The role of sea ice algae in Arctic marine ecosystems and oceanic emissions of dimethylsulfide. Hakase Hayashida Candidacy Exam August 19, 2015

Transcription

1 The role of sea ice algae in Arctic marine ecosystems and oceanic emissions of dimethylsulfide Hakase Hayashida Candidacy Exam August 19, 2015

2 Introduction: Role of sea ice algae in Arctic marine ecosystems Light reducer Nutrient competitor Food supplier Plant seeder Food & debris supplier

3 Introduction: Relative significance of bottom ice algal DMS production in Arctic marine ecosystems [Levasseur 2013]

4 Introduction: Role of oceanic DMS in the global sulfur cycle and in the climate system. Planktonic and microbial food web. The dominant natural source in the global sulfur budget. The almost exclusive source of DMS in the atmosphere. Influence on the climate via: direct solar scattering. cloud formation/growth. Important for the Arctic summer: clean atmosphere. a drastic change in the DMS emission rate in response to climate change?? [Simo, 2001]

5 Research Questions How important is the presence of sea ice algae for the marine ecosystem and for the marine sulfur cycle in the Arctic? What are the key processes for the production and consumption of DMS(P) in sea ice, and how do they differ from the pelagic environment? What are the projected changes in the emissions of DMS from the Arctic Ocean in response to ongoing climate change?

6 Methods: 1-D Model Simulation at Resolute Passage Model experiments: Baseline simulation ice camp sites Two-year ( ) run. Attempt to reproduce a reasonable seasonal cycle. ~10 km Model-data comparison Short (May 7-June 30) run. Attempt to reproduce the observed variability in weather stations this study

7 Methods: Schematic diagram for the sympagic-pelagic ecosystem model Stars: Major modifications from the original models. Arrows in black: Nitrogen flow. Arrows in red: Silicon flow.

8 Methods: Schematic diagram of the DMS(P) model in sea ice and water column Star: New feature added to the original model. Ovals: Modeled prognostically. Rectangles: Not modeled but the relevant processes are parametrized. Arrows in black: Present in both sea ice and ocean. Arrows in red: Absent in sea ice.

9 Results: Baseline simulation at Resolute in 2010 Good: Timing of snow melting. Magnitude of ice algal biomass. Bad: Too thick ice (should be ~1.4 m). Too little PAR prior to snow melting. As a result, ice algal bloom delayed by 2 months compared to observations. Constant snowfall rate. Ice algal contribution: 12% Improvements since the candidacy report submission: Variable snowfall rate (Environment Canada s hourly dataset). Improved PAR parametrization based on Abraham et al. (2015).

10 Results: Improved baseline simulation at Resolute in 2010 Bottom ice PAR and algae Snow and sea ice thickness BEFORE AFTER Still delayed by 1.5 months compared to obs. Initiates and blooms ~0.5 month earlier.

11 Results: Baseline simulation at Resolute in 2010 In comparison to observations: DMSPp: reasonable DMSPd: too low DMS: no data available DMS flux: reasonable Again, reasonable magnitude, but NOT reasonable timings.. DMSPp, DMSPd, and DMS in bottom 3cm sea ice and sea-to-air DMS flux

12 Snow thickness Ice thickness Bottom 3cm ice Chl-a Bottom 3cm ice DMSPd Bottom 3cm ice DMSPp Results: Model-Data Comparison at Resolute in 2010 Reasonable comparison, except for DMSPd.

13 Summary The baseline simulation produces a ice algal bloom that is reasonable in magnitude but not in timing, compared to the observations. In both the baseline simulation and the model-data comparison experiments, DMSPd in sea ice is too low compared to observations*. Further model refinement is required to be able to simulate the observed variability.

