Simulation of the oil spill processes in the Sea of Japan with regional ocean circulation model

A simulation of the movement of spilled oil after the incident of the Russian tanker Nakhodka in the Sea of Japan, in January 1997, was performed by a particle tracking model incorporating advection by currents, random diffusion, the buoyancy effect, the parameterization of oil evaporation, biodegradation, and beaching. The currents advecting spilled oil were defined by surface wind drift superposed on the three-dimensional ocean currents obtained by the Geophysical Fluid Dynamics Laboratory modular ocean model (GFDL MOM), which was forced by the climatological monthly mean meteorological data, or by the European Center for Medium Range Weather Forecasts (ECMWF) daily meteorological data, and assimilated sea surface topography detected by satellite altimeter. A number of experiments with different parameters and situations showed that the wide geographical spread of oil observed is not explained by wind drift alone, and that including the simulated climatological currents gives better results. The combination of surface wind drift and daily ocean currents shows the best agreement between the model and observations except in some coastal areas. The daily meteorological effect on the ocean circulation model results in a stronger variability of currents that closely simulates some features of the nonlinear large-scale horizontal turbulent diffusion of oil. The effect of different parameterizations for the size distribution of model oil particles is discussed. Received for publication on July 26, 1999; accepted on Nov. 17, 1999     

Simulation of the mean zero-up-crossing wave period using artificial neural networks trained with a simulated annealing algorithm

The aim of this work was to develop a predictive model to forecast the mean zero-up-crossing wave periods (T _z ) for 3-hourly sea states at a location in the Pacific using artificial neural networks (ANNs). Seven multilayer ANNs were trained with a simulated annealing algorithm. The output of each trained ANN was used to estimate each of the seven parameters of a new distribution called the hepta-parameter spline proposed for the conditional distribution of T _z , given some mean zero-up-crossing wave periods and significant wave heights. After estimating the parameters of the distribution, the model was used to simulate and predict future values of T _z . Forecasting a sea state and developing the joint distribution of sea state characteristics with the help of the simulated characteristics are also discussed in this article.     

Environmental changes and the responses of the ecosystems of the Yellow Sea during 1976–2000

The changes in the environmental features of the Yellow Sea during the last 25 years of the 20th century were studied using a set of seasonally monitored data along a transect (at 36°N) maintained by the State Oceanic Administration of China. The data included the ocean temperature (T), salinity (S) and biogenic elements, such as dissolved oxygen (DO), phosphorus (PO₄-P), silicon (SiO₃-Si) and dissolved inorganic nitrogen (DIN).The seasonal (summer and winter) values and the annual mean of these elements showed significant changes during the monitored period. Time series of T, S, DIN and N:P ratios exhibited positive trends, while those of DO, P and Si exhibited negative trends. During this period, the annual mean of T and DIN in the Yellow Sea increased by 1.7 °C and 2.95 μmol L⁻¹, respectively, while those of DO, P and Si decreased by 59.1, 0.1 and 3.93 μmol L⁻¹, respectively. In the 1980s, particularly in between 1985 and 1989, concentrations of P and Si dropped to near the ecological threshold for growth of diatoms. The N:P ratio increased from 4 in 1984 to over 16 in 2000. The climate trend coefficients, R_xt, for these time series are all above 0.43 with significance levels of 95%, except for salinity. The increases in T were consistent with the recent climate warming in northern China and the adjacent seas, i.e. the Bohai Sea and the East China Sea. The reduction of DO was probably attributable to the increase in T and decrease in primary production in these regions. The positive trend of DIN was mainly attributable to precipitation and partly to Changjiang River discharge. The negative concentration trends of P and Si were due to the decreases in their concentrations in seawater that flowed to the Yellow Sea from the Bohai Sea. As a result, N:P ratios greatly increased in the seawater of the Yellow Sea.Moreover, some important responses of the Yellow Sea ecosystems to the changes in physical variables and chemical biogenic elements were obviously displayed. These responses include strengthening nutrient limitation, decreasing chlorophyll a, primary production and phytoplankton abundance, succession of dominant phytoplankton species from diatoms to non-diatoms, changes in fish community structure and species diversity.     

