tions (e.g., lack of knowledge on subgrid scales, inadequate diagnoses of vertical velocities, possible inconsistency between reality and assimilation model physics) that could have a significant impact, especially on the concentrations of short-lived species.

Almost all global CTM studies of aerosols so far have been mass-only simulations that do not resolve the aerosol size distribution, mixing across components, or phase. This is evidently problematic for radiative forcing calculations and, in particular, prevents simulations of the indirect effect except through loose empirical relationships between cloud droplet number concentrations and preexisting aerosol mass concentrations (Boucher and Lohmann, 1995). There is a major computational problem because accounting for aerosol microphysics and allowing for an ensemble of aerosol mixing states rapidly increases the number of prognostic model variables. It appears unlikely that this problem will be solved over the next decade by simple increases in computing resources. Innovative algorithms for simulating aerosol microphysics are needed, such as the method of moments (McGraw, 1997) or new sectional approaches (Adams et al., 2003). Better understanding is also needed of the fundamental processes driving aerosol microphysics, particularly nucleation.

Inverse Models

The standard way for specifying emission inventories in CTMs uses “bottom-up” approaches in which knowledge of the underlying processes, and of the associated emission factors, is parameterized and extrapolated on the basis of globally available socioeconomic or environmental information. The bottom-up approach provides the fundamental tool for ascribing sources to specific emission processes and for making future projections. However, there are often large uncertainties in the emission factors and their extrapolation. One can attempt to reduce this uncertainty with “top-down” constraints on emissions that combine information on observed atmospheric concentrations with CTM-derived relationships between concentrations and sources. Formal inverse models combine these bottom-up and top-down approaches by seeking an optimum solution for the emissions that best accommodates the a priori constraints from bottom-up inventories and information from observations (Kasibhatla et al., 2002).

Global observations from long-term surface-based networks (e.g., NOAA CMDL and ALE/GAGE networks) have been used extensively in inverse model studies of sources for CO2 (e.g., Peylin et al., 2002), CO (e.g. Kasibhatla et al., 2002; Petron et al., 2002), methane (Wang et al., 2004), and halocarbons (Mahowald et al., 1997). Inverse model studies for CO2 have played a key role in quantifying the terrestrial sink of CO2 at northern midlatitudes. Observations from aircraft campaigns and from satellites are

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