Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45
DOI 10.1186/s40645-015-0075-0

REVIEW

Open Access

Overview of the development of the
Aerosol Loading Interface for Cloud
microphysics In Simulation (ALICIS)
Takamichi Iguchi1,2*, In-Jin Choi3, Yousuke Sato4, Kentaroh Suzuki5 and Teruyuki Nakajima6

Abstract
This review summarizes the scientific background of and past/prospective update strategies for the development of
the Aerosol Loading Interface for Cloud microphysics In Simulation (ALICIS). ALICIS provides a novel approach forcoupling downscaled mesoscale cloud-resolving simulations to large-scale aerosol-transport simulations. Realistic
aerosol loading, including spatio-temporal aerosol variations and particle-size spectra, is implemented in the cloudresolving simulations. Prior studies employing ALICIS have demonstrated how the interface introduction
significantly improves the reproducibility of the simulated microphysical cloud structure through better
representation of aerosol effects on cloud.
Keywords: Cloud microphysics, Cloud and aerosol, Regional modeling, Dynamical downscaling

Review
Introduction
Aerosol effects on cloud in atmospheric models

The coexistence of the three phases of hydrogen monoxide is, within the solar system, unique to the Earth’s
atmosphere. The liquid and solid phases in the atmosphere (i.e., cloud and precipitation) play critical roles
in determining the Earth’s radiation balance andhydrological cycle by driving considerable changes in
the atmospheric hydrometeor distribution and characteristics. The hydrometeor behavior is controlled byvarious factors, not limited to phase changes affectedby temperature, dry-air and vapor pressure, advection
by background winds, and gravitational falling.
Particulate matter that is dispersed colloidally with avariety of the particle sizes, shapes, and chemical components in the Earth’s atmosphere is referred to as aerosol.
Aerosol particles are closely related to the liquid and solid
particles making up clouds and precipitation; they are not
only necessary for the nucleation process of hydrometeor
particles but are also a source of impurities in these* Correspondence: takamichi.iguchi@nasa.gov
1
Earth System Science Interdisciplinary Center, University of Maryland,College Park, MD, USA
2
Code 612 NASA Goddard Space Flight Center, Greenbelt, MD, USA
Full list of author information is available at the end of the article

particles. To date, the aerosol influence on cloud has been
investigated in a number of ways, from the perspectives ofweather and climate research, as well as in the context ofweather modification and geo-engineering, starting from aseries of studies by Twomey and Squires who examined
microphysical cloud structure under various airmass conditions using in situ aircraft measurements in the 1950s
and 1960s (e.g., Squires 1956). A well-known example
demonstrating the effects of aerosol is that of the systematic difference in microphysical properties between continental and oceanic clouds. Continental clouds generally
have a smaller mean particle size and a higher particle
number concentration than oceanic clouds. These differences are attributed to the disparity in aerosol concentration and characteristics between the regions.
Aerosol was not represented explicitly in most numerical models of the Earth’s atmosphere on global or regional
scales until the 1990s, despite its significance for clouds
and climate. This was due in part to the fact the models
were generally aimed at analyses of the atmosphere’s
dynamical structure and at land-surface rainfall predictions. Researchers thus considered aerosol unimportant in
the basic framework of atmospheric models, because it
had no direct influence on mass or energy conservation in
the atmosphere and its fluid dynamics. In addition, computational resources were insufficient to explicitly include© 2015 Iguchi et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

aerosol. Aerosol effects on cloud were thus tacitly included in the models in the form of parametrizations consisting of empirical equations. For example, saturation
adjustment (Soong and Ogura 1973; Tao et al. 1989) is areasonable assumption for calculations of the nucleationof cloud droplets and their condensation growth fromwater vapor. Even though the presence of aerosol is arequirement for the droplets’ nucleation process, thedetails are not important because of the wealth of aerosol
particles that can be activated as cloud condensation
nuclei (CCN) in the troposphere.
In the 1990s and 2000s, increasing interest in aerosol
brought the issue under consideration even in thecontext of atmospheric modeling approaches. The mostsignificant turning point was the recognition that aerosol
might counter the anthropogenic gases that cause global
warming through their greenhouse effect. Troposphericaerosol particles have a direct impact on the Earth’s
radiative balance through the scattering and absorptionof short- and longwave radiation (i.e., aerosol direct
radiative forcing). In addition, aerosol particles have anindirect influence on the Earth’s radiative balancethrough altering optical cloud properties by serving as
CCN (i.e., aerosol indirect radiative forcing). An increasein the concentration of tropospheric aerosol particles
may enhance cloud reflectance for solar irradiance as aresult of the increased droplet number concentration
and reduced droplet size for a fixed liquid water path
(Twomey 1974, 1977; Twomey et al. 1984). On the other
hand, a decrease in droplet size may inhibit droplet
growth through collisional coagulation during warmrain formation; this effect has an influence on the Earth’s
cloud coverage through altering cloud lifetimes owing to
the suppression of rain formation (Albrecht 1989).
Including the aerosol influence proved necessary inassessments of the Earth’s radiation balance and, consequently, the surface air temperature, not only in the
future but also in the past. Simultaneously, researchers
have become gradually more interested in changes in the
precipitation amount through modification of cloud dynamical/microphysical structures by anthropogenic factors related to aerosol (Levin and Cotton 2008; Tao et al.
2012). Many numerical models have been developed
competitively to represent aerosol–cloud interactions
and their impacts on climate and weather phenomena
on global and regional scales. In addition, remote and insitu measurements have been conducted worldwide toprovide chances for modelers to justify their results. Satellite measurements have produced global projections of
the distribution and characteristics of aerosol and clouds
on the basis of measurements of their optical properties
through spaceborne remote sensors (e.g., Nakajima et al.
2001; Rosenfeld et al. 2014). Continuous observations ofaerosol by ground-based networks allow researchers toPage 2 of 23

trace long-term transitions in their characteristics (e.g.,
Aerosol Robotic Network (AERONET) Holben et al.
1998; US Department of Energy (DOE), atmospheric radiation measurement (ARM) Stokes and Schwartz 1994).
Many intensive field campaigns have also been conducted to produce comprehensive views of aerosol–
cloud interaction by coupling observation results obtained from spaceborne satellites with ground-based,seaborne, and airborne instruments, as well as model
simulations (e.g., INDian Ocean Experiment (INDOEX)
Ramanathan et al. 2001; Asian Pacific Regional Aerosol
Characterization Experiment (ACE–Asia) Huebert et al.
2003; Atmospheric Brown Clouds (ABC) Nakajima et al.
2007; Ramanathan et al. 2007; and Distributed Regional
Aerosol Gridded Observation Networks (DRAGON) Eck
et al. 2012).
Modeling approaches to aerosol and its activation in
droplet nucleation

An essential physical process connecting aerosol with cloudis the nucleation of hydrometeor particles. All aerosol particles that can potentially be activated as nuclei of cloud
droplets are referred to as condensation nuclei (CN). CCN
are a subset of CN; they can be activated to initiate droplet
formation at the low supersaturation observed in the troposphere. In contrast, ice-forming nuclei (IN) are defined asaerosol particles that act as the base of the deposition nucleation of cloud–ice particles or the heterogeneous freezing of supercooled droplets. At present, our knowledge of
IN is less complete than that of CCN because of insufficientavailability of measurements and experimental data tomodel the complicated ice-nucleation process (Pruppacher
and Klett 1997).
A fundamental theory about CCN activation is summarized in a chart relating the relationship between thevapor saturation ratio and the equilibrium droplet size
formed in CCN (Köhler 1936, Appendix 1). CN size distribution spectra, as well as ambient supersaturation andtemperature, are essential factors in determining thedroplet nucleation process. Several additional factors
have a potential impact on the balance of the vapor saturation ratio and the equilibrium droplet size undermore complex assumptions, other than the basic Köhler
theory, such as changes in the amount of dissolving solutes in a droplet, temperature variations through latentheating/cooling by vapor condensation and evaporation,and the existence of insoluble and multiple soluble
chemical components in impacted CN.
The question of how to numerically handle the two critical components (i.e., the size distribution spectra of aerosol
particles and ambient supersaturation) is an important issue
in the representation of the droplet nucleation process incloud microphysics schemes. Existing approaches are generally categorized into the following four types, according

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

to the temporal and spatial resolutions, and the aerosol representation in the models: (i) the use of supersaturation andaerosol size distribution spectra explicitly predicted for eachspatial grid point; (ii) the use of parametrization involving
predicted grid-scale supersaturation or updraft wind velocity, as well as the mass and/or number concentration ofaerosol particles; (iii) the use of parametrization including
the diagnosed maximum supersaturation or updraft windvelocity instead of predicted grid-scale values, as well as themass and/or number concentration of aerosol particles; and(iv) the use of parametrization that implicitly assumes thepresence of aerosol.
The first approach has been employed in a limited
number of cloud-resolving models (CRMs) that aim ataccurate calculation of cloud microphysics (e.g., Kogan
1991; Chen and Lamb 1994; Feingold et al. 1999; Khainet al. 2000; Geresdi and Rasmussen 2005; Lynn et al.
2005a; Kuba and Fujiyoshi 2006; Suzuki et al. 2006,
2010; Li et al. 2008; Shima et al. 2009; Misumi et al.
2010; Xue et al. 2010; Fan et al. 2012). The so-called
sectional representation (e.g., Abdul-Razzak and Ghan
2002) is employed to numerically handle aerosol size
distribution spectra in most of these models. Köhler
theory is used to directly calculate the CCN number
concentration and, consequently, the increased cloud
droplet number concentration (CDNC) from the predicted aerosol size distribution spectra and supersaturation. Consumption of aerosol particles through thenucleation process is explicitly represented in the size distribution spectrum predicted. Miscellaneous aerosol types
are often simplified to monotype CN to reduce the complexity. These CRMs sometimes lack a grid structure offull three-dimensional Cartesian coordinates, i.e., they
may have only a two- or one-dimensional grid structure.
Large computational resources are required to ensure sufficient accuracy to explicitly resolve aerosol size distribution spectra. In addition, very fine grid spacings in bothtime and space are required to represent minute variations
in supersaturation. Such CRMs are generally employed tosimulate idealized and simplified clouds to investigate thebasic mechanisms of cloud and precipitation formation,and to discuss the sensitivity to various factors, including
aerosol concentration. Typical size distribution forms ofaerosol particles are often prescribed using reference
measurement data.
The second approach is employed in high-resolution
regional models that can be used for aerosol–cloudinteraction studies based on idealized experiments orcase studies. Generalized aerosol size distribution functions are usually assumed instead of their explicitly resolved spectra. The total or species-distinguished aerosol
concentration is explicitly predicted in bulk-moment
forms in some models (e.g., Hashino and Tripori 2007;
Muhlbauer and Lohmann 2009; Onishi and Takahashi