Assessment of sea surface temperature observational networks in the Baltic Sea and North Sea

The satellite and in situ Sea Surface Temperature (SST) observational networks in the Baltic Sea and North Sea are evaluated based on the quality of the gridded SST products generated from the networks. A multi-indicator approach is applied in the assessment. It includes evaluation of data quality, effective data coverage, field reconstruction error and model nowcast error. The results show that the best available full-coverage SST product is generated by assimilating the SST observations to obtain a yearly mean model bias of 0.07 °C and RMSE of 0.64 °C. The effective data coverage rate is 31% by using AVHRR (Advanced Very High Resolution Radiometer) data from NOAA (National Ocean and Atmosphere Administration) satellites 12, 14 and 16. The data redundancy increases rapidly with the number of infrared sensors. Using either NOAA satellite 12 or all 3 satellites makes a small difference with regard to derived effective coverage and the ocean model nowcast error. The influence of using the in situ SST observations in the SST field reconstruction is negligibly small. Instead, the major role of in situ SST observations is in calibrating the satellite observations. To study the relative importance of data quality and data coverage, an assessment is done for two satellite products: one product is based entirely on NOAA 12 data and has larger coverage but lower quality. The other product is a subset of the SAF products (derived from NOAA 14 and 16) and has lower coverage but higher quality. Based on current monitoring, modelling and assimilation technology, the results suggest that the data quality is an important factor in further improving the quality of the gridded SST products. Recommendations are made for possible further improvements of the existing SST observational networks.     

Enrichment, concentration and retention processes in relation to anchovy (Engraulis ringens) eggs and larvae distributions in the northern Humboldt upwelling ecosystem

A Lagrangian model is used to simulate and quantify in the northern Humboldt upwelling ecosystem the processes of enrichment, concentration and retention, identified by Bakun [Bakun, A., 1996. Patterns in the ocean. Ocean processes and marine population dynamics. University of California Sea Grant, California, USA, in cooperation with Centro de Investigaciones Biologicas de Noroeste, La Paz, Baja California Sur, Mexico, 323 pp.] as being important for the survival and recruitment of early life stages of pelagic fish. The method relies on tracking the positions of particles within water velocity fields generated by a three-dimensional hydrodynamic model. Simple criteria for considering particles as participating to enrichment, concentration or retention are used to derive indices of the three processes. We analyse the spatial distribution of and seasonal variability in these indices. The results are discussed in relation to anchovy (Engraulis ringens) eggs and larvae distributions off Peru, and to a comparable study conducted in the southern Benguela upwelling ecosystem.     

CO2 air–sea disequilibrium and preformed alkalinity in the Pacific and Indian oceans calculated from subsurface layer data

This work estimates new regionalized empirical parameterizations for preformed alkalinity (A_T^o) and the CO₂ air–sea disequilibrium (?C_dis). Both are key terms for the computation of anthropogenic CO₂ in the back-calculation methods. Data from the subsurface layer (75–180 m depth range) covering an area from North to South and from 19°E to 67.5°W (Pacific and Indian oceans) were taken from GLODAP (The Global Ocean Data Analysis Project) database. The subsurface layer is proved as a reliable reference for representing the main characteristics of the different water masses of the oceans. Besides, handing data from the two ocean basins altogether makes the new parameterizations of A_T^o and ?C_dis to be more globally consistent. Nevertheless, each ocean basin, at least in some regions, has different oceanographic characteristics based on its proper dynamical processes and water masses formation. In order to maintain each ocean basin ‘identity’ the whole domain was divided in six different regions (two of them sharing waters from Pacific and Indian oceans) and parameterizations in each region for both terms were obtained. Previously, data were transformed into a grid of 4°lat. × 5°lon. and the results obtained from the parameterizations were visualized and compare with pCO₂ climatologies. From the comparisons with previous ?C_dis estimations good results are obtained showing the reliability and robustness of the new regionalized empiric parameterizations.     

