OS2.2

The statistical analysis of turbulent air-sea fluxes is not a common study for the Mediterranean Sea but an important one to characterize the probability distribution of air-sea fluxes and relates with the atmospheric variables.  For this study, we intend to compute the turbulent air-sea fluxes for a longer period in the Mediterranean Sea. On the base of computed air-sea fluxes, this study aims to investigates the characteristics of probability density distribution. We analyze the probability distribution of turbulent air-sea fluxes using high-resolution model forecasts from the ECMWF.  We assume that a two parameter Weibull probability density function (PDF) would be a good fit to model the probability distribution of the turbulent fluxes, while three parameters Skew normal distribution is an alternative one to characterize the tail of the distribution with both positive and negative value range.  This statistical study focuses on the probability distribution of air-sea fluxes at the regional sea level which is related with the uncertainty analysis of ocean forecasting. In addition, the usage of higher resolution atmospheric forecast data would give us newer aspect in the probability distribution of air-sea fluxes. It would be an interesting study, how the parameters of the PDFs may vary over the short and longer time span as well as over the Mediterranean Sea domain. Overall, this study on the air-sea heat fluxes and its probability distribution will extend our knowledge on the air-sea energy exchange distribution for interannual and seasonal variability.

How to cite: Ghani, M. H., Pinardi, N., Trotta, F., and Ligouri, G.: The statistical characteristics of turbulent air-sea fluxes in the Mediterranean Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13007, https://doi.org/10.5194/egusphere-egu22-13007, 2022.

Lagrangian Particle Tracking Models (PTMs) have a wide range of applications in the marine environment, from predicting the dispersal of microplastics to larval transport. In two decades computational power have increased exponentially allowing PTMs to move from probabilistic approaches (e.g. advection and random walk) to more deterministic methods (e.g. including animal behaviour and buoyancy).

Validation of hydrodynamic models simulating oceanographic processes has allowed confidence in their accuracy at simulating mean-flow fields (i.e. at the order of tens of metres and minutes). However, methods to validate PTMs appear less developed due the complexity of biophysical process interactions; for example, wind and wave combined impact on surface currents and larvae behaviour such as vertical and horizontal swimming.

Here, we use a novel set of data representing the travel of drifters in the Irish Sea during summer 2021. The experiment aim is to reduce the near surface flow uncertainty influencing particle dispersal (i.e. larvae, microplastics and pollutant). Data were collecting using a range of drifters designs released in coastal, estuarine and offshore locations of a tidally dominate shelf-sea (Irish Sea): 1) variation of  drogue depth between 1m and 5m; 2)  variation of period from tidal cycles to spring-neap cycles; and 3) some with reduced “windage” designs (no drogue and minimal exposure above surface).

The results allowed us to measure the difference of dispersal between PTM created associated to high-resolution 3D hydrodynamic model and data collected. The validation of a deterministic PTM created will be presented, with a discussion of wind and wave impact on surface current flow and uncertainty of the PTM. For example, we find some scales of oceanographic processes that affect transport, such as turbulent eddies and waves, were not resolved - and yet our predictions broadly matched observations.

How to cite: Demmer, J., Lewis, M., Rushton, R., and Neill, S.: Reducing uncertainty in dispersal predictions: validation of particle tracking model with drifter data., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2514, https://doi.org/10.5194/egusphere-egu22-2514, 2022.

The vertical stability of the ocean in the Gulf of Lion responds to the atmospheric forcing on both the seasonal and anomaly timescale, with the latter predominantly driven by the Mistral winds. The inter-annual variability of the atmospheric forcing on both timescales determines the occurrence of deep convection in the gulf. Deep convection is the major process in the Western Mediterranean Basin leading to dense water formation, which assists with the general circulation of the Mediterranean Sea, and also leads to years of phytoplankton blooming, due to increasing the oxygen and nutrient content along the water column.

