tween the surface and tropospheric surface temperature changes in simulations and observations (Chase et al., 2004), which could be attributed to deficiencies in either models or observations, or a combination (NRC, 2001; Christy and Norris, 2004; Mears et al., 2003; Vinnokov and Grody, 2003, Pielke and Chase, 2004; Fu et al., 2004). Other studies, however, find good agreement between observations and the model-predicted spatial and vertical fingerprints of radiatively forced climate change in recent decades (Allen et al., 2000; Stott et al., 2000; Wigley et al., 2000; Barnett et al., 2001; Santer et al., 2000, 2003a,b; Karoly et al., 2003). Additional evaluations of the ability of models to reproduce regional and global climate in recent decades—including tropospheric temperature, ocean heat content, and other climate variables in addition to surface temperature—should be a major priority for further quantifying model predictive skill. Models should also be encouraged to incorporate forward radiance calculations as model diagnostics to compare with observed radiances.

In order to narrow down the uncertainties associated with radiative forcing effects on climate, models have to be improved in many aspects. Of particular importance is improving the representation of cloud processes, the coupling between the atmosphere and the land surface and ocean, the impacts of regional variability in diabatic heating, and the simulation of regional-scale climate.

Clouds and Microphysics

Uncertainties in relating aerosol to cloud droplet populations seriously limits our ability to quantify the indirect aerosol effects. To treat cloud droplet formation accurately, the aerosol number concentration, its chemical composition, and the vertical velocity on the cloud scale need to be known. Abdul-Razzak and Ghan (2000) developed a parameterization based on the Köhler theory that can describe cloud droplet formation for a multimodal aerosol. This approach has been extended by Nenes and Seinfeld (2003) to include kinetic effects, that is, considering that the largest aerosols do not have time to grow to their equilibrium size. To apply one of these parameterizations, the updraft velocity relevant for cloud formation needs to be known. Some climate models apply a Gaussian distribution or use the turbulent kinetic energy as a surrogate for updraft velocity (Ghan et al., 1997; Lohmann et al., 1999). Others avoid this issue completely and use empirical relationships between aerosol mass and cloud droplet number concentration instead (Menon et al., 2002a). This method is limited because of the scarce observational database. At present, the relationship can only be derived between cloud droplet number and sulfate aerosols, sea salt, and organic carbon; no concurrent data for dust or black carbon and



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