Direct covariance measurement of CO2gas transfer velocity during the 2008 Southern Ocean Gas Exchange Experiment: Wind speed dependency

Edson, James B.; Fairall, C. W.; Bariteau, Ludovic; Zappa, Christopher J.; Cifuentes-Lorenzen, Alejandro; McGillis, Wade R.; Pezoa, Sergio; Hare, J. E.; Helmig, Detlev

doi:10.1029/2011jc007022

Cited by 87 publications

(149 citation statements)

References 59 publications

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“…This dependency was demonstrated empirically in a number of local and regional experimental studies, using diverse methods such as covari- ance flux or deliberate tracer techniques (i.e., helium, 3 He, and sulfur hexafluoride, SF 6 ) . These studies have led to k dependencies on wind speed of quadratic (k 660 = c·U 10 2 ; Ho et al, 2006Ho et al, , 2011Jacobs et al, 1999;Kuss et al, 2004), cubic (k 660 = a + d · U 10 3 with a ≥ 0; Edson et al, 2011;Kuss et al, 2004;McGillis et al, 2001McGillis et al, , 2004Prytherch et al, 2010;Wanninkhof and McGillis, 1999) and linear-quadratic (k 660 = b · U 10 + c · U 10 2 ; Nightingale et al, 2000;Weiss et al, 2007) forms. Other studies have followed a distinct approach and constrained k − U 10 relationships for the global ocean on the basis of the global ocean bomb 14 C inventory (Broecker et al, 1985;Naegler et al, 2006;Sweeney et al, 2007) and global wind fields.…”

Section: K − U 10 Parameterizationmentioning

confidence: 99%

Uncertainty in the global oceanic CO2 uptake induced by wind forcing: quantification and spatial analysis

et al. 2018

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Abstract. The calculation of the air-water CO 2 exchange (F CO 2 ) in the ocean not only depends on the gradient in CO 2 partial pressure at the air-water interface but also on the parameterization of the gas exchange transfer velocity (k) and the choice of wind product. Here, we present regional and global-scale quantifications of the uncertainty in F CO 2 induced by several widely used k formulations and four wind speed data products (CCMP, ERA, NCEP1 and NCEP2). The analysis is performed at a 1 • × 1 • resolution using the sea surface pCO 2 climatology generated by Landschützer et al. (2015a) for the 1991-2011 period, while the regional assessment relies on the segmentation proposed by the Regional Carbon Cycle Assessment and Processes (REC-CAP) project. First, we use k formulations derived from the global 14 C inventory relying on a quadratic relationship between k and wind speed (k = c · U 10 2 ; Sweeney et al., 2007;Takahashi et al., 2009;Wanninkhof, 2014), where c is a calibration coefficient and U 10 is the wind speed measured 10 m above the surface. Our results show that the range of global F CO 2 , calculated with these k relationships, diverge by 12 % when using CCMP, ERA or NCEP1. Due to differences in the regional wind patterns, regional discrepancies in F CO 2 are more pronounced than global. These global and regional differences significantly increase when using NCEP2 or other k formulations which include earlier relationships (i.e., Wanninkhof, 1992;Wanninkhof et al., 2009) as well as numerous local and regional parameterizations derived experimentally. To minimize uncertainties associated with the choice of wind product, it is possible to recalculate the coefficient c globally (hereafter called c * ) for a given wind product and its spatiotemporal resolution, in order to match the last evaluation of the global k value. We thus performed these recalculations for each wind product at the resolution and time period of our study but the resulting global F CO 2 estimates still diverge by 10 %. These results also reveal that the Equatorial Pacific, the North Atlantic and the Southern Ocean are the regions in which the choice of wind product will most strongly affect the estimation of the F CO 2 , even when using c * .

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Section: K − U 10 Parameterizationmentioning

confidence: 99%

Uncertainty in the global oceanic CO2 uptake induced by wind forcing: quantification and spatial analysis

et al. 2018

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“…Some EC measurements tend to support a cubic wind-speed dependence for k CO 2 , (e.g. McGillis et al, 2001;Prytherch et al, 2010;Edson et al, 2011), whereas results from tracer studies (e.g. Nightingale et al, 2000;Ho et al, 2011) and more recent EC studies (Miller et al, 2010;Butterworth and Miller, 2016a) are better fitted by a quadratic model.…”

Section: Introductionmentioning

confidence: 99%

“…The non-dispersive infrared CO 2 gas analysers used in most EC studies have cross sensitivities to water vapour, which lead to large uncertainties in the measurements and unrealistic transfer velocity estimates (Kondo and Osamu, 2007;Prytherch et al, 2010;Edson et al, 2011;Blomquist et al, 2014). The cross-sensitivity effect can be mitigated by the use of closed-path systems in combination with a dryer to remove water vapour fluctuations in the measurement volume (Miller et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Nightingale et al, 2000;Ho et al, 2006) which integrate the flux over S. Landwehr et al: Dependence of air-sea gas transfer of CO 2 on friction velocity timescales of days, and (iii) direct eddy covariance (EC) flux measurements on hourly timescales (e.g. McGillis et al, 2001McGillis et al, , 2004Kondo and Osamu, 2007;Miller et al, 2009Miller et al, , 2010Prytherch et al, 2010;Edson et al, 2011;Blomquist et al, 2014). Some EC measurements tend to support a cubic wind-speed dependence for k CO 2 , (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Using eddy covariance to measure the dependence of air–sea CO2 exchange rate on friction velocity

