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Abstract:
How will marine low-level cloudiness change in a warming climate? To answer this ques-
tion a better process understanding of low-level cloudiness is needed. This dissertation
uses a multitude of observations and large-eddy simulations to explore how meso-scale
patterns of shallow convection relate to this challenging question. This study focuses
on the downwind trades and its meso-scale patterns that only recently raised interest
based on the work of Stevens et al. (2020) who supplemented the traditional classes of
meso-scale patterns of the upstream trades. These new classes are named based on their
visual impression Sugar, Gravel, Flowers and Fish. Here they are further investigated in
terms of their climatic relevance, physical characteristics, atmospheric environment and
emergence.
The core of these investigations consists of deep neural networks that have been
trained to identify these patterns in satellite images. At the same time, the deep neural
networks proved to be a valuable tool to derive a common perception of subjectively
defined classes that do not have a ground truth. Although the crowd-sourced labels were
therefore very noisy, the neural networks ranked among the highest in inter-annotator
agreements.
The classification of the neural network reveals that the patterns are common to the
trades beyond the winter season in the western North Atlantic and can represent more
than 40 % of the observed variability depending on season and region. This variability
expresses itself not only in changes of the visual appearance but also physically in the
cloud cover. A linear relationship between the cloud cover and the cloud radiative effect
makes the processes leading to the patterns relevant for the climate.
The underlying physical processes of each meso-scale pattern are related to the air-
mass origin with an influence of diurnal variations that are potentially modulating the
large-scale factors. One large-scale factor that is most distinct among the patterns is wind
speed. Other factors are only related to a particular pattern but can be a necessity for the
pattern to form. Fish for example is associated with anomalously strong convergence.
Sugar favors warmer surface temperatures. Both the forcing of Fish and Sugar are related
to air-masses intruding from outside the trades, leaving Gravel and Flowers be the only
native trade-wind patterns.
Large-eddy simulations reveal that they are in general capable of replicating the
observed variability in meso-scale cloud patterns. However, they are unable to match
the observed vertical distribution of cloudiness in both their absolute values and their
variability in particular for Flowers and Fish. Nevertheless, the distribution of moisture
and the presence of meso-scale circulations indicates that the responsible processes for
the formation of the different patterns are captured and the simulations are a valuable
tool to complement the observations to gain a better process understanding.
Based on the relationships between large-scale forcing and mesoscale patterns found
in this dissertation, conditions preferred by patterns with higher cloud amount and
more negative cloud radiative effect are expected to occur less frequently in a warming climate.