Page 3 of 23

2012; Saleeby and van den Heever 2013; Thompson andEidhammer 2014), whereas the aerosol concentration is
not predicted but tacitly parametrized in some models(e.g., Rasmussen et al. 2002; Seifert and Beheng 2006;
Morrison and Grabowski 2007; Seiki and Nakajima
2014; Seiki et al. 2015). In most of these models, the activated CCN number concentration is parametrized on
the basis of the basic Köhler theory, using the predictedsupersaturation or vertical wind velocity. An empiricalparametrization was suggested by Twomey (1959a,
1959b) to calculate the activated CCN number concentration from the predicted supersaturation or verticalwind velocity and prescribed coefficients representing
the ambient aerosol loading. A more generalized parametrization was proposed by Abdul-Razzak et al. (1998),
which combines Twomey’s parametrization with a lognormal representation of the aerosol size distribution(Ghan et al. 1993); these authors also extended theparameterization to cases including multiple externally
mixed aerosol types (Abdul-Razzak and Ghan 2000). On
the other hand, Saleeby and Cotton (2004) proposed theuse of pre-calculated lookup tables through Lagrangianparcel model calculations instead of empirical parametrizations; aerosol size distribution spectra are explicitly
represented using a sectional approach in the parcel
model. The lookup tables are used to calculate the proportion of aerosol particles activated for droplet nucleation by referring to the vertical wind velocity predictedin their main model framework.
The third approach is used in most global or coarseresolution regional models for studies of aerosol–cloudinteractions, even on a climatic scale (e.g., Storelvmo et
al. 2006, 2008; Lohmann et al. 2007; Morrison and Gettelman 2008; Suzuki et al. 2008; Takemura et al. 2009;
Wang et al. 2011; Wang et al. 2013; Park et al. 2014; Shiet al. 2014). Supersaturation and the vertical wind velocity cannot be resolved well on model grid points withcoarse intervals. Saturation adjustment approaches arenecessarily employed in the calculation of phase changes
between vapor and cloud water/ice. The approach used
to introduce aerosol-to-cloud effects is significantly different among models according to the type of bulk cloud
microphysics employed. Models using double-momentbulk cloud microphysics generally employ empirical parametrizations (e.g., Abdul-Razzak et al. 1998; AbdulRazzak and Ghan 2000) to obtain the relationship between the aerosol number concentration and CDNC.
Since the grid-scale vertical velocity is not a suitable input into the parametrizations, diagnostic variables (e.g.,turbulent kinetic energy) are often included in modified
parametrizations to determine the activated CCN number concentration through diagnosis of the maxima of
the vertical wind velocity and supersaturation (e.g., Morrison et al. 2005). In contrast, single-moment bulk cloudIguchi et al. Progress in Earth and Planetary Science (2015) 2:45

microphysics cannot directly incorporate the activated
CCN number concentration into the microphysics calculations in the form of a CDNC change. Diagnosed
CDNC can be included in the calculation of the autoconversion rate from cloud to rain to represent the ambient CN effect on clouds (e.g., Liu and Daum 2004).
The fourth approach is adopted in model simulations
on any scale. Since aerosol particles are necessary for the
nucleation of cloud particles, the presence of aerosol
particles is always assumed in cloud microphysics in a
steady form. Explicit representation of aerosol and its
effect on cloud may cause excess computational costs
and uncertainties in regular operational runs for shorttime weather forecasting. Speedy and stable simulation
is important in these runs. Even coarse parametrizations
based on empirical formulas work reasonably well forsuch short-time simulations where aerosol loading has
little direct influence and minor feedback on the prognostic variables.
Solutions to the problem related to aerosol-to-cloudmodeling

The best approach to accurately simulating aerosol-tocloud effects is obviously the first option discussed in“Modeling approaches to aerosol and its activation in
droplet nucleation”, i.e., using the explicitly predictedsupersaturation and aerosol size distribution. However,predictions of the two components under realistic conditions are contradictory problems in a single-model framework because of scale gaps between aerosol and cloud. A
small computational domain is preferable to define thegrid spacing as finely as possible to represent minor variations in supersaturation. In contrast, a large computational domain is preferred to strictly predict transitions in
the aerosol concentration and size distribution fromsources of aerosol-particle formation. The limited capability of present computational resources places results in insufficient domain size and grid spacing in a single-model
framework.
Several methods can be employed to solve the scale dilemma in aerosol-to-cloud modeling in the present computing environment. Thompson and Eidhammer (2014)
used monthly aerosol climatology data as input into their
Weather Research and Forecasting (WRF) model simulations (Skamarock et al. 2005). The aerosol climatology
data were derived from multiannual global aerosol model
simulations using the NASA Goddard Earth Observing
System model coupled with the Goddard Chemistry Aerosol Radiation and Transport model (GEOS–GOCART,
Ginoux et al. 2001; Chin et al. 2002; Colarco et al. 2010).
The climatological data were composed of mass-mixing
ratios of the five aerosol species with 0.5° × 1.25° horizontal grid spacing. The mass-mixing ratios were converted
into two bulk number concentrations. One is the number

Page 4 of 23

concentration of simplified hygroscopic aerosol particles
based on combining all aerosol species except for dust
and black carbon. The second is that of simplified icenucleating particles based on the accumulation of dust
particles with diameters larger than 0.5 μm. Both number
concentrations were explicitly predicted in the WRF
model simulations as tracers affected by interactions withhydrometeors and emission from the land surface. The
predicted aerosol number concentrations were used in
calculating the activation process of aerosol particles
in the bulk cloud microphysics scheme. Aerosol activation (i.e., cloud droplet nucleation) was calculatedusing a method employing pre-calculated lookup tables (e.g., Saleeby and Cotton 2004). Cloud-ice nucleation and heterogeneous freezing of supercooled
droplets were calculated using the parametrizations ofBigg (1953), Koop et al. (2000), Phillips et al. (2008),
and DeMott et al. (2010).
Shi et al. (2014) used aerosol and chemical-transportmodel simulations as a tool to provide a realistically diagnosed aerosol field for their cloud microphysics calculation in the main model framework with the same
domain configuration. They incorporated the following
two individual models off-line: the GOCART solomodule version of the WRF coupled with chemistry(WRF–Chem; Grell et al. 2005), and the National Aeronautics and Space Administration (NASA) Unified WRF(NU–WRF; Peters-Lidard et al. 2015). The activated
CCN number concentration in the NU–WRF simulation
was diagnosed based on the air temperature and supersaturation on the NU–WRF side, and the mass concentration of multiple aerosol species on the WRF–Chem
GOCART side. The diagnosed CCN number concentration was used to modulate the autoconversion rate fromcloud to rain through CDNC diagnosis in the frameworkof the single-moment bulk cloud microphysics in the
NU–WRF simulation.
Another compromise adopted to address this problem
was suggested by Iguchi et al. (2008; hereafter IG08).
Their approach was based on dynamical downscaling ofrealistic aerosol fields from large-scale aerosol (andchemical) transport model simulations to small-scale
CRM simulations. The basic numerical framework ofaerosol downscaling was similar to that of dynamical
downscaling for standard prognostic variables such as
wind velocity, temperature, and vapor-mixing ratio. Initial and time-variant aerosol boundary conditions wereprepared using four-dimensional output from the largescale simulation. However, aerosol size distribution spectra were incompletely resolved in the large-scale aerosol
transport model. This deficiency might be problematicfor accurate calculation of the activated CCN number
concentration on the basis of Köhler theory on the CRM
side. To deal with this problem in a reasonable manner,

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

the authors proposed the use of an additional intermediate
module. The intermediate aerosol loading module complemented the information of the aerosol size distribution
spectra in the dynamical downscaling framework. The
aerosol size distribution spectra and their total number/mass concentrations should be as realistic as possible,
such as those observed at times and locations pertaining
to the simulations.
The objective of this review is to summarize our development activities and prospective update strategies for theaerosol loading interface since the publication of IG08.
The interface connects small-scale CRM simulations tolarge-scale aerosol transport simulations in a dynamical
downscaling framework and complements information
about aerosol size distribution spectra. This interface iscurrently undergoing development in order to couple itwith regional models, including a detailed cloud microphysical scheme, referred to as spectral-bin microphysics(SBM; e.g., Khain et al. 2015). Since this paper aims at
reviewing the aerosol loading interface in detail, detailed
descriptions of SBM are not included. Such descriptions
can be found in Khain et al. (2000, 2015), among others.
Description of ALICIS
Brief overview

Our aerosol loading module is referred to as the Aerosol
Loading Interface for Cloud microphysics In Simulation(ALICIS). Figure 1 provides a schematic illustration of how
ALICIS works as an intermediate module between largescale aerosol transport models and regional-scale CRMs. So
far, this interface has been employed in the framework connecting global-scale simulations using the spectral radiationtransport model for aerosol species (SPRINTARS; e.g.,

