Representing Key Phytoplankton Groups in Ocean Carbon Cycle Models

Sponsored by: National Aeronautic Space Administration & US J.G.O.F.S.
Project Period: March 15, 1998 – March 14, 2001
Principle Investigator: Paul G. Falkowski
Co-Principle Investigator: Michael J. Behrenfeld
Co-Principle Investigator: Zbigniew S. Kolber

The primary goal of this project is to improve ocean carbon models by describing how physical and chemical forcing affects the statistical distribution of key functional phytoplankton groups. This information is critical in predicting how changes in ocean physics and chemistry will influence total and new production in future ocean model scenarios. The research is coordinated with the Ocean Carbon-cycle Modeling Intercomparison Project (OCMIP), an international project initiated in 1995 by the Global Analysis, Interpretation and Modeling (GAIM) Task Force of the International Geosphere-Biosphere Program (IGBP).
The project focuses on the development of algorithms that predict how ocean physics and chemistry affect the spatial distribution of:

  • trichodesmium sp.,the major nitrogen fixing organisms;
  • diatoms, the major group responsible for export production;
  • coccolithophores, which, as a consequence of calcification, raise pCO2; and
  • the polytaxonomic group of picoplankton, which, while they are the major carbon fixers, contribute little to carbon export.

The statistical distribution of these four functional groups will be analyzed using remotely sensed information in conjunction with sea truth data, and, based on the statistics of their distributions, “functional group profiles” will be generated. The “functional group profiles” give a probability of encountering each of the four groups in each grid cell of an OGCM. Based on these profiles, we can specify physical and chemical criteria that maximize and minimize the distributions of each group, and hence prospectively infer their distributions in climate change scenarios. From knowledge of the distributions of each group, the forcing and feedback between ocean circulation, chemistry and biological processes can be represented much more realistically in ocean general circulation/biogeochemical models.

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