1. Introduction
Air–sea exchange of oxygen is an area of increasing scientific interest. Oxygen in the ocean is of crucial importance, as it is strongly linked to both the carbon dioxide and nitrogen cycles (Arrigo 2005; Keeling et al. 2010) and is a key component in eutrophication (Karlsson et al. 2002). In the fifth and latest assessment report from the Intergovernmental Panel on Climate Change (Stocker et al. 2013), it was concluded that the ocean has absorbed about 30% of the emitted anthropogenic carbon dioxide, causing ocean acidification. Variability of oxygen gives additional information about the carbon cycle, not contained in the variability of CO2, due to the different reactivity and equilibrium time scale of oxygen and CO2 (Körtzinger et al. 2008).
During the last decade, there has been increasing interest in oxygen minimum zones (OMZ). OMZ are zones in the water column typically 200–1000 m in depth where oxygen concentration is at its lowest and hypoxia occurs. OMZ constitute the main areas of nitrogen loss to the atmosphere via denitrification and anaerobic ammonium oxidation (Codispoti et al. 2001) and could also mitigate the ocean biological sequestration of atmospheric CO2 (Paulmier et al. 2008). In the last few years, new evidence of expanding OMZ has been found in the tropical northeast Atlantic as a consequence of global warming (Stramma et al. 2008), capable of threatening the sustainability of pelagic fisheries and marine ecosystems (Stramma et al. 2012). Recently, the Surface Ocean Lower Atmosphere Study (SOLAS) launched a program related to OMZ as a key player to understand the nitrogen cycle and the role of the ocean in atmospheric greenhouse gas control. The oxygen concentration of these zones is very sensitive to changes in air–sea fluxes of oxygen and interior ocean advection; hence, dissolved oxygen is an important parameter for the understanding of the ocean’s role in the earth’s climate system (Joos et al. 2003). One major uncertainty of the ocean oxygen dynamics is the air–sea gas exchange. Therefore, measurements of oxygen fluxes across the air–sea surface are fundamental to the understanding of how the increased emissions of greenhouse gases—for example, CO2, CH4, and NO2—affect the global climate dynamics.
The most direct method to measure fluxes of gases across the air–sea interface is the eddy covariance method. This method is widely used within the meteorological community to determine, for example, fluxes of CO2, and sensible and latent heat. Oxygen eddy covariance measurements in water have been performed successfully over the last decade in studying water–sediment exchange (e.g., Berg et al. 2003, 2009; Kuwae et al. 2006; Brand et al. 2008; McGinnis et al. 2008; Reimers et al. 2012; Chipman et al. 2012). Atmospheric eddy covariance measurements of oxygen have not previously been performed, since instrumentation with sufficient response time and resolution was not available.
The magnitude and direction of a gas flux at the air–sea interface is determined by the air–sea difference in partial pressure of the gas and by the efficiency of the transfer process (described by the transfer velocity). Traditionally, the air–sea fluxes of oxygen have been determined through mass balance techniques using the oxygen disequilibrium between the atmosphere and the surface ocean and using a wind-dependent expression for the transfer velocity (e.g., Stigebrandt 1991; McNeil et al. 2006; Kihm and Körtzinger 2010). Measurements of sparingly soluble gases such as CO2 suggest linear, quadratic, or cubic dependencies of transfer velocity on wind speed (Liss and Merlivat 1986; Wanninkhof 1992; Wanninkhof and McGillis 1999; McGillis et al. 2004; Woolf 2005; McNeil and D’Asaro 2007). The relative importance of various processes for the air–sea exchange is still not fully understood (Garbe et al. 2014). In the low to intermediate wind speed regime, the primary driving mechanism is presumed to be near surface turbulence (e.g., Fairaill et al. 2000). Additionally, microwave breaking (Zappa et al. 2001), spray and bubbles (Woolf 1993, 1997), and water-side convection (Rutgersson et al. 2011) are of importance. The relative importance of different processes is expected to vary for gases of different solubility. Dimethyl sulfide (DMS) has shown less wind speed dependence than CO2 for high winds (Blomquist et al. 2006). By introducing eddy covariance measurements of yet another gas with different solubility (oxygen), knowledge about transport processes could fundamentally increase.
Here we evaluate the potential use of the Microx TX3 oxygen sensor for eddy covariance applications, investigating whether when it meets the resolution and response requirements for eddy covariance measurements in atmospheric marine environments, it will be exposed to, for example, high humidity and possible sea salt loading. A sensor comparison between two types of tapered oxygen sensors is performed, where response time and signal quality are studied. We also investigate the validity of scalar similarity on oxygen, by comparing cospectra of oxygen with cospectra of carbon dioxide and humidity. Additionally, we present corrections that need to be applied prior to a final flux estimate.
