Direct CO2 flux measurements over the open ocean

W. R. McGillis1, J. B. Edson1, J. Hare2 and C. W. Fairall3

1Woods Hole Oceanographic Institution, Woods Hole, MA

2CIRES, University of Colorado, NOAA / ETL Boulder, CO

3NOAA / ETL, Boulder, CO

Abstract

We report results of direct covariance air-sea carbon dioxide flux measurements over the open ocean, performed during June 1998 in the North Atlantic in an area of significant sink for CO2. For the first time, these direct estimates are in general agreement with the traditional geochemical isotope constraints. The covariance, or eddy correlation, technique directly measures the CO2 flux over a 1/2 hour time scale by correlating the fluctuations of carbon dioxide concentration with the turbulent vertical velocity perturbations in the atmospheric surface layer. These unique measurements quantify the transfer of CO2 between the atmosphere and ocean over a range of wind speeds, and therefore improve our understanding of the environmental factors controlling the flux. The relatively large flux of carbon dioxide in the study region, together with improved analytical techniques, facilitated the measurement. The one-half hour mean wind speeds varied from 0.9 to 16.3 ms-1 over the month-long experiment, and the estimated carbon dioxide flux based on 14C parameterizations at this wind speed is approximately 3.9 mol m-2 yr-1. The mean measured pCO2 during the study period was -85.8 ± 16.0 ppm, the average observed wind speed was 7.7 ms-1, and the mean covariance carbon dioxide flux was estimated at 4.6 mol m-2 yr-1. At high winds, where the relationship between gas physical properties, surface processes and air-sea gas exchange is still elusive, direct CO2 flux measurements are crucial. Our measurements for winds in excess of 11 ms s-1 show a general enhancement of gas transfer velocity over previous indirect measurements, and we believe that this enhancement can be explained by the fact that the indirect methods cannot discriminate surface process variabilities such as atmospheric stability, upper ocean mixing, wave age, wave breaking, or surface films.

1. Introduction

Carbon dioxide gas exchange across the air-sea interface is an extremely important component in global climate dynamics, given the theorized possible absorption in the ocean of a significant fraction of the anthropogenically-produced CO2. In spite of this, the kinetics of the air-sea CO2 transfer are not well understood, as the many different mechanisms and processes that control the flux have large spatial and temporal variability. For example, the surface pCO2 can vary with temperature, salinity, bubble entrainment, and bioproductivity. In addition, the gas transfer rates are known to vary with wind speed, air-sea concentration difference, sea-state, and a host of surface processes [Jahne and Monahan reference]. Most of the current estimates of the air-sea flux of CO2 rely on gas exchange models that integrate sea surface concentration measurements with various surface-layer transport algorithms, and indirect estimates of the transfer velocity are then used to estimate the gas flux. These transport models have a number of limitations. Particular areas of concern are that (1) measurements of radon have been used as the basis for the previous global flux parameterizations, which may not be appropriate for all gases, and (2) the role of sea surface microlayer chemical and physical processes, such as wind speed and sea state variability, are not adequately described. The latter concern is essentially due to the time (one week to several months) required to obtain a statistically stable estimate of the gas transfer velocity, which averages out even the synoptic-scale variability of atmospheric forcing.

The quantification of carbon dioxide flux between the ocean and atmosphere is a controversial subject (Broecker et al., 1986). For example, indirect methods such as radiocarbon and radon methods provide transfer velocities ranging from 12-24 cm hr-1 (Broecker and Peng, 1982; Jähne, 1985; Peng et al., 1979). Most previous estimates of the transfer velocity using direct covariance flux estimates are an order of magnitude larger (Jones and Smith, 1977; Wesely et al., 1982; Smith and Jones, 1985). While these studies have provided a number of possible explanations for the observed differences, the actual cause for the discrepancies is ambiguous. One of the questions that remains to be adequately quantified is how certain environmental parameters influence the transfer velocity of CO2, which, in turn, determines the flux. Many wind-wave experiments have shown that the gas transfer velocity is a function of wind speed (Broecker et al., 1978, Jähne et al., 1980). However, the lack of sufficiently decisive data from field experiments has made relationships between wind speed and transfer velocity questionable when applied to the ocean. This is particularly true at high wind speeds which are rarely sustained long enough to match the measurement time required by the indirect methods. Therefore, large uncertainties in the estimation of air-sea CO2 flux at moderate to high wind speeds exist, particularly where the gas transfer is related to the wind speed nonlinearly [Wanninkhof 1982 ref?]. As a result of the uncertainty, different climate models employ various transfer velocity parameterizations, which ultimately produces a wide variety of long-term climate forecasts. The use of a single accurate parameterization for the CO2 transfer velocity in all climate models would remove one major source of ambiguity in long-term global climate forecasts.

