Representing Key Phytoplankton Groups in Ocean Carbon Cycle Models
Paul Falkowski
Institute of Marine and Coastal Science and Dept. of Geology
Rutgers University

Project Summary

A quantitative understanding of the global carbon cycle is essential for the assessment of anthropogenic effects on the Earth's environment. Such an understanding requires the integration of ecological processes in mathematical models. We propose to develop and integrate observational data with mathematical models leading to the development of a comprehensive biogeochemical representation of key functional groups the ocean carbon cycle. Specifically, we propose to:

(1) develop satellite-based observational approaches for distinguishing major functional biological groups, including nitrogen fixers, diatoms, and coccolithophores, that influence the temporal and spatial distribution of sources and sinks for carbon dioxide in the world ocean;

(2) develop modeling approaches for incorporating these functional groups in ecosystem models; and

(3) simulate the response of functional groups to future climate scenarios using coupled atmosphere-ocean models.

The proposed research will examine the relationship between remotely sensing data products, such as ocean color, SST, and wind stress, with the spatial and temporal distribution of the key functional groups. We propose to develop algorithms that represent biogeochemical processes such as nitrogen fixation, calcification and export production. The algorithms are intended to be included in global ocean circulation models in which the effects of climate change on the spatial and temporal distributions can be explored. The proposed effort will be developed in coordination with the SMP Ocean Carbon-cycle Modeling Intercomparison Project (OCMIP). From the model representations, we can explore how feedbacks between ocean circulation, stratification and heat storage (and their forcing by changes in the atmospheric radiative balance) will affect the internal ocean carbon cycle.

This research effort combines capabilities in remote sensing with expertise in physiological ecology and ocean biogeochemical process models. This interaction will be facilitated by collaborations with key researchers that specialize in one or more of the individual functional groups.