4.1.1. Photographic Measurements of Whitecap Fraction
 Five new data sets of whitecap fraction have been reported, four in coastal regions under fetch-limited conditions [Lafon et al., 2004, 2007; Sugihara et al., 2007; Callaghan et al., 2008a] and one in open ocean (unlimited fetch) conditions [Callaghan et al., 2008b]. Details of these data sets (Table 3) show the ranges of various meteorological and oceanographic variables (in addition to wind speed) that were recorded to investigate possible dependencies on these other quantities and the means by which the images were collected and processed.
Table 3. Location and Time of Observations and Accompanying Meteorological and Oceanographic Factors (U10, X, Tw, ΔT) for New Measurements of Whitecap Fraction Shown in Figure 2a
|Reference||Platform||Location||Time Period||U10 Range (m s−1)||X (km)||Tw (°C)||ΔT (°C)||Additional Parametersb||Data Points||Images per Data Point||Averaging Period (min)||Medium||Image Rate (s−1)||Geometric Mean of Ratio to MO'M80c|
|Lafon et al. ||ship||Gulf of Lyon, Mediterranean||Mar-Apr 1998||6–17||8–90||13||−2.65 to +6.25||u*, fp, Hs||45||10–25||5 to 12.5||film||0.033||0.64|
|Lafon et al. ||tower||Toulon-Hyères Bay, Mediterranean||Oct-Nov 2001||10–18||<30||14||NA||u*, fp, Hs||29||24||12||film||0.033||0.52|
|Sugihara et al. ||tower||Tanabe Bay, Wakayama, Japan||Nov-Dec 2003; Feb-Mar 2004||4.4–16.4||coastal||NA||−11 to 0||u*, Tp, cp,Hs||91||600||10||digital video||1||0.35|
|Callaghan et al. [2008a]||tower||Martha’s Vineyard, Massachusetts, USA||Nov 2002||3–12||3–20||NA||NA||ϕw, fp, cp/u*, ||73||400–1200||20||digital photo||1||0.24|
|Callaghan et al. [2008b]||ship||Northeast Atlantic||Jun-Jul 2006||4.5–23||200, 500, >500||13–14||−3.86 to −0.13||Ta, Tw for 44 data points||107||100–782||30||digital video||0.5||0.46|
 Recent developments in image processing of sea state photographs have aimed at decreasing the uncertainty in measured whitecap fraction in two ways, both of which have been facilitated by developments in digital technology. One is removing the subjectivity in determining the intensity threshold that distinguishes whitecaps from the surrounding water. The other is averaging a large number of “instantaneous” W values measured during an observation period to obtain a single W data point.
 To determine more objectively the intensity thresholds separating whitecaps from the surrounding water, the change in instantaneous W values when the threshold was varied was examined by Sugihara et al. [2007, Figure 5]. An optimum threshold was identified for which a change in threshold of ±6% resulted in a relative change in W of 10–20%; this same threshold was selected and applied to all processed images. An automated whitecap extraction technique was devised by Callaghan and White  that involved two major elements: an “image structure,” defined as the fraction of pixels with intensities greater than a given threshold, which decreased as the threshold was increased from a predetermined minimum intensity to the maximum intensity of the image, and analysis of the first, second, and third derivatives of this image structure with respect to the threshold intensity. The image structure was used to identify whether an image contains a whitecap, and the derivative analysis was used to determine the intensity threshold for an image containing a whitecap. This procedure yielded a unique threshold applicable to an individual image [Callaghan et al., 2008a].
