1. Conceptual Models and Parameterizations of Air- Water Gas Transfer Coefficients Proposal for Advancement to Candidacy Applicant: Damon Turney Committee Chair: Jeff Dozier Bren School of Environmental Science and Management Committee Members: Jeff Dozier, Sanjoy Banerjee, Sally MacIntyre, Jordan Clark

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3. ● ● Air-water gas transfer…sounds boring…but it’s important for many environmental issues.

4. ● Reareation of water with inputs of BOD or COD ● Pathway for loss of toxic (or potentially toxic) chemicals ● Potentially a pathway for loss of nutrients ● Pathway for loss of carbon from terrestrial ecosystems ● Method of measuring community respiration ● On the average, the ocean absorbs carbon from atm. ● In tropics and subtropics the ocean losses carbon while in the mid or polar latitudes the ocean gains carbon ● Loss of dimethylsulfide to atmosphere ● Reareation of water with inputs of BOD or COD ● Potentially a pathway for loss of nutrients ● Pathway for loss of toxic (or potentially toxic) chemicals ● Reareation of water with inputs of BOD or COD ● Pathway for loss of toxic (or potentially toxic) chemicals ● Pathway for loss of carbon from terrestrial ecosystems ● Potentially a pathway for loss of nutrients

5. ● ● Global Carbon Cycle ● ● Terrestrial regions often show that a significant amount of carbon is lost to the atmosphere through wetlands and lakes. The amount can reach up to 50% of net annual ecosystem carbon production. Richey, J. E., J. M. Melack, et al. (2002). "Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2." Nature 416(6881): 617-620. Kling, G. W., G. W. Kipphut, et al. (1991). "Arctic Lakes and Streams as Gas Conduits to the Atmosphere - Implications for Tundra Carbon Budgets." Science 251(4991): 298-301. ● ● Oceanic regions are absorbing ~30% of the anthropogenic CO 2 emitted annually. Since the Industrial Revolution the oceans have absorbed about half of the total anthropogenic CO 2. Sabine, C. L., R. Feely, et al. (2004). "The Oceanic Sink for Anthropogenic CO2." Science 305: 367-371.

6. ● ● Loss of toxic (or potentially toxic) chemicals from water bodies ● ● USEPA: http://www.epa.gov/OGWDW/dwh/t-soc/pcbs.html http:// www.epa.gov/OGWDW/dwh/t-soc/dioxin.html http://www.epa.gov/OGWDW/dwh/t-soc/pcbs.html http:// www.epa.gov/OGWDW/dwh/t-soc/dioxin.html ● ● Community Action: http://www.copa.org/library/articles/bv/volatile.htm http://www.foxriverwatch.com/volatilization_pcbs_1.html http://www.copa.org/library/articles/bv/volatile.htm http://www.foxriverwatch.com/volatilization_pcbs_1.html ● ● Scientific Literature: Shannon, J. D. and E. C. Voldner (1995). "Modeling Atmospheric Concentrations of Mercury and Deposition to the Great-Lakes." Atmospheric Environment 29(14): 1649-1661. Dewulf, J. P., H. R. Van Langenhove, et al. (1998). "Air/water exchange dynamics of 13 volatile chlorinated C1- and C2-hydrocarbons and monocyclic aromatic hydrocarbons in the southern North Sea and the Scheldt Estuary." Environmental Science & Technology 32(7): 903-911. etc…

7. ● ● Reaeration of anoxic water ● ● Gulf of Mexico “dead zone” ● ● This is also a problem in highly eutrophic lake and river waters, particularly downstream of sewage outfall.

8. ● ● The methods used to determine an air-water gas transfer rate could use improvement.

9. ● ● The current options: ● ● Assume a rate that is within previously accepted values. ● ● Measure a rate locally and then assume that it applies over the period/location of interest. ● ● Use some kind of an empirical parameterization, e.g. F = k ( [c] b - [c] eq ) where k is determined from the empirical fit. ● ● Each of these will benefit from a solid conceptual and mechanistic model of the process.

10. ● ● Typical Parameterizations ● ● from Nightingale et al. (2000) from Clark JF et al. online at http://www.ldeo.columbia.edu/res/pi/Hudson/articles/article1b.html

11. ● ● both plots from NOAA website http://www.aoml.noaa.gov/ http://www.aoml.noaa.gov/ ● ● Testing

12. ● ● Wanninkhof et al. 1992Banerjee and MacIntyre, 2004

13. ● ● For a given wind speed there seems to be uncertainty by a factor of 3 to 4. ● ● Wind may not be the dominant forcing of the process at low wind speeds, and even at intermediate wind speeds there can exist complexities such as surfactants. ● ● Recent work has pointed to convection as a significant forcing. Eugster, W., G. Kling, et al. (2003). "CO2 exchange between air and water in an Arctic Alaskan and midlatitude Swiss lake: Importance of convective mixing." Journal of Geophysical Research-Atmospheres 108(D12). ● ● McGillis, W. R., J. B. Edson, et al. (2004). "Air-sea CO2 exchange in the equatorial Pacific." Journal of Geophysical Research-Oceans 109(C8).

14. ● ● Takahashi et al. (2002)

15. ● ● What conceptual model will accurately describe all the complexity? ● ● How do we make the transition to large space and time scales?

16. ● ● The most recent geophysical research is turning to surface divergence models of Soloviev and Schlussel (1994), Reynolds number models of Banerjee (1990), and surface divergence models of Chan and Scriven (1970) for conceptual models. ● ● The surface divergence models are probably the most accurate, but this has not rigorously been proven and they have not caught on in the geophysical community.

