Introduction
Atmospheric aerosol particles of both anthropogenic and natural origin have
important effects on human health (Dockery et al., 1993), visibility (Malm et
al., 2000) and climate (Boucher et al., 2013). The magnitude of these aerosol
effects depend on the concentration, composition and size of the particles.
The particles may affect climate directly by scattering and absorbing solar
radiation (the aerosol direct effect; Charlson et al., 1992) and indirectly
by acting as cloud condensation nuclei (CCN, the seeds for cloud-droplet
formation) and affecting cloud radiative properties (the aerosol indirect
effect; Twomey, 1974; Albrecht, 1989). Uncertainties in these aerosol-climate
effects are among the leading uncertainties in recent climate forcing changes
(Boucher et al., 2013).
The particle size and composition distribution is shaped in the atmosphere by
primary emissions of particles, removal of particles through dry and wet
deposition, coagulation, aerosol- and cloud-phase chemistry, and condensation
from and evaporation to the vapor phase. In remote regions of the atmosphere,
away from major anthropogenic sources of particles (e.g., remote oceans and
polar regions), understanding the removal processes becomes increasingly
important in simulating aerosol-climate effects (Carslaw et al., 2013; Lee et
al., 2013; Croft et al., 2014). Lee et al. (2013) show that uncertainties in
dry and wet deposition were ranked first and third, respectively, as the
largest contributors to CCN uncertainty in clean remote regions (out 27
uncertain parameters investigated). As aerosol-climate effects are strongly
sensitive to CCN concentrations in remote regions due to the low baseline CCN
concentrations, particle removal mechanisms must be well represented in
aerosol-climate simulations (Carslaw et al., 2013).
Coagulation is an important removal mechanism of particle number and it
moves particle mass towards larger particle sizes. Brownian coagulation, the
process where particles collide by diffusion through air, is the dominant
coagulation mechanism for aerosol particles (Seinfeld and Pandis, 2006). The
coagulation kernel, the rate constant for coagulation between particles of
two different sizes, increases as the diameter of the smaller particle
decreases (increasing the diffusivity of the smaller particle) and as the
larger particle increases (increasing the size of the target for the
smaller, diffusing particle). The Brownian coagulation kernel reaches a
minimum when the particles have the same size.
In clouds, CCN-sized particles (particles with dry diameters larger than
30–100 nm depending on particle composition and cloud conditions) will
activate into cloud droplets, and their diameters will typically grow to
5–20 µm (Rogers and Yau, 1989). The smaller particles will not
activate and will continue to have wet diameters below ∼ 100 nm in the
cloud. These unactivated particles are referred to as interstitial particles.
The increase in size of CCN to cloud droplets will enhance the Brownian
coagulation rate between the CCN particles (now cloud droplets) and the
interstitial particles. Other effects, such as thermophoresis,
diffusiophoresis, turbulence and electrical effects (e.g., charged droplets
and/or particles) may also increase the collection of interstitial particles
by cloud droplets but are less well understood than Brownian coagulation
(Pruppacher and Klett, 1997). Furthermore, if cloud droplets (or ice crystals
in ice clouds) grow to diameters beyond ∼ 20 µm, the
droplets/crystals will have non-trivial fall speeds relative to the
interstitial particles, and gravitational collection of the particles will
also contribute to and may dominate coagulation (Rogers and Yau, 1989).
In the case of non-precipitating clouds, the cloud droplets will generally
not grow to diameters beyond 20 µm, and Brownian coagulation will
dominate the coagulation between droplets and interstitial particles. Any
coagulation between the droplets and the interstitial particles will lead to
a reduction of interstitial particle number and an increase in the size of
the CCN-sized particles after the cloud dries. This coagulation may impact
climate in two ways: (1) the removed interstitial particles that may have
otherwise grown to CCN sizes via condensation and increased CCN
concentrations (Pierce and Adams, 2007; Westervelt et al., 2013, 2014). Thus,
this coagulation may lower CCN concentrations and lead to a warming through a
reduction in the magnitude of the aerosol indirect effect. (2) The shift of
particle mass from the smaller, interstitial sizes to the larger sizes of the
activated particles may result in a change in the mass scattering and
absorption efficiencies of the particles and change the magnitude of the
aerosol direct effect (Seinfeld and Pandis, 2006).
