they were applied at similar spatial resolution as the regional models, thus confirming the practical validity of the nested regional modeling approach.
More recently, global variable-resolution models using unstructured grids have become feasible (Skamarock et al., 2011). By eliminating the physical boundaries, these models provide local mesh refinement with improved accuracy of the numerical solutions. However, the challenge of developing physics parameterizations that work well across the variable resolution is significant. Systematic evaluation and comparison of different approaches is important for developing a more robust framework to model regional climate. Besides increasing grid resolution, subgrid classification is used in some land-surface models or even atmospheric models (e.g., Leung and Ghan, 1998) to capture the effects of land-surface heterogeneity such as vegetation and elevation to improve simulations of regional climate.
There is considerable evidence that refining the horizontal spatial resolution of climate models improves the fidelity of their simulations. At the most fundamental level, increasing resolution should improve the accuracy of the approximate numerical solutions of the governing equations that are at the heart of climate simulation. However, because the climate system is complex and nonlinear, numerical accuracy in solving the dynamical equations is a prerequisite to climate model fidelity, but is not the only consideration.
One of the more obvious impacts of improving climate model resolution is the representation of geographic features. Resolving continental topography, particularly mountain ranges and islands, can significantly improve the representation of atmospheric circulation. Examples include the South Asian monsoon region and the vicinities of the Rockies, Andes, Alps, and Caucasus, where the mountains alter the large-scale flow and give rise to small-scale eddies and instabilities. Resolving topography can also improve simulations of land-surface processes such as snowpack and runoff that rely strongly on orographically modulated precipitation and temperature (e.g., Leung and Qian, 2003) and may also have upscaled or downstream effects on atmospheric circulation (e.g., Gent et al., 2010) and clouds (e.g., Richter and Mechoso, 2006). Similarly, weather and climate variability associated with landscape heterogeneity, as well as coastal winds influenced by local topography and coastlines, are better represented in models with refined spatial resolution, which can also lead to improved simulation of tropical variability through improved coastal forcing (Navarra et al., 2008).
Many processes in the ocean and sea ice can benefit from increasing spatial resolution. Improved resolution of coastlines, shelf and slope bathymetry, and sills separating basins can significantly improve the simulation of boundary and buoyancy-driven