The well-established direct covariance or eddy-correlation flux measurement technique is the standard for surface layer gas flux measurements as it is a direct computation of the covariance of the gas concentration with the air vertical velocity at the height of measurement. By definition, the kinematic vertical flux of a scalar species, , is , where and are the turbulent perturbations of the vertical wind component and of the gas concentration, respectively, and the overline denotes an average over a large ensemble of turbulent eddies. We can make estimates of the ensemble average to within some statistical sampling uncertainty with the appropriate selection of an averaging time interval (Mahrt et al. 1996; Lenschow et al. 1994).

This expression demonstrates the relative ease with which eddy correlation estimates of the turbulent fluxes can be made. It appears that all that is necessary to obtain the turbulent flux is the computation of the covariance. However, on a seagoing research vessel the measurement of the velocity components necessary to compute the covariance becomes difficult due to the motion of the platform. In the case of a ship, the anemometer is fixed with respect to the frame of the ship and moves with it, requiring significant correction to the measured vertical wind velocity component.

In addition, the eddy correlation technique has the potential to circumvent many of the problems associated with the indirect measurements of gas exchange because it is a measurement of the gas flux at the scale of the turbulent transport. Concerns related to the application of the direct covariance method at sea include 1) having adequate signal levels to compute the gas flux using currently available sensors and 2) the ability to adequately remove the motion contamination and minimize the effect of flow distortion around an ocean-going vessel. Below we report results from the use of the covariance technique to obtain direct air-sea CO2 flux measurements during a field experiment that was performed in the North Atlantic.

2. Field Experiment

2.1. Interdisciplinary Air-Sea Gas Exchange Experiment - GasEx98

In May and June of 1998, an interdisciplinary air-sea gas exchange experiment, named GasEx98, was performed in the North Atlantic at 46° 6 N and 20° 55 W aboard the NOAA SHIP RONALD H. BROWN. This cruise included a wide range of independent air-sea gas exchange measurement efforts, which were simultaneously conducted for comparison. Continuous measurements of the 10-m referenced wind speeds and the sea-air pCO2 are shown in Figures 1 and 2, respectively. These fundamental CO2 gas exchange forcing parameters are seen from the figures to vary on hourly time scales.

Besides the direct covariance measurements reported here, other estimates of air-sea gas exchange included deliberate-release dual-tracers (SF6 and the isotope 3He), dimethysulfide atmospheric gradients, CO2 atmospheric gradients, and eddy accumulation carbon dioxide measurements [REF? Wanninkhof?]. The deliberate tracer release also served as a water mass marker, as the overall experiment was conducted within a persistent warm-core eddy with a cool top. Drifting marker buoys were also deployed during the experiment to follow the movement of the eddy. Tagging water parcels with deliberate tracers and drifters is a near-essential prerequisite to perform a Lagrangian process study, as the tracers can clearly delineate a homogeneous water parcel. At later stages when the patch becomes more chemically heterogeneous, addition tracers such as salinity and pCO2 are necessary. Preliminary investigations of the rich data set indicate an extremely successful cruise that has yielded several important breakthroughs in our understanding of air-sea fluxes of CO2 and the biogeochemical cycles.

[Wade, I think this section definitely needs to be beefed up to describe more about the experiment. Can you plagarize something from Wanninkhof's web page. Just general descriptions of the purpose and participants. "There were x scientists from y different institutions, etc". Also, is Jon Ware's name to appear in the author's list? Has anyone given a thought to having Rik as a co-author as well?].

