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 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.

scholarly journals Eddy covariance flux measurements confirm extreme CH<sub>4</sub> emissions from a Swiss hydropower reservoir and resolve their short-term variability

2011 ◽  
Vol 8 (9) ◽  
pp. 2815-2831 ◽  
Author(s):  
W. Eugster ◽  
T. DelSontro ◽  
S. Sobek
Keyword(s):  
Greenhouse Gas ◽  
Eddy Covariance ◽  
Land Surface ◽  
Lake Level ◽  
Driving Forces ◽  
Open Water ◽  
Short Term ◽  
Flux Measurements ◽  
Floating Chamber ◽  
Hydropower Reservoir

Abstract. Greenhouse gas budgets quantified via land-surface eddy covariance (EC) flux sites differ significantly from those obtained via inverse modeling. A possible reason for the discrepancy between methods may be our gap in quantitative knowledge of methane (CH4) fluxes. In this study we carried out EC flux measurements during two intensive campaigns in summer 2008 to quantify methane flux from a hydropower reservoir and link its temporal variability to environmental driving forces: water temperature and pressure changes (atmospheric and due to changes in lake level). Methane fluxes were extremely high and highly variable, but consistently showed gas efflux from the lake when the wind was approaching the EC sensors across the open water, as confirmed by floating chamber flux measurements. The average flux was 3.8 ± 0.4 μg C m−2 s−1 (mean ± SE) with a median of 1.4 μg C m−2 s−1, which is quite high even compared to tropical reservoirs. Floating chamber fluxes from four selected days confirmed such high fluxes with 7.4 ± 1.3 μg C m−2 s−1. Fluxes increased exponentially with increasing temperatures, but were decreasing exponentially with increasing atmospheric and/or lake level pressure. A multiple regression using lake surface temperatures (0.1 m depth), temperature at depth (10 m deep in front of the dam), atmospheric pressure, and lake level was able to explain 35.4% of the overall variance. This best fit included each variable averaged over a 9-h moving window, plus the respective short-term residuals thereof. We estimate that an annual average of 3% of the particulate organic matter (POM) input via the river is sufficient to sustain these large CH4 fluxes. To compensate the global warming potential associated with the CH4 effluxes from this hydropower reservoir a 1.3 to 3.7 times larger terrestrial area with net carbon dioxide uptake is needed if a European-scale compilation of grasslands, croplands and forests is taken as reference. This indicates the potential relevance of temperate reservoirs and lakes in local and regional greenhouse gas budgets.

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 Options to correct local turbulent flux measurements for large-scale fluxes using a LES-based approach

2021 ◽  
Author(s):  
Matthias Mauder ◽  
Andreas Ibrom ◽  
Luise Wanner ◽  
Frederik De Roo ◽  
Peter Brugger ◽  
...  
Keyword(s):  
Eddy Covariance ◽  
Large Scale ◽  
Heat Fluxes ◽  
Eddy Covariance Method ◽  
Boundary Layer Height ◽  
Secondary Circulations ◽  
Low Pass ◽  
Measurement Height ◽  
Flux Measurements ◽  
Dispersive Fluxes

Abstract. The eddy-covariance method provides the most direct estimates for fluxes between ecosystems and the atmosphere. However, dispersive fluxes can occur in the presence of secondary circulations, which can inherently not be captured by such single-tower measurements. In this study, we present options to correct local flux measurements for such large-scale transport based on a non-local parametric model that has been developed from a set of idealized LES runs for three real-world sites. The test sites DK-Sor, DE-Fen, and DE-Gwg, represent typical conditions in the mid-latitudes with different measurement height, different terrain complexity and different landscape-scale heterogeneity. Different ways to determine the boundary-layer height, which is a necessary input variable for modelling the dispersive fluxes, are applied, either from operational radio-soundings and local in-situ measurements for the flat site or from backscatter-intensity profile obtained from collocated ceilometers for the two sites in complex terrain. The adjusted total fluxes are evaluated by assessing the improvement in energy balance closure and by comparing the resulting latent heat fluxes with evapotranspiration rates from nearby lysimeters. The results show that not only the accuracy of the flux estimates is improved but also the precision, which is indicated by RMSE values that are reduced by approximately 50 %. Nevertheless, it needs to be clear that this method is intended to correct for a bias in eddy-covariance measurements due to the presence of large-scale dispersive fluxes. Other reasons potentially causing a systematic under- or overestimation, such as low-pass filtering effects and missing storage terms, still need to be considered and minimized as much as possible. Moreover, additional transport induced by surface heterogeneities is not considered.

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