1. Effect of Variable Flux Footprint on Measurement of Air/Sea DMS Transfer Velocity A Southern Ocean Case Study Thomas Bell Presented by Mingxi Yang with contributions from: Warren De Bruyn, Christa Marandino, Scott Miller, Cliff Law, Murray Smith, Brian Ward, Kai Christensen and Eric Saltzman

3. Modified from Wanninkhof et al. 2009 Sol. Sc No. Flux ΔC ΔC K Wave Controlling Factors on K

4. How well do we know K over the ocean? Numerous Field Measurements of waterside controlled gases, though few in high winds Fair agreement in low to moderate winds Large divergence among different gases/methods in high winds & rough seas K DMS Lower than K of less soluble gases above U ~ 8 m/s Solubility DMS > CO 2 > 3 He

5. Why Measure Air/Sea DMS Exchange? Environmental importance: –Large biogenic sulphur source to atmosphere –Clouds, albedo and climate? Useful tracer for K: –Grossly supersaturated in surface ocean (strong flux signal) –Highly sensitive detector (CIMS) available for eddy covariance –A proxy for interfacial (i.e. tangential) gas exchange Relevant to other gases: e.g. CO 2, N 2 O, CH 4, CO, O 2, acetone, etc

6. Wave influence? k DMS measurements from Knorr_11 cruise in N. Atlantic Bell et al. ACP (2013)

7. Waves = 20% reduction in k DMS Rhee et al. (2007) Wave influence? Wind-wave tank measurements

8. DOGEE Cruise Surfactants? k DMS from DOGEE cruise in N. Atlantic Salter et al. (2011) U 10n (m/s)

9. Southern Ocean Aerosol Production (SOAP) Cruise Feb/March 2012 High productivity waters Natural surfactants and K? Waves and K?

10. Micrometeorological technique: Eddy Covariance Covariation between vertical wind velocity (w) and gas concentration in air (c) Timescale = 10 minutes ~ 1 hour Useful for assessing processes affecting gas transfer (e.g. waves, surfactants) BUT 1) Turbulence is stochastic – requires averaging 2) Spatial separation between ΔC and Flux Gas Flux U 10 Flux Footprint

11. SOAP Setup 3-D Winds (Sonic Anemometer) Atm. Inlet (90 L/min) Motion Sensor Internal Standard Seawater DMS Ship’s Inlet Atmospheric flux mast Atmospheric instruments in container lab

12. 10 min average data

13. Bin Average - Fairly good agreement on the mean with previous DMS studies up to 14 m/s

14. - Scattering not just random - Positive and negative biases in 10 min K relative to COARE model Short timescales may contain information about physical processes that become lost during bin-averaging

15. Spearman’s ρ = 0.57, p<0.01, n=1327 10 min average Scatter: Random noise + systematic bias Other processes? Waves? Surfactants? 10-m Wind speed (m/s)

16. Wave influence? No clear relationship with significant wave height

17. Surfactant influence? No obvious relationship with chlorophyll as a proxy for surfactant What else can be causing the scatter?

18. Flux footprint analysis 18 hour transect Consistent conditions Into bloom Into wind

19. Distance from Bloom (km) Neutral-stable atmosphere

20. DMS sw vs Flux/U 10 lag analysis - F DMS /U 10 peaks earlier than DMS sw - 8±2 min lag = max. correlation between DMS sw and F DMS /U 10 SOAP raw SOAP LagCorr COARE

21. ~30% reduction in scatter Wind speed (m/s) No Lag Shift Seawater DMS Shifted by 8 minutes

22. Footprint Size Distance from sensor (m) Proportion of flux signal (%) SOAP Cruise 8 min lag (ship speed = 5.1 m/s) suggests footprint = 2.5 km (peak) Footprint model (Kormann and Meixner, 2001) predicts peak flux at 0.8 km (range = 0.3 – 1.9 km, depending on stability) Peak flux

23. Conclusions Scatter in SOAP K DMS –Random + systematic –Masks potential impacts of other processes e.g. surfactants, wave properties Accounting for lag between flux and seawater concentration improves gas transfer estimates –Reduces scatter in K Peak flux distance estimates: –Flux footprint model < lag-based estimate

24. Extra slides

25. Seawater DMS (UCI miniCIMS) PTFE porous membrane counter- flow equilibrator residual gas analyzer (SRS) liquid d3-DMS standard DL ~0.1 nM @ 20°C (2 min avg)

26. DMS m/z 63 d3-DMS m/z 66 10 Hz data acq. ~100 cps/ppt (Hz/ppt) Atmospheric DMS (UCI mesoCIMS)

27. Ho et al. (2006) Liss and Merlivat (1984) Nightingale et al. (2000) Global excess 14 C Budget techniques: Sparingly soluble gases U 10 (Horizontal Wind Speed) Gas Transfer Velocity (K) cm/hr calm (buoyancy) moderate wind (shear stress) rough (waves, bubbles) Dual tracer ( 3 He / SF 6 ) Timescale = hours-days