Page 5 of 23

Takemura et al. 2000) with regional-scale simulations using
the Japan Meteorological Agency Non-Hydrostatic Model(JMA–NHM; Saito et al. 2006) coupled with SBM (IG08).
The SBM scheme originates from the corresponding partof the Hebrew University Cloud Model (HUCM; e.g., Khainet al. 2000). ALICIS was first published by IG08 and subsequently updated by Sato et al. (2012; hereafter SA12) andChoi et al. (2014; hereafter CH14).
CN modeling in the original HUCM

The representation of aerosol in the original HUCM issignificantly simplified for idealized CRM simulations(e.g., Khain et al. 1999). The CN size distribution
spectrum for a single chemical component is calculated
as prognostic variables for each of the model’s spatial
grid points. The chemical composition of CN is assumed
to consist of pure ammonium sulfate. The CN particle
shape is assumed to be completely spherical. The size
distribution spectrum is approximated by 33 discrete sizebins, covering a particle radius ranging from 1.23 × 10−3 to2 μm. The representative particle radius of each CN size
bin is defined as follows. The CN particle mass of the
maximum size bin (i.e., the 33rd bin) is assumed to be
identical to the particle mass of the smallest droplet sizebin; the smallest droplet radius is assumed to be 2 μm.
The CN particle mass of each size bin is determined bydoubling the mass bins; that is, a series of CN bins arecharacterized in the form of a geometric progression ofparticle masses with a ratio of 2 between subsequent bins.
The CN particle radius of each size bin is tied to the particle mass of the bin and the CN particle density.
The initial CN size distribution spectrum in the HUCM
is determined using the Köhler equation (Eq. 11) and

Fig. 1 Conceptual diagram of the coupling framework between a large-scale aerosol transport model and a regional-scale cloud resolving modelusing the ALICIS module. Gray components have not yet been implemented but will be included in future updates

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 6 of 23

Twomey’s empirical parametrization described by NCCN =N0Sk. Here, NCCN is the number concentration of CCN
activated at supersaturation S, and N0 and k areenvironment-dependent coefficients (e.g., Twomey 1959a,
1959b). The following equation for the CN number density function can be obtained by coupling both equations(Khain et al. 2000):

1:5k
dN CN
4A3
;
¼ 1:5N 0 kdðlnr CN Þ
27Br CN

ð1Þ

where A and B are calculated from Eq. 8 for atemperature of 288.15 K, and the molecular weight, thevan ’t Hoff factor, and the CN particle density are identicalto those of a typical ammonium sulfate aerosol particle.
The spectral shape of the initial CN size distribution iscommon to all spatial model grid points. The initial spatial
distribution is vertically inhomogeneous, since the CN
number density function decreases exponentially with ascale height of 2 km above an altitude of 2 km (Khain andSednev 1996). CN particles with radii larger than 0.6 μm
are eliminated from the size distribution spectrum instandard simulations to prevent artificially rapid formationof large droplets at the initial simulation stage.
The governing equation of the number density concentration in each CN bin (Eq. 3.4 of Khain and Sednev 1996)
is calculated numerically for all model grid points andeach time step. Gravitational sedimentation of CN particles is neglected. CN production in the atmosphere or on
the terrain surface is ignored. No direct CN source term isassumed. The CN sink is limited to consumption throughthe droplet nucleation process. Scattering and absorptionof short- and longwave radiative fluxes by CN particles are
not included, because no radiation process is calculated in
the HUCM. A flat horizontal gradient of the CN number
density concentration is assumed at the model’s lateralboundaries; the CN size distribution spectra are thus horizontally constant near the lateral boundaries. Consequently, CN consumed within the simulation domain isreplenished by advection from the lateral boundaries.
The microphysical process of CN is limited to activation of droplet nucleation. The process is calculated on
the basis of the following approach. If the predictedsupersaturation at a grid point is positive, the critical radius of CCN is calculated based on Eq. 11 using supersaturation and the air temperature. All CN larger thanthe critical radius are immediately activated and converted into cloud droplets, with their corresponding radii
calculated using Eq. 10. Hygroscopic growth of CN isneglected.
An early version of ALICIS: Iguchi et al. (2008)

Recently, regional models using SBM have been widely
applied, even to realistic simulations resolving clouds.

However, most studies have employed a similarly coarse
approach to the CN representation (Lynn et al. 2005a,
2005b; Khain et al. 2010; Fan et al. 2012; Iguchi et al.
2012a, 2012b, 2014). Such an approach may be insufficient in realistic simulations, because in reality, thespatial distribution of aerosol particles is inhomogeneousand variable. The environment-dependent parameters in
Eq. 1, N0 and k, are determined on the basis of limited
reference observations (Pruppacher and Klett 1997).
Substitution of arbitrary values for those parameterscauses large uncertainties in determining the CN concentration and size distribution spectra that should beconsistent with their real counterparts. In particular, theuncertainties may be increased under conditions whereanthropogenic pollution has a large impact on the aerosol distribution. Aerosol–cloud interaction is of greatinterest as a research problem under such conditions. In
addition, the simplified vertical CN distribution may beproblematic. The vertical CN profile assumed in the
HUCM is based on a typical observed profile of the vertical aerosol distribution. However, severe aerosol contamination is sometimes observed even in the middle
troposphere (Marenco et al. 2014); such highly concentrated aerosol is transported from distant intensive
sources through lifting by convection and it even has asignificant impact on the local cloud properties as wellas on the radiation balance.
IG08 developed the aerosol loading module ALICIS toovercome the deficiencies in CN modeling associated withthe original HUCM. The module provides inhomogeneous initial CN and time-variant lateral boundary conditions with a specified particle-size distribution through adynamical downscaling approach using output from global aerosol transport simulations of SPRINTARS (e.g.,
Takemura et al. 2000). The SPRINTARS model includes
the five types of tropospheric aerosol, i.e., organic carbonaceous, black carbonaceous, soil dust, sulfate, and sea salt.
Aerosol mass-mixing ratios are predicted in the framework of the atmospheric general circulation model(AGCM). Aerosol particle size spectra are incompletely
resolved in SPRINTARS.
Aerosol transport simulations using the SPRINTARS
model have been validated in various ways. For example,
the simulated aerosol optical thickness and Ångström
exponents have been compared with those retrieved
from the space-borne Advanced Very High Resolution
Radiometer (AVHRR) measurements on a global scale(Takemura et al. 2000); these authors also evaluated thesurface aerosol concentration at multiple global locations by comparison with those estimated from opticalmeasurements of the AERONET network. The singlescattering albedo of simulated aerosol was also validated
using AERONET data (Takemura et al. 2002). An aerosol data assimilation system has been developed on the

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 7 of 23

basis of a four-dimensional variational data assimilation
method (Yumimoto and Takemura 2013). The verticalaerosol distribution was validated through a comparison of the aerosol extinction with lidar measurements
at several locations in Japan (Goto et al. 2015). In
addition, the SPRINTARS model is included in the
Aerosol Comparisons between Observations andModels (AEROCOM) project (e.g., Kinne et al. 2006).
Model performance has been evaluated by comparing
the global modeling outcomes of numerous aerosol
modules.
IG08 determined the CN size distribution spectrum of
the initial and time-variant lateral boundary conditionsin their nested model simulations through the following
process. Among the five types of tropospheric aerosol,organic carbonaceous, sulfate, and sea salt aerosol particles are assumed to be CN activatable as CCN. Bulk
number concentrations of the three types of aerosol in
SPRINTARS are calculated based on the predictedmass-mixing ratios for the grid points of the global
simulation under the assumption that all aerosol particles are dry. The calculated bulk number concentrations
are then temporally and spatially interpolated at the grid
points of the simulation domain of the nested model.
The interpolated bulk number concentrations areconverted into aerosol size distribution spectra at eachgrid point. The conversion employs different built-in size
distribution functions, according to aerosol type. Finally,
accumulating the three-type aerosol size spectra yields
the CN size distribution spectrum at a given grid point
for the initial and boundary conditions.
The particle size distribution of organic carbonaceous
aerosol is assumed to be a lognormal function with asingle mode. The corresponding fraction of organiccarbonaceous aerosol in the CN size distribution spectrum
is given in units of the number density concentration,
2 8 
 92 3
1=3
1=3
<
=
ln
B
=B
⋅r
=r
CN
m
ð
OC
Þ
dN CNðOCÞ
N bðOCÞ
CN
OC
6 1
7
exp4−
¼ pﬃﬃﬃﬃﬃﬃ
5;
:
;
2
σ
dðlnr CN Þ
2πσ OCOC

ð2Þ
where Nb is the bulk aerosol number concentration of
the SPRINTARS simulation, and rm and σ are the mode
radius and the standard deviation of the lognormal aerosol
size distribution, respectively. The subscripts, CN and OC,
distinguish the variables pertaining to CN and organiccarbonaceous aerosol, respectively; rm(OC) = 0.1 μm andσOC = 1.8 is assumed. Note that Eq. 2 applies the adjustment of the difference in the B coefficients (Eq. 8) between organic carbonaceous aerosol and CN with acertain soluble component.
Sulfate aerosol is also assumed to have a single-mode
lognormal particle size distribution. The corresponding

portion of the sulfate aerosol in the CN size distribution
spectrum is also given by Eq. 2 in units of the number
density concentration, but for parameters typical ofsulfate aerosol; rm = 0.0695 μm and σ = 2.03 is assumed.
The particle size distribution function of sea salt aerosol is assumed to be a power law. The corresponding
fraction of sea salt aerosol in the CN number density
concentration is given by
dN CNðSAÞ
BSA r mðSAÞ k;¼ 1:5N bðSAÞ kdðlnr CN Þ
BCN r 3CN

