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Data from profiling floats in the Black Sea revealed complex temporal and spatial relationships between physical variables and oxygen, chlorophyll and the backscattering coefficient at 700 nm, as well as some limits in understanding the details of biogeochemistry dynamics. To account for different interdependences between physical and biogeochemical properties, a feedforward backpropagation neural network (NN) was used. This NN learns from data recorded by profiling floats and predicts biogeochemical states using physical measurements only. The performance was very high, particularly for oxygen, but it decreased when the NN was applied to older data because the interrelationships between the physical and biogeochemical properties have changed recently. The biogeochemical states reconstructed by the NN using physical data produced by a coupled physical–biogeochemical operational model were better than the biogeochemical outputs of the same coupled model. Therefore, the use of data from profiling floats, physical properties from numerical models and NNs appears to be a powerful approach for reconstructing the 4D dynamics of the euphotic zone. Basin‐wide patterns and temporal variabilities in oxygen, backscattering coefficient and chlorophyll were also analyzed. Of particular interest is the reconstruction of short‐lived biogeochemical features, particularly in coastal anticyclone areas, which are difficult to observe with available floats at the basin scale. Plain Language Summary: This study addresses the biogeochemical dynamics of the euphotic layer in the Black Sea. Observations are provided from profiling floats, and the observed biogeochemical parameters include oxygen, the backscattering coefficient at 700 nm and chlorophyll‐a. Data analysis showed complex temporal and spatial relationships between physical and biogeochemical variables and some limits in understanding the details of biogeochemical dynamics. A feedforward backpropagation neural network was developed, which can be considered an input–output mapping in which the neurons combine the input data in such a way that the output can be considered a nonlinear combination of input data. When applied to older data, the reconstruction performance decreases, suggesting a change in the dependency of biogeochemical characteristics on physical drivers caused by known climate change. A comparison with simulations from a coupled operational biogeochemical model shows that the neural network outperforms the numerical model. The newly proposed method, combining data from profiling floats, physical properties from numerical models and a backpropagation neural network, allows us to reconstruct the 4D dynamics of the euphotic layer over the period 2013–2020. Key Points: Machine learning helps identify fundamental biogeochemical mechanisms in the Black SeaA feedforward backpropagation neural network performs better than a coupled physical‐biogeochemical modelData from profiling floats, physical data from numerical models and machine learning enabled the analysis of 4D biogeochemical dynamics [ABSTRACT FROM AUTHOR]

Recent studies have indicated changes in the light climate of the North Sea. An overall reduction of water clarity over the twentieth century could be observed in measurements, and more recent analysis suggests that these trends continue. Inorganic sediment is often named one of the driving factors in these changes and it has been shown to locally increase. With 20 years' worth of satellite‐derived sediment data, we were able to conduct basin wide investigations of the temporal dynamics of the biogeochemical state of the North Sea. To identify the impact of inter‐annual and seasonal changes in sediment, we fed from two different remote sensing sources (GlobColour & IFREMER) into a 3D coupled hydrodynamic and biological model. The light scheme in the Carbon Silicon Nitrogen Ecosystem model (CoSiNE) was modified to account for sediment specific attenuation. We performed a total of five numerical experiments for the period of 2000–2017. The main two experiments were conducted using monthly averaged data. Additionally, as controls, one experiment with annually averaged and one with a 20 years average of sediment, as well as a fifth one without sediment were performed. Our model showed a clear relation between changes in sediment and water clarity. Phytoplankton biomass was reduced only in areas with high nutrient availability. Plain Language Summary: Studies using satellite or field data have shown that the North Sea has undergone decreases in water clarity and increases in sediment content, while simultaneously showing decreases in phytoplankton biomass over the past century. Declining water clarity would imply inhibited photosynthesis and phytoplankton growth. Phytoplankton itself reduces water clarity. Therefore, the coupling between the three quantities is complex. In this study, we used satellite‐derived sediment data of the years 2000–2017 from two different sources in a three‐dimensional model to account for sediment specific effects on underwater light. This way, we were able to determine how changes in sediment content affect water clarity and phytoplankton biomass. The two data sets show different long‐term behavior, with one showing increases and the other decreases. Changes in sediment directly corresponded to trends in water clarity. Long‐term changes in the light climate led to changes in biomass in areas of high nutrient availability. Key Points: Satellite‐derived sediment concentration data is fed into a model to study long‐term trends in water clarity over the period of 2000–2017Changes in sediment content correspond to changes in water clarity in the modelPhytoplankton biomass changes do not directly correspond to changes in water clarity in the model [ABSTRACT FROM AUTHOR]

