[1] Knowledge of the size-and composition-dependent production flux of primary sea spray aerosol (SSA) particles and its dependence on environmental variables is required for modeling cloud microphysical properties and aerosol radiative influences, interpreting measurements of particulate matter in coastal areas and its relation to air quality, and evaluating rates of uptake and reactions of gases in sea spray drops. This review examines recent research pertinent to SSA production flux, which deals mainly with production of particles with r 80 (equilibrium radius at 80% relative humidity) less than 1 mm and as small as 0.01 mm. Production of sea spray particles and its dependence on controlling factors has been investigated in laboratory studies that have examined the dependences on water temperature, salinity, and the presence of organics and in field measurements with micrometeorological techniques that use newly developed fast optical particle sizers. Extensive measurements show that water-insoluble organic matter contributes substantially to the composition of SSA particles with r 80 < 0.25 mm and, in locations with high biological activity, can be the dominant constituent. Order-of-magnitude variation remains in estimates of the size-dependent production flux per white area, the quantity central to formulations of the production flux based on the whitecap method. This variation indicates that the production flux may depend on quantities such as the volume flux of air bubbles to the surface that are not accounted for in current models. Variation in estimates of the whitecap fraction as a function of wind speed contributes additional, comparable uncertainty to production flux estimates.

[1] We present estimates of whitecap coverage on a global scale from satellite-measured brightness temperature of the ocean surface. This is a first step in a larger framework aiming at more realistic modeling of the high variability of whitecap coverage as a function of wind speed and a suite of additional environmental and meteorological factors. The involvement of oceanic whitecaps in various physical and chemical processes important for climate studies such as production of sea-salt aerosols, enhancement of airsea gas exchange, and influence on retrievals of ocean surface wind and ocean color motivates this effort. A critical review of the physical variables causing the high variability of whitecap coverage and existing approaches modeling this variability establishes the need for a database of whitecap coverage and concomitant measurements of additional factors. The necessity to build such an extensive database justifies the quest for a method estimating whitecap coverage from satellite data. We describe the physical concept, a possible implementation, error analysis, results, and evaluation of a method for estimating whitecap coverage from routine satellite measurements. The advantages of the concept and the drawbacks and necessary improvements of the implementation are discussed.

[1] Despite decades of effort to accurately quantify whitecap fraction W using in situ photography of the ocean surface, there remains significant scatter in estimates for any given 10 m wind speed (U 10 ). It is believed that the resulting, commonly used, W(U 10 ) parameterizations do not fully account for the true variability in W, by failing to incorporate the impact of the wavefield and other environmental conditions. This paper attests to the variability in whitecap fraction attributed to these additional factors, by analyzing satellitederived W estimates over the globe for a full year. A comparison is made between the wind speed dependence of satellite estimates and three W(U 10 ) relationships formulated from in situ photographic data. The influence of various secondary factors on W is investigated once the dominant wind speed dependence is accounted for. The W retrieval's sensitivity to secondary forcings is dependent upon microwave frequency; at 37 GHz it varies by up to 25% of the mean at a given wind speed, while at 10 GHz it is a maximum of 8%. This results from a frequency-dependent sensitivity to foam depth; at 10 GHz predominantly foam from active breaking waves is detected, while at 37 GHz thin foam in residual whitecaps is also seen. Principal component analysis is used to rank variables by their success in accounting for variability in W. After wind speed, the most important secondary factor that accounts for variability in W is the wavefield. A wind-wave Reynolds number accounts for almost as much variability in W as wind speed.

[1] We present a systematic investigation of the applicability of a group of mixing rules for obtaining the dielectric constant (permittivity) of sea foam (whitecaps) at microwave frequencies, 1.4 to 37 GHz. By demonstrating that the foam scattering is weak at these frequencies, we justify our interest in basic mixing rules, which do not involve explicit scattering computation, namely, the Maxwell Garnett, Polder-Van Santen, Coherent potential, Looyenga, and Refractive models. The complex dielectric constant of sea foam obtained with these mixing rules is presented and the dependence of foam permittivity on foam void fraction, radiation frequency, sea surface temperature, and salinity is reported. With the exception of the Coherent potential model, all selected mixing rules give reasonable values for the sea foam dielectric constant. To further examine the suitability of a permittivity model for computing the dielectric constant of sea foam, the performance of each mixing rule is evaluated on the basis of three criteria: (1) how well a permittivity model deals with a wide range of void fractions, (2) how a permittivity model behaves approaching the foam-air and foam-water boundaries, and (3) how the choice of a permittivity model affects estimates of emissivity and brightness temperature due to foam. The suitability of the basic mixing rules for computing the complex dielectric constant of sea foam at microwave frequencies can be ranked as:(1) Refractive model, (2) Looyenga model, (3) Maxwell Garnett model, and (4) PolderVan Santen model.

The wind tearing of breaking wave crests produces spume drops. The authors report preliminary laboratory data from direct and unambiguous observation of this process under various wind conditions using a video imaging technique. Results include the size distribution and production rates of these drops. The curves for production rates at different wind speeds merge effectively when normalized by the number of breaking events. This confirms that wave breaking occurrence, not the wind speed, is a dominant factor in spume production.