1 Introduction

The atmospheric aerosol is very important for the geosciences study. Both aerosol impacts: direct effect (absorption and scattering of light jointly called extinction) and its importance as cloud condensation nuclei (indirect effect) is well known and extremely popular in recent literature. Especially interesting and insufficiently explained is the parameterization of aerosol emission from the Earth surface. This study is very important for atmospheric and global climate modeling. The most common aerosol is sea spray aerosol (SSA). 70 % of Earth surface is covered by oceans and seas, that is why the aerosol emission from the sea surface is the highest. According to the newest Intergovernmental Panel on Climate Change (IPCC) report (Boucher et al. 2013) the global emission of SSA is estimated in range 1,400–6,800 (Tg/year), which, as presented in Table 1 dominates all other emissions of the main natural aerosols species, obtained from various model simulations.

Table 1 Global natural emissions of aerosols and aerosol precursors (Boucher et al. 2013)
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Aside from aerosol origin, it is possible to distinguish classification due to particle sizes. SSA particles exist in two main ranges of sizes, the so called coarse and accumulation modes (particle diameters >2.5 μm, and from 0.1 to 2.5 μm, respectively, Seinfeld and Pandis 2012). The lifetime of SSA in troposphere ranges from 1 day to 1 week. SSA scatters the solar radiation, it has very hygroscopic properties so that means it is very active cloud condensation nuclei (CCN). As regards the chemical composition, according to Seinfeld and Pandis (2012) SSA contains by weight 55.7 % Cl, 0.19 % Br, and 0.00002 % I. The SSA has also primary organic aerosol (POA) compound which is difficult to parameterize (O’Dowd et al. 2004; Gantt et al. 2011; Ovadnevaite et al. 2011; Westervelt et al. 2012; Schmitt-Kopplin et al. 2012). The POA separately has quite different properties. It is observed as Aitken (from 0.01 to 0.1 μm) and accumulation mode and it comes from biologically active marine regions. The POA represents the smallest aerosol particles so they lifetime is approximately 1 week and they are active CCN.

Another very important aerosol species with the second largest global emission is mineral dust. The mineral dust aerosol species exist especially in coarse and super coarse modes. Similarly to SSA mineral dust lives from 1 day to 1 week, depending on particle size and takes part not only in light scattering but also in absorption. It can be active ice nuclei (IN) and takes active role in greenhouse effect (Boucher et al. 2013). Terrestrial primary biological aerosol particles (PBAP) are mainly coarse particles. It has the same lifetime as SSA and mineral dust and may form large CCN and be active IN. Precise description of all atmospheric aerosol compound are presented in (Seinfeld and Pandis 2012).

2 The Main Aspects of the SSA Studies

There are four general areas of SSA study: 1. in situ field measurements, 2. Laboratory Experiments, 3. Remote Sensing, 4. Regional and Global Modeling. Each of these research aspects have their own particular approach and problems. In this part of the article problems connected with each of these issues are shortly presented.

2.1 In Situ Measurements

In situ measurements are probably the most problematic and also the most important aspect of SSA study. The direct aerosol measurements in natural marine environment are the closest to reflecting the real phenomena. Unfortunately, conducting measurements on the open ocean is very expensive and technically difficult to carry. In recent years there were a number of measurement campaigns (inter alia described by: Kulmala et al. 2011; Kleinman et al. 2012; Norris et al. 2012; Lewandowska and Falkowska 2013; Petelski et al. 2014). Kulmala et al. (2011) presented the main achievements of the European Aerosol Cloud Climate and Air Quality Interactions project (EUCAARI) which lasted from 1 January 2007 to 31 December 2010. That big project consisted of multidisciplinary investigations such as observation of physical and chemical composition and optical properties of aerosol particles over Europe, and comprehensive modeling of aerosol processes from nano to global scale and their effects on climate and air quality. Kleinman et al. (2012) presented results from the VOCALS Regional Experiment. In this experiment aircraft observations of aerosol chemistry and physics, stratocumulus clouds properties and atmospheric gaseous composition were conducted. Norris et al. (2012) discussed results of 3 week measurements of marine aerosol fluxes on board the ship in the open Atlantic Ocean region. In this work eddy correlation method was used for wind speed range 4–18 m s−1 and size 0.176 < R80 < 6.61 µm. Lewandowska and Falkowska (2013) presented results from observation of SSA chemical composition in the open southern Baltic Sea region and coastal zone. Petelski et al. (2014), presented results of sea spray flux measurements from numerous cruises around the southern Baltic Sea region on board r/v ‘Oceania’. Other campaign carried out in the Baltic Sea region is presented by Byčenkienė et al. (2013). Based on the in situ measurements and trajectory-based approach, the Potential Source Contribution Function analysis was performed. Such tool allows to estimate the possible contribution of long-range and local aerosol number concentration transport. All the above mentioned studies are very challenging because of their multidisciplinary character. It is necessary to posses, first of all knowledge in meteorology, atmospheric physic, physical oceanography or biogeochemistry.

