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 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 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.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Modelling random uncertainty of eddy covariance flux measurements

2019 ◽  
Vol 33 (3) ◽  
pp. 725-746 ◽  
Author(s):  
Domenico Vitale ◽  
Massimo Bilancia ◽  
Dario Papale
Keyword(s):  
Eddy Covariance ◽  
Random Uncertainty ◽  
Flux Measurements

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.

CO2 flux measurements in Russian Far East tundra using eddy covariance and closed chamber techniques

Tellus B ◽  
2003 ◽  
Vol 55 (4) ◽  
pp. 879-892 ◽  
Author(s):  
D. G. ZAMOLODCHIKOV ◽  
D. V. KARELIN ◽  
A. I. IVASCHENKO ◽  
W. C. OECHEL ◽  
S. J. HASTINGS
Keyword(s):  
Eddy Covariance ◽  
Russian Far East ◽  
Far East ◽  
Co2 Flux ◽  
Closed Chamber ◽  
Flux Measurements

scholarly journals Comparison of floating chamber and eddy covariance measurements of lake greenhouse gas fluxes

2013 ◽  
Vol 10 (11) ◽  
pp. 18309-18335 ◽  
Author(s):  
E. Podgrajsek ◽  
E. Sahlée ◽  
D. Bastviken ◽  
J. Holst ◽  
A. Lindroth ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Carbon Sink ◽  
Co2 Flux ◽  
Flux Footprint ◽  
Gas Fluxes ◽  
Partial Pressure Of Co2 ◽  
Flux Measurements ◽  
Eddy Covariance Measurements ◽  
Terrestrial Carbon Sink ◽  
Carbon Dioxide Co2

Abstract. Fluxes of carbon dioxide (CO2) and methane (CH4) from lakes may have a large impact on the magnitude of the terrestrial carbon sink. Traditionally lake fluxes have been measured using the floating chambers (FC) technique, however, several recent studies use the eddy covariance (EC) method. We present simultaneous flux measurements using both methods at the lake Tämnaren in Sweden during field campaigns in 2011 and 2012. Only very few similar studies exist. For CO2 flux, the two methods agree relatively well during some periods, but deviate substantially at other times. The large discrepancies might be caused by heterogeneity of partial pressure of CO2 (pCO2w) in the EC flux footprint. The methods agree better for CH4 fluxes, it is, however, clear that short-term discontinuous FC measurements are likely to miss important high flux events.

scholarly journals Eddy covariance flux measurements of gaseous elemental mercury over a grassland

2020 ◽  
Vol 13 (4) ◽  
pp. 2057-2074 ◽  
Author(s):  
Stefan Osterwalder ◽  
Werner Eugster ◽  
Iris Feigenwinter ◽  
Martin Jiskra
Keyword(s):  
Detection Limit ◽  
Eddy Covariance ◽  
Co2 Flux ◽  
Elemental Mercury ◽  
Terrestrial Ecosystems ◽  
Co2 Uptake ◽  
Global Networks ◽  
Gaseous Elemental Mercury ◽  
Flux Measurements

Abstract. Direct measurements of the net ecosystem exchange (NEE) of gaseous elemental mercury (Hg0) are important to improve our understanding of global Hg cycling and, ultimately, human and wildlife Hg exposure. The lack of long-term, ecosystem-scale measurements causes large uncertainties in Hg0 flux estimates. It currently remains unclear whether terrestrial ecosystems are net sinks or sources of atmospheric Hg0. Here, we show a detailed validation of direct Hg0 flux measurements based on the eddy covariance technique (Eddy Mercury) using a Lumex RA-915 AM mercury monitor. The flux detection limit derived from a zero-flux experiment in the laboratory was 0.22 ng m−2 h−1 (maximum) with a 50 % cutoff at 0.074 ng m−2 h−1. We present eddy covariance NEE measurements of Hg0 over a low-Hg soil (41–75 ng Hg g−1 in the topsoil, referring to a depth of 0–10 cm), conducted in summer 2018 at a managed grassland at the Swiss FluxNet site in Chamau, Switzerland (CH-Cha). The statistical estimate of the Hg0 flux detection limit under outdoor conditions at the site was 5.9 ng m−2 h−1 (50 % cutoff). We measured a net summertime emission over a period of 34 d with a median Hg0 flux of 2.5 ng m−2 h−1 (with a −0.6 to 7.4 ng m−2 h−1 range between the 25th and 75th percentiles). We observed a distinct diel cycle with higher median daytime fluxes (8.4 ng m−2 h−1) than nighttime fluxes (1.0 ng m−2 h−1). Drought stress during the measurement campaign in summer 2018 induced partial stomata closure of vegetation. Partial stomata closure led to a midday depression in CO2 uptake, which did not recover during the afternoon. The median CO2 flux was only 24 % of the median CO2 flux measured during the same period in the previous year (2017). We suggest that partial stomata closure also dampened Hg0 uptake by vegetation, resulting in a NEE of Hg0 that was dominated by soil emission. Finally, we provide suggestions to further improve the precision and handling of the “Eddy Mercury” system in order to assure its suitability for long-term NEE measurements of Hg0 over natural background surfaces with low soil Hg concentrations (< 100 ng g−1). With these improvements, Eddy Mercury has the potential to be integrated into global networks of micrometeorological tower sites (FluxNet) and to provide the long-term observations on terrestrial atmosphere Hg0 exchange necessary to validate regional and global mercury models.

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