Here, we revisit the existing concepts of the vertical structure of deep layers in the Black Sea using data from sensors deployed on profiling floats. The deep transition layer (DTL) between 700 and 1,700 m acts as an interface between the baroclinic layer and the largest bottom convective layer (BCL) of the world oceans. On top of DTL are the warm intermediate layer and deep cold intermediate layer. They both showed strong trends in the last 15 years due to warmer climate and intensification of warmer intrusions from Bosporus. A "salinity wave" was detected in 2005–2009 below ∼1,700 m, which evidenced for the first time the penetration of gravity flow from Bosporus down to the bottom. The layering of water masses was explained as resulting from the different distribution of sources of heat and salt, double diffusion, and balances between the geothermal and salinity flows in the BCL. Plain Language Summary: A total of 5,927 profiles from 31 profiling floats deployed in the Black Sea after 2005 were used to study the thermohaline dynamic. The density ratio, which sets the conditions under which double diffusion could occur qualifies most of the depths below 600 m as a double‐diffusive environment, which was confirmed by the observed staircase structure of vertical profiles. The qualitative differences between the temperature and salinity profiles in DTL are explained by the different distributions of sources and sinks of heat and salt. This layer acts as a transition zone between the bottom convective layer and the baroclinic ocean. A deep cold intermediate layer (DCIL) occupies the top of the DTL. The former became pronounced in recent years as a consequence of the warming of upper water layers. The large salinity variations from 2005 to 2009 observed at the bottom of the DTL evidenced traces of the penetration of the Bosporus plume down to the bottom for the first time. Combining known values of geothermal flow and concepts from the theory of dynamics of salinity‐stratified tanks heated from below made it possible to quantify the mean annual volume of Bosporus water reaching the bottom. Key Points: Profiling floats registered for the first time traces of Bosporus intrusions down to the bottomThe deep warm and cold intermediate layers are responsive to climate warmingDifferent sources of heat and salt dominate the transition between the baroclinic ocean and bottom convection layer [ABSTRACT FROM AUTHOR]

Abstract: Numerical model experiments are used to study the effects of multiple channel bends on estuarine dynamics and, in particular, on secondary flows. These effects are demonstrated by comparing experiments with two different idealized trumpet‐shaped estuaries, one straight and another one with a ∼8 km meandering section in the middle of the estuary. Meanders complicate the flow field by introducing secondary processes. For instance, meanders increase turbulence and associated mixing locally within the water column, as well as outside the meandering portion. Furthermore, meanders transform up to 30% of the along‐channel momentum into secondary circulation. Production of turbulence and secondary currents is different at flood and ebb tidal phases. At flood, meanders lead to unstable stratification and increased turbulence. At ebb, the flow develops a helical pattern and adjusts to the channel curvature with minimal decrease in density stability. The secondary circulation asymmetry is caused by an interplay between the across‐channel baroclinic pressure gradient force and the centrifugal force. During ebb both forces enhance each other, whereas they oppose during flood. As a consequence of this interaction between baroclinic forcing and curving morphology, ebb flows and horizontal buoyancy fluxes increase relative to flood. The enhanced ebb dominance shifts a density front toward the mouth of the estuary, thus reducing salt intrusion. [ABSTRACT FROM AUTHOR]