Yearly NEMO ocean simulations were run over the span of 20 years, from 1993 to 2013, through the RegIPSL regional climate model and forced by atmospheric outputs from a coupled WRF/ORCHIDEE simulation, also produced through the RegIPSL model. Two ocean simulations per year were run, a control and a seasonal run, with the latter forced by a filtered atmospheric forcing, to separate the ocean's response at the seasonal and anomaly timescales.

These simulations revealed the importance of the magnitude and variability of the seasonal atmospheric forcing regarding the vertical stability, or stratification, of the Gulf of Lion. On average, roughly 50% of the relative destratification over the course of the preconditioning period (the period leading up to a potential deep convection event) came from the seasonal change in stratification. Years with deep convection not only had a less than average yearly maximum stratification, but also had a greater than average (greater than 50%) seasonal contribution to the preconditioning destratification. The anomaly timescale contribution typically only provided, on average, about 27% of the destratification required for deep convection to occur, with its contribution during deep convection years hovering slightly above, at around 30%. The necessary additional 70% required came from the above average seasonal contribution mentioned beforehand, demonstrating the importance of the seasonal contribution and its variability.

The increased seasonal contribution was explained by the use of a simple model that relates the seasonal atmospheric heat flux components to the stratification index, a diagnostic for the vertical stability. The seasonal forcing varied greatly over the 20-year span, and years with larger upward latent and sensible heat fluxes and lower downward shortwave radiation fluxes were more likely to be deep convection years. The anomaly forcing also showed variability, and years with more frequent and stronger Mistral events were also more likely to be deep convection years.

If future years shift towards having larger downward shortwave radiation fluxes, such as years with less cloud cover, and/or weaker upward latent/sensible heat fluxes, such as years with warmer advected air masses, then deep convection may occur less often. This could then lead to a shift in the Mediterranean Sea circulation and alter biological processes in the region.

How to cite: Keller Jr, D., Givon, Y., Pennel, R., Raveh-Rubin, S., and Drobinski, P.: Variability of the Mistral and Seasonal Atmospheric Forcing on Deep Convection in the Gulf of Lion, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6863, https://doi.org/10.5194/egusphere-egu22-6863, 2022.

This study is dedicated to the dynamics in Marine Protected Areas (MPAs) in the German Bight under different forcing scenarios. A large amount of data has been collected in the North Sea over the last decades to characterize MPAs, which can shed light on long-term changes in the North Sea dynamics from abiotic part to ecosystem. At the moment, a question is raised about the interconnection between MPAs and their representativeness for the larger area. Nowadays, this issue can be resolved with the existing numerical instruments and accumulated observations. We paid particular attention to the tidal dynamics in the North Sea since tidal residual circulation and asymmetric tidal cycles significantly define circulation patterns, transport and accumulation of biogeochemical material, and the distribution of bedforms in this relatively shallow region. We analyzed in detail the tidal energy transformation and the role of higher harmonics in the domain. The tidal ellipses, maximum tidally induced velocities, energy fluxes and residual circulation maps are constructed and analyzed. The numerical tool used in this study is the FESOM-C model (Androsov et al., 2019), which works with triangular, rectangular or mixed grids and is equipped with a wetting/drying option. A grid with a resolution of up to 10 meters in the flooded areas is used.

Androsov, A., Fofonova, V., Kuznetsov, I., Danilov, S., Rakowsky, N., Harig, S., Brix, H., and Wiltshire, K. H.: FESOM-C v.2: coastal dynamics on hybrid unstructured meshes, Geoscientific Model Development, 12, 1009-1028, 10.5194/gmd-12-1009-2019, 2019.

How to cite: Rubinetti, S., Fofonova, V., Androsov, A., Kuznetsov, I., Rick, J. J., Mielck, F., Sander, L., and Wiltshire, K. H.: Dynamics in Marine Protected Areas in the German Bight (North Sea) under different forcing scenarios, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12057, https://doi.org/10.5194/egusphere-egu22-12057, 2022.

