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.

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 Uncertainty analysis of eddy covariance CO<sub>2</sub> flux measurements for different EC tower distances using an extended two-tower approach

2014 ◽  
Vol 11 (8) ◽  
pp. 11943-11983
Author(s):  
H. Post ◽  
H. J. Hendricks Franssen ◽  
A. Graf ◽  
M. Schmidt ◽  
H. Vereecken
Keyword(s):  
Eddy Covariance ◽  
Reference Method ◽  
Co2 Flux ◽  
Weather Conditions ◽  
Co2 Fluxes ◽  
Random Uncertainty ◽  
Extended Approach ◽  
Uncertainty Estimates ◽  
Flux Measurements

Abstract. The use of eddy covariance CO2 flux measurements in data assimilation and other applications requires an estimate of the random uncertainty. In previous studies, the two-tower approach has yielded robust uncertainty estimates, but care must be taken to meet the often competing requirements of statistical independence (non-overlapping footprints) and ecosystem homogeneity when choosing an appropriate tower distance. The role of the tower distance was investigated with help of a roving station separated between 8 m and 34 km from a permanent EC grassland station. Random uncertainty was estimated for five separation distances with an extended two-tower approach which removed systematic differences of CO2 fluxes measured at two EC towers. This analysis was made for a dataset where (i) only similar weather conditions at the two sites were included and (ii) an unfiltered one. The extended approach, applied to weather-filtered data for separation distances of 95 m and 173 m gave uncertainty estimates in best correspondence with the independent reference method The introduced correction for systematic flux differences considerably reduced the overestimation of the two-tower based uncertainty of net CO2 flux measurements, e.g. caused by different environmental conditions at both EC towers. It is concluded that the extension of the two-tower approach can help to receive more reliable uncertainty estimates because systematic differences of measured CO2 fluxes which are not part of random error are filtered out.

scholarly journals A model study on the sensitivity of surface ocean CO<sub>2</sub> pressure with respect to the CO<sub>2</sub> gas exchange rate

2011 ◽  
Vol 8 (6) ◽  
pp. 10797-10821
Author(s):  
P. Landschützer ◽  
J. F. Tjiputra ◽  
K. Assmann ◽  
C. Heinze
Keyword(s):  
Exchange Rate ◽  
Gas Exchange ◽  
Co2 Flux ◽  
Gas Transfer ◽  
Co2 Fluxes ◽  
Transfer Velocity ◽  
Carbon Cycle Model ◽  
Spatial And Temporal Scales ◽  
Gas Transfer Velocity ◽  
Gas Exchange Rate

Abstract. Rising CO2 concentrations in the atmosphere and a changing climate are expected to alter the air-sea CO2 flux through changes in the respective control factors for gas exchange. In this study we determine the sensitivity of the CO2 fluxes on the gas transfer velocity using the MICOM-HAMOCC isopycnic carbon cycle model. The monthly generated MICOM-HAMOCC output data are suitable to investigate seasonal variabilities concerning the exchange of CO2. In a series of 3 sensitivity runs the wind dependent gas exchange rate is increased by 44%, both in the northern and southern westerly regions, as well as in the equatorial area to investigate the effect of regional variations of the gas transfer rate on the air-sea fluxes and the distribution of the ocean surface pCO2. For the period between 1948–2009, the results show that locally increasing gas transfer rates do not play an important role concerning the global uptake of carbon from the atmosphere. While effects on a global and annual scale are low, the regional and intra-annual variability shows remarkable variations in the gas fluxes and the surface pCO2. An accurate quantification of the variable gas transfer velocity therefore provides a potential source to enhance model predictions over small spatial and temporal scales and to successfully reconcile model results on surface pCO2 and air-sea CO2 fluxes with observations.

scholarly journals Uncertainty analysis of eddy covariance CO<sub>2</sub> flux measurements for different EC tower distances using an extended two-tower approach

2015 ◽  
Vol 12 (4) ◽  
pp. 1205-1221 ◽  
Author(s):  
H. Post ◽  
H. J. Hendricks Franssen ◽  
A. Graf ◽  
M. Schmidt ◽  
H. Vereecken
Keyword(s):  
Eddy Covariance ◽  
Reference Method ◽  
Co2 Flux ◽  
Weather Conditions ◽  
Data Set ◽  
Random Uncertainty ◽  
Extended Approach ◽  
Uncertainty Estimates ◽  
Flux Measurements

Abstract. The use of eddy covariance (EC) CO2 flux measurements in data assimilation and other applications requires an estimate of the random uncertainty. In previous studies, the (classical) two-tower approach has yielded robust uncertainty estimates, but care must be taken to meet the often competing requirements of statistical independence (non-overlapping footprints) and ecosystem homogeneity when choosing an appropriate tower distance. The role of the tower distance was investigated with help of a roving station separated between 8 m and 34 km from a permanent EC grassland station. Random uncertainty was estimated for five separation distances with the classical two-tower approach and an extended approach which removed systematic differences of CO2 fluxes measured at two EC towers. This analysis was made for a data set where (i) only similar weather conditions at the two sites were included, and (ii) an unfiltered one. The extended approach, applied to weather-filtered data for separation distances of 95 and 173 m gave uncertainty estimates in best correspondence with an independent reference method. The introduced correction for systematic flux differences considerably reduced the overestimation of the two-tower based uncertainty of net CO2 flux measurements and decreased the sensitivity of results to tower distance. We therefore conclude that corrections for systematic flux differences (e.g., caused by different environmental conditions at both EC towers) can help to apply the two-tower approach to more site pairs with less ideal conditions.

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.

scholarly journals Accounting for surface waves improves gas flux estimation at high wind speed in a large lake

2021 ◽  
Author(s):  
Pascal Perolo ◽  
Bieito Fernández Castro ◽  
Nicolas Escoffier ◽  
Thibault Lambert ◽  
Damien Bouffard ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wind Shear ◽  
Diffusion Flux ◽  
Gas Transfer ◽  
Co2 Fluxes ◽  
Large Lake ◽  
Lake Geneva ◽  
Transfer Velocity ◽  
Near Surface ◽  
Gas Transfer Velocity

Abstract. The gas transfer velocity (k) is a major source of uncertainty when assessing the magnitude of lake gas exchange with the atmosphere. For the diversity of existing empirical and process-based k models, the transfer velocity increases with the level of turbulence near the air-water interface. However, predictions for k can vary by a factor of 2 among different models. Near-surface turbulence results from the action of wind shear, surface waves and buoyancy-driven convection. Wind shear has long been identified as a key driver, while recent lake studies have shifted the focus towards the role of convection, particularly in small lakes. In large lakes, wind fetch can however be long enough to generate surface waves and contribute to enhance gas transfer, as widely recognised in oceanographic studies. Here, field values for gas transfer velocity were computed in a large hardwater lake, Lake Geneva, from CO2 fluxes measured with an automated (forced diffusion) flux chamber and CO2 partial pressure measured with high frequency sensors. k estimates were compared to a set of reference limnological and oceanic k models. Our analysis reveals that accounting for surface waves generated during windy events significantly improves the accuracy of k estimates in this large lake. The improved k model is then used to compute k over a one-year time-period. Results show that episodic extreme events with surface waves (6 % occurrence, significant wave height > 0.4 m) can generate more than 20 % of annual cumulative k and more than 25 % of annual net CO2 fluxes in Lake Geneva. We conclude that for lakes whose fetch can exceed 15 km, k-models need to integrate the effect of surface waves.

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