EMAC AOD to different water assumptions considering different
EMAC setups: no aging (blue stars), no water without aerosol water
(orange circles), 50 % aging (pink crosses), and 90 % aging (light
blue squares); see Tables and
(Sect. ). The sensitivity is based on ISORROPIA II.
EMAC AOD based on the aging setup versus AERONET observations for
the year 2005, complementing Fig. . Different time
averages (full time resolution in light colors with statistics in the upper
left corner) are shown for the results of ISORROPIA II (ISO2) and EQSAM4clim
(EQ4c) based on 537 AERONET station locations (shown in
Fig. S1).
EMAC AOD versus AERONET and satellites
The EMAC hygroscopic growth calculations of EQSAM4clim and ISORROPIA II are
first compared to independent AOD results of
(PO2015) for the period 2000–2010. To give a compact but representative
picture of our analysis, we focus on a selection of AERONET stations that
represent different regions of the Earth.
Figure shows the selected station locations,
Fig. S1 the corresponding regions.
Figure shows the results of the AOD comparison
(from left to right, top to bottom): GSFC (North America), Sao Paulo (South
America), Cape San Juan (Latin America), Cabo Verde (West Africa), Canberra
(Australia), Yekaterinburg (Siberia), Forth Crete (EMME), Dakar (West
Africa), Yakutsk (Siberia), Amsterdam Island (Indian Ocean), Lampedusa (North
Africa), and Beijing (East Asia). Figure shows the
corresponding Taylor diagrams (standard deviation and correlation
coefficient) of the AOD comparison of EQSAM4clim, ISORROPIA II, and PO2015.
The comparison includes different observations from independent satellite
instruments, i.e, MODIS, MODIS-Aqua, MODIS-Deep Blue, MISR, SeaWIFS, and
ENVISAT, which are discussed in detail in our extended evaluation study. All
satellite products and model results are compared against the AERONET
observations for the period 2000–2010 (based on globally averaged seasonal
means using a 5-hourly model output and accordingly averaged AERONET
observations – details are given in , which
also outlines our interpolation procedure in time and space). The
corresponding scatter plots are shown in
Figs. S2–S4 and include
the statistics root-mean-square error (RMSE), correlation coefficient
(R), mean biased error (MBE), standard deviation of the model
results (σm), and AERONET observations (σo). The
equations are given in Appendix : Evaluation metrics.
The comparison shows that the differences associated with the two
partitioning schemes are smaller compared to the differences associated with
the two different EMAC setups, i.e., our EMAC version with EQSAM4clim (orange
circles) and ISORROPIA II (blue stars), and the independent PO2015 setup
(pink crosses). But all AOD model results are relatively close to the AERONET
observations, despite the distinct different underlying approaches to obtain
the mixed-solution aerosol water uptake. The largest differences occur for
regions that are dominated by mineral DU outbreaks, as indicated by the
AERONET stations Cabo Verde and Dakar (Fig. ).
The reason is that PO2015 uses prescribed DU emissions, while our setup
calculates the DU emission fluxes online with the EMAC meteorology
(Sect. ). Although the same is true for the SS
emissions, differences there are much less pronounced (see, for example, Amsterdam
Island). The prescribed DU emissions basically yield a mean DU
concentration with a too low variability, which is reflected in a too low
variability in the AOD results (see pink crosses for Dakar in
Fig. , for example). Conversely, our EMAC version results show
too low minimum values for certain periods (e.g., for 2002–2008), but the
magnitude of the seasonal cycle is much closer to the AERONET observations
(black circles). Conversely, the setup of PO2015 is based on the
T106L31 resolution (≈1.1×1.1∘), while our results are
based on a T42L31 (≈2.8×2.8∘) setup. Although the
coarser resolution somewhat affects the statistics of the analysis (see
Supplement), our results are also within the range of the satellite results
when compared to the AERONET observations (Fig. ).
Notably, spring and summer seasons are better resolved
than the winter months for our T42L31 setup. Altogether the results indicate that we may
underestimate the chemical aging of bulk particles, which is therefore
scrutinized in Sect. .
