scholarly journals Thermal enhancement of gas transfer velocity of CO2in an Amazon floodplain lake revealed by eddy covariance measurements

2013 ◽  
Vol 40 (9) ◽  
pp. 1734-1740 ◽  
Author(s):  
Pierre Polsenaere ◽  
Jonathan Deborde ◽  
Guillaume Detandt ◽  
Luciana O. Vidal ◽  
Marcela A. P. Pérez ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Gas Transfer ◽  
Floodplain Lake ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Amazon Floodplain ◽  
Thermal Enhancement ◽  
Eddy Covariance Measurements

scholarly journals DMS gas transfer coefficients from algal blooms in the Southern Ocean

2014 ◽  
Vol 14 (21) ◽  
pp. 28453-28482
Author(s):  
T. G. Bell ◽  
W. De Bruyn ◽  
C. A. Marandino ◽  
S. D. Miller ◽  
C. S. Law ◽  
...  
Keyword(s):  
Wind Speed ◽  
Southern Ocean ◽  
Eddy Covariance ◽  
Algal Blooms ◽  
Biologically Active ◽  
Gas Transfer ◽  
Transfer Coefficients ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Wind Speeds

Abstract. Air/sea dimethylsulfide (DMS) fluxes and bulk air/sea gradients were measured over the Southern Ocean in February/March 2012 during the Surface Ocean Aerosol Production (SOAP) study. The cruise encountered three distinct phytoplankton bloom regions, consisting of two blooms with moderate DMS levels, and a high biomass, dinoflagellate-dominated bloom with high seawater DMS levels (>15 nM). Gas transfer coefficients were considerably scattered at wind speeds above 5 m s−1. Bin averaging the data resulted in a linear relationship between wind speed and mean gas transfer velocity consistent with that previously observed. However, the wind speed-binned gas transfer data distribution at all wind speeds is positively skewed. The flux and seawater DMS distributions were also positively skewed, which suggests that eddy covariance-derived gas transfer velocities are consistently influenced by additional, log-normal noise. A~flux footprint analysis was conducted during a transect into the prevailing wind and through elevated DMS levels in the dinoflagellate bloom. Accounting for the temporal/spatial separation between flux and seawater concentration significantly reduces the scatter in computed transfer velocity. The SOAP gas transfer velocity data shows no obvious modification of the gas transfer-wind speed relationship by biological activity or waves. This study highlights the challenges associated with eddy covariance gas transfer measurements in biologically active and heterogeneous bloom environments.

Using eddy covariance to estimate air–sea gas transfer velocity for oxygen

2016 ◽  
Vol 159 ◽  
pp. 67-75 ◽  
Author(s):  
Andreas Andersson ◽  
Anna Rutgersson ◽  
Erik Sahlée
Keyword(s):  
Eddy Covariance ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Gas Transfer Velocity

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

2018 ◽  
Vol 18 (6) ◽  
pp. 4297-4315 ◽  
Author(s):  
Sebastian Landwehr ◽  
Scott D. Miller ◽  
Murray J. Smith ◽  
Thomas G. Bell ◽  
Eric S. Saltzman ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Friction Velocity ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Wind Speeds ◽  
Physically Based ◽  
Flux Measurements ◽  
Physically Based Models ◽  
Eddy Covariance Measurements ◽  
Ocean Carbon

Abstract. Parameterisation of the air–sea gas transfer velocity of CO2 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 CO2 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 (u10 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 u10 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 bubble-mediated transfer predicted by physically based models.

scholarly journals Dimethylsulfide gas transfer coefficients from algal blooms in the Southern Ocean

2015 ◽  
Vol 15 (4) ◽  
pp. 1783-1794 ◽  
Author(s):  
T. G. Bell ◽  
W. De Bruyn ◽  
C. A. Marandino ◽  
S. D. Miller ◽  
C. S. Law ◽  
...  
Keyword(s):  
Wind Speed ◽  
Southern Ocean ◽  
Eddy Covariance ◽  
Algal Blooms ◽  
Biologically Active ◽  
Gas Transfer ◽  
Transfer Coefficients ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Wind Speeds

Abstract. Air–sea dimethylsulfide (DMS) fluxes and bulk air–sea gradients were measured over the Southern Ocean in February–March 2012 during the Surface Ocean Aerosol Production (SOAP) study. The cruise encountered three distinct phytoplankton bloom regions, consisting of two blooms with moderate DMS levels, and a high biomass, dinoflagellate-dominated bloom with high seawater DMS levels (> 15 nM). Gas transfer coefficients were considerably scattered at wind speeds above 5 m s−1. Bin averaging the data resulted in a linear relationship between wind speed and mean gas transfer velocity consistent with that previously observed. However, the wind-speed-binned gas transfer data distribution at all wind speeds is positively skewed. The flux and seawater DMS distributions were also positively skewed, which suggests that eddy covariance-derived gas transfer velocities are consistently influenced by additional, log-normal noise. A flux footprint analysis was conducted during a transect into the prevailing wind and through elevated DMS levels in the dinoflagellate bloom. Accounting for the temporal/spatial separation between flux and seawater concentration significantly reduces the scatter in computed transfer velocity. The SOAP gas transfer velocity data show no obvious modification of the gas transfer–wind speed relationship by biological activity or waves. This study highlights the challenges associated with eddy covariance gas transfer measurements in biologically active and heterogeneous bloom environments.