In the case of precipitating clouds, the coagulation of particles below
clouds by falling drops via gravitational collection directly contributes to
wet scavenging/deposition of these particles. The gravitational collection of
particles below clouds by precipitation is typically included in global
aerosol models and has been investigated in earlier studies of collection
efficiency (Greenfield, 1957; Klett and Davis, 1973; Lin and Lee, 1975;
Schlamp et al., 1976; Wang et al., 1978; Hall, 1980), parameterizations
(Slinn, 1984; Jung and Lee, 1997; Croft et al., 2009; Wang et al., 2014) and
recent reviews (Zhang et al., 2013), so we will not focus on these effects in
this paper. Brownian coagulation of cloud droplets and interstitial particles
still occurs in precipitating clouds, but the collecting cloud droplets will
only be wet scavenged/deposited if they are converted into a precipitation
drop.
Brownian coagulation of interstitial particles with cloud droplets is often
ignored in aerosol models, and we are not aware of any papers that have
quantified the importance of this process on aerosol direct and indirect
effects on the global scale. Hoose et al. (2008) included Brownian
coagulation of interstitial particles with cloud droplets, along with other
aerosol-cloud interactions, in the ECHAM-HAM climate model with online
aerosol and cloud microphysics. While Hoose et al. (2008) provides zonal mass
budgets for how this coagulation impacts the aerosol size modes in their
model, they do not explicitly quantify the impact of this coagulation on
global aerosol size distributions and climate. The Brownian coagulation of
interstitial particles with cloud droplets is also included in the MIRAGE
model (Easter et al., 2004; Ghan et al., 2006) but, like ECHAM-HAM, we are
not aware of a detailed evaluation of this process. Finally, this process is
included in HADAM4 (Jones et al., 2001), but this is a mass-only model and
does not consider the evolution of the aerosol size distribution. To our
knowledge, the Brownian coagulation between interstitial particles and cloud
droplets has not previously been considered in many of the other global
aerosol microphysics models, including GEOS-Chem-TOMAS ((Goddard Earth Observing System-Chemistry TwO-Moment Aerosol Sectional); D'Andrea et al.,
2013; Pierce et al., 2013; Trivitayanurak et al., 2008), GISS-TOMAS (Adams
and Seinfeld, 2002; Pierce and Adams, 2009), GLOMAP (Spracklen et al., 2005a,
b, 2008; Mann et al., 2012), GLOMAP-Mode (Mann et al., 2010, 2012; Lee et
al., 2013), GEOS-Chem-APM (Yu and Luo, 2009; Yu, 2011) and IMPACT (Herzog et
al., 2004; Wang and Penner, 2009).
In this paper, we estimate the effects of Brownian coagulation of
interstitial particles with cloud droplets in shaping the aerosol size
distribution and aerosol-climate effects, globally. Additionally, we compare
the simulated size distributions with and without interstitial coagulation to
measurements at 21 sites globally to determine if the inclusion of this
coagulation improves our simulated size distributions. We only consider
Brownian coagulation between the interstitial particles and cloud droplets,
and we do not consider thermophoresis, diffusiophoresis, turbulence, or
electrical effects. These other effects will change the rate of coagulation
of the interstitial particles in the accumulation-mode size range with the
cloud droplets, and will have less influence on the smaller particles
relative to the effects of Brownian diffusion. In the following section, we
describe the GEOS-Chem-TOMAS global chemical transport model with online
aerosol microphysics used in this study, the modifications we made to the
model, and the various model simulations. In Sect. 3, we provide the results
and analysis of our model simulations estimating the effect of interstitial
particle coagulation by cloud droplets, and we compare our simulated results
to measurements. Finally, we provide conclusions in Sect. 4.
