Observations of oceanic whitecaps in the north polar waters of the Atlantic

Stramska, Małgorzata; Petelski, Tomasz

doi:10.1029/2002jc001321

Cited by 116 publications

(145 citation statements)

References 45 publications

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“…10) is negative and, following the usual interpretation, yields a threshold wind speed of about 1.1 m s −1 for whitecap inception. This is in the range of previously published values from 0.6 (Reising et al, 2002) to 6.33 (Stramska and Petelski, 2003). Meanwhile, the positive y intercept b for W 37 (Eq.…”

Section: Wind Speed Dependencesupporting

confidence: 74%

Parameterization of oceanic whitecap fraction based on satellite observations

et al. 2016

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Abstract. In this study, the utility of satellite-based whitecap fraction (W ) data for the prediction of sea spray aerosol (SSA) emission rates is explored. More specifically, the study aims at evaluating how an account for natural variability of whitecaps in the W parameterization would affect SSA mass flux predictions when using a sea spray source function (SSSF) based on the discrete whitecap method. The starting point is a data set containing W data for 2006 together with matching wind speed U 10 and sea surface temperature (SST) T . Whitecap fraction W was estimated from observations of the ocean surface brightness temperature T B by satellite-borne radiometers at two frequencies (10 and 37 GHz). A global-scale assessment of the data set yielded approximately quadratic correlation between W and U 10 . A new global W (U 10 ) parameterization was developed and used to evaluate an intrinsic correlation between W and U 10 that could have been introduced while estimating W from T B . A regional-scale analysis over different seasons indicated significant differences of the coefficients of regional W (U 10 ) relationships. The effect of SST on W is explicitly accounted for in a new W (U 10 , T ) parameterization. The analysis of W values obtained with the new W (U 10 ) and W (U 10 , T ) parameterizations indicates that the influence of secondary factors on W is for the largest part embedded in the exponent of the wind speed dependence. In addition, the W (U 10 , T ) parameterization is able to partially model the spread (or variability) of the satellite-based W data. The satellite-based parameterization W (U 10 , T ) was applied in an SSSF to estimate the global SSA emission rate. The thus obtained SSA production rate for 2006 of 4.4 × 10 12 kg year −1 is within previously reported estimates, however with distinctly different spatial distribution.

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Section: Wind Speed Dependencesupporting

confidence: 74%

Parameterization of oceanic whitecap fraction based on satellite observations

et al. 2016

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“…Recent studies indicated that the whitecap coverage is influenced by the state of the wind wave development. It has been shown that the larger whitecap coverage, the more developed wind waves at the same wind speed (Stramska and Petelski, 2003;Lafon et al, 2004;Sugihara et al, 2007;Callaghan et al, 2008;Goddijn-Murphy et al, 2011), while the existence of swell appears to suppress the whitecap coverage (Sugihara et al 2007;Goddijn-Murphy et al, 2011). Based on comprehensive observational data, Zhao and Toba (2001) found that whitecap coverage is better correlated with the windÁsea Reynolds number R B than the wind speed alone, and they proposed a quasi-linear relationship:…”

Section: Model Of Gas-transfer Velocity In the Presence Of Wave Breakingmentioning

confidence: 99%

Gas transfer velocity in the presence of wave breaking

Zhao

2016

Tellus B: Chemical and Physical Meteorology

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A B S T R A C T Wave breaking is known to cause air entrainment and enhancement of the near-surface turbulence. Thus, it intensifies the gas exchange across the airÁsea interface. Based on the combination of the vertical distribution of the turbulence in the wave-affected layer and the breaking wave-energy dissipation rate in the wave-breaking layer, we proposed a composite model for the gas transfer velocity in the presence of wave breaking. The gas transfer velocity was calculated as a function of the air frictional velocity, wave age, and whitecap coverage. The model was validated by the dependences on winds and wave ages by field and laboratory measurements. The results supported the hypothesis that the large uncertainties in the traditional gas transfer velocities based on wind speed alone at moderate-to-high wind speeds can be ascribed to the neglect of the windÁwave effect, which is mainly attributed to the whitecap coverage as a function of the windÁsea Reynolds number.

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“…Slower sampling rates and lower resolutions were used during HiWASE, since the cameras were serviced only every two or three months rather than every day. A grayscale image analysis similar to that employed by Stramska and Petelski (2003) was used to isolate whitecaps from the surrounding sea. Initial results show an increase of whitecap fraction with wind speed (Fig.…”

Section: Wave Breaking and Whitecap Measurements Whitecap Coverage Ementioning

confidence: 99%