 The changes in the value of W that resulted from increasing the number of individual determinations of W obtained in series of measurements during 30 min periods to yield an average was also investigated by Callaghan and White . The relative difference of each such value of W from the data set mean was as great as ±25% when 10–30 values were averaged, gradually decreasing to about ±10% when 100 values were averaged and to less than ±3% when about 500 values were averaged. Such decrease in the relative difference would be consistent with expectation for averages of independent measurements. Although use of a greater number of images reduced the difference from the mean calculated from 700 images, there did not appear to be any bias associated with using fewer images (as would also be consistent with expectation for averages of independent measurements). Similar findings were reported by Callaghan et al. [2008a]. Additionally, it was found that the value of W for many of the images would not be substantially different if sampled only 1 or 2 s apart. Callaghan et al. [2008a] noted that the optimal sampling frequency (beyond which little improvement is seen) was once every 3–4 s, approximately the lifetime of an individual whitecap. Several of these data sets would appear to contain valuable information concerning statistics on the lifetimes and sizes of individual whitecaps and on the temporal autocorrelation of W which have not yet been fully exploited.
 The new whitecap fraction data are plotted in Figure 2 as a function of wind speed, U10, together with previous measurements that are summarized in Table 20 of LS04 and in Table 2 of Anguelova and Webster . The W(U10) relationship from MO'M80 (equation (9)) is also shown. As determinations of W by analog video are thought to not be as accurate as those by film photography [LS04], the “previous” measurements in Figure 2 include only photographic determinations of W [LS04, Table 20]. Three of these new data sets were obtained using digital photography or digital video (Table 3); digital video has better resolution and lower noise than analog video, although it is not yet as good as film photography in spatial resolution and dynamic range [Brady and Legge, 2009; Kroeker, 2009].
 The newly measured values of W appear to exhibit less scatter than, but are consistently less than, the bulk of those of the previous data sets. Geometric means of the ratios of the new values of W to those calculated according to the MO'M80 relationship ranged from 0.24 to 0.64 for the new data sets (Table 3). Furthermore, the wind speed dependence of W for these new data sets seems to differ from that of the older data sets: At low wind speeds (U10 < 7 m s−1), the new measurements indicate that W(U10) increases faster than MO'M80, resulting in a strong increase of W (from ∼10−5 to ∼5 × 10−4) over a narrow range of wind speeds (5–7 m s−1). In contrast, and in agreement with the previous results, W(U10) increases slowly for U10 > 16 m s−1, and the few data for U10 > 20 m s−1 seem to plateau at a constant value; albeit the new data are consistently lower than the MO'M80 curve throughout the entire range of wind speeds. As the new data sets were based on both film photography (two sets) and digital imagery (three sets) and were characterized by both limited fetch (four sets) and open ocean (one set), there seems to be no obvious reason for the consistently lower values.
 Most of the new whitecap data [Lafon et al., 2004, 2007; Sugihara et al., 2007; Callaghan et al., 2008a] have also been examined for their dependence on friction velocity u*, but there seems to be little or no decrease of the scatter in plots of W versus u* compared to that in plots of W versus U10, a similar conclusion to that reached from the analysis of previous data by LS04. It has been suggested that u* could be more accurately determined if the expression of roughness length explicitly included wavefield characteristics (or combinations of them) such as wave age (a measure of and proxy variable for fetch), significant wave height, wave steepness, or energy dissipation in the breaking waves [e.g., Drennan et al., 2005]. By the same token, models of W that directly involve wavefield characteristics might better account for variability in whitecap fraction [cf. Massel, 2007, chapter 7]. For example, using the so-called breaking wave parameter or windsea Reynolds number, Rb = u*2/(νafp) [Zhao and Toba, 2001], where νa is the kinematic viscosity of air and fp the frequency peak of the wave spectrum, to represent the sea state-dependent whitecap fraction has yielded improved prediction of the transfer velocity of CO2 [Woolf, 2005; Soloviev et al., 2007]. Consequently, it has been suggested that parameterizations of W in terms of wave age [Lafon et al., 2004, 2007; Guan et al., 2007; Sugihara et al., 2007; Callaghan et al., 2008a] might lead to similar improvement in predicting the SSA particle flux in equation (8) through improved estimates of W.
