A simulated Antarctic ast ice ecosystem

Abstract

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, specffal irradiance, nutrient concentration, and salinity. The physical component utilizes atmospheric d,•,ta to simulate congelation ice growth, initial brine entrapment, desalination, and nutrient flux. Tempefixture 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 ac•cumulation in the upper congelation ice is controlled by microzooplankton grazing. Microalgae in the skeletal layer and upper platelet ice are susceptible to nutrient linfitation at this time due to diminished flux and high nutrient demand. Light limits microalgal gro•wth 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. INTRODUCTION The Southern Ocean, which extends from the continental margin of Antarctica northward to the subtropical convergence, encompasses an area equivalent to approximately 40% of the global ocean surface. A large but variable fraction of this area is covered by annually forming sea ice, ranging from 3 x 106 km 9-at the end of the austral summer to 20 x 106 km 9-in late winter [Zwally et al., 1979]. The advance and retreat of the ice sheet effectively divides the Southern Ocean into three provinces: the permanent open...

Role of sea ice in global biogeochemical cycles: emerging views and challenges

“…There are instances of mono-band [e.g., Lavoie et al, 2005] or multi-band schemes [Arrigo et al, 1993] in the literature, but all suffer from uncertainties, the largest being associated with the optical properties of snow [Pogson et al, 2011]. Furthermore, the required components for proper eco-dynamics are not known either.…”

“…These models (see Figure 4c) can be viewed as testing tools focussing on undeformed sea ice and can be used in a single location but not globally. Originally inspired from ocean biogeochemistry models, they are based on simple N-P formulations [see, e.g., Sarmiento and Gruber, 2006] and include (i) a single plankton group (diatoms) located at a prescribed depth in the ice (surface or bottom), (ii) one or several limiting nutrients, assimilated by ice algae with prescribed elemental (Redfield) ratios, and simple physics [Arrigo et al, 1993;Lavoie et al, 2005;Jin et al, 2006;Saenz and Arrigo, 2012]. Those 1D models reasonably well simulate Arctic and the Antarctic sea ice algal developments over a few months.…”

The multiphase physics of sea ice: a review for model developers

“…Early sea ice biogeochemistry models handled the bio-physical coupling in various ways. Arrigo et al (1993) represent the ocean-to-ice nutrient flux as the product of the nutrient concentration in seawater and a brine volume flux. This brine volume flux is formulated empirically, as a function of the sea ice growth rate, based on the laboratory data of Wakatsuchi and Ono (1983).…”

Fast ice distribution in Adélie Land, East Antarctica: interannual variability and implications for emperor penguins Aptenodytes forsteri

“…As it can attain a considerable thickness (> 5 m for perennial fast ice, Massom et al 2001), it likely comprises a substantial proportion of the total Antarctic sea ice volume (Giles et al 2008) and forms an important component of the ocean freshwater budget. Moreover, fast ice is of critical ecological importance as a key habitat for microorganisms (Garrison 1991), a region of enhanced primary productivity (Satoh & Watanabe 1988, Arrigo et al 1993, McMinn et al 2000 and a breeding platform for Weddell seals Leptonychotes weddelli (Thomas & DeMaster 1983) and emperor penguins (Kirkwood & Robertson 1997a, Kooyman & Burns 1999. The population status of emperor penguins is potentially an important indicator of possible trends in both fast ice, pack ice and distributions of prey species (Ainley 1983, Barber-Meyer et al 2008), yet little is currently known of the impact of different fast ice conditions on the breeding ecology of this species.…”