Author

Nathan Briggs

Date of Award

12-2014

Level of Access

Campus-Only Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Oceanography

Advisor

Mary Jane Perry

Second Committee Member

Andrew Thomas

Third Committee Member

Emmanuel Boss

Abstract

In this dissertation I develop, apply, and test methods for estimating primary productivity and particle size from low-power autonomous floats and gliders deployed during the three-month North Atlantic Bloom 2008 (NAB08) project in the Iceland Basin. I find the primary productivity methods to be accurate within uncertainty bounds of ~30%, and similar in magnitude to previous studies of the North Atlantic bloom. I derive an entirely new method for autonomously estimating mean particle size in suspension using existing instruments. Laboratory testing quantitatively validates the method and field testing provides encouraging qualitative validation. I use a combination of the primary productivity estimates and mixed-layer budgets from a Lagrangian (water following) float to show that the spring phytoplankton bloom was initiated primarily by the classical bottom-up mechanism of shoaling mixed layer and increased light, and further show that most biomass accumulation was associated with two short (2-3 day) fair weather events (high light, low wind). The bloom was ended by a combination of silicate limitation, grazing, and sinking loss. I compare autonomous primary productivity timeseries with the rate of organic carbon flux through the mesopelagic (100-1000 m) at a much broader spatial scale (50 km) and higher temporal resolution (2 d) than traditional measurements permit, and use these results to help resolve a large discrepancy between previous estimates of the efficiency of carbon export and sequestration during the North Atlantic bloom. I also show a strong temporal correlation between the efficiency of carbon transport to depth and the mean size of particles at the surface. In addition to improving understanding of the North Atlantic spring bloom, this work provides and validates important methods that promise to improve global understanding of planktonic ecosystems and biogeochemistry when applied to the growing global array of autonomous platforms.

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