2.2 Wind speed and pCO2 measurements

In order to accomplish our objective of making direct measurements of carbon dioxide flux from a ship, a turbulent flux measurement system, which was designed at the Woods Hole Oceanographic Institution, was placed on the forward jackstaff mast of the Brown. Central to this turbulent flux system is an ultrasonic ('sonic') anemometer / thermometer and a platform motion measuring system described below. The sonic anemometer also provides the means as presented in Figure 1. Air-sea gas transfers rates are typically parameterized in terms of the mean wind speed and the gas concentration gradient across the interface. As Figure 1 shows, a number of atmospheric disturbances passed through the study area with wind speeds in excess of 8 m/s. The frequency of the systems was approximately every 4 days with sustained wind speeds greater than 10 m/s persisting from a few hours to more than a day. The frequency distribution of winds during this study, shown in Figure 2, is representative of the global wind speed distribution.

The air and water pCO2 measurements were obtained with two independent closed-path Non-
Dispersive Infrared Detector (NDIR) instruments, the Licor-xxx. The water-side carbon dioxide mean concentration measurements were obtained from the head space gas of an ocean gas [is this the proper name for the equilibrator?] equilibrator with one of the NDIR detectors. Equilibration of the sample gas with ocean water is done by continuously pumping and draining seawater into an enclosed chamber. Gas is pumped from the head space of the chamber, through a condenser that removes approximately 90% of the water vapor, through a Mg(Cl04)2 drier, and finally passed through the detector and back to the equilibrator. As with the air measurement, the output from the detector is corrected for the effects of pressure, temperature, and any remaining water vapor on the measured CO2 concentration. Mean air-side CO2 estimates were also obtained with an identical NDIR.

During this study most of the carbon and nutrient input into the mixed layer can be directly related to storm events. It appears that these episodic events, to large extent control the evolution of the spring blooms and pCO2 in the area. Figure 3 shows the ocean and air pCO2 difference measured during the course of the GasEx98 experiment. This time series shows a generally constant pCO2 concentration in the ocean within the algal bloom while the wind speed varied substantially (see Figure 1). The variability of the atmospheric carbon dioxide around the mean of 368 ppm resulted in pCO2 levels between 70 and 110 ppm. The average pCO2 during the course of the study period was -85.8 ± 16.0 ppm. The temporary decrease on yearday 156 and the more permanent decrease on yearday 158 were caused by high wind events entraining water masses from below the mixed layer. Overall, the pCO2 was relatively large for ocean surface waters, which greatly increased our ability to measure the flux. After yearday 166, the algal bloom decayed and the warm core eddy began to dissipate, and this fact is reflected in the reduced mean and increased variability of the pCO2. Generally, the pCO2 varies on time scales of less than a day, which further illustrates the necessity for direct measurements of the flux.

3. Direct Flux Methods

3.1 Direct covariance momentum flux estimates

On a seagoing research vessel, the measurement of the vertical wind velocity necessary to compute the covariance is complicated by the platform motion, and this motion contamination must be removed before the fluxes can be estimated. The error arises from three sources: (1) instantaneous tilt of the anemometer due to the pitch, roll, and heading variations of the ship; (2) angular velocities at the anemometer due to rotation of the ship about its local coordinate system axes; and (3) translational velocities of the ship with respect to a fixed frame of reference.

We have developed an integrated Direct Covariance Flux System (DCFS) capable of correcting the motion contamination, and this system is thoroughly described in Edson et al (1998). The heart of the DCFS is the Solent three-axis ultrasonic (often abbreviated as 'sonic') anemometer/thermometer and a Systron-Donner MotionPak system of three orthogonally-oriented angular rate sensors and accelerometers. The MotionPak is mounted directly beneath the sonic anemometer, which allows us to accurately align the MotionPAK and sonic axes in addition to insuring that the wind and motion measurements are collocated. The application of this 'strap-down' system allows us to express the true wind vector as:

where is the desired wind velocity vector in the Earth reference coordinate system; and are the measured wind and platform angular velocity vectors in the ship frame of reference, respectively; is the coordinate transformation matrix from the ship frame coordinate system to the reference coordinates; is the position vector of the wind sensor with respect to the motion package and is negligible for the strap-down configuration; and is the translational velocity vector measured at the location of the motion package. A more complete description of the ship motion correction can be found in Edson et al (1998).