A generalized model of pelagic biogeochemistry for the global ocean ecosystem. Part II: Numerical simulations

This paper presents a global ocean implementation of a multi-component model of marine pelagic biogeochemistry coupled on-line with an ocean general circulation model forced with climatological surface fields (PELAgic biogeochemistry for Global Ocean Simulations, PELAGOS). The final objective is the inclusion of this model as a component in an Earth System model for climate studies. The pelagic model is based on a functional stoichiometric representation of marine biogeochemical cycles and allows simulating the dynamics of C, N, P, Si, O and Fe taking into account the variation of their elemental ratios in the functional groups. The model also includes a parameterization of variable chlorophyll/carbon ratio in phytoplankton, carrying chl as a prognostic variable. The first part of the paper analyzes the contribution of non-local advective–diffusive terms and local vertical processes to the simulated chl distributions. The comparison of the three experiments shows that the mean chl distribution at higher latitudes is largely determined by mixing processes, while vertical advection controls the distribution in the equatorial upwelling regions. Horizontal advective and diffusive processes are necessary mechanisms for the shape of chl distribution in the sub-tropical Pacific. In the second part, the results have been compared with existing datasets of satellite-derived chlorophyll, surface nutrients, estimates of phytoplankton community composition and primary production data. The agreement is reasonable both in terms of the spatial distribution of annual means and of the seasonal variability in different dynamical oceanographic regions. Results indicate that some of the model biases in chl and surface nutrients distributions can be related to deficiencies in the simulation of physical processes such as advection and mixing. Other discrepancies are attributed to inadequate parameterizations of phytoplankton functional groups. The model has skill in reproducing the overall distribution of large and small phytoplankton but tends to underestimate diatoms in the northern higher latitudes and overestimate nanophytoplankton with respect to picoautotrophs in oligotrophic regions. The performance of the model is discussed in the context of its use in climate studies and an approach for improving the parameterization of functional groups in deterministic models is outlined.     

Operational Oceanography System applied to the Prestige oil-spillage event

This contribution describes the procedure used during the Prestige oil-spillage event, by means of an Operational Oceanography System, and the behaviour of the present prediction tools (hydrodynamic and dispersion models) applied to it. The accuracy of these tools is estimated by a reanalysis of field data transmitted by a sea surface drifting buoy, released at the time of the oil spill. The numerical models applied were the Regional Ocean Modeling System (ROMS), fed by the available six-hourly NCEP atmospheric information, together with a Lagrangian Particle-Tracking Model (LPTM). ROMS has been used to estimate the current fields for the Bay of Biscay, whilst the LPTM has provided the oil spill trajectories. The results demonstrate that the accuracy of the numerical models depends upon the quality of the meteorological input data. In this case, the current fields at the sea surface, derived by ROMS, have been underestimated by the wind fields of the NCEP reanalysis data. An efficient calibration of these wind fields, with data provided by the Gascony buoy (fixed oceanic and atmospheric station), achieves more realistic looking results; this is reflected in the comparison between the buoy trajectory predicted numerically and the tracked movements of the drifting buoy.     

An approach to estimation of near-surface turbulence and CO2 transfer velocity from remote sensing data

The air–sea CO₂ exchange is primarily determined by the boundary-layer processes in the near-surface layer of the ocean since it is a water-side limited gas. As a consequence, the interfacial component of the CO₂ transfer velocity can be linked to parameters of turbulence in the near-surface layer of the ocean. The development of remote sensing techniques provides a possibility to quantify the dissipation of the turbulent kinetic energy in the near-surface layer of the ocean and the air–sea CO₂ transfer velocity on a global scale. In this work, the dissipation rate of the turbulent kinetic energy in the near-surface layer of the ocean and its patchiness has been linked to the air–sea CO₂ transfer velocity with a boundary-layer type model. Field observations of upper ocean turbulence, laboratory studies, and the direct CO₂ flux measurements are used to validate the model. The model is then forced with the TOPEX POSEIDON wind speed and significant wave height to demonstrate its applicability for estimating the distribution of the near-surface turbulence dissipation rate and gas transfer velocity for an extended (decadal) time period. A future version of this remote sensing algorithm will incorporate directional wind/wave data being available from QUIKSCAT, a now-cast wave model, and satellite heat fluxes. The inclusion of microwave imagery from the Special Sensor Microwave Imager (SSM/I) and the Synthetic Aperture Radar (SAR) will provide additional information on the fractional whitecap coverage and sea surface turbulence patchiness.     

