Modelling sea ice biogeochemistry: key findings from 1-D sensitivity experiments and plans for 3-D study Hakase Hayashida1, Eric Mortenson1, Nadja Steiner2,3, Adam Monahan1 1 School of Earth and Ocean Sciences, UVic. 2Institute of Ocean Science, DFO. 3 CCCma, EC. April 4, 2016 UBC Physical Oceanography Seminar
Outline Introduction Arctic sea ice habitats Marine sulfur cycle Objectives Methods 1-D model Results Simulated and observed features Sensitivity experiments Conclusions and future work
Intro: Arctic sea ice Provides diverse habitats for microalgae and bacteria. Is NOT necessarily a barrier to gas exchange processes between the ocean/ice and the atmosphere. [Levasseur 2013]
[Levasseur 2013] Intro: Bottom ice algae Bottom ice (5-10 cm) is often the most biologically productive sea ice habitat [Arrigo 2014]. Roles of bottom ice algae (diatoms): 1. 2. 3. 4. 5. Food for pelagic zooplankton. Export food/organic matter to benthic ecosystem. Seeding pelagic phytoplankton bloom. Reducing light levels in the water column. Reducing nutrient levels in the upper water column.
Intro: DMS Dimethylsulfide (DMS) is a biogenic sulfur compound. DMSP (dimethylsulfoniopropionat e) acts as osmolyte, antioxidant, cryoprotectant (anti-freeze). DMS serves as chemical cue for foraging. Source: https://culturingscience.wordpress.com/2010/07/19/dms/
Intro: DMS and Climate CLAW hypothesis: Climate regulation via oceanic DMS emissions [Charlson et al. 1987] CLAW probably does not exist... [Quinn & Bates 2011] [Wikipedia]
Intro: Oceanic DMS emissions could exert a significant influence on the Arctic (summer) climate because: 1. Arctic air is relatively clean (free of anthropogenic influence and low in CCN) during summer. 2. High levels of DMS can accumulate in diverse sea ice habitats during the melt season. 3. Global warming is expected to promote the DMS production, while the ongoing reduction of sea ice extent is expected to enhance the DMS emissions.
Intro: Research objectives Design and develop a numerical model which represents sea ice biogeochemical processes in the Arctic. Quantify the relative contribution of sea ice biogeochemistry to the total (sea ice and ocean) primary production and oceanic emissions of DMS. Identify the key biogeochemical processes in the sea ice.
Methods: 1-D model setup Location: Resolute Passage in CAA Model components: Sea ice thermodynamics Ocean physics Sea ice and ocean biogeochemistry Nitrogen- and silicon-based lowertrophic level ecosystem Sulfur cycle Carbon cycle Adapted from [Mundy et al., 2014]
[Mortenson et al. in prep.] Methods: Ecosystem model Sea ice: 3N1P Ocean: 3N2P2Z2D Black arrows: Nitrogen flow Red arrows: Silicon flow
Methods: Equation for ice algae 1. Growth (photosynthesis) 2. Mortality 3. Flushing Growth limiting factors: Nitrate, silicate, light, ice
Methods: Sulfur cycle model Blue: Prognostic Yellow: Diagnostic Green: Ecosystem model variables Red: Not simulated but relevant processes are parameterized. [Hayashida et al. in prep.]
Results: Simulated snow, melt pond, and ice A. Snow and melt pond thicknesses a. Snow accumulated up to ~20 cm in mid. to late May, started melting in the end of May and melted completely by mid May. b. Melt pond depth ~ 6cm. B. Ice grew gradually to ~145cm, started melting in early June, and melted completely by early July.
Results: Simulated ecosystem A. Ice algae bloom started in late March, peaked in mid May, and declined in late June. B. An under-ice bloom (late June), a subsurface chlorophyll maximum (early July), an open-water bloom (August). C. Growth rate of ice algae was limited by light and silicate. D. Ice algal production contributed ~10% of the entire (ice algae + pelagic phytoplankton) production.
Results: Simulated sulfur cycle 1. In the bottom ice, concentrations of sulfur species were two orders of magnitude higher than those in the water column. 2. In the water column, DMS accumulated up to 14 nm in the upper water column during the melt period, ~30% of which comes from the bottom ice.
Results: Sensitivity experiments Sensitivity of simulated ice algae bloom to: Pre-bloom biomass Photosynthetic parameters Mortality function Intracellular silicon-to-nitrogen ratio Sensitivity of simulated DMS production and emissions to: Presence and absence of sea ice sulfur cycle Open water fraction during ice-covered period Ecological and microbial process rates
Results: Sensitivity of ice algae bloom to pre-bloom biomass The pre-bloom (initial) biomass of ice algae is known to vary greatly (e.g. 0.3-26.8 mg Chl a m-3 ; Garrison et al. 1983). The onset of bloom (>100 mg Chl a m-3) was affected by this range (from late March to early May), while the peak biomass did not vary much as it was most likely dependent on available nutrient concentrations.
Results: Sensitivity of DMS emissions to open water fraction Oceanic emissions of DMS during icecovered periods are possible through various ice features. In the flux equation, open water fraction (owf) was introduced to represent the emissions through small (2%) and large (10%) leads and near (50%) and at (100%) iceedge. The ice-covered flux of DMS could be an important source of the seasonal (June) and the annual (up to 12% increase) fluxes. Flux = owf * k * DMS where k is the gas transfer velocity and DMS is the seawater DMS in the uppermost layer.
Work in progress: PanArctic 3-D modelling NEMO-CanOE with NAA configuration. Add the following biogeochemical model components: Sea ice ecosystem Sea ice and ocean sulfur cycles Conduct various model simulations: Historical, Future, and sensitivity [Hu & Myers 2013]
Conclusions and future work The model developed in the 1-D framework simulated reasonable primary production and sulfur cycling in the bottom ice and the water column in Resolute Passage. Senstivivity experiments highlighted the control of biogeochemical parameters on the simulated timing and magnitude of ice algae bloom and associated production of DMS in the sea ice, suggesting the need for more field measurements to further constrain these parameters and improve model parameterizations. In the 3-D modelling work, we will quantify the contribution of sea ice biogeochemistry to the primary production and DMS emissions under present and future climates.
Observed data provided by: M. Blais, M. Gosselin (ISMER) Acknowledgements V. Galindo, C.J. Mundy (U of Manitoba) Restoration data provided by: X. Hu (U of Alberta)
Thanks! Questions?