  We perform an out-of-distribution analysis of ~12,000,000 semi-independent
128x128 pixel^2 SST regions, which we define as cutouts, from all nighttime
granules in the MODIS R2019 Level-2 public dataset to discover the most complex
or extreme phenomena at the ocean surface. Our algorithm (Ulmo) is a
probabilistic autoencoder, which combines two deep learning modules: (1) an
autoencoder, trained on ~150,000 random cutouts from 2010, to represent any
input cutout with a 512-dimensional latent vector akin to a (non-linear) EOF
analysis; and (2) a normalizing flow, which maps the autoencoder's latent space
distribution onto an isotropic Gaussian manifold. From the latter, we calculate
a log-likelihood value for each cutout and define outlier cutouts to be those
in the lowest 0.1% of the distribution. These exhibit large gradients and
patterns characteristic of a highly dynamic ocean surface, and many are located
within larger complexes whose unique dynamics warrant future analysis. Without
guidance, Ulmo consistently locates the outliers where the major western
boundary currents separate from the continental margin. Buoyed by these
results, we begin the process of exploring the fundamental patterns learned by
Ulmo, identifying several compelling examples. Future work may find that
algorithms like Ulmo hold significant potential/promise to learn and derive
other, not-yet-identified behaviors in the ocean from the many archives of
satellite-derived SST fields. As important, we see no impediment to applying
them to other large, remote-sensing datasets for ocean science (e.g., sea
surface height, ocean color).