2. Theory
a. Microx TX3
(a) The EC system with a close-up of the NOI sensor mounted at the CSAT3 during P5, (b) the EC system used during P5 at the height of 27 m, and (c) a close-up of the NOI sensor tip.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
(a) The EC system with a close-up of the NOI sensor mounted at the CSAT3 during P5, (b) the EC system used during P5 at the height of 27 m, and (c) a close-up of the NOI sensor tip.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
(a) The EC system with a close-up of the NOI sensor mounted at the CSAT3 during P5, (b) the EC system used during P5 at the height of 27 m, and (c) a close-up of the NOI sensor tip.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
A thermistor is connected to the Microx TX3 to correct for changes in quenching frequency due to temperature variations. This measurement may be bypassed by setting a fixed temperature, for which then the oxygen concentration is determined; this is, however, not the recommended setup when the best possible resolution is required. Here we test both setups, that is, using a fixed temperature and with the thermistor connected. The two types of tapered sensors used in this study are the optical isolated (OI) sensor and the nonoptical isolated (NOI) sensor. The NOI sensor has the advantage of a faster response time (t90) < 0.5 s and accuracy of ±1% air saturation compared to the OI sensor with t90 < 1 s with the same accuracy, where t90 is the response time for 90% of the measured signal. The disadvantages of the NOI sensor compared to the OI sensor are the more fragile construction and a presumably larger signal drift as a result of faster photodecomposition of the oxygen sensitive part, since it is more exposed to ambient light (Loligo Systems 2005). A signal drift will induce errors in the estimated flux if not corrected.
b. Eddy covariance method
c. Spectra and cospectra theory
3. Site and instrument setup
a. Site description
The site used for the oxygen measurements is located on a small island east of Gotland in the Baltic Sea. A 30-m tower is located at the southern tip of the island with the base 1 ± 0.5 m above mean sea level. The tower is equipped with both slow response instruments for profiles and high-frequency instruments for turbulent fluxes. The fluxes of momentum, sensible heat, and latent heat are shown to represent open sea conditions for wind directions (WD) 80° < WD < 210° (Högström et al. 2008), while fluxes of CO2 are shown to represent open sea conditions for wind direction 80° < WD < 160° (Rutgersson et al. 2008). Data from the site have been used for numerous studies on varies aspects on air–sea interaction and gas exchange (e.g., Rutgersson and Smedman 2010; Smedman et al. 1999). A more detailed description of the site and instrument setup can be found in, for example, Rutgersson et al. (2008).
b. Instrumental setup
The Microx TX3 was used in two different eddy covariance systems during the years 2010–13; see Table 1 for descriptions of the setups and measuring periods (P1–P5). For P1–P4 (2010–12), the eddy covariance system consisted of one Gill Windmaster sonic anemometer (Gill Instruments Ltd., Lymington, Hampshire, United Kingdom) combined with the Microx TX3 using a tapered sensor, while for P5 the Gill sonic was replaced by a CSAT3 (Campbell Scientific, North Logan, Utah). In addition an open-path gas analyzer, LI-7500 (LI-COR Inc., Lincoln, Nebraska), was installed to determine the fluxes of CO2 and humidity (Fig. 1). The eddy covariance (EC) system was mounted and evaluated at two different heights, 10 and 27 m. The sensor comparison test was performed at 10-m height (OI sensor), while data used for spectral and cospectral comparison with CO2 and humidity were performed at 27-m height. The instruments were mounted at the outermost part of a 3.5-m bar attached to the tower facing south. The oxygen sensor was mounted to one of the supporting legs of the sonic anemometer, and the LI-7500 was mounted 0.35 m horizontally apart from the sonic anemometer with the detection volume in-line with the detection volume of the sonic. The thermistor of the Microx TX3 was mounted within the tower inside a ventilated radiation shield. The sampling rate of the EC system was set to 20 Hz. To get the best possible temporal resolution, the Microx TX3 was set to operate in “fast sampling” mode, which means the fastest possible update of the analog signal corresponding to about 2 Hz. Since the sampling rate for the EC system is larger compared to the analog update frequency of the Microx TX3, data in between updates were kept at a constant sample value. Prior to every new measuring period, a manual two-point calibration was performed on the oxygen sensor: the first point for 100% air saturation (in air) and the second point for 0% air saturation (in liquid).
Dataset and setup during the five field campaigns including date for field campaign, hours with data (n), deployment height of the EC system (z), and the sensor type (ST) used for the respective period.
4. Measurements
a. Data analysis
The details of atmospheric conditions during the measuring periods are presented in Table 2. To avoid disturbance from land and flow distortion effects from the tower, only data with a prevailing wind direction from the sector 60°–250° were used, for spectral analysis data were filtered to 80°–160° in order to represent open sea conditions for all scalars. Periods of rain and high relative humidity (>93%) were excluded by using the relative humidity measured at 8 m. Oxygen data were first screened to exclude periods of low signal-to-noise ratio, identified using the ratio of the oxygen fluctuation and the tabulated precision of 0.1% air saturation. For sonic wind data, a rotation of the coordinate system was performed, by aligning the sonic x axis into the mean wind and a second rotation giving a zero vertical wind speed. Spectral analysis and turbulence statistics were compiled in 30-min blocks. For every 30-min block, the 10-min average wind directions are allowed to vary within ±10°, to ensure a persistent wind direction. To remove trends affecting the scalar averages, a linear detrend was applied on the raw data for every 30-min block. Time lags were checked by cross-correlating signals on every 30-min block in order to get the most accurate flux estimation. The time lag between oxygen and w was found to be zero. For w–CO2 and w–q having a distance to the sonic of 0.35 m, a time lag of ±0.6 s was found, depending on wind speed and wind direction. A spike removal algorithm similar to Sahlée et al. (2012) was then conducted on the remaining data used for cospectral analysis.