Marine heatwaves (MHW) have emerged as critical factors driving shifts and negative impacts on marine ecosystems and to ecosystem services. MHW frequency and severity is expected to increase in response to climate change and there is a need to assess the effect of past MHW on marine ecosystems in order to better understand future trends and impacts.

The aim of this work is provide a long term downscaled analysis of the Mediterranean MHW and cold spells (MCS) at 0.05° spatial resolution, and to further focus this analysis to four specific domains (Northern Adriatic, Ligurian Sea, Gulf of Lion, Catalan Sea) and 43 coastal locations in the Mediterranean basin. This analysis can be used to explore the links with other environmental and ecological data. 

Multidecadal L4-gridded satellite SST measurements are used to analyze statistical properties of MHW and MCS in the Mediterranean basin from 1989 until present. Methodology from Hobday et al. (2016, 2018) is used for the analysis. For each of the locations, cumulative MHW and MCS intensities are aggregated by year. All locations indicate a steep rise of MHW cumulative intensity and a sharp decline of MCS. Generalized extreme value analysis is performed to estimate multidecadal variability of SST anomaly return periods. Results show that at all locations a given return period gets associated with more and more extreme SST anomalies as time progresses.

How to cite: Licer, M., Zunino, S., and Canu, D.: Multidecadal Marine Heat Wave Variability in the Mediterranean, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2377, https://doi.org/10.5194/egusphere-egu22-2377, 2022.

Sea level is rising, threatening coastal population and infrastructures. While the causes for global sea level rise are relatively well determined and the projections can be made based on most likely climate scenarios, sea level changes in coastal regions are far less understood. Apart from the global temperature rise and the melting of ice sheets and glaciers, coastal areas are also affected by the spatio-temporal temperature and salinity variations, atmospheric conditions, and even vertical land motions, such as those due to glacial isostatic adjustment. It is therefore necessary to determine which factors contribute most to the sea level change in each specific region, in order to mitigate the effect sea level changes will have on local infrastructure. 
We here use long short-term memory (LSTM) neural networks to find the connection between the sea level variations and its many potential drivers in the Baltic, North, and Nordic Seas, at daily to decadal scales. We test all the different combinations of local atmospheric (surface pressure, wind, and precipitation from reanalysis) and oceanic (temperature and salinity, from in-situ observations), along with the remote ones (e.g. Greenland ice sheet runoff, large scale water mass circulation) to predict sea level variations at selected locations that have uninterrupted long-term (>50 years) tide gauge observations.
By comparing the quality of sea level prediction from these different combinations, we find that the long-term sea level trend and low-frequency variations at most locations in our region of interest are mainly driven by the temperature rise, both local and remote, while the higher frequency variations are predominantly driven by the changes in local wind. As expected, northern locations are also affected by glacial isostatic adjustment, which counteracts the sea level rise. Precipitation, even during major storm events, seems to play an insignificant role in our region. The exception is the Baltic Sea, where wind plays less of a role, and the sea level is more affected by the influx of fresh water.  While most regions are affected by sea level rise to some extent, because the causes for the local sea level changes vary, the protection from flooding and the warning techniques have to be adapted for each location.

How to cite: Poropat, L. and Heuzé, C.: Drivers of sea level variability in the Baltic/North/Nordic Seas using neural networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-201, https://doi.org/10.5194/egusphere-egu22-201, 2022.