There are several aerosol flux measurement methods. De Leeuw et al. (2011), described the following groups of methods:

  • Steady State Dry Deposition Method (Smith et al. 1993; Petelski and Piskozub 2006);

  • Statistical Wet Deposition Method (Lewis and Schwartz 2004);

  • Whitecap Method (Monahan et al. 1986);

  • Micrometeorological Methods: eddy correlation (Nilsson and Rannik 2001), gradiental method (Petelski 2003; Petelski and Piskozub 2006; Andreas 2007)

  • Multiple Methods (Lewis and Schwartz 2004);

The main disadvantage of all methods, except micrometeorological is the fact that they are based on more qualitative estimation than direct flux determination. However, micrometeorological method and especially eddy correlation (EC) are more and more popular in field measurements over open oceans. Micrometeorological Methods rely on strong physical foundations as Monin-Obukhov theory or Reynolds Decomposition. Eddy covariance method (Lee et al. 2004; Aubinet et al. 2012) is commonly used in air-sea gas transfer measurements, by using high frequency concentration and wind speed measurements (~20 Hz, commonly used ultrasonic anemometers work even with 50 Hz speed). Unfortunately, it is still impossible to measure aerosol concentration with a frequency as high as in the case of trace gases. However, thanks to technological progress, it is possible to construct increasingly faster particle counters (~1 Hz is acceptable level, De Leeuw et al. 2007). During SEASAW campaign (described by Norris et al. 2012) there was used A Compact Lightweight Aerosol Spectrometer Probe (CLASP) which allows to measure aerosol concentration with even 10 Hz speed in 0.24 μm < D p  < 18.5 μm range. High frequency measurements are necessary to record air turbulence spectrum. In fluid dynamics there is common mathematical technique, which allow to separate average \(\overline{x}\) and fluctuating x′ parts of given quantity (Müller 2006; Foken and Nappo 2008):

$$x = \bar{x} + x^{\prime}$$
(1)

In turbulent flow with assumptions of negligible density fluctuations, negligible mean vertical flow (no divergence/convergence), there is possible to determine the net flux of each meteorological parameter as:

$$F \approx \overline{{\rho_{a} \,w^{'} s^{'} }}$$
(2)

where ρ a is an air density, \(\overline{{w^{'} s^{'} }}\) is a covariance of vertical fluctuating component of wind (w′) and fluctuation of a given meteorological parameter s′.

The Gradient method (GM), in contrast to the EC, relies on continuous measurements of aerosol concentration on at least three levels. To calculate aerosol flux based on the M-O theory, there is an assumption of the particle concentration as a scalar property of the air. Based on this and under the condition of horizontal uniformity, vertical flux equals to the emission from the sea surface. It is possible to fully determine horizontal uniformity by using such parameters as momentum flux τ, sensible heat flux Q and buoyancy parameter β = g/T (g-gravitational acceleration, T-air temperature). These parameters allow to define following scales: friction velocity: u *  = (τ/ρ) 1/2, Temperature: T *  = −Q/κu * and Length: L = −u 3 /κβQ (ρ-density of the air, κ-Von Kármán constant. The scale of particle concentration is defined as:

$$N_{*} = F_{N} /u_{*}$$
(3)

where: F N is the aerosol flux.

It is possible to express the non-dimensional aerosol concentration gradient by the universal function of z/L:

$$(z/N_{*} )\partial {\text{N/}}\partial {\text{z}} = \Phi (z/L)$$
(4)

Using Eq. 4 it is possible to derive the final equation using asymptotic forms from the M-O theory. The most popular for near water atmospheric boundary layer is the following formula:

$$N(z) = N_{*} {\text{ln(z) + }}C$$
(5)

Measurements of concentration on 3 levels above sea surface, allow to calculate N * and thus aerosol fluxes. In measurements presented by Petelski (2003), Petelski and Piskozub (2006), Petelski et al. (2014), there are presented GM measurements on board s/y Oceania, where there is Classical Aerosol Spectrometer (CSASP-100-HV, Zielinski 2004) used. The probe is placed on a special lift on board of the vessel. The aerosol concentration is measured on five levels above sea surface 8, 11 14, 17 and 20 m. Another newest achievement using gradient method is presented in Savelyev et al., (2014). In this paper there are successfully compared in situ measurements of aerosol production (GM and dry deposition) and direct passive microwave remote sensing.

