2-2 A warming-cloud, aerosol, and radiation feedback precision evaluation

a. Summary

In this subject of research, in order to evaluate the climate influence of the interaction of clouds and aerosol, it aims at developing the numerical model which can express the fine physics process of aerosol transportation and clouds in detail. A numerical simulation which paid its attention to the influence of aerosol was done about the optical characteristic of the clouds which are the results acquired in the last fiscal year using non-statics bin method detailed cloud physics model, and the feature acquired by satellite observation was reproduced. Moreover, the work which mounts aerosol chemistry transportation model SPRINTARS in nonhydrostatic icosahedral atmospheric model NICAM developed in the Frontier Research Center for Global Change was done.

b. Research purpose

In order for the accuracy which can trust the radiation legal force by the large aerosol indirect effect of uncertainty when understanding and predicting a climate change to estimate, he needs to understand quantitatively the influence aerosol affects the optical characteristic and the precipitation characteristic of clouds. The actual condition of such an interaction of aerosol and clouds has effective numerical modeling, in order to interpret those observational data quantitatively and to understand the impact to the climate of an aerosol-cloud interaction in detail, although it is becoming clear on a scale of all balls in observation with satellite observation in recent years. So, it aims at development of the numerical model which can express aerosol transportation and the interaction of cloud physics process in detail on a scale of all balls in this research sub theme.

c. A research program, a method, scheduling

In this subject of research, in order to investigate quantitatively first the influence aerosol affects the particle size distribution of clouds, and the optical characteristic and the precipitation generation characteristic, bin method cloud model which can express the fine physics process of clouds in detail is developed, and a numerical simulation using it is done. Furthermore, the interaction of a cloud system also including a convective cloud and an aerosol transport process is calculated on a scale of all balls by mounting aerosol transportation model SPRINTARS on it, using nonhydrostatic icosahedral atmospheric model NICAM as a dynamics platform. In this simulation, the influence of the cloud, precipitation, and radiation process on aerosol is taken in from the relation of calculation cost based on formulization of bulk cloud physics for the time being.

d. The research program in the Heisei 17 fiscal year

A numerical simulation for investigating the influence (aerosol indirect effect) whose fine physics process of a system in which aerosol, the quality of a matter of chance, and a ice grain child live together aerosol exerts on the optical characteristic of a water cloud using bin method detailed cloud physics model which can be expressed in detail is done. Simultaneously, the mounting work of aerosol chemistry transportation model SPRINTARS to nonhydrostatic icosahedral atmospheric model NICAM is done.

e. Fruits of work in the Heisei 17 fiscal year

A numerical simulation for investigating the influence whose fine physics process of a system in which the aerosol, and the water and the ice grain child who were developed in Center for Climate System Research, University of Tokyo live together aerosol exerts on the optical characteristic of clouds using the non-statics detailed cloud physics resolving model (Suzuki and 2004) which can be expressed in detail was done. This is the model which bin method detailed cloud physics module for calculating the spatial distribution and time development of the particle system which consists of a non-statics frame for expressing explicitly the convection which plays a role important for cloud generation, and aerosol, liquid water particles and a ice grain child combined. In the portion of bin method detailed cloud physics module, signs that direct handling and they change a particle-size-distribution function with various fine physics process about each of aerosol, liquid water particles (cloud particle), and various ice grain children (needle ice, hail, and hail) are forecast explicitly.

A numerical simulation which generates the water cloud of few stories using such a cloud model was done, and analysis which paid its attention to the fine physics characteristic and the optical characteristic of clouds was conducted. The cloud particle effective radius r_e and optical thickness showing such the physical characteristic of clouds which are physical quantity τ_c is defined by the following formula from particle-size-distribution function n (r), respectively. :

