Wind and fetch dependence of gas transfer velocity in an Arctic sea-ice lead determined from eddy covariance CO2 flux measurements

2020 ◽  
Author(s):  
John Prytherch
Keyword(s):  
Sea Ice ◽  
Eddy Covariance ◽  
Co2 Flux ◽  
Arctic Sea Ice ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Arctic Sea ◽  
Flux Measurements

scholarly journals Direct determination of the air-sea CO2 gas transfer velocity in Arctic sea ice regions

2017 ◽  
Vol 44 (8) ◽  
pp. 3770-3778 ◽  
Author(s):  
John Prytherch ◽  
Ian M. Brooks ◽  
Patrick M. Crill ◽  
Brett F. Thornton ◽  
Dominic J. Salisbury ◽  
...  
Keyword(s):  
Sea Ice ◽  
Direct Determination ◽  
Arctic Sea Ice ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Co2 Gas ◽  
Gas Transfer Velocity ◽  
Arctic Sea

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.

Eddy covariance flux measurements of momentum, heat and carbon dioxide in Hudson Bay at the onset of sea ice melt 

2021 ◽  
Author(s):  
Richard Sims ◽  
Brian Butterworth ◽  
Tim Papakyriakou ◽  
Mohamed Ahmed ◽  
Brent Else
Keyword(s):  
Carbon Dioxide ◽  
Gas Exchange ◽  
Sea Ice ◽  
Eddy Covariance ◽  
Open Water ◽  
The Arctic ◽  
Gas Transfer ◽  
Hudson Bay ◽  
Flux Measurements ◽  
Ice Fraction

&lt;p&gt;Remoteness and tough conditions have made the Arctic Ocean historically difficult to access; until recently this has resulted in an undersampling of trace gas and gas exchange measurements. The seasonal cycle of sea ice completely transforms the air sea interface and the dynamics of gas exchange. To make estimates of gas exchange in the presence of sea ice, sea ice fraction is frequently used to scale open water gas transfer parametrisations. It remains unclear whether this scaling is appropriate for all sea ice regions. Ship based eddy covariance measurements were made in Hudson Bay during the summer of 2018 from the icebreaker CCGS Amundsen. We will present fluxes of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;), heat and momentum and will show how they change around the Hudson Bay polynya under varying sea ice conditions. We will explore how these fluxes change with wind speed and sea ice fraction. As freshwater stratification was encountered during the cruise, we will compare our measurements with other recent eddy covariance flux measurements made from icebreakers and also will compare our turbulent CO&lt;sub&gt;2&amp;#160;&lt;/sub&gt;fluxes with bulk fluxes calculated using underway and surface bottle pCO&lt;sub&gt;2&lt;/sub&gt;&amp;#160;data.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Dried, closed-path eddy covariance method for measuring carbon dioxide flux over sea ice

2018 ◽  
Vol 11 (11) ◽  
pp. 6075-6090 ◽  
Author(s):  
Brian J. Butterworth ◽  
Brent G. T. Else
Keyword(s):  
Carbon Dioxide ◽  
Sea Ice ◽  
Eddy Covariance ◽  
Co2 Flux ◽  
Carbon Dioxide Flux ◽  
The Arctic ◽  
Closed Path ◽  
Eddy Covariance Method ◽  
Open Path ◽  
Flux Measurements

Abstract. The Arctic marine environment plays an important role in the global carbon cycle. However, there remain large uncertainties in how sea ice affects air–sea fluxes of carbon dioxide (CO2), partially due to disagreement between the two main methods (enclosure and eddy covariance) for measuring CO2 flux (FCO2). The enclosure method has appeared to produce more credible FCO2 than eddy covariance (EC), but is not suited for collecting long-term, ecosystem-scale flux datasets in such remote regions. Here we describe the design and performance of an EC system to measure FCO2 over landfast sea ice that addresses the shortcomings of previous EC systems. The system was installed on a 10 m tower on Qikirtaarjuk Island – a small rock outcrop in Dease Strait located roughly 35 km west of Cambridge Bay, Nunavut, in the Canadian Arctic Archipelago. The system incorporates recent developments in the field of air–sea gas exchange by measuring atmospheric CO2 using a closed-path infrared gas analyzer (IRGA) with a dried sample airstream, thus avoiding the known water vapor issues associated with using open-path IRGAs in low-flux environments. A description of the methods and the results from 4 months of continuous flux measurements from May through August 2017 are presented, highlighting the winter to summer transition from ice cover to open water. We show that the dried, closed-path EC system greatly reduces the magnitude of measured FCO2 compared to simultaneous open-path EC measurements, and for the first time reconciles EC and enclosure flux measurements over sea ice. This novel EC installation is capable of operating year-round on solar and wind power, and therefore promises to deliver new insights into the magnitude of CO2 fluxes and their driving processes through the annual sea ice cycle.

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 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

2012 ◽  
Vol 9 (8) ◽  
pp. 9993-10017
Author(s):  
P. Otero ◽  
X. A. Padín ◽  
M. Ruiz-Villarreal ◽  
L. M. García-García ◽  
A. F. Ríos ◽  
...  
Keyword(s):  
Wind Speed ◽  
Co2 Flux ◽  
Forecast Model ◽  
Gas Transfer ◽  
Co2 Uptake ◽  
Bay Of Biscay ◽  
Transfer Velocity ◽  
Gas Transfer Velocity ◽  
Different Sources ◽  
Meteorological Models

Abstract. The estimation of sea-air CO2 fluxes are 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 using different sources of wind speed such as three different global reanalysis meteorological models (NCEP/NCAR 1, NCEP/DOE 2 and ERA-Interim), one regional high-resolution forecast model (HIRLAM-AEMet) 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 may differ up to 240% depending on the wind speed product and the gas exchange parameterization. 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 this region, QuikSCAT has the best performing, although ERA-Interim becomes the best choice in areas near the coastline or when the time resolution is the constraint.

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