A model-based analysis of physical and biological controls on ice algal and pelagic primary production in Resolute Passage

Mortenson, Eric; Hayashida, Hakase; Steiner, Nadja; Monahan, Adam H.; Blais, Marjolaine; Gale, Matthew A.; Galindo, Virginie; Gosselin, Michel; Hu, Xianmin; Lavoie, Diane; Mundy, C. J.

doi:10.1525/elementa.229

Cited by 26 publications

(54 citation statements)

References 68 publications

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“…The coupled sea ice-ocean ecosystem model is described and evaluated in Mortenson et al (2017). In this earlier study, the model was used to study the physical and biological controls on the ice algal and under-ice phytoplankton blooms observed in Resolute Passage during the spring of 2010.…”

Section: Ecosystem Modelmentioning

confidence: 99%

Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

et al. 2017

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Abstract. Sea ice represents an additional oceanic source of the climatically active gas dimethyl sulfide (DMS) for the Arctic atmosphere. To what extent this source contributes to the dynamics of summertime Arctic clouds is, however, not known due to scarcity of field measurements. In this study, we developed a coupled sea ice-ocean ecosystem-sulfur cycle model to investigate the potential impact of bottom-ice DMS and its precursor dimethylsulfoniopropionate (DMSP) on the oceanic production and emissions of DMS in the Arctic. The results of the 1-D model simulation were compared with field data collected during May and June of 2010 in Resolute Passage. Our results reproduced the accumulation of DMS and DMSP in the bottom ice during the development of an ice algal bloom. The release of these sulfur species took place predominantly during the earlier phase of the melt period, resulting in an increase of DMS and DMSP in the underlying water column prior to the onset of an under-ice phytoplankton bloom. Production and removal rates of processes considered in the model are analyzed to identify the processes dominating the budgets of DMS and DMSP both in the bottom ice and the underlying water column. When openings in the ice were taken into account, the simulated sea-air DMS flux during the melt period was dominated by episodic spikes of up to 8.1 µmol m −2 d −1 . Further model simulations were conducted to assess the effects of the incorporation of sea-ice biogeochemistry on DMS production and emissions, as well as the sensitivity of our results to changes of uncertain model parameters of the sea-ice sulfur cycle. The results highlight the importance of taking into account both the sea-ice sulfur cycle and ecosystem in the flux estimates of oceanic DMS near the ice margins and identify key uncertainties in processes and rates that should be better constrained by new observations.

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Section: Ecosystem Modelmentioning

confidence: 99%

Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

et al. 2017

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“…The submodel for sea-ice biogeochemistry is a modified version of a three-compartment (ice algae, nitrate, and ammonium) ecosystem based on Mortenson et al (2017) and a two-compartment (DMS and DMSPd) sulfur cycle based on Hayashida et al (2017).…”

Section: Sea-ice Biogeochemistrymentioning

confidence: 99%

“…However, this simplification has negligible effect on ocean biogeochemistry given the relatively-thin sea-ice biogeochemical layer as demonstrated in Hayashida et al (2017). For ice algae only, a minimum biomass threshold is set at 10 mmol C m −3 in order to mimic reasonable overwintering biomass (Mortenson et al, 2017).…”

mentioning

confidence: 99%

“…The biological and chemical processes represented as sources and sinks of the sea-ice biogeochemical state variables are described in detail in Mortenson et al (2017) and Hayashida et al (2017). For the ecosystem component, these processes include photosynthesis, mortality, and remineralization of dead organic matter.…”

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confidence: 99%

“…The growth rate of ice algae is dependent on ambient temperature, PAR, and nutrient concentrations (nitrate and ammonium). Note that the growth rate dependence on ice melt considered in Mortenson et al (2017) has been neglected in the present study because: 1) our preliminary results indicated 25 that ice algal blooms were generally insensitive to it; 2) the parameterization for ice melt limitation was applied for a specific location and might not be appropriate for other locations; and 3) the parameterization lacks observational evidence.…”

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confidence: 99%

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CSIB v1: a sea-ice biogeochemical model for the NEMO community ocean modelling framework

Hayashida

Christian

Holdsworth

et al. 2018

Preprint

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Abstract. Numerical models are a useful tool for studying marine ecosystems and associated biogeochemical processes in icecovered regions where observations are scarce. To this end, CSIB v1 (Canadian Sea-ice Biogeochemistry version 1), a new seaice biogeochemical model has been developed and embedded into the Nucleus for European Modelling of the Ocean (NEMO) modelling system. This model consists of a three-compartment (ice algae, nitrate, and ammonium) sea-ice ecosystem and a twocompartment (dimethylsulfoniopropionate and dimethylsulfide) sea-ice sulfur cycle which are coupled to pelagic ecosystem 5 and sulfur-cycle models at the sea ice-ocean interface. In addition to biological and chemical sources and sinks, the model simulates the horizontal transport of biogeochemical state variables within sea ice through a one-way coupling to a dynamicthermodynamic sea-ice model (LIM2). This paper describes technical aspects of implementing sea-ice biogeochemistry into NEMO and provides discussion on the results of several model experiments. Results of the reference simulation were evaluated by comparing the model outputs to observations and previous modelling studies. Additional simulations were conducted to 10 assess the model sensitivity to 1) the temporal resolution of the snowfall forcing data, 2) the representation of light penetration through snow, 3) advective and eddy-diffusive horizontal transport of sea-ice biogeochemical state variables, and 4) light attenuation by ice algae. The sea-ice biogeochemical model has been developed within the generic framework of NEMO to facilitate its use within different configurations and domains, and can be adapted for use with other NEMO-based submodels such as LIM3 and PISCES.

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Modeling Arctic sea‐ice algae: Physical drivers of spatial distribution and algae phenology

et al. 2017

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Algae growing in sea ice represent a source of carbon for sympagic and pelagic ecosystems and contribute to the biological carbon pump. The biophysical habitat of sea ice on large scales and the physical drivers of algae phenology are key to understanding Arctic ecosystem dynamics and for predicting its response to ongoing Arctic climate change. In addition, quantifying potential feedback mechanisms between algae and physical processes is particularly important during a time of great change. These mechanisms include a shading effect due to the presence of algae and increased basal ice melt. The present study shows pan‐Arctic results obtained from a new Sea Ice Model for Bottom Algae (SIMBA) coupled with a 3‐D sea‐ice–ocean model. The model is evaluated with data collected during a ship‐based campaign to the Eastern Central Arctic in summer 2012. The algal bloom is triggered by light and shows a latitudinal dependency. Snow and ice also play a key role in ice algal growth. Simulations show that after the spring bloom, algae are nutrient limited before the end of summer and finally they leave the ice habitat during ice melt. The spatial distribution of ice algae at the end of summer agrees with available observations, and it emphasizes the importance of thicker sea‐ice regions for hosting biomass. Particular attention is given to the distinction between level ice and ridged ice. Ridge‐associated algae are strongly light limited, but they can thrive toward the end of summer, and represent an additional carbon source during the transition into polar night.

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A model-based analysis of physical and biological controls on ice algal and pelagic primary production in Resolute Passage

Cited by 26 publications

References 68 publications

Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

Implications of sea-ice biogeochemistry for oceanic production and emissions of dimethyl sulfide in the Arctic

CSIB v1: a sea-ice biogeochemical model for the NEMO community ocean modelling framework

Modeling Arctic sea‐ice algae: Physical drivers of spatial distribution and algae phenology

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