We show that from 1955 to 2013, the inventory of oxygen in the Black Sea has decreased by 36% and the basin-averaged oxygen penetration depth has decreased from 140m in 1955 to 90m in 2013, which is the shallowest annual value recorded during that period. The oxygenated Black Sea surface layer separates the world's largest reservoir of toxic hydrogen sulphide from the atmosphere. The threat of chemocline excursion events led to hot debates in the past decades arguing on the vertical stability of the Black Sea oxic/suboxic interface. In the 1970s and 1980s the Black Sea faced severe eutrophication. Enhanced respiration rates reduced the thickness of the oxygenated layer. The consecutive increase of oxygen inventory in 1985-1995 supported arguments in favor of the stability of the oxic layer. Concomitant with a reduction of nutrient loads, this increase also supported the perception of a Black Sea recovering from eutrophication. More recently, atmospheric warming was shown to reduce the ventilation of the lower oxic layer by lowering Cold Intermediate Layer (CIL) formation rates. The debate on the vertical migration of the oxic interface also addressed the natural spatial variability affecting Black Sea properties when expressed in terms of depth. Here we show that using isopycnal coordinates does not free from a significant spatial variability of oxygen penetration depths. Considering this spatial variability, the analysis of a composite historical set of oxygen profiles evidenced a significant shoaling of the oxic layer, and showed that the transient "recovery" of the 1990s was mainly a result of increased CIL formation rates during that period. As both atmospheric warming and eutrophication are expected to increase in the near future, monitoring the dynamics of the Black Sea oxic layer is required to assess the threat of further shoaling. [ABSTRACT FROM AUTHOR]

An observation network operating three Wellen Radars (WERAs) in the German Bight, which are part of the Coastal Observing System for Northern and Arctic Seas (COSYNA), is presented in detail. Major consideration is given to expanding the patchy observations over the entire German Bight on a 1-km grid and producing state estimates at intratidal scales, and 6- and 12-h forecasts. This was achieved with the help of the proposed spatiotemporal optimal interpolation (STOI) method, which efficiently uses observations and simulations from a free model run within an analysis window of one or two tidal cycles. In this way the method maximizes the use of available observations and can be considered as a step toward the 'best surface current estimate.' The performance of the analysis was investigated based on the achieved reduction of the misfit between model and observations. The complex dynamics of the study domain was illustrated based on the spatial and temporal changes of tidal ellipses for the M2 and M4 constituents from HF radar observations. It was demonstrated that blending observations and numerical modeling facilitates physical interpretation of processes such as the nonlinear distortion of the Kelvin wave in the coastal zone and in particular in front of the Elbe and Weser estuaries. Comparisons with in situ data acquired outside the area covered by the HF radar demonstrated that the analysis method is able to propagate the HF radar information to larger spatial scales. [ABSTRACT FROM AUTHOR]

The quality of water level time series data strongly varies with periods of high and low quality sensor data. In this paper we are presenting the processing steps which were used to generate high quality water level data from water pressure measured at the Time Series Station (TSS) Spiekeroog. The TSS is positioned in a tidal inlet between the islands of Spiekeroog and Langeoog in the East Frisian Wadden Sea (southern North Sea). The processing steps will cover sensor drift, outlier identification, interpolation of data gaps and quality control. A central step is the removal of outliers. For this process an absolute threshold of 0.25 m/10 min was selected which still keeps the water level increase and decrease during extreme events as shown during the quality control process. A second important feature of data processing is the interpolation of gappy data which is accomplished with a high certainty of generating trustworthy data. Applying these methods a 10 years dataset of water level information at the TSS was processed and the results were submitted to WDC MARE data base system PANGAEA (http://doi.pangaea.de/10.1594/PANGAEA.843740). [ABSTRACT FROM AUTHOR]