The resulting corrected velocity components are then used to compute the covariance momentum, gas, sensible heat, and moisture fluxes. For example, the momentum flux is described by:

where is the air density, and are the streamwise horizontal and vertical wind velocity components, repectively, and the primes denote the turbulent departure from the mean. As flow distortion is a concern for measurements from ships, Edson et al. (1998) provide a comparison of the momentum fluxes computed on board a ship with those measured aboard the R/P FLIP.

Friction velocities were directly measured with eddy correlation during Gas-Ex98 as in

The bin-averaged friction velocities from GasEx98, corrected to neutral conditions, are shown in Figure 4. For comparison, also shown in the figure are friction velocities estimated from the TogaCoare Bulk Flux Algorithm (Fairall, et al 199x). For winds in excess of 5 m/s, there is very good agreement between the measurements and the model, while the model underestimates the friction velocities for the lighter wind conditions. This common observation is a result of statistical uncertainties of the covariances at low wind speeds.

3.2 Direct covariance CO2 flux measurements

The objective of our efforts during GasEx98 was to refine the direct covariance system to obtain accurate gas flux measurements from an ocean-going research vessel. In an analogous fashion to the discussion above, the covariance gas flux can be directly measured from the gas concentration, c, and the vertical velocity, , using the expression:

where the primes indicate fluctuations about the mean, the first term defines the turbulent flux and the second term accounts [for mesoscale transport?.....I'm not sure this is technically correct. Maybe re- read the Webb paper (which is short) to get an idea of how to phrase this. It may be simpler to simply say that it is caused by non-zero mean vertical flows caused by buoyant plumes, etc] caused by buoyant plumes where non-zero mean vertical flows transport gas (Webb et al., 1980). This mean vertical velocity is given by:

where T and q are the temperature and specific humidity, respectively.

In addition to the motion contamination, the accuracy of making direct CO2 flux measurements using the covariance method [I think we shouldn't mention flow distortion any further than what was mentioned in the previous section......and only refer the reader to Edson et al.] depends on the sensitivity of the CO2 analyzer to higher frequency fluctuations (to approximately 2 Hz). [Why 2 Hz?....is this mentioned because the Licor resolution is good to 2 Hz, or is it because very little covariance exists beyond 2Hz?. If it is the former, then it is poor reasoning. If it is meteorologically- based, then we should provide an expression.....F z / U < 2, for example, and a reference].

Atmospheric CO2 fluctuations are measured by pulling air samples through a closed cell NDIR which simultaneously measures water vapor concentration in the air sample. The air sample inlet was positioned adjacent to the ultrasonic anemometer probe volume, while the detector, filters, calibration and equilibration systems were by mounted on a scaffold 6 m below the sample area. Any water vapor effects were removed to calculate the mixing-ratio of CO2 using XXXXX [Insert description of the procedure for removing the H2O]. Infrared absorption by carbon dioxide is very sensitive to pressure and temperature, and therefore, in-line thermal and pressure equilibration was performed in the sampling system using XXXXXXX [Insert description of the pressure reservoir and the thermal equilibrator]. The carbon dioxide concentration, water vapor concentration, pressure and temperature were oversampled in the closed cell at 20 Hz, although the response of the CO2 detector is approximately 2 Hz.

[Wade, I've forgotten....did you estimate the flux by integrating the cospectrum, or did you compute the flux by the covariance? If it is the former, then we need to be clear about that.] [Also, there has been no description of the need to lag the velocity for the purpose of computing the correlation with the CO2 concentration. That should be inserted here. You should find ready-made descriptions in the literature.....for example, the Luning papers.] The directly-measured w'-c' cospectra, averaged in wind speed bins from the entire GasEx98 deployment are shown in Figure 5. As expected, the cospectral levels increase with increasing wind speed, but the overall shape of each cospectrum is similar. The cospectra show signs of wave-induced signal and noise at the wave frequencies (from x Hz to y Hz), which accounts for some of the uncertainty in our flux estimates. Experiments performed during the cruise indicate that this noise is primarily caused by changes in pressure due to the vertical displacement of the ship and by gyroscopic effects on the chopper within the CO2 sensor. [The wave-induced effects generally increase the correlation on one side of the wave band and suppress the correlation on the other...........Explain or omit].