14 Future Work 1-D modeling (Aug. - Dec. 2015) Parameter optimization via data assimilation Model-data comparison In the water column Sensitivity analysis Comprehensive parameter sensitivity Fate of ice algae Exclusion of ice algae and ice-dms(p) 3-D modeling Implementation (Jan. - Apr. 2016)

15 Pan-Arctic 3-D model implementation My task: ice algae model. sympagic-pelagic DMS(P) model.

16 Future Work 3-D modeling Implementation (Jan. - Apr. 2016) Model-data comparison (Jan. - Aug. 2016) Sensitivity runs (May - Dec. 2016) Selected parameter sensitivity Fate of ice algae Exclusion of ice algae and ice-dms(p) Projection runs (May - Dec. 2016) RCP climate change scenario

17 Expected publications 1. A 1-D model study of lower-trophic marine ecosystems within the sea ice and the water column at Resolute Passage. (Submit by Dec. 2015) 2. A 1-D model study of marine sulfur cycle within the sea ice and the water column at Resolute Passage. (Submit by Feb. 2016) 3. A 3-D model study of pan-arctic sea ice algal production and DMS emissions under present and future climates. (Submit by Aug. 2017) ***Anticipated defence in Summer 2017.

18 Thanks.

19 Chlorophyll, DMSPp, and DMSPd in the bottom 3cm ice and upper 50m at Resolute Passage from early May to late June in 2010 [Galindo et al. 2014] (1) Sea ice vs seawater concentrations. (2) Vertical structures. (3) Observations in the Arctic are scarce.

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21 Results: Baseline simulation at Resolute in 2010

22 TOP: PAR in the first ocean layer + constant snow fall rate MIDDLE: Improved PAR in the bottom ice + constant snow fall rate BOTTOM: Improved PAR in the bottom ice + variable snow fall rate based on EC obs.

23 Sensitivity Experiment Effects of snowfall rates (0, 0.1, 0.5, 1, 1.5, 2 mm per day) on ice, bottom ice PAR, and ice algae

24 Snow thickness [m] Ice thickness [m] Bottom ice PAR [W/m2] Ice algae [mg-chl/m3] Sufficient PAR is available for ice algae to bloom in mid-may for no snow case only.

25 Multi-year run 1

26 Multi-year run 2

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32 Campbell et al. 2015

33 High snow Medium snow Low snow where 4.56 = conversion factor from [uein./m2] to [W/m2] and x is PAR in [W/m2]. Light limitation formulation from Lavoie et al. 2005

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35 Bubbles in the Arctic [Norris, 2011] Present in the absence of both 1) sufficient wind and 2) ice-free surface (e.g. lead). Bubbles are generated by processes under the ice, instead of wave breaking. These processes include: release of bubbles trapped in melting sea ice rejection by freezing seawater respiration of phytoplankton released from the sea bed (seafloor)

36 Linear U10 parametrization is sufficient for DMS. Bubble effect is relatively small for DMS because DMS has relatively high solubility compared to other gases (e.g. CO2). Huebert et al Bubble-mediated transfer is proportional to whitecap fraction (which is proportional to wind speed...), while it is inversely proportional to solubility [Woof, 1997].

37 DMS in the atmosphere Under atmospheric conditions sulfuric acid (H2SO4) is the only final product of the DMS oxidation that can form new aerosol particles, all other products may only condense onto (and thereby enlarge) existing particles [Glasow & Crutzen, 2004]. In the cloud-abundant Arctic air, aerosols are scavenged by droplets before being large enough to nucleate (i.e. to act as CCN).

38 [Glasow & Crutzen, 2004]

39 CCNs Their number and type can affect the lifetime, the radiative properties, and the amount of clouds. Raindroplet ~ 2 mm, cloud droplet ~ 0.02 mm, and CCNs ~ 0.1 um in diameter. CCN numbers (i.e. concentrations) are fewer (~10 cm3) in the Arctic compared to the lower latitudes (~ cm-3), hence fewer but larger cloud droplets. Cloud formation in the central Arctic is frequently limited by CCN availability.

40 1-D Model basics [GOTM manual] horizontally homogeneous, hence depends only on the vertical coordinate. horizontal gradients can be either: neglected (d/dx = d/dy = 0) parametrized/estimated taken from observations In the momentum equations, horizontal advection and diffusion terms are neglected. vertical velocity is assumed to be zero everywhere (e.g. continuity equation). However, W can be prescribed as a linear profile (w=0 at bottom and w=d(eta)/dt at surface, where eta is the change in sea surface height).

41 Turbulence model [GOTM manual] Computed as the product of a positive turbulent diffusivity and a mean flow gradient: <u w > = - k du/dz where k is the vertical diffusivity parametrized by the product of a typical velocity (q) and length (l) scales of turbulence.