Atmospheric conditions during the field campaigns with the atmospheric stability range (z/L), the average wind stress defined as 
b. Corrections
1) Air density effects
2) Flux estimation from spectral attenuated signals
The most frequently used method to determine the air–sea flux of oxygen is Eq. (4). However, when a correction due to limitations in frequency response is needed, the interpolated cospectrum Eq. (7) is preferably used. To compensate for the frequency loss, different methods have been suggested, such as different transfer functions developed for spectral attenuation (Horst 2000) or using in situ measurements and assuming cospectral similarity of a reference signal shown to be free from attenuation (Hicks and McMillen 1988; Horst et al. 1997). The transfer functions are usually smooth curves based on field measurements over flat terrain (Moore 1986; Kaimal and Finnigan 1994); however, as showed in several studies (Laubach and McNaughton 1998; Massman 2000, 2001) and summarized in Lee et al. (2004), half-hour spectra never resemble this smooth shape and the nondimensional frequencies were shown to be site specific rather than universal, causing errors in the transfer functions.
5. Results and discussion
a. Sensor comparison
In this section sensor and instrument limitations in terms of response time, signal drift, and flux detection limit are studied. Both the OI type and the NOI type of sensors were used. The measurements with the OI sensor were performed at P1, while measurements with the NOI sensor were performed during four field campaigns over the years 2011–13. Figure 2a shows a detrended time series of the O2 measurements with the NOI sensor during 30 June 2011. Both large-scale and small-scale fluctuations are observed; additionally, it is clear that the sensor displays a discrete stepwise response. The Microx TX3 shows this discrete signal regardless of the sensor tip used. However, as displayed in Fig. 2a, fluctuations of the order of 0.5% air saturation are detected within 30-s interval between 2925 and 2955 LST, and can be distinguished from the typical precision error of ±0.1% air saturation. This is most likely due to the combination of the limited resolution and the oversampled system (sampling at 20 Hz but the sensor outputs only at about 2 Hz). For eddy covariance measurement in water, the stepwise behavior was shown to have a minor effect on the calculated fluxes (Chipman et al. 2012). However, this does not necessary have to be the case for atmospheric eddy covariance measurements of oxygen where the concentration fluctuations are small relative to the absolute concentration.
Time series (LST) of oxygen raw data in % air saturation measured at the height of 27 m during P2: (a) 50-s series displaying the discrete behavior of the oxygen signal and (b) 17-h series showing the large signal drift (3.7 %airsaturation h−1) of the NOI sensor.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Time series (LST) of oxygen raw data in % air saturation measured at the height of 27 m during P2: (a) 50-s series displaying the discrete behavior of the oxygen signal and (b) 17-h series showing the large signal drift (3.7 %airsaturation h−1) of the NOI sensor.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Time series (LST) of oxygen raw data in % air saturation measured at the height of 27 m during P2: (a) 50-s series displaying the discrete behavior of the oxygen signal and (b) 17-h series showing the large signal drift (3.7 %airsaturation h−1) of the NOI sensor.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
For all field campaigns, a nonphysical trend in oxygen signal was seen as an increase in oxygen concentration with time (Fig. 2b). This trend in oxygen concentration starts immediately after a new sensor tip has been installed and measurements started. Figure 2b shows a time series of oxygen concentration measured during P2 with a new two-point calibrated NOI sensor tip installed. Regardless of the type of sensor tip, a signal drift could be distinguished; however, this signal drift was found to be much larger for the nonoptical isolated sensor than for the isolated sensor tip, sometimes as large as 3.7% air saturation per hour (Fig. 2b). This drift has not been recognized for use in aquatic environments (Chipman et al. 2012) and is probably due to photo-bleaching of the sensor tip (Loligo Systems 2005. The drift in oxygen signal could, if not reduced to its minimum, potentially have large influences on the computed oxygen fluxes. The impact from the drift on the oxygen flux is investigated by comparing the evolution of the oxygen flux [difference in flux compared to the initial flux Fo(t) − Fo(0)] with the evolution of the partial pressure of oxygen Po(t) − Po(0) (Fig. 3a). The oxygen partial pressure increases with 35 hPa due to the drift. The signal drift is largest in the beginning of the measuring period and levels out after about 40 h; therefore, most of the data are found for Po(t) − Po(0) > 20 hPa. From Po(t) − Po(0) = 15 to 33 hPa, a small tendency is found for increasing oxygen fluxes. This is, however, more likely explained by natural trends in dynamical or biological forcing affecting the magnitude of the oxygen flux rather than the signal drift. For air–sea oxygen fluxes over such a relatively short period of time, dynamical forcing from wind speed is assumed to be the most important parameter affecting the magnitude of the oxygen flux.
