Accounting for surface waves improves gas flux estimation at high wind speed in a large lake

The gas transfer velocity (k) is a major source of uncertainty when assessing the magnitude of lake gas exchange with the atmosphere. For the diversity of existing empirical and process-based k models, the transfer velocity increases with the level of turbulence near the air–water interface. However, predictions for k can vary by a factor of 2 among different models. Near-surface turbulence results from the action of wind shear, surface waves, and buoyancy-driven convection. Wind shear has long been identified as a key driver, but recent lake studies have shifted the focus towards the role of convection, particularly in small lakes. In large lakes, wind fetch can, however, be long enough to generate surface waves and contribute to enhance gas transfer, as widely recognised in oceanographic studies. Here, field values for gas transfer velocity were computed in a large hard-water lake, Lake Geneva, from CO2 fluxes measured with an automated (forced diffusion) flux chamber and CO2 partial pressure measured with high-frequency sensors. k estimates were compared to a set of reference limnological and oceanic k models. Our analysis reveals that accounting for surface waves generated during windy events significantly improves the accuracy of k estimates in this large lake. The improved k model is then used to compute k over a 1-year time period. Results show that episodic extreme events with surface waves (6 % occurrence, significant wave height > 0.4 m) can generate more than 20 % of annual cumulative k and more than 25 % of annual net CO2 fluxes in Lake Geneva. We conclude that for lakes whose fetch can exceed 15 km, k models need to integrate the effect of surface waves.

Uncertainties in eddy covariance air–sea CO₂ flux measurements and implications for gas transfer velocity parameterisations

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.

Accounting for surface waves improves gas flux estimation at high wind speed in a large lake

The gas transfer velocity (k) is a major source of uncertainty when assessing the magnitude of lake gas exchange with the atmosphere. For the diversity of existing empirical and process-based k models, the transfer velocity increases with the level of turbulence near the air-water interface. However, predictions for k can vary by a factor of 2 among different models. Near-surface turbulence results from the action of wind shear, surface waves and buoyancy-driven convection. Wind shear has long been identified as a key driver, while recent lake studies have shifted the focus towards the role of convection, particularly in small lakes. In large lakes, wind fetch can however be long enough to generate surface waves and contribute to enhance gas transfer, as widely recognised in oceanographic studies. Here, field values for gas transfer velocity were computed in a large hardwater lake, Lake Geneva, from CO2 fluxes measured with an automated (forced diffusion) flux chamber and CO2 partial pressure measured with high frequency sensors. k estimates were compared to a set of reference limnological and oceanic k models. Our analysis reveals that accounting for surface waves generated during windy events significantly improves the accuracy of k estimates in this large lake. The improved k model is then used to compute k over a one-year time-period. Results show that episodic extreme events with surface waves (6 % occurrence, significant wave height > 0.4 m) can generate more than 20 % of annual cumulative k and more than 25 % of annual net CO2 fluxes in Lake Geneva. We conclude that for lakes whose fetch can exceed 15 km, k-models need to integrate the effect of surface waves.

Mesoscale Temporal Wind Variability Biases Global Air–Sea Gas Transfer Velocity of CO2 and Other Slightly Soluble Gases

The significance of the water-side gas transfer velocity for air–sea CO2 gas exchange (k) and its non-linear dependence on wind speed (U) is well accepted. What remains a subject of inquiry are biases associated with the form of the non-linear relation linking k to U (hereafter labeled as f(U), where f(.) stands for an arbitrary function of ), the distributional properties of (treated as a random variable) along with other external factors influencing k, and the time-averaging period used to determine k from . To address the latter issue, a Taylor series expansion is applied to separate f(U) into a term derived from time-averaging wind speed (labeled as ⟨U⟩ where indicates averaging over a monthly time scale) as currently employed in climate models and additive bias corrections that vary with the statistics of . The method was explored for nine widely used f(U) parameterizations based on remotely-sensed 6-hourly global wind products at 10 m above the sea-surface. The bias in k of monthly estimates compared to the reference 6-hourly product was shown to be mainly associated with wind variability captured by the standard deviation around or, more preferably, a dimensionless coefficient of variation [...]

Estimating the air-sea gas transfer velocity from a statistical reconstruction of ocean turbulence observations

<p>Although the air-sea gas transfer velocity k is usually parameterized with wind speed, the so-called small-eddy model suggests a relationship between k and the ocean surface turbulence in the form of the dissipation rate of turbulent kinetic energy ε. However, available observations of ε from oceanographic cruises are spatially and temporally sparse. In this study, we use a Gaussian Process (GP) model to investigate the relationship between the observed profiles of ε and co-located atmospheric and oceanic fields from the ERA5 reanalysis. The model is then used to construct monthly maps of ε and to estimate the climatological air-sea gas transfer velocity from existing parametrizations. As an independent  validation,  the same model is also trained on EC-Earth3 outputs with the objective of reproducing the temporal and spatial patterns of turbulence kinetic energy as simulated by EC-Earth3. The ability to predict ε is instrumental to achieve better estimates of air-sea gas exchange that take into account multiple sources of upper ocean turbulence beyond wind stress.</p>