EQSAM4clim versus ISORROPIA II for 2000–2013
To further evaluate EQSAM4clim and ISORROPIA II, we compare the AOD and the
total particulate matter (PM) that drives the model AOD with AERONET and EMEP
observations for the period for 2000–2013.
Figure shows that the AOD and PM time series
and the climatological year (14-year average) are close to independent
observations of the EMEP station Harwell and the AERONET site Chilbolton
(United Kingdom, Fig. ). The two sites lie
within one model grid box and are chosen since no other site provides
long-term observations of both AOD and PM. Only Cabauw in the Netherlands,
which is one of the few EMEP and AERONET super-sites, provides AOD and PM
observations with some reasonable overlap and supports these results as shown
in Fig. . To complement the picture, the
corresponding aerosol water (H2O), which is associated with the total
model PM, is also shown yielding consistent results for EQSAM4clim and
ISORROPIA II (but no observations are available).
Figure S5 (Sect. S1.2) shows the corresponding size-resolved PM, aerosol
water, number concentration, and wet radius for each aerosol mode: nucleation
soluble (ns), Aitken soluble (ks), accumulation soluble (as), coarse soluble
(cs), Aitken insoluble (ki), accumulation insoluble (ai), and coarse
insoluble (ci) for ISORROPIA II (left column) and EQSAM4clim (right
column). The sum
of the modes (for PM, H2O) is identical to
Fig. and also supports this finding.
Figure further shows various PM time series of
EQSAM4clim and ISORROPIA II (top panels) in comparison with EMEP stations,
which provide long-term PM observations, i.e., Cabo de Creus,
Hyytiälä, Illmitz, and Vredepeel. The station locations are shown in
Fig. , the corresponding climatological year
below each time series (Fig. ). Interestingly,
despite the distinct different regions and climates, our EMAC model results
are close to these long-term PM observations. The corresponding global
aerosol PM and associated water (H2O) distributions (14-year average)
are shown in Figs. and : meridional means
(left columns), zonal means (middle columns), surface distributions (right
columns), ISORROPIA II (ISO2, top row), EQSAM4clim (EQ4c, middle row), and
together with the differences between both simulations (EQ4c–ISO2, bottom
row). Notably, our water mass
results are lowest in the western desert of the US in agreement with
and , for example.
Importantly, both the AOD and PM model results nicely compare with various
surface observations for the entire evaluation period (2000–2013). However,
the global surface and vertical distributions from both EMAC simulations are also
in close agreement for the aerosol PM and H2O, which supports our
previous finding (Sect. ) that the difference between
EQSAM4clim and ISORROPIA II is negligible on climate simulation timescales.
Figure additionally shows scatter plots of the model
AOD versus AERONET observations for the period 2000–2013 and the year 2005.
For each period, three different time averages are shown, i.e., 5-hourly
averages (full time resolution), monthly means, and station means based on 537
AERONET stations all over the Earth (locations are shown in
Fig. S1). The statistics
included in each panel summarize the results and show that both EMAC
simulations are comparable in terms of statistical key metrics, i.e., RMSE, standard deviation (σ),
R, and MBE (equations
are given in Appendix ). Interestingly, the statistics of all
time averages indicate that the results of EQSAM4clim are slightly closer to
the AERONET observations compared to ISORROPIA II. Note that
Fig. complements Fig.
with the results for 2005 with our EMAC aging setup that is discussed
further in Sect. .
EQSAM4clim versus ISORROPIA II for 2005
In order to scrutinize this result, we zoom into a single location and compare
the EMAC AOD of EQSAM4clim and ISORROPIA II for the AERONET observations at
Cabo Verde for both 5-hourly and monthly averages
(Fig. ). Cabo Verde is one of the more
difficult stations because of the frequent Sahara DU outflows
. In our setup the DU outflow is
associated with elevated calcium loadings, which can cause differences in the
subsequent sulfate–bisulfate neutralization (Sect. ).
