  Clouds play a critical role in the Earth's energy budget and their potential
changes are one of the largest uncertainties in future climate projections.
However, the use of satellite observations to understand cloud feedbacks in a
warming climate has been hampered by the simplicity of existing cloud
classification schemes, which are based on single-pixel cloud properties rather
than utilizing spatial structures and textures. Recent advances in computer
vision enable the grouping of different patterns of images without using
human-predefined labels, providing a novel means of automated cloud
classification. This unsupervised learning approach allows discovery of unknown
climate-relevant cloud patterns, and the automated processing of large
datasets. We describe here the use of such methods to generate a new AI-driven
Cloud Classification Atlas (AICCA), which leverages 22 years and 800 terabytes
of MODIS satellite observations over the global ocean. We use a
rotation-invariant cloud clustering (RICC) method to classify those
observations into 42 AI-generated cloud class labels at ~100 km spatial
resolution. As a case study, we use AICCA to examine a recent finding of
decreasing cloudiness in a critical part of the subtropical stratocumulus deck,
and show that the change is accompanied by strong trends in cloud classes.
