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A laboratory study of breaking waves

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This paper discusses problems of estimating the net long-wave radiation flux at the sea surface on the basis of easily measurable meteorological quantities (air and sea surface temperatures, near-surface water vapour pressure, cloudiness). Empirical data and existing formulae are compared. Additionally, an improved formula for the southern Baltic region is introduced, with a systematic error of less than 1 W m−2 and a statistical error of less than 20 W m−2.
Breaking waves (whitecaps) are one of the most important and least understood processes associated with the evolution of the surface gravity wave field in the open sea. This process is the principal means by which energy and momentum are transferred away from a developing sea. However, an estimation of the frequency of breaking waves or the fraction of sea surface covered by whitecaps and the amount of dissipated energy induced by breaking is very difficult to carry out under real sea conditions. A controlled experiment, funded by the European Commission under the Improving Human Potential Access Infrastructures programme, was carried out in the Ocean Basin Laboratory at MARINTEK, Trondheim (Norway). Simulation of random waves of the prescribed spectra by wave makers provided a very realistic pattern of the sea surface. The number of breaking waves was estimated by photographing the sea surface and recording the noise caused by the breaking waves. The experimental data will serve for calibration of the theoretical models of the sea surface fraction related to the whitecaps.
A simple spectral model of solar energy input to the sea surface was extended to incorporate space-borne data. The extension involved finding a method of determining aerosol optical thickness (on the basis of AVHRR data) and the influence of cloudiness (on the basis of METEOSAT data) on the solar energy flux. The algorithm for satellite data assimilation involves the analysis of satellite images from the point of view of cloud identification and their classification with respect to light transmission. Solar energy input values measured at the Earth’s surface by traditional methods were used to calibrate and validate the model. Preliminary evaluation of the results indicates a substantial improvement in the accuracy of estimates of solar energy input to the sea surface in relation to models utilising only traditionally obtained data on the state of the atmosphere.
The Arctic Ocean (AO) is an important basin for global oceanic carbon dioxide (CO2) uptake, but the mechanisms controlling air—sea gas fluxes are not fully understood, especially over short and long timescales. The oceanic sink of CO2 is an important part of the global carbon budget. Previous studies have shown that in the AO differences in the partial pressure of CO2 (DpCO2) and gas transfer velocity (k) both contribute significantly to interannual air—sea CO2 flux variability, but that k is unimportant for multidecadal variability. This study combined Earth Observation (EO) data collected in 2010 with the in situ pCO2 dataset from Takahashi et al. (2009) (T09) using a recently developed software toolbox called FluxEngine to determine the importance of k and DpCO2 on CO2 budgets in two regions of the AO — the Greenland Sea (GS) and the Barents Sea (BS) with their continental margins. Results from the study indicate that the variability in wind speed and, hence, the gas transfer velocity, generally play a major role in determining the temporal variability of CO2 uptake, while variability in monthly DpCO2 plays a major role spatially, with some exceptions.
We suggest to transfer the empirical downscaling methodology, which was developed mostly for atmospheric dynamics and impacts, to regional ocean problems. The major problem for doing so is the availability of decades-long and homogeneous and spatially detailed data sets. We have examined the performance of the STORM multidecadal simulation, which was run on a 0.18 grid and forced with 1950—2010 NCEP re-analyses, in the South China Sea and found the data suitable. For demonstration we build with this STORM-data downscaling model for the regional throughflow. The STORM data is compared with AVISO satellite observations and the ocean re-analysis dataset C-GLORS. We find the seasonal patterns and the inter-annual variability of sea surface height anomaly in both the C-GLORS data and the STORM simulation consistent with the AVISO- satellite data. Also the strong westward intensification and the seasonal patterns of South China Sea circulation steered by the monsoon have been presented well. As an important indicator of vertical movement, the sea surface temperature distribution maps are also very close, especially for the narrow upwelling region in summer. We conclude that the output of the STORM simulation is realistically capturing both the large-scale as well as the small-scale dynamical features in the South China Sea.
An experiment has been performed to determine the upward water-leaving radiance by non-contact measurement of the total upward and downward radiance above the sea surface from a moving ship. The method for achieving this aim is described: the radiance meters are both tilted in such a way that the upward radiance meter can ‘see’ that part of the measured downward radiance which would be reflected if the water surface were smooth and which is not derived directly from solar glitter. Both meters are firmly fixed in a special frame, which ensures that the required orientation is the most probable one. Time records of the measured parameters are analysed. The results are presented in several forms: frequency (histogram) analysis appears to be the most promising one.
Among over 150 maps of sea surface temperature in the Polish Baltic coastal region derived from satellite data during the warm period of the year (April–October) in 2000–2002, 41 cases were noted where its distribution showed characteristic features indicating the occurrence of coastal upwelling. The fundamental parameters of range, probability of occurrence and temperature modification caused by water from deeper sea layers raised by an upwelling event and spreading across the surface were established for three regions (Hel, Łeba and Kołobrzeg). The Kołobrzeg upwelling region had the largest spatial range (up to 5000 km2). The region with the smallest spatial range (Hel, up to 1400 km2) had the largest surface temperature amplitude (to 14◦C), the largest maximum temperature gradient (5◦C km−1) and the largest average sea surface temperature decrease in the centre in relation to the background value.
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