[1] With a global aerosol transport-radiation model coupled to a general circulation model, changes in the meteorological parameters of clouds, precipitation, and temperature caused by the direct and indirect effects of aerosols are simulated, and its radiative forcing are calculated. A microphysical parameterization diagnosing the cloud droplet number concentration based on the Köhler theory is introduced into the model, which depends not only on the aerosol particle number concentration but also on the updraft velocity, size distributions, and chemical properties of each aerosol species and saturation condition of the water vapor. The simulated cloud droplet effective radius, cloud radiative forcing, and precipitation rate, which relate to the aerosol indirect effect, are in reasonable agreement with satellite observations. The model results indicate that a decrease in the cloud droplet effective radius by anthropogenic aerosols occurs globally, while changes in the cloud water and precipitation are strongly affected by a variation of the dynamical hydrological cycle with a temperature change by the aerosol direct and first indirect effects rather than the second indirect effect itself. However, the cloud water can increase and the precipitation can simultaneously decrease in regions where a large amount of anthropogenic aerosols and cloud water exist, which is a strong signal of the second indirect effect. The global mean radiative forcings of the direct and indirect effects at the tropopause by anthropogenic aerosols are calculated to be À0.1 and À0.9 W m À2 , respectively. It is suggested that aerosol particles approximately reduce 40% of the increase in the surface air temperature by anthropogenic greenhouse gases on the global mean.Citation: Takemura, T., T. Nozawa, S. Emori, T. Y. Nakajima, and T. Nakajima (2005), Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model,

International audienceThe collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint ESA-JAXA EarthCARE satellite mission, scheduled for launch in 2017, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, CALIPSO, and Aqua. Specifically, EarthCARE's Cloud Profling Radar, with 7 dB more sensitivity than CloudSat, will detect more thin clouds and its Doppler capability will provide novel information on convection, precipitating ice particle and raindrop fall speeds. EarthCARE's 355-nm High Spectral Resolution Lidar will measure directly and accurately cloud and aerosol extinction and optical depth. Combining this with backscatter and polarization information should lead to an unprecedented ability to identify aerosol type. The Multi-Spectral Imager will provide a context for, and the ability to construct the cloud and aerosol distribution in 3D domains around the narrow 2D retrieved cross-section. The consistency of the retrievals will be assessed to within a target of ±10 W m⁻² on the (10 km²) scale by comparing the multi-view Broad-Band Radiometer observations to the top-of-atmosphere fluxes estimated by 3D radiative transfer models acting on retrieved 3D domains

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This study describes an approach for combining CloudSat and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations to investigate the microphysical processes of warm clouds on the global scale. MODIS column optical thickness is vertically distributed between the cloud top and cloud bottom according to adiabatic and condensational growth assumptions and used as a vertical coordinate system to analyze profiles of CloudSat-observed radar reflectivity. The reflectivity profiles thus rescaled as a function of the in-cloud optical depth clearly depict how the cloud-to-rain particle growth processes take place within the cloud layer and how these processes vary systematically with variations in MODIS-derived effective particle radius. It is also found that the effective radii retrieved using two different wavelengths of MODIS tend to trace the microphysical change of reflectivity profiles in a different way because of the difference in the layer depth that characterizes these two effective radii. The reflectivity profiles as a function of optical depth are also interpreted in terms of drop collection processes based on the continuous collection model. The slope of the reflectivity change with optical depth provides a gross measure of the collection efficiency factor. The systematic changes of reflectivity profiles with MODIS-derived particle sizes are then interpreted as demonstrating a strong dependency of the collection efficiency on particle size. These results provide a quantitative insight into the drop collection process of warm clouds in the real atmosphere.

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Hydrometeor droplet growth processes are inferred from a combination of Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) cloud particle size observations and CloudSat/Cloud Profiling Radar (CPR) observations of warm water clouds. This study supports the inferences of a related paper (Part I) (i) that MODIS-retrieved cloud droplet radii (CDR) from the 3.7-mm channel (R37) are influenced by the existence of small droplets at cloud top and (ii) that the CDR obtained from 1.6-(R16) and 2.1-mm (R21) channels contain information about drizzle droplets deeper into the cloud as well as cloud droplets. This interpretation is shown to be consistent with radar reflectivities when matched to CDR that were retrieved from MODIS data. This study demonstrates that the droplet growth process from cloud to rain via drizzle proceeds monotonically with the evolution of R16 or R21 from small cloud drops (on the order of 10-12 mm) to drizzle (CDR greater than 14 mm) to rain (CDR greater than 20 mm). Thus, R16 or R21 is an indicator of hydrometeor droplet growth processes whereas R37 does not contain information about coalescence. A new composite analysis, the contoured frequency diagram, is introduced to combine CloudSat/CPR reflectivity profiles and reveals a distinct trimodal population of reflectivities corresponding to cloud, drizzle, and rain modes.

This study examines the sensitivity of the retrieved cloud droplet radii (CDR) to the vertical inhomogeneity of droplet radii, including the existence of a drizzle mode in clouds. The focus of this study is warm water-phase clouds. Radiative transfer simulations of three near-infrared Moderate Resolution Imaging Spectroradiometer (MODIS) channels centered on wavelengths of 1.6, 2.1, and 3.7 mm reveal that the retrieved CDR are strongly influenced by the vertical inhomogeneity of droplet size including (i) the existence of small cloud droplets at the cloud top and (ii) the existence of the drizzle mode. The influence of smaller droplets at cloud top affects the 3.7-mm channel most, whereas the presence of drizzle influences radiances of both the 2.1-and 1.6-mm channels more than the 3.7-mm channel. Differences in the CDR obtained from MODIS 1.6-, 2.1-, and 3.7-mm channels that appear in global analysis of MODIS retrievals and the CDR derived from data collected during the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) intensive observation period in 1987 can be explained by the results obtained from the sensitivity experiments of this study.

[1] This study developed an algorithm for estimating solar radiation from space using a neural network (NN) with an improved learning algorithm to approximate radiative transfer code. The NN solver for the solar radiation budget is based on radiative transfer calculations. All data sets for testing and training the NN were generated from radiative transfer code. Thus the NN traces the radiative transfer calculation that is approximated by a learning algorithm. To demonstrate the effectiveness of the NN approach for high-speed estimation and multiparameter problems, the NN was applied to data from a geostationary satellite and a Sun-synchronous subrecurrent orbit satellite. The developed algorithm was applied to data from the Multi-functional Transport Satellite-1 Replacement (MTSAT-1R) geostationary satellite, and estimations were validated against in situ observations for March 2006 at four SKYNET sites. Byproducts of the algorithm include UVA, UVB, and photosynthetically active radiation (PAR) fluxes as well as direct and diffuse components. The NN approach enables semi-real-time analysis of these products by high-speed calculation. In addition, the NN allows for consideration of detailed particle optical parameters in the solar radiation budget without the need for a massive database. The method was also applied to observations from the Advanced Earth Observing Satellite-II/Global Imager (ADEOS-II/GLI) for May 2003. The results showed trends in the direct and diffuse components of downward solar radiation over the North Pacific Ocean. This report outlines the construction of the NN for radiation budget estimation and demonstrates the effectiveness of the NN approach.