Gas Transfer Velocity Parameterizations Research Articles

Air–sea CO2 exchange in the Baltic Sea—A sensitivity analysis of the gas transfer velocity

Air–sea gas fluxes are commonly estimated using wind-based parametrizations of the gas transfer velocity. However, neglecting gas exchange forcing mechanisms – other than wind speed – may lead to large uncertainties in the flux estimates and the carbon budgets, in particular, in heterogeneous environments such as marginal seas and coastal areas. In this study we investigated the impact of including relevant processes to the air–sea CO 2 flux parametrization for the Baltic Sea. We used six parametrizations of the gas transfer velocity to evaluate the effect of precipitation, water-side convection, and surfactants on the net CO 2 flux at regional and sub-regional scale. The differences both in the mean CO 2 fluxes and the integrated net fluxes were small between the different cases. However, the implications on the seasonal variability were shown to be significant. The inter-annual and spatial variability were also found to be associated with the forcing mechanisms evaluated in the study. In addition to wind, water-side convection was the most relevant parameter controlling the air–sea gas exchange at seasonal and inter-annual scales. The effect of precipitation and surfactants seemed negligible in terms of the inter-annual variability. The effect of water-side convection and surfactants resulted in a reduction of the downward fluxes, while precipitation was the only parameter that resulted in an enhancement of the net uptake in the Baltic Sea. • Variability of air–sea CO 2 fluxes is modulated by mechanisms other than wind speed. • Gas transfer velocity parameterizations are not adequate for coastal/marginal seas. • Water-side convection enhances air–sea fluxes during winter in the Baltic Sea. • Surfactants suppress CO 2 downward fluxes during summer. • Precipitation represents a net increase of the carbon uptake.

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Abstract. Marine dimethyl sulfide (DMS) is important to climate due to the ability of DMS to alter Earth's radiation budget. Knowledge of the global-scale distribution, seasonal variability, and sea-to-air flux of DMS is needed in order to improve understanding of atmospheric sulfur, aerosol/cloud dynamics, and albedo. Here we examine the use of an artificial neural network (ANN) to extrapolate available DMS measurements to the global ocean and produce a global climatology with monthly temporal resolution. A global database of 82 996 ship-based DMS measurements in surface waters was used along with a suite of environmental parameters consisting of latitude–longitude coordinates, time of day, time of year, solar radiation, mixed layer depth, sea surface temperature, salinity, nitrate, phosphate, and silicate. Linear regressions of DMS against the environmental parameters show that on a global-scale mixed layer depth and solar radiation are the strongest predictors of DMS. These parameters capture ∼9 % and ∼7 % of the raw DMS data variance, respectively. Multilinear regression can capture more of the raw data variance (∼39 %) but strongly underestimates DMS in high-concentration regions. In contrast, the artificial neural network captures ∼66 % of the raw data variance in our database. Like prior climatologies our results show a strong seasonal cycle in surface ocean DMS with the highest concentrations and sea-to-air fluxes in the high-latitude summertime oceans. We estimate a lower global sea-to-air DMS flux (20.12±0.43 Tg S yr−1) than the prior estimate based on a map interpolation method when the same gas transfer velocity parameterization is used. Our sensitivity test results show that DMS concentration does not change unidirectionally with each of the environmental parameters, which emphasizes the interactions among these parameters. The ANN model suggests that the flux of DMS from the ocean to the atmosphere will increase with global warming. Given that larger DMS fluxes induce greater cloud albedo, this corresponds to a negative climate feedback.

Inferring Air‐Sea Carbon Dioxide Transfer Velocities From Sea Surface Scatterometer Measurements

AbstractC‐band microwave sea surface radar backscatter observations from the FINO‐2 tower in the western Baltic are analyzed with respect to their relevance for air‐sea CO2 transfer velocity parameterizations. The scatterometer measurements observed from a height of 25 m above the sea surface using a multifrequency scatterometer instrument of the University of Hamburg were obtained quasi‐simultaneously with eddy covariance CO2 flux measurements. Both data sets are merged here to derive a gas transfer velocity parameterization based on radar backscatter measurements. At the location of the FINO‐2 tower, the resulting time‐averaged gas transfer velocity amounts to 26.95 cm/hr. In combination with measurements available from the vicinity of the FINO‐2 platform, a time‐mean CO2 flux of 0.23 μmol·m−2·s−1 into the Baltic was estimated. Applied to monthly mean satellite‐based C‐band ASCAT scatterometer data, the newly derived gas transfer velocity parameterization provides estimates of seasonal and annual mean global maps of air‐sea transfer velocities. The new results agree in their general pattern with previous estimates using wind speed parameterization. However, the backscatter‐based transfer velocities appear smaller at higher latitudes. Globally averaged air‐sea CO2 fluxes would thereby be reduced by 20%. To what extent this is a robust result, or if it depends on the fact that the training data set did not represent conditions, has to be investigated in the future.

