• feedbacks between tropical instability waves in the ocean and wind speed in atmospheric eddies (Chelton and Xie, 2010);
• air-sea interactions in presence of a sea-ice cover, which depend on the accuracy of detailed representation of sea-ice states, including ice edge position, thickness distribution, and deformations; and
• ice sheet-ocean interactions, which require representation of local flow under and into the ice, including fjord circulation and exchanges.
However, increasing spatial resolution is not a panacea. Climate models rely on parameterizations of physical, chemical, and biological processes to represent the effects of unresolved or subgrid-scale processes on the governing equations. Increasing spatial resolution does not automatically lead to improved accuracy of simulations (e.g., Duffy et al., 2003; Kiehl and Williamson, 1991; Leung and Qian, 2003; Senior, 1995). Often, the assumptions in the parameterizations are scale dependent, although so-called scaleaware parameterization development has been pursued recently (e.g., Bennartz et al., 2011). As model resolution is increased, the assumptions may break down, leading to a degradation of the simulation fidelity. Even if the assumptions remain valid over a range of model resolutions, there is still a need to recalibrate the parameters in the parameterizations as resolution is refined (sometimes called model tuning), and the tuning may only be valid for the time period for which observations used to constrain the model parameters are available. The lack of understanding and formulation of the interactions between parameterizations and spatial resolution makes it hard to quantify the influence of spatial resolution on model skill. Furthermore, structural differences among parameterizations may have comparable, if not larger, effects on the simulations than spatial resolution.
Climate projections at finer scales (such as resolving climatic features for a small state, single watershed, county, or city) are typically produced using one of two approaches: either dynamical downscaling using higher-resolution (50 km or finer) regional climate models nested in the global models or empirical statistical downscaling of projections developed from global climate model output and observational data sets. Neither downscaling approach can reduce the large uncertainties in climate projections, which derive in large part from global-scale feedbacks and circulation changes, and it is important to base such downscaling on model output from a representative set of global climate models to propagate some of these uncertainties into the downscaled predictions. The modeling assumptions inherent in the downscaling step add further uncertainty to the process. There has been inadequate work done to date to systematically evaluate and compare the value added by various downscaling techniques for different user needs in different types of geographic regions. However, as the grid spacing of the global climate model becomes finer, simple statistical down-