A simple two-dimensional (z,t) model of first year sea ice structure and dynamics is coupled to a high resolution, time-dependent model of microalgal growth in which simulated physiological responses are determined by ambient temperature, spectral irradiance, nutrient concentration, and salinity. The physical component utilizes atmospheric data to simulate congelation ice growth, initial brine entrapment, desalination, and nutrient flux. Temperature gradient, sea ice salinity, brine salinity, and brine volume are also computed. The biological component is based on the concept of a maximum temperature-dependent algal growth rate which is reduced by limitations imposed from insufficient light or nutrients, as well as suboptimal salinity. Estimated gross primary productivity is reduced by respiration and grazing terms. Preliminary simulations indicate that, during a bloom, microalgae are able to maintain their vertical position relative to the lower congelation ice margin and are not incorporated into the crystal matrix as the ice sheet thickens. Model results imply that land fast sea ice contains numerous microhabitats that are functionally distinct based upon the unique suite of processes that control microalgal growth and accumulation within each. In the early stages of the spring bloom, high brine salinity inhibits microalgal growth at all depths within the congelation ice, except near the skeletal layer. Light is predicted to be the limiting resource throughout the congelation ice and platelet ice at this time. Later in the bloom when environmental conditions are more favorable for algal growth, model results suggest that biomass accumulation in the upper congelation ice is controlled by microzooplankton grazing, Microalgae in the skeletal layer and upper platelet ice are susceptible to nutrient limitation at this time due to diminished flux and high nutrient demand. Light limits microalgal growth in the lower platelet ice throughout the bloom. Results indicate that land fast sea ice in McMurdo Sound can support a production rate of approximately 0.5 g C m−2 d−1 under optimal conditions, 76% of which is associated with the platelet layer where rates of nutrient exchange are relatively high. While adjustments in any biological coefficient will alter the magnitude of production in the model, the range of results permitted by uncertainty in their values is well within the bounds likely to result from normal variations in snow cover, or from the uncertainty in the rate of nutrient flux.
If you can't find a tool you're looking for, please click the link at the top of the page to "Go to old article view". Alternatively, view our Knowledge Base articles for additional help. Your feedback is important to us, so please let us know if you have comments or ideas for improvement.