The full benefit of a move towards use of radiometric estimates of W in prediction of gas transfer velocities can only be realised if more refined models for air-sea gas-exchange are developed. Further information is needed to better relate breaking waves and bubble plume production to transfer coefficients. For example, wave information (wave height and slope) is required to better quantify transfer enhanced by wave breaking dissipation mechanisms. Likewise, bubble plume information (such as penetration depth, and bubble size distribution) needs to be considered for better representation of both exchange across bubble surfaces and exchange due to disruption of the surface microlayer by bubble surfacing and bursting.
Summary and recommendations
7.1 Summary
This study has presented an analysis of whitecap fraction estimated from satellite-based radiometric observations at microwave frequencies of 10 GHz (W10) and 37 GHz (W37).
Results and conclusions have been framed in the context of more traditional approaches to the measurement, interpretation, and parameterisation of W . By providing global coverage, along with a several order of magnitude increase in the number of individual estimates of W compared to historical in situ data, the satellite-based approach is unquestionably a significant step in the quest to obtain more accurate estimates of whitecap fraction which in turn will lead to improved representation of whitecaps in models and remote sensing applications.
W10 and W37 estimates have been directly validated against ship-based photographic estimates from two cruises. In situ W estimates were calculated by temporally aver-aging photographic data obtained from ship-borne digital cameras. Large day-to-day variation in the volume and quality of data was apparent; during several days it was not possible to obtain the number of photographs needed to converge on a statis-tically stable characteristic value of W over the time-averaging window. The wind speed dependence of the in situ estimates is similar to the Callaghan et al. [2008a]
W (U10) relationship, which was obtained using the same automated processing al-gorithm. Other published W (U10) parametrisations, such as that of Stramska and Petelski[2003] and Monahan and O’Muircheartaigh[1980] predict higher W than the
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SEASAW formulation over much of the wind speed range, and have a stronger wind speed dependence, resulting in larger deviations from SEASAW estimates in high winds (i.e., U10> 12 m s−1). To directly validate the retrieval, a spatial-temporal approach was used to obtain matchups between temporally-averaged in situ data and spatially averaged satellite data. Despite the small number of data points, (only fifteen ship-satellite matchups were identified), it was possible to determine that ship-satellite-based W is biased high at low wind speeds (more so for W37) and low at high wind speeds when compared to photographic W estimates. The bias between satellite and photo-based estimates is generally larger for W37 than for W10, and generally increases as wind speed increases. Differences in U10 estimates (i.e., ship-based estimates of ‘true’ U10 versus the QuikSCAT equivalent-neutral U10 estimates paired with satellite W data) is likely the cause of some of the bias.
The global distribution and seasonal dependence of satellite-based W have been de-scribed. Seasonal means of the two estimates have similar geographical distributions, with W37 seasonal means a factor 1.5–2 higher than those for W10. At low latitudes (equatorward of 30◦N and S), seasonal means rarely reach 1% for W10 and 1.5% for W37. Seasonal changes in mid to high latitudes are stronger in the northern hemisphere than in the southern hemisphere; this reflects the effects of the asymmetry in distribu-tion of continental land masses between the hemispheres. Highest seasonal W occurs during the boreal winter (December–February) in the North Atlantic and during the austral winter months (June–August) in the Southern Ocean.
A comparison has been made between satellite-based W estimates and those obtained from the widely used W (U10) relationship of Monahan and O’Muircheartaigh [1980]
(MM80). Differences are driven primarily by their differing wind speed dependence, which is weaker for the satellite-based estimates. The weaker wind-speed dependence results in satellite estimates higher than those obtained from MM80 in regions (such as the tropics) where mean wind speeds are low (U10≤ 8 m s−1), but lower than MM80 in high latitudes where mean wind speeds are higher. Overestimation of MM80 due to extrapolation beyond its range of validity is likely a key bias at high wind speeds.
These differences are robust if a comparison is made between MM80 W estimates and estimates from wind speed parameterisations of W10 and W37. The satellite-based pa-rameterisations have been derived from W estimates on a global scale and so their wind speed dependence will in part reflect the influence of factors other than wind speed
which co-vary with the wind geographically; for example SST, biological surfactant concentration, and fetch-dependent wave state. As the dataset of satellite-based W es-timates is not yet freely available for use, the satellite-based W (U10) parameterisations can be used in lieu of observed W10 and W37 estimates.
Estimation of satellite-derived whitecap fraction is dependent upon the retrieval’s ra-diometric wavelength. Different frequencies are able to discriminate—albeit somewhat crudely—between foam layers depending on their thickness. Our results show un-ambiguously that wind speed and secondary factors affect W10 and W37 differently.
Importantly, these differences can be plausibly explained with different influences of the secondary factors on foam layers associated with different whitecap lifetime stages.
This lends support to the notion that W10 and W37represent different mixes of active and residual whitecaps. This is also an independent confirmation of the conclusions regarding the frequency sensitivity to foam layer thicknesses which Anguelova and Gaiser[2011] obtained on the basis of purely physical considerations. Again it should be stressed that ‘microwave radiometry is suitable for remote sensing of the surface foam layers which represent the horizontal extent of (i.e., the area covered by) the whitecaps. That is, passive microwave remote sensing measures predominantly the surface expression of breaking waves’ [Anguelova,2008].