Methods
GEOS-Chem-TOMAS overview
In this paper, we simulate global aerosol size distributions using the
GEOS-Chem-TOMAS model, which is a coupling of the GEOS-Chem global chemical
transport model (www.geos-chem.org, Bey et al., 2001) with the TOMAS
microphysics scheme (Adams and Seinfeld, 2002; Lee and Adams, 2012). In this
work, model simulations use GEOS-Chem version 9.02 at
4∘ × 5∘ resolution globally with 47 layers extending
from the surface to 0.01 hPa. Modeled meteorology is taken from the National
Aeronautics and Space Administration (NASA) Global Modeling and Assimilation
Office (GMAO) Goddard Earth Observing System version 5 (GEOS-5) assimilated
meteorology product. All simulations use year 2012 meteorology and emissions
following a 3-month spin-up at the end of 2011. GEOS-Chem includes simulation
of 50 gas-phase species including aerosol precursor gases such as SO2
and NH3.
TOMAS in this work tracks the number and mass of particles within each of
15 size sections. The first 13 size sections are logarithmically spaced and
span diameters from approximately 3 nm to 1 µm, and the 2 final
size sections span 1–10 µm (Lee and Adams, 2012). Particle
composition includes sulfate, ammonia, sea spray, hydrophilic organics,
hydrophobic organics, internally mixed black carbon, externally mixed black
carbon, dust and water. Particle nucleation is estimated using the ternary
scheme (H2SO4 + NH3 + H2O) of Napari et
al. (2002) with nucleation rates scaled by 10-5, which showed good
agreement versus observations in Westervelt et al. (2013) and the binary
(H2SO4+H2O) nucleation scheme of Vehkamäki et al. (2012)
in the regions with NH3 mixing ratios below the Napari et al. (2002)
threshold. Particle sizes below 3 nm are approximated using the Kerminen et
al. (2004) scheme, which has been evaluated in TOMAS in Lee et al. (2013).
Secondary organic aerosol (SOA) includes both a biogenic contribution and an
anthropogenic (or anthropogenically enhanced yields of biogenic SOA)
contribution and is considered to be non-volatile for the condensation
parameterization as in D'Andrea et al. (2013). Emissions in GEOS-Chem-TOMAS
are described in detail in Stevens and Pierce (2014).
Coagulation between particles in GEOS-Chem-TOMAS occurs using the Brownian
coagulation scheme of Fuchs (1964). Prior to this work, the particle size for
the coagulation parameterization was found using the grid-box-mean relative
humidity for water uptake (capped at 99 %) both in and out of clouds.
Thus, in clouds, previous versions did not account for Brownian coagulation of
interstitial aerosols with particles that have grown to cloud droplet size.
This previous model version that lacked coagulation between interstitial
particles and cloud droplets will be our base assumption for comparison in
this paper.
Brownian coagulation between interstitial particles with cloud
droplets
In this work, we add Brownian coagulation between interstitial particles and
particles that are assumed to have activated and grown to cloud droplet
size. In the GEOS-5 assimilated meteorological fields, model grid boxes are
divided into cloudy fractions and non-cloudy fractions. For the cloudy
fraction of the grid box, we assume that all particles above a certain size
threshold activated into cloud droplets (this size threshold is varied in
different simulations described below). We assume that the activated
aerosols have a fixed wet size equal to the assumed cloud droplet size,
which is varied between simulations, described below. The coagulation kernel
is then calculated between all particle size-bin combinations regardless of
whether the particles in the bin are activated or not. Similarly, we
calculate the coagulation kernel for the non-cloudy portion of the grid box
for all size-bin combinations assuming that all bins are of non-activated
wet size. The grid-box-mean coagulation rate between any two size bins is
then calculated as follows:
Ji,j=(1-fcloudy)Kclear;i,jNiNj+fcloudyKcloudy;i,jNiNj,
where Ji,j is the coagulation rate between particles in bins i and bin
j, fcloudy is the fraction of the grid box that is cloudy,
Kclear;i,j is the coagulation kernel between bins i and j in
the clear portion of the grid box, Kcloudy;i,j is the coagulation
kernel between bins i and j in the cloudy portion of the grid box, Ni
is the number concentration of particles in bin i, and Nj is the
number concentration of particles in bin j.
Summary of simulations.
Simulation name
Interstitialcoagulation
Critical diameterfor activation
Assumed dropletdiameter
Minimumtemperature foruse of revisedcoagulation
BASE
No
N/A
N/A
N/A
INT_65nm_10µm_238K
Yes
65 nm
10 µm
238 K
INT_65nm_13µm_238K
Yes
65 nm
13 µm
238 K
INT_40nm_10µm_238K
Yes
40 nm
10 µm
238 K
INT_65nm_10µm_258K
Yes
65 nm
10 µm
258 K
In our base simulations, for comparison, we do not consider Brownian
coagulation between interstitial particles and cloud droplets, which is
equivalent to assuming the fcloudy is 0 in Eq. (1).
