Uncertainty in the radiative forcing from anthropogenic activities since the Industrial Revolution is dominated by how clouds respond to aerosol. Climate projections are limited by this uncertainty. The cloud response to aerosol is influenced by the meteorological conditions of the atmosphere wherein the cloud is suspended. Understanding the covariation between meteorological state and cloud response to aerosol is a path forward to improve our understanding of aerosol effects on the climate. In this thesis we study aerosol-cloud interactions while controlling for meteorology using clustering techniques. This allows us to study the interactions per meteorological regime and gain deeper understanding of the effect of meteorology on aerosol-cloud interactions. Cloud-controlling factors are clustered using k-means clustering. Six meteorological clusters are found and satellite observations over ocean between -60 and 60 degrees latitude are used to study cloud response to changes in aerosol concentrations per cluster. The choice of clustering does not create significant variability in the sensitivity and the radiative forcing of the cloud albedo effect, but the sensitivity of cloud liquid water path and cloud fraction adjustments do show variability between clusters. This indicates that controlling for meteorology is specifically important for the adjustments to the cloud albedo effect. Our results show that the effective radiative forcing from aerosol-cloud interactions over our study domain since 1850 is -1.0 watts per square meter with a 90% confidence interval of [-1.6, -0.48] watts per square meter. There are variations in the forcing estimates depending on the number of clusters, but this signal is small compared to other sources of uncertainty. Our findings corroborate recent findings and present a novel method to control for meteorological covariation using cluster analysis.

How clouds change due to global warming is a major source of uncertainty in global climate models. A large part of this uncertainty originates from the feedback of low clouds over the subtropical ocean. The processes that shape these clouds range from microphysical processes of condensation and evaporation to the large-scale circulations spanning thousands of kilometers. While different models can resolve the large scale circulations and the small scale of individual clouds increasingly well, there is a gap in understanding the scale in between: the mesoscale with its fascinating patterns. They exhibit a large continuous variability that affects not only their visual appearance, but also the amount of sunlight they reflect. This thesis aims to analyze the temporal evolution of the mesoscale cloudiness by following the clouds on their path over the Atlantic along the trade winds and characterize their patterns by cloud metrics, cloud cover and mean cloud object length, computed on geostationary satellite images. Both decrease as the cloud fields move over warmer waters. The most prominent feature is the diurnal cycle in the cloud metrics that can at least partially be explained by oscillations in the atmospheric stability and the surface wind speed of the large-scale environment. Following the line of evidence found from these cloud controlling factors, we conclude that the decrease in low cloud amount and size due to increase in sea-surface temperature will most likely overcompensate the rise in cloud object size due to increase of stability. Especially stratocumulus clouds might become much less and also smaller. However, there remains a large, seemingly random variability in the cloud metrics that has little mean contribution to the climatology. As we cannot expect this to be the case in a warmer climate, we still need to understand what causes this variability. Therefore, the trade wind cloudiness needs to be further investigated with higher temporal resolution of the cloud controlling factors, more trajectories to bin for a fixed large-scale and better understanding of the interplay of governing processes both from models and observations.