Controls of the landfast ice–ocean ecosystem offshore Barrow, Alaska

Jin, Meibing; Deal, Clara; Wang, Jia; Shin, Kyung‐Hoon; Tanaka, Nori; Whitledge, Terry E.; Lee, Sang‐Heon; Gradinger, Rolf

doi:10.3189/172756406781811709

Cited by 76 publications

(169 citation statements)

References 23 publications

(28 reference statements)

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“…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). This approach was used later by Jin et al (2006). Fritsen et al (1998) included the contribution of flooding by seawater near the surface.…”

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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“…The ice algal component of mBGC represents colonies in a 3 cm layer at the bottom of each sea ice thickness category, coupled to the pelagic model through nutrient and biotic fluxes. This submodel was based on biophysical ice core data collected in land-fast ice offshore from Barrow, Alaska [Jin et al, 2006] and coupled with a pelagic ecosystem model in vertically 1-D models [Jin et al, 2007] and global POP-CICE model settings [Deal et al, 2011;Jin et al, 2012Jin et al, , 2016.…”

Section: A13 Model 14mentioning

confidence: 99%

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Lee

Matrai

Friedrichs

et al. 2016

J. Geophys. Res. Oceans

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The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality‐controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan‐Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice‐free versus ice‐influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

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“…In other words, cell lysis may be slaved too tightly to the autotrophic source profile. Following a long-standing tradition in ice biogeochemistry modeling, secondary consumption is represented here solely as a fixed proportion of growth [14,25,33,54]. The usual justification is that understanding simply remains inadequate for the higher trophic levels.…”

Section: Discussion: Influence On Structure and Future Directionsmentioning

confidence: 99%

“…A salinity-growth retardation is computed for consistency with the local porosity. Zooplankton are treated in the mechanism as a non-modeled background entity skimming a constant small fraction of primary production [33,54]. This is a typical assumption even in contemporary ice ecodynamic simulations and it will be considered in more detail in the discussion section, since the organics are strongly affected.…”

Section: Biogeochemistrymentioning

confidence: 99%

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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Controls of the landfast ice–ocean ecosystem offshore Barrow, Alaska

Cited by 76 publications

References 23 publications

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

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

Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

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

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