Neural network-based fuel consumption estimation for container ships in Korea

ABSTRACT

Due to the outstanding strength of advanced machine-learning techniques, they have become increasingly common in predictive studies in recent years, particularly in predicting ship energy performance. In constructing predictive models, prior studies have mostly employed vessels’ technical parameters to establish machine-learning algorithms. To bridge this research gap and enable wider applications, this paper presents the design of a multilayer perceptron artificial neural network (MLP ANN) as a machine-learning technique to estimate ship fuel consumption. We utilized the real operational data from 100–143 container ships to estimate fuel consumption for five different container ships grouped by size. We compared the performance of two ANN models and two multiple-regression models. Four input parameters (sailing time, speed, cargo weight, and capacity) were included in the first ANN and the first regression model, while the other two models only consider two inputs from physical function. The mean absolute percentage error of the ANN models with four inputs was the smallest and less than those in extended statistical models, demonstrating the MLP’s superiority over the statistical model. The MLP ANN model can thus be applied to confirm the effectiveness of the slow-steaming method for achieving energy efficiency.     

Modelling the silica pump in the Permanently Open Ocean Zone of the Southern Ocean

A coupled 1D physical–biogeochemical model has been built to simulate the cycles of silicon and of nitrogen in the Indian sector of the Permanently Open Ocean Zone of the Southern Ocean. Based on a simplified trophic network, that includes two size classes of phytoplankton and of zooplankton, and a microbial loop, it has been calibrated by reference to surface physical, chemical and biological data sets collected at the KERFIX time-series station (50°40′S–68°25′E). The model correctly reproduces the high nutrient low chlorophyll features typical of the studied area. In a region where the spring–summer mixed layer depth is usually deeper than 60 m, the maximum of chlorophyll never exceeds 1.5 mg m⁻³, and the annual primary production is only 68 g C m⁻² year⁻¹. In the surface layer nitrate is never exhausted (range 27–23.5 mmoles m⁻³) while silicic acid shows strong seasonal variations (range 5–20 mmoles m⁻³). On an annual basis 71% of the primary production sustained by nanophytoplankton is grazed by microzooplankton. Compared to North Atlantic, siliceous microphytoplankton is mainly prevented from blooming because of an unfavourable spring–summer light-mixing regime. Silicic acid limitation (high half saturation constant for Si uptake: 8 mmoles m⁻³) also plays a major role on diatom growth. Mesozooplankton grazing pressure excerpts its influence especially in late spring. The model illustrates the efficiency of the silica pump in the Southern Ocean: up to 63% of the biogenic silica that has been synthetized in the photic layer is exported towards the deep ocean, while only 11% of the particulate organic nitrogen escapes recycling in the surface layer.     

Using the SKAGEX dataset for evaluation of ocean model skills

Numerical ocean models are now being applied in numerous oceanographic studies. However, the qualities of the model results are often uncertain and there is a great need for standards and procedures for evaluation of the skills of numerical general circulation models. In this paper measurements from repeated hydrographical sections across Skagerrak taken in 1990, the SKAGEX dataset, are used to evaluate the skills of two σ-coordinate ocean models and to study the sensitivity of these models to model parameters. A methodology for quantification of model skills based on observations from repeated hydrographical sections in general is suggested. Area averages of absolute differences are for Skagerrak completely dominated by the discrepancies in the upper few meters of the ocean and may not be used to assess models' abilities to reproduce the fields in the larger and deeper part of the ocean. Therefore, discrepancies between average values in time from the observed fields and time averaged values from model outputs are related to the natural variability of the fields. The numbers produced with the suggested measure are relative numbers that will be specific for each section and for each series of observation. Ideally we would therefore like to see the measures computed for a number of sections for various models and choices of model parameters in order to assess model skills. The value of the SKAGEX dataset as a tool for model improvements is demonstrated. Evidence to support the importance of applying non-oscillatory, gradient preserving advection schemes in areas with sharp density fronts is given. The method is used to identify that the forcing/initial values/boundary values for the temperature field are inferior to the corresponding values for the salinity field. With the present coarse resolution, 11 layers in the vertical, it is shown that it is far from obvious that the quality of the model results improve when replacing simple Richardson number formulations for vertical mixing processes with higher order turbulence closure in the Skagerrak area.     