Hydromorphodynamic modelling has as one of its pillars the speed of obtaining results. Sometimes it is only due to the necessity to finish some work or to make other simulations but for example when talking about operational systems, this problematic takes even more strength. If other coastal operational modelling have to be coupled after, the model itself has to be fed by other models or the results have to be published periodically, the importance of the simulation speed is clear. A problem appears because there is an insoluble paradigm which doesn’t allow to make simulations combining the best approaches for this three variables: low ”computational time”, high ”resolution” and huge ”study area”. For example, if we want to simulate a huge coastal area but we need to present the results as soon as possible (i.e. low computational time), we will not be able to use a high-resolution grid because it will fail in the second statement. This problem appears in all the other possible scenarios involving this three variables where the best response for all of them cannot be satisfied at the same time due to the paradigm. Then, since it is impossible to totally solve this problematic, a lot of efforts have been made to reduce its effects and to work with the best possible approaches. In this work, two methodologies that try to improve the performance of the hydromorphodynamic simulations with XBeach model are tested and statically compared with the typical performance. On one hand, a direct reduction of the computational time using parallel computing, specifically Message Passing Interface (MPI) with Cluster application. On the other hand, the conversion of the grids that are used for the simulation from rectangular to curvilinear in order to increase only the resolution of the area of interest in the grid, maintaining lower resolutions for the rest of the area.

This research received funding from INTERREG EFA 344/19 MARLIT  Project  European Union / European Regional Development Fund thanks (FEDER) and was done with the support from the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund. We also want to thank the Research, Development and Innovation Program through the grant to ECOPLANTS project (REF: PID2020-119058RB-I00).

How to cite: Sánchez-Artús, X., Gracia, V., Espino, M., and Sánchez-Arcilla, A.: Statistical comparison of different strategies to reduce computational time within high resolution hydromorphodynamic modelling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7847, https://doi.org/10.5194/egusphere-egu22-7847, 2022.

Coastal flooding is one of the most important natural hazards worldwide, increasingly growing in a changing climate. Various problems concerning the current and potential future risk of coastal areas, highlighting the needs for reliable forecast of storm conditions that can reach the coasts and of consequent estimated of the probability of flooding of urbanized areas.

The study presented here aims to develop a method to predict flooding in coastal areas through Artificial Neural Networks (ANNs). The method was applied to the village of Granelli, in the South-Eastern part of Sicily (Italy). The present work was organized into two-phase: the first was dedicated to create the database of the flooded areas, through a physically-based modelling of wave propagation from offshore to the coast; the second was dedicated to study how to use the relate through machine learning approach onshore flooding results to offshore wave characteristics.

As regards the first phase, the wave data used in the present study were obtained using two nested numerical models: SWAN and Xbeach. For an accurate simulation of wave propagation both in the nearshore zone and on the beach, a bathymetric survey of the submerged foreshore and a morphological survey of the emerged beach were carried out. The first one was surveyed through a Multibeam eco-sounder up to a depth of -12 m. The survey of the emerged beach was carried out using a Trimble TX8 Laser Scanner. As regards the boundary conditions, more than 1600 scenarios were simulated by changing both the offshore wave characteristics and the water elevation.

During the second phase, the offshore wave characteristics and the resulting flooded areas were used to train several configurations of an ANN. After a calibration process of the ANN configuration, the best one was identified through comparison with the results of Xbeach.

The proposed approach allows both to significantly reduce the time required for the estimation of flooding areas (8-9 hours required by Xbeach for each sea state against a few seconds required by ANN for the entire storm) and to obtain extremely reliable results with an accuracy of 0.05 m² in terms of root mean square error.

Acknowledgments

This work has been partly funded by the project “NEWS - Nearshore hazard monitoring and EarlyWarning System" (code C1-3.2-60) in the framework of the programme INTERREG V-A Italia Malta 2014-2020, by the project “ISYPORT - Integrated System for navigation risk mitigation in PORT” financed under the program PNR 2015-2020 and by University of Catania funded project "Interazione Moto Ondoso - Strutture (IMOS)".

How to cite: Cavallaro, L., Iuppa, C., Sanfilippo, M., Musumeci, R. E., and Foti, E.: Short term predictions of coastal flooded areas using a machine-learning approach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11550, https://doi.org/10.5194/egusphere-egu22-11550, 2022.