ð3Þ

where rm(SA) = 0.1 μm and k = 0.6 is assumed. The
subscript SA denotes sea salt aerosol. This equation also
includes adjustment of the difference in the B coefficient
between CN and sea salt aerosol.
In the version of ALICIS presented here, the CN size
spectrum is discretely approximated by 13 size bins witha radius ranging from 10−3 to 1 μm. The use of a small
number of CN bins compared with the 33 bins in theoriginal HUCM yields improved computational efficiency. The CN concentration in each bin is numericallycomputed every time step, using almost the same governing equations as the standard scalar tracers.
The chemical component of CN in the nested model
can be arbitrarily determined by substituting a proper,
corresponding value into the B coefficient of CN in theconversion equations (Eqs. 2 and 3). No microphysicalprocesses causing a change in the CN size spectra are
assumed, except for activation to droplet nucleation.
Droplet nucleation is calculated using the same approach as that employed in the original HUCM; thevalue of the B coefficient of CN in Eqs. 2 and 3 issubstituted into Eqs. 10 and 11 to determine the critical
CCN radius and the corresponding radius of the generated cloud droplet. Although atmospheric radiation is
calculated in the nested model, absorption and scatteringof the radiative fluxes by CN are not included at present.
The reason for this is that the optical properties of eachaerosol species are eliminated once the multiple aerosol
types have been bundled into standardized CN. In
addition, the other two types of aerosol (i.e., black carbonaceous and dust aerosol) are not included in thenested model, so that this version of ALICIS has a limited capability to simulate direct aerosol effects on atmospheric radiation.
IG08 demonstrated the validity and effectiveness of theaerosol loading module in model simulations of two precipitation events observed during the 3rd Experiment of
the Asian Atmospheric Particulate Environment Change
Studies (APEX–E3). First, vertical profiles of the CCN
number concentration in the nested model simulations
were evaluated in direct comparison with those acquiredby aircraft in situ measurements. The measurement

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

methods of the CCN number concentration using a CCN
counter were summarized by Ishizaka (2004) and Adhikari
et al. (2005); some additional detailed information about
the measurements on the event days can also be found in
IG08. Figure 2 compares the results for the two individual
cases. The simulations reproduced the observed CCN
concentration profiles reasonably well. In the April 2 case(Fig. 2a), the observed CCN concentration ranged fromapproximately several tens to close to 1000/cm3. The observed concentration decreased a little with increasing
altitude. The three-type simulated CCN concentrations
ranged from approximately 100 to 1000 cm−3 at heights of
up to 5500 m; the small decrease with increasing altitude
was generally reproduced. In the April 8 case (Fig. 2b), theobserved CCN concentration was approximately 1000 cm−3
at heights below 1000 m, which decreased with increasing altitude to roughly 100 cm−3 at 3000 m. The verticalprofiles of the simulated CCN concentrations showed a
log-linear decrease with increasing altitude, similar to theobserved profile.
Second, the horizontal distribution of the liquid cloudproperties in the nested model simulations was compared with that estimated from data of the Modulate
Resolution Imaging Spectroradiometer on the Terrasatellite (Terra/MODIS) based on the satellite’s retrieval
algorithm (Nakajima and Nakajima 1995; Nakajima et al.
2005); plots of the horizontal distribution are shown in
Fig. 6 of IG08. Figure 3 shows the relationship between
the liquid cloud-top temperature and the droplets’ effective radius near the cloud tops in the satellite measurements and the simulation for the April 2 case. These
parameters were averaged separately in the northern andsouthern halves of the simulation domain to highlightspatial differences in the droplet radius distribution. The
north–south difference is likely to have resulted from ahorizontal bias in the CN concentration. The model
simulation performed satisfactorily in reproducing theoverall tendency of the north–south gradients of theobserved droplet radius in warm-cloud regimes withtemperatures in excess of 273 K, despite some overestimation (Fig. 3a). The observed radius monotonously increased with decreasing cloud-top temperature. The
observed radii averaged in the northern half had larger
values than those in the southern half; the maximum
north–south difference was roughly 5 μm. The simulated radius generally increased with decreasing cloudtop temperature down to 275 K and roughly decreasedor remained constant below 275 K. The simulated radiiin the northern half attained larger values than those in
the southern half for temperatures greater than 275 K;
the north–south difference was roughly 5 μm on average. In addition, two sensitivity simulations were conducted to highlight the effects of the aerosol loading
module on the simulation results (Fig. 3b, c). The

Page 8 of 23

simulation assuming horizontally homogeneous initialand boundary CN conditions failed to reproduce thestructure of the north–south gradients (Fig. 3b). The
simulated profiles of the average droplet effective radius
are almost identical for the northern and southernhalves. The simulated north–south gradients in thecontrol run were thus caused by a horizontally inhomogeneous CN distribution introduced by aerosol downscaling. This result proved that the implementation ofrealistic, inhomogeneous CN fields is essential for moreaccurate simulations of cloud microphysical properties.
On the other hand, the simulation assuming a doubled
CN number concentration compared with the control
run had smaller effective radii (Fig. 3c). The simulated
effective radius profiles were in better agreement withthe observed profiles than those in the control run. This
result indicates that the control run underestimated the
CCN number concentration.

Update by Choi et al. (2014): Aerosol particle size
distribution revision and type multiplication

The IG08 version of ALICIS still contained many limitations. Among them was the absence of two aerosol species, i.e., black carbonaceous aerosol and dust particles.
In reality, they often work as CCN if soluble components are combined with the particles, either externallyor internally (e.g., Sullivan et al. 2009). In addition, themodeling of the other three types of aerosol still offered
room for significant improvements. For example, the application of a power-law function to the size distribution
spectra of sea salt aerosol might cause an overestimationof the activated CCN number concentration because of
the continuous increase in the CCN number concentration with increasing supersaturation.
The aerosol loading module was comprehensively updated by CH14. The conversion from the various types
of aerosol to standardized CN, as implemented by IG08,
has been removed. The same five aerosol species used in
the SPRINTARS model are employed in nested models.
The discretization of the size distribution spectra of eachaerosol type has been extended to 17 size bins with radii
ranging from 10−3 to 10 μm. The use of more size bins
and a wider range of particle sizes compared with thosein the old version allows the representation of aerosol
particles with radii larger than 1 μm. Such large aerosol
particles are possibly included in the sea salt and dust
aerosol categories (Fig. 4).
The size distribution function of each aerosol type wasassumed to be of a multi-modal lognormal form by CH14.
The size distribution spectra described in this manner are
consistent with those observed in reality (e.g., Brechtel
et al. 1998). The number density concentration of thesize distribution of each aerosol type is given by

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 9 of 23(a) April 2, 2003
Flight Measurement
Sim (control; 30.5N)
Sim (cloud-free; 32.5N)

Sim (cloud-free; 30.5N)
Sim (cloud-free; 31.5N)

6000

Altitude (m)

5000
4000
3000
2000
1000
0
1

10

100

1000

10000

-3

CCN number concentration (cm )(b) April 8, 2003
Flight Measurement
Simulation (cloud-free; 30.5N)
Simulation (control; 30.5N)

3500
3000

Altitude (m)

2500
2000
1500
1000
500
0
1

10

100

1000

10000

CCN number concentration (cm-3)
Fig. 2 Vertical distribution of the observed and simulated CCN number concentrations over the East China Sea on a April 2, 2003 and b April 8, 2003.
The observed CCN number concentration in the flight measurements was obtained for liquid-phase supersaturation ranges of 0.07–0.22 % on April 2
and 0.09–0.32 % on April 8. The simulated CCN number concentration in the regional model was calculated for a liquid-phase supersaturation of 0.1 %in the April 2 case and 0.2 % in the April 8 case. Lines of simulated concentration show vertical profiles of horizontal averages in three domains ofradius ±0.5° centered at 129.5° E and 30.5° N, 31.5° N, or 32.5° N. The two types of simulated concentration are plotted separately from the control
and cloud-free runs (where all cloud microphysical processes were switched off to prevent CN consumption through droplet nucleation). Adaptedfrom Iguchi et al. (2008)

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 10 of 23(a) Droplet effective radius near cloud top (um)
0

5

10

15

20

25

30

250

Cloud top temperature (K)

255
260
265
270
275
280
MODIS (north)
MODIS (south)
Sim (north)
Sim (south)

285
290
295

(b) Droplet effective radius near cloud top (um)
0

5

10

15

20

25

30

250

Cloud top temperature (K)

255
260
265
270
275
280
MODIS (north)

285

MODIS (south)290

Sim (uni-CN, north)
Sim (uni-CN, south)295(c) Droplet effective radius near cloud top (um)
0

5

10

15

20

25

250
255
Cloud top temperature (K)Fig. 3 Dependence of droplet effective radii near cloud tops on
cloud-top temperature calculated based on simulations with threedifferent settings and from the retrieval results derived from Terra/
MODIS measurements over the East China Sea on April 2, 2003.
Simulation profiles are calculated in a the control run, b the run
characterized by a homogenous CN field, and c the run withdoubled CN. Plotted radii are averaged individually in the northernand southern halves of the analysis domain. Adapted from Iguchi
et al. (2008)

260
265
270
275
280
MODIS (north)

285
290

MODIS (south)
Sim (doubled CN, north)
Sim (doubled CN, south)