Scatterplot of the evolution of the absolute value of the oxygen flux with time 
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Scatterplot of the evolution of the absolute value of the oxygen flux with time
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Scatterplot of the evolution of the absolute value of the oxygen flux with time
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
During P5 the oxygen fluxes for the sector 80°–160° are found within the range 0.3–6.6 μmol m−2 s−1, where the variation is mostly due to variations in wind speed. These oxygen fluxes are somewhat larger than the modeled 50-yr average oxygen flux for June of 0.6 µmol m−2 s−1 for the eastern Gotland basin (Norman et al. 2013). However, prior to evaluating the magnitude of the absolute air–sea scalar flux, several corrections need to be applied and flux uncertainty determined (Blomquist et al. 2010; Rowe et al. 2011; Yang et al. 2013); additionally, the air–sea scalar gradient needs to be verified by measurements. Here the oxygen fluxes are corrected according to Eq. (9) and for the contribution from the latent heat flux [second term on the right-hand side in Eq. (8)] affecting the measured density flux. In Fig. 4a the evolution of the absolute value of Fo(t) − Fo(0) is shown as a function of time, and Fig. 4b shows the wind speed during the period. After about 30 h, the wind starts to increase from 5 m s−1 to reach its maximum of 14 m s−1 at 72 h after deployment. For most cases the large increase in oxygen flux coincides with high wind speeds, showing wind speed to be the dominant forcing on the oxygen flux rather than the influence from the signal drift.
Time series of (a) the absolute value of 
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Time series of (a) the absolute value of
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Time series of (a) the absolute value of
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
To ascertain the most suitable oxygen sensor for eddy covariance measurements, normalized mean oxygen spectra of the OI sensor (Fig. 5) and the NOI sensor (Fig. 6) as a function of frequency are studied. The mean spectra in Fig. 5 consist of 14 half-hour runs measured with the OI sensor during P1. In agreement with the Kolmogorov theory for other scalars, oxygen spectra show a slope close to −⅔ in the inertial subrange; however, for frequencies higher than 0.3 Hz, a sudden drop in energy can be seen for the OI sensor. The energy loss occurs at frequencies higher than 0.3 Hz, which is slightly lower than what is to be expected for the OI sensor, which, according to the specification is capable of resolving eddies of sizes up to 0.5 Hz. The normalized oxygen power spectra for the NOI sensor (Fig. 6) represent an average of 50 half-hour runs measured during conditions close to neutral. The NOI sensor shows a slope close to −⅔ up to about 1 Hz, with a small tendency of a more spiky structure of the spectra in the range 0.5–1 Hz. After 1 Hz a drastic drop in oxygen power spectra is observed, indicating a response limited to 1 Hz, which corresponds to the tabulated response time of 0.5 s for the NOI sensor. Energy in the frequency range 1.0–10 Hz should be regarded as noise, since the update rate of the instrument is limited to 2 Hz. As expected there is a shift in the spectral peak frequency toward lower frequencies when measuring at 27-m height compared to 10-m height, which makes it possible to resolve a larger part of the spectrum, shown in Figs. 5 and 6.
Normalized mean power spectra of oxygen measured with the OI sensor plotted against frequency consisting of 14 consecutive half hours of data during P1 at 10-m height. Straight line indicates a −⅔ slope.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean power spectra of oxygen measured with the OI sensor plotted against frequency consisting of 14 consecutive half hours of data during P1 at 10-m height. Straight line indicates a −⅔ slope.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean power spectra of oxygen measured with the OI sensor plotted against frequency consisting of 14 consecutive half hours of data during P1 at 10-m height. Straight line indicates a −⅔ slope.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean power spectra measured with the NOI sensor plotted against frequency. The spectra is averaged over 50 half hours of data from 29 to 30 Aug 2012 at a height of 27 m. Straight line indicates a −⅔ slope.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean power spectra measured with the NOI sensor plotted against frequency. The spectra is averaged over 50 half hours of data from 29 to 30 Aug 2012 at a height of 27 m. Straight line indicates a −⅔ slope.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean power spectra measured with the NOI sensor plotted against frequency. The spectra is averaged over 50 half hours of data from 29 to 30 Aug 2012 at a height of 27 m. Straight line indicates a −⅔ slope.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Half hour average of 
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Half hour average of
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Half hour average of
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
b. Spectral analysis
As previously discussed the peak frequency is shifted toward lower frequencies when measuring at 27-m height compared to 10-m height, meaning that the NOI sensor resolves a larger part of the spectra and cospectra. Because of scalar similarity, we expect the cospectra of oxygen to behave similar as the cospectra for CO2 and humidity following a −
Normalized mean cospectra of oxygen (blue), humidity (green), and CO2 (red) plotted against normalized frequency: (a) in a log–log representation and (b) in a linear–log representation. The mean cospectra contain 22 half hours of data from 30 Aug 2012. Straight black line in (a) indicates a −
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean cospectra of oxygen (blue), humidity (green), and CO2 (red) plotted against normalized frequency: (a) in a log–log representation and (b) in a linear–log representation. The mean cospectra contain 22 half hours of data from 30 Aug 2012. Straight black line in (a) indicates a −
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Normalized mean cospectra of oxygen (blue), humidity (green), and CO2 (red) plotted against normalized frequency: (a) in a log–log representation and (b) in a linear–log representation. The mean cospectra contain 22 half hours of data from 30 Aug 2012. Straight black line in (a) indicates a −
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
As mentioned previously the area enclosed by the curve is proportional to the flux. For lower frequencies the normalized cospectra follow each other as expected. For higher frequencies, however, the cospectrum of oxygen is found to be consistently lower compared to cospectra of humidity and CO2. For f > 1, the nCwo cospectrum is found to be zero. However, less than 1% of the total fluxes of CO2 and water vapor are associated with eddies in this range. The total loss in the normalized oxygen cospectra compared to the normalized cospectra of humidity and CO2 is of importance for the magnitude of the total oxygen flux. To correct for this loss, the normalized cospectral shape of CO2 and humidity or any other scalar can be used as the true shape of the normalized oxygen cospectra for frequencies higher than the drop-off frequency [cf. section 4b(2)].
