Project Summary

Marine Primary Production

Producing an Updated Synthesis of the Arctic's Marine Primary Production Regime and Its Controls

Lead PIs:
  • Patricia Matrai, Bigelow Laboratory for Ocean Sciences
  • Louis Codispoti, University of Maryland, Center for Environmental Sciences
  • Richard Zimmerman, Old Dominion University Research Foundation
  • Victoria Hill, Old Dominion University Research Foundation
  • Michael Steele, Polar Science Center, University of Washington

Primary production provides the energy that fuels the Arctic Ocean (AO) ecosystem, as in all ecosystems. Understanding marine primary production (PP) and its controls is a critical step towards appreciating the Arctic Ocean as a system and allowing diagnostic modeling of its current status as well as prognostic modeling of future change.

The focus of this proposal is to synthesize existing studies and data relating to AO PP and its changing physical controls such as light, nutrients, and stratification, and to use this synthesis to better understand how PP varies in time and space and as a function of climate change.

Specifically, this work will:

-Synthesize estimates of PP, using complementary methods that emphasize different spatial and temporal scales into a consistent pan-AO data set (ARCSS-PP). These methods are:

(a) 14C uptake (measured; instantaneous gross to net algal primary production),

(b) nutrient, O2 and inorganic carbon production/consumption (derived; seasonal and regional scale net community production),

(c) remote sensing (derived; seasonal, regional, and real-time pan-AO net community production) and,

(d) bio-physical algorithms (derived; net primary production as a function of physical, chemical and physiological factors).

-Employ ARCSS-PP to test hypotheses regarding the controls of PP and to prepare a marine PP dataset for AO modelers to calibrate biogeochemical numerical models, both in collaboration with existing or proposed ARCSS-funded projects.

-Define functional regions of the AO that operate similarly with respect to PP with similar temporal and spatial variability.

-Investigate potential future changes in PP using analogues from the historical data record.