et al. 2018

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Abstract. Parameterisation of the air-sea gas transfer velocity of CO 2 and other trace gases under open-ocean conditions has been a focus of air-sea interaction research and is required for accurately determining ocean carbon uptake. Ships are the most widely used platform for air-sea flux measurements but the quality of the data can be compromised by airflow distortion and sensor cross-sensitivity effects. Recent improvements in the understanding of these effects have led to enhanced corrections to the shipboard eddy covariance (EC) measurements.Here, we present a revised analysis of eddy covariance measurements of air-sea CO 2 and momentum fluxes from the Southern Ocean Surface Ocean Aerosol Production (SOAP) study. We show that it is possible to significantly reduce the scatter in the EC data and achieve consistency between measurements taken on station and with the ship underway. The gas transfer velocities from the EC measurements correlate better with the EC friction velocity (u * ) than with mean wind speeds derived from shipboard measurements corrected with an airflow distortion model. For the observed range of wind speeds (u 10 N = 3-23 m s −1 ), the transfer velocities can be parameterised with a linear fit to u * . The SOAP data are compared to previous gas transfer parameterisations using u 10 N computed from the EC friction velocity with the drag coefficient from the Coupled Ocean-Atmosphere Response Experiment (COARE) model version 3.5. The SOAP results are consistent with previous gas transfer studies, but at high wind speeds they do not support the sharp increase in gas transfer associated with bubblemediated transfer predicted by physically based models.

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“…The most recent results from application of the algorithm to CO 2 and DMS transfer for SO GasEx can be found in Edson et al (2011), while an analysis of the implementation of COAREG for ozone is outlined in Bariteau et al (2010) and Helmig et al (2011). Current application of COAREG includes input of satellite fields of sea surface temperature, near surface humidity and temperature, and wind speed to obtain global estimates of air-sea gas transfer (Jackson et al 2011).…”

Section: Noaa-coarementioning

confidence: 99%

Transfer Across the Air-Sea Interface

Garbe

Rutgersson

Boutin

et al. 2013

Springer Earth System Sciences

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The efficiency of transfer of gases and particles across the air-sea interface is controlled by several physical, biological and chemical processes in the atmosphere and water which are described here (including waves, large-and small-scale turbulence, bubbles, sea spray, rain and surface films). For a deeper understanding of relevant transport mechanisms, several models have been developed, ranging from conceptual models to numerical models. Most frequently the transfer is described by various functional dependencies of the wind speed, but more detailed descriptions need additional information. The study of gas transfer mechanisms uses a variety of experimental methods ranging from laboratory studies to carbon budgets, mass balance methods, micrometeorological techniques and thermographic techniques. Different methods resolve the transfer at different scales of time and space; this is important to take into account when comparing different results. Air-sea transfer is relevant in a wide range of applications, for example, local and regional fluxes, global models, remote sensing and computations of global inventories. The sensitivity of global models to the description of transfer velocity is limited; it is however likely that the formulations are more important when the resolution increases and other processes in models are improved. For global flux estimates using inventories or remote sensing products the accuracy of the transfer formulation as well as the accuracy of the wind field is crucial. IntroductionThe transfer of gases and particles across the air-sea interface depends not only on the concentration difference between the water and the air, but also on the efficiency of the transfer process. The efficiency of the transfer is controlled by complex interaction of a variety of processes in the air and in the water near the interface. Here we treat both gases and particles since the transfer, to some extent, is governed by similar mechanisms. Studies of transfer across the air-sea interface include a variety of methods and techniques ranging from laboratory studies, modeling and large-scale field studies. Various methods reach somewhat different conclusions, due to representation of different

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Direct covariance measurement of CO₂gas transfer velocity during the 2008 Southern Ocean Gas Exchange Experiment: Wind speed dependency

Cited by 87 publications

References 59 publications

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Using eddy covariance to measure the dependence of air–sea CO<sub>2</sub> exchange rate on friction velocity

Transfer Across the Air-Sea Interface

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Direct covariance measurement of CO2gas transfer velocity during the 2008 Southern Ocean Gas Exchange Experiment: Wind speed dependency

Cited by 87 publications

References 59 publications

Uncertainty in the global oceanic CO&lt;sub&gt;2&lt;/sub&gt; uptake induced by wind forcing: quantification and spatial analysis

Uncertainty in the global oceanic CO&lt;sub&gt;2&lt;/sub&gt; uptake induced by wind forcing: quantification and spatial analysis

Using eddy covariance to measure the dependence of air–sea CO&lt;sub&gt;2&lt;/sub&gt; exchange rate on friction velocity

Transfer Across the Air-Sea Interface

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Product

Resources

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Direct covariance measurement of CO₂gas transfer velocity during the 2008 Southern Ocean Gas Exchange Experiment: Wind speed dependency

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Uncertainty in the global oceanic CO<sub>2</sub> uptake induced by wind forcing: quantification and spatial analysis

Using eddy covariance to measure the dependence of air–sea CO<sub>2</sub> exchange rate on friction velocity