c. Corrections of O2 fluxes
Using the normalized mean cospectra of CO2 and humidity, the oxygen flux is corrected by applying Eq. (7). The correction on the oxygen flux is (for cospectra in Fig. 8) 16% and 26% using 
Cospectral corrected flux (FCC) normalized with the mean flux (Fm) for 50 half hours as a function of uncorrected flux (FUC) normalized during P4. Red line displays the best curve fitting by the least squares method. Blue line shows the 1:1 relation between corrected and uncorrected flux.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Cospectral corrected flux (FCC) normalized with the mean flux (Fm) for 50 half hours as a function of uncorrected flux (FUC) normalized during P4. Red line displays the best curve fitting by the least squares method. Blue line shows the 1:1 relation between corrected and uncorrected flux.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
Cospectral corrected flux (FCC) normalized with the mean flux (Fm) for 50 half hours as a function of uncorrected flux (FUC) normalized during P4. Red line displays the best curve fitting by the least squares method. Blue line shows the 1:1 relation between corrected and uncorrected flux.
Citation: Journal of Atmospheric and Oceanic Technology 31, 11; 10.1175/JTECH-D-13-00249.1
6. Summary and conclusions
Using high-frequency oxygen data from the Östergarnsholm site, we show that the Microx TX3 with the nonoptical isolated (NOI) sensor attains sufficient response time and precision to be used in the eddy covariance system at 27 m to measure larger air–sea fluxes of O2. The optical isolated (OI) sensor shows a more stable signal than the NOI sensor in terms of signal drift, not shown here; however, the slow response time of the OI sensor makes it insufficient for ground-based eddy covariance measurements. The signal drift of the NOI sensor will result in errors in the flux estimation if not corrected. This drift can be reduced by regular calibration and by lowering the sensor’s light-emitting diode (LED) intensity; however, a lower LED intensity will also decrease the instrumental resolution, which is already a limitation for this instrument. This study reveals a significant limitation in sensor lifetime for the NOI sensor, which limits the application of the Microx TX3 in atmospheric eddy covariance systems. The sensor lifetime varied between 1 and 4 days, where the variations in sensor lifetime were found to depend on atmospheric conditions, such as solar radiation and direct impact from precipitation. We therefore encourage a signal stability control like the one presented in this study to select periods of data used for further analysis, and if necessary a manual spectral–cospectral inspection. To reduce the forcing on the oxygen sensor, land-based measurements are preferred where motions of the EC system are minimized.
Spectra of oxygen measured with the NOI sensor follow a −⅔ slope within the inertial subrange; however, above 1.0 Hz a pronounced drop in frequency response is seen. This study also reveals the importance of measuring at the highest possible altitude for instruments with a limited response time, such as the Microx TX3. Comparing normalized cospectra of oxygen, humidity, and CO2 shows that they coincide and follow a −
Acknowledgments
We would like to give a special thanks to the anonymous reviewers for their most valuable feedback. We also thank all the people involved in the field campaigns and especially Eva Podgrajsek, for her technical assistance.
REFERENCES
Anderson, D. E., and Verma S. B. , 1985: Turbulence spectra of CO2, water vapor temperature and wind velocity fluctuations over a crop surface. Bound.-Layer Meteor., 33, 1–14, doi:10.1007/BF00137033.
Arrigo, K. R., 2005: Marine microorganisms and global nutrient cycles. Nature, 437, 349–355, doi:10.1038/nature04159.
Aubinet, M., Vesala T. , and Paple D. , Eds., 2012: Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Springer Atmospheric Sciences Series, Springer, 436 pp.
Berg, P., Røy H. , Janssen F. , Meyer V. , Jørgensen B. , Huettel M. , and de Beer D. , 2003: Oxygen uptake by aquatic sediments measured with a novel non-invasive eddy-correlation technique. Mar. Ecol. Prog. Ser., 261, 75–83, doi:10.3354/meps261075.
Berg, P., Glud R. N. , Hume A. , Stahl H. , Kazumasa O. , Meyer V. , and Kitazato H. , 2009: Eddy correlation measurements of oxygen uptake in deep ocean sediments. Limnol. Oceanogr. Methods, 7, 576–584, doi:10.4319/lom.2009.7.576.