Kcloudy;i,j is determined by the size of the interstitial and
activated particles, which we vary between sensitivity simulations and are
discussed next. We apply this interstitial coagulation mechanism only when
temperatures are above 238 K (threshold for homogeneous freezing; Koop et
al., 2000) because the crystal size distributions and concentrations in ice
clouds are much more variable than those of liquid clouds. As glaciation
often occurs at warmer temperatures, we perform a sensitivity simulation to
this temperature cutoff, described below. We justify this temperature
threshold since super-cooled liquid clouds can exist at temperatures as cold
as 238 K, although the onset of glaciation can occur at temperatures as warm
as 258–263 K (Rosenfeld and Woodley, 2000; Rosenfeld et al., 2011). Recent
studies indicate that low-level liquid clouds are ubiquitous in all seasons
in remote regions such as the Arctic (e.g., Cesana et al., 2012). Thus, we
also perform a simulation where we limit interstitial coagulation to
temperatures warmer than 258 K.
Simulations
Table 1 summarizes the simulations performed for this paper. As stated
earlier, in our BASE simulation there is no interstitial coagulation by
aerosols of cloud droplet size, and the coagulation scheme for the entire
grid box assumes all particles are of non-activated size
(Kclear;i,j). In the INT_65nm_10µm_238K
simulation, all particles with dry diameters larger than 65 nm in the cloudy
fraction of grid boxes warmer than 238 K are assumed to have wet diameters of
10 µm (i.e., the critical diameter for activation is 65 nm, and the
cloud droplet diameter is 10 µm for all droplets).
Kcloudy;i,j is calculated using this new wet diameter for the
particles with dry diameters larger than the critical 65 nm value. Thus, the
coagulation rate between any two particles, both with dry diameters smaller
than 65 nm, remains unchanged from the clear-sky value. For clouds colder
than 238 K, we do not consider interstitial coagulation. We test the
sensitivity of our results of this temperature cutoff as well as the critical
activation dry diameter and the assumed cloud droplet diameter. In the
INT_65nm_13µm_238K simulation, particles in the cloudy
fraction of the grid box with dry diameters larger than 65 nm are assumed to
have wet diameters of 13 µm. In the
INT_40nm_10µm_238K simulation, particles in the cloudy
fraction of the grid box with dry diameters larger than 40 nm are assumed to
have wet diameters of 10 µm. Finally, in the
INT_65nm_10µm_258K simulation, interstitial scavenging is
only considered in clouds warmer than 258 K.
None of these simulations capture the variability in clouds throughout the
globe (e.g., minimum activation diameters, cloud droplet sizes, glaciation
temperatures); however, in this work, we are attempting to bound the aerosol
and climate effects of interstitial scavenging that are frequently
overlooked in aerosol simulations.
Radiative forcing calculations
The direct radiative effect (DRE) is calculated using the parameterization of
Chylek and Wong (1995), which uses the single-scatter approximation. Optical
properties for aerosols are calculated from monthly averaged GEOS-Chem-TOMAS
aerosol number and mass size distributions with refractive indices for each
aerosol species from the Global Aerosol Data Set (GADS) (Koepke et al., 1997;
d'Almedia et al., 1991). We calculate the direct radiative effect assuming
the particles are internally mixed, and scattering species (e.g., sulfate and
organics) form a shell around black carbon (core-shell assumption). We volume
weight the refractive index of the scattering species. Scattering and
absorption efficiencies and the asymmetry parameter were calculated using the
Bohren and Huffman (1998) coated sphere Mie code (BHCOAT). Surface albedo and
cloud fraction are taken as monthly averages from GEOS-5. We assume no
aerosol effects in cloudy columns, and our all-sky DRE is the clear-sky DRE
multiplied by the clear-sky fraction.
Summary of the global mean change in aerosol number (N10 and
N80) and the radiative effect for all sensitivity simulations versus the
base case.
