  We extracted ~2.8M nearly cloud-free 144x144 km^2 cutout images from the
2012-2020 Level-2 VIIRS Sea Surface Temperature (SST) dataset to quantitatively
compare with MIT ocean general circulation model outputs, specifically the one
year LCC4320 1/48 deg global-ocean simulation starting on November 17, 2011,
matched in geography and day-of-year to VIIRS observations. We analyzed these
cutouts using an unsupervised probabilistic autoencoder (PAE) to learn the SST
structure on ~10-to-80 km scales (submesoscale-to-mesoscale). A key finding is
that, in general, the LLC4320 simulation accurately reproduces the observed SST
patterns, both globally and regionally. Global structure distribution medians
match within 2 sigma for 65% of the ocean, despite a modest, latitude-dependent
offset. Regionally, model outputs mimic mesoscale SST pattern variations in
VIIRS data revealed by PAE, including subtle features influenced by bathymetry
variations. There are however some areas showing significant differences in the
distribution of SST patterns: (1) near western boundary currents' separation
from the continental margin, (2) in the ACC, particularly in the eastern half
of the Indian Ocean, and (3) in an equatorial band equatorward of 15 deg. The
discrepancy in (1) results from premature separation of simulated western
boundary currents. In (2), the Southern Indian Ocean, the model output predicts
more structure than observed, possibly due to mixed layer misrepresentation or
energy dissipation and stirring inaccuracies in the simulation. The differences
in (3), the equatorial band, may also stem from model errors, potentially
arising from the simulation's shortness or insufficient
high-frequency/wavenumber atmospheric forcing. While the exact causes of these
model-data differences remain uncertain, such comparisons are expected to guide
future developments in high-resolution global-ocean simulations.

Atmospheric and Oceanic Physics