Rising sea levels and a potential increase in intensity and frequency of storm events due to climate change are increasingly intensifying flood risks caused by storm surges in the coastal areas of the North Sea. The ebb tidal delta (ETD) sandbanks off the coast of the East Frisian Islands change dynamically – a single storm surge may change them significantly – and, as a natural barrier, exert a huge impact on the nearshore wave climate.
Therefore, accurate predictions of storm surge wave heights are of particular interest and essential for coastal protection. Typically, these predictions are made by time-consuming numerical models like the third-generation wave model SWAN. Therefore, we designed a machine learning method that reasonably accelerates these simulations and wave height predictions and, for the first time, takes the ETD dynamics into account. To train the model, we created a dataset by driving an unstructured grid SWAN model with in-situ measurements of wave heights (also used for model validation), water level, and wind as boundary conditions. A dynamic bathymetric input was used by simulating various potential ETD bathymetries with geostatistical variogram analysis and random field simulation. Our proposed method is a mixed-data CNN-LSTM neural network for wave height prediction. While CNN neural networks are designed for processing image data (spatial bathymetric maps), LSTM units are optimized for processing long-term time series data. The model is capable of predicting nearshore wave heights after training with the SWAN-generated events and multiple simulated bathymetries. While the SWAN model took about 60 days to simulate 6480 events, our proposed neural network improved the computational time for a single event by a factor of 100.
These results can be used to explore potential future sea states under the influence of climate change and their local impact on the East Frisian coast in no time, as well as to warn the inhabitants of the affected areas and to install location-specific e.g. sandbags and flood protection walls by using the latest water level and wind forecasts as input.

How to cite: Jörges, C. and Stumpe, B.: Regional nearshore wave height prediction using a mixed-data CNN-LSTM neural network and dynamic bathymetric maps for the East Frisian North Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5151, https://doi.org/10.5194/egusphere-egu22-5151, 2022.

The transport of cohesive sediments in estuaries and coastal oceans can be simulated by coupling flocculation models with circulation models. Since size-resolved flocculation models allow the dynamic evolution of floc size distribution, these types of models can well characterize the transport and fate of cohesive sediments in water columns. However, due to the addition of tens of floc variables, the computational cost of these types of models is extremely high and has limited their application in real environments.

To improve the calculation efficiency, we proposed an efficient and accurate method for the representation of floc size distributions (FSD) in sediment transport models. A log-normal approximation of the floc size distribution is obtained by fitting the FSD with three parameters: the total mass concentration , the medium floc size  and the standard deviation parameter . The approximation is used for advection in circulation models. Instead of advection of floc concentration in each size bin, the  and the  are advected. After the advection is done, the FSD on each grid is obtained from the new and  as well as the constant .

Applications of the method in simulations of cohesive sediment transport in an idealized estuary for aggregation, breakup, gravitational settling and tidal mixing are described. The results are compared to results from the same simulation but without the log-normal approximation. According to these comparisons, the new method can improve the calculation efficiency by about 50%, and the differences are less than 10%. So the method has a great potential to be applied in size-resolved cohesive sediment transport modeling.

How to cite: Fang, Z. and Xu, F.: Log-normal Approximation of Floc Size Distribution for Advection in Size-resolved Sediment Transport Modeling, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8012, https://doi.org/10.5194/egusphere-egu22-8012, 2022.

Quality physical oceanographic data such as currents and waves are needed to understand processes and produce forecasts for multiple scientific, management and industry applications. Some of the applications that require currents and waves data are the estimation of climate change impacts, marine species distribution changes, litter accumulation in beaches and vessels fuel consumption. The X-band radar can provide such currents and waves data in near real time.