Simulation name
N10 2 km (new-BASE)
N80 2 km (new-BASE)
AIE (new-BASE)
INT_65nm_10µm_238K
-18.4 %
-10.2 %
+1.02 W m-2
INT_65nm_13µm_238K
-20.8 %
-11.7 %
+1.18 W m-2
INT_40nm_10µm_238K
-21.3 %
-10.9 %
+1.25 W m-2
INT_65nm_10µm_258K
-15.0 %
-10.5 %
+0.48 W m-2
We calculate the cloud-albedo aerosol indirect effect (AIE) offline by
calculating a change in monthly averaged cloud reflectivity due to a change
in monthly average aerosol number and mass size distributions. We calculate
the number of activated particles for each simulation using the Abdul-Razzak
and Ghan activation parameterization (Abdul-Razzak and Ghan, 2002) and
assuming a constant updraft velocity of 0.2 m s-1. Cloud optical depth
is then calculated using monthly averaged activated particle concentrations,
and the monthly averaged liquid water content in the mean cloudy fraction of
each grid box from the GEOS5 met fields. Cloud reflectivity is calculated
from the cloud optical depth using the two-stream approximation assuming a
non-absorbing, horizontally homogenous cloud (Lacis and Hansen, 1974), which
may lead to an overprediction of cloud albedo of as much as 10 %
(Oreopoulis et al., 2007). The change in cloud-albedo forcing for two
simulations is then the product of the change in total albedo, incoming solar
radiation, cloud area, surface albedo, and atmospheric transmittance (Lacis
and Hansen, 1974).
While both our DRE and AIE calculations include simplifying assumptions
(e.g., monthly mean aerosol and cloud fields, a single-scatter approximation
for DRE, and no DRE in cloudy columns), these calculations should be
sufficient for determining the general range of DRE and AIE changes that are
expected from inclusion of coagulation of interstitial particles by cloud
droplets. These simplified calculations allow us to determine if the
interstitial scavenging is important in shaping radiative effects.
Results
Sensitivity of aerosols and radiative forcing to interstitial
scavenging
Table 2 shows the global, 2 km altitude and annual-mean relative changes in
N10 and N80 (the number concentration of particles larger than 10
and 80 nm, respectively) and absolute changes of the AIE between the various
interstitial scavenging simulations and BASE simulation. The 2 km layer is
shown here as being representative of low-level clouds. The global-mean
changes in N10 and N80 are -18.4 and -10.2 %, respectively,
between INT_65nm_10µm_238K and BASE. Thus, not only are
particles with dry diameters smaller than the 65 nm activation cutoff being
reduced in concentration due to interstitial scavenging, particles larger
than this (e.g., N80) are also being reduced in concentration. In
clouds, the coagulation rate between the particles with diameters larger than
65 nm would be slower than outside of the cloud because we only consider
Brownian coagulation and we assume all cloud droplets have the same diameter
(uniform sizes leads to reduced coagulation rates compared to polydisperse
sizes). Thus, the reduction in N80 is due to a reduction of the number
of particles with dry diameters smaller than 65 nm and a subsequent
reduction of the number of particles growing from these smaller sizes to
80 nm through condensation growth.
Annual-mean percent changes in N10 (a and c)
and N80 (b and d) changes between
INT_80nm_10µm_238K and BASE. (a) and (b)
show the changes for the 2 km model layer (representative of low clouds),
and (c) and (d) show the zonal-mean changes throughout the
troposphere.
Figure 1 shows the spatial patterns of annual-mean changes in N10 and
N80 between the INT_65nm_10µm_238K simulation and the
BASE simulation. Figure 1a shows the change in N10 in the 2 km layer of
the model (to represent the boundary layer). The largest changes are in
remote regions with low aerosol source strengths (e.g., the Arctic and
extratropical oceans) where the reductions in N10 exceed 25 %. There
are some regions with little (< 1 %) change in N10. These are
generally in regions of low cloud-cover amount that are downwind of cloudier
regions. There is enhanced nucleation in these low cloud-cover-amount regions
due to a lower condensation sink advecting in from cloudier regions upwind.