2.2 Laboratory Experiments

The aim of laboratory experiments is to develop knowledge of SSA emission processes. SSA is generated from the sea surface as water drops through several processes. The collapsing wind waves are the main mechanism in which the SSA is transported to the atmosphere. Therefore, the emission depends on amount of wind wave energy, dissipated in the breaking process. Such phenomenon is however, very difficult to parameterize (Massel 2007).

The nature of aerosol emission is strongly correlated with wind speed. For wind speed in range from 5 to 10 m/s emission from bursting bubbles created during wave collapsing (so-called film and jet drops) is the dominating process. In higher wind speed conditions, the spume tearing from wave crests (spume drops) dominates the emission. This process generates the largest aerosol droplets (reaching even the 1,000 μm in radius). The secondary process consists of large droplets falling to the sea surface and creating smaller drops within the impact (splash drops).

Laboratory experiments allow scientists to study all mechanisms in an isolated system. In this approach this is possible to better understand each single mechanism. With such unquestionable advantage, there are still some issues bothering the scientists, which include problems with standardization and representativeness the measurements (for example standardization of sea water, Meskhidze et al. 2013). There are also problems with terminology unification.

With all advantages and disadvantages laboratory experiments are very important in the SSA flux study. Through such investigation it is possible to find the most universal parameter describing the SSA emission.

The most influential articles in this subject from recent years concentrate mainly on contribution of organic compounds to SSA chemical, physical and size distribution properties. Fuentes et al. (2010, 2011) presented results of investigating impacts of phytoplankton on properties of primary marine aerosol and source fluxes. Park et al. (2014a) investigate marine aerosol production based on measurements of insoluble submicrometer particles and biological materials in sea water. In this article influence of anthropogenic contribution on size distribution is described along with observed seasonal variability of marine aerosol concentration changes with biological composition of sea water. Park et al. (2014b) investigated mixing state of submicrometer SSA and measured differences between natural sea water with artificial water in the laboratory environment. Prather et al. (2013) presented results of newly implemented approach of laboratory experiment so called mesocosm experiment. In this experiment it is possible to reproduce the chemical complexity of SSA in artificial environment. For natural seawater, SSA concentration variability was measured, depending on parameters such as phytoplankton and heterotrophic bacteria concentration or chlorophyll-a.

2.3 Remote Sensing

Remote sensing consists of all passive measurements, based on extinction (absorption and scattering) of electromagnetic radiation by aerosol particles, in given area or cross section. The most important remote sensing systems investigating the aerosol include:

  • Maritime Aerosol Network (Smirnov et al. 2009), based on aerosol optical depth (AOD) measurements using Microtops II sun photometers on board the ships over the World’s Oceans.

  • Aerosol Robotic Network, is the international program involving many ground remote sensing aerosol networks established by NASA and PHOTONS (PHOtométrie pour le Traitement Opérationnel de Normalisation Satellitaire). This federation gathers institutes, national agencies, individual scientist all over the world. Collaboration via AERONET provides observation of AOD and all related meteorological or other aerosol properties parameters.

  • Moderate resolution Imaging Spectroradiometer (MODIS), one of the most important remote sensing device. Involves two satellites: Terra and Aqua. This system is designed for large-scale global dynamics monitoring. Placed into the orbit by NASA.

  • Multi-angle Imaging Spectral Radiometer (MISR), another satellite in this measurements unique is that the instrument is equipped in cameras pointed in 9 different directions. As a result, it is possible to gain very accurate information about radiation coming from earth. Also launched by NASA.

  • Advanced Along Track Scanning Radiometer (AATSR), main purpose of this satellite is the global Sea Surface Temperature (SST) monitoring. Besides SST this system provides data about surface temperature in general, clouds, aerosols, vegetation and snow. Belongs to European Space Agency (ESA).

  • Polarization and Directionality of the Earth’s Reflectances (POLDER), another satellite developed by French space agency CNES.

  • Medium-spectral Resolution Imaging Spectrometer (MERIS), device placed on board Envisat platform put into the orbit by ESA.

  • Sea-viewing Wide Field-of-view Sensor (SeaWiFS), project based on the satellite observations designed to collect global ocean surface biological data. Another system provide by NASA.

  • Cloud-Aerosol Lidar and Infrared Pathfinder Observations (CALIPSO), involves lidar measurements and passive infrared and visible observations from satellites to investigate vertical profile of atmosphere.

