Understanding cloud structures with machine learning- An algorithm to represent sub-grid scale variability in stratocumulus clouds

Changes to low-level cloud properties and their associated feedback in a warming climate are a significant source of uncertainty in global climate models (GCMs). “Local’’ processes at the droplet scales, such as drizzle growth by collision-coalescence, are not well represented in GCMs and constitute a significant uncertainty in model predictions. Parameterization schemes often derived from empirical fits to spatially averaged cloud size distributions have been used to represent clouds and hence do not fully account for the subgrid-scale variabilities. We hypothesize that inhomogeneities in cloud microphysical properties may be captured by a small number of distinct droplet size distributions called “characteristic distributions” and developed an algorithm capable of retrieving them.

To do this, we developed an algorithm by combining hypothesis testing with a machine-learning clustering algorithm. The test does not presume any specific distribution shape, is parameter-free, and avoids biases from binning. Importantly, for the clustering algorithm, the number of clusters is not an input parameter but is independently determined in an unsupervised fashion. As implemented, it works on an abstract space from the hypothesis test results, and hence spatial correlation is not fundamental for members classified to a characteristic distribution. To validate the algorithm's robustness, we test it on a synthetic dataset that mimics cloud drop distributions. The algorithm successfully identifies the predefined distributions at plausible noise levels.

When implemented on cm-scale cloud samples taken using Holographic Detector for Clouds (HOLODEC) deployed during Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA), the algorithm reveals that local characteristic distribution types are ubiquitous in stratocumulus clouds. These distribution types are generally narrow with distinct modes and do not resemble the averaged size distribution shape. Each characteristic distribution represents identical-looking local cloud volumes which tend to occur in spatial blocks of varying extent, usually of order 1s to 10s of km. These observations have implications for understanding small-scale cloud properties and can guide the development of novel parameterizations of sub-grid-scale variability for coarse-resolution models. Subsequently, we show the first results from an investigation of characteristic distributions for LESs. The algorithm is general and helps in finding similarities in data representable as CDFs and is expected to have broader applicability in earth sciences.