Figure 1c shows that the zonal changes in N10 are larger than 10 %
throughout nearly all of the troposphere. Figure 1b shows the 2 km changes
in N80. N80 are reduced by over 1 % over the entire 2 km layer
due to interstitial scavenging, and are greater than 5 % in regions
outside of the tropical continental regions. Decreases exceed 10 % in
remote midlatitude and polar regions. The decreases in the Arctic exceed
20 %. Figure 1d shows that the decreases in N80 due to interstitial
scavenging are generally between 10 and 20 % in the free troposphere.
These decreases in remote regions show that away from sources, the effects of
interstitial scavenging on CCN-sized particles might have significant
climatic effects.
Figure 2 shows the predicted annual-mean AIE between the
INT_65nm_10µm_238K simulation and the BASE simulation. The
global mean AIE of interstitial scavenging (Table 2) is +1.02 W m-2
between these simulations, showing that the interstitial scavenging causes a
reduction in the amount of cooling of the AIE. The AIE of interstitial
scavenging is over +1 W m-2 throughout most tropical and midlatitude
oceanic regions and over +1.5 W m-2 throughout much of the Northern
Hemisphere oceans. There are regions of smaller AIE of interstitial
scavenging over continents due to different combinations of (1) bright
surfaces (e.g., northern Africa, Middle East, Australia), (2) lower
cloud-cover amounts (same regions), and (3) very high CCN concentrations
saturating AIE changes (e.g., China, eastern North America, Europe, southern
Africa).
Annual-mean AIE between the INT_65nm_10µm_238K and
BASE simulations.
We also calculated the DRE between the
INT_65nm_10µm_238K simulation and the BASE simulation. The
global mean DRE was cooling, but smaller in magnitude than
-0.01 W m-2 for all interstitial scavenging simulations relative to
the BASE simulation (not shown due to the small magnitude). The slight
cooling was due to a shift in the aerosol mass distribution towards slightly
larger sizes due to the enhanced coagulation between the ultrafine particles
and the activated particles in clouds. These larger sizes are closer to the
peak size of the mass scattering efficiency and thus there is a net negative
DRE between the simulations with interstitial scavenging and the BASE
simulation (there was also a enhancement in absorption due to the shift in
size, but in smaller magnitude to the scattering effect). While there was a
large change in N80 number concentrations, which greatly affected the
AIE, the DRE was largely insensitive to the interstitial scavenging because
the changes in mass and mass-scatter/absorption efficiencies were small.
Observed and simulated (BASE and
INT_65nm_10µm_238K) annual-mean aerosol number size
distributions described in D'Andrea et al. (2013).
Table 2 also shows the global-mean N10, N80 and AIE changes of the
interstitial scavenging sensitivity studies versus BASE. We do not show maps
for each of these sensitivity cases because the spatial patterns are
qualitatively similar to Figs. 2 and 3. Increasing the diameter of the cloud
droplets to 13 µm (INT_65nm_13µm_238K) leads to
∼ 10–20 % strengthening of the decreases in N10 and N80
and a 15 % strengthening of the increase in AIE difference relative to
the case with 10 µm cloud droplets. This strengthening of the
interstitial scavenging effects is due to enhanced Brownian coagulation rates
because of the larger cloud droplets.
Decreasing the activation cutoff diameter to 40 nm
(INT_40nm_10µm_238K) leads to enhanced reduction of N10
relative to the 65 nm cutoff at the same temperature threshold and cloud
droplet size (-20.8 % rather than -18.4 %); however, the
reduction in N80 is similar to the 65 nm cutoff (-10.9 % rather than
-10.2 %). The 40 nm cutoff means that more particles will activate and
participate as scavengers than compared to the simulations with the 65 nm
cutoff; however, the 40–65 nm particles no longer undergo enhanced
scavenging. The AIE for the 40 nm cutoff diameter is about 20 % stronger
than in the 65 nm cutoff case because in many remote locations particles
smaller than 80 nm activate in our AIE calculation.
Increasing the glaciation temperature to 258 K from 238 K
(INT_65nm_10µm_258K) reduces the effects of the
interstitial scavenging because fewer clouds included the interstitial
scavenging in the simulation, particularly at high latitudes and altitudes.
The magnitude of the AIE for the 258 K case is roughly half of the AIE from
the 238 K case. Thus, the uncertainties in interstitial scavenging in ice
clouds which we ignore here, as well as the temperature at which clouds
glaciate, contribute large uncertainties to the strength of interstitial
scavenging effects.
