Climate models are an important tool in understanding the mechanisms of the climate system and for predicting future climate. However, clouds remain poorly simulated by models. Clouds play an important role in the climate system by reducing the amount of shortwave radiation reaching the surface and by trapping longwave radiation leaving the surface. On average, this balance leads to more cooling than warming, but this is dependent on cloud type and location. Improving the understanding of clouds and how they can be simulated will lead to more accurate models and better predictions of future climate. This study builds a method whereby clouds are grouped into dynamical regimes in order to gain insight into the connection between large-scale dynamics and cloud properties. This method can also be used to understand the sources of modeling errors and help discern ways to improve these models. An additional aspect of the study addresses the lack of understanding of how the balance that clouds create between cooling from reflection of shortwave radiation and warming from absorption of longwave radiation might change as the atmosphere warms due to anthropogenic increases in greenhouse gases like carbon dioxide. This study builds a method whereby data analysis can assist in estimates of the cloud-climate feedback.