Pan-Arctic simulation of coupled nutrient-sulfur cycling due to sea ice biology: Preliminary results

• Published in: Journal of Geophysical Research: Biogeosciences Vol. 117; no. G1
• Main Authors: Elliott, S., Deal, C., Humphries, G., Hunke, E., Jeffery, N., Jin, M., Levasseur, M., Stefels, J.
• Format: Journal Article
• Language: English
• Published: Washington, DC Blackwell Publishing Ltd 01.03.2012; American Geophysical Union
• Subjects: Algae; Algal growth; Arctic; Arctic environments; Biogeochemistry; Cryosphere; DMS; Earth sciences; Earth, ocean, space; Ecosystem biology; Ecosystems; Exact sciences and technology; Geobiology; geocycling; Grazing; ice algae; Metabolites; Nutrient cycles; Nutrients; Sea ice; Sulfides; Sulfur; Sulfur cycle; polar regions; Arctic region; High latitude; thickness; simulation; global; nutrients; Heterokontophyta; Bacillariophyta; gels; sea ice; Dynamic model; Pack; Thallophyta; interfaces; biology; Algae; nitrogen; Plantae; chlorophyll; Source sink relationship; Mixed layer; major elements; silicon; diatoms; ecosystems; growth; kinetics; melts; Pan
• ISSN: 0148-0227; 2169-8953; 2156-2202; 2169-8961
• DOI: 10.1029/2011JG001649

A dynamic model is constructed for interactive silicon, nitrogen, sulfur processing in and below Arctic sea ice, by ecosystems residing in the lower few centimeters of the distributed pack. A biogeochemically active bottom layer supporting sources/sinks for the pennate diatoms is appended to thickness categories of a global sea ice code. Nutrients transfer from the ocean mixed layer to drive algal growth, while sulfur metabolites are reinjected from the ice interface. Freeze, flux, flush and melt processes are linked to multielement geocycling for the entire high‐latitude regime. Major element kinetics are optimized initially to reproduce chlorophyll observations, which extend across the seasons. Principal influences on biomass are solute exchange velocity at the solid interface, optical averaging in active ice and cell retention against ablation. The sulfur mechanism encompasses open water features such as accumulation of particulate dimethyl sulfoniopropionate, grazing and other disruptive releases, plus bacterial/enzymatic conversion to volatile dimethyl sulfide. For baseline settings, the mixed layer trace gas distribution matches sparging measurements where they are available. However, concentrations rise to well over 10 nM in remote, unsampled locations. Peak contributions are supported by ice grazing, mortality and fractional melting. The model bottom layer adds substantially to a ring maximum of reduced sulfur chemistry that may be dominant across the marginal Arctic environment. Sensitivity tests on this scenario include variation of cell sulfur composition and remineralization, routings/chemical time scales, and the physical dimension of water layers. An alternate possibility that peripheral additions are small cannot be excluded from the outcomes. It is concluded that seagoing dimethyl sulfide data are far too sparse at the present time to distinguish sulfur‐ice production levels. Key Points Nutrients flux into ice ecosystems and so drive a sulfur cycle below the pack Algae and a DMS model are attached to the global CICE code to simulate effects Sparse data cannot exclude contributions from ice systems to mixed layer DMS