Numerical study of the aerosol effect on water cloud optical properties
with non-hydrostatic explicit microphysics cloud model
Kentaroh Suzuki1, Teruyuki Nakajima1, Takamichi Iguchi1,Gorou Asanuma 1, and Takashi Y. Nakajima 2( 1: CCSR, University of Tokyo, 2: EORC/JAXA )Center for Climate System Research, University of Tokyo, Tokyo, Japan
4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, JAPANe-mail : kenta@ccsr.u-tokyo.ac.jp
Abstract
In the present study, we developed a non-hydrostatic spectral microphysics cloud model andperformed numerical experiments with the model to investigate the aerosol effect on a water cloud
microphysics. Numerical model used for the experiments is a coupled model of non-hydrostatic
dynamical framework and spectral microphysics binned cloud model, both of which were newly
constructed in the present study. Numerical experiments were performed with this model to investigate
the interaction between aerosol and water cloud. It was found that the simulated correlation pattern
between cloud particle effective radius and optical thickness was consistent with those acquired bysatellite observation. Cloud effective radius and optical thickness are positively correlated when the cloudis not drizzling, contrary to negative correlation when the cloud is accompanied by drizzle-sized particles.
We also investigated the dependence of cloud properties on column aerosol particle number obtained
from the simulation. Cloud particle effective radius and optical thickness was simulated to be negatively
and positively correlated with column aerosol particle number, respectively, resulting an approximate
constancy of Liquid Water Path (LWP) independent of aerosol particle number because LWP is
proportional to optical thickness multiplied by effective radius. Column cloud particle number wassimulated to increase with column aerosol particle number. These characteristics are qualitatively
consistent with those reported from the statistics obtained by satellite observation, although the sensitivityof cloud properties to aerosol amount was somewhat larger in this simple numerical experiment thanglobal statistics from satellite observation.
Keyword: aerosol, cloud microphysics, aerosol indirect effect, cloud-aerosol interaction

1. Introduction
Cloud microphysical structure, which has afundamental role in determining the optical andhydrologic properties of clouds, is known to bemodified by atmospheric aerosols acting as cloud
condensation nuclei (CCN). This indirect effect ofaerosol is one of key issues important inunderstanding the global climate change, but theuncertainty in the evaluation of aerosol indirect
radiative forcing is still very large (IPCC, 2001).
Recent progress in satellite remote sensing
provided us with useful data of cloud and aerosol
optical properties over global scale. Global
analysis with the data revealed that the water
cloud optical properties such as effective particle
radius and optical thickness are significantlycorrelated with aerosol particle number (Nakajimaet al., 2001). Numerical simulations with General
Circulation Model (GCM) have been performedby parameterizing the aerosol effect on cloudproperty. Recent modeling studies (Lohmann andLesins, 2002; Suzuki et al., 2004) compared themodel-simulated result of aerosol indirect effectwith satellite observation at global scale andreported that the global sensitivity of cloudproperties to aerosol amount largely depends onthe choice of parameterization for cloud
microphysics. Such ambiguity in cloudparameterization is a main source of uncertainty
in the estimate of aerosol indirect radiativeforcing.
It is necessary for reducing the uncertainty to
study with numerical cloud model which
explicitly calculates the cloud particle growth bytreating microphysical processes in proper way
rather than bulk parameterizations. In the present
study, we developed a non-hydrostatic spectral
microphysics cloud model and performed
numerical simulations for investigating the cloudparticle growth process and the aerosol effect onwater cloud optical properties. Model description
and experimental design are briefly introduced insection 2, and the results of numerical simulation
are given in section 3. The conclusion of thisstudy will be presented in section 4.
2.

Model experiment

The model used for numerical experiment
was newly developed in the present study. This isa coupled model of non-hydrostatic dynamicalframework and spectral bin microphysics cloudscheme. Non-hydrostatic dynamics was chosen
for explicitly calculating updraft velocity, which
is important in the formation of cloud and rain.
Since the aim of this study is an investigation ofaerosol effect on cloud microphysics, spectral bin
method was used for explicitly calculating aparticle growth process. We consider several
types of particles, i.e., aerosol, liquid droplet, ice
crystals, snowflake, graupel and hail, andexplicitly predict the size distribution functions(SDF) of these particles by taking into account the
microphysical processes responsible for thechange of SDFs. Aerosol and cloud particles areinteracting through the nucleation and wash out
processes. Nucleation is based on Kohler theory
and the wash-out is calculated from collision
between aerosol and water droplet. The latent heatrelease through the change of SDFs due tocondensation, evaporation and sublimation causes
a feedback on dynamics and SDFs are modified
by advection with wind field from dynamicalprocess.
Numerical experiment was performed withthis model in two-dimensional computational
domain of 20km in horizontal and 4km in vertical.
The resolution was horizontally 500m andvertically 200m. We set an initial profile oftemperature and humidity which is conditionallyunstable in lower layer (0-2km in height) withinversion layer in upper layer (2-4km in height)
for forming a low cloud, which is known to be
most effectively modified by aerosols. The warm
bubble was initially located to trigger aconvection. We get a cycle of cloud and drizzle
formation in this simple experiment.