295

30



nm
X
dN
N SPR f ilnr a −lnr mipﬃﬃﬃﬃﬃﬃ exp −;¼
2σ i 2dðlnr a Þ
2π σ ii¼1

ð4Þwhere ra is the aerosol particle radius, i the index of agiven mode, nm the total number of modes in the size
distribution, NSPR the bulk number concentration ofeach aerosol type obtained from the SPRINTARS simulation, and f the weight factor required to normalize themulti-modal distribution. The variables nm, f, rm, and σ
are the functions of aerosol type as listed in Table 1 of
CH14; the values have been determined on the basis ofobservational results (d’Almeida et al. 1991; Chuang
et al. 1997; Penner et al. 1998; Herzog et al. 2004).
Figure 4 compares the aerosol size distribution spectra. Two size distribution spectra were calculated foreach aerosol type using the assumptions employed by
CH14 and IG08. No size distribution was assumed fordust or black carbonaceous aerosol in the study by
IG08. A set of observed spectra was obtained from insitu measurements during the ACE–Asia field campaign in 2001 (Huebert et al. 2003). The set of spectra
was calculated from the size-segmented aerosol massconcentration measured using the Micro-Orifice
Uniform Deposit Impactor (MOUDI) onboard a research vessel; the marine aerosol sampling performed
during ACE–Asia 2001 is summarized by Mochida
et al. (2007). Another set of observed spectra was
obtained from ground-based measurements using
MOUDI at the Gosan site on Jeju Island (Republic ofKorea) during the Atmospheric Brown Cloud–East
Asian Regional Experiment (ABC–EAREX) in 2005
(Nakajima et al. 2007).
In Fig. 4, the size distribution spectra employed by
CH14 are in better agreement with those from the measurements, although the observed size spectra lack
multi-modal distributions. The observed aerosol number densities monotonously decrease with increasing
aerosol particle size. The observed size distributions ofsulfate and organic/black carbonaceous aerosol extend
to the radii of 2 μm, whereas those of dust and sea salt
aerosol extend to over 5 μm. The spectra of CH14 reproduce the distinct structure of the observed spectra
according to aerosol type reasonably well, especiallyIguchi et al. Progress in Earth and Planetary Science (2015) 2:45

(a)

(b)

(d)

(e)

Page 11 of 23(c)Fig. 4 Size distribution spectra of the five aerosol species, i.e., a sulfate, b dust, c sea salt, d organic carbon, and e black carbon, assumed in the updated
and outdated ALICIS modules, and obtained from in situ measurements during ACE–Asia 2001 and ABC–EAREX 2005. Cited from Choi et al. (2014)

those of sulfate and organic/black carbonaceous aerosol. In contrast, the spectra of IG08 exhibit several severe insufficiencies. The number density concentrationof sulfate aerosol is overestimated across the full particle size range shown and the sea salt aerosol concentration is significantly underestimated. The organiccarbonaceous aerosol concentration is underestimatedfor particle radii smaller than 0.1 μm and overestimatedfor radii larger than 0.4 μm.
The activation process of aerosol particles to form cloud
droplets was calculated for each aerosol type on the basisof basic Köhler theory by CH14, unlike in IG08. Because
the five types of aerosol have different soluble capabilities,
different B coefficients are employed in determining thecritical radius of CCN in Eq. 11. The B coefficients are assumed to be 0.51 for sulfate, 1.16 for sea salt, 0.14 for dust,
0.14 for organic carbonaceous, and 0.05 × 10−5 for black
carbonaceous aerosol (Ghan et al. 2001).
CH14 employed the same build-up process of initial and time-variant boundary conditions of binned
aerosol concentrations in their nested model simulations as IG08. The time evolution of each binned
concentration was numerically computed for eachtime step. Microphysical processes producing or losing aerosol particles were excluded, except for consumption through the nucleation of cloud droplets.

Any direct aerosol effects on atmospheric radiation
were excluded, even by CH14, although the five
types of aerosol were fully managed.
CH14 evaluated the performance of the updated versionof the aerosol loading module in their simulation analysis.
They conducted numerical simulations of two cloudy cases
observed during the ABC–EAREX 2005 field campaign
over an East China Sea region. Corresponding simulations
using the old version of the aerosol loading module of IG08
were performed simultaneously for comparison.
First, the simulated CN and CCN number concentrations were evaluated in comparison with those obtained
from ground-based measurements. The CN comparison
yielded a new perspective on the validation of ALICIS,
in addition to the CCN comparison also discussed by
IG08. Figure 5 compares the concentrations (CCN at
0.6 % supersaturation) derived from the different assumptions made by both IG08 and CH14 with thoseobtained from in situ measurements at the Gosan site.
The observed CN and CCN concentrations were measured using a TSI (Shoreview, MN, USA) condensation
particle counter model 3010, which detected particles
with diameters larger than 10 nm, and using a streamwise thermal-gradient CCN counter (Roberts andNenes 2005); details of the measurements can be foundin Yum et al. (2007). The large underestimation of theIguchi et al. Progress in Earth and Planetary Science (2015) 2:45

(a)Page 12 of 23(b)Fig. 5 Surface-level a CN and b CCN (at 0.6 % liquid-phase supersaturation) number concentrations simulated near the ABC–EAREX 2005 Gosan
site and measured at the site on March 13, 2005 (case 1) and March 25, 2005 (case 2). Green bars show number concentrations in a regionalmodel simulation using the ALICIS module based on Iguchi et al. (2008); the concentrations are calculated at grid points near the site (averaged
within an area of 126.0° E–126.4° E and 33.1° N–33.5° N at the model bottom level) for the initial time step of the simulation. Error bars show
standard deviations from the averages. Red bars show the same concentrations but using assumptions proposed by Choi et al. (2014). Observed
CN and CCN were measured simultaneously using a TSI condensation particle counter and a stream-wise thermal gradient CCN counter deployedat the Gosan site, respectively

number concentrations in the outdated IG08 version
has been alleviated in the updated CH14 version. The
improvement is attributed to the application of more
realistic distribution functions to the aerosol size spectra rather than the new inclusion of dust and black
carbonaceous aerosol into the categories. However,underestimation of CN and CCN concentrations still
remained in the simulations using the updated CH14
version, especially for the second case. This insufficiency is probably caused by an underestimation of theaerosol concentration in the SPRINTARS simulation
over highly polluted regions affected by anthropogenic
aerosol emissions.
Second, CH14 evaluated droplet effective radii nearcloud tops, liquid water paths, and cloud optical
thicknesses calculated in their simulations in comparison with those obtained from the Terra/MODIS satellite measurements through retrieval (see Table 2 inCH14); details of the retrieval algorithm are describedby Nakajima et al. (2005). Simulations using the updated and outdated aerosol loading modules led todifferent values of the variables, although the cloud
distribution patterns were similar. The droplet effective radii were smaller, and the liquid water paths andcloud optical thicknesses were larger in the updated
simulation compared with the outdated simulation.
These differences resulted from larger CDNC attributed to the larger CN and CCN number concentrations. Overall, the values in the updated simulation
were in better agreement with those estimated fromthe satellite measurements, reflecting the better
reproduction of the CN and CCN concentrations in
the updated version.

Update by Sato et al. (2012): Aerosol emission from
evaporated cloud droplets

SA12 introduced an aerosol particle generation processfrom completely evaporated droplets into the frameworkof the HUCM SBM, coupled with the ALICIS module.
Absence of an aerosol regeneration process might cause
underestimation of the CN and CCN number concentrations and, consequently, an underestimation of CDNC,
particularly in long-term simulations. The process was
not included in the original HUCM SBM, and was notconsidered by IG08 or CH14, mostly because of the following technical issue. Once the activation to droplet
nucleation has been calculated in the models, the sizeand composition information about the aerosol particles
used was eliminated. Some state-of-the-art models fully
predict solute components in hydrometeor particles atthe expense of increased computational cost (e.g., Chen
and Lamb 1994). Such models have the advantage ofenabling explicit calculations of aerosol particle emission
from completely evaporated or sublimated hydrometeor
particles.
The parametrization of the aerosol emission processby SA12 was based on the third approach described by
Feingold et al. (1996). SA12 assumed that one aerosol
particle was regenerated when one droplet had completely evaporated (Mitra et al. 1992). Solute components in the droplet were not fully predicted, so that thesizes of regenerated aerosol particles could not be calculated explicitly; alternatively, the size distribution
spectrum of the regenerated aerosol was determinedbased on the change of the domain-averaged aerosol
number concentration from the initial condition to thepresent time step. The additional aerosol number

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 13 of 23

concentration to the k-th aerosol size bin resulting fromthis regeneration process (Rk) is as follows (see Eqs. 3
and 4 in Feingold et al. 1996):
ϕ
Rk ¼ N R nbin k ;
X
ϕk

ð5Þ

k¼1

ϕk ¼

 0;k −N
k
N
;