An X-band radar (transmission frequency: 9410 ± 30 MHz) was installed in Biarritz in March 2021 and it is expected to acquire data until the end of February 2022, as part of the SusTunTech project (https://www.sustuntech.eu/the-project/). Since it´s installation, the x-band radar has been measuring data non-stop, except during few days (due to power failures). The radar is monitoring oceanographic processes at coastal scales in this shallow water coastal area up to 3.2 km offshore. The main parameter extracted from the radar data are spectral wave parameters such as significant wave height, peak wave period and direction as well as surface current fields. During a short calibration phase, the marine radar has to be calibrated for significant wave heights utilizing in situ measurements. For accurate surface current measurements from the X-band radar, the tidal water depth variation of up to 4 m in the study area is being taken into account.  Four ADCPs and two pressure sensors were installed in the area covered by the x-band radar from March to May 2021 as part of MARLIT project (https://www.suez.com/en/news/marlit-project-prevention-storm-related-risks-protection-coast-against-climate-change) to strengthen the x-band radar data information.

The presented multiplatform experiment (in-situ data from MARLIT project and X-band from SusTunTech project) is allowing to compare the measurements of the same variable by different instruments and find complementarities of these instruments to characterize the wave regime and the surface currents of the study area. In addition, these measurements will be utilized to validate and better understand the remarkable intensification of significant wave heights suggested by local wave models at specific locations within the area covered by the X-band area (Varing et al. 2020). Finally, the better knowledge acquired about the longshore distribution of wave and current energy will help to assess local early warning systems to prevent storm-related risks along the highly urbanized coastline of Biarritz. Nowadays, such systems are increasingly being used by scientists, policy and industry in their activities. Therefore, the importance of producing quality data to feed modelling and its applications.

How to cite: Solabarrieta, L., Delpey, M., Rubio, A., Caballero, A., Liria, P., Fernandes-Salvador, J. A., Carrasco-Álvarez, R., Somdecoste, T., Mader, J., Horstmann, J., and Clot, A.: A multiplatform data experiment to characterize waves and currents in front of Biarritz main beach, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7223, https://doi.org/10.5194/egusphere-egu22-7223, 2022.

The coastal area is one of the most vulnerable areas that is connecting lands and seas under the intensive human activities subjecting to the ocean forces. Nowadays, as the climate conditions are highly concerned, coastal morphodynamics, one of the most important elements for coastal areas, would become more uncertainty under the climate changes due to its non-linear interaction to the water forces. Therefore, an investigation of tide-wave-morphodynamic interactions is required by including sea level rise in order to involve various responses of morphodynamics to changing climate. In the conference, we will present results from our process-based coupled framework of tide-wave-morphodynamics modelling to consider the climate impacts on the morphodynamic changes in application on Wadden Sea of German Bight, which is one of the most vulnerable coastal areas subjects to sea level rise. The well-evaluated third-generation phase-resolved wave model WAVEWATCH III (WW3) is set up, coupled to the well-validated General Estuary Circulation Model (GETM) including the sediment transport and morphology modules. We applied ensemble-based simulations to reduce the uncertainties of climate effects in downscaling procedure. It is proved that this process-based model is capable for application on climate scenarios in a long term aspect as long as involving specific data analysis. It is desired that the process-based numerical investigation could be one of the most promising methods for studying the coastal morphodynamic responses to climate change as the physical processes could be examined straightforward for this non-linear interactions.

Based on the preliminary results from the framework, it is indicated that the wave could propagate further more under sea level rise while the currents are observed to be increased at some locations, particularly at the region (i.e. ebb flats) of North Frisian Wadden Sea (NFW). As a result, the NFW becomes more dynamic under the sea level rise conditions especially at the intertidal areas, whereas the Elbe mouth might has less exchange of sediment far field to east and north Frisian Sea but might be highly dynamic in local, which is much similar to the pattern of observed datasets. The dam
that connecting Sylt and main land, interested by lots of previous studies, also shows impact on both hydrodynamic and morphodynamic pattern under climate conditions. Based on the sea level rise scenarios, the significance of bed level changes at most areas of German Bight would be more serious
by keeping the identical pattern of morphological changes as that in present scenarios. However, it might also correlate to storminess, where we could looking in detail based on the process-based modelling. When considering the entire German Bight, it is found that the predominant forces from tide and wave might vary between the North Frisian Sea and East Frisian Sea due to the specific geometry. Tide-predominated and wave-dominated coastal system would be able to coexist in the German Bight. The detailed quantitative and qualitative results would be present in the conference.