3. Result3.1. Correlation between cloud effective
radius and optical thickness
The result of the numerical experimentmentioned in previous section was analyzed froma viewpoint of cloud optical property such as
cloud particle effective radius and optical
thickness. These two properties have been
retrieved from satellite remote sensing and thecorrelation pattern between them is known tohave remarkable characteristics related to cloud
lifecycle (Nakajima and Nakajima, 1995). Cloud
optical thickness is positively correlated witheffective particle radius at no-drizzle stage,illustrating that the cloud water increases with thegrow of cloud particles. On the contrary, cloudoptical thickness is negatively correlated witheffective radius in drizzling stage, implying adecrease of cloud water due to a conversion intodrizzle-sized particles with the grow of particles.
These two correlations are combined to form atriangular pattern as schematically shown in Fig.1,
which illustrates a microphysical lifecycle ofcloud.
Effective Radius

Optical Thickness
Fig.1. Schematic diagram of effective radius and optical
thickness over cloud lifecycle. Green, blue and red arrowcorresponds to the cloud regime of before drizzling, drizzling
and after drizling, respectively.

We made a similar scatterplot between
effective radius and optical thickness also fromthe result of model calculation as shown in Fig.2.
The upper panel of Fig.2 shows a simulated
correlation over whole lifecycle of the cloud,which is composed by two branches of positive
and negative correlation. Positive and negative
correlation branch represents a cloud regime
before drizzling and drizzling, respectively. The
lower panel of Fig.2 shows a correlation for thecase of simulation without collision-coagulation
process. It is found that only positive correlation
branch appeared when the collision-coagulation
process was removed from the simulation because
the drizzle particles are not formed.
Sensitivity experiment to change aerosol
amount was also performed for investigating theimpact of aerosol on the correlation pattern, andthe result is shown in Fig.3. The increase inaerosol amount causes more abundant cloudparticles, which in turn reduces thesupersaturation due to the consumption of water
vapor by condensational growth of the particles.
The decrease in supersaturation delays thecondensational growth of particles and thus thecloud tends to have smaller effective radius and tobecome optically thicker, resulting a modificationof correlation pattern as shown in Fig.3. This
result illustrates the effect of aerosols on cloud
microphysical lifecycle.

optical thickness is negatively and positively
correlated with Na, respectively. LWP tends to be
independent of Na for turbid air condition of Na >107.5 cm-2 because LWP is proportional to optical
thickness multiplied by effective radius. Column
cloud particle number Nc is increased withincreasing Na. These characteristics are
qualitatively consistent with global statistics
obtained from satellite observation. Cloud particle
number Nc is roughly linear with aerosol particle
number Na following the relationship Nc ∝ Nak.
The exponent k represents a sensitivity of cloudproperty to aerosol amount. The value of
exponent k was estimated to be 0.72 in the present
simulation. This is close to those from past
research Kaufman et al. (1991), which reported
k=0.7-0.8 based on observational analysis ofbiomass burning event. Global statistics obtained
from satellite observation (Nakajima et al., 2001)
reported somewhat smaller sensitivity as k=0.50
because various types of clouds are included in
global analysis and thus the signal of aerosol
indirect effect is diluted.

Fig.2. Scatterplot of effective radius and optical
thickness obtained from simulation with (upper) andwithout (lower) collision-coagulation process.

Fig.3. Scatterplot of effective radius and optical thickness
obtained from simulation with different aerosol amount(blue: clean, green: modest, red: polluted).

3.2. Sensitivity of cloud properties toaerosol particle numberInprevioussubsection,
thecloudmicrophysical lifecycle was found to be affectedby aerosol amount as revealed by modification ofcorrelation pattern between optical thickness andeffective radius. This subsection is contributed todiscussion on the sensitivity of cloud properties toaerosol particle number. Fig.4 shows a scatterplotof cloud effective radius re, optical thickness τc,liquid water path LWP and column cloud particle
number Nc as a function of column aerosol
particle number Na. Cloud effective radius andFig.4. Scatterplot of cloud effective radius, optical thickness,
liquid water path and column cloud particle number as afunction of column aerosol particle number obtained from thesimulation. Different colors (red and green) show the resultswith different function for collision efficiency between
particles. Several lines within a same color show the results
under different dynamical stability condition.

4. Conclusion
In the present study, non-hydrostatic spectral
microphysics cloud model was developed and thenumerical experiment was performed for theformation of water clouds in lower layer of 1-2
km height. Correlation pattern between cloudoptical thickness and effective radius wassimulated to be similar to those obtained fromsatellite observation. Cloud optical thickness wasfound to be positively correlated with effective

radius for no-drizzle clouds, contrary to negative
correlation for drizzling clouds. The correlation
pattern was simulated to be sensitive to aerosol
amount. The simulated sensitivity of cloudproperties to aerosol particle number was also
found to be qualitatively consistent with satellite
observation. Quantitative comparison of thesensitivity implied that the global statistics fromsatellite observation estimated somewhat smaller
dependence of cloud on aerosol amount due to themixture of various cloud types.

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