 The analysis of whitecap observations by Callaghan et al. [2008b] supports this premise. Callaghan et al. sorted data into periods with decreasing and increasing wind as surrogates for developed (old) seas (defined as a sea state produced by winds blowing steadily for fetch of hundreds of kilometers and duration of several days) and undeveloped (young) seas, respectively, and reported that for U10 below 9 m s−1, there seemed to be no difference in the relation between W and U10 between the two data sets, whereas for U10 greater than 9 m s−1, W values from periods of decreasing wind were 30–70% higher than those from periods of increasing wind. Although such measurements demonstrate the contribution of sea state to the variability of W at a given U10, the reported dependence accounts for only a small fraction of this order-of-magnitude variability.
4.1.2. Satellite-Based Measurements of Whitecap Fraction
 Measurements made with satellite-borne microwave sensors infer W from surface brightness temperature, TB, determined from the emitted radiance, which increases with increasing whitecap fraction, as opposed to detecting individual whitecaps. Although the dependence of W on TB might be calculated from a simple empirical relationship [Wang et al., 1995], a physically sound approach for obtaining W requires an algorithm containing multiple steps. The feasibility of acquiring whitecap fraction globally from space using TB and variables necessary for the atmospheric correction (columnar water vapor and cloud liquid water path) from the Special Sensor Microwave/Imager (SSM/I) was demonstrated by Anguelova and Webster . Because the algorithm uses satellite observations with a wide cross-track swath, W is determined twice a day (once in the daytime and once at night) at almost every oceanic location on Earth. Each satellite-based determination of W is a value spatially averaged over the sensor footprint (approximately 50 km × 50 km) at a specific local time for a given location.
 There are two main contributions to the uncertainty of satellite-based estimates of W. One is the error associated with the accuracy of models used in the algorithm that represent the various relationships needed for determining W, e.g., the emissivities of the rough sea surface and of whitecaps at microwave frequencies. This error might be characterized by comparing satellite- and surface- or aircraft-based observations collocated in time and space. The second source of uncertainty is the measurement error, which results from random and systematic errors in the data used in the determination of W. Random error is quantified as the variance, σW2, of the calculated W. This method does not identify or quantify systematic errors. In their feasibility study, Anguelova and Webster [2006, section 3.4] evaluated the measurement error and assigned a standard deviation σW to each W estimate; lack of concurrent in situ measurements prevented evaluation of the modeling error. Analysis of whitecap fraction determined by the satellite-based method for all days in 1998 showed that the relative standard deviation, σW/W, was less than 30% for about half of the determinations, whereas less than one-third of the individual photographic measurements available at the time had this accuracy [Anguelova and Webster, 2006].
 The satellite-based results for W from the algorithm of Anguelova and Webster , binned (as arithmetic means) by wind speed in intervals of 1 m s−1, are compared in Figure 3 to bin (arithmetic) averages of W determined from photographic measurements and to the W(U10) parameterization of MO'M80. These determinations of W yield a nearly constant value of approximately 0.03, independent of wind speed over the range 8 m s−1 < U10 < 17 m s−1, with somewhat lower W as wind speed decreases for U10 < 8 m s−1, in contrast to the much stronger wind speed dependence exhibited by the photographic data and MO'M80 parameterization.
Figure 3. Whitecap fraction W as a function of wind speed at 10 m above the sea surface U10, arithmetically averaged in intervals of 1 m s−1, obtained with the algorithm of Anguelova and Webster  (blue) using annually averaged (1998) observations of brightness temperature TB from SSM/I in clear sky (no clouds) locations all over the globe. The corresponding U10 values are also from SSM/I. Error bars on W values represent 1 standard deviation of the data points falling in each U10 bin; the apparent asymmetry of the error bars is a consequence of plotting on the logarithmic ordinate scale. Also shown (gray) are bin-average values of W from previous photographic determinations shown in Figure 2 and the formulation of Monahan and Ó Muircheartaigh , equation (9).
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 The differences between the satellite results and in situ photographic measurements are likely due to three factors. First, the signal from a whitecap may be different in different regions of the spectrum because of difference in the observed physical process, e.g., skin depth of the foam in the microwave region versus penetration depth of scattered visible radiation. Second, the satellite retrieval algorithm may be incomplete; for instance, simplified emissivity models were employed for foamy and rough surfaces by Anguelova and Webster [2006, section 5]. Finally, the influence of various geophysical factors captured by the satellite estimates of W, which are not currently extracted nor reliably quantified, may be important. Improvement of the satellite-based estimates of W requires understanding and characterizing all these factors.