Despite the slight underestimation of the AOD observations by both model
simulations, the results of EQSAM4clim and ISORROPIA II are very close
throughout the year. Even the distinct AOD peaks in May, which can be
attributed to Saharan DU outbreaks, are well resolved at the 5-hourly
output frequency, although the comparison based on monthly averages seems to
be less impressive. Nevertheless, the absolute comparison is overall very
good for a chemistry–climate model.
To evaluate the aerosol composition that drives the hygroscopic growth, we
further compare our aerosol ammonium (NH4+) results against EMEP
observations at the measurement site Vredepeel. NH4+ is the
weakest cation considered in our simulations and driven out of the aerosol
phase by all nonvolatile cations because of its semi-volatility. It is one
of the most difficult aerosol species to model, if the mineral cations
Na+, K+, Mg2+, and Ca2+ are
considered, for example, through a chemical speciation of the aerosol emission
fluxes (Sect. ). For
cation-rich locations NH4+ therefore shows the largest sensitivity in our aerosol calculations
(shown by the results of Sect. S1.3, for example).
Only in the case that NH4+ is the only cation that neutralizes the anions
SO42-, HSO4-, NO3-, and Cl-,
is
it preferentially bound with sulfate for which the aerosol concentrations are
usually in good agreement with observations. However, including mineral cations
through a chemical speciation of emission fluxes complicates the modeling
enormously. Despite these challenges, our comparison with observations in
Fig. shows that the total particulate
ammonium, i.e., the sum of all liquid and solid NH4+ cations,
compares well for different time averages for the year 2005. Differences
between EQSAM4clim and ISORROPIA II are also rather small for the daily,
monthly, and even 14-year monthly mean (climatological year).
To further evaluate our EMAC results on a global scale,
Fig. compares the annual mean AOD of ISORROPIA II (left
panels) and EQSAM4clim (right panels) against AERONET observations (included as
squares) for 2005 (top and middle rows). The top row represents our no
aging case and excludes chemical aging and hysteresis effects
(Sect. ), while the middle row represents our aging
case and includes both effects (they are discussed further in
Sect. ). The bottom row shows independent satellite
observations from MODIS and MISR. Altogether, this comparison shows that the
EMAC results based on EQSAM4clim and ISORROPIA II are also very similar on a
global scale and that the EMAC results labeled aging compare better with
the satellite observations than the no aging results. This qualitative
comparison indicates that the overall assumption on the water uptake is
important. But it also shows that the differences between the two different
EMAC setups (comparing upper and middle row) are larger than the differences
between the two distinct different gas–aerosol partitioning schemes
(comparing left and right panels).
Sensitivity of EMAC AOD to different RH cutoffs (see
Sect. ).
Sensitivity study (year 2005)
To scrutinize the importance of the aerosol water calculations, we compare our
EMAC results in a sensitivity study that excludes (Sect. )
and includes (Sect. ) the aerosol water and bulk water uptake
(Sect. ) due to the chemical aging of primary particles
(Sect. ).
EMAC setup – without chemical aging of bulk species
Our EMAC setup without chemical aging omits the water uptake of bulk aerosols
(OC, BC, SS, DU) in contrast to the aging case (Sect. ),
which considers that the bulk particle hygroscopicity can change with time
(Sect. ). For both setups we consider the chemical speciation
of the emission fluxes (Sect. ) to obtain chemically
specified aerosol mass fractions in terms of cations and anions. But for the
no aging case, we limit the water uptake to the neutralization products
(ion pairs), which are calculated with the partitioning schemes
(Sect. ). Our reasoning for this limited setup is that the
aerosol water mass of bulk species (Sect. ), as well as the
hysteresis effect (Sect. ), can regionally reduce
potential differences of the aerosol water mass calculations if the total
aerosol water mass is dominated by one of these effects. For both processes
explicit RHD calculations and the associated uncertainties
are excluded. The no aging setup is
therefore most sensitive to potential differences in the water uptake
calculation approaches of EQSAM4clim and ISORROPIA II, though differences are
rather small on a global scale as discussed in Sect.