Impact of Nonzero Intercept Gas Transfer Velocity Parameterizations on Global and Regional Ocean–Atmosphere CO2 Fluxes

Carbon dioxide (CO2) fluxes between the ocean and atmosphere (FCO2) are commonly computed from differences between their partial pressures of CO2 (ΔpCO2) and the gas transfer velocity (k). Commonly used wind-based parameterizations for k imply a zero intercept, although in situ field data below 4 m s−1 are scarce. Considering a global average wind speed over the ocean of 6.6 m s−1, a nonzero intercept might have a significant impact on global FCO2. Here, we present a database of 245 in situ measurements of k obtained with the floating chamber technique (Sniffle), 190 of which have wind speeds lower than 4 m s−1. A quadratic parameterization with wind speed and a nonzero intercept resulted in the best fit for k. We further tested FCO2 calculated with a different parameterization with a complementary pCO2 observation-based product. Furthermore, we ran a simulation in a well-tested ocean model of intermediate complexity to test the implications of different gas transfer velocity parameterizations for the natural carbon cycle. The global ocean observation-based analysis suggests that ignoring a nonzero intercept results in an ocean-sink increase of 0.73 Gt C yr−1. This corresponds to a 28% higher uptake of CO2 compared with the flux calculated from a parameterization with a nonzero intercept. The differences in FCO2 were higher in the case of low wind conditions and large ΔpCO2 between the ocean and atmosphere. Such conditions occur frequently in the Tropics.

Insights from year-long measurements of air–water CH<sub>4</sub> and CO<sub>2</sub> exchange in a coastal environment

Abstract. Air–water CH4 and CO2 fluxes were directly measured using the eddy covariance technique at the Penlee Point Atmospheric Observatory on the southwest coast of the United Kingdom from September 2015 to August 2016. The high-frequency, year-long measurements provide unprecedented detail on the variability of these greenhouse gas fluxes from seasonal to diurnal and to semi-diurnal (tidal) timescales. Depending on the wind sector, fluxes measured at this site are indicative of air–water exchange in coastal seas as well as in an outer estuary. For the open-water sector when winds were off the Atlantic Ocean, CH4 flux was almost always positive (annual mean of ∼0.05 mmol m−2 d−1) except in December and January, when CH4 flux was near zero. At times of high rainfall and river flow rate, CH4 emission from the estuarine-influenced Plymouth Sound sector was several times higher than emission from the open-water sector. The implied CH4 saturation (derived from the measured fluxes and a wind-speed-dependent gas transfer velocity parameterization) of over 1000 % in the Plymouth Sound is within range of in situ dissolved CH4 measurements near the mouth of the river Tamar. CO2 flux from the open-water sector was generally from sea to air in autumn and winter and from air to sea in late spring and summer, with an annual mean flux of near zero. A diurnal signal in CO2 flux and implied partial pressure of CO2 in water (pCO2) are clearly observed for the Plymouth Sound sector and also evident for the open-water sector during biologically productive periods. These observations suggest that coastal CO2 efflux may be underestimated if sampling strategies are limited to daytime only. Combining the flux data with seawater pCO2 measurements made in situ within the flux footprint allows us to estimate the CO2 transfer velocity. The gas transfer velocity and wind speed relationship at this coastal location agrees reasonably well with previous open-water parameterizations in the mean but demonstrates considerable variability. We discuss the influences of biological productivity, bottom-driven turbulence and rainfall on coastal air–water gas exchange.

The influence of transformed Reynolds number suppression on gas transfer parameterizations and global DMS and CO<sub>2</sub> fluxes

Abstract. Eddy covariance measurements show gas transfer velocity suppression at medium to high wind speed. A wind–wave interaction described by the transformed Reynolds number is used to characterize environmental conditions favoring this suppression. We take the transformed Reynolds number parameterization to review the two most cited wind speed gas transfer velocity parameterizations: Nightingale et al. (2000) and Wanninkhof (1992, 2014). We propose an algorithm to adjust k values for the effect of gas transfer suppression and validate it with two directly measured dimethyl sulfide (DMS) gas transfer velocity data sets that experienced gas transfer suppression. We also show that the data set used in the Nightingale 2000 parameterization experienced gas transfer suppression. A compensation of the suppression effect leads to an average increase of 22 % in the k vs. u relationship. Performing the same correction for Wanninkhof 2014 leads to an increase of 9.85 %. Additionally, we applied our gas transfer suppression algorithm to global air–sea flux climatologies of CO2 and DMS. The global application of gas transfer suppression leads to a decrease of 11 % in DMS outgassing. We expect the magnitude of Reynolds suppression on any global air–sea gas exchange to be about 10 %.