Surface chlorophyll in the Black Sea over 1978–1986 derived from satellite and in situ data

Absolute values of chlorophyll a concentration and its spatial and seasonal variations in the Black Sea were assessed by using satellite CZCS and in situ data. Since the satellite CZCS had operated for the 1978–1986 period, CZCS data was used for assessing the past state of the Black Sea just before the onset of drastic changes observed in late 1980s. The approach used for the calculation of the absolute values of chlorophyll a concentration from CZCS data was based on the direct comparison of in situ chlorophyll a data and those of CZCS and by applying the algorithm developed for the transformation of CZCS data into chlorophyll a values. CZCS Level 2 data related with pigment concentration having a spatial resolution of 1 km at nadir were used. The daily Level 3 files were derived by binning Level 2 values into 4-km grid cells and the monthly and seasonal Level 3 files were created by averaging the daily Level 3 files over the corresponding period. In situ chlorophyll a data were obtained by spectrophotometric and fluorometric methods in 15 scientific cruises over the 1978–1986 period. Total number of ship-measured data used for the comparison with those CZCS values was 590.Chlorophyll a concentration (Chl) was derived from CZCS values (C) with regression equations Chl=kC; the coefficient of transformation k was calculated from six different data sets by taking into account distinctions between subregions and seasons. The reasons for difference in the k values have been analyzed.Statistical comparison of the chlorophyll a values measured in situ and those derived from CZCS data was based on log-transformed data and gave the following results: regression SLOPE=0.842, regression INTERCEPT=−0.081, coefficient of determination (R²)=0.806, root–mean–square ERROR=0.195. The mean monthly chlorophyll a distributions derived from CZCS data over 1978–1986 have been constructed and the mean seasonal chlorophyll a values in different regions have been calculated and analyzed. The significant difference in chlorophyll concentration between the western shelf regions and the open part of the Black Sea has been demonstrated, especially in warm season. At almost all seasons, the highest chlorophyll concentration is observed in the western interior shelf region which is under strong influence of Danube. The summer mean chlorophyll concentration in this region is 18 times higher than that in the open parts and about nine times higher than in the eastern shelf region. The greatest seasonal variations are observed in the open part of the Black Sea: chlorophyll concentration in cold season is four to six times higher than in summer and three to five times higher than in April and October. To the contrary, in the western interior shelf regions, the concentration is higher in May–October (about twice than that in November–March). Seasonal variations in the western outer shelf regions are smoothed out as compared with both the western interior shelf and the open regions.     

Out of sight is not out of mind: Public opposition to ocean incineration

Abstract

During the early 1980s, ocean incineration emerged as one of the hottest issues in the field of hazardous waste management. Ocean incineration involves the thermal destruction of liquid hazardous wastes on specially designed ships and was promoted by the Environmental Protection Agency and powerful industry groups as part of the solution to liquid hazardous waste disposal. Attempts to adopt ocean incineration led to heated debate and apparent defeat of efforts to introduce this technology. The arguments advanced by proponents and opponents are examined. Success of environmental groups opposed to ocean incineration is explained with reference to technical, environmental, socio‐political, and legal factors. Ocean incineration, promoted as a solution to “not‐in‐my‐backyard”; opponents, instead ran afoul of region wide opposition by coastal residents for whom the sea is a common backyard.     

