The representation of clouds in simulations of future climate is the largest source in uncertainty in predicting the surface temperature response to a doubling of atmospheric carbon dioxide. Since climate models do not consistently represent the sign and magnitude of cloud feedbacks on the climate system, we instead estimate these quantities from observed data. In order to do so, we must distinguish changes in cloud properties due to temperature alone from changes in cloud properties due to dynamical processes. We did this by using a k-means clustering algorithm to group midlatitude oceanic clouds with similar properties and then calculated the difference between clouds associated with warm temperatures and cold temperatures in each cluster. The warm and cold subgroups were further constrained to have similar vertical and horizontal temperature advection in order to minimize the influence of dynamics. Our analysis used daily satellite cloud data from ISCCP and dynamical information from the NCEP reanalysis. We found that cloud fraction generally decreases and cloud albedo generally increases for warmer temperature under similar dynamical conditions and vertical stratification. The reduction in cloud fraction has a bigger impact on radiation flux, suggesting that midlatitude oceanic clouds have a net positive feedback on the climate system.