New parameterizations of air-sea CO2 gas transfer velocity on wave breaking

<p>Ocean surface waves and wave breaking play a pivotal role in air-sea Carbon Dioxide (<em>CO<sub>2</sub></em>) gas exchange by producing abundant turbulence and bubbles. Contemporary gas transfer models are generally implemented with wind speed, rather than wave parameters, to quantify <em>CO<sub>2</sub></em> transfer velocity (<em>K<sub>CO2</sub></em>). In our work, the direct relationship of <em>K<sub>CO2</sub></em> and waves is explored through the combination of laboratory experiment, field observational data and estimation of global ocean uptake of <em>CO<sub>2</sub></em>.</p><p>In laboratory, the waves and <em>CO<sub>2 </sub></em>transfer at water surface are forced for simultaneous measurements in a wind-wave flume. Three types of waves are exercised: mechanically generated monochromatic waves, pure wind waves with 10-meter wind speed ranging from 4.5 <em>m/s</em> to 15.5 <em>m/s</em>, and the coupling of monochromatic waves with superimposed wind force. The results show that <em>K<sub>CO2 </sub></em>is well correlated with wave height and orbital velocity. In the connection of <em>K<sub>CO2 </sub></em>with breakers, wave breaking probability (<em>b<sub>T</sub></em>) should also be considered. The wind speed is competent too in describing <em>K<sub>CO2 </sub></em>but may be inadequate for varied wave ages. A non-dimensional formula (hereafter the RHM model) is proposed in which gas transfer velocity is expressed as a main function of wave Reynolds number (<em>R<sub>HM </sub>= U<sub>w</sub>H<sub>s</sub>/ν<sub>w</sub></em>, where <em>U<sub>w</sub></em> is wave orbital velocity, <em>H<sub>s</sub></em> is significant wave height, <em>ν<sub>w</sub></em> is viscosity of water) while wind is accounted as an enhancement factor (<em>1+Û</em>, where <em>Û </em>is non-dimensional wind speed denoting the reverse of wave age). For wave breaking dominated gas exchange, second formula (hereafter the BT model) is developed by replacing components of <em>R<sub>HM </sub></em>with breaker’s statistics and integrates an additional factor of <em>b<sub>T. </sub></em></p><p>Utilizing campaign observations from open ocean, the RHM model can effectively reconcile the laboratory and field data sets. The BT model related with wave breaking, on the other hand, is adapted by including a complementary term of bubble-mediated gas transfer in which the bubble injection rate is parameterized with <em>R<sub>HM</sub></em>. The updated BT model also performs well for the data. The conventional wind-based models show similar features as in laboratory experiments: the wind speed successfully captures the variation of gas transfer for respective observation yet is insufficient to neutralize the gaps among data sets.</p><p>Our wave-based gas transfer models are applied for the estimation of net annual <em>CO<sub>2</sub></em> fluxes of global ocean in the period of year 1985-2017. The results are in high agreement with previous studies. The wind-based gas transfer models might underestimate the <em>CO<sub>2</sub></em> fluxes although the estimations still distribute within the range of uncertainty. Moreover, the models using wave parameters are found advantageous over the wind-based models in reducing the uncertainties of gas fluxes.</p>

Is remote sensing of the surfactant effect on gas transfer velocity possible?

<p>The air-sea gas flux is proportional to the difference of partial pressure between the sea-water and the overlying atmosphere multiplied by gas transfer velocity <em>k</em>, a measure of the effectiveness of the gas exchange. Because wind is the source of turbulence making the gas exchange more effective, <em>k</em> is usually parameterized by wind speed. Unfortunately, measured values of gas transfer velocity at a given wind speed have a large spread in values. Surfactants have been long suspected as the main reason of this variability but few measurements of gas exchange and surfactants have been performed at open sea simultaneously and therefore their results were inconclusive. Only recently, it has been shown that surfactants may decrease the CO<sub>2</sub> air-sea exchange by up to 50%. However the labour intensive methods used for surfactant study make it impossible to collect enough data to map the surfactant coverage or even create a gas transfer velocity parameterization involving a measure of surfactant activity. This is why we propose to use optical fluorescence as a proxy of surfactant activity.</p><p> </p><p>Previous research done by our group showed that fluorescence parameters allow estimation the surfactant enrichment of the surface microlayer, as well as types and origin of fluorescent organic matter involved. We plan to measure, from a research ship, all the variables needed for calculation of gas transfer velocity <em>k</em> (namely CO<sub>2</sub> partial pressure both in water and in air as well as vertical flux of this trace gas) and to use mathematical optimization methods to look for a parameterization involving wind speed and one of the fluorescence parameters which will minimize the residual <em>k</em> variability. Although our research will still involve water sampling and laboratory fluorescence measurements, the knowledge of which absorption and fluorescence emission bands are the best proxy for surfactant activity may allow to create remote sensing products (fluorescence lidars) allowing continuous measurements of surfactant activity at least from the ship board, if not from aircraft and satellites. The improved parameterization of the CO<sub>2</sub> gas transfer velocity will allow better constraining of basin-wide and global air-sea fluxes, an important component of global carbon budget.</p><p> </p><p>If an improved gas transfer velocity parametrization based on surfactant fluorescence spectrum in concert with a turbulence proxy (wind) were to be found, a tantalizing possibility arises of a remote sensing estimation of <em>k</em>. Namely a UV lidar can both excite and measure the fluorescence band identified as proxy of the surfactant effect on the gas transfer velocity. Depending on the wavelength bands needed to be utilized, the effect could be measured from a moving ship (already an improvements on methods needing sampling), an aircraft or possibly even a satellite. We intend to pursue this idea in cruises to both the Baltic and the North Atlantic, possibly in cooperation with other air-sea interaction groups (this presentation is in part an invitation to cooperation).</p>