Date of Award

Spring 5-2014

Degree Type

Masters Thesis

Degree Name

Master of Science (MS)

Department

Marine Science

Committee Chair

Jeremy Wiggert

Committee Chair Department

Marine Science

Committee Member 2

Scott Milroy

Committee Member 2 Department

Marine Science

Committee Member 3

Robert Arnone

Committee Member 3 Department

Marine Science

Abstract

Bio-physical coupling in the Gulf of Mexico (GMx) is explored in this research using an integrated satellite sensor approach. Physical features are identified using Sea Level Anomaly (SLa) produced by Archiving, Validation, and Interpretation of Satellite Oceanographic data (AVISO). Biological characteristics of the GMx have been recognized as ocean color identified by the satellites SeaWiFS and MODISA. A climatology data set and a chlorophyll anomaly (CLa) was made using the 14-year mean determined from the ocean color files. The hypotheses are that there will be an inverse relationship between SLa and CLa, that mesoscale SLa features will significantly modulate local biological variability compared to the typical seasonal conditions, and that interannual climate variability will influence this climatological state of biological variability. Using MATLAB, Generic Mapping Tools (GMT), and other programs, statistics were performed and images of both parameters were created. Individual images of SLa contours superimposed on CLa as well as Hovmöller diagrams allowed qualitative analysis with image analysis to aid in determining the hypotheses. Due to the non-parametric nature of SLa and CLa, a Spearman’s Rank Correlation was performed along lines of latitude and longitude and across the GMx in conjunction with the Ives-Gibbons dichotomous correlation, which allowed for magnitude to be dismissed. A significant inverse relationship was found between SLa and CLa. The second hypothesis of SLa modulating local biological conditions was also proven through qualitative and quantitative analysis, though the third hypothesis of interannual variability did not have enough data to support it.

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