Cloud microphysical structure as investigated witha non-hydrostatic cloud resolving model*T. Iguchi1, T. Nakajima1, A. Khain2, K. Saito3, T. Takemura4, Y. Ishizaka5, T. Y. Nakajima6(1: CCSR, the University of Tokyo, 2: The Institute of Earth Sciences, The Hebrew University of Jerusalem, 3: NPD,
Japan Meteorological Agency, 4: Research Institute for Applied Mechanics, Kyushu University, 5: HyARC, Nagoya
University and 6: JAXA/EORC)* Center for Climate System Research, the University of Tokyo, Japan, 153-8904, Komaba,
Meguro-ku, Tokyo, Japan. e-mail: iguchi@ccsr.u-tokyo.ac.jp
Abstract
We’ve developed a non-hydrostatic model coupled with a detailed cloud microphysical scheme described by an explicit bin
method. Model simulations have been carried out for a domain within a radius of 1,400 km whose center is the sea near theKyusyu region and for the second half of March and the first half of April in 2003. The results have been compared withsatellites and aircraft observation datasets for validation.
Keyword: cloud, aerosol

1. Introduction
An increase of tropospheric aerosols, which act as cloud
condensation nuclei (CCN), can cause an increase in thecloud albedo and lifetime, through which aerosol has astrong impact on the climate system of the earth, known as
the indirect climate effect of aerosols. Because clouds andaerosols are short-lived constituents distributed
inhomogenerously, it is difficult to access the accurate
forcing by the aerosol indirect effect. General circulation
models are insufficient for studying the aerosol-cloudinteraction process because of lack of detailed
microphysical mechanism in the models. On the other hand,use of a cloud resolving model including an explicit cloudmicrophysical scheme is promising, because satellite
datasets of cloud parameters became available in thesedays for detailed comparison with simulation results.

(Fig. 1). The calculation periods are from 18:00 to 6:00
(UTC) of each day within the second half of March and thefirst half of April in 2003.
The horizontal grid size is 7km, and the number ofvertical layer is 38 (the interval of bottom layer = 40m; toplayer = 580m) and the height of the top boundary is about
12km. The time step is 20 seconds for dynamics and 6.7
seconds for cloud microphysics, with a variable time stepfor the condensation process. The tracers of cloud
microphysics are only CCN and water droplets resolved
into 33 size bins, and ice cloud particles (ice crystals,snowflakes, graupels and hails) are excluded because of the
problem of calculation cost.

2. Model description
In this study, we’ve developed a non-hydrostatic cloud
resolving model based on the Meteorological ResearchInstitute / Numerical Prediction Division unitied
Nonhydrostatic Model [Saito et al., 2001] coupled with thecloud microphysical scheme with a spectral explicit binmethod of the Hebrew University Cloud Model [Khain etal., 2000] which treats the CCN effect explicitly. In
detailed, as cloud microphysical processes this model treats
nucleation from CCN, condensation growth, evaporationand collision coagulation growth. The model is nested to
the re-analysis data as for dynamical variables such as
horizontal velocities, temperature and mixing ration ofwater vapor. In addition to that, this model can be nested as
for CCN. Then, we make use of the output of 3-D aerosol
transport model, SPRINTARS [Takemura et al., 2001] forthis nesting.

3. Numerical experiments
3.1 Setting
The calculation area is a domain within a radius of 1,400
kilometers whose center is the sea near the Kyushu region

Fig. 1: The calculation area of this simulation.

3.2 Results
Fig. 2 illustrates snapshots of horizontal distribution ofsome cloud parameters at 3:00 on April 8th, 2003,
comparingwiththeretrieved
datasets
fromTERRA/MODIS satellite-borne imager (Fig. 3). Then, Fig.
4 shows one example of a comparison between CCN
concentration as the input of these model simulations
obtained from the results of SPRINTARS and that fromaircraft observation. Also, Fig. 5 shows a comparison
between effective radius of cloud droplets as the output ofmodel simulations and that from aircraft observation.

Fig. 5: The vertical distribution of effective radiusof cloud droplets. Red dots correspond to theinput of this model simulation on April 8th. Blue
dots are datasets of aircraft observation.

4. Discussion
Fig. 2: The snapshots of liquid water path, droplet
effective radius on cloud top, optical thickness andtemperature on cloud top at 3:00 on April 8th, 2003by the numerical simulation.

A general agreement between numerical simulation
results and satellite-retrieved results of cloud liquid waterand effective particle radius of low level clouds. However,there are some problems, for example, in Fig.2 low andthin clouds on East China Sea cannot be resolved. As forthe comparison between model simulation and aircraft
observation, generally the inputs of CCN concentration inmodel simulations are underestimated than datasets ofobservations and then the effective radiuses of cloud
droplets in model simulations are overestimated thanobservations due to that.
We will show results on other days also. In addition tothat, sensitivity test simulations for an amount of CCN
concentration to cloud parameters will be carried out.

References

Fig. 3: The snapshots of the same cloud parameteras Fig.2 by TERRA/MODIS retrieval datasets.

Fig. 4: The vertical distribution of activated CCN
concentration. Red line corresponds to the inputof this model simulation on April 8th. Blue dots
are datasets of aircraft observation.

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