The meridional overturning circulation (MOC) is a global-scale conveyor belt of deep ocean currents that transports heat from tropics to the poles.  It is characterized by the sinking of cold dense water at high latitudes and the ultimate upwelling at lower latitudes through turbulent diapycnal mixing, and plays a critical role in setting and moderating the Earth's climate.  Despite its importance, our understanding of the spatial and temporal variability of turbulence and diapycnal mixing throughout the global ocean is poorly understood, owing in part to limited observations. The primary outcome of this proposal was to develop and implement processing procedures to allow estimates of mixing from full depth temperature profiles measured from chi-pods, highly-sensitive turbulence sensors that are mounted on the standard ship-based CTD rosette along repeated GO-SHIP ocean transects.  chi-pod data was collected along 15 (9 prior to this award, 6 more during this award) GO-SHIP cruises providing coast-to-coast full depth meridional and zonal ocean transects (see image).  Typically 3-4 chi-pods were mounted on the rosette - half looking upward, and half downward - taking measurements on both the up and down cast. 

A major outcome of this proposal was to make mixing data publicly available to the community.  A primary challenge in making turbulence measurements from instruments lowered from a ship is that the motion of the ship is transmitted to the instruments being lowered, even when these are more than a mile beneath the ship.  During periods of inclement weather and large sea-states, the CTD rosette can be heaved up and down while being lowered, creating turbulence associated with its own wake.  It is critical to understand the instrument’s trajectory in order to identify CTD wake turbulence and exclude these 'contaminated' data from the true signals.  During the proposal, robust code was developed that automates much of the processing of the χ-pods data and isolate the wake-free measurements collected on both up and down casts in order to calculate mixing.  

During this award, all acquired data from 15 cruises (which includes raw chi-pod data and CTD data used for data processing), was standardized and posted publicly to microstructure.ucsd.edu and the individual GO-SHIP cruise CCHDO.ucsd.edu cruise page.  In addition, 5 of the 15 cruises were fully processed to provide the 2-dar binned product. An example of one final section data is shown in the attached image.  Processing of mixing estimates from the remaining 10 cruises are being finalized (see attached table for details).

A second major outcome of this proposal was to leverage the chi-pod data to advance our understanding of the finescale parameterizations in the deep ocean.  Finescale 'parameterizations' allow us to relate mixing to larger-scale characteristics of the ocean;chi-pod data permit us to explore the physical processes underlying mixing, and to investigate its temporal evolution and the geographic distribution throughout the ocean.  To this end, we have used a combination of traditional and machine learning techniques:

First, an in-depth analysis of the deep ocean mixing in the Southwest Pacific Basin was conducted using estimates of basin mean dissipation rates captured by the chi-pod measurements along P06 across the basin to show closure of the deep ocean heat budget. This study demonstrated how direct observations of mixing can be used in traditional ocean heat budgets instead of inferred, thus allowing for better estimates of uncertainty.  

Second, we conducted a global analysis examining the validity of finescale parameterization applications by using an unsupervised learning model to identify dominant shear and strain spectra shapes. Clusters showed distinct spectral characteristics with marked vertical and horizontal spatial dependence. Regions of significant disparities between parameterized and chi-pod microstructure observations of turbulent dissipation rate epsilon were identified and associated with spectral shapes deviating significantly from the canonical Garrett-Munk (GM) model shape. This method emphasizes the need for more microstructure measurements and a deeper understanding of the dynamics in order to develop a more robust parameterization of oceanic turbulent mixing.

Last Modified: 01/09/2025
Modified by: Sarah G Purkey