A simple model of iron limitation on phytoplankton growth in the Gulf of Alaska

J. Fiechter (a), A.M. Moore (a), C.A. Edwards (a), K.W. Bruland (a), E. Di Lorenzo (b), C.V.W. Lewis (c), T.M. Powell (d), E.N. Curchitser (e), and K. Hedstrom (f).

(a) Department of Ocean Sciences, University of California, Santa Cruz,
(b) School of Earth and Atmospheric Sciences, Georgia Institute of Technology,
(c) NATO Undersea Research Center, La Spezia, Italy,
(d) Department of Integrative Biology, University of California, Berkeley,
(e) Institute of Marine and Coastal Sciences, Rutgers University,
(f) Arctic Region Supercomputing Center, Fairbanks.

A simple NPZD lower trophic ecosystem model with explicit iron limitation on nutrient uptake is coupled to a three-dimensional coastal ocean circulation model to investigate the regional ecosystem dynamics of the northwestern coastal Gulf of Alaska (CGOA). Iron limitation is included in the NPZD model by adding governing equations for two micro-nutrient compartments: dissolved iron and phytoplankton-associated iron. The model thus involves separate budgets for nitrate (the limiting macro-nutrient in the standard NPZD model) and for iron, with iron limitation on nitrate uptake being imposed as a function of the phytoplankton realized Fe:C ratio. Simulated nitrate and chlorophyll concentrations exhibit striking similarities with available remotely-sensed and in situ observations. In addition to reproducing the seasonal variability in a “climatological” sense over a 10-year period, the model provides useful insight on the different modes of variability affecting the physical and biological variables as a function of cross-shelf position. Simulated SSH, nitrate concentrations, and chlorophyll concentrations typically exhibit a strong seasonal cycle on the inner- and mid-shelf, and become more significantly modulated by mesoscale events at the shelfbreak. Overall, the results suggest that a simple NPZD ecosystem model, which includes iron-limiting effects, may be used to investigate the processes that control biological variability on monthly, seasonal, and interannual timescales. The ability of the model to differentiate between light-, nitrate-, and iron-limited growth regimes is also a first step towards understanding the role of environmental gradients in defining the complex phytoplankton community structure in the CGOA.