Overall, our sensitivity studies show that the interstitial scavenging of
particles by cloud drops may reduce aerosol number concentrations by about
10–20 % and decrease the amount of cloud cooling (AIE) by about
1 W m-2; however, these magnitudes are uncertain on the order of a
factor of 2 due to uncertainties or variability in activation diameter, cloud
droplet size, and ice cloud physics.
Comparisons to aerosol size-distribution observations
In this section, we compare our simulations to aerosol size-distribution
measurements to determine how the addition of interstitial scavenging
changes model performance. These results should be viewed with caution
because aerosol microphysics models may have canceling errors, and improved
results may occur for wrong reasons. Nonetheless, this comparison shows how
the current state of the model changes relative to observations with the
addition of new physics.
For the comparisons, we use long-term (1 year or longer) aerosol size
distribution observations from 21 field sites. These data are described in
detail in D'Andrea et al. (2013) with a map of the locations in Fig. 1 of
that study. The measurements at these sites were from either scanning
mobility particle sizers (SMPSs) or differential mobility particle sizers
(DMPSs).
Annual-mean N10, N40, N80 and N150
GEOS-Chem-TOMAS simulation-to-measurement comparisons for the five simulations
at the 21 SMPS measurement sites.
Figure 3 shows the annual-mean size distributions at each location for the
measurements and the model for the BASE and
INT_65nm_10µm_238K simulations. The inclusion of
interstitial coagulation decreases the number of sub-100 nm particles at
many remote locations. Figure 4 shows comparisons of modeled to measured
annual-mean N10, N40, N80 and N150 at the 21 sites for
each of the five simulations. The statistics of the comparisons are given in
Table 3. The statistics are log-mean bias (LMB), slope (m), and coefficient
of determination (R2). We use the coefficient of determination rather
than the correlation coefficient (R) because the coefficient of
determination quantifies the fraction of the variance in the measurements
that is captured by the model. Including interstitial scavenging improves the
slope of the comparison of N10, N40 and N80 to measurements
for all four interstitial scavenging simulations relative to the BASE
simulation. The predicted concentration of N10, N40 and N80 in
clean regions was, on average, too high in the base simulation, and the
interstitial scavenging corrects this to some degree. The
INT_65nm_13µm_238K simulation, which had the most
aggressive interstitial scavenging, had the best slopes for N10,
N40 and N80, so it is possible that increasing the rate of
interstitial scavenging beyond that of this simulation may further improve
the slopes. The slope of the N150 relative to measurements does not
change because particles with diameters larger than 150 nm were less
affected by the addition of interstitial scavenging than smaller particles
(fewer particles grow to 150 nm than to 80 nm).
The inclusion of interstitial scavenging lowers the mean predicted values of
N10, N40, N80 and N150, which means that the LMB has more
negative values for the interstitial scavenging simulations relative to BASE.
Whether or not interstitial scavenging improves the LMB depends on the LMB of
the BASE simulation. The LMB for N10 and N150 in the BASE
simulation are positive, and the inclusion of interstitial scavenging brings
the LMB to values closer to zero. For N40 and N80, the inclusion of
interstitial scavenging brings the LMB to negative values that are further
from zero than the BASE simulation. Thus, the inclusion of interstitial
scavenging brings the LMB to more negative values, but neither shows a clear
improvement or deterioration compared to the BASE simulation. Finally, the
inclusion of interstitial scavenging does little to change the scatter across
the various sites, so the R2 values do not change greatly.
Statistical summary of the comparisons of simulated to measured
N10, N40, N80 and N150 across the 21 sites. Included
statistics are log-mean bias (LMB), slope (m), and coefficient of
determination (R2). Bold font indicates the simulation performing best
for each statistic.
