scaling approaches become more justifiable and attractive because the climate model is already simulating more of the weather and surface features that drive local climate variations.
Finding 3.1: Climate models are continually moving toward higher resolutions via a number of different methods in order to provide improved simulations and more detailed spatial information; as these higher resolutions are implemented, parameterizations will need to be updated.
Finding 3.2: Although different approaches to achieving high resolution in climate models have been explored for more than two decades, there remains a need for more systematic evaluation and comparison of the various downscaling methods, including how different grid refinement approaches interact with model resolution and physics parameterizations to influence the simulation of critical regional climate phenomena.
The climate system includes a wide range of complex processes, involving spatial and temporal scales that span many orders of magnitude. As our understanding of these processes expands, climate models need to become more complex to reflect this understanding. The balance between increased complexity and increased resolution, subject to computational limitations, represents a fundamental tension in the development of climate models.
Model complexity can broadly be described in terms of the sophistication of the model parameterizations of the physical, chemical, and biological processes, and the scope of Earth system interactions that are represented. Increasing sophistication of model parameterizations is evident in models of all Earth system components. Atmospheric models, for example, have grown from specified clouds to simulated clouds using simple convective adjustment and relative humidity-based cloud schemes to a host of shallow and cumulus convective parameterizations with different convective triggers, mass flux formulations and closure assumptions, and cloud microphysical parameterizations that represent hydrometeors (mass and number concentrations) in multiple phases. Today most climate models include some representation of atmospheric chemistry and aerosols, including aerosol-cloud interactions (e.g., Liu et al., 2011). Although model parameterizations have become more detailed, uncertainty in process representations remains high as different formulations of parameterizations can lead to large differences in model response to greenhouse gas forcing (e.g., Bony and Dufresne, 2005; Kiehl, 2007; Soden and Vecchi, 2011).