(i.e., shown by the comparison in Fig. a, b).
We note that the relatively largest deviations occur in our no aging EMAC
setup for stations that are subject to high DU loads, e.g., Dakar and Cabo
Verde (see Supplement). But the aerosol properties that are most important
for climate modeling, i.e., the total (dry) PM and the associated aerosol
water mass concentrations, are mostly close to a one-by-one line for all
simulations and all stations. Differences are mainly caused by differences in
the bisulfate–sulfate partitioning of both schemes. In contrast to
ISORROPIA II, EQSAM4clim does not treat the dissolution of weak acids
(HNO3, HCl) and bases (NH3), which can cause differences
in the sulfate neutralization levels and the subsequent water coating of
mineral DU particles. The Kelvin effect is also not considered in
ISORROPIA II in contrast to EQSAM4clim, which can have an effect on the water
uptake of Aitken mode but not coarser particles. Nevertheless, overall
differences are small in terms of mass concentrations as shown by the
extended analysis included in the Supplement.
Note that the Supplement (Sect. S1.3) shows both time series and scatter
plots for 2005 for our no aging case, which are based on all 537 AERONET
stations of Fig. S1. The results include the PM (Fig. S6) and H2O
(Fig. S7) concentrations [µgm-3(air)], as well as those of the
lumped aerosols, i.e., sulfate (SO42-), bisulfate
(HSO4-), nitrate (NO3-), chloride (Cl-),
ammonium (NH4+), sodium (Na+), potassium (K+),
magnesium (Mg2+), and calcium (Ca2+), shown in
Figs. S8–S16. The corresponding scatter plots (Figs. S17–S20) show the
annual means for three soluble (key) aerosol modes of GMXe
(Sect. ): coarse (top row), accumulation (middle row), and
Aitken (bottom row) and include the growth factor (GF; see
). Each panel includes the statistics
RMSE, R, MBE, and standard deviation of ISORROPIA II
(x-SD) and EQSAM4clim (y-SD). Table complements the time
series and scatter plots with some additional statistics of key EMAC tracers.
EMAC setup – with chemical aging of bulk species
The EMAC setup labeled aging extends the no aging setup
(Sect. ) with the water mass calculation of bulk aerosol
species and the hysteresis effect (Sect. ) such that the
bulk particle hygroscopicity can change with time (Sect. ) –
note Table . Both can become regionally important. As noted
in Sect. , our EMAC aging setup compares better
with observations than the no aging case. This is especially true for
regions over the open oceans, intense biomass burnings, or DU outbreaks,
including the transatlantic DU transport as shown in
Fig. . But despite the more complex chemical aging setup
of bulk species, our EMAC version still somewhat underestimates the AOD
observations. This finding is supported by the AERONET observations, which
are included in Fig. (squares with the same color
scale). One reason could be that our default aging setup only considers
a partial aging of 50 % of the bulk aerosol mass for the additional
water uptake calculations.
To scrutinize the effect of aging level on the AOD comparison, we apply
different levels of bulk aging according to Table .
Figure shows the results of four different
EMAC simulations, i.e., case 1 with no aging (blue stars), case 2 with no
water (orange circles), case 3 with 50 % aging (pink crosses), and case 4
with
90 % aging (light blue squares). The upper two rows compare the model
results of EQSAM4clim and ISORROPIA II based on case 4 for the AERONET
observations at Lampedusa and Beijing for the year 2005. The first and third
rows show the 5-hourly means, while the second and fourth rows show the corresponding
monthly means. The lowest two rows present the key results of our sensitivity
study.