Generally, over-ocean CO2 fluctuation are relatively small, and therefore, quantification of the signal to noise is critical. The signal strength is a function of the air-sea concentration difference and the wind speed. The noise in our system is due to a number of factors such as pressure fluctuations due to pump noise, height changes of the sample intake, and by gyroscopic effects on the rotating component (chopper wheel) in the NDIR detector caused by wave-induced motions. We reduce the noise in our CO2 flux system by: 1) regular automated calibration of the system using 0.005 ppm accurate gas standards; 2) reduction of the vacuum pump pressure noise fluctuations with a flow reservoir; 3) incorporating an in-line heat exchanger to minimize temperature fluctuations, 4) implementing flow controllers to insure constant intake-to-sample lags; and 5) performing simultaneous data acquisition of all relevant meteorological flux parameters.

To estimate the uncertainty of our flux estimates due to naturally occurring variability (?) and (?) remaining system noise we used the data collected during this experiment. [Data collected over the course of GasEx98 were used to estimate the combined uncertainty of the measured fluxes due to systematic and random error sources.]. This uncertainty was estimated by computing the standard deviation of the CO2 fluxes divided by their mean within a specified range of wind speeds. We find that the uncertainty varies between 40% at high wind speeds to 80% at low wind speeds [Is there a figure to demonstrate this?]. The decrease in uncertainty with increasing wind speed is due to the increase in the signal-to-noise ratio at higher wind speeds, and this uncertainty is similar to the that found in direct estimates of the sensible and latent heat fluxes [Is there a figure or reference to demonstrate this?]

Null test for CO2 flux

Constant calibration of the closed-path NDIR was performed throughout the duration of the cruise by flushing CO2-free gas through the analyzer for X-minutes every four hours. This provides a continual operational measure of the noise caused by inherent detector noise and the ship induced noise. Toward this end, we computed the covariance of zero-gas with the motion-corrected vertical velocity fluctuations from every calibration period using [the last?] 3 minutes of the calibration time series, which insures that the zero-gas has equilibrated in the system and that the computed covariance includes many wave periods. Prior to the computation of the covariance, the detector signal is lagged exactly as is done during measurement periods. [We need to mention the need for the lag earlier in the paper].

Some of the results of the motion-error covariance spectra are shown in Figure 6 which demonstrates conclusively that the wave induced noise produces a systematic positive offset that actually slightly increases [makes more negative or less negative?] our mostly negative fluxes. A conservative estimate of the accumulated noise threshold is calculated to be less than 2 mol/m2/yr for the direct flux technique. The offset appears to be a weak function of wind speed (shown in Figure 6 inset), and its magnitude matches the discrepancy in the flux we found at very low wind speeds. That is, at zero wind speed, the flux goes to zero (the null test [Is this wording 'null test' included for Tann's benefit? Maybe we should leave it out?.]), and there is a slightly enhanced flux at high wind speeds after correction of the cospectrum (covariance?) for the platform motion noise.

4. Results and Discussion

Continuous covariance measurements of the flux of CO2 flux were performed for XX days during GasEx98. A timeseries of the cospectral (covariance) estimates of the CO2 flux (ten-minute, half hour?), corrected for the accumulated [why accumulated noise?] noise measured during the null tests, is shown in Figure 7. During the periods of high winds, CO2 fluxes into the ocean often exceeded 7 mol m2 yr-1, and the variability of the flux occurs on time scales of less than a day [hours?].

Bulk estimates of the gas fluxes rely on the air-sea concentration difference, and the flux of CO2 is usually expressed using the gas transfer velocity (Wanninkhof, 1992):

where is the transfer velocity of CO2 [is it pCO2?], s is the solubility of carbon dioxide in seawater, and CO2 is the difference between the concentration in the water and the concentration in the air. Gas transfer velocities are commonly normalized to a value corresponding to a nominal Schmidt number, Sc=600, through the following relationship which accounts for differences in gas diffusivity:

This transfer velocity is computed by dividing the covariance flux estimates by the air-sea CO2 concentration difference using Equation X. Wind speed bin averages of the transfer velocity versus the 10-m windspeed corrected to neutral conditions is shown in Figure 8. For comparison, also shown on Figure 8 are the relationships of Wanninkhof (1992) and Liss-Merlivat (1986), referred hereafter to W-92 and LM-86, respectively. The W-92 gas transfer velocity has a quadratic relationship with wind speed that satisfies the global isotopic constraint, while the LM-86 relationship is based on laboratory and lake gas exchange experiments. In neither relationship are direct gas flux field measurements considered. At the lower wind speeds, the GasEx98 results show a finite positive transfer velocity. Although these small fluxes are within the uncertainty of our measurements, we have sufficiently quantified the instrument noise levels to have confidence that these mean transfer velocities are valid.