 In view of concerns over the accuracy of the space-based microwave determination of W, Anguelova and Webster  suggested several possible modifications of their initial algorithm, including different models for foamy and rough surfaces and independent data sets for atmospheric correction. Microwave observations from the new satellite radiometric sensor WindSat [Gaiser et al., 2004; Bettenhausen et al., 2006; Freilich and Vanhoff, 2006] in addition to those of SSM/I provide a possibility to use independent data sets. To better represent the emissivity of whitecaps in different lifetime stages, Anguelova and Webster suggested using a depth profile of the void fraction within the thickness of the whitecaps instead of a constant value and assuming a distribution of whitecap thicknesses over the ocean. Details of these suggestions are given by Anguelova  and Reul and Chapron , respectively.
4.1.3. Laboratory Experiments on SSA Production
Table 4. Experimental Investigations of Sea Spray Production by Laboratory Bubble Plumes
|Study||Medium||Organic Added||Bubble Production Method(s)||Bubble Rise Distance (cm)||White Area (cm2)||Temperature (°C)||Salinity|
|Mårtensson et al. ||artificial seawater||none||diffusera||4||3||−2, 5, 15, 25||0, 9.2, 33|
|Sellegri et al. ||artificial seawater||sodium dodecyl sulfate||weir, diffusersb||2||not stated||4, 23||not stated, presumably near 35|
|Tyree et al. ||seawater, artificial seawater, mixtures||oleic acid||diffusersc||32–40||180||20–25||1, 10, 20, 33, 70|
|Keene et al. ||seawater||none||diffuserb||∼115||∼150–300||∼27||not stated, presumably near 35|
|Facchini et al. ||seawater||none||water jet||not stated||400||not stated, presumably ∼13||not stated, presumably near 35|
|Fuentes et al. ||artificial seawater, seawater||Thalassiosira rotula exudate||water jets, diffusersd||4–10||200||18–20||35|
 The range of conditions in these experiments could, in principle, provide a test of the key premise of the whitecap method (section 3.5), specifically the assumption that the size-dependent production flux per white area is independent of the means by which that white area is formed. However, several of the investigations reported only normalized concentrations and/or did not report the white area characterizing their experiment. Nonetheless, under the assumption of negligible particle loss such normalized concentrations would exhibit the same size dependence as production fluxes, permitting comparison of the results of the several studies. Those experiments which provided sufficient data to allow determination of both a magnitude and size distribution of a production flux are discussed further in section 5.1.
 There are several concerns with laboratory experiments simulating SSA production. One is the extent to which laboratory whitecaps can accurately simulate breaking waves over the ocean as discussed in section 3.5. All of the laboratory whitecaps discussed in this section, whether formed by diffusers or water jets, were continuous, as opposed to open ocean whitecaps, which are discrete. Large bubbles (those thought to be responsible for the production of most of the small drops, i.e., those with r80 less than several tenths of a micrometer, which are thought to be film drops) rise quickly to the surface, and after several seconds the only bubbles that remain in the ocean are smaller ones, which are thought to be too small to produce film drops. Thus the vast majority of the film drops would be produced during only a small fraction of the lifetime of a whitecap in the ocean, in contrast to the laboratory whitecaps. Another concern with laboratory experiments is the possible influences of the sides of the container on the resultant whitecap. In some experiments [e.g., Keene et al., 2007; Tyree et al., 2007] the white area was constrained by the size of the tank such that the white area was nearly the same for a range of bubble volume fluxes (i.e., the rate of air volume in bubbles reaching the surface divided by the white area, which Tyree et al. called the superficial bubbling velocity). Other experiments used only one bubble volume flux or varied this quantity only slightly. However, whether the values chosen are in the range of those in oceanic whitecaps, and the possible consequences of those values not being in the oceanic range, are not known.
