Simulated aerosol key optical properties over global scale using an aerosol transport model coupled with a new type of dynamic core

Atmospheric aerosols greatly impact the Earth's climate in many ways, and to date, not all of them are well known. Aerosols are considered to be one of the factors inducing climate change primarily through two effects: (a) a direct effect in which aerosol particles scatter and absorb the solar and thermal radiation (Coakley et al., 1983), and (b) an indirect effect in which they change the microphysical and optical properties of cloud droplets acting as cloud condensation nuclei (Albrecht, 1989).

To evaluate aerosol effects on the climate system, we need to accurately estimate aerosol optical properties, such as aerosol optical depth (AOD), Ångström Exponent (AE), and single scattering albedo (SSA). Aerosol optical properties are determined not only by aerosol amount but also by physical and optical parameters such as size distribution of particles, mixing state of particles, and refractive index (especially for absorbing particles, e.g., soot and dust). These parameters are usually described differently within global aerosol models, and there are large model diversities in aerosol dispersal and consequently optical properties (Textor et al., 2006, Textor et al., 2007). It has become evident that aerosol modeling suffers from both poorly known emission inventories and aerosol physical and optical parameters. Thus, the modeled aerosol properties have to be validated by observations to ensure high confidence in the modeled results.

AErosol RObotic NETwork (AERONET) is to date the most dedicated effort in establishing a global surface network with the purpose of observing aerosol behavior, and its data have been commonly used for model validations. Monthly mean AODs, AEs and SSAs simulated by Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with an atmospheric general circulation model, MIROC (Model for Interdisciplinary Research on Climate), were compared with the observations collected at dozens of AERONET sites (Takemura et al., 2002). In an attempt to provide an absolute measure for model skill, AODs simulated with aerosol modules of seven global models were compared to the observations from 20 AERONET sites (Kinne et al., 2003). To investigate the ability of the Community Multiscale Air Quality (CMAQ) model to simulate the aerosol distribution in Europe, the modeled results were compared with surface-measured PM10 values and AERONET AODs (Matthias, 2008). Compared to AERONET retrievals, the simulated AODs with a global chemical and transport model (GEOS-Chem) were systematically overestimated over northern Africa and southern Europe (Generoso et al., 2008). Chin et al. (2009) evaluated the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model simulated key aerosol optical parameters against AERONET retrievals at seven different regions worldwide and concluded the model underestimated AODs for biomass burning aerosols by 30–40%.

The SPRINTARS module has also been implemented into the Nonhydrostatic ICosahedral Atmospheric Model (NICAM). The simulated results with a spatial resolution of 7 km were compared with satellite observations, but the period was very limited only during July 1–8, 2006 (Suzuki et al., 2008). In addition, although NICAM model with a coarse spatial resolution of 224 km was also applied for a passive tracer model to simulate CO₂ distribution (Niwa et al., 2011), NICAM + SPRINTARS with a coarse resolution has not been evaluated using ground-based remote sensing measurements. Therefore, we simulate the global temporal and spatial distributions of aerosol characteristics using this new aerosol-coupled version of NICAM and evaluate the simulated aerosol key optical properties with the AERONET retrievals over a total of 196 sites during the period 2006–2008 in this study.