At wind speeds greater than 11 ms-1, the measured transfer velocities are higher than either the W-92 or LM-86 parameterizations. Fairall et al. (1999) discusses the physical processes that may contribute to the enhanced exchange, mostly owing to breaking waves and bubble mediation. The line fit through the measurements in Figure 8 describes a cubic dependence on wind speed and passes through zero at zero wind speed. This relationship is important because it satisfies the global isotopic constraint, believed to be a necessary condition for air-sea gas exchange.

It is obvious that wind speed is not the only parameter controlling the gas flux. A relationship that provides a better representation of the physical processes controlling the aqueous mass boundary layer is given in terms of the friction velocity by:

where the variable [beta needs a better definition] is dependent on the hydrodynamic regime. The exponent, n, describes the Schmidt number dependence of the surface rheology over a hydrodynamic regime and varies from 0.67 to 0.4. The relationship between the CO2 transfer velocity and friction velocity is shown in Figure 9. Similar to the gas transfer velocity, the directly-measured friction velocity is dependent on sea state and surface conditions. For this reason, it is regarded as a much better parameterization than Equation XXX [?]. Unfortunately, it relies on measurements and models of the surface stress?????. Also shown for comparison is the wind speed parameterizations converted to u* using a neutral drag coefficient. [I don't understand what is happening with these last 2 sentences]

The most convenient method for estimating gas fluxes from meteorological data is with the wind speed. The Dalton number, defined here by k600/u10 is shown in Figure 10 and is consistent with the trends from laboratory measurements. A minimum is observed near a 3 m/s wind speed, while for winds in excess of 3 m/s, the Dalton number monotonically increases. This indicates that the processes controlling CO2 transfer, namely the aqueous boundary layer, are not the same as the process controlling the water vapor flux (?) [How so?].

A bulk expression for the air-sea carbon dioxide flux can also be given in terms of the air-sea concentration difference and the friction velocity:

where is the CO2 transfer coefficient . If the friction velocity is not measured directly, the bulk model for the CO2 flux is given as:

where CD is the air-sea drag coefficient and U is the difference between the 10-m reference wind speed and the surface current. In Figure 11, the CO2 transfer coefficient is plotted versus the roughness Reynolds number defined as:

where zo is the Charnock roughness height, and a is the kinematic viscosity of air. As shown in the figure, the transfer coefficient for CO2 measured during Gas-Ex98 continuously increases with wind speed (or does it increase with Re?). Shown for comparison are the laboratory results for a clean surface, laboratory results with surface films, and the W-92 relationship. At small Re*, the field data lie between the clean and surface films results, but at higher Reynolds numbers, both the W-92 relationship and the field data increase above the laboratory results. This is attributed to the fact that the laboratory measurements do not provide the scales representative of the open ocean environment. At even greater wind speeds (or is it Re?), the GasEx98 field data increase faster than W-92.

5. Conclusions

The environmental conditions during Gas-Ex98 were optimal for accurate direct covariance CO2 flux measurements. A relatively large delta pCO2 was maintained within the study area, and relatively low humidity fluxes reduced the dilution effect errors [we need to define dilution effect earlier in the paper]. For the first time, covariance measurements of CO2 transfer velocity are in general agreement with the isotopic and indirect methods results. In particular, there is very good agreement for wind speeds less than 11 m s-1. At higher wind speeds, however, the direct measurements of gas transfer velocity increase above those reported by LM-86 and W-92. The cubic wind speed dependence may be due, in part, to the presence of breaking waves. [Future estimates of the transfer coefficient may remove some of the variability of wave state and atmospheric stability......... ?? needs to be stated more clearly]. The direct measurement and bulk estimates of friction velocity agree very well suggesting that robust large scale extrapolation of CO2 fluxes may be feasible using u* [If this is true, it needs to be mentioned in the text].

Acknowledgments

This work was supported by the National Science Foundation Grant OCE-9711218 and the NOAA OACES program.