 Another concern with laboratory experiments as models for oceanic behavior of bubbles is the short bubble rise times and distances compared to those for bubbles produced by breaking ocean waves, which reach depths of up to several meters, depending on wave height, as shown by acoustic observations of bubble plumes near the ocean surface [e.g., Thorpe, 1992]. Rise distances in laboratory studies are often much shorter. For example, Sellegri et al.  and Fuentes et al.  used bubble rise distances of only a few centimeters. Tyree et al.  used rise distances of ∼0.35 m, which they claimed approximated the circulation depth of oceanic bubbles. Keene et al.  used bubble rise distances greater than 1 m, over which distance they assumed that the equilibrium size distribution would be attained before bubbles reached the surface and burst.
 A possible basis for a dependence of drop production on bubble rise times or distance is the time required for organic substances to equilibrate on the air-water interface of the bubbles. This equilibration time was examined by Fuentes et al. , who provided a theoretical analysis demonstrating that equilibrium with respect to adsorption of organics would be reached within 0.05 ms, much shorter than rise times of bubbles even for the short distances of some of the laboratory studies. On the basis of this analysis, Fuentes et al. concluded that the depth of bubble generation would play little role in the effect of organics on production and properties of SSA. However, this result would seem to be in contradiction with findings reported in a series of laboratory studies by Blanchard and colleagues, which indicated that the equilibrium attachment of organics to the air-water interface of bubbles is attained much more slowly. Blanchard and Syzdek [1972, 1975] reported that ejection heights of jet drops exhibited a dependence on bubble rise distance over the range 6–23 cm and on bubble age for up to 10–20 s. Detwiler and Blanchard  reported that both bubble rise speeds and top jet drop ejection heights decreased with increasing bubble age (time spent in the water), with rise speeds decreasing by nearly a factor of 2 over the first 10 s or so, effects that they attributed to attachment of organic material to the bubble interface. In several studies, Blanchard and colleagues examined the dependence of enrichment of bacterial concentration in drops relative to the bulk concentration on bubble age or rise distance. Blanchard and Syzdek  reported that bacterial enrichment in the top jet drop increased by approximately a factor of 5 when the bubble rise distance increased from 1 to 30 cm. Blanchard and Syzdek  and Blanchard et al.  reported that bacterial enrichment in jet drops increased with increasing bubble age for ages of 20 s or more. All of these results, which were attributed to organic attachment to the bubbles, would appear to indicate that this process does not rapidly attain equilibrium.
 Several of the size-dependent production flux measurements obtained in the newly reported studies, normalized to the maximum values in the representation dF/dlogr80, are shown in Figure 4. A common feature is a rather broad maximum of the production flux in this representation at r80 near 0.05–0.1 μm, which is rather independent of the means of production and of the bubble size distribution, although the spectral shapes differ markedly among the different examples. The large differences in the size distributions of the normalized concentration (and thus of the production flux), which may be as great as 2 orders of magnitude at r80 = 0.01 μm, rather strongly refute the assumption that the production flux per white area is independent of the means by which the white area is produced. The results presented in Figure 4 were obtained for different conditions such as artificial versus natural seawater, water temperature, salinity, effects of surfactants, and bubble generation method, the effects of which were assessed in different studies. The results of these studies are presented here and possible causes for differences are examined.
 The effect of salinity on the production flux size distribution was examined by Mårtensson et al.  (salinities 0, 9.2, and 33) and by Tyree et al.  (salinities 1, 10, 20, 33, and 70). Both studies reported an increase in particle number production with increasing salinity. Mårtensson et al. (their Figure 5) reported that size distributions for r80 between ∼0.05 and 0.1 μm were nearly the same for salinities 9.2 and 33 and that for larger SSA particles the number fluxes for salinity 33 were increasingly greater than for salinity 9.2 as r80 increased, up to nearly an order of magnitude for r80 larger than approximately 1 μm. Mårtensson et al. argued that the size distributions near r80 = 0.05 μm shifted to slightly lower sizes at lower salinity, consistent with the hypothesis that formation radii were independent of salinity, although this shift did not occur for larger particles. In contrast to these results, Tyree et al. observed little change in the shape of their size distributions, with only a small increase (∼15%) in the value of r80 of particles with increasing salinity from 10 to 70 (their Figure 4). Tyree et al. did, however, report an increase in total particle number production by a factor of 2.5 with salinity increasing from 10 to 70. As discussed by LS04, there is a transition in the coalescence behavior of bubbles that occurs near salinity 10, which results in very different bubble size distributions and thus perhaps SSA particle size distributions between lower and higher salinities to which it may be possible to attribute some of these results.