Simulation
LMB
m
R2
N10
N40
N80
N150
N10
N40
N80
N150
N10
N40
N80
N150
BASE
0.077
-0.01
0.027
0.046
0.77
0.83
0.82
0.86
0.79
0.82
0.80
0.74
INT_65nm_10µm_238K
-0.034
-0.101
-0.043
0.015
0.86
0.90
0.86
0.86
0.79
0.82
0.81
0.75
INT_40nm_10µm_238K
-0.053
-0.107
-0.041
0.015
0.85
0.89
0.86
0.86
0.79
0.82
0.81
0.75
INT_65nm_10µm_258K
-0.022
-0.09
-0.035
0.019
0.82
0.87
0.85
0.86
0.79
0.83
0.81
0.75
INT_65nm_13µm_238K
-0.05
-0.114
-0.054
0.010
0.87
0.91
0.87
0.86
0.78
0.82
0.81
0.76
In summary, the inclusion of interstitial scavenging improves the slope of
N10, N40 and N80 comparisons to observations but has little
effect on the slope of N150 and the LMB and R2 of all sizes. Again,
the improvement of the slopes shown here could be due to interstitial
scavenging canceling errors from elsewhere in the model; however, because
interstitial scavenging is a physical processes that was lacking in our model
previously, it is encouraging that the model performance improved through its
inclusion.
Conclusions
In this paper, we test the sensitivity of the global aerosol size
distributions and radiative forcing to the scavenging of interstitial aerosol
particles by cloud droplets. We limit this study to scavenging in liquid
clouds. We make simple assumptions about cloud droplet activation and the
size of the cloud droplets as a starting point for understanding the impact
of interstitial scavenging. The inclusion of interstitial scavenging was
found to decrease the total number of particles larger than 10 nm (N10)
by 15–21 % at 2 km, relative to a simulation with no interstitial
scavenging. The range was due to different simulations where we changed the
cutoff temperature for ice clouds, the minimum aerosol activation diameter,
and the cloud droplet diameter. The number of particles larger than 80 nm
(N80, a proxy for CCN) decreased by 10–12 % at 2 km even though
particles of this size were not directly removed by the interstitial
scavenging. N80 was reduced when interstitial scavenging was included
because of fewer particles grew to 80 nm diameters from smaller sizes. The
global-mean aerosol indirect effect of including interstitial scavenging was
+0.5 to +1.3 W m-2, but the aerosol direct effect of this process
was negligible (∼ -0.01 W m-2) because neither the total mass
nor the mass-scatter/absorption efficiencies changed.
While the simulations in this paper use simplified assumptions regarding the
critical aerosol activation diameter and diameter of cloud droplets, our
sensitivity tests show that the scavenging of interstitial particles by cloud
droplets yields important (> 10 %) changes in the aerosol size
distribution, particularly in remote regions away from sources. These changes
provided an improvement in comparison of the simulated aerosol size
distribution to SMPS/DMPS measurements at 21 global sites; however, we
acknowledge that these improvements could be due to a canceling of other
errors in the model. We only consider Brownian coagulation between the
interstitial particles and cloud droplets, and we do not consider
thermophoresis, diffusiophoresis, turbulence, or electrical effects. These
effects are expected to be less important for collection of particles at the
size range of the interstitial aerosols.
Thus, while the scavenging of interstitial particles by cloud droplets has
often been left out of previous aerosol-climate studies, we recommend aerosol
microphysics models include this process since the effects on aerosols and
climate are substantial in many global regions. Interstitial scavenging has
aerosol-climate effects of similar magnitude as uncertainties in nucleation
(Merikanto et al., 2009; Pierce and Adams, 2009; Reddington et al., 2011;
Spracklen et al., 2008; Wang and Penner, 2009), primary emissions (Adams and
Seinfeld, 2003; Pierce and Adams, 2006, 2009; Reddington et al., 2011;
Spracklen et al., 2011), wet/dry deposition (Croft et al., 2012) and other
factors (Lee et al., 2013); hence, since significant effort is put into
improving these other processes in models, we recommend attention be paid to
the coagulation of interstitial particles by cloud droplets. Our simple
methods here may be further refined by including online schemes that
calculate aerosol activation from updraft velocities and the aerosol size
distribution (e.g., Nenes and Seinfeld, 2003; Abdul-Razzak and Ghan, 2000).
The MIRAGE and ECHAM-HAM models (Herzog et al., 2004; Ghan et al., 2006;
Hoose et al., 2008) already include these online activations schemes as well
as the interstitial coagulation described here, so these models may be seen
as state-of-the-art for this process.