Physical and Biogeochemical Factors Affecting Deep Oxygen Minimum Zone Variability at the Bermuda Atlantic Time Series Site 

Samuel Stevens, Bermuda Institute of Ocean Sciences, St.George's, GE, Bermuda, Rodney Johnson, Bermuda Institute of Ocean Sciences, St.George's, Bermuda, Nicholas Robert Bates, Bermuda Institute of Ocean Sciences, BATS, St. George's, Bermuda and Rachel Jane Parsons, Bermuda Institute for Ocean Sciences, BIOS, St. George's, Bermuda
Abstract:
Oxygen Minimum Zones (OMZs) in the world’s oceans are increasing in extent and this expansion has the potential to effect biogeochemical systems globally, thus prompting the need to understand the processes impacting these systems. An assessment of dissolved oxygen profiles at the Bermuda Atlantic Time series Site (BATS) has revealed large variability in the size, form and depth of the deep OMZ in the Sargasso Sea. Local physical and biogeochemical forcing are hypothesised to play a significant role in OMZ variability and a number of different physical and biogeochemical parameters have been assessed to attempt to account for this variance. Analysis of the dissolved oxygen profiles from deep (>1000m) CTD casts at BATS since 1988 reveals that the depth of the minimum oxygen concentration in the OMZ varies between 650m and 1000m. Additionally, a strong negative correlation (R=-0.57, P=<0.0001) is found between OMZ depth and sea surface height whereby a 10cm change in sea surface height leads to a variation in OMZ depth of ~ 47m. Furthermore it has been found that the extent of the anoxia in the OMZ is negatively correlated with nitrate levels (R=-0.3634, P=<0.0001), indicating increased levels of nitrification as a result of reduced oxygen availability. Bacterial abundance is hypothesised to play a key role in the oxygen utilization in the OMZ at the BATS site. This is reflected in recent data, where it is found to negatively correlate with oxygen content. Finally, we investigate the OMZ at the BATS site to help detail short term variability associated with seasonal and mesoscale features, and identify longer term modes of variability.