17. ● ● Exactly what is the surface divergence model and why should we focus on it? surface renewal surface divergence

18. Where and How My Research Plan is Going To Make An Improvement » » What Exactly The Research Plan Is » » What I’ve already done » » What I plan to do ● ● Major Impediments, Time Schedule, and Financial Situation of the Research Plan

19. ● Advection-diffusion equation: ● No sources/sinks on a time scale fast enough to be in this equation ● The equations are linear in [c], and so we can recast them as: ● Where [c] n is non-dimensionalized using ([c]-[c] eq )/([c] b -[c] eq )

20. ● ● If we take a typical air-water gas transfer rate (say ~1x10 -2 mol/m 2 /s from McGillis et al. 2004) and use the idea that advection is non-operative near the interface we see that 10 -2 mol/m 2 /s = F = -D d[c] n /dx ([c] b -[c] eq ) and so d[c] n /dx <~ 10 4 m ● ● So the concentration boundary layer is less than 100 microns. But this close to the water interface the fluid motions are dominated by viscosity, since the viscous layer is ~10*(viscosity)/(velocity scale) ~ 10 -3 m or about a centimeter ● ● This ensures that: 1) in the concentration boundary layer we can model the fluid motion as stagnation flow, 2) we can neglect surface deformation in most locations

21. ● ● The only physical scales are D, ([c] b -[c] eq ), and we can non-dimensionalize the advection-diffusion equation to get ● ● The natural velocity scale that now arises is ● ● MATLAB demo: Here is a 1-D example. ● ● Note that for steady state the solution is the error function in the variable z n /2 and therefore d[c] n /dz n =0.5

22. ● Turney et al. 2005

23. ● ● 3.6 mps channel wind speed. 125 fps or 250 fps

25. ● ● Tests of the surface divergence theory ● Banerjee et al. 2004 Turney et al. 2005 and McKenna and McGillis 2004

26. ● ● My PhD Research Plan ● ● Objective 1: Test the surface divergence model under a variety of physical conditions. Characterize the time and space scales of the fluid motions. Determine which features of the wind waves are producing the important fluid motions. Search for a relationship between wave properties (i.e. morphology, dynamics) and important fluid motions. Assure the validity of the assumptions constituting the model. ● ● Objective 2: Connect the fluid motions that drive the surface divergence model with common meteorological observables. ● ● Objective 3: Connect the fluid motions that drive the surface divergence model with common satellite observables.

27. ● ● Novel technique for imaging the surface divergence. Possibly useful for field studies. ● Dime under 3mm of water dime under 1mm of water ● Both are taken at wavelengths of 1300nm to ~2500nm

28. ● ● Unpublished figure, data that I took

29. ● ● The experiments are described best in section 4 of my qualification exam write up. ● ● Collect collocated images (with pixel resolution of ~O(.1mm)) of surface velocities and surface morphology at frame rates of ~O(100 fps) ● ● Independent variables are wind speed, wave characteristics, surface heat loss, surface force dynamics, water depth (unfortunately).

30. ● ● Can we determine a gas transfer velocity from satellite observables? ● ● The necessary information is a relationship between surface morphology and fluid motions, and also a calculation of heat loss from the ocean surface? ● ● This sounds like a tall order, but the connection between surface morphology and satellite observables is well established. Satellite methods for calculating energy budgets at the ocean surface also exist. Gautier, C., Diak, G., Masse, S., 1980: A Simple Model to Estimate the Incident Solar Radiation at the Surface from GOES Satellite Data. J. of Appl. Meter. 19 1005-1012. ● ● Researchers in Japan have already proposed a very similar idea for determining wind speed from satellite altimeters, scatterometers, and sun-glitter (essentially surface morphology). If this approach is valid, the extension to gas transfer coefficients should follow straight-forward. Ebuchi, N. and S. Kizu (2002). "Probability distribution of surface wave slope derived using sun glitter images from Geostationary Meteorological Satellite and surface vector winds from scatterometers." Journal of Oceanography 58(3): 477-486. ● ● Zhao, D. L. and Y. Toba (2003). "A spectral approach for determining altimeter wind speed model functions." Journal of Oceanography 59(2): 235-244.

31. ● ● Approach: obtain gas transfer rate data from the Gas-Ex experiment, SOFeX experiment, or elsewhere and collocate it with remotely sensed data that gives mean square slope, significant wave height, surface irradiance, and surface temperature. Use the satellite data to calculate the “wave age”, maximum wave height, and also a surface heat flux. ● ● The likely remote sensing platforms will be ● ● Topex/Poseidon altimeter ● ● NSCAT or other scatterometer data ● ● Visible sun-glitter data ● ● GOES data for surface irradiance ● ● AVHRR for sea surface temperature ● ● Look at 5 to 10 cases. Can we estimate surface divergence from this data? If so, do we see agreement between model and measurements?

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33. ● ● Budget Potential Money Sources Fannie and John Hertz Foundation Fellowship NSF oceanography, Jan 15 EPA STAR Fellowship Air & Waste Management Association Robert and Patricia Switzer Foundation Link Foundation Fellowship in Ocean Engineering American Water Works Association NASA Graduate Student Researchers Program NASA Earth System Science Fellowships Equipment Needed New Solenoid Switch for Water Chiller Polyethylene Filters Tanks of Nitrogen Tanks of SF6 Tanks of He Four Week Rental of Infrared Camera Thermocouple probe Silvered Particles Water heater and insulation Humidity sensor Surface tensiometer (I can borrow this) Topex/NSCAT/GOES/AVHRR Images??

34. ● ● Time Table

35. ● ● end

36. ● ● Testing ● ● Template