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

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Flux =KΔC

Modified from Wanninkhof et al. 2009

Sol. Sc No.

Flux

ΔC

K

Wave

Controlling Factors on K

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

• KDMS Lower than K of less soluble gases above U ~ 8 m/s

SolubilityDMS > CO2 > 3He

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. CO2, N2O, CH4, CO, O2, acetone, etc

Wave influence? kDMS measurements from Knorr_11 cruise in N. Atlantic

Bell et al. ACP (2013)

Waves = 20% reduction in kDMS

Rhee et al. (2007)

Wave influence? Wind-wave tank measurements

DOGEE Cruise

Surfactants? kDMS from DOGEE cruise in N. Atlantic

Salter et al. (2011)

U10n (m/s)

Southern Ocean Aerosol Production (SOAP) CruiseFeb/March 2012

High productivity waters

Natural surfactants and K?

Waves and K?

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

U10

Flux Footprint

C

FluxK

Δ=

SOAP Setup

3-D Winds (Sonic Anemometer)

Atm. Inlet(90 L/min)

Motion Sensor

Internal Standard

Seawater DMSShip’s Inlet

Atmospheric flux mast

Atmospheric instruments in container lab

10 min average data

C

FluxK

Δ=

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

C

FluxK

Δ=

- 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

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)

Wave influence?

No clear relationship with significant wave height

Surfactant influence?

No obvious relationship with chlorophyll as a proxy for surfactant

What else can be causing the scatter?

Flux footprint analysis

• 18 hour transect• Consistent conditions• Into bloom• Into wind

Distance from Bloom (km)

Neutral-stable atmosphere

DMSsw vs Flux/U10 lag analysis

- FDMS/U10 peaks earlier than DMSsw - 8±2 min lag = max. correlation between DMSsw and FDMS/U10

SOAP rawSOAP LagCorrCOARE

~30% reduction in scatter

Wind speed (m/s) Wind speed (m/s)

No Lag Shift Seawater DMS Shifted by 8 minutes

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

Conclusions

• Scatter in SOAP KDMS

– 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

Extra slides

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)

DMS m/z 63

d3-DMS m/z 66

10 Hz data acq.

~100 cps/ppt

(Hz/ppt)

Atmospheric DMS (UCI mesoCIMS)

Ho et al. (2006)

Liss and Merlivat (1984)

Nightingale et al. (2000)

Global excess 14C

Budget techniques: Sparingly soluble gases

U10 (Horizontal Wind Speed)

Gas Transfer Velocity

(K)cm/hr

calm(buoyancy)

moderate wind(shear stress)

rough(waves, bubbles)

Dual tracer (3He / SF6)

Timescale = hours-days