However, Q_ext is an extinction efficiency factor and Q_ext=2 are realized in good approximation to a cloud particle. In a bin method detailed cloud physics model, since the particle-size-distribution function n(r) of a cloud particle is forecast explicitly, it is the big merit of this model that an effective radius and optical depth are calculable according to the upper definition. Although these two physical quantity is the fundamental amounts of observations which can be found from satellite remote sensing, it is known that a correlation pattern which is different among these from the past observation research corresponding to the developmental stage of clouds exists. That is, it is reported by the clouds accompanied by a positive correlation and rain (Dorri Zulu particles) in both with clouds without rain (or the Dorri Zulu particles which are the precursor particle) that many negative correlations are observed, respectively (Nakajima et al., 1991; Han et al., 1994; Nakajima and Nakajima, 1995; Asano et al., 1995). Although it was as such a correlation pattern having reported that bin method cloud model was also reproduced in the last fiscal year, the influence of aerosol on still such a correlation pattern was investigated in the current fiscal year. Therefore, a numerical simulation which made the number of aerosol given to calculation of cloud generation as an initial condition fluctuate was done, and it was investigated how the correlation pattern of an effective radius and optical thickness would change. The result is shown in Fig. 34. according to this, on clear conditions (Fig. 34 upper row) with little aerosol, on the dirty conditions (Fig. 34 lower berth) with much aerosol, only the positive-correlation division was conversely reproduced to the thing with few positive-correlation divisions by a plot mainly consisting of a negative-correlation division, and it turned out that a negative-correlation division is not formed. The difference in such a correlation pattern takes place, when the fine physics particle growth pattern of clouds changes with change of the amount of aerosol. Since the number density of the cloud particle generated is small when there is little aerosol, the condensation growth rate of particles is large, the size by which a collision coalescence process is activated is easily reached, since there is little expenditure of steam and the degree of supersaturation is high and the Dorri Zulu particles are generated actively, a negative-correlation pattern stands high. On the other hand, when there is much aerosol, since the number density of the cloud particle generated is large, many steam is consumed, the degree of supersaturation falls, and the condensation growth rate of particles becomes small. As a result, since a cloud particle cannot reach the size by which collision annexation is activated and Dorri Zulu particles are not generated, only a positive correlation portion is formed, without a negative correlation portion appearing. This simulation result shows the feature well similar to the result reported in observation by the satellite remote sensing by Nakajima and Nakajima (1995). According to Nakajima and Nakajima (1995), the correlation patterns of an effective radius and optical depth differ notably in a FIRE area (offing of California), and an ASTEX area (North Atlantic Ocean), a negative correlation excels in a FIRE area, a positive correlation division receives slightly and its positive correlation is dominant in an ASTEX area. The former is similar to the model simulation result (Fig. 34 upper row) under conditions with little aerosol, and the latter is similar to the calculation result (Fig. 34 lower berth) under conditions with much aerosol (Suzuki et al., 2006). From this, the difference in the correlation pattern observed in these two domains can be interpreted as what was hung down even if it reflected the difference in the fine physics particle growth process by the difference in the amount of aerosol. Thus, by combining a model experiment and satellite observational data, the correlation pattern between the effective radii and optical thickness which are being obtained in observation to the clouds of all balls can be matched with the number of aerosol, and can be classified, and it is thought that it will become possible to investigate the pattern of fine physics particle growth of clouds on a scale of all balls from now on.

In this sub theme, the model which combined aerosol chemistry transportation model SPRINTARS (Takemura et al., 2000, 2002) with nonhydrostatic icosahedral atmospheric model NICAM (Satoh, 2002, 2003; Tomita et al., 2002, 2004) is developed in order to perform all the ball simulations of influence that aerosol exerts on a cloud system in parallel to such detailed detailed cloud physics modeling. In order to perform all ball simulations, we decided to perform introduction of an aerosol indirect effect using the formulization (Suzuki et al., 2004; Takemura et al., 2005) based on a bulk cloud physics scheme for the time being from restrictions of calculation cost. In this formulization, an average effective particle radius is computed from cloud particle number density and liquid water content in consideration of an aerosol second kind indirect effect by calculating cloud particle number density diagnostically based on the aerosol number density obtained from an aerosol transportation model, and calculating the conversion to precipitation from an itinerant Buddhist in the form depending on the cloud particle number density obtained in this way. By taking in such an indirect effect calculation module to global cloud resolving models, it is an essentially different point from the research using the conventional GCM that the reaction of the aerosol to the convective cloud which is becoming clear like a observation in recent years can be evaluated. Now, about 80 percent of SPRINTARS mounting work is completed, and it goes into the culmination of work. As an example of first stage-of a calculation result, distribution of a sulphate and carbon nature aerosol is shown in Fig. 35. Signs that sulphate aerosol (Fig. 35 upper row) exists in an industrial active region mostly, and carbon nature aerosol (Fig. 35 lower berth) exists in an industrial active region and the generating area of a forest fire mostly are seen.

f. Consideration

Two kinds of routes, the method of performing calculation in a small domain using bin method cloud model which treats detailed cloud physics process in detail, and the method of performing all the ball simulations combined with the aerosol transport process using the bulk cloud physics scheme, exist in the modeling for the impact evaluation which aerosol exerts on the optical characteristic and the precipitation generation characteristic of clouds through the influence of the fine physics process on clouds. Although it seems that these two methods are unified in the future, at present, it is considered to be realistic approach to perform these in parallel. Development of the model which combined aerosol transportation model SPRINTARS with nonhydrostatic icosahedral atmospheric model NICAM in the meaning needs to advance combination of NICAM and SPRINTARS immediately, in order to belong to research of the latter and to advance aerosol climate influence using a cloud resolving model.

