Gas Exchange Rates in the Parker River Estuary, Massachusetts

Carini, Stephen A.; Weston, Nathaniel B.; Hopkinson, Charles S.; Tucker, James P.; Giblin, Anne E.; Vallino, Joseph J.

doi:10.1086/bblv191n2p333

Cited by 57 publications

(71 citation statements)

References 3 publications

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“…Atmosphere 2018, 9, x FOR PEER REVIEW 11 of 18 et al [30] parameterization for the Parker river estuary, or similar to that estimated by the Wanninkhof [29] parameterization for the global ocean. Consequently, the root mean square deviation (RMSD) between the Woolf [44] formulation (henceforth 'W05va') and the E-C transfer velocities was 6.34 cm•h −1 , whereas between the Wan92 and the E-C transfer velocities it was 8.4 cm•h −1 .…”

Section: Transfer Velocity Estimates From Field Datasupporting

confidence: 56%

“…On the other hand, the physically based algorithms, by accounting for the effects of sea state, breaking waves and atmospheric stability, showed a greater adaptability to local conditions ( Figure 5). As an example, depending on the sea state, the formulations by Woolf [44] or Zhao and Xie [47] estimated k w similar to that estimated by the Carini et al [30] parameterization for the Parker river estuary, or similar to that estimated by the Wanninkhof [29] parameterization for the global ocean. Consequently, the root mean square deviation (RMSD) between the Woolf [44] formulation (henceforth 'W05va') and the E-C transfer velocities was 6.34 cm·h −1 , whereas between the Wan92 and the E-C transfer velocities it was 8.4 cm·h −1 .…”

Section: Transfer Velocity Estimates From Field Datamentioning

confidence: 62%

“…For sparingly soluble gases or for CO 2 in alkaline waters, and under low winds, the transfer velocity is enhanced by the hydration reaction (b T ), which is sensitive to temperature. Other simple empirical formulations based only on u 10 [30,31], or also accounting for bottom-generated turbulence [32], used data collected in estuaries under low wind conditions. However, modelling the coastal ocean at finer resolutions requires an enhanced representation of the multitude of processes involved.…”

Section: Transfer Velocitymentioning

confidence: 99%

“…Concomitantly, there emerged empirical formulae best fitting low wind data collected from estuaries. Such are the formulations proposed by Carini et al [30] or Raymond and Cole [31], accounting only for u 10 , or by Borges et al [32], adding current drag with the bottom. Meanwhile, new evidence emerged for the effects of the sea surface cool-skin and warm-layer [33][34][35][36][37][38], atmospheric stability [39,40], wave-breaking [41][42][43][44][45][46][47][48][49][50], water surface renewal [28,[51][52][53][54][55][56][57], surfactants [28,[58][59][60] and rain [61][62][63][64], just to mention a few examples from an extensive list of published works.…”

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 Field Datasupporting

confidence: 56%

Section: Transfer Velocity Estimates From Field Datamentioning

confidence: 62%

Section: Transfer Velocitymentioning

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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“…The term [CO 2 ] water Ϫ [CO 2 ] air is the concentration gradient (mmol m Ϫ3 ) between the water and the air. In order to estimate k, we modeled k as a function of wind speed using data from two estuarine studies that measured directly k and the wind speed in tidal systems (Clark et al 1994, Carini et al 1996. Both studies modeled k as a function of wind speed and reported values of k for a gas with a Schmidt number of 600 (k 600 ), which is the Schmidt number for CO 2 at 20ЊC.…”

Section: Methodsmentioning

confidence: 99%

Atmospheric CO₂ evasion, dissolved inorganic carbon production, and net heterotrophy in the York River estuary

Raymond

Bauer

Cole

2000

Limnology & Oceanography

262

182

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Direct measurements of the partial pressure of CO 2 (pCO 2 ) and dissolved inorganic carbon (DIC) were made over a 2-yr period in surface waters of the York River estuary in Virginia. The pCO 2 in surface waters exceeded that in the overlying atmosphere, indicating that the estuary was a net source of CO 2 to the atmosphere at most times and locations. Salinity-based DIC mixing curves indicate there was also an internal source of both DIC and alkalinity, implying net alkalinity generation within the estuary. The DIC and alkalinity source displayed seasonal patterns similar to that of pCO 2 and were reproducible over a 2-yr study period.We propose that the source of inorganic carbon necessary for both the sustained CO 2 evasion to the atmosphere and the advective export of DIC is respiration in excess of primary production (e.g., net heterotrophy). The rates of CO 2 evasion and DIC export were estimated to provide an annual rate of net heterotrophy of ϳ100 g C m Ϫ2 yrϪ1 . Approximately 40% of this excess inorganic carbon production was exported as DIC to the coastal ocean, whereas 60% was lost as CO 2 evasion to the atmosphere. The alkalinity generation needed to sustain the export of inorganic carbon, as HCO 3 Ϫ , is most likely provided by net sulfate reduction in sediments. Accumulation of sulfide in the sediments of a representative site directly adjacent to the York River estuary is sufficient to account for the net export of alkalinity. The seasonality of net heterotrophy causes large variations in annual CO 2 and DIC concentrations, and it stresses the need for comprehensive temporal data sets when reporting annual rates of CO 2 evasion, DIC advection, and net heterotrophy.

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Air‐water gas exchange and CO₂ flux in a mangrove‐dominated estuary

Ferrón

Engel

et al. 2014

Geophysical Research Letters

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[1] Mangrove forests are highly productive ecosystems, but the fate of mangrove-derived carbon remains uncertain. Part of that uncertainty stems from the fact that gas transfer velocities in mangrove-surrounded waters are not well determined, leading to uncertainty in air-water CO 2 fluxes. Two SF 6 tracer release experiments were conducted to determine gas transfer velocities (k(600) = 8.3 ± 0.4 and 8.1 ± 0.6 cm h À1 ), along with simultaneous measurements of pCO 2 to determine the air-water CO 2 fluxes from Shark River, Florida (232.11 ± 23.69 and 171.13 ± 20.28 mmol C m À2 d À1), an estuary within the largest contiguous mangrove forest in North America. The gas transfer velocity results are consistent with turbulent kinetic energy dissipation measurements, indicating a higher rate of turbulence and gas exchange than predicted by commonly used wind speed/gas exchange parameterizations. The results have important implications for carbon fluxes in mangrove ecosystems.

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Gas Exchange Rates in the Parker River Estuary, Massachusetts

Cited by 57 publications

References 3 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

Atmospheric CO₂ evasion, dissolved inorganic carbon production, and net heterotrophy in the York River estuary

Air‐water gas exchange and CO₂ flux in a mangrove‐dominated estuary

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Gas Exchange Rates in the Parker River Estuary, Massachusetts

Cited by 57 publications

References 3 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

Atmospheric CO2 evasion, dissolved inorganic carbon production, and net heterotrophy in the York River estuary

Air‐water gas exchange and CO2 flux in a mangrove‐dominated estuary

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Atmospheric CO₂ evasion, dissolved inorganic carbon production, and net heterotrophy in the York River estuary

Air‐water gas exchange and CO₂ flux in a mangrove‐dominated estuary