The comparison of cases 1–4 shows that aerosol water calculations are
essential. Excluding aging or aerosol water at all, our EMAC simulation
largely underestimates the AOD (case 1–2), while considering the bulk water
uptake (aging case 3–4) improves the AOD comparison. However, the
improvement strongly depends on the AERONET location and the assumed level of
aging. For instance, our EMAC results based on a 90 % aging level
(case 4) can overestimate the AOD observations at certain locations such as
for Lampedusa, while at the same time the results compare best with other
observations such as at the AERONET site of Beijing. With a decreasing level of
aging, the AOD observations become more underestimated for Beijing, while
they are
improved for Lampedusa. This fact points to missing processes that cannot be
resolved by applying constant chemical aging parameters. To improve our
results further, a more comprehensive chemical aging parameterization is
needed by an extension of the water uptake framework to organic
compounds as considered by , for example.
This
study included the neutralization of major carboxylic acids for
neutralization by the cations Na+, K+, Ca2+,
and
Mg2+ to form salt compounds (formates, acetates, oxalates,
citrates; see their Table 1), which can contribute to the overall aerosol
water mass and hence regionally improve the AOD. Yet, such extensions are
beyond the scope of this work. Here, we focus on a consistent model intercomparison of EQSAM4clim and ISORROPIA II and the importance of aerosol water
mass for the model evaluation in terms of AOD. Nevertheless, our EMAC results
based on the higher aging level (case 4) improve the global-scale
comparison of Fig. (discussed in
Sect. ) as shown by Fig. .
Note that the hysteresis assumption (Sect. ) comes on top
of both, i.e., our aging and no aging (Sect. )
assumption, but is negligible in our EMAC setup compared to the aging
effect, which is why we have not separated it from the sensitivity analysis.
Thus, the differences in AOD between aging and no aging are basically
caused by the associated water uptake of bulk compounds (SS, DU, OC, BC).
Overall, our sensitivity analysis indicates the potential limitations
associated with the lack of water uptake on organic aerosol, the effects of
organic aerosol on inorganic partitioning and resulting water uptake, and
water uptake and resulting AOD. With Fig.
we show the results of different aging assumptions. Although we do not
explicitly treat organic aerosols, the 50 % and 90 % aging cases also
include water uptake of organic aerosols through our consideration of OC bulk
mass (with the parameters given in Table ). Clearly, only
certain regions are dominated by organic aerosols and the water uptake of
organic aerosols is usually much less than that of the inorganic counterparts
(if normalized to the aerosol mass). Nevertheless, certain regions such as
Beijing can be dominated by organic aerosols and the effect on AOD can be
significant as shown by Fig. – compare no
water without aerosol water (orange circles), 50 % aging (pink crosses),
and 90 % aging (light blue squares) for the monthly means.
Importance of aerosol water
The sensitivity of our AOD calculations with respect to the RH cutoff is
analyzed next. Such a cutoff is required for all aerosol water mass
calculations and applied to prevent overlap between aerosol hygroscopic
growth and parameterized cloud formation. Most of our EMAC simulations use a
default cutoff (maximum) RH = 95 or 98 %, while there is no
minimum RH by default. In our EMAC simulations the minimum RH is determined
automatically by the aerosol composition, i.e., by the single solute or mixed-solution deliquescence RH (this is detailed in Sect. 2.6 of
).
Here we consider four different RH cutoff cases for which AOD results (2005,
annual mean) of four EMAC simulations are shown in Fig.
(from left to right, top to bottom): (a) RH = 0 [%], i.e., no aerosol
water; (b) RH =97 [%]; (c) RH = 98 [%]; and (d)
RH = 99.9 %. The four simulations only
differ by the assumption on the aerosol water uptake limitation, i.e., the
upper RH value that is used to limit the water uptake calculation for both
EQSAM4clim and ISORROPIA II. While our first and last sensitivity simulations
represent an extreme case (with unrealistic AOD results), the two simulations
with RH = 97 and 98 % cutoffs
yield similar AOD results that are relatively close to many AERONET
observations (colored squares). Noticeably, the AOD values significantly
increase for the high RH = 99.9 %
case. Of course, any RH cutoff is arbitrary if the aerosol water mass is not
consistently linked with cloud formation. To avoid an inconsistent
aerosol–cloud–radiation coupling,
proposed a mass conservative
coupling to limit the aerosol water mass by an approach that needs
to be further scrutinized too (presented elsewhere).