References

Broecker, H. C., J. Petermann, W. Siems, The influence of wind on CO2-exchange in wind/wave

tunnel, including the effects of monolayers, Journal of Marine Research, 36, 595-610, 1978.

Broecker, W. S., and T.-H. Peng, Tracers in the Sea, Eldigo Press, Palisades, New York, 1982.

Broecker, W. S., J. R. Ledwell, T. Takahashi, R. Weiss, L. Merlivat, L. Memery, T. H Peng, B. Jähne, and K. O. Munnich, Isotopic versus micrometeorologic ocean CO2 fluxes: A serious conflict, Journal of Geophysical Research, 91, 10517-10527, 1986.

Edson, J. B., A. A. Hinton, K. E. Prada, J. E. Hare and C. W. Fairall, Direct covariance flux estimates from mobile platforms at sea, , J. Atmos. Oceanic Tech., 15, 1998.

Fairall, C. W., E. F. Bradley, D. P. Rogers, J. B. Edson, and G. S. Young, Bulk parameterization of air-sea fluxes for TOGA COARE. J. Geophys. Res., 101, 3747-3764, 1996.

Hare, J. E., J. B. Edson, E. J. Bock, and C. W. Fairall, Progress on direct covariance measurements of air-sea fluxes from ships and buoys. Tenth Symp. Turbulence and Diffusion, Portland, OR, Amer. Meteor. Soc., 281-284,1992.

Jähne, B., On transfer processes at a free air-water interface, Habilitation thesis, Faculty of Physics and Astronomy, University of Heidelberg, 1985.

Jones, E. P., and S. D. Smith, A first measurement of sea-air CO2 flux by eddy correlation, Journal of Geophysical Research, 82, 5990-5992, 1977.

Liss, P. S., and L. Merlivat, Air-sea gas exchange rates: Introduction and synthesis, in The Role of Air-
Sea Exchange in Geochemical Cycling, edited by P. Buat-Ménard, pp. 113-129, D. Reidel, Hingham, Mass., 1986.

Liss, P. S., and P. G. Slater, Flux of Gases across the Air-Sea Interface, Nature, 247, 181-184, 1974.

Peng, T.-H., W. S. Broecker, G. G. Mathieu, Y. H. Li, and A. E. Bainbridge, Radon evasion rates in the Atlantic and Pacific oceans as determined during the GEOSECS program, Journal of Geophysical Research, 84, 2471-2486, 1979.

Smith, S. D., and E. P. Jones, Evidence for wind-pumping of air-sea exchange based on direct measurements of CO2 fluxes, Journal of Geophysical Research, 90, 869-875, 1985.

Wanninkhof, R, Relationship between gas exchange and wind speed over the ocean. J. Geophys. Res., 97, 7373-7381, 1992.

Webb, E. K., G. I. Pearman, and R. Leuning, Correction of flux measurements for density effects due to heat and water vapor transport, Q. J. R. Meteorol. Soc., 106, 85-100, 1980.

Wesely, M. L., D. R. Cook, R. L. Hart, and R. M. Williams, Air-sea exchange of CO2 and evidence for enhanced upward fluxes, Journal of Geophysical Research, 87, 8827-8832, 1982.