N 0;k

ð6Þ

where NR is the total number concentration of the regenerated aerosol particles, which is equivalent to thenumber concentration of completely evaporated droplets; nbin is the total number of aerosol size bins; and k are the domain-averaged aerosol number 0;k and N
N
concentrations of the k-th size bin in the initial andpresent conditions, respectively. SA12 assumed that all
CN were composed of ammonium sulfate; therefore, thechemical component of aerosol particles generated
through this emission process was assumed to be ammonium sulfate.
SA12 evaluated the performance of the aerosol regeneration parametrization in their simulations by resolving
stratocumulus over a region off the coast of California.
Two types of simulation (i.e., with and without the regeneration parametrization) were conducted simultaneously to investigate the influence of this process. The
simulation results were validated using retrieval resultsfrom the space-borne AVHRR/2 data over the region ofinterest (Nakajima and Nakajima 1995).
Figure 6 shows the horizontal distribution of the cloudoptical thickness retrieved from the AVHRR/2 spaceborne measurements (Nakajima and Nakajima 1995)
and calculated in the regional model simulations excluding or including the aerosol regeneration process. Cloud
optical thickness ranged from 10 to 60 over the domain
of interest in the observation region. The southern halfof the domain exhibited relatively thick stratocumulus.
In contrast, the simulated cloud fields show a more dispersed structure. Cloud optical thicknesses simulatedwith the aerosol regeneration parametrization were atleast in better agreement with the satellite measurements than those not based on this parameterization.
Large optical thicknesses of more than 30 are patchily
and widely distributed in the simulation including theregeneration process, whereas the corresponding values
were less than 20 in the simulation without the regeneration process, except for areas that were close to a lateral boundary of the simulation. Implementation of theaerosol regeneration process caused an increase of approximately 20 % in the aerosol number concentration
averaged over the domain. A large fraction of aerosol
particles emitted from evaporated droplets was usedagain as CCN. The regeneration of aerosol particles
caused increased CDNC and cloud optical thickness.
Sato (2012) tested another approach used by Feingold
et al. (1996) for the aerosol regeneration process, because the parametrization based on Eq. 5 was not very
appropriate for downscaled realistic simulations. The
new approach assumed that the particle size distributionof regenerated aerosol was the same as the horizontal
average at the corresponding vertical level for the initialconditions. Sato’s (2012) tests using this approach
showed that the reproducibility of cloud optical thickness was improved as well.
SA12 also conducted a quantitative comparison on thebasis of correlation analysis between the droplet effective
radii and cloud optical thicknesses (e.g., Fig. 8 of SA12).
Only simulations including the aerosol regeneration parametrization reproduced a characteristic pattern in thecorrelation plots based on the satellite measurements.
Their results demonstrate that implementation of theaerosol regeneration process is necessary to adequately
simulate the cloud microphysical structure of stratocumulus over the region of interest.
Prospective ALICIS update strategy
IN modeling

The behavior of aerosol particles as IN is implicitly included in the framework of the HUCM SBM tied to thepresent ALICIS module. The IN effect is tacitly containedin parametrizations to calculate ice-nucleation and freezing processes without consumption of predicted aerosol
particles in the model. This coarse approach is due to thepresent poor knowledge about complicated microphysics
in ice/mixed-phase clouds. Nevertheless, introducing an
explicit representation of IN activation and the resultant
depletion is important for developing a more comprehensive scheme of cloud microphysics for aerosol–cloudinteraction studies. Improvement of the SBM and ALICIS
to manage the IN effect is a worthwhile challenge.
Solid hydrometeor particles in the atmosphere primarily form through either homogenous freezing of supercooled droplets without IN action or heterogeneous
nucleation/freezing with catalysis by IN. In addition, thelatter heterogeneous processes can be divided into four
different categories (Vali 1985): deposition nucleation,condensation-freezing nucleation, contact freezing, andimmersion freezing.
Deposition nucleation is completed when water vapor
directly deposits onto the surface of an aerosol particle,
provided that supersaturation with respect to ice is positive.
The aerosol particle does not have to be hydroscopic. On
the other hand, condensation-freezing nucleation originatesfrom the nucleation process of a droplet under the condition that supersaturation with respect to water is positive.
The aerosol particle is activated as CCN but does not have

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 14 of 23(a) AVHRR/2-derived cloud optical thickness
at 1500UTC July 10, 1987

Simulation domain(b) CTL-run(c) RGN-run

Fig. 6 Horizontal distribution of cloud optical thickness observed at 1500 UTC on July 10, 1987: a retrieved from the space-borne AVHRR/
2 measurement results, b simulated by the regional model without aerosol regeneration parametrization (CTL-run) at 1400 UTC on thesame day, and c with regeneration parameterization (RGN-run). The red rectangle in the top panel shows the model simulation domain

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

to completely dissolve in the droplet. The droplet that isformed then immediately freezes from the non-dissolved
impurity. Distinguishing these two types of nucleation
process is difficult in measurements where supersaturation
with respect to water is positive. Supersaturation (i.e., humidity) rather than temperature is an important factor controlling the progress of these processes. Contact freezing
possibly occurs when supercooled droplets collidewith aerosol particles. The collision is usually caused
by Brownian diffusion, thermophoresis, diffusiophoresis, electrophoresis, and/or inertial impaction. The
concentration of aerosol particles that can act as
contact-freezing IN at the relevant temperature is themost important factor in determining the freezing
probability. Immersion freezing originates from impurities already present inside a supercooled droplet.
Unlike condensation-freezing nucleation, immersion
freezing does not have to start immediately upon theformation of a supercooled droplet. The freezing canoccur when the temperature of the supercooled droplet decreases.
Among the four types of heterogeneous cloud glaciation
processes, deposition nucleation is the simplest because
the process does not proceed through a state of supercooled droplets. An increase in the number of aerosol particles that can work as deposition IN directly causes thenumber of ice cloud particles to increase, just like the relationship between CCN and cloud droplets. In contrast,
cloud glaciation through the other three processes is more
complicated. If the number concentrations of all types ofaerosol increase, promotion of heterogeneous freezing
through increasing IN may be in competition with depression of the freezing efficiency caused by a decrease in theparticle sizes of supercooled droplets through increasing
CDNC. In contrast, increased cloud height and decreased
cloud temperature caused by the particle size change ofsupercooled droplets (e.g., Pincus and Baker 1994) may
accelerate cloud glaciation through heterogeneous or
homogenous freezing.
Unlike droplet nucleation summarized in Köhler theory,
no comprehensive theory has been developed to formulate
each ice-nucleation or freezing process. Parametrizationsof these processes are constructed empirically on the basisof in situ measurement data or experimental laboratory
data acquired under certain conditions. The four types ofmechanisms of heterogeneous nucleation/freezing canrarely be distinguished through measurements or experiments, so that empirical parametrization may cover multiple components for each of the four types of themechanisms. In addition, identifying the chemical components of IN is difficult for most measurements.
Appendix 2 summarizes our testbed for how to improve IN parametrization in the HUCM SBM coupledwith the ALICIS module. Fan et al. (2014) improved thePage 15 of 23

ice-nucleation parts of the HUCM SBM by introducing
up-to-date IN parametrizations that include prognostic
dust aerosol concentrations. The parametrizations werebased on results by Tobo et al. (2013) and DeMott et al.
(2015) for deposition/condensation-freezing nucleationand immersion freezing, and by Muhlbauer andLohmann (2009) for contact freezing. We may modify
the parametrizations into suitable forms to use the aerosol size distribution spectra predicted by models coupledwith the ALICIS module. In addition, more comprehensive and generalized parametrizations of heterogeneous
ice-nucleation/freezing should be implemented in future
updates. Phillips et al. (2008, 2013) proposed a flexible
framework where various empirical parametrizations canbe bundled into a versatile form since parameters in theformulation can be easily constrained by various observational data. Note that the application of any proposedparametrizations to actual model simulations is stillaffected by large uncertainties. The uncertainties comefrom the limited applicability of findings from in situ
measurement and laboratory experiment results for IN,
and the coarse translation from results to developerfriendly parametrizations in a numerical scheme.

Introduction of regional variability into the aerosol size
distribution and hygroscopicity parameters

The present ALICIS module includes fixed aerosol size
distribution functions with constant parameters, as described in “An early version of ALICIS: Iguchi et al.
(2008)”. The size distribution spectra were validated for
application to simulations of regions in Northeast Asia
through comparisons with those obtained from on-sitemeasurements. The spectra might thus be inappropriate
if applied to simulations pertaining to other regions. For
example, mineral dust aerosol particles in East Asia andNorth Africa (Sahara) have different physical and chemical properties (Formenti et al. 2011). The dust properties depend not only on the source region but also on
the distance from the emission site.
We will introduce regional variability into the aerosol
size distribution functions and the hygroscopicity parameters assumed in the ALICIS module. First, thesefunctions and parameters should have different forms
and values in accordance with the type of simulation region, e.g., continental, coastal, or maritime. For example,
the parameters determining the particle size distributionof sulfate aerosol used by CH14 originate from those formaritime scenes from Chuang et al. (1997). The latter
authors assumed a bimodal function with different parameter values for continental regions instead of the trimodal function employed for maritime scenes. An
average of the two size spectrum forms can also be used
for intermediate (i.e., coastal) scenes.

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Second, introducing intercontinental variability into
the ALICIS module is a valuable challenge for further
improvement. Although many measurements of theaerosol size distribution have been conducted worldwide,most obtained a bulk aerosol size distribution containing
all chemical components. Internal mixing of aerosol particles is highly problematic in constraining the aerosol
size distribution of an individual chemical component inrelation to the measurement data. Few recent studies
have conducted global simulations using an aerosol
transport model predicting the aerosol size distribution(e.g., Spracklen et al. 2005; Zhang et al. 2010; Jacobson2012). These simulation results are useful for complementary provision of aerosol size distribution information, particularly over pelagic regions and in the upper
troposphere where few in situ measurements are available. The results may be employed to construct climatology data of the aerosol size distribution or they may be
used as direct input to simulations through dynamical
downscaling approaches.
Introduction of aerosol representation to internal mixing,
source/sink, and direct radiative effects

Compared with typical aerosol transport models, thepresent SBM coupled with the ALICIS module still lacks
modeling of many aspects of aerosol behavior and its effects. Some may have a critical influence on cloud microand macrophysics. Their implementation is necessary to
conduct more comprehensive simulations of aerosol–cloudinteractions (e.g., Saleeby and van den Heever 2013). Various approaches have been proposed to introduce these aspects into models. We will include them in future updates
while considering the limitations of the available computational resources.
In reality, aerosol particles are often characterized byinternally mixed states with multiple chemical compositions. These aerosol particles are mostly dominant in regions at great distances from their major emission
sources. The chemical and physical properties of aerosol
particles may change through gas absorption/depositionor they may coagulate with other particles during migration. The question of how to numerically model an internal mixture is a delicate problem in calculating thenucleation process of cloud hydrometeor particles fromaerosol particles, as well as their scattering and absorption of atmospheric radiation. Various modifications of
the Köhler theory have been proposed to represent thehygroscopic growth of internally mixed particles. For example, reformulation of the Köhler equation was suggested to consider the effects of soluble gases and slightly
soluble solid substances in the droplet nucleation process(Laaksonen et al. 1998). A modified Köhler theory with asingle solute hygroscopicity parameter (κ-Köhler theory)
has also been proposed (Petters and Kreidenweis 2007);

