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Measurements collected by a REMUS 600 AUV off the coast of southern California demonstrate large-scale coherent wave-driven vortices, consistent with Langmuir turbulence (LT), played a dominant role in structuring turbulent dissipation within the oceanic surface boundary layer. During a 10-hour period with sustained wind speeds of 10 m/s, Langmuir circulations were limited to the upper third of the surface mixed layer by persistent stratification within the water column. The ensemble-averaged circulation, calculated using conditional averaging of AD2CP velocity profiles using elevated backscattering intensity associated with subsurface bubble clouds, indicates that LT vortex pairs were characterized by an energetic downwelling zone flanked by broader, weaker upwelling regions with vertical velocity magnitudes similar to previous numerical studies of LT. Horizontally-distributed microstructure estimates of turbulent kinetic energy dissipation rates were lognormally-distributed near the surface in the wave mixing layer with the majority of values falling between wall layer scaling and wave transport layer scaling. Partitioning dissipation rates between downwelling centers and ambient conditions suggests that LT may play a dominant role in elevating dissipation rates in the OSBL by redistributing wave-breaking turbulence. 

Abstract Divergence time estimation is crucial to provide temporal signals for dating biologically important events from species divergence to viral transmissions in space and time. With the advent of high-throughput sequencing, recent Bayesian phylogenetic studies have analyzed hundreds to thousands of sequences. Such large-scale analyses challenge divergence time reconstruction by requiring inference on highly correlated internal node heights that often become computationally infeasible. To overcome this limitation, we explore a ratio transformation that maps the original $N-1$ internal node heights into a space of one height parameter and $N-2$ ratio parameters. To make the analyses scalable, we develop a collection of linear-time algorithms to compute the gradient and Jacobian-associated terms of the log-likelihood with respect to these ratios. We then apply Hamiltonian Monte Carlo sampling with the ratio transform in a Bayesian framework to learn the divergence times in 4 pathogenic viruses (West Nile virus, rabies virus, Lassa virus, and Ebola virus) and the coralline red algae. Our method both resolves a mixing issue in the West Nile virus example and improves inference efficiency by at least 5-fold for the Lassa and rabies virus examples as well as for the algae example. Our method now also makes it computationally feasible to incorporate mixed-effects molecular clock models for the Ebola virus example, confirms the findings from the original study, and reveals clearer multimodal distributions of the divergence times of some clades of interest.