Evolution of mesoscale organization in trade cumulus clouds

Abstract

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.