Limitations of Current Small to Mesoscale Observations

Traditional field programs have advanced our process-level understanding of the climate system and the representation of cloud processes in detailed models. Still, it has proven far more challenging to apply the field observations to constrain the parameterizations and large-scale properties of clouds in models. Challenges arise from the disparities in spatial scales between point measurements and climate models (the so-called "process-parameterization gap") as well as from disparities in temporal scales between short-term field programs and long-term climate change.

To engage climate model development with fi eld measurements, we suggest that sensitivity studies with large-scale models be used more frequently to guide the design of field programs. For example, intermodel differences across ensembles of climate models could be used to identify processes, cloud systems, or aerosol-cloud interaction processes that represent major sources of uncertainty in simulations of climate perturbations. These differences could then be targeted for detailed field programs. Consideration should be given to methods that bridge the spatial and temporal gaps, so that field measurements could be used to test climate sensitivity.

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