Bottom mixed layer derivation and spatial variability over the central and eastern abyssal Pacific Ocean
Abstract. The bottom mixed layer (BML) of the abyssal ocean regulates heat exchange between the deep interior and seafloor, driving water-mass transformation and influencing global circulation. Spatial variability of the BML was examined in the under-sampled abyssal Pacific Ocean using surface-to-seafloor temperature and pressure observations over 4 months in 2023–24. Given the typical decadal repeat rate of global hydrographic sections, subdecadal variability in the abyssal ocean has remained poorly resolved. While constrained in coverage, our observations contribute towards filling this gap for the central and eastern abyssal Pacific Ocean. Four methods were used to determine the BML thickness, with the threshold method providing the most reliable estimates. The mean BML thickness was 226 ± 172 with added repeat hydrographic sections providing context and additional data points. At each BML data point we determined the slope, the terrain roughness and the extracted predicted internal tide energy dissipation (over five different low-mode processes and high-mode local processes) at 50 km scales from publicly available datasets. These factors were input into a Random Forest Regressor (RF) model, the first time machine learning techniques have been applied to investigate BML thickness. The RF feature importance scores identified bottom depth, total internal tide energy dissipation, followed by slope, as the strongest predictors of BML thickness, revealing the importance of low-mode internal wave energy losses in this abyssal setting. Targeted and sustained observations near the seafloor at gateway regions of abyssal pathways are vital for understanding energy exchange that influences meridional overturning circulation. Our results highlight a regime where sustained low-mode internal tide energy loss, modulated by topographic slope and depth, governs the BML thickness in the abyssal Pacific. However, the rate at which BML thickness changes over time and the processes that cause these changes remain key unresolved factors.
In this manuscript (MS), the authors utilize novel field data to reveal the characteristics of bottom mixed layer (BML) thickness in the central and eastern Pacific and identify key controls on BML variability (ocean depth, total internal tide dissipation, slope). The RF analysis in the MS is first application of machine learning to BML thickness, identifies physically intuitive predictors, which is a notable strength. Several issues related to the RF regression and result interpretation need refinement to enhance the manuscript’s scientific rigor and impact. The detailed comments are provided below.