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The test bed is a new computational framework to streamline the process of testing and evaluating aerosol process modules over a range of spatial and temporal scales. Many of the uncertainties associated with estimates of direct (via scattering and absorption of radiation by aerosols) and indirect (via droplet nucleation influenced by aerosols) radiative forcing in climate models (Solomon et al. 2007) can be attributed to inaccurate simulations of the spatial and temporal variations of aerosol mass, number, composition, mixing state, size distribution, hygroscopicity, and optical properties. For example, the formation and transformation of secondary organic aerosols (SOAs; e.g., Volkamer et al. 2006) and the nature of many cloud-aerosol interactions (e.g., Lohmann and Feichter 2005) are still poorly understood and consequently inadequately represented in models. The coarse horizontal and vertical grid spacings usually employed by global climate models, which cannot resolve the observed spatial variability of atmospheric aerosols as well as meteorological factors that contribute to aerosol-radiation-cloud-chemistry interactions (e.g., Haywood et al. 1997;Petch 2001), are another factor that contributes to uncertainties in predictions of aerosol radiative forcing.Regional and global models are becoming more complex as they incorporate new representations for the size distribution of aerosol mass and number and new parameterizations of aerosol processes. Journal articles that describe new parameterizations of aerosol processes usually employ a single model along with a dataset for a specific region and/or time period to quantify the performance of the new parameterization. The models, evaluation datasets, and other factors differ from study to study. One consequence of the current modeling paradigm is that the performance and computational efficiency of multiple treatments for a specific aerosol process cannot be quantitatively compared, because many other processes among aerosol models are different

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A U.S. Environmental Protection Agency (EPA)-approved diagnostic wind model [California Meteorological Model (CALMET)] was evaluated during a typical lake-breeze event under fair weather conditions in the Chicago region. The authors focused on the performance of CALMET in terms of simulating winds that were highly variable in space and time. The reference winds were generated by the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) assimilating system, with which CALMET results were compared. Statistical evaluations were conducted to quantify overall model differences in wind speed and direction over the domain. Below 850 m above the surface, relative differences in (layer averaged) wind speed were about 25%-40% during the simulation period; wind direction differences generally ranged from 6°to 20°. Above 850 m, the differences became larger because of the limited number of upper-air stations near the studied domain. Analyses implied that model differences were dependent on time because of time-dependent spatial variability in winds. Trajectory analyses were made to examine the likely spatial dependence of CALMET deviations from the reference winds within the domain. These analyses suggest that the quality of CALMET winds in local areas depended on their proximity to the lake-breeze front position. Large deviations usually occurred near the front area, where observations cannot resolve the spatial variability of wind, or in the fringe of the domain, where observations are lacking. Results simulated using different datasets and model options were also compared. Differences between CALMET and the reference winds tended to be reduced with data sampled from more stations or from more uniformly distributed stations. Suggestions are offered for further improving or interpreting CALMET results under complex wind conditions in the Chicago region, which may also apply to other regions.

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