On the parameters influencing air‐water gas exchange

Jähne, Bernd; Münnich, K. O.; Bösinger, Rainer; Dutzi, Alfred; Huber, W. C.; Libner, P.

doi:10.1029/jc092ic02p01937

Cited by 655 publications

(569 citation statements)

References 33 publications

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“…We show the comparison with the iWLP implemented with the kwind formulation by Jähne et al [66] and the kbubble formulation by Woolf [44] using the kinematic viscosity of air (the W05va). Their kw estimates diverged mostly under unstable SBL, very rough or very smooth sea-surfaces, or higher friction velocities ( Figure 6).…”

Section: Transfer Velocity Estimates From Level 4 Datamentioning

confidence: 99%

“…The k wind , taken from Jähne et al [66], related to the transfer mediated by the turbulence generated by wind drag (Equation (13)). The k bubble = 850 W related to the transfer mediated by the bubbles generated by breaking waves.…”

Section: Transfer Velocitymentioning

confidence: 99%

“…The preponderance of surface renewal and micro-scale wave breaking at the coastal ocean turn it more susceptible to additional factors driving k w . As an example, under the low-moderate wind speeds often verified at the coastal ocean, the hydration reaction enhances k w [19] and surfactants attenuate surface renewal [28,58,59,65,66]. [20] to compilation by Sarmiento and Gruber [2].…”

Section: Transfer Velocity Estimates From Field Datamentioning

confidence: 99%

“…For instance, while surfactants increase surface tension, although decreasing the water surface renewal and micro-scale wave breaking [28,65], there is no report of them affecting whitecap from breaking waves. Surfactants are expected to be more relevant on the coastal ocean due to their supply from land and their effect being more pronounced under low-moderate winds [60,66].…”

Section: Introductionmentioning

confidence: 99%

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The FuGas 2.3 Framework for Atmosphere–Ocean Coupling: Comparing Algorithms for the Estimation of Solubilities and Gas Fluxes

et al. 2018

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Accurate estimates of the atmosphere-ocean fluxes of greenhouse gases and dimethyl sulphide (DMS) have great importance in climate change models. A significant part of these fluxes occur at the coastal ocean which, although much smaller than the open ocean, have more heterogeneous conditions. Hence, Earth System Modelling (ESM) requires representing the oceans at finer resolutions which, in turn, requires better descriptions of the chemical, physical and biological processes. The standard formulations for the solubilities and gas transfer velocities across air-water surfaces are 36 and 24 years old, and new alternatives have emerged. We have developed a framework combining the related geophysical processes and choosing from alternative formulations with different degrees of complexity. The framework was tested with fine resolution data from the European coastal ocean. Although the benchmark and alternative solubility formulations generally agreed well, their minor divergences yielded differences of up to 5.8% for CH 4 dissolved at the ocean surface. The transfer velocities differ strongly (often more than 100%), a consequence of the benchmark empirical wind-based formulation disregarding significant factors that were included in the alternatives. We conclude that ESM requires more comprehensive simulations of atmosphere-ocean interactions, and that further calibration and validation is needed for the formulations to be able to reproduce it. We propose this framework as a basis to update with formulations for processes specific to the air-water boundary, such as the presence of surfactants, rain, the hydration reaction or biological activity.

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Section: Transfer Velocity Estimates From Level 4 Datamentioning

confidence: 99%

Section: Transfer Velocitymentioning

confidence: 99%

Section: Transfer Velocity Estimates From Field Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The FuGas 2.3 Framework for Atmosphere–Ocean Coupling: Comparing Algorithms for the Estimation of Solubilities and Gas Fluxes

et al. 2018

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“…This di$culty arises because gas transfer is a function not only of wind speed but turbulence at the air}water interface, boundary layer stability, temperature, the presence of surfactants, and surface conditions such as breaking waves and bubble injection (Jahne et al, 1987;Wanninkhof, 1992;Erickson, 1987). Some processes such as wave breaking, which might indicate strong turbulence, may actually inhibit certain mechanisms that control air-water gas exchange (Erickson, 1987).…”

Section: Introductionmentioning

confidence: 99%

Mass transfer coefficients for polycyclic aromatic hydrocarbons (PAHs) to the water surface sampler:

Odabaşı

Sofuoğlu

Holsen

2001

Atmospheric Environment

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On the coefficients of small eddy and surface divergence models for the air‐water gas transfer velocity

et al. 2015

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Recent studies suggested that under low to moderate wind conditions without bubble entraining wave breaking, the air-water gas transfer velocity k 1 can be mechanistically parameterized by the nearsurface turbulence, following the small eddy model (SEM). Field measurements have supported this model in a variety of environmental forcing systems. Alternatively, surface divergence model (SDM) has also been shown to predict the gas transfer velocity across the air-water interface in laboratory settings. However, the empirically determined model coefficients (a in SEM and c 1 in SDM) scattered over a wide range. Here we present the first field measurement of the near-surface turbulence with a novel floating PIV system on Lake Michigan, which allows us to evaluate the SEM and SDM in situ in the natural environment. k 1 was derived from the CO 2 flux that was measured simultaneously with a floating gas chamber. Measured results indicate that a and c 1 are not universal constants. Regression analysis showed that a $ logðeÞ while the near-surface turbulence dissipation rate e is approximately greater than 10 26 m 2 s 23 according to data measured for this study as well as from other published results measured in similar environments or in laboratory settings. It also showed that a scales linearly with the turbulent Reynolds number. Similarly, coefficient c 1 in the SDM was found to linearly scale with the Reynolds number. These findings suggest that larger eddies are also important parameters, and the dissipation rate in the SEM or the surface divergence b 0 in the SDM alone may not be adequate to determine k 1 completely.

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On the parameters influencing air‐water gas exchange

Cited by 655 publications

References 33 publications

The FuGas 2.3 Framework for Atmosphere–Ocean Coupling: Comparing Algorithms for the Estimation of Solubilities and Gas Fluxes

The FuGas 2.3 Framework for Atmosphere–Ocean Coupling: Comparing Algorithms for the Estimation of Solubilities and Gas Fluxes

Mass transfer coefficients for polycyclic aromatic hydrocarbons (PAHs) to the water surface sampler:

On the coefficients of small eddy and surface divergence models for the air‐water gas transfer velocity

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