Blomquist, B. W., Fairall C. W. , Huebert B. J. , Kieber D. J. , and Westby G. R. , 2006: DMS sea-air transfer velocity: Direct measurements by eddy covariance and parameterization based on the NOAA/COARE gas transfer model. Geophys. Res. Lett., 33, L07601, doi:10.1029/2006GL025735.
Blomquist, B. W., Huebert B. J. , Fairall C. W. , and Faloona I. C. , 2010: Determining the sea-air flux of dimethylsulfide by eddy correlation using mass spectrometry. Atmos. Meas. Tech., 3, 1–20, doi:10.5194/amt-3-1-2010.
Brand, A., Mcginnis D. F. , Wehrli B. , and Wueest A. , 2008: Intermittent oxygen flux from the interior into the bottom boundary of lakes as observed by eddy correlation. Limnol. Oceanogr., 53, 1997–2006, doi:10.4319/lo.2008.53.5.1997.
Chipman, L., Huettel M. , Berg P. , Meyer V. , Klimant I. , Glud R. , and Wenzhoefer F. , 2012: Oxygen optodes as fast sensors for eddy correlation measurements in aquatic systems. Limnol. Oceanogr. Methods, 10, 304–316, doi:10.4319/lom.2012.10.304.
Codispoti, L., Brandes J. , Christensen J. , Devol A. , Naqvi S. , Paerl H. , and Yoshinari T. , 2001: The oceanic fixed nitrogen and nitrous oxide budgets: Moving targets as we enter the anthropocene? Sci. Mar., 65, 85–105, doi:10.3989/scimar.2001.65s285.
Fairaill, C. W., Hare J. E. , Edson J. B. , and McGillis W. R. , 2000: Measurement and parameterization of air–sea gas transfer. Bound.-Layer Meteor., 96, 63–105, doi:10.1023/A:1002662826020.
Garbe, C. S., and Coauthors, 2014: Transfer across the air-sea interface. Ocean-Atmosphere Interactions of Gases and Particles, P. S. Liss and M. T. Johnson, Eds., Springer Earth System Sciences, 55–112, doi:10.1007/978-3-642-25643-1_2.
Hicks, B. B., and McMillen R. T. , 1988: On the measurement of dry deposition using imperfect sensors and in non-ideal terrain. Bound.-Layer Meteor., 42, 79–84, doi:10.1007/BF00119876.
Högström, U., 1996: Review of some basic characteristics of the atmospheric surface layer. Bound.-Layer Meteor., 78, 215–246, doi:10.1007/BF00120937.
Högström, U., and Coauthors, 2008: To what extent can we believe measurements on a land-based tower to represent upwind open sea conditions. Boreal Environ. Res., 13, 475–502.
Horst, T. W., 2000: On frequency response corrections for eddy covariance flux measurements. Bound.-Layer Meteor., 94, 517–520, doi:10.1023/A:1002427517744.
Horst, T. W., Oncley S. P. , and Semmer S. R. , 1997: Measurement of water vapor fluxes using capacitance RH sensors and cospectral similarity. Preprints, 12th Symp. on Boundary Layers and Turbulence, Vancouver, BC, Canada, Amer. Meteor. Soc., 360–361.
Joos, F., Plattner G. K. , Stocker T. F. , Körtzinger A. , and Wallace D. W. R. , 2003: Trends in marine dissolved oxygen: Implications for ocean circulation changes and the carbon budget. Eos, Trans. Amer. Geophys. Union, 84, 197–201, doi:10.1029/2003EO210001.
Kaimal, J. C., and Finnigan J. J. , 1994: Atmospheric Boundary Layer Flows. Oxford University Press, 289 pp.
Kaimal, J. C., Wyngaard J. C. , Izumi Y. , and Coté O. R. , 1972: Spectral characteristics of surface layer turbulence. Quart. J. Roy. Meteor. Soc., 98, 563–589, doi:10.1002/qj.49709841707.
Karlsson, K., Rosenberg R. , and Bonsdorff E. , 2002: Temporal and spatial large-scale effects of eutrophication and oxygen deficiency on benthic fauna in Scandinavian and Baltic waters: A review. Oceanogr. Mar. Biol., 40, 427–489.
Keeling, R. F., Körtzinger A. , and Gruber N. , 2010: Ocean deoxygenation in a warming world. Annu. Rev. Mar. Sci.,2, 199–229, doi:10.1146/annurev.marine.010908.163855.
Kihm, C., and Körtzinger A. , 2010: Air-sea gas transfer velocity for oxygen derived from float data. J. Geophys. Res., 115, C12003, doi:10.1029/2009JC006077.
Körtzinger, A., Send U. , Wallace D. W. R. , Karstensen J. , and DeGrandpre M. , 2008: Seasonal cycle of O2 and PCO2 in the Labrador Sea: Atmospheric, biological, and physical implications. Global Biogeochem. Cycles, 22, GB1014, doi:10.1029/2007GB003029.
