An evaluation of the LLC4320 global ocean simulation based on the submesoscale structure of modeled sea surface temperature fields

Abstract. We have assembled 2,851,702 nearly cloud-free cutout images (sized 144×144 km²) of Sea Surface Temperature (SST) data from the entire 2012–2020 Level-2 Visible Infrared Imaging Radiometer Suite (VIIRS) dataset to perform a quantitative comparison to the ocean model output from the MIT general circulation model (MITgcm). Specifically, we evaluate outputs from the LCC4320 global-ocean simulation for a one-year period starting on November 17, 2011 but otherwise matched in geography and day-of-year to the VIIRS observations. In lieu of simple (e.g., mean, standard deviation) or complex (e.g., power spectrum) statistics, we analyze the cutouts of SST anomalies with an unsupervised Probabilistic AutoEncoder (PAE) trained to learn the distribution of structures in SST anomaly (SSTa) on ~10-to-80-km scales (i.e., submesoscale-to-mesoscale). A principal finding is that the LLC4320 simulation reproduces well, over a large fraction of the ocean, the observed distribution of SST patterns, both globally and regionally. Globally, the medians of the structure distributions match to within 2σ for 65 % of the ocean, despite a modest, latitude-dependent offset. Regionally, the model outputs reproduce mesoscale variations in SSTa patterns revealed by the PAE in the VIIRS data, including subtle features imprinted by variations in bathymetry. We also identify significant differences in the distribution of SSTa patterns in several regions: (1) in the vicinity of the point at which western boundary currents separate from the continental margin, (2) in the Antarctic Circumpolar Current (ACC), especially in the eastern half of the Indian Ocean, and (3) in an equatorial band equatorward of 15°. It is clear that (1) is a result of premature separation in the simulated western boundary currents. The model output in (2), the Southern Indian Ocean, tends to predict more structure than observed, perhaps arising from a misrepresentation of the mixed layer or of energy dissipation and stirring in the simulation. The differences in (3), the equatorial band, are also likely due to model errors, perhaps arising from the shortness of the simulation or from the lack of high-frequency/wavenumber atmospheric forcing. Although we do not yet know the exact causes for these model-data SSTa differences, we expect that this type of comparison will help guide future developments of high-resolution global-ocean simulations.