How to cite: Yin, Y., Gräwe, U., Klingbeil, K., Niehüser, S., Arns, A., Lorenz, M., and Burchard, H.: The impacts of wave-tide interaction on the coastal morphodynamics in changing climate, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10478, https://doi.org/10.5194/egusphere-egu22-10478, 2022.

The exposure to wave overtopping is growing worldwide which forces coastal communities to adopt methodologies for anticipating the risks associated with it. In areas with shallow foreshores, like those created by extended ebb-tidal deltas, at the entrance of estuaries or harbours for instance, infragravity waves play an important, if not dominant, role. Therefore, hydrodynamic and topographic data collected on the downdrift side of the entrance to Figueira da Foz harbour, along the wave exposed western coast of Portugal, were used to calibrate a local XBbeach 2DH-surfbeat model to (1) investigate the role of infragravity waves and (2) the ability of a phase average model to account for the main drivers of coastal overtopping in similar locations. The local model was forced on its open boundary by water levels and 2D wave spectra dynamically downscaled using an operational model workflow developed for providing near real-time forecasts. The local dissipation of short-waves in the surf zone was calibrated based on the hydrodynamic data. This data, collected under a moderate swell, was also used to ensure the model’s resolution and numerical scheme were correctly setup to reproduce the energy and shape of the infragravity wave’s frequency spectrum. Lastly, the model’s option to use an unconventional breaking criterion was found useful to improve the model’s ability to reproduce an overtopping event. For this event, which was observed and surveyed during slightly energetic waves combined with high tides, results from the surfbeat mode were compared to results from the non-hydrostatic phase-resolving mode of XBeach (applied with a resolution four times thinner in both horizontal directions). In both cases, the modelled overtopping extents were similar and matched the data. However, it was found that the wave-induced setup was much larger in the surfbeat model. Furthermore, the extra energy brought in by accounting a fraction of the instantaneous wave height into the equation of the wave breaking criterion allowed the water to overtop the dune crest in similar proportion as in the phase-resolving case. So, the finely tuned surfbeat model was run for scenarios of a storm surge with a return period of ~70 years, combined with present day sea level and projections for 2050 and 2100. Like in the calibration runs, in these three scenarios the wave spectra for the chosen 2014’s Hercules storm were dynamically downscaled. Again, the inundation maps produced with this methodology were compared to those created with the phase-resolving version of XBeach. It transpired that, for those scenarios, the wave-induced setup and the runup of the infragravity waves were the dominant drivers of overtopping along the waterfront in the shadow of the large ebb-tidal delta from the harbour’s entrance. Our study therefore suggests that with minimal observation data it was possible to calibrate the phase-averaged version of XBeach to reproduce and map overtopping. Moreover, for similar coastal zones, where wave-setup and infragravity waves dominate, the inundation maps may be more accurate that those produced with its phase-resolving counterpart, and this at a lower computational cost.

How to cite: Nahon, A., Fortunato, A. B., Oliveira, F. S. B. F., and Freire, P.: Mapping coastal overtopping in the shadow of large ebb-tidal deltas with XBeach surfbeat: insights from the western coast of Portugal, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12388, https://doi.org/10.5194/egusphere-egu22-12388, 2022.