The temporal and spatial variability of the upper ocean hydrochemistry in the Black Sea is analysed using data originating from profiling floats with oxygen sensors and carried out with a coupled three-dimensional circulationbiogeochemical model including 24 biochemical state variables. Major focus is on the dynamics of suboxic zone which is the interface separating oxygenated and anoxic waters. The scatter of oxygen data seen when plotted in density coordinates is larger than those for temperature, salinity and passive tracers. This scatter is indicative of vigorous biogeochemical reactions in the suboxic zone, which acts as a boundary layer or internal sink for oxygen. This internal sink affects the mixing patterns of oxygen compared to the ones of conservative tracers. Two different regimes of ventilation of pycnocline were clearly identified: a gyre-dominated (cyclonic) regime in winter and a coastal boundary layer (anticyclonic eddy)-dominated regime in summer. These contrasting states are characterized by very different pathways of oxygen intrusions along the isopycnals and vertical oxygen conveyor belt organized in multiple-layered cells formed in each gyre. The contribution of the three-dimensional modelling to the understanding of the Black Sea hydro-chemistry, and in particular the coast-to-open-sea mixing, is also demonstrated. Evidence is given that the formation of oxic waters and of cold intermediate waters, although triggered by the same physical process, each follow a different evolution. The difference in the depths of the temperature minimum and the oxygen maximum indicates that the variability of oxygen is not only just a response to physical forcing and changes in the surface conditions, but undergoes its own evolution. [ABSTRACT FROM AUTHOR]

This paper describes the implementation and evaluation of a pre-operational three dimensional variational (3DVAR) data assimilation system for the North/Baltic Sea. Univariate analysis for both temperature and salinity is applied in a 3DVAR scheme in which the horizontal component of the background error covariance is modeled by an isotropic recursive filter (IRF) and the vertical component is represented by dominant Empirical Orthogonal Functions (EOFs). Observations of temperature and salinity (T/S) profiles in the North/Baltic Sea are assimilated in the year of 2005. Effect of the 3DVAR scheme is assessed by a comparison between data assimilation run and control run. The statistical analysis indicates that the model simulation is significantly improved with the 3DVAR scheme. On average, the root mean square errors (RMSE) of temperature and salinity are reduced by 0.2 °C and 0.25 psu in the North/Baltic Sea. In addition, the bias of temperature and salinity is also decreased by 0.1 °C and 0.2 psu, respectively. Starting from an analyzed initial state, one month simulation without assimilation is carried out with the aim of examining the persistence of the initial impact. It is shown that the assimilated initial state can impact the model simulation for nearly two weeks. The influence on salinity is more pronounced than temperature. [ABSTRACT FROM AUTHOR]

Oil spill modeling is considered to be an important part of a decision support system (DeSS) for oil spill combatment and is useful for remedial action in case of accidents, as well as for designing the environmental monitoring system that is frequently set up after major accidents. Many accidents take place in coastal areas, implying that low resolution basin scale ocean models are of limited use for predicting the trajectories of an oil spill. In this study, we target the oil spill in connection with the "Full City" accident on the Norwegian south coast and compare operational simulations from three different oil spill models for the area. The result of the analysis is that all models do a satisfactory job. The "standard" operational model for the area is shown to have severe flaws, but by applying ocean forcing data of higher resolution (1.5 km resolution), the model system shows results that compare well with observations. The study also shows that an ensemble of results from the three different models is useful when predicting/analyzing oil spill in coastal areas. [ABSTRACT FROM AUTHOR]

A coastal observing system for Northern and Arctic Seas (COSYNA) aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a preoperational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF) radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data assimilation significantly improved the performance of the model with respect to both SST and SSS and demonstrated a good skill not only in the vicinity of the Ferry track, but also over larger model areas. The examples provided in this study are considered as initial steps in establishing new coastal ocean products enhanced by the integrated COSYNA-observations and numerical modelling. [ABSTRACT FROM AUTHOR]