Page 16 of 23

the new parameter represents the CCN capability of dry
particles with arbitrary mixtures and can be constrained
easily with observational data.
Wet scavenging (i.e., impaction of aerosol particles
by hydrometeor particles) does not only cause a significant decrease in aerosol concentration but also
triggers several cloud microphysical processes, such as
contact or immersion freezing. The scavenging
process is divided into two types: dissolution or impaction as a result of the nucleation process throughcondensation or deposition of water vapor on aerosol
particles, and impaction through collision between
aerosol particles and already extant hydrometeor particles. As highlighted in the section about contact
freezing, Brownian diffusion, thermophoresis, diffusiophoresis, electrophoresis, and/or inertial impaction
are major causes of collisions between aerosol andhydrometeor particles. There are significant uncertainties associated with modeling these effects, especiallyas regards the electromagnetic attraction between
aerosol and hydrometeor particles, because of insufficient theoretical knowledge and measurement data. In
addition, tracing solutions and impurity components
in hydrometeor particles is technically difficult if only
limited computational resources are available. At
present, a few models explicitly predict solution andimpurity components in hydrometeor particles in their
SBM frameworks (e.g., Chen and Lamb 1994). As analternative approach, solution and impurity components are assumed to be statistically (Poissonian)
distributed according to the volume of a hydrometeor
particle when the total mass of the components scavenged by hydrometeor particles is predicted (Phillips
et al. 2008). Although these authors applied thisapproach to their parametrization for immersion
freezing, the same approach is applicable to the calculation of aerosol particle emission from evaporatedhydrometeor particles.
Scattering and absorption of atmospheric radiation by
aerosol particles can change the radiation budget in the
different atmospheric layers and on the Earth’s surface;
this is a well-known direct effect of aerosol on the
Earth’s radiation balance. Direct aerosol effects on atmospheric radiation have a little impact on cloud physics. However, strong absorption of the solar irradianceby aerosol particles potentially has a drastic influence on
cloud structure. Heating through absorption may affectthe surroundings of the particles on a minor scale.
When such absorptive aerosol particles or impurities inhydrometeor particles exist in great volumes at layers
near the cloud top, an increase in temperature throughheating may promote evaporation of hydrometeor particles. This effect may cause a change in the Earth’s radiation balance through cloud structure change. This kind

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

of aerosol influence is referred to as the aerosol semidirect radiative effect (Ackerman et al. 2000).
Aerosol optical properties are a key determinant of theirinfluence on the Earth’s radiation budget. We plan to follow standard methods employed in existing aerosol transport models (e.g., GOCART, SPRINTARS, ECHAM:
Pozzoli et al. 2011; the Hadley Centre Global Environment
Model HadGEM: Bellouin et al. 2011) to determine theaerosol optical properties. The single-scattering propertiesof spherical aerosol particles can be approximated using
Mie scattering theory in radiative flux calculations. The
wavelength of light, the radius of a particle, and the refractive index of its medium at the relevant wavelength
are required to determine the extinction coefficient,
single-scattering albedo, and asymmetry factor. The effectof water uptake on hygroscopic aerosol particles is approximated by calculating the particle radius and refractive index for a series of relative humidity values; therefractive indices are calculated on the basis of thevolume-weighted mixing assumption between water anddry components (e.g., Haywood et al. 1997; Takemura etal. 2002; Eq. 3). The internal mixture effect is also approximated using the volume-weighted mixing assumption for
multiple different components. Non-spherical dust particles are approximated as their corresponding sphericalparticles in calculating their optical properties, because
relative errors introduced by this assumption are verysmall (Fu et al. 2009). We will generate lookup tables ofextinction coefficients, single-scattering albedos, andasymmetry factors computed using a Mie theory program
for the discretized series of aerosol particle radii, relative
humidity values, and band wavelengths as applicable toeach aerosol type. An internal mixture will be assumed
only for organic and black carbonaceous aerosol particles
at this stage to avoid heavy computational loads by including all mixture combinations; lookup tables for the mixed
particles will also be prepared.

Conclusions
This review paper describes the scientific backgroundand update strategies in the development of the aerosol
loading module referred to as ALICIS. The module isused for coupling a downscaled CRM simulation with alarge-scale aerosol transport simulation. The most important function of the module is to provide realistic
temporally and spatially inhomogeneous distributions ofaerosol combined with accurate aerosol size distribution
information for downscaled simulations. Introduction of
the module yields better simulations of cloud microphysical structure by avoiding the adoption of inadequate aerosol loading as artificially defined by users.
ALICIS has been subject to two major updates since its
initiation by IG08. The assumption of the aerosol size distribution spectra was significantly improved as the

Page 17 of 23

spectrum shapes better matched their observed counterparts in the first update (CH14). The aerosol emission
process from evaporated cloud droplets was added in thesecond update (SA12). However, many points remain to be
improved in future updates, including but not limited tothe management of heterogeneous ice nucleation/freezing,aerosol direct/semi-direct effects on atmospheric radiation,
addressing internal mixtures of different chemical components in aerosol particles, the introduction of regionalvariability into the aerosol size distribution, and the representation of wet scavenging.
So far, ALICIS has been employed only in the framework connecting SPRINTARS aerosol transport simulations to JMA–NHM coupled with the HUCM SBM. We
are now working on implementing the aerosol loading
module into the bridge between the Modern Era
Retrospective analysis for Research and Applications
Aerosol Reanalysis (MERRAero) or GEOS–GOCART
model simulations and WRF coupled with HUCM
SBM (WRF–SBM) (e.g., Iguchi et al. 2012a). Thisextension will readily increase the applicability of
ALICIS to more diverse simulation scenes and styles.
In addition, we plan to develop the coupling of
ALICIS to bulk cloud microphysics instead of SBM toenhance the simulation capability.
In future updates, models coupled with the ALICIS
module will include a more comprehensive representation of aerosol and related chemical species, like the
WRF–Chem model. Such state-of-the-art models alsoenable better simulation of cloud microphysics, although
they require much larger computational resources andloads. Improvement in model functionality is in competition with improvement in various resolutions such as
spatial grid spacing, time-step sampling, and/or particle
size bins. Keeping a balance between model performanceand computational efficiency in the context of limited
resources is a delicate problem in model development.
Adequate sorting of the priorities between the introduction of various model functions and improvement invarious resolutions is an important task assigned toresearchers.

Appendix 1: Basic Köhler theory for droplet
nucleation
A chart of Köhler curves (Köhler 1936) summarizes a fundamental theory about CN activation and stabilization of
the formed droplets. The chart illustrates relationships between the ambient vapor saturation ratio and droplet radius under equilibrium conditions with the environment.
Two competitive effects are determinants of the relationships: the Raoult effect, where dissolution depresses thesurface vapor pressure on the solution; and the Kelvin effect, where the surface vapor pressure on a curved liquidsurface is higher than that on a corresponding flat surface.

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 18 of 23

The supersaturation around a droplet in equilibrium withthe ambient vapor is determined by the following equation(e.g., Rogers and Yau 1989), assuming that the dissolved
amount of solutes in the droplet does not change:
A Br 3S w ¼ − 3CN ;
rw
rw

ð7Þ



3:3  10−5
4:3v 4πρCN;A≈ðcmÞandB≈
T
3
M CN

ð8Þ

where Sw is the equilibrium supersaturation, rw thedroplet radius, and T the skin temperature of the droplet; rCN is the radius of the dry CN particle before dissolution; and v, MCN, and ρCN are the van ’t Hoff factor(e.g., Low 1969), molecular weight, and density of the
CN component, respectively. The CN particle is assumed to be composed of a single soluble ingredientthat has been completely dissolved in the droplet. In
Eq. 7, there is a unique peak of the supersaturation
Scrit(w) under the condition that dSw/drw = 0,
2A;
3r critðwÞ

ð9Þ

rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
3Br 3CN;r critðwÞ ¼
A

ð10Þ

S critðwÞ ¼

where rcrit(w) is the critical radius of the droplet thatmatches Scrit(w). Once the ambient supersaturation
reaches approximately Scrit(w), droplets with radii greaterthan rcrit(w) can exist stably. The critical radius of CN,
rcrit(CN), can also be determined uniquely:
r critðCNÞ ¼

4 A3 127 B S critðwÞ 2

!1=3
:

ð11Þ

Equation 11 represents the critical radius of CN as afunction of supersaturation. If the radius of CN is larger
than rcrit(CN), CN is activated as CCN and the formed
droplet exists stably under the ambient supersaturation,
in excess of Scrit(w).