 The effect of water temperature on the resultant size distribution was investigated by Mårtensson et al.  (−2, 5, 15, and 25°C) and by Sellegri et al.  (4 and 23°C). Mårtensson et al. found nearly identical size distributions for −2 and 5°C and little change between these and the size distribution at 15°C, although at both 15 and 25°C there was a decrease in the magnitude of the production flux by a factor of 2–3 for r80 < 0.1 μm and an increase by a factor of 5–10 for r80 > 1 μm. Sellegri et al. reported an increase in the production flux of particles with r80 < 0.7 μm at 4°C relative to that at 23°C and a decrease at greater r80, although much of this difference could alternatively be attributed to a decrease in the values of r80 by ∼30% at the lower temperature.
 The effects of different bubble generation methods on the resultant aerosol size distribution and properties were examined by Sellegri et al. , Tyree et al. , and Fuentes et al. . Sellegri et al. noted different size distributions (their Figure 2) for different methods, a weir and diffusers with three pore sizes, with dN/dlogr80 exhibiting a maximum near r80 = 0.1 μm for each method but with the size distribution produced by the weir having a narrower distribution near this maximum and an additional contribution from particles with r80 near 0.35 μm. Tyree et al. reported that the production flux per white area obtained using a diffuser with a pore size (presumably diameter) 140 μm was up to a factor of 4 greater than when using one with pore size 80 μm at the same bubbling rate. Fuentes et al. reported large differences in the magnitude and shape of the number size distribution (their Figure 6) and hence of inferred SSA production flux, produced by plunging water jets and by diffusers with different pore sizes, with the size distribution (in the form dN/dlogr80) produced by the water jets being bimodal with maxima at r80 near 0.05 and 0.15 μm, with that from an aquarium diffuser having a single broad maximum near r80 = 0.06 μm, and with that from a sintered glass filter (pore size, presumably diameter, 30 μm) having a narrow maximum near r80 = 0.06 μm with a much smaller secondary maximum near r80 = 0.25 μm. These size distributions are also shown in Figure 4.
 The dependence of production flux on bubble volume flux was investigated by Tyree et al.  and Keene et al. . Tyree et al. reported that a higher bubble volume flux could yield more than an order of magnitude increase in the total number production flux per white area. Keene et al. also reported an increase in the magnitude of this quantity with increased bubble volume flux, although shapes of size distributions were similar. These dependences together with results of Mårtensson et al.  are shown in Figure 5. In view of the strong dependences shown in Figure 5, bubble volume flux would seem to be an important property of whitecaps influencing the SSA production flux per white area. Certainly it would seem that a whitecap property such as this would be much more useful than an arbitrary threshold of “white” in relating SSA production flux to white area and ultimately in developing more accurate parameterizations for SSA production flux.
 The effects of surfactants on SSA production were investigated by Sellegri et al. , who added sodium dodecyl sulfate (SDS) to artificial seawater; Tyree et al. , who investigated natural seawater containing different organic compositions and artificial seawater to which 0.1 and 10 mg L−1 oleic acid was added; and Fuentes et al. , who added exudate of the diatom Thalassiosira rotula to natural filtered seawater at a concentration 512 μM (representative of dissolved organic carbon (DOC) concentration in seawater in areas of high biological activity).