Sea surface temperature, chlorophyll, and turbidity are three variables of the coastal environment commonly measured by monitoring networks. The observation networks are often based on coastal stations, which do not provide a sufficient coverage to validate the model outputs or to be used in assimilation over the continental shelf. Conversely, the products derived from satellite reflectance generally show a decreasing quality shoreward, and an assessment of the limitation of these data is required. The annual cycle, mean, and percentile 90 of the chlorophyll concentration derived from MERIS/ESA and MODIS/NASA data processed with a dedicated algorithm have been compared to in-situ observations at twenty-six selected stations from the Mediterranean Sea to the North Sea. Keeping in mind the validation, the forcing, or the assimilation in hydrological, sediment-transport, or ecological models, the non-algal Suspended Particulate Matter (SPM) is also a parameter which is expected from the satellite imagery. However, the monitoring networks measure essentially the turbidity and a consistency between chlorophyll, representative of the phytoplankton biomass, non-algal SPM, and turbidity is required. In this study, we derive the satellite turbidity from chlorophyll and non-algal SPM with a common formula applied to in-situ or satellite observations. The distribution of the satellite-derived turbidity exhibits the same main statistical characteristics as those measured in-situ, which satisfies the first condition to monitor the long-term changes or the large-scale spatial variation over the continental shelf and along the shore. For the first time, climatologies of turbidity, so useful for mapping the environment of the benthic habitats, are proposed from space on areas as different as the southern North Sea or the western Mediterranean Sea, with validation at coastal stations. [ABSTRACT FROM AUTHOR]

A coastal observing system for Northern and Arctic Seas (COSYNA) aims at construction of a long-term observatory for the German part of the North Sea, elements of which will be deployed as prototype modules in Arctic coastal waters. At present a coastal prediction system deployed in the area of the German Bight integrates near real-time measurements with numerical models in a pre-operational way and provides continuously state estimates and forecasts of coastal ocean state. The measurement suite contributing to the pre-operational set up includes in situ time series from stationary stations, a High-Frequency (HF) radar system measuring surface currents, a FerryBox system and remote sensing data from satellites. The forecasting suite includes nested 3-D hydrodynamic models running in a data-assimilation mode, which are forced with up-to-date meteorological forecast data. This paper reviews the present status of the system and its recent upgrades focusing on developments in the field of coastal data assimilation. Model supported data analysis and state estimates are illustrated using HF radar and FerryBox observations as examples. A new method combining radial surface current measurements from a single HF radar with a priori information from a hydrodynamic model is presented, which optimally relates tidal ellipses parameters of the 2-D current field and the M2 phase and magnitude of the radials. The method presents a robust and helpful first step towards the implementation of a more sophisticated assimilation system and demonstrates that even using only radials from one station can substantially benefit state estimates for surface currents. Assimilation of FerryBox data based on an optimal interpolation approach using a Kalman filter with a stationary background covariance matrix derived from a preliminary model run which was validated against remote sensing and in situ data demonstrated the capabilities of the pre-operational system. Data assimilation significantly improved the performance of the model with respect to both SST and SSS and demonstrated a good skill not only in the vicinity of the Ferry track, but also over larger model areas. The examples provided in this study are considered as initial steps in establishing new coastal ocean products enhanced by the integrated COSYNA-observations and numerical modelling. [ABSTRACT FROM AUTHOR]

High-Frequency (HF) radars measure the ocean surface currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each boundary condition perturbation. This cost function ensures that the boundary condition perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM). Optimized boundary values are obtained by assimilating all observations using the covariances of the ensemble simulation. [ABSTRACT FROM AUTHOR]

High-Frequency (HF) radars measure the ocean currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each perturbation. This cost function ensures that the perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimension General Estuarine Ocean Model (GETM). Optimized boundary values are obtained using all observations within the assimilation period using the covariances of the ensemble simulation. [ABSTRACT FROM AUTHOR]