An evaluation of gas transfer velocity parameterizations during natural convection using DNS

AbstractDirect numerical simulations (DNS) of free surface flows driven by natural convection are used to evaluate different methods of estimating air‐water gas exchange at no‐wind conditions. These methods estimate the transfer velocity as a function of either the horizontal flow divergence at the surface, the turbulent kinetic energy dissipation beneath the surface, the heat flux through the surface, or the wind speed above the surface. The gas transfer is modeled via a passive scalar. The Schmidt number dependence is studied for Schmidt numbers of 7, 150 and 600. The methods using divergence, dissipation and heat flux estimate the transfer velocity well for a range of varying surface heat flux values, and domain depths. The two evaluated empirical methods using wind (in the limit of no wind) give reasonable estimates of the transfer velocity, depending however on the surface heat flux and surfactant saturation. The transfer velocity is shown to be well represented by the expression, , where is a constant, is the buoyancy flux, is the kinematic viscosity, is the Schmidt number, and the exponent depends on the water surface characteristics. The results suggest that and and for slip and no‐slip boundary conditions at the surface, respectively. It is further shown that slip and no‐slip boundary conditions predict the heat transfer velocity corresponding to the limits of clean and highly surfactant contaminated surfaces, respectively.

Net sea–air CO<sub>2</sub> flux uncertainties in the Bay of Biscay based on the choice of wind speed products and gas transfer parameterizations

Abstract. The estimation of sea–air CO2 fluxes is largely dependent on wind speed through the gas transfer velocity parameterization. In this paper, we quantify uncertainties in the estimation of the CO2 uptake in the Bay of Biscay resulting from the use of different sources of wind speed such as three different global reanalysis meteorological models (NCEP/NCAR 1, NCEP/DOE 2 and ERA-Interim), one high-resolution regional forecast model (HIRLAM-AEMet), winds derived under the Cross-Calibrated Multi-Platform (CCMP) project, and QuikSCAT winds in combination with some of the most widely used gas transfer velocity parameterizations. Results show that net CO2 flux estimations during an entire seasonal cycle (September 2002–September 2003) may vary by a factor of ~ 3 depending on the selected wind speed product and the gas exchange parameterization, with the highest impact due to the last one. The comparison of satellite- and model-derived winds with observations at buoys advises against the systematic overestimation of NCEP-2 and the underestimation of NCEP-1. In the coastal region, the presence of land and the time resolution are the main constraints of QuikSCAT, which turns CCMP and ERA-Interim in the preferred options.

Error estimations of dry deposition velocities of air pollutants using bulk sea surface temperature under common assumptions

It is well known that skin sea surface temperature (SSST) is different from bulk sea surface temperature (BSST) by a few tenths of a degree Celsius. However, the extent of the error associated with dry deposition (or uptake) estimation by using BSST is not well known. This study tries to conduct such an evaluation using the on-board observation data over the South China Sea in the summers of 2004 and 2006. It was found that when a warm layer occurred, the deposition velocities using BSST were underestimated within the range of 0.8–4.3%, and the absorbed sea surface heat flux was overestimated by 21 W m −2. In contrast, under cool skin only conditions, the deposition velocities using BSST were overestimated within the range of 0.5–2.0%, varying with pollutants and the absorbed sea surface heat flux was underestimated also by 21 W m −2. Scale analysis shows that for a slightly soluble gas (e.g., NO 2, NO and CO), the error in the solubility estimation using BSST is the major source of the error in dry deposition estimation. For a highly soluble gas (e.g., SO 2), the error in the estimation of turbulent heat fluxes and, consequently, aerodynamic resistance and gas-phase film resistance using BSST is the major source of the total error. In contrast, for a medium soluble gas (e.g., O 3 and CO 2) both the errors from the estimations of the solubility and aerodynamic resistance are important. In addition, deposition estimations using various assumptions are discussed. The largest uncertainty is from the parameterizations for chemical enhancement factors. Other important areas of uncertainty include: (1) various parameterizations for gas-transfer velocity; (2) neutral-atmosphere assumption; (3) using BSST as SST, and (4) constant pH value assumption.

