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\section{Observational mechanisms}  We observed the sea state and fluxes in a high winds regime, during the HIWINGS experiment project. Our region of interest was the North Atlantic ocean, south of Greenland, in the path of strong and frequent cyclones. There was an acute positive NAO (North Atlantic Oscillation) index during that period, namely from the beginning of October to middle November, 2013. Because of this phenomenon, a powerfull jet stream and intense horizontal pressure gradients were present, hence the repetitive cyclones. We succeeded to perform the measurements during winds of over 20 m/s, which makes the data and the future results more valuable, because it is something that probably hasn't been realized in the past. A notable variety of instruments that measured the sea state and fluxes were used during this field experiment. The scientific crew was partioned in different teams having specific goals regarding their interest for the data, but a unique goal when it comes to understand the big picture of the air-sea coupled system dynamics. Our specific scientifc goal is to understand the mechanisms of primary source for aerosol production, and hence aerosol turbulent fluxes. We already made the consideration that aerosol turbulent fluxes occur via bubble, whitecap, and bubble plume dynamics. Our main focus is on bubble plume and whitecap physics, but on individual bubble physics as well. In order to have a deep understanding of this natural process in a high winds regime, we need to harmonize and correlate the measurements performed using this rich variety of instruments. We used a foam camera to detect whitecap fractions, a submerged bubble camera to explore the optical properties of bubbles within bubble plumes, two resonators to assess the acoustical properties of bubbles within bubble plumes, and a sonar to detect the bubble plumes dynamics. All instruments were set in place along a 11 meters spar buoy. Just under the foame camera, we had wires that could measure the sea state. More detailed informations on the sea state can be generated by the Wave Rider, which was separated from the spar buoy. Other instruments took measurements from the ship's board, including measurements of aerosol and gas fluxes.  \subsection{Theoretical approaches} modelling}  Bubbles encapsulate water vapors and other gases. We are interested in analyzing bubbles, because, as mentioned, bubbles are a direct cause for transfer velocity of different gases. Aerosols are produced as sea spray particles by the breaking of the surface tension of individual bubbles. The bubble film layers are fragmentated into small droplets (film droplets). Bubbles usually cary salt and organic particles so they are called "dirty" bubbles. The more "loaded" bubbles are, the more effective they are in generating cloud condensation nucleis. The difficulty is that loaded bubbles have a higher dragging effect, which comes with a decrease in vertical velocity (upward motion) of the bubbles. Coarser salt or organic particles carried by bubbles have a higher inertia, so the "popping" momentum in launching aerosols into the atmospheric boundary layer is inhibited.  Molecular diffusion for subsurface bubbles is an important feature for gas transfer, because it actually gas transfer. The higher the molecular diffusion through bubble walls, the less available the number of bubbles at the surface which can organize in foam patches. If the foam patches contain gases with low solubility, they become void fractions. There is a relation between void fractions and interstitial water, which involves the transfer velocity. For highly insoluble gases, the capacity of the interstitial water may be smaller than that of the bubble even for small void fractions \cite{en_Liddicoat_Baker_et_al__2007}. This is called restriction of gas transfer in a dense isolated plume "suffocation". High subsurface bubbles molecular diffusion inhibits the buoyancy of bubbles and, hence, the vertical upward velocity of bubbles. In this sense, the Wolf model shows that there is a higher gas transfer for low Schmidt number, $Sc$, therefore subsurface lower molecular diffusion, $D$, generates more gas available at the surface, hence a higher transfer rate. Low Schmidt number means higher upward vertical velocity for subsurface bubbles, therefore more available bubbles for surfacing. Wolf model assumes two cases for the transfer velocity: one without void fraction and the other with 20 \% void fraction (Figure \ref{fig:fig1}). It can be noticed that the transfer velocity is reduced at low Schmidt number with 20 \% void fraction present. Correlation between observed gases with different solubilities and bubble size distribution would give an insight on what type of fluxes, from the chemical point of view, are more likely to be enhanced. The presence of void fractions assumes that gas is trapped at the surface, therefore the transfer velocity is suppressed. Considering this phenomenon, one can correlate void fraction with whitecap time decay and, therefore, with transfer velocity in low winds, because in high winds condition, this phenomenon might not be so relevant.   \subsection{Possible correlations and parameterizations}  Our interest is to go right to source of whitecap variability and, therefore, of aerosol fluxes. Other fluxes, such as momentum fluxes especially, have no or little dependency on whitecap variability. One should dynamically couple whitecaps and bubble plumes characteristics in order to explicitly quantify aerosol production potential.  A range of several parameterizations for whitecap coverage have been performed. Whitecap fraction as a function of wind speed only is the most common, and it takes the form of a power-law relation between $W$ and ${U}_{10}$,   \begin{equation}  W = a{U}^b_{10},  \end{equation} with coefficients $a$ and $b$ derived from a set of observations.  In order Because it was clear that $W$ cannot vary with wind speed in a linear way, some autors tried to link $W$ with the cubed wind speed,   \begin{equation}   W = a({U}_{10} + b)^3   \end{equation}   According to Philips, in 1985,  to obtain the whitecap fraction, one can refer to an average bubble persistence time $\tau_{bub}$ to obtain $W$: \begin{equation}  W = \int_{0}^{\infty}\tau _{bub}c\Lambda (c)dc _{bub}c\Lambda(c)dc,  \end{equation} where ${tau}_{bub}$ is the bubble persistence time, $Lambda(c)$ is the Philips parameter and $c$ is the forward speed of the beaking wave crest.