The modeled response of trade-wind cumulus to climate change is highly uncertain, leading to large uncertainties in the radiative feedback and resulting climate sensitivity (Bony & Dufresne, 2005). This uncertainty is linked to the inability of models to capture the relationship between cloud cover and the large-scale circulation (Nuijens et al., 2015a). Observations of trade-wind clouds show that the strongest variability comes from stratiform regions at altitudes of 1.5-2 km on timescales of a few hours with less variability at the cloud base (Nuijens et al., 2014); however, while models capture the climatological-mean cloud cover, they do not capture the variability, instead the instantaneous profiles of cloud cover are typically unrealistic (Nuijens et al., 2015a). This was shown to be because models too strongly relate cloud cover to single large-scale parameters, such as mixed-layer relative humidity or inversion strength (Nuijens et al., 2015b), whereas in reality, the dependence of cloud cover on the large-scale circulation is more complex and can't be predicted by a single parameter on synoptic timescales (Brueck et al., 2015). High climate sensitivity arises when warming leads to an increased convective mixing which can lead to a reduction in the amount of low clouds; however, this response is strongly dependent on the