Kuwae, T., Kamio K. , Inoue T. , Miyoshi E. , and Uchiyama Y. , 2006: Oxygen exchange flux between sediment and water in an intertidal sandflat, measured in situ by the eddy-correlation method. Mar. Ecol. Prog. Ser., 307, 59–68, doi:10.3354/meps307059.
Laubach, J., and McNaughton K. G. , 1998: A spectrum-independent procedure for correcting eddy fluxes measured with separated sensors. Bound.-Layer Meteor., 89, 445–467, doi:10.1023/A:1001759903058.
Lee, X., Massman W. , and Beverly L. , 2004: Handbook of Micrometeorology. Kluwer Academic Publishers, 245 pp.
Liss, P. S., and Merlivat L. , 1986: Air–sea gas exchange rates: Introduction and synthesis. The Role of Air–Sea Exchange in Geochemical Cycling, P. Buat-Menard, Ed., NATO Science Series C, Vol. 185, Springer, 113–127.
Loligo Systems, 2005: Microx TX3 fiber-optic oxygen meter. Instruction Manual, Software Version TX3v531, 96 pp. [Available online at http://www.loligosystems.com/getfile_d.php?id=4104.]
Massman, W. J., 2000: A simple method for estimating frequency response corrections for eddy covariance systems. Agric. For. Meteor., 104, 185–198, doi:10.1016/S0168-1923(00)00164-7.
Massman, W. J., 2001: Reply to comment by Rannik on “A simple method for estimating frequency response corrections for eddy covariance systems.” Agric. For. Meteor., 107, 247–251, doi:10.1016/S0168-1923(00)00237-9.
McGillis, W. R., and Coauthors, 2004: Air-sea CO2 exchange in the equatorial Pacific. J. Geophys. Res., 109, C08S02, doi:10.1029/2003JC002256.
McGinnis, D. F., Berg P. , Brand A. , Lorrai C. , Edmonds T. J. , and Wuest A. , 2008: Measurements of eddy correlation oxygen fluxes in shallow freshwaters: Towards routine applications and analysis. Geophys. Res. Lett., 35, L04403, doi:10.1029/2007GL032747.
McNeil, C. L., and D’Asaro E. , 2007: Parameterization of air-sea gas fluxes at extreme wind speeds. J. Mar. Syst., 66, 110–121, doi:10.1016/j.jmarsys.2006.05.013.
McNeil, C. L., Ward B. , McGillis W. R. , DeGrandPre M. D. , and Marcinowski L. , 2006: Fluxes of N2, O2, and CO2 in nearshore waters off Martha’s Vineyard. Cont. Shelf Res., 26, 1281–1294, doi:10.1016/j.csr.2006.04.008.
Monin, A. S., 1962: Empirical data on turbulence in the surface layer of the atmosphere. J. Geophys. Res., 67, 3103–3109, doi:10.1029/JZ067i008p03103.
Moore, C. J., 1986: Frequency response corrections for eddy correlation systems. Bound.-Layer Meteor., 37, 17–35, doi:10.1007/BF00122754.
Norman, M., Rutgersson A. , Sørensen L. L. , and Sahlée E. , 2012: Methods for estimating air–sea fluxes of CO2 using high-frequency measurements. Bound.-Layer Meteor., 144, 379–400, doi:10.1007/s10546-012-9730-9.
Norman, M., Rutgersson A. , and Sahlée E. , 2013: Impact of improved air–sea gas transfer velocity on fluxes and water chemistry in a Baltic Sea model. J. Mar. Syst., 111–112, 175–188, doi:10.1016/j.jmarsys.2012.10.013.
Ohtaki, E., 1982: Kolmogorov constant for carbon dioxide in the atmospheric surface layer over a paddy field. Bound.-Layer Meteor., 23, 153–159, doi:10.1007/BF00123293.
Ohtaki, E., 1985: On the similarity in atmospheric fluctuations of carbon dioxide, water vapor and temperature over vegetated fields. Bound.-Layer Meteor., 32, 25–37, doi:10.1007/BF00120712.
Ohtaki, E., and Matsui T. , 1982: Infrared device for simultaneous measurement of fluctuations of atmospheric carbon dioxide and water vapor. Bound.-Layer Meteor., 24, 109–119, doi:10.1007/BF00121803.
Paulmier, A., Ruiz-Pino D. , and Garçon V. , 2008: The oxygen minimum zone (OMZ) off Chile as intense source of CO2 and N2O. Cont. Shelf Res., 28, 2746–2756, doi:10.1016/j.csr.2008.09.012.
Reimers, C. E., Özkan-Haller H. T. , Berg P. , Devol A. , McCann-Grosvenor K. , and Sanders R. D. , 2012: Benthic oxygen consumption rates during hypoxic conditions on the Oregon continental shelf: Evaluation of the eddy correlation method. J. Geophys. Res., 117, C02021, doi:10.1029/2011JC007564.
Rowe, M. D., Fairall C. W. , and Perlinger J. A. , 2011: Chemical sensor resolution requirements for near-surface measurements of turbulent fluxes. Atmos. Chem. Phys., 11, 5263–5275, doi:10.5194/acp-11-5263-2011.
Rutgersson, A., and Smedman A. , 2010: Enhanced air–sea CO2 transfer due to water-side convection. J. Mar. Syst., 80, 125–134, doi:10.1016/j.jmarsys.2009.11.004.
