Lagrangian surface drifter analyses from observations and numerical modeling in the subpolar North Atlantic

Abstract

Lagrangian surface drifters are powerful tools to study the dynamics of the ocean across a variety of spatial and temporal scales, ranging from regional to global and monthly to climatological, respectively. This dissertation investigates the utility of Lagrangian surface drifters for estimating the mechanical input of energy into the ocean by the atmosphere, and for gathering information about the underlying dynamics driving oceanic variability. The basis for the analysis was a large dataset of 88 surface drifters deployed in the subpolar North Atlantic between 2018 and 2019. In addition, numerical drifters from both idealized and realistic ocean models were used to supplement the observations. The study region is characterized by pronounced mesoscale eddy activity and, due to its proximity to the North Atlantic storm track, strong atmospheric storms causing energetic near-inertial oscillations. It is hence well-suited for the analyses presented here. We introduced a novel surface drifter instrument, the Minimet, that measures sea surface wind in situ along the drifter track. Estimates of in situ Minimet wind power input were found to be over 40% higher than those using a reanalysis wind product. This discrepancy was likely due to Minimets accurately capturing strong high-frequency wind events that were misrepresented in the reanalysis product, highlighting the utility of the Minimets for both wind power input calculations and the important validation of gridded wind products. We currently lack a basic understanding of the Lagrangian velocity frequency spectrum and how it relates to the underlying dynamics. We therefore investigated the Lagrangian spectral shape and found significant variability linked to eddy kinetic energy. Lastly, we established a direct link between the Lagrangian velocity frequency spectrum and Eulerian kinetic energy wavenumber spectrum. This link had not previously been made from single particles and together with a better understanding of the Lagrangian frequency spectrum furthers our ability to efficiently utilize Lagrangian data.