Appendix 2: Future plans for improving IN
parametrization
Deposition and condensation-freezing nucleation

A common approach to measuring ambient IN is bycounting the change in the number of ice particles in asampled airmass for a series of air temperature or supersaturation measurements. DeMott et al. (2010) provided
an equation for the activated IN number concentration
as a function of both temperature and the number concentration of aerosol particles with diameters larger than
0.5 μm, by combining measurement data at multiple

locations from the Arctic to the Amazon Basin. Thisequation is represented by
nIN;T k ¼ að273:16−T k Þb ðna>0:5 Þðcð273:16−T k ÞþdÞ ;ð12Þ

where a = 0.594 × 10−4, b = 3.33, c = 0.0264, and d =0.0033; Tk is the cloud temperature (in Kelvin), na>0.5 isthe number concentration (in cm−3) of aerosol particles
with diameters larger than 0.5 μm, and nIN,Tk is the active
IN number concentration (std L−1). This parametrization
may cover all heterogeneous ice nucleation types (i.e., deposition nucleation, condensation-freezing nucleation,
contact freezing, and immersion freezing) because it isbased on measurement data under water saturation conditions. However, Demott et al. (2010) indicated that nIN,Tk
corresponds to the maximum number concentration ofambient IN activated mostly through deposition nucleation, because deposition nucleation is the primary contributor in mixed-phase clouds at temperatures above−35 °C (Phillips et al. 2008). The measurement data were
sampled at a temperature range between −9 and −35 °C.
Several issues need to be solved before implementationof the parametrization described by Eq. 12 into a model
coupled with the ALICIS module. Equation 12 can beinterpreted in several ways in terms of how to connect
the maximum number concentration of ambient IN at agiven temperature to the number growth rate of cloud
ice particles in a microphysics scheme. An approach described by Khain et al. (2000) can be appropriated todeal with this problem. A possible assumption is that
new IN activation occurs only if the temperature decreases at a certain grid point. The number concentration of newly activated IN per time interval is given in adifferential form:
8dnIN dT; if dT=dt ≤ 0dnIN < −dT dt¼; ð13Þ
:dt
0;
if dT =dt > 0


dT
∂T
∂T
∂T
∂T
¼
þu
þv
þw
;
ð14Þ
dt
∂t
∂x
∂y
∂z
where u, v, and w are the wind velocities in the framework of the three-dimensional Cartesian coordinates x,y, and z, respectively. The number of newly activated IN
is directly applied to that of newly generated cloud ice
particles.
Another problem is how to connect na>0.5 in Eq. 12 to
the predicted size distribution spectra of aerosol in amodel coupled with the ALICIS module. Sulfate, black
carbon, organic, dust, and sea salt aerosols were assumed in the global model simulations of Demott et al.
(2010); they predicted aerosol mass and number concentrations for the three specific modes in particle size foreach aerosol species. The term na>0.5 in Eq. 12 wasIguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Page 19 of 23

directly calculated from the size distribution of the four
aerosol species, except for sea salt. A similar approach
can be employed in a model coupled with the ALICIS
module; na>0.5 can be calculated from the predicted size
distribution spectra of the same four aerosol species. Depletion of IN is represented by reducing the number
concentration of aerosol particles with diameters larger
than 0.5 μm. The priority (i.e., what type and size ofaerosol particles are preferentially activated) was not discussed by Demott et al. (2010). At present, larger aerosol
particles are preferentially consumed; the type dependence is determined arbitrarily.
Contact freezing

Unlike deposition and condensation-freezing nucleation,
contact freezing has hardly been fully parametrized ormodeled in earlier atmospheric models. It is difficult toobtain appropriate measurement data to construct a parametrization scheme or an empirical formula. Lohmann
and Diehl (2006) employed a parametrization based on
Levkov et al. (1992) to calculate the contact-freezing probability only through Brownian diffusion of aerosol particles in their global aerosol transport model with bulk
cloud microphysics. The freezing rate Qfrz,cnt (in m−3 s−1)
is given by
Qfrz;cnt ¼ mi Dap 4πr w N a;cnt
Dap ¼

kT C c;6πηr am

N w2
;
ρqw

ð15Þ
ð16Þ

where mi is the mass of the newly formed ice particle
through contact freezing, Dap is the Brownian aerosol
diffusivity, rw is the volume mean radius of the ambient
supercooled droplets, Na,cnt is the number concentrationof aerosol particles that can be activated as contact
freezing nuclei at the relevant temperature, Nw is thenumber concentration of the supercooled droplets, qw is
the mass-mixing ratio of the supercooled droplets, ρ isthe air density, k is the Boltzmann constant, Cc the Cunningham correction factor, η the viscosity of air, and ram
the mode radius of the aerosol particles.
Na,cnt is the most important parameter for determining
the efficiency of contact freezing in Eq. 15. Lohmann
and Diehl (2006) assumed that hydrophilic black carbon
and accumulation-mode dust particles were able to work
as contact freezing nuclei; the activation probability wasdependent on the particle composition and size, and on
the temperature (see their Fig. 1). Dust aerosol particles
can work as contact freezing nuclei at higher temperatures than black carbonaceous aerosol particles. If mineral dust particles are composed of montmorillonite, theonset temperature of contact freezing is approximately−3 °C; all dust particles can work as contact freezing

nuclei at −8 °C. The onset temperature of contact freezing mediated by black carbonaceous particles is approximately −10 °C; the probability of freezing is largely
dependent on the particle size.
This parametrization can be implemented in a model
coupled with the ALICIS module without major modifications. If SBM is employed in the model, Eq. 15 is applied to all combinations between supercooled droplet
bins and aerosol bins, on a one-by-one basis. On theother hand, if bulk cloud microphysics is employed,
Eq. 15 directly provides the production rates of the
mass-mixing ratio and the number concentration ofcloud ice formed through contact freezing, as describedby Lohmann and Diehl (2006).
Immersion freezing

Some prior studies already developed several empiricalparametrization schemes to calculate immersion freezing
rates under conditions that the particle composition andconcentration of immersion freezing nuclei inside supercooled droplets are specified (e.g., Diehl and Wurzler
2004). However, the present SBM coupled with the
ALICIS module does not explicitly predict the concentration or composition of impurities that potentially act
as immersion freezing nuclei inside supercooled droplets. Accordingly, the present model needs to adopt anempirical parametrization that excludes the detailed
characteristics of immersion freezing nuclei. As an alternative approach, Lohmann and Diehl (2006) employed
the assumption that the concentration of potential IN
inside supercooled droplets was identical to that in ambient air (i.e., aerosol particle concentration). Similarly,
Fan et al. (2014) employed a parametrization that included a mineral dust aerosol concentration with particle diameters larger than 0.5 μm (Tobo et al. 2013;
DeMott et al. 2015). These approaches are still affectedby large uncertainties, even though the ambient aerosol
concentration is related to the number of activatable
immersion IN inside supercooled droplets through wet
scavenging.
Abbreviations
ABC: Atmospheric Brown Clouds; ABC–EAREX: Atmospheric Brown Cloud–East Asian Regional Experiment; ACE–Asia: Asian Pacific Regional Aerosol
Characterization Experiment; AEROCOM: Aerosol Comparisons between
Observations and Models; AERONET: AErosol RObotic NETwork;
AGCM: atmospheric general circulation model; ALICIS: Aerosol Loading
Interface for Cloud microphysics In Simulation; APEX–E3: 3rd Experiment of
the Asian Atmospheric Particulate Environment Change Studies;
ARM: Atmospheric Radiation Measurement; AVHRR: (space-borne) Advanced
Very High Resolution Radiometer; CCN: cloud condensation nuclei;
CDNC: cloud droplet number concentration; CN: condensation nuclei;
CRM: cloud resolving model; DOE: Department of Energy;
DRAGON: Distributed Regional Aerosol Gridded Observation Networks;
GEOS: NASA Goddard Earth Observing System model; GOCART: Goddard
Chemistry Aerosol Radiation and Transport model; HadGEM: Hadley Centre
Global Environment Model; HUCM: Hebrew University Cloud Model; IN: iceforming nuclei; INDOEX: INDian Ocean EXperiment; JMA–NHM: Japan

Iguchi et al. Progress in Earth and Planetary Science (2015) 2:45

Meteorological Agency Non-Hydrostatic Model; MERRAero: Modern Era
Retrospective analysis for Research and Applications Aerosol Reanalysis;
MODIS: Modulate Resolution Imaging Spectroradiometer; MOUDI: MicroOrifice Uniform Deposit Impactor; NASA: National Aeronautics and Space
Administration; NU–WRF: NASA Unified WRF; SBM: spectral bin microphysics;
SPRINTARS: Spectral Radiation Transport model for Aerosol Species;
WRF: Weather Research and Forecasting model; WRF–Chem: Weather
Research and Forecasting model coupled with chemistry; WRF–SBM: WRF
coupled with HUCM SBM.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
TI proposed and designed the study and wrote most parts of the paper. IC
and YS substantially contributed to writing the subsections of “Update by
Choi et al. (2014): Aerosol particle size distribution revision and typemultiplication” and “Update by Sato et al. (2012): Aerosol emission from
evaporated cloud droplets,” respectively. KS and TN collaborated with theother authors in the context of manuscript composition. All authors readand approved submission of the final manuscript.
Acknowledgements
This research was supported by the Global Environment Research Fund B-4 of the
Ministry of Environment, Japan, by project RR2002 and the Data Integration for
Earth Observation project of the Japanese Ministry of Education, Sports, Science,
Culture, and Technology (MEXT). YS was supported by a Grant-in-Aid for JSPS
Fellows (22-7893). IC was supported by the research and development projecton the development of global numerical prediction systems of the Korea Instituteof Atmospheric Prediction Systems (KIAPS), funded by the Korea Meteorological
Administration (KMA). The authors thank the two anonymous reviewers and thejournal editor for their helpful comments in improving this paper.
Author details
1
Earth System Science Interdisciplinary Center, University of Maryland,College Park, MD, USA. 2Code 612 NASA Goddard Space Flight Center,Greenbelt, MD, USA. 3Korea Institute of Atmospheric Prediction Systems,
Seoul, Republic of Korea. 4RIKEN Advanced Institute for Computational
Science, Kobe, Japan. 5Atmosphere and Ocean Research Institute, The
University of Tokyo, Kashiwa, Japan. 6Earth Observing Research Center, Japan
Aerospace Exploration Agency, Tsukuba, Japan.
Received: 30 June 2015 Accepted: 7 December 2015

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