1. Wanninkhof, R. J. Geophys. Res. 97, 7373-7381, (1992).

2. Watson, A.J., Upstill-Goddard, R.C. and Liss, P.S. Nature. 349, 145-147, (1991).

3. Liss, P.S. and Merlivat, L. The Role of Air-Sea Exchange in Geochemical Cycling. Edited by

P. Buat-Ménard, pp. 113-129, D. Reidel, Hingham, Mass. (1986).

4. Erickson, D.J., J. Geophys. Res. 98, 8471-8488, (1993).

5. Siegenthaler, U. and Sarmiento, J.L. Nature 365: 119-125 (1993).

6. Broecker, W.S. et al. J. Geophys. Res. 91, 10517-10527, (1986).

7. Broecker, W.S. and Peng, T.-H., Tracers in the sea. Eldigo Press, Palisades, NY, (1982).

8. Jähne, B., Habilitation thesis, Faculty of Phys. and Astronomy, U. Heidelberg, (1985).

9. Peng, T.-H. et al. J. Geophys. Res. 84, 2471-2486, (1979).

10. Jones, E.P. and Smith, S. D. J. Geophys. Res. 82, 5990-5992, (1977).

11. Wesely, M. L. et al. J. Geophys. Res. 87, 8827-8832, (1982).

12. Smith, S.D., and Jones, E.P., J. Geophys. Res. 90, 869-875, (1985).

13. Broecker, H.C., Petermann, J. and Siems, W., J. Mar. Res. 36, 595-610, (1978).

14. Jähne, B. et al. J. Geophys. Res. 92, 1937-1949 (1987).

15. Webb, E. K. et al. Q.J.R., Meteorol. Soc. 106, 85-100, (1980).

16. Fairall, C.W. et al. J. Geophys. Res. 101, 3747-3764, (1996).

17. Edson, J.B. et al. J. Atmos. Oceanic Tech. 15, 547-562, (1998).

18. Edson, J.B. et al. J. Geophys. Res. 96, 10689-10711, (1991).

19. Monahan, E.C., Natural Physical Sources of Underwater Sound, edited by B. R. Kerman,

503-517, Kluwer, Dordrecht, The Netherlands, (1993).

List of Figures

Figure 1: Time series of 10-meter windspeed during Gas-Ex98 in the N. Atlantic measured using an ultrasonic anemometer corrected from ship motion.The sonic was mounted on the forward bow-mast of the NOAA SHIP Ronald H. Brown. Wind speeds varied on hour scales and over a wide range with gusts up to 17 m/s. The study site location was approximately 46° 6 N and 20° 55 W in a warm core eddy.

Figure 2: The distribution of winds during the Gas-Ex98 experiment. The global wind speed distribution is shown for comparison.

Figure 3: Time series of atmospheric-ocean pCO2 differences, pCO2, during Gas-Ex98. This study site was chosen on the basis of the strength and constancy of the pCO2 signal. The large signal was desired for increased CO2 flux measurement accuracy. Initially, an algal bloom continued to increase the pCO2 . The temporary decrease on yearday 156 and the more permanent decrease on yearday 158 were caused by high wind events entraining water masses from below the mixed layer. Overall, the pCO2 was large for ocean surface waters. After yearday 166 the pCO2 decreased and greater variability was observed. These features are present because the peak of the algal bloom was over and the warm core eddy was eroding. Note the pCO2 varies on times scales less than a day.

Figure 4: Average atmospheric friction velocity versus 10-m windspeed corrected to neutral conditions. The solid line is the prediction using neutral drag coefficients.

Figure 5: Cospectra of vertical wind fluctuations and atmospheric CO2 fluctuations measured during Gas-
Ex98 with the direct CO2 flux system. Ensemble spectral densities for wind range 3-6 ms-1 (squares), 6-9 ms-1 (circles), and 9-12 ms-1 (X), and 12-15 ms-1 (diamonds).

Figure 6: Cospectra of vertical wind fluctuations and zero reference gas measured during Gas-Ex98 calibration intervals. Ensemble spectral densities are shown for windspeeds less than 6 ms-1 (dotted line), 6-10 ms-1 (dashed line), and 10-15 ms-1 (solid line). The NDIR optical chopper wheel is affected by rotational accelerations and the predominant noise occurs at the frequency of ship motion caused by waves. (Inset) Averaged noise versus windspeed.

Figure 7: Time series of air-sea CO2 flux. Fluxes have been corrected for detector noise.

Figure 8: Direct covariance CO2 air-sea transfer velocities corrected to Sc=600 versus 10-m windspeed during Gas-Ex98. Transfer velocities based on 14C budgets using a quadratic windspeed dependence (dotted line), a cubic windspeed dependence (solid line), and based on lake and wind-tunnel experiments (dashed line) are shown for comparison.

Figure 9: Gas-Ex98 transfer velocities from direct covariance versus friction velocity. W-92 and LM-86 windspeed relationships are shown for comparison. Referenced windspeeds are converted to friction velocities using neutral drag coefficients.

Figure 10: Dalton number for CO2 transfer versus 10-m windspeed.

Figure 11: CO2 transfer coefficient CCO2 versus roughness Reynolds number, Re*. The W-92 (solild line), laboratory clean results (dashed line), and laboratory results with surfactants (dotted line) are shown for comparison.