 Particle size distributions produced using artificial seawater were reported by Sellegri et al.  as being similar to those using natural seawater, although they were shifted toward smaller values of r80 for SDS concentrations greater than 3 mg L−1. The investigators stated that these results should be considered exploratory because their comparison to long-term, seasonally varying data of particle size distributions obtained at the Mace Head atmospheric research station (located on the west coast of Ireland) showed that SDS does not accurately simulate the effects of the surfactants present in the natural environment.
 The natural seawater samples of Tyree et al.  exhibited differing organic composition because they had been collected in winter (DOC concentration = 2.3 mg C L−1, chlorophyll concentration = 0.1 mg m−3) and summer (DOC concentration = 3.1 mg C L−1, chlorophyll concentration = 1.8 mg m−3). The size distributions of the SSA particles produced in their experiments were nearly the same, regardless of the type of water (artificial, filtered, or unfiltered seawater), with little dependence on the amount of surfactant added. The winter samples of natural seawater produced 20–40% more SSA particles than the summer samples. Comparison of the size distributions of the SSA particles produced with the summer and winter samples showed that the natural organic matter exerted little effect on the numbers or radii of the produced SSA particles. Bubbling artificial seawater artificially enriched with oleic acid produced approximately twice as many drops as natural seawater. The investigators concluded that the nature of organic matter affects foam droplet production and that oleic acid is a poor surrogate for natural organic matter for studies of foam production. These findings, as well as those of Sellegri et al. , would seem to raise questions over the accuracy of laboratory experiments as models for oceanic SSA production.
 Hygroscopic growth and CCN activity for artificial seawater were examined by Fuentes et al. , who reported that these properties were not affected by the bubble generation technique; however, for the organically enriched natural seawater, hygroscopic growth was suppressed, with the degree of suppression depending on the aerosol generation technique. The main differences in hygroscopic growth resulting from different generation techniques were observed for RH > 75%, with the plunging water jet presenting the greatest suppression of growth. The influence of organics on the CCN activity exhibited little size dependence, with only a slight increase in the critical supersaturation compared to seawater samples to which no organics were added.
 Chamber studies aimed at determining the size-dependent mass fraction of organic material in SSA particles produced from natural seawater were conducted by Keene et al.  and Facchini et al. . Keene et al. used highly oligotrophic seawater (concentrations of organic substances such as formate, acetate, oxalate, and methylsulfonate were below detection limits) pumped from the ocean into a laboratory near the coast of Bermuda and produced SSA by bubbling the water through diffusers. Facchini et al. used highly productive seawater (average chlorophyll-a concentration of 1.4 mg m−3) pumped into a sealed tank on a ship in the North Atlantic west of Ireland during an algae bloom and produced SSA using a water jet.
 Enrichment of calcium with respect to surface water concentrations (median enrichment factor of 1.2), which may have been caused by fragments of biogenic CaCO3 or from complexes with organic matter, was reported by Keene et al. . These investigators also reported that all size-resolved and bulk aerosol samples were highly enriched in organics, with the enrichment decreasing from greater than 105 for r80 near 0.06 μm (the lowest size range) to slightly greater than 102 for r80 near 4 μm and again increasing slightly to near 103 for r80 near 14 μm; the median enrichment factor for all samples was near 400. The organic mass fraction exhibited similar behavior, decreasing from near 80% for r80 near 0.06 μm to 40–50% for r80 between 0.06 and 0.6 μm and to less than a few percent for r80 between 0.6 and 4 μm then increasing again to near 40% for r80 near 14 μm. In all size ranges except the smallest, the dominant contribution to aerosol mass was provided by sea salt.
