UC San Diego

The ocean's overturning circulation is a large-scale conveyor belt responsible for transporting mass, heat and tracers around the global oceans driven primarily by heat and density gradients between different water masses. Two distinct cells of the global MOC have been proposed based upon observations guided by physical constraints, the upper cell (u-MOC) associated with waters sinking to lower to mid-depth in the northern reaches of the North Atlantic Ocean, and the lower or bottom cell (b-MOC) which is linked to the sinking of waters formed around Antarctica to abyssal depths. The deep and abyssal oceans are responsible for absorbing a significant fraction of the global heat budget. Processes that govern the sequestration of heat and carbon in the deep ocean and its redistribution into the interior ocean have huge consequences for the large scale circulation, sea level rise, and the global climate system as a whole. Studying the abyssal ocean depths below 2000 m has historically been limited due to the paucity of high-quality observational data. Only in recent decades have advances in autonomous float technologies, satellite remote sensing, and regular ship-based observational programs begun to reduce the existing data deficit. This thesis uses data from decades of ship-based observations, thousands of profiles from autonomous Argo floats worldwide, and other novel instrumentation to understand and characterize some of the fundamental questions regarding the contemporaneous changes in the abyssal ocean and its impact on climate.

In Chapter 2, we construct a heat budget in the Southwest Pacific Basin and utilize ship-based observations gathered over three decades to understand the changes in the large-scale abyssal circulation in the basin. We further calculate the estimates of turbulent mixing in the basin, reconciling them using three different techniques of backing out the turbulent diffusivities in the basin. In Chapter 3, we demonstrate a methodology deploying a novel turbulence profiler called x-Pod and develop a method to reduce spikes in the error-prone data. In Chapter 4, we use a novel unsupervised machine learning technique to characterize different internal wave spectra observed in the ocean, using observations from 15 repeat hydrographic sections around the globe. Lastly, in Chapter 5 we quantify the rate of sea level rise and the contribution of the warming in the abyssal ocean in the Southwest Pacific Basin using data from 4954 profiles from Deep Argo floats. These chapters provide a detailed view of critical processes in the abyssal ocean measured by novel instrumentation to better understand the role of the oceans in a changing global climate.