Inorganic carbon fluxes through the boundaries of the Greenland Sea Basin based on in situ observations and water transport estimates

A carbon budget for the exchange of total dissolved inorganic carbon C_T between the Greenland Sea and the surrounding seas has been constructed for winter and summer situations. An extensive data set of C_T collected over the years 1994–1997 within the European Sub-polar Ocean Programmes (ESOP1 and ESOP2) are used for the budget calculation. Based on these data, mean values of C_T in eight different boxes representing the inflow and outflow of water through the boundaries of the Greenland Sea Basin are estimated. The obtained values are then combined with simulated water transports taken from the ESOP2 version of the Miami Isopycnic Coordinate Ocean Model (MICOM). The fluxes of inorganic carbon are presented for three layers; a surface mixed layer, an intermediate layer and a deep layer, and the imbalance in the fluxes are attributed to air–sea exchange, biological fixation of inorganic carbon, and sedimentation. The main influx of carbon is found in the surface and the deep layers in the Fram Strait, and in the surface waters of direct Atlantic origin, whereas the main outflux is found in the surface layer over the Jan Mayen Fracture Zone and the Knipovich Ridge, transporting carbon into the Atlantic Ocean via the Denmark Strait and towards the Arctic Ocean via the Norwegian Sea, respectively. The flux calculation indicates that there is a net transport of carbon out of the Greenland Sea during wintertime. In the absence of biological activity, this imbalance is attributed to air sea exchange, and requires an oceanic uptake of CO₂ of 0.024±0.006 Gt C yr⁻¹. The flux calculations from the summer period are complicated by biological fixation of inorganic carbon, and show that data on organic carbon is required in order to estimate the air–sea exchange in the area.     

Determination of anthropogenic CO2 in the North Atlantic Ocean using water mass ages and CO2 equilibrium chemistry

A new method to calculate the anthropogenic CO₂ (ΔDIC_ant) within the water column of the North Atlantic Ocean is presented. The method exploits the equilibrium chemistry of the carbonate system with reference to temperature, salinity and the partial pressure of atmospheric CO₂ (pCO_2,atm). ΔDIC_ant is calculated with reference to the ventilation ages of water masses derived from tracer data and to the time history of pCO_2,atm. The method is applied to data recorded during the WOCE program on the WHP A1/E transect in the North Atlantic Ocean, where we characterise six key water masses by their relationships of dissolved inorganic carbon (DIC) and apparent oxygen utilisation (AOU). The error in determining ΔDIC_ant is reduced significantly by minimising the number of values referred to, especially by avoiding any use of remineralisation ratios of particulate organic matter. The distribution of ΔDIC_ant shows highest values of up to 45 μmol kg⁻¹ in the surface waters falling to 28–33 μmol kg⁻¹ in the Irminger Sea west of the Mid-Atlantic Ridge. The eastern basin is imprinted by older water masses revealing decreasing values down to 10 μmol kg⁻¹ ΔDIC_ant in the Antarctic Bottom Water. These findings indicate the penetration of the whole water column of the North Atlantic Ocean by anthropogenic CO₂.     

自主式水下潜器海洋环境监测系统技术概念

介绍了被誉为未来海洋卫星的自主式水下潜器ＡＵＶ（Ａｕｔｏｎｏｍｏｕｓ Ｕｎｄｅｒｗａｔｅｒ Ｖｅｈｉｃｌｅｓ）的国外发展新动态、自主式海洋采样网络ＡＯＳＮ（Ａｕｔｏｎｏｍｏｕｓ Ｏｃｅａｎ Ｓａｍｐｌｉｎｇ Ｎｅｔｗｏｒｋ）和海洋中的“卫星”技术概念和应用前景。     

改进的BP神经网络在船舶与海洋工程中的应用研究

人工神经网络作为一个具有高度非线性映射能力的计算模型,在工程中具有广泛的应用前景.在数值预测方面,它不需要预选确定样本的数学模型,仅通过学习样本数据即可以进行预测.文章介绍了BP神经网络,并针对实际应用中收敛速度慢,平台效应等问题对网络进行了改进并优化,详尽地给出了改进的三层BP神经网络数值预测算法.为测试该算法.选用了著名的XOR(异或)问题和和一个高度非线性的0-1矩阵预测问题对其进行了验证.计算结果表明文中算法能给出令人满意的精度.最后结合船舶与海洋工程的两个实际问题,探讨了利用改进的BP神经网络进行数值预测的方法和应该注意的问题,并给出了一些有益的建议.实践表明,文中给出的改进的BP神经网络数值预测算法值得在船舶与海洋工程中加以应用并推广.     

