共查询到20条相似文献,搜索用时 31 毫秒
1.
《Ocean Modelling》2009,26(3-4):154-171
Ocean surface mixing and drift are influenced by the mixed layer depth, buoyancy fluxes and currents below the mixed layer. Drift and mixing are also functions of the surface Stokes drift Uss, volume Stokes transport TS, a wave breaking height scale Hswg, and the flux of energy from waves to ocean turbulence Φoc. Here we describe a global database of these parameters, estimated from a well-validated numerical wave model, that uses traditional forms of the wave generation and dissipation parameterizations, and covers the years 2003–2007. Compared to previous studies, the present work has the advantage of being consistent with the known physical processes that regulate the wave field and the air–sea fluxes, and also consistent with a very large number of in situ and satellite observations of wave parameters. Consequently, some of our estimates differ significantly from previous estimates. In particular, we find that the mean global integral of Φoc is 68 TW, and the yearly mean value of TS is typically 10–30% of the Ekman transport, except in well-defined regions where it can reach 60%. We also have refined our previous estimates of Uss by using a better treatment of the high frequency part of the wave spectrum. In the open ocean, Uss ≃ 0.013U10, where U10 is the wind speed at 10 m height. 相似文献
2.
粗糙海面L 和C 双波段的代价函数多参量遥感反演分析 总被引:1,自引:0,他引:1
利用代价函数(cost function)方法,通过分析粗糙海面L和C双波段多极化遥感亮温对海表盐度、温度、风速和有效波高等参数的敏感性以及L和C双波段多极化的代价函数收敛特性,建立了反演海表盐度、温度、风速和有效波高等多参数的L和C双波段多极化代价函数模式。双波段遥感模式分析结果表明:(1)对于双参数的联合反演,L和C双波段垂直极化代价函数联合反演海表盐度和温度可以获得较好的反演结果。(2)L波段垂直极化和C波段水平极化代价函数联合反演海表盐度和风速较好。(3)对于三参数联合反演,L波段垂直极化和C波段的双极化联合反演盐度、温度和风速的精度较高。(4)L波段亮温对有效波高的敏感性较低(C波段经验模式不含有效波高),使得有效波高反演误差较大,L和C波段经验模式不适合反演有效波高参数。另外,为了定量分析L和C双波段代价函数的多参量遥感反演结果,采用加性噪音模拟亮温方法,对上述L和C双波段多极化模式的盐度、温度和风速等多参数联合反演误差进行了分析,均得出较好的结果。结论表明L和C双波段代价函数联合反演多参量可以明显提高参量反演精度,为粗糙海表面多参量的反演提供了新的方法和途径。 相似文献
3.
Changes from winter (July) to summer (February) in mixed layer carbon tracers and nutrients measured in the sub-Antarctic zone (SAZ), south of Australia, were used to derive a seasonal carbon budget. The region showed a strong winter to summer decrease in dissolved inorganic carbon (DIC; 45 µmol/kg) and fugacity of carbon dioxide (fCO2; 25 µatm), and an increase in stable carbon isotopic composition of DIC (δ13CDIC; 0.5‰), based on data collected between November 1997 and July 1999.The observed mixed layer changes are due to a combination of ocean mixing, air–sea exchange of CO2, and biological carbon production and export. After correction for mixing, we find that DIC decreases by up to 42 ± 3 µmol/kg from winter (July) to summer (February), with δ13CDIC enriched by up to 0.45 ± 0.05‰ for the same period. The enrichment of δ13CDIC between winter and summer is due to the preferential uptake of 12CO2 by marine phytoplankton during photosynthesis. Biological processes dominate the seasonal carbon budget (≈ 80%), while air–sea exchange of CO2 (≈ 10%) and mixing (≈ 10%) have smaller effects. We found the seasonal amplitude of fCO2 to be about half that of a study undertaken during 1991–1995 [Metzl, N., Tilbrook, B. and Poisson, A., 1999. The annual fCO2 cycle and the air–sea CO2 flux in the sub-Antarctic Ocean. Tellus Series B—Chemical and Physical Meteorology, 51(4): 849–861.] for the same region, indicating that SAZ may undergo significant inter-annual variations in surface fCO2. The seasonal DIC depletion implies a minimum biological carbon export of 3400 mmol C/ m2 from July to February. A comparison with nutrient changes indicates that organic carbon export occurs close to Redfield values (ΔP:ΔN:ΔC = 1:16:119). Extrapolating our estimates to the circumpolar sub-Antarctic Ocean implies a minimum organic carbon export of 0.65 GtC from the July to February period, about 5–7% of estimates of global export flux. Our estimate for biological carbon export is an order of magnitude greater than anthropogenic CO2 uptake in the same region and suggests that changes in biological export in the region may have large implications for future CO2 uptake by the ocean. 相似文献
4.