Calculating long‐term global air‐sea flux of carbon dioxide using scatterometer, passive microwave, and model reanalysis wind data

Global air‐sea flux of carbon dioxide (CO2) is calculated from wind data acquired by the satellite scatterometer QuikSCAT, the passive microwave radiometer AMSR‐E, and the model reanalysis ERA‐40 using four of the most commonly used wind speed dependent parameterizations of gas transfer velocity. Assuming QuikSCAT as reference, the results are compared to obtain an estimate of that relative uncertainty in the flux calculations which results solely from the origin of the input wind data. We illustrate the discrepancies between these data sets and quantify the uncertainty in the computed air‐sea CO2 flux that arises from data processing such as temporal and spatial averaging using AMSR‐E as an example data set. The impact of temporal variability of wind speed is shown to be significantly greater than that of spatial variability. However, simple parameterizations of temporal variability are found to be sensor‐specific and cannot be applied in a straightforward way to data sets with lower temporal resolutions from other sensors. We show a simple methodology to correct monthly mean data in such a way that seasonally and zonally varying parameterizations of temporal variability derived from QuikSCAT data can be applied to data from AMSR‐E and ERA‐40. This allows us to produce a global 44‐year time series of gas transfer velocity and to present a more coherent estimate of air‐sea transfer of carbon dioxide from the three most commonly available types of wind data.

Comment on “Measurements of air‐sea gas exchange at high wind speeds in the Southern Ocean: Implications for global parameterizations” by D. T. Ho et al.

[1] Recently, Ho et al. [2006] presented the gas transfer velocity measurements at high wind speeds from the SOLAS New Zealand Air-Sea Exchange (NZ-SAGE) experiment conducted in the western Pacific sector of Southern Ocean. Gas transfer velocities were determined using the He/SF6 dual gas tracer technique. The NZ-SAGE experiment alone has more than doubled the number of He/SF6-derived gas transfer velocity measurements from the open ocean, and the measurements spread into high wind speed range (above 10 m s ), which is extremely useful in determining empirical parameterizations of gas transfer velocity. One of the conclusions from the paper is that ‘‘The results clearly reveal a quadratic relationship between wind speed and gas transfer velocity, rather than a recently proposed cubic relationship.’’ While we congratulated their significant success in collecting gas transfer velocity measurements at high wind speeds through the NZSAGE experiment, we would like to point out that one of the assumptions implied in the data presentation, namely the form of enhancement factor, e, that was adopted, favors the quadratic relationship between wind speed and gas transfer velocity. Therefore the above conclusion is less convincing, and we also fear that hasty readers could get the wrong impression when comparing gas transfer velocity measured at different timescales. This is of great importance to the outcomes of data-fitting parameterizations that are currently employed in estimating air-sea CO2 fluxes and in climate models for study of CO2 cycles.

Correction to “A joint atmosphere‐ocean inversion for surface fluxes of carbon dioxide: 1. Methods and global‐scale fluxes”

