Description
- Abstract:
- Submesoscale features and processes with length scales of 1-10km strongly affect the state of the upper ocean by providing routes for large-scale energy to be dissipated, and by altering the distribution and transport of floating debris, sea ice, marine life, pollutants, heat, freshwater, and nutrients. This thesis utilizes drifter observations and numerical simulations to characterize turbulent dispersion in the Gulf of Mexico, with heavy emphasis on how features over the submesoscale range of motion affect their single- and two-point statistics. Numerical simulations of the Northern Gulf of Mexico are paired with Lagrangian particle tracking to understand the impact of convergence zones and vortices on turbulence statistics derived from synthetic surface drifters. Differences between Eulerian and semi-Lagrangian velocity structure functions are explained by correlated divergence and curl structure functions over the submesoscale range of motion, which leads to shallower second-order structure functions and larger third-order structure functions. Observed velocity structure functions from Eulerian X-band radar measurements and semi-Lagrangian surface drifter measurements are also compared, and disagree in a way consistent with the results from the numerical simulations. However, the biases investigated with the observations are two-fold. In addition to the accumulation bias from oversampling frontal regions and vortices, the differences between Eulerian and surface drifter velocity structure function are also shown to be due to a background bias from non-zero first-order structure functions. Unlike the accumulation bias, the background bias is correctable. Transport and dispersion are investigated through the spatial distribution of corrected Lagrangian integral timescales and eddy diffusivity. Corrected estimates of both these variables are found to be nearly ten times larger in magnitude than uncorrected estimates. The evolution of tracers (such as oil) in the ocean depends on the interaction of a wide spectrum of time and space scales, making it inherently difficult to predict and elucidate. This is particularly true over the submesoscale range of motion due to observational and numerical constraints. The results and conclusions of this thesis provide insights into submesoscale turbulence which is hard to characterize by observation platforms, identifies the limitations of using surface drifters to estimate sought after Eulerian statistics, and offers observational constraints on important parameters used in tracer transport modelling.
- Notes:
- Thesis (Ph. D.)--Brown University, 2020
Access Conditions
- Rights
- In Copyright
- Restrictions on Use
- Collection is open for research.
Citation
Pearson, Jenna,
"Submesoscale Statistics from Surface Drifters: Biases and Benefits"
(2020).
Earth, Environmental and Planetary Sciences Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/sssa-hx40
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Earth, Environmental and Planetary Sciences Theses and Dissertations
Theses and Dissertations for the Earth, Environmental and Planetary Sciences department....