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

2018 ◽  
Author(s):  
Alexander Zavarsky ◽  
Christa A. Marandino
Keyword(s):  
Reynolds Number ◽  
Wind Speed ◽  
Wind Wave ◽  
Wave Interaction ◽  
Gas Transfer ◽  
Data Sets ◽  
High Wind Speed ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Eddy Covariance Measurements

Abstract. Eddy covariance measurements show gas transfer velocity limitation at medium to high wind speed. A wind-wave interaction described by the transformed Reynolds number is used to characterize environmental conditions favoring this limitation. We take the transformed Reynolds number parameterization to review the two most cited wind speed gas transfer velocity parameterizations, Nightingale 2000 and Wanninkhof 1992/2014. We propose an algorithm to correct for the effect of gas transfer limitation and validate it with two gas transfer limited directly measured DMS gas transfer velocity data sets. A correction of the Nightingale 2000 parameterization leads to an average increase of 22 % of its predicted gas transfer velocity. The increase for Wanninkhof 2014 is 9.85 %. Additionally, the correction is applied to global air-sea flux climatologies of CO2 and DMS. The global application of gas transfer limitation leads to a decrease of 6–7 % for the uptake CO2 by the oceans and to decrease of 11 % of oceanic outgassing of DMS. We expect the magnitude of Reynolds limitation on any global air-sea gas exchange to be 10 %.

scholarly journals Uncertainties in eddy covariance air-sea CO<sub>2</sub> flux measurements and implications for gas transfer velocity parameterisations

2021 ◽  
Author(s):  
Yuanxu Dong ◽  
Mingxi Yang ◽  
Dorothee C. E. Bakker ◽  
Vassilis Kitidis ◽  
Thomas G. Bell
Keyword(s):  
Eddy Covariance ◽  
Co2 Flux ◽  
Gas Transfer ◽  
Co2 Fluxes ◽  
Relative Uncertainty ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Random Uncertainty ◽  
Flux Measurements ◽  
The Mean

Abstract. Air-sea carbon dioxide (CO2) flux is often indirectly estimated by the bulk method using the air-sea difference in CO2 fugacity (ΔfCO2) and a parameterisation of the gas transfer velocity (K). Direct flux measurements by eddy covariance (EC) provide an independent reference for bulk flux estimates and are often used to study processes that drive K. However, inherent uncertainties in EC air-sea CO2 flux measurements from ships have not been well quantified and may confound analyses of K. This paper evaluates the uncertainties in EC CO2 fluxes from four cruises. Fluxes were measured with two state-of-the-art closed-path CO2 analysers on two ships. The mean bias in the EC CO2 flux is low but the random error is relatively large over short time scales. The uncertainty (1 standard deviation) in hourly averaged EC air-sea CO2 fluxes (cruise-mean) ranges from 1.4 to 3.2 mmol m−2 day−1. This corresponds to a relative uncertainty of ~20 % during two Arctic cruises that observed large CO2 flux magnitude. The relative uncertainty was greater (~50 %) when the CO2 flux magnitude was small during two Atlantic cruises. Random uncertainty in the EC CO2 flux is mostly caused by sampling error. Instrument noise is relatively unimportant. Random uncertainty in EC CO2 fluxes can be reduced by averaging for longer. However, averaging for too long will result in the inclusion of more natural variability. Auto-covariance analysis of CO2 fluxes suggests that the optimal timescale for averaging EC CO2 flux measurements ranges from 1–3 hours, which increases the mean signal-to-noise ratio of the four cruises to higher than 3. Applying an appropriate averaging timescale and suitable ΔfCO2 threshold (20 µatm) to EC flux data enables an optimal analysis of K.

scholarly journals Uncertainties in eddy covariance air–sea CO<sub>2</sub> flux measurements and implications for gas transfer velocity parameterisations

2021 ◽  
Vol 21 (10) ◽  
pp. 8089-8110
Author(s):  
Yuanxu Dong ◽  
Mingxi Yang ◽  
Dorothee C. E. Bakker ◽  
Vassilis Kitidis ◽  
Thomas G. Bell
Keyword(s):  
Eddy Covariance ◽  
Co2 Flux ◽  
Gas Transfer ◽  
Co2 Fluxes ◽  
Relative Uncertainty ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Random Uncertainty ◽  
Flux Measurements ◽  
The Mean

Abstract. Air–sea carbon dioxide (CO2) flux is often indirectly estimated by the bulk method using the air–sea difference in CO2 fugacity (ΔfCO2) and a parameterisation of the gas transfer velocity (K). Direct flux measurements by eddy covariance (EC) provide an independent reference for bulk flux estimates and are often used to study processes that drive K. However, inherent uncertainties in EC air–sea CO2 flux measurements from ships have not been well quantified and may confound analyses of K. This paper evaluates the uncertainties in EC CO2 fluxes from four cruises. Fluxes were measured with two state-of-the-art closed-path CO2 analysers on two ships. The mean bias in the EC CO2 flux is low, but the random error is relatively large over short timescales. The uncertainty (1 standard deviation) in hourly averaged EC air–sea CO2 fluxes (cruise mean) ranges from 1.4 to 3.2 mmolm-2d-1. This corresponds to a relative uncertainty of ∼ 20 % during two Arctic cruises that observed large CO2 flux magnitude. The relative uncertainty was greater (∼ 50 %) when the CO2 flux magnitude was small during two Atlantic cruises. Random uncertainty in the EC CO2 flux is mostly caused by sampling error. Instrument noise is relatively unimportant. Random uncertainty in EC CO2 fluxes can be reduced by averaging for longer. However, averaging for too long will result in the inclusion of more natural variability. Auto-covariance analysis of CO2 fluxes suggests that the optimal timescale for averaging EC CO2 flux measurements ranges from 1 to 3 h, which increases the mean signal-to-noise ratio of the four cruises to higher than 3. Applying an appropriate averaging timescale and suitable ΔfCO2 threshold (20 µatm) to EC flux data enables an optimal analysis of K.

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