The wave transmission, reflection and energy dissipation characteristics of ‘’-type breakwaters were studied using physical models. Regular and random waves in a wide range of wave heights and periods and a constant water depth were used. Five different depths of immersion (two emerged, one surface flushing and two submerged conditions) of this breakwater were selected. The coefficient of transmission, Kt, and coefficient of reflection, Kr, were obtained from the measurements, and the coefficient of energy loss, Kl was calculated using the law of balance of energy. It was found that the wave transmission is significantly reduced with increased relative water depth, d/L, whether the vertical barrier of the breakwater is surface piercing or submerged, where ‘d’ is the water depth and ‘L’ is the wave length. The wave reflection decreases and energy loss increases with increased wave steepness, especially when the top tip of the vertical barrier of this breakwater is kept at still water level (SWL). For any incident wave climate (moderate or storm waves), the wave transmission consistently decreases and the reflection increases with increased relative depth of immersion, Δ/d from −0.142 to 0.142. Kt values less than 0.3 can be easily obtained for the case of Δ/d=+0.071 and 0.142, where Δ is the height of exposure (+ve) or depth of immersion (−ve) of the top tip of the vertical barrier. This breakwater is capable of dissipating wave energy to an extent of 50–80%. The overall performance of this breakwater was found to be better in the random wave fields than in the regular waves. A comparison of the hydrodynamic performance of ‘’-type and ‘T’-type shows that ‘T’-type breakwater is better than ‘’-type by about 20–30% under identical conditions. 相似文献
5.
Estimation of the leeway drift of small craft 总被引:1,自引:0,他引:1
Small craft (<6·4 m) leeway is determined as a function of the wind speed in the range of 5–20 knots (3·6–10·3 m/sec). Leeway is calculated relative to the surface current by measurement of the separation distance of the small craft from a dyed patch of surface water at sea, using time-sequenced aerial photography. Leeway increases linearly with wind speed for small craft equipped with or without a sea anchor in the wind range studied. Leeway for small craft without sea anchor can be calculated from the equation UL = 0.07 UW + 0.04 where UW is the wind speed at 2 m elevation. Leeway for small craft drifted off the be calculated from the equation ULD = 0·05 UW − 0·12. The small craft drifted off the downwind direction in about 80% of the experiments. The drift angle is variable and difficult to predict. 相似文献
6.
S. Aranuvachapun 《Ocean Engineering》1987,14(2)
Monte Carlo simulation of wave spectra was carried out to provide an assessment of JONSWAP spectral model and parameters. The simulation method is found to be satisfactory because (a) it excludes the spectral variability due to geophysical factors from the sampling errors in the spectral estimates and the statistical uncertainty in determining the model parameters; and (b) the simulated spectra can represent ideal spectral estimates where the sampling errors have been minimized by increasing the degrees of freedom of the spectra. The latter (b) allows both the magnitude of sampling errors to be evaluated and errors due to statistical uncertainty to be isolated. Thus, the stimulation study provides a useful error analysis to assess the JONSWAP spectral model and parameters. For instance, it is found from the results that the sampling errors could be as high as 20% while errors due to uncertainty in determining the model parameter could be as high as 17%. However, the overall errors may be reduced to the minimum of approx. 15% if the simulated spectra have 80 degrees of freedom and constant values of σa and σb i.e. σa = 0.07 and σb = 0.09. This implies that the maximum accuracy of 85% may be achieved in JONSWAP spectral model even though the α parameter has been underestimated by about 1.5%. The overestimated values of γ might come from the underestimated α and the biased φm estimator caused by the statistical uncertainty in the presence of a sharp spectral peak. Although the scale parameters (α and φm) exhibit smaller errors and variability than the shape parameters (ψ, σa and σb), they are more sensitive to the degrees of freedom of the spectra and their estimators are not better than the estimators of shape parameters. The simulation experiments have also shown that simulated spectra at 20–40 degrees of freedom contain a substantial amount of sampling errors. Therefore, the measured wave spectra at the same degrees of freedom (20–40) are not suitable and should not be used for evaluating the accuracy of any wave spectral model. 相似文献
7.
Predictions of maximum wave height Hmax are made at Cromer, Happisburgh and Lowestoft on the East Anglian Coast using the formula Hmax = CKRKS U2/g where C is a constant, KR, KS are refraction and shoaling cofficients, U is wind speed and g is the acceleration due to gravity. Comparisonsare made with the models of Darbyshire Draper (1963) and Bretschneider (1958) Using this wave prediction formula, an estimate of the wave climate in the southern North Sea is deduced for the gales of 2–3 January 1976. 相似文献
8.
The rates of the reduction of Cr(VI) with S(IV) were measured in deaerated NaCl solution as a function of pH, temperature and ionic strength. The rates of the reaction were found to be first order with respect to Cr(VI) and second order with respect to S(IV), in agreement with previous results obtained at concentrations two order higher than the present study. The reaction also showed a first-order dependence of the rates on the concentration of the proton and a small influence of temperature with an apparent energy of activation ΔHapp of 22.8 ± 3.4 kJ/mol. The rates were independent of ionic strength from 0.01 to 1 M. The rate of Cr(VI) reduction is described by the general expression
−d[Cr(VI)]/dt=k[Cr(VI)][S(IV)]2