Turbulent mixing in the ocean is often parameterized in terms of the downscale energy transfer by internal waves. Expressed in terms of the vertical wavenumber spectrum of oceanic velocity shear ($V^2_z $) and isopycnal strain ($ \zeta^2_z $), the “finescale parameterization” relies on several parameters, including key assumptions relating to the spectral properties. Here we use an unsupervised learning model to identify spatial correlations between embedded parameters of the finescale parameterization based upon data from 1875 full-depth hydrographic profiles from 15 sections traversing the global ocean. The clustered patterns along the sections have marked horizontal and vertical spatial dependence associated with distinct modes of spectral variation. Two clustered regions are identified where the underlying spectra deviate significantly from the canonical Garrett-Munk (GM) spectrum, suggesting potential departures from implicit assumptions about the downscale energy cascade. Spectral composites in these two regions show intensification of variance in the low and high wavenumber regimes respectively, as well as distinction in overall spectral levels and geographic prevalence. Furthermore, these clusters are found to be associated with regions where parameterized estimates of the turbulent dissipation rate $\epsilon$ differ significantly (exceeding a factor of 5) from co-located in-situ observations measured using $\chi$-pod temperature microstructure. Extending the methodology to other hydrographic datasets has the potential to reveal reasons for this parameterization bias and to identify the dynamical underpinnings leading to more robust parameterizations of oceanic turbulent mixing.ere
