Global changes in cloud properties have the potential to significantly impact the Earth's energy balance. Due to the strong cooling effect of stratocumulus clouds, it is of particular importance to quantify their sensitivities to changing aerosol and dynamical forcings. Prior observational studies have shown instantaneous correlations between aerosols and cloud properties, but have generally been unable to test if these correlations reflect a true causal relationship. Mauger and Norris (GRL, accepted) recently presented a new technique for separately quantifying the impacts of aerosol and dynamical forcings on clouds. The method uses HySPLIT back trajectories to control for the influence of meteorological history on cloudiness. By combining MODIS observations with ECMWF operational analyses, Mauger and Norris found that covariation between aerosol optical depth and lower tropospheric stability (LTS) during the previous 48 hours led to an overestimate of the cloud sensitivity to aerosols. Controlling for variations in LTS reduced the sensitivity by 54%. The present work builds on these results by using clustering analysis to group trajectories from similar meteorological regimes and thereby identify systematic aerosol-meteorology correlations. New and independent measurements are also added to the analysis, including microwave retrievals of cloud water from SSM/I and AMSR-E, surface winds from QuikScat, top-of-atmosphere shortwave flux measurements from CERES and aerosol optical depth from MISR. Partial derivatives are estimated by compositing data into high, low, and middle terciles, and considering variations in one variable while holding others constant. The results represent a set of statistically robust estimates of stratocumulus sensitivities with respect to a variety of aerosol and dynamical forcings, useful for assessing cloud feedbacks as well as aerosol-climate impacts. Future work will apply the technique to the validation of model parameterizations, by comparing observed with modeled sensitivities.