Innovative Observational Global and Regional Ocean Water Mass, Circulation, and Mixing Analyses

Abstract

This dissertation applies novel methodology to existing oceanographic datasets in order to gain new insights into the circulation and mixing of water masses at both regional and global scales. Given the boom of in-situ data from programs such as Argo over the last two decades, in addition to the rising prevalence and accessibility of novel methods such as machine learning, we hope to build on the foundational understanding of these processes obtained from analyses of previous, sparser datasets. We investigate the water mass properties, circulation, and inferred near-bottom mixing of the ocean from a mean state perspective. This allows us to use as many observations as possible in our analyses of these important features of the ocean and to get at the “baseline” behavior of the ocean. The first portion of this dissertation focuses on using conductivity-temperature-depth (CTD) and velocity measurements from Argo floats to assess the mean transport of the Alaskan Stream (AS). This current is a western boundary current at the north edge of the North Pacific subpolar gyre that flows west-southwestward along the south side of Alaska and the Aleutian Island Arc to strongly influence physical and biological processes downstream in the Sea of Okhotsk and the Bering Sea. Since the start of the Argo program, a sufficient number of Argo floats have sampled the AS such that we are now able to quantify the zonal evolution of this current by mapping Argo data to across-current transects at a number of locations along the current’s extent. Alongshore absolute geostrophic transports in the top 2000 dbar (obtained by combining mean absolute 1000-dbar velocities from float displacements with the geostrophic velocity fields) were found to generally increase to the west. Full-depth transports are estimated by fitting a barotropic and the first two baroclinic modes calculated from a climatology to the absolute geostrophic velocities in the upper 2000 dbar and applying the velocities from these fits from 2000 dbar to the seafloor. As it flows west from its formation region the full-depth AS becomes stronger, more barotropic, and narrower. Mean concentrations of the relatively warm, salty, oxygen-poor, and nutrient-rich Pacific Equatorial Water in the AS decrease westward as more water is entrained from the offshore gyre. This work introduces a number of new methods for using Argo data to analyze the volume transports and water mass structure of a boundary current. In the second part of this dissertation, we developed a new global bottom water climatology using a novel machine learning-based method that we hope can be applied to other sparse oceanographic datasets in the future. The most recent global synthesis of mean bottom water properties relied on expert hand-contouring to create maps from sparse shipboard measurements. Here we created a new bottom water climatology by using random forest regression followed by objective mapping to map the sparsely sampled quantities of temperature, salinity, oxygen, silicate, nitrate, and phosphate to a 0.5-degree grid based on smoothed ETOPO-2 bathymetry. We applied the regressions in an iterative manner, using the new maps of the better sampled fields (e.g., temperature) to help improve the prediction of the more sparsely sampled fields (e.g., silicate), an approach we have named the “stacked” random forest and objective mapping method. Using these maps, we are able to trace the flow of North Atlantic Deep Water and Antarctic Bottom Water globally from their source regions, through several topographic choke points such as various sills and fracture zones, and along the rest of the seafloor. The new, high-quality oxygen and nutrient maps produced here illustrate the influence of a variety of regional biogeochemical processes on the bottom water property distributions, which have been difficult to resolve in previous, coarser-resolution global analyses of the bottom waters. Lastly, we performed a global census of the bottom waters in temperature-salinity and oxygen-salinity space to improve our understanding of how different water masses mix and are affected by biogeochemical processes along the seafloor. The final set of analyses included here aims to provide a description of different dynamical bottom mixed layer (BML) regimes found throughout the North Atlantic Ocean. We have adapted a quantitative approach from the surface mixed layer literature for identifying BMLs in high-resolution CTD profiles known as the split-and-merge method. This method automatically identifies not only the BML but also the important overlying layer known as the stratified mixing layer (SML) in these profiles. Under certain conditions, weakly-stratified BMLs over sloped topography may act as conduits for diapycnal upwelling of bottom waters, an important mechanism for closing the global meridional overturning circulation. We performed k-means clustering on the thickness and mean stratification values for the identified BMLs and SMLs along with information about the underlying bathymetry (e.g., slope and roughness) to identify dynamically similar BMLs in our dataset that may play a role in the diapycnal upwelling of bottom waters.