Based on comprehensive (ground, satellite, aircraft-based) measurements such interdisciplinary kind of approach, allows to estimate not only the global AOD distribution or the SSA emission, but also budget of SSA, surface wind speed, surface wave phase velocity, Chl-a concentration, colored dissolved organic matter (CDOM) concentration whitecap fraction and sea surface temperature (SST), (Meskhidze et al. 2013). The reason for such wide-ranging measurements is to estimate and compare the influence of above parameters on aerosol emission from the marine surface. Ground remote measurements are necessary to validate satellite measurements (Melin et al. 2013).

2.4 Regional and Global Modeling

More and more models of climate or atmosphere have implementation of interactive sea-salt aerosol emission part. The role of such modeling is to estimate the global emission of SSA to the atmosphere. Very important is also prediction of SSA influence on weather and cloud-creative processes. The main problem for such research based on modeling is lack of in situ data. It is very important to compare theoretical predictions with real measurements. The most popular data from direct measurements is the mass concentration measurements. For improvement quality of modeling processes connected with SSA there are plans to develop large comprehensive databases with in situ measurement data (Meskhidze et al. 2013). Example of such data base is the data base of the University of Miami (Mishchenko et al. 2002). It is essential to gather long data base, to compare modeled data with observations of the seasonal variability and trends.

Other subject is a choice of parameters used in models. The main and easiest to apply is the wind speed at 10 m above sea surface. It is the simplest meteorological parameter to use, because of direct relation between surface wave height or whitecap generation. Science community still discuses about using other parameters instead. The main other parameters are (Lewis and Schwartz 2004): Atmospheric Stability, Wind Friction Velocity, Sea Water Temperature, Wave Phase Velocity, Fetch, Salinity, Surface-Active Substances. Atmospheric Stability strongly affects air mass movements so there is strong dependence for marine aerosol drops coming from whitecaps. Wind friction velocity influences not only stability of atmosphere but also whitecap ratio. This parameter is quite easy to obtain, so it is convenient using it during source function estimation or model execution. Sea water temperature is strongly related to the kinetic viscosity of seawater so also existing of whitecaps also should be related. The state of sea surface has its specific inertia. Parameter which informs about state of the sea surface is wave phase velocity. Other parameter connected with sea surface inertia is fetch. Fetch is the distance over water that the wind has blown and may affect the wind spectrum. There is no direct relation between aerosol emission and salinity. However, there are differences in observations for brackish seas and open oceans with fresh water. Surface active substances may change the sea state, roughness length, surface tension of the seawater-air layer and affect on the lifetime of foam on the sea surface. Unfortunately, all these relations are very difficult to parameterize. The very interesting parameterization was presented by Ovadnevaite et al. (2014) where the Reynolds Number was used instead of all the above parameters. Advantage of such approach is that Reynolds Number brings information about wind speed, kinematic viscosity of water and indirectly: wave height, wind history, friction velocity or viscosity.

The new approach for determining SSA fluxes is presented by Grythe et al. (2013). In this paper I have reviewed 21 SSA source functions known from the literature. For each function a global SSA emission was described. In applying this task the FLEXPART Lagrangian particle dispersion model was used. Additionally, the authors proposed a new source function. This function, based on modeling estimation determined the functional relation of the SSA emission versus such parameters as wind speed (power dependence, ~u 3.5) or sea surface temperature and aerosol diameter (D p  < 10 μm, lognormal relation enclosing 3 aerosol modes). This is the first function determined using modeling estimation. The comparison of the SSA emission obtained from all 21 source functions is presented in Table 2 (Grythe et al. 2013).

Table 2 Comparison of the source functions used by Grythe et al. (2013) with selected components
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3 Summary

In this chapter I have described the latest efforts in sea spray aerosol flux studies. The SSA fluxes have a great influence on air-sea interaction processes and thus the climate processes. Several aspects of the studies on the SSA transfer have still low scientific understanding. Meskhidze et al. (2013) presented an understanding level of each SSA study aspects (Table 3).

Table 3 Prioritization matrix by Meskhidze et al. (2013)
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The main components with the lowest understanding and the greatest impact, according to Meskhidze et al. (2013) include:

  1. 1.

    Size-resolved chemical composition/hygroscopicity,

  2. 2.

    Mixing state

  3. 3.

    Wet removal,

  4. 4.

    Photochemical aging,

  5. 5.

    Cloud processing,

  6. 6.

    Seawater/microlayer chemical composition,

  7. 7.

    Size resolved organic speciation.

Only by conducting interdisciplinary measurements and research, it will be possible to provide a comprehensive description of marine boundary layer physics.