Using collocated and concurrent measurements of a variety of physically relevant quan-tities in addition to wind speed, the influence of secondary factors on whitecap fraction has been quantified. At a given wind speed, variability due to secondary factors is more pronounced for W37 than for W10 with W37 changing by as much as 20% when wave height, wave period, or the mean wave slope are considered, and by up to 25% when SST or Ta are considered. This clear SST trend is largely due to the strong suppres-sion of W37 in high SST (low viscosity) waters. The influence of the degree of wave development has also been assessed by partitioning the W estimates according to the relationship between the bulk wave parameters available as part of the database (sig-nigicant wave height and peak wave period), and U10. The results have been discussed in the context of the inconclusive findings of several previous studies using in situ data.
At a given wind speed, satellite-based W is lower in conditions of a developing sea com-pared with a fully developed sea. The effect on W of the wave state in swell conditions is more complex; these findings require further investigation with more detailed wave
measurements including directional information, and ideally, measures quantifying the wind sea and swell parts of the wave spectrum separately.
Based on the magnitude of the influence of secondary forcing factors on W10and W37, we conclude that much of the variability in foam layer might be due to the behavior of the thinner, decaying foam patches, variability that is not captured by the retrieval using the 10 GHz channel. Variability in W is smaller for active whitecaps (W10) and larger for total whitecap fraction which includes both active and residual whitecaps (W37). Within each secondary dependence, the effect of the secondary parameter is, in most cases, stronger at higher winds than at lower, supporting the findings from the in situ studies ofCallaghan et al.[2008a] and Goddijn-Murphy et al. [2011].
Principal component analysis has been used to assess the success of wind speed and secondary forcing parameters in accounting for variability in W . Though we show that the influence of secondary factors on W can be appreciable (especially for W37), the PCA results suggest that wind speed alone accounts for most of the variability in both W10 and W37. Interestingly, two Reynolds number parameters—combining measures of wind speed, wave height or period, and viscosity—perform almost as well as wind speed alone. Different measures of the degree of wave development (wave age and mean wave slope) can account for between 80 and 85% of variability in W . The first principal component for air temperature, SST, and the viscosity of water account for roughly the same level of variance in W ; 71% for W10, and 74% for W37.
Whitecap fraction is currently used to parameterise numerous bubble-dependent air-sea processes, but it is expected that these parameterisations could be improved through use of measures of W that discriminate between active breakers and residual foam. As such, microwave measurements of whitecap fraction could prove beneficial to parame-terization of such processes. It has been shown that use of W (U10) parameterisations based on limited in situ data can lead to biases in the global distribution of W . This in turn leads to biases in predictions of the magnitude and spatial distribution of several bubble-dependent processes which are parameterised in terms of W . Such biases are consistent with recent results showing both general overestimation of modeled SSA concentrations in high latitudes and underestimation in the tropics. These biases can be reduced with use of satellite-based estimates of W to estimate SSA source fluxes.
An improved representation of the spatial and temporal distribution of W will also
benefit parameterisations of air-sea interaction processes and the accuracy of remote sensing retrievals. Routine satellite observations of whitecap fraction can provide such improved spatial and temporal distribution of W .
Recent results on the time evolution of individual whitecaps have been combined with a dynamical model of whitecap fraction based on breaking wave statistics. The frame-work enables one to more closely relate whitecap fraction to the dynamics and statis-tics of breaking waves. It has been shown that such a semi-empircal approach may be favourable to constraining active whitecap fraction WA which is thought to be more closely related to the dynamic processes associated with wave breaking (such as pro-duction of spume droplets) and bubble plume generation (such bubble-mediated gas exchange). Measured total W depends explicitly on the foam decay mechanism; in some situations—for example, when whitecap decay rates are controlled by surfactant stabilisation—estimates of W will be strongly influenced by factors that are not di-rectly related to the active dynamics of the breaking waves. Consideration of both a constant decay rate and one that scales with properties of the breaking waves illus-trates the large effect that different decay rate schemes have one estimates of total W (stage A and B whitecaps).
Though our discussion on the utility of satellite-based W estimates in predicting the magnitude of air-sea interaction processes focuses mainly on efforts to improve SSA source flux predictions, we stress that satellite-based estimates of W will also benefit modeling of other air-sea interaction processes associated with whitecaps. These in-clude gas exchange, storm intensification, global radiation budget, and ocean albedo.
It would also improve the accuracy of remote sensing retrievals of geophysical variables such as wind vector, sea surface salinity, and ocean color. Though whitecap fraction is an indirect, bulk measure of wave breaking, it provides a convenient approach to predicting the magnitude of bubble-related air-sea mechanisms. However, W needs to be more explicitly linked to these different processes; improved measurement and parameterisation of W needs to be complemented with a more refined understanding of the links between wave breaking, bubble plume production, formation and decay or surface foam layers, and related physical processes.