the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Mar 2026
Airborne eddy covariance measurements of ocean-air VOC fluxes: Distinguishing signal from noise
Abstract. Ocean-atmosphere exchange plays an important but uncertain role for many volatile organic compounds (VOCs). Airborne eddy covariance (EC) enables direct flux quantification over large areas, but VOC applications have largely been performed over land. Here we combine the EC methodology with aircraft-based measurements from the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) and use the results to characterize air-sea VOC fluxes and to elucidate random and systematic drivers of error. Using perturbation experiments, we show that uncorrelated sensor noise (USN) causes flux biases by obscuring the sensor-wind time lag; such biases are avoided by imposing a time-lag constraint (e.g., from a higher-flux compound or time). We define the flux signal-to-noise ratio SNRf and characterize its dependence on USN and sampling regime. Results show a transition from a USN-dominated regime to one where SNRf is limited by turbulent stochasticity. The NAAMES VOC fluxes are noise-limited, whereas H2O and sensible heat fluxes lie respectively in turbulence-limited and transitional regimes. We provide a methodology for determining sensor noise levels needed for robust flux detection: for the NAAMES subset examined here, a factor of 18 USN reduction would enable 75 % (rather than 15 %) of measured VOC fluxes to attain SNRf > 3. The airborne NAAMES results reveal VOCs with universally upward (e.g., dimethyl sulfide), downward (e.g., acetone), bidirectional (e.g., acetaldehyde), and undetectable (e.g., monoterpenes) air-sea exchange, with controls including wind speed and planktonic activity. Findings highlight the importance of USN for VOC flux quantification by airborne EC and lay a foundation for expanded use of this technique.
Competing interests: One of the co-authors is a member of the editorial board for Atmospheric Measurement Techniques. The authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-976', Anonymous Referee #1, 07 Apr 2026
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RC2: 'Comment on egusphere-2026-976', Mingxi Yang, 17 Apr 2026
This is a highly technical paper that reports aircraft VOC fluxes measured by a PTR-tof-MS. The paper looks in detail at sources of random and systematic uncertainties in the flux measurements. Due to the small magnitude of the VOC fluxes, the authors found that only DMS (emission) and acetone (deposition) fluxes were unambiguously detected on a consistent basis. Overall, I think this is a useful methods paper. Though it doesn’t seem that the flight plans were designed to optimize flux measurements, and the numbers of flux segments were very small, thus it’s hard to say how representative the measured fluxes are of air-sea exchange in the North Atlantic.
Detailed comments:
The introduction had a very comprehensive review of previous techniques.
Sensitivity cps/ppb of PTR-tof-MS for main compounds of interest? What’s the mass resolution?
Line 105. Altitude of low level legs?
Line 110. This might have been described briefly, but is worth repeating. Was the PTR calibrated against standard gases? What was used as the background or blank measurement? Type/length of inlet? Flow rate? What’s the expected e-folding time (response time) of the system?
Line 135. Was a correction for the aircraft motion on the measured relative winds necessary for these flights?
Figure 2. please use different marker shapes for people who might struggle with the colors.
Line 176. Random uncertainty in flux can usually be reduced by more averaging (scales with 1/sqrt(N)). Here a rather large range in averaging time (and so space) was chosen: 3-25 km, which by itself preassembly leads to quite different noise levels. Why not just using 3 km averaging period through out? And if needed, further average the 3-km fluxes afterwards?
Line 186. How different are flux results using this approach vs a simple linear detrend?
Line 190. What is roughly the expected lag time based on the inlet dimensions and flow rates? Have authors tested with injections of VOC standards to see whether different VOC show comparable or different responses within their inlet?
Figure 3. on my screen there looks to be breaks in the cospectral density plots?
Line 254. Again, what’s the response time of the PTR/inlet system? It might be that the cospectra are too noisy to say with confidence that high frequency attenuation is negligible. Were there conditions of stable atmosphere, where attenuation might be more pronounced?
Line 260. The detrend method used implicitly assumes that there are no low frequency eddies that contribute to flux, right? Again, a simple comparison with this approach vs linear detrend would be useful.
Line 314. The use of ‘VOCs’ here is ambiguous. Surely the random error contribution to well resolved fluxes such as DMS and maybe acetone is smaller than for VOCs such as benzene and monoterpenes?
Figure 5. This is a neat way to illustrate the importance of getting the lag time correctly. But I don’t get why such a large lag wind window (+/- 50s) is needed for the lag correlation calculation. Was the pump flow so variable (e.g. with altitude)?
Figure 7. Again, I think it’d be useful to specify which VOC here, as the S/N must be very different for say DMS vs benzene.
Table 1. where it says ‘uncertainty of the mean flux’, this seems to be 3 stdev. Is this the flux noise or the flux detection limit? It seems like it might be the latter to me.
How are error-weighted mean flux and its uncertainty computed?
Section 5.1 This section is interpreted in the context of the expected air-sea fluxes. However the authors never mentioned the height of their ‘low level legs’. EC fluxes are generally thought to be representative of surface fluxes if they’re measured within the surface layer of the atmosphere, which is roughly lowest 10% of the marine boundary layer. Thus if the boundary layer is 500 m, the aircraft would need to fly below 50 m for the flues to be representative of surface exchange. Above the surface layer there’s expected to be a vertical gradient in flux (driven by the strength of surface flux and entrainment flux at top of the marine boundary layer).
Section 5.2 Eddy covariance fluxes, as the authors have shown, have relatively large random uncertainties that require sufficient averaging to draw robust conclusions. I’m not saying that the conclusions in this section is wrong, but given the very low number of above LOD flux segments for these VOCs, it’s very difficult to say how representative the aircraft fluxes are. I’d be tempted to minimize/remove this section. But if the authors want to keep it in, put a disclaimer at the beginning of the section about how limited the data coverage is.
Note: I co-reviewed this paper with my PhD student, Irene Monreal-Campos
Citation: https://doi.org/10.5194/egusphere-2026-976-RC2
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Review of Chen et al.: Airborne eddy covariance of ocean-air VOC fluxes: distinguishing signal from noise
The manuscript uses PTR-TOF-MS-measured VOC data from an airborne campaign over the North Atlantic to calculate airborne fluxes. The data is characterized by low signal-to-noise ratios, which the authors use as an opportunity to explore different sources of errors, especially uncorrelated sensor noise. This systematic analysis will be a valuable source of information and inspiration for scientists who want to conduct similar measurements and have to decide which kind of sensor to take on board, or who are analyzing airborne flux data and are wondering which sources of error to take into account. I do not have many criticisms, my main concern being that the authors ignored vertical flux divergence as a potential source of systematic uncertainty and as a potential reason for underestimation of the flux values which they report. The text is generally well written, the figures could partly be presented in a clearer way. Some more specific points are below. They should be addressed before the manuscript can be accepted for publishing in ACP.
References
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