Physical/Bio-Optical Interactions and Productivity Estimates from Oceanic Time Series

  • J. Wiggert
  • T. Dickey
  • M. Hamilton
  • T. Granata
  • J. Marra
  • C. Langdon
  • D. Siegel
  • L. Washburn
Part of the Environmental Science Research book series (ESRH, volume 43)

Abstract

We collected in situ bio-optical and physical oceanographic data using the MVMS (Multi-Variable Moored System) 34N 70W in the Sargasso Sea for nine months in 1987 in the upper 160 meters. The sampled oceanographic parameters include temperature, currents, photosynthetically available radiation, beam attenuation coefficient, stimulated fluorescence, and dissolved oxygen concentration. Meteorological measurements were made, and sea surface temperature maps and Geosat altimetry data were used to investigate the mesoscale field associated with the dynamics of the Gulf Stream. Time series indicate seasonal cycling of heat and momentum fields, the diel cycle in bio-optical variables, episodic events related to advected water masses and local meteorological forcing, a phytoplankton bloom concentrated at 20 meters during the spring, and the development of sub-subsurface chlorophyll and particle maxima during the summer. Spectral analysis reveals a correspondence between internal wave and bio-optical variability. High-resolution time series of gross primary production were estimated with the Kiefer-Mitchell model using the time series of stimulated fluorescence and PAR as input. Particle production rate also was calculated using the time series of beam attenuation coefficient. By combining the two estimates, time series of Chl: Cratio were created. Our results show that high-frequency sampling (minimizing aliasing) is essential in revealing the dynamical changes occurring in upper ocean primary productivity and carbon flux.

Keywords

Internal Wave Gulf Stream Diel Cycle Seasonal Cycling Beam Attenuation Coefficient 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • J. Wiggert
    • 1
  • T. Dickey
    • 1
  • M. Hamilton
    • 1
  • T. Granata
    • 2
  • J. Marra
    • 3
  • C. Langdon
    • 3
  • D. Siegel
    • 4
  • L. Washburn
    • 4
  1. 1.USC Ocean Physics GroupLos AngelesUSA
  2. 2.Department of BiologySoutheastern Massachusetts UniversityNorth DartmouthUSA
  3. 3.Lamont-Doherty Geological ObservatoryColumbia UniversityPalisadesUSA
  4. 4.Department of GeographyUniversity of California at Santa BarbaraSanta BarbaraUSA

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