Rutgersson, A., Norman M. , Schneider B. , Pettersson H. , and Sahlée E. , 2008: The annual cycle of carbon dioxide and parameters influencing the air–sea carbon exchange in the Baltic Proper. J. Mar. Syst., 74, 381–394, doi:10.1016/j.jmarsys.2008.02.005.
Rutgersson, A., Smedman A. , and Sahlée E. , 2011: Oceanic convective mixing and the impact on air–sea gas transfer velocity. Geophys. Res. Lett., 38, L02602, doi:10.1029/2010GL045581.
Sahlée, E., Smedman A. S. , Rutgersson A. , and Högström U. , 2008: Spectra of CO2 and water vapour in the marine atmospheric boundary layer. Bound.-Layer Meteor., 126, 279–295, doi:10.1007/s10546-007-9230-5.
Sahlée, E., Drennan W. M. , Potter H. , and Rebozo M. A. , 2012: Waves and air-sea fluxes from a drifting ASIS buoy during the Southern Ocean Gas Exchange experiment. J. Geophys. Res., 117, C08003, doi:10.1029/2012JC008032.
Smedman, A., Högström U. , Bergström H. , Rutgersson A. , Kahma K. , and Pettersson H. , 1999: A case-study of air-sea interaction during swell conditions. J. Geophys. Res., 104, 25 833–25 851, doi:10.1029/1999JC900213.
Stern, O., and Volmer M. , 1919: Über die Abklingzeit der Fluoreszenz. Phys. Z., 20, 183–188.
Stigebrandt, A., 1991: Computations of oxygen fluxes through the sea surface and the net production of organic matter with application to the Baltic and adjacent seas. Limnol. Oceanogr., 36, 444–454, doi:10.4319/lo.1991.36.3.0444.
Stocker, T. F., and Coauthors, 2013: Climate Change 2013: The Physical Science Basis. Cambridge University Press, 1535 pp. [Available online at www.climatechange2013.org/images/report/WG1AR5_ALL_FINAL.pdf.]
Stramma, L., Johnson G. C. , Sprintall J. , and Mohrholz V. , 2008: Expanding oxygen-minimum zones in the tropical oceans. Science, 320, 655–658, doi:10.1126/science.1153847.
Stramma, L., and Coauthors, 2012: Expansion of oxygen minimum zones may reduce available habitat for tropical pelagic fishes. Nat. Climate Change,2, 33–37, doi:10.1038/nclimate1304.
Verma, S., and Anderson D. , 1984: Kolmogorov constants for CO2, wind velocity, air temperature, and humidity fluctuations over a crop surface. Bound.-Layer Meteor., 28, 161–167, doi:10.1007/BF00119461.
Wanninkhof, R., 1992: Relationship between wind speed and gas exchange over the ocean. J. Geophys. Res., 97, 7373–7382, doi:10.1029/92JC00188.
Wanninkhof, R., and McGillis W. R. , 1999: A cubic relationship between gas transfer and wind speed. Geophys. Res. Lett., 26, 1889–1893, doi:10.1029/1999GL900363.
Wanninkhof, R., Asher W. E. , Ho D. T. , Sweeney C. , and McGillis W. R. , 2009: Advances in quantifying air-sea gas exchange and environmental forcing. Annu. Rev. Mar. Sci., 1, 213–244, doi:10.1146/annurev.marine.010908.163742.
Webb, E. K., Pearman G. I. , and Leuning R. , 1980: Correction of flux measurements for density effects due to heat water vapor transport. Quart. J. Roy. Meteor. Soc., 106, 85–100, doi:10.1002/qj.49710644707.
Woolf, D. K., 1993: Bubbles and the air–sea transfer velocity of gases. Atmos.–Ocean, 31, 517–540, doi:10.1080/07055900.1993.9649484.
Woolf, D. K., 1997: Bubbles and their role in air–sea gas exchange. The Sea Surface and Global Change, P. Liss and R. Duce, Eds., Cambridge University Press, 173–205.
Woolf, D. K., 2005: Parameterization of gas transfer velocity and sea-state-dependent wave breaking. Tellus, 57B, 87–94, doi:10.1111/j.1600-0889.2005.00139.x.
Wyngaard, J. C., 2010: Turbulence in the Atmosphere. Cambridge University Press, 393 pp.
Wyngaard, J. C., and Cote O. R. , 1972: Cospectral similarity in the atmospheric surface layer. Quart. J. Roy. Meteor. Soc., 98, 590–603, doi:10.1002/qj.49709841708.
Yang, M., Beale R. , Smyth T. , and Blomquist B. , 2013: Measurements of OVOC fluxes by eddy covariance using a proton-transfer-reaction mass spectrometer—Method development at a coastal site. Atmos. Chem. Phys., 13, 6165–6184, doi:10.5194/acp-13-6165-2013.
Zappa, C. J., Asher W. E. , and Jessup A. T. , 2001: Microscale wave breaking and air‐water gas transfer. J. Geophys. Res., 106, 9385–9391, doi:10.1029/2000JC000262.
