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Horizontal and vertical wavenumbers (kx, kz) immediately below the Ozmidov wavenumber (N3/ε)1/2 are spectrally distinct from both isotropic turbulence (kx, kz > 1 cpm) and internal waves as described by the Garrett–Munk (GM) model spectrum (kz < 0.1 cpm). A towed CTD chain, augmented with concurrent Electromagnetic Autonomous Profiling Explorer (EM-APEX) profiling float microstructure measurements and shipboard ADCP surveys, are used to characterize 2D wavenumber (kx, kz) spectra of isopycnal slope, vertical strain, and isopycnal salinity gradient on horizontal wavelengths from 50 m to 250 km and vertical wavelengths of 2–48 m. For kz < 0.1 cpm, 2D spectra of isopycnal slope and vertical strain resemble GM. Integrated over the other wavenumber, the isopycnal slope 1D kx spectrum exhibits a roughly +1/3 slope for kx > 3 × 10−3 cpm, and the vertical strain 1D kz spectrum a −1 slope for kz > 0.1 cpm, consistent with previous 1D measurements, numerical simulations, and anisotropic stratified turbulence theory. Isopycnal salinity gradient 1D kx spectra have a +1 slope for kx > 2 × 10−3 cpm, consistent with nonlocal stirring. Turbulent diapycnal diffusivities inferred in the (i) internal wave subrange using a vertical strain-based finescale parameterization are consistent with those inferred from finescale horizonal wavenumber spectra of (ii) isopycnal slope and (iii) isopycnal salinity gradients using Batchelor model spectra. This suggests that horizontal submesoscale and vertical finescale subranges participate in bridging the forward cascade between weakly nonlinear internal waves and isotropic turbulence, as hypothesized by anisotropic turbulence theory. 

Abstract Microstructure observations in the Pacific cold tongue reveal that turbulence often penetrates into the thermocline, producing hundreds of watts per square meter of downward heat transport during nighttime and early morning. However, virtually all observations of this deep-cycle turbulence (DCT) are from 0°, 140°W. Here, a hierarchy of ocean process simulations, including submesoscale-permitting regional models and turbulence-permitting large-eddy simulations (LES) embedded in a regional model, provide insight into mixing and DCT at and beyond 0°, 140°W. A regional hindcast quantifies the spatiotemporal variability of subsurface turbulent heat fluxes throughout the cold tongue from 1999 to 2016. Mean subsurface turbulent fluxes are strongest (∼100 W m −2 ) within 2° of the equator, slightly (∼10 W m −2 ) stronger in the northern than Southern Hemisphere throughout the cold tongue, and correlated with surface heat fluxes ( r 2 = 0.7). The seasonal cycle of the subsurface heat flux, which does not covary with the surface heat flux, ranges from 150 W m −2 near the equator to 30 and 10 W m −2 at 4°N and 4°S, respectively. Aseasonal variability of the subsurface heat flux is logarithmically distributed, covaries spatially with the time-mean flux, and is highlighted in 34-day LES of boreal autumn at 0° and 3°N, 140°W. Intense DCT occurs frequently above the undercurrent at 0° and intermittently at 3°N. Daily mean heat fluxes scale with the bulk vertical shear and the wind stress, which together explain ∼90% of the daily variance across both LES. Observational validation of the scaling at 0°, 140°W is encouraging, but observations beyond 0°, 140°W are needed to facilitate refinement of mixing parameterization in ocean models. Significance Statement This work is a fundamental contribution to a broad community effort to improve global long-range weather and climate forecast models used for seasonal to longer-term prediction. Much of the predictability on seasonal time scales is derived from the slow evolution of the upper eastern equatorial Pacific Ocean as it varies between El Niño and La Niña conditions. This study presents state-of-the-art high-resolution regional numerical simulations of ocean turbulence and mixing in the eastern equatorial Pacific. The results inform future planning for field work as well as future efforts to refine the representation of ocean mixing in global forecast models.