The aerosol effects on the climate system are roughly divided into three categories: direct, indirect, and semi-direct effects. Observations from satellites and ground with remote sensing and numerical models have been developed to understand and estimate aerosol effects on a global scale. In the latest assessment report of the Intergovernmental Panel on Climate Change (IPCC) , however, there are still large uncertainties in their radiative forcings in comparison with the estimation of long-lived greenhouse gases. To reduce the uncertainties, we have to study the three-dimensional aerosol distributions and the cloud-aerosol interaction more accurately. It is important to observe aerosol vertical profiles with lidar, one of the active sensors, to understand the three-dimensional aerosol distributions as well as to continue observations with passive sensors. The data assimilation, which harmonizes numerical models with observations, is also an effective method to reduce the uncertainties. A cloud resolving model coupled with an aerosol transport model is a useful tool to better understand the cloud-aerosol interaction. Efforts to analyze the aerosol climate effects quantitatively will result in more reliable projection of the future climate change and elucidating climate system.
Vertical distributions of aerosol optical properties derived from lidar measurements are essential information for evaluating climate change. The recent development of lidar, communication, and computer technologies has enabled us to conduct ground-based network observations and satellite borne, ship borne, and airborne measurements with multichannel lidar. These lidar observations and related data analyses have provided detailed aerosol information. This paper reports the current status of aerosol observations conducted with lidars by the National Institute for Environmental Studies (NIES) and those performed around the world. Several up-to-date developed algorithms that estimate aerosol optical and microphysical properties using multichannel lidar data are also reported. NIES future strategies for lidar observation and aerosol retrieval algorithms are also presented based on the current status.
This paper reviews a recent study of aerosol indirect effect using a global cloud-resolving model. The new global cloud-resolving model NICAM recently developed for the Earth Simulator was coupled with SPRINTARS aerosol transport model for simulating the aerosol effects on convective clouds. Numerical experiments were performed using the NICAM-SPRINTARS model with horizontal resolution of 7 km, and the simulated results were compared with satellite observations of clouds and aerosols. The results showed several important aspects of cloud microphysical properties and their interactions with aerosols, which have been difficult to simulate with traditional climate models. This includes detailed spatial structures of cloud droplet effective radius, global correlation statistics of liquid water path with aerosols and vertical growth patterns of cloud droplets interacting with aerosols. These results demonstrate that the interactions of aerosols with convective clouds, for the first time, are represented by global-scale model, providing a new capability for studying the climatic effects of aerosols. We also discuss several issues with the model that should be addressed in future studies.
Data assimilation has been developed in numerical weather prediction (NWP) and modeling of oceanography. Recently, store and expansion of observations and development of numerical modeling have enabled data assimilation techniques to be applied to aerosol transport models. In this paper, we introduce information about applications (e.g., forecast, inverse modeling, reanalysis, sensitivity analysis) and recent studies about data assimilation with atmospheric aerosol observations and numerical models. We also show a preliminary experiment of ensemble-based data assimilation with global aerosol climate model (SPRINTARS) and Aerosol Optical Thickness (AOT) measured by MODIS/AQUA. In the experiment, the data assimilation improves under-estimates in East Asia, North Pacific Ocean, Central America, Middle East and Central Africa, and over-estimates in oceans over the southern hemisphere. Root mean square difference (RMSD) between SPRINTARS and MODIS AOT is reduced by 21 %, and long-wave aerosol direct forcing at the tropopause increased where dust and carbon aerosol are increased by the data assimilation.
Temperature and airflow distributions around heated pans by a gas-oven and an IH-heater were compared, and the difference in steam generation was studied. As a result, we found that the existence of flame around a pan affected the airflow and the temperature distribution, etc. We also found that water evaporated by IH-heaters was more easily changed into mist. The number concentration and the size distribution of water mist were measured using a PDPA system. The number of water mist generated with the IH-heater was 10 to 100 times larger than the number of mist generated with the gas-oven. The mode diameter of mist generated with the IH-heater was 8 to 10 μm, and that with gas-oven was 4 to 5 μm. We concluded that the mist size difference resulted from the difference in temperature distribution and the existence of hot flame around the pan in the case of gas-oven.
Allocation method of contaminant source in a cleanroom was developed. With this method, the distribution of contaminant concentration was given as a weighted sum of influences from each contaminant source. The weighting function of each contaminant source was obtained by treating it as a linear inversion-problem. Generalized inverse matrix technique was employed to solve the problem. As a result, the contribution of each contaminant source was determined, and therefore the result was useful to improve the cleanliness level of cleanrooms. The method was further improved to solve unsteady state problems. The improved method gave a good agreement in the locations with previously assumed sources, and it was found that the method was applicable to the continuous monitoring of cleanrooms.