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Bioinformatic Mapping of Ocean Biogeochemical Provinces

Matthew Oliver, Institute of Marine and Coastal Sciences, Rutgers University, oliver@imcs.rutgers.edu (Presenting)
Andrew Irwin, Mt. Allison University, airwin@mta.ca
Oscar Schofield, Institute of Marine and Coastal Sciences, Rutgers University, oscar@imcs.rutgers.edu
Paul Falkowski, Institute of Marine and Coastal Sciences, Rutgers University, falko@imcs.marine.rutgers.edu

The concept of ocean biogeochemical provinces was introduced in 1995 by Alan Longhurst. This concept provided a framework to compare and contrast biogeochemical processes over broad regions of the ocean biome. Province designations have been used to understand many biogeochemical processes, including primary production and carbon fluxes. What has been difficult to assess is the spatial and temporal variability in these provinces, which are known to be important on short term (hurricanes and eddies) and long term (Pacific Decadal Oscillation [PDO], North Atlantic Oscillation [NAO]) time scales. The temporal variability of province distribution and interaction remains a vexing issue in discriminating between secular changes (i.e., anthropogenically induced trends) and decadal cycles in the ocean system (i.e., natural variability). While oceanic biogeochemical provinces oscillate seasonally, there appears to be secular changes in oceanic provinces, the underlying causes of which we know little about (e.g. increase in global chlorophyll over the last two decades). A clearer understanding of the processes that control the distribution of oceanic provinces requires an objective method to resolve these provinces in a time-dependent manner. In the work presented here, we develop and implement a biogeochemical classification scheme based on bioinformatic analysis of ocean color and sea surface temperature that overcomes the technical difficulties of fixed boundary province classification in order to objectively elucidate the time dependent distribution of provinces. We have successfully demonstrated this approach on regional scales, and propose to apply this approach to the historic global data to elucidate the dynamics of biogeochemical province distribution.

Presentation Type:  Poster

Abstract ID: 109

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