Air–sea gas transfer velocity estimates from the Jason-1 and TOPEX altimeters: Prospects for a long-term global time series

Estimation of global and regional air–sea fluxes of climatically important gases is a key goal of current climate research programs. Gas transfer velocities needed to compute these fluxes can be estimated by combining altimeter-derived mean square slope with an empirical relation between transfer velocity and mean square slope derived from field measurements of gas fluxes and small-scale wave spectra [Frew, N.M., Bock, E.J., Schimpf, U., Hara, T., Hauβecker, H., Edson, J.B., McGillis, W.R., Nelson, R.K., McKenna, S.P., Uz, B.M., Jähne, B., 2004. Air–sea gas transfer: Its dependence on wind stress, small-scale roughness and surface films, J. Geophys. Res., 109, C08S17, doi: 10.1029/2003JC002131.]. We previously reported initial results from a dual-frequency (Ku- and C-band) altimeter algorithm [Glover, D.M., Frew, N.M., McCue, S.J., Bock, E.J., 2002. A Multi-year Time Series of Global Gas Transfer Velocity from the TOPEX Dual Frequency, Normalized Radar Backscatter Algorithm, In: Gas Transfer at Water Surfaces, editors: Donelan, M., Drennan, W., Saltzman, E., and Wanninkhof, R., Geophysical Monograph 127, American Geophysical Union, Washington, DC, 325–331.] for estimating the air–sea gas transfer velocity (k) from the mean square slope of short wind waves (40–100 rad/m) and derived a 6-year time series of global transfer velocities based on TOPEX observations. Since the launch of the follow-on altimeter Jason-1 in December 2001 and commencement of the TOPEX/Jason-1 Tandem Mission, we have extended this time series to 12 years, with improvements to the model parameters used in our algorithm and using the latest corrected data releases. The prospect of deriving multi-year and interdecadal time series of gas transfer velocity from TOPEX, Jason-1 and follow-on altimeter missions depends on precise intercalibration of the normalized backscatter. During the Tandem Mission collinear phase, both satellites followed identical orbits with a mere 73-s time separation. The resulting collocated, near-coincident normalized radar backscatter (σ°) data from both altimeters present a unique opportunity to intercalibrate the two instruments, compare derived fields of transfer velocity and estimate the precision of the algorithm. Initial results suggest that the monthly gas transfer velocity fields generated from the two altimeters are very similar. Comparison of along-track Ku-band and C-band σ° during the collinear phase indicates that observed discrepancies are due primarily to small offsets between TOPEX and Jason-1 σ°. The Jason-1 k values have an apparent bias of + 4% relative to TOPEX, while the precision estimated from the two observation sets is 5–7% and scales with k. The resultant long-term, global, mean k is 16 cm/h.     

States’ obligations relating to marine monitoring and observation

Ocean data determining the state of the ocean are required for various purposes. The data are needed to understand the ocean and its interrelation, for instance, with the climate as well as to provide information and benefits for numerous sectors, such as for the coastal protection or pollution control. In order to meet the multiple needs for ocean information, a global and sustained system for ocean observation activities is necessary. Steps towards such a global system have been taken within the last decades, especially by setting up the Global Ocean Observing System (GOOS). However, the existing international structures and systems have been lacking governmental support and are held to be insufficient. In this context, the questions are raised whether states are already required by international law to observe and monitor the ocean and to cooperate in doing so and whether states are even obliged to contribute to international ocean observing programmes. Although to some extent, states’ obligations can be found in international conventions, such as in the UN Convention on the Law of the Sea (UNCLOS), they cannot meet all the various needs for ocean data. Specific and substantive obligations in international law meeting the multiple needs for ocean data are, however, required. If such obligations were included in the UNCLOS, issues relating to Marine Scientific Research would have to be kept in mind. Another option of creating specific commitments could be seen in a new agreement relating to the GOOS.