Fig. 34: Correlation of effective radius and optical thickness which were obtained by bottle method cloud model (upper row : Lower-berth: clear conditions, dirty conditions)

Fig. 35: Example computation of aerosol concentration distribution near altitude of 1km obtained by NICAM+SPRINTARS (upper row: a sulphate, lower-berth:carbon nature)

g. Bibliography

Asano, S., M. Shiobara, and A. Uchiyama, Estimation of cloud physical parameters from airborne solar spectral reflectance measurements for stratocumulus clouds, J. Atmos. Sci., 52, 3556-3576, 1995.

Han, Q., W. B. Rossow, J. Chou, and R. M. Welch, Global survey of the relationships of cloud albedo and liquid water path with droplet size using ISCCP, J. Climate, 11, 1516-1528, 1998.

Nakajima, T., M. D. King, and J. D. Spinhirne, Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part II: Marine stratocumulus observations. J. Atmos. Sci., 48, 728-750, 1991.

Nakajima, T. Y. and T. Nakajima, Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX regions. J. Atmos. Sci., 52, 4043-4059, 1995.

Satoh, M., Conservative scheme for a compressible non-hydrostatic models with moist processes, Mon. Wea. Rev., 131, 1033-1050, 2003.

Satoh, M., Conservative scheme for the compressible non-hydrostatic models with the horizontally explicit and vertically implicit time integration scheme, Mon. Wea. Rev., 130, 1227-1245, 2002.

Suzuki, K., T. Nakajima, A. Numaguti, T. Takemura, K. Kawamoto, and A. Higurashi, A study of the aerosol effect on cloud field with simultaneous use of GCM modeling and satellite observation, J. Atmos. Sci., 61, 179-194, 2004.

Suzuki, K., T. Nakajima, T. Y. Nakajima, and A. Khain, Correlation pattern between optical thickness and effective radius of water clouds simulated by a spectral bin microphysics cloud model, Geophys. Res. Lett. in review.

Takemura, T., H. Okamoto, Y. Maruyama, A. Numaguti, A. Higurashi, and T. Nakajima, Global three-dimensional simulation of aerosol optical thickness distribution of various origins, J. Geophys. Res., 105, 17853-17873, 2000.

Takemura, T., T. Nakajima, O. Dubovik, B. N. Holben, and S. Kinne, Single-scattering albedo and radiative forcing of various aerosol species with a global three-dimensional model, J. Climate, 15, 333-352, 2002.

Takemura, T., T. Nozawa, S. Emori, T. Y. Nakajima, and T. Nakajima, Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model, J. Geophys. Res., 110, D02202, doi:10.1029/2004JD005029, 2005.

Tomita, H., M. Satoh, K. Goto, An optimization of the icosahedral grid modified by the spring dynamics, J. Comput. Phys., 183, 307-331, 2002.

Tomita, H., and M. Satoh, A new dynamical framework of nonhydrostatic global model using the icosahedral grid, Fluid Dyn. Res., 34, 357-400, 2004.

The research on the numerical modeling of the detailed cloud physics process in connection with Kentaro Suzuki and particle growth, the University of Tokyo physical science system graduate course earth planet science speciality doctoral dissertation, and September, 2004

h. The announcement of a result

<Printing announcement>

Suzuki, K., T. Nakajima, T. Y. Nakajima, and A. Khain, 2006: Correlation pattern between optical thickness and effective radius of water clouds simulated by a spectral bin microphysics cloud model. Geophys. Res. Lett. in review.

<Oral announcement>

Suzuki, K., T. Nakajima, T. Y. Nakajima, and T. Iguchi, 2005: Numerical study of the aerosol effect on water cloud optical properties with non-hydrostatic spectral microphysics cloud model. International Association of Meteorology and Atmospheric Science (IAMAS), Scientific Assembly, Beijing, China, 2-11. August.

Suzuki, K., T. Nakajima, and T. Y. Nakajima, 2005: Characteristics of water cloud optical property as simulated by non-hydrostatic spectral microphysics cloud model. Cloud Modeling Workshop, Fortcollins, CO, 6-8 July.

Kentaro Suzuki, Teruyuki Nakajima, Takashi Nakajima: The interpretation by bin method cloud model of the optical characteristic of the water cloud obtained by satellite observation, the Meteorological Society of Japan autumn convention, Kobe University, and 20-November 22, 2005.