Flood-freeze cycles and microalgal dynamics in Antarctic pack ice

Fritsen, Christian H.; Ackley, Stephen F.; Kremer, James N.; Sullivan, Cornelius W.

doi:10.1029/ar073p0001

Cited by 39 publications

(62 citation statements)

References 48 publications

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“…This approach was used later by Jin et al (2006). Fritsen et al (1998) included the contribution of flooding by seawater near the surface. In other studies, the nutrient fluxes were prescribed diffusivity values (e.g.…”

Section: Sea Ice Biogeochemistrymentioning

confidence: 99%

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

et al. 2011

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Abstract. Rather than being solid throughout, sea ice contains liquid brine inclusions, solid salts, microalgae, trace elements, gases, and other impurities which all exist in the interstices of a porous, solid ice matrix. This multiphase structure of sea ice arises from the fact that the salt that exists in seawater cannot be incorporated into lattice sites in the pure ice component of sea ice, but remains in liquid solution. Depending on the ice permeability (determined by temperature, salinity and gas content), this brine can drain from the ice, taking other sea ice constituents with it. Thus, sea ice salinity and microstructure are tightly interconnected and play a significant role in polar ecosystems and climate. As large-scale climate modeling efforts move toward "earth system" simulations that include biological and chemical cycles, renewed interest in the multiphase physics of sea ice has strengthened research initiatives to observe, understand and model this complex system. This review article provides an overview of these efforts, highlighting known difficulties and requisite observations for further progress in the field. We focus on mushy layer theory, which describes general multiphase materials, and on numerical approaches now being explored to model the multiphase evolution of sea ice and its interaction with chemical, biological and climate systems.

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Section: Sea Ice Biogeochemistrymentioning

confidence: 99%

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

et al. 2011

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“…A very similar physiological approach was extended to the full ice column for generic Antarctic situations in both references [43] and [83]. It has also made its way into several Arctic pack ice biology models, including those in CICE [13][14]18,33] Identical photosynthetic and limitation parameters were adopted to simulate nutrient cycling along both the Palmer Peninsula and coastal Beaufort/Chukchi Seas.…”

Section: Appendix B: Parametersmentioning

confidence: 99%

“…Constants in the system are provided in Appendix B. Essentially we combine approaches from the Fritsen, Lavoie, Jin and Elliott groups [14,25,33,43], while acknowledging the pioneering work of Arrigo and company [44,54,83]. Additional concepts are borrowed from well-known middle to high latitude pelagic ecodynamics models [57][58][95][96][97]109].…”

Section: Appendix A: Equationsmentioning

confidence: 99%

“…Freeboard material is positioned above the porosity cut off, and so blooms typically form under stringent initial mass limitations [40]. A combination of upward percolation and flooding saturates the top of the ice when it is weighted down sufficiently by snow (infiltration [43]). Thresholds for submersion were determined by Archimedes Principle, and the possibility of multiple flooding events was explored.…”

Section: Appendix A: Equationsmentioning

confidence: 99%

“…Emphasis is placed on organic compound production and loss within the brine, as set by ice algal nutrient uptake and traditional carbon cycling [4,[15][16][17]. thickness ratios which determine pack submersion [43]. In a baseline run, organics are supported solely by traditional cell disruption and with the exception of prescribed humics, pack-internal carbon is biased low relative to observations [15,20,29].…”

Section: Introductionmentioning

confidence: 99%

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Strategies for the Simulation of Sea Ice Organic Chemistry: Arctic Tests and Development

Elliott¹,

Jeffery²,

Hunke³

et al. 2017

Preprint

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Abstract:A numerical mechanism connecting ice algal ecodynamics with the buildup of organic macromolecules is tested within modeled pan-Arctic brine channels. The simulations take place offline in a reduced representation of sea ice geochemistry. Physical driver quantities derive from the global sea ice code CICE, including snow cover, thickness and internal temperature. The framework is averaged over ten boreal biogeographic zones. Computed nutrient-light-salt limited algal growth supports grazing, mortality and carbon flow. Vertical transport is diffusive but responds to pore structure. Simulated bottom layer chlorophyll maxima are reasonable, though delayed by about a month relative to observations due to uncertainties in snow variability. Upper level biota arise intermittently during flooding events. Macromolecular concentrations are tracked as proxy proteins, polysaccharides, lipids and refractory humics. The fresh biopolymers undergo succession and removal by bacteria. Baseline organics enter solely through cell disruption, so that the internal carbon content is initially biased low. By including exudation, agreement with dissolved organic or individual biopolymer data is achieved given strong release coupled to light intensity. Detrital carbon then reaches hundreds of micromolar, sufficient to support structural changes to the ice matrix.

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Antarctic pack ice algal distribution: Floe‐scale spatial variability and predictability from physical parameters

Meiners

Arndt

Bestley

et al. 2017

Geophysical Research Letters

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Antarctic pack ice serves as habitat for microalgae which contribute to Southern Ocean primary production and serve as important food source for pelagic herbivores. Ice algal biomass is highly patchy and remains severely undersampled by classical methods such as spatially restricted ice coring surveys. Here we provide an unprecedented view of ice algal biomass distribution, mapped (as chlorophyll a) in a 100 m by 100 m area of a Weddell Sea pack ice floe, using under‐ice irradiance measurements taken with an instrumented remotely operated vehicle. We identified significant correlations (p < 0.001) between algal biomass and concomitant in situ surface measurements of snow depth, ice thickness, and estimated sea ice freeboard levels using a statistical model. The model's explanatory power (r2 = 0.30) indicates that these parameters alone may provide a first basis for spatial prediction of ice algal biomass, but parameterization of additional determinants is needed to inform more robust upscaling efforts.

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Flood-freeze cycles and microalgal dynamics in Antarctic pack ice

Cited by 39 publications

References 48 publications

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

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

Strategies for the Simulation of Sea Ice Organic Chemistry: Arctic Tests and Development

Antarctic pack ice algal distribution: Floe‐scale spatial variability and predictability from physical parameters

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