involved in the IPCC assessments. This chapter discusses issues of model resolution and complexity as well as future trends in the hierarchy of models and development pathways.
A major component of climate models is the dynamical core that numerically solves the governing equations of the system components. Computation of the solution is carried out on a three-dimensional spatial grid. Increasing model resolution enables better resolution of processes, but this comes at considerable computational cost. For example, increasing horizontal resolution by a factor of 2 (say from 100 to 50 km2) generally requires a factor of 2 decrease in time step for numerical stability. Thus, the overall computational cost is a factor of 8. Furthermore, to avoid distortion of the results, the horizontal resolution cannot be increased without concomitant increases in vertical resolution. Increasing complexity independently adds to the computational cost of a model, so a balance must be sought between resolution and complexity. In practice, the ensemble of these considerations has led to an increase in atmospheric grid resolution from ~500 km to ~100 km in state-of-the-science climate models since the 1970s.
To enable higher resolution within computational constraints, alternative approaches such as regional climate models and global models with variable resolution, stretched grids, or adaptive grids have been developed to provide local refinements for geographic regions or processes of interest. Similar to global atmospheric models, regional climate models numerically and simultaneously solve the conservation equations for energy, momentum, and water vapor that govern the atmospheric state. Solving these equations on limited-area domains requires lateral boundary conditions, which can be derived from global climate simulations or global analyses. Because of the dependence on large-scale circulation, biases in global climate simulations used to provide lateral boundary conditions propagate into the nested regional climate simulations. Similar to global models, regional models are sensitive to model resolution and physics parameterizations. However, the nesting approach can introduce additional model errors and uncertainties. This issue has been addressed in a series of studies using an idealized experimental framework, known as “Big Brother Experiments (BBE)” (Denis et al., 2002). As summarized by Laprise et al. (2008), the BBE show that, given large-scale conditions provided by the GCMs, regional climate models can downscale to produce finer-scale features absent from the GCMs. Moreover, the fine scales produced by the regional models are consistent with what the GCMs would generate if