IPCC TAR 1.4 ± 0.7 1.7 ± 0.5 T3L1 1.3 ± 1.4 1.5 ± 1.4 T3L2 1.5 ± 1.0 1.3 ± 1.0 Bopp O2/N2 1.7 ± 0.9 ( 1.2 ± 0.9) 1.9 ± 0.9 ( 2.3 ± 0.7) Keeling O2/N2 1.7 ± 0.8 ( 1.3 ± 0.8) 1.4 ± 0.6 ( 1.9 ± 0.6) Joint 1.1 ± 0.2 2.1 ± 0.1 0.4 ± 0.2 1.7 ± 0.2 Gloor inverse 1.9 ± 0.3 ( 1.7 ± 0.3) 0.4 ± 0.3 1.5 ± 0.4 ( 1.8 ± 0.4) McNeil CFCs 1.5 ± 0.4 ( 2.0 ± 0.4) Matsumoto OCMIP 1.7 ± 0.2 ( 2.2 ± 0.2) Tak99 k u 2.1 Tak02 k u 1.6 Tak02 k u 2.3 MOM3 forward 2.2 ± 0.2 0.4 ± 0.1 1.7 ± 0.2 Estimates have been corrected so that river carbon flow is manifested as a land sink and a preindustrial ocean source of 0.45 PgC yr 1 (see auxiliary material). Contemporary air-sea fluxes (Fcontemp = Fpreindust + Fanthro) have been scaled to the 1992–1996 period by assuming the anthropogenic component is proportional to the atmospheric concentration perturbation, but air-land fluxes are not scaled. Original estimates, uncorrected for river carbon and unscaled in time, are given in parentheses. ‘‘IPCC-TAR 90s’’ is the estimate of Prentice et al. [2001] for the 1990s; ‘‘T3L1’’ and ‘‘T3L2’’ are the TransCom3 control inversions for level 1 [Gurney et al., 2002] and level 2 [Gurney et al., 2004], respectively, both for the period 1992–1996. ‘‘BoppO2/N2’’ and ‘‘Keeling O2/N2’’ represent the oxygen analyses of Bopp et al. [2002] for the period 1990–1996 and Keeling and Garcia [2002] for the 1990s, respectively. ‘‘Joint’’ is the current joint inversion for the period 1992–1996. ‘‘Gloor inverse’’ is the previous ocean inversion of Gloor et al. [2003] scaled to 1992–1996, a result nearly identical to that of McNeil et al. [2003] from CFC analysis. ‘‘Matsumoto OCMIP’’ is the summary of forward ocean carbon cycle simulations [Orr et al., 2001], as reported by Matsumoto et al. [2004], also scaled to 1992–1996. ‘‘Tak99’’ and ‘‘Tak02’’ represent estimates based on the DpCO2 analyses of Takahashi et al. [1999, 2002] respectively, using quadratic (‘‘k u’’ [Wanninkhof, 1992]) and cubic (‘‘k u’’ [Wanninkhof and McGillis, 1999]) gas transfer velocity parameterizations. ‘‘MOM3 Forward’’ are the 1992–1996 fluxes from OCMIP2 biotic simulations for the five models of the MOM3 suite used in the present study (see Table 1 of Jacobson et al. [2007]). For the period of the 1990s. Air-sea fluxes scaled to 1992–1996 by assuming that the anthropogenic flux is proportional to the atmospheric CO2 perturbation. For the period 1990–1996. Includes 0.45 ± 0.18 PgC yr 1 to account for river carbon fluxes (see auxiliary material). For the period 1990–1991. GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 21, GB2025, doi:10.1029/2007GB003012, 2007 Click Here for Full Article

Application of new parameterizations of gas transfer velocity and their impact on regional and global marine CO 2 budgets

One of the dominant sources of uncertainty in the calculation of air–sea flux of carbon dioxide on a global scale originates from the various parameterizations of the gas transfer velocity, k, that are in use. Whilst it is undisputed that most of these parameterizations have shortcomings and neglect processes which influence air–sea gas exchange and do not scale with wind speed alone, there is no general agreement about their relative accuracy. The most widely used parameterizations are based on non-linear functions of wind speed and, to a lesser extent, on sea surface temperature and salinity. Processes such as surface film damping and whitecapping are known to have an effect on air–sea exchange. More recently published parameterizations use friction velocity, sea surface roughness, and significant wave height. These new parameters can account to some extent for processes such as film damping and whitecapping and could potentially explain the spread of wind-speed based transfer velocities published in the literature. We combine some of the principles of two recently published k parameterizations [Glover, D.M., Frew, N.M., McCue, S.J. and Bock, E.J., 2002. A multiyear time series of global gas transfer velocity from the TOPEX dual frequency, normalized radar backscatter algorithm. In: Donelan, M.A., Drennan, W.M., Saltzman, E.S., and Wanninkhof, R. (Eds.), Gas Transfer at Water Surfaces, Geophys. Monograph 127. AGU,Washington, DC, 325–331; Woolf, D.K., 2005. Parameterization of gas transfer velocities and sea-state dependent wave breaking. Tellus, 57B: 87–94] to calculate k as the sum of a linear function of total mean square slope of the sea surface and a wave breaking parameter. This separates contributions from direct and bubble-mediated gas transfer as suggested by Woolf [Woolf, D.K., 2005. Parameterization of gas transfer velocities and sea-state dependent wave breaking. Tellus, 57B: 87–94] and allows us to quantify contributions from these two processes independently. We then apply our parameterization to a monthly TOPEX altimeter gridded 1.5° × 1.5° data set and compare our results to transfer velocities calculated using the popular wind-based k parameterizations by Wanninkhof [Wanninkhof, R., 1992. Relationship between wind speed and gas exchange over the ocean. J. Geophys. Res., 97: 7373–7382.] and Wanninkhof and McGillis [Wanninkhof, R. and McGillis, W., 1999. A cubic relationship between air−sea CO2 exchange and wind speed. Geophys. Res. Lett., 26(13): 1889–1892]. We show that despite good agreement of the globally averaged transfer velocities, global and regional fluxes differ by up to 100%. These discrepancies are a result of different spatio-temporal distributions of the processes involved in the parameterizations of k, indicating the importance of wave field parameters and a need for further validation.