 A strong dependence of the organic (water-soluble and water-insoluble organic matter) mass fraction on particle size (Figure 6), with the enrichment factor (relative to the bulk seawater) increasing with decreasing particle size, was also reported by Facchini et al. . SSA particles with ambient radii greater than 0.5 μm contained more than 90% of the inorganic sea salt mass; particles with ambient radii less than 0.25 μm consisted mainly of organic matter, most of which was water insoluble. This water-insoluble organic matter (WIOM) exhibited substantial enrichment (relative to the bulk solution) with mass fraction increasing from 3 to 77% as radius (at 50–70% RH) decreased from 4 to 0.06 μm, with only a very minor fraction (∼3%) of water-soluble organic matter (WSOM); the remaining mass was sea salt. The WIOM was attributed to colloids and aggregates exuded by phytoplankton on the basis of functional nuclear magnetic resonance spectroscopy. Such an increasing fraction of organic matter with decreasing drop radius is consistent with the volume fraction of adsorbed surfactant organic matter as a function of SSA particle size as evaluated with a thermodynamic model [Oppo et al., 1999]. Despite the small mass fraction of organic matter in larger particles (radius of 2–4 μm at 50–70% RH), the total mass of organic matter in these particles was approximately half the total organic mass in aerosol particles with radius (at these RH values) less than 4 μm. Facchini et al. also reported that the mass ratio of WIOM to sea salt was similar to that observed in aerosol samples at Mace Head.
Figure 6. Mass fraction of sea salt, WSOM, and WIOM as a function of particle radius sampled at approximately 70% RH (a) for seawater bubble-bursting chamber experiments with fresh seawater, conducted in a shipboard laboratory in a plankton bloom over the northeast Atlantic (May-June 2006); (b) for clean marine air at Mace Head, Ireland, May-June 2006; and (c) for clean marine air 200–300 km offshore west-northwest of Mace Head in a plankton bloom coincident in time with aforementioned samples. Adapted from Facchini et al. .
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4.1.4. Surf Zone Measurements
 The production of SSA in a surf zone was determined by Clarke et al.  from measurements on a 20 m tower, 20–30 m from the water's edge on a beach in Hawaii, during onshore winds (typical wind speed of 7 m s−1). Aerosol properties were characterized using a differential mobility analyzer (DMA), an OPC, and an aerodynamic particle sizer (APS), which together covered the size range 0.01 μm < r80 < 8 μm. The DMA and OPC included options for sampling aerosol at ambient temperature or at 300–360°C to permit determination of size distributions of volatile and residual refractory aerosol (the latter being typically sea salt, nonvolatile organics, dust, or soot). These instruments were complemented with two condensation particle counters (CPCs), one operated at ambient temperature and the other at 360°C; a tandem DMA (TDMA) and a humidified TDMA (HTDMA) to examine the thermal and humidification response of selected sizes; and a three-wavelength nephelometer to determine particle light scattering. Inlets for all these instruments were placed at heights of 5, 10, and 20 m and sampling was cycled at regular intervals.
 Comparison of measurements at these three heights showed that the highest level was not influenced by surf production and could thus be used for determining the upwind background concentration. SSA production in the surf zone was evaluated from the SSA concentrations measured at 5 m after correction for background concentrations using the 20 m data. The production flux per white area was determined as described in section 3.5 using a mean whitecap fraction in the surf zone of 40%, based on visual examinations of images. Substantial production of particles with dry radius less than 0.05 μm was found.
 Heated and ambient sample volumes were used by Clarke et al.  to discriminate between refractory aerosol particles, assumed to be mainly sea salt, and other components. To further ascertain whether the detected particles were sea salt, the investigators made several tests. First, they noted the strong correlation between the concentrations of the refractory particles, most of which had dry radii less than 0.05 μm, and light scattering, which would be dominated by particles with dry radius greater than 0.25 μm. Chemical analysis using a flame photometric aerosol sodium detector confirmed that particles with r80 > 0.09 μm were composed mainly of sea salt. SSA particles with r80 of 0.05 μm (previously heated to 300°C) exhibited a humidity growth factor near 1.8 from low RH to RH of 76%, as expected for sea salt particles, from which Clarke et al. concluded that these particles were composed mainly or entirely (80% up to possibly 100%) of sea salt. They further concluded that most of the particles with r80 ≳ 0.03 μm produced from breaking waves were primarily sea salt. Based on their measurements, Clarke et al. presented an SSSF that extended to r80 as small as 0.01 μm. This formulation, presented in Appendix A and discussed in section 5.1, is shown in Figure 4 as a normalized size distribution.