The Rhone River is one of the largest rivers in the North-West Mediterranean Sea. Freshwater and particle (sediments, nutrients and contaminants) inputs make the adjacent coastal area as a remarkable ROFI known as a hotspot of biodiversity in the Gulf of Lions. The dynamics and behavior of riverine particles is often observed from classical moorings, buoys as well as remote sensing. These observing systems only permit limited measurements in 1D for single-point observations in the water column and at the water surface during cloud-free days from remote sensing data. But there is a lack of spatio-temporal observations especially during extreme events when sea campaign investigations are difficult. An autonomous underwater vehicle, also named glider, equipped with a Laser In-Situ Scattering and Transmissometry (LISST) sensor was deployed in front of the Rhone River in February 2019 in order to investigate the small-scale characteristics of particles in the coastal area. In-situ particle size measurements and volume concentrations of suspended particles were related to mass concentrations in order to estimate the density and settling velocity. Results revealed the presence of highly dynamic surface, intermediate and bottom nepheloid layers composed of particles with distinct characteristics. Those results give useful informations to undestand the behavior of particles in the coastal area and for amelioration of regional hydro-sedimentary models.

How to cite: Bourrin, F., Gentil, M., and Miles, T.: Suspended Particle Characteristics from Glider Observations in a Region of Freshwater Influence, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5170, https://doi.org/10.5194/egusphere-egu22-5170, 2022.

Bulgarian National Operational Marine Observing System (NOMOS) is a module of MASRI - Infrastructure for Sustainable Development of Marine Research and Participation in the European Infrastructure Euro-Argo, a project of the National roadmap for scientific Infrastructure (2020 – 2027) of Republic of Bulgaria. NOMOS consist of six components, one of which is a waves and currents monitoring system managed in collaboration by the Institute of Oceanology, Bulgarian Academy of Sciences (IO-BAS) and the National Institute of Meteorology and Hydrology (NIMH). The development of the waves observing system started in 2020 with the deployment of six moored wave buoys, three by IO-BAS and three by NIMH. Next year another three wave buoys were deployed by NIMH. The deployment positions were chosen to provide optimal coverage of the Bulgarian Black Sea coast. The buoy measurements are transmitted using GPRS or satellite communications and are stored in databases both at the Bulgarian National Oceanographic Data Center and at the NIMH data center. WEB sites were developed to deliver real time wave data to stakeholders. The wave observing system has been in operation for over a year and sufficient data has been collected for an initial analysis. During the operation period, experience was gained in maintaining the system in order to provide reliable sea waves data. Biofouling and vandalism are assessed as the main factors influencing system performance. The wave observing system is a unique source of in-situ wave data in the Black Sea that provides real-time wave data and long series of data for science and marine industry.   In-situ wave data are also distributed through CMEMS and used for the assimilation in wave models and quality assessment of forecasts and reanalyzes in Copernicus marine services.

How to cite: Palazov, A., Ivanov, I., Marinova, V., and Ivanova, V.: Sea wave observing system – initial results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3123, https://doi.org/10.5194/egusphere-egu22-3123, 2022.

Quality ocean information from observations and forecasting is crucial to support evidence-based decision making and providing a crucial framework for underpinning the scientific basis for policies that regulate the use of the oceans, coastal regions and port areas, maintaining their healthy ecosystems, protecting the development of the littoral zones, and monitoring the environmental mitigation efforts. However, there are fundamental gaps in our ocean observing and forecasting capabilities, limiting our capacity to manage the oceans, coastal regions, and port areas sustainably. Therefore, particularly for coastal areas, it is necessary to ensure high-level integration for coordinated observations that can be sustained in the long term and help to improve ocean forecasting to ensure safe and sustainable human-coastal interaction.

The EuroSea initiative [1] is an innovative action of the European Union that brings together key European actors in ocean observation and forecasting with key European end-users in ocean observation, thereby promoting a genuinely interdisciplinary ocean observing system and providing oceanographic products and services. Furthermore, it enables high-resolution coastal operations and forecasting systems in restricted domains such as local ports, beaches, and nearby coastal waters. The EuroSea Project aims to advance scientific knowledge about ocean climate, marine ecosystems, and their vulnerability to human impacts and demonstrate the ocean's importance for a healthy and economically viable society.

Within the EuroSea project framework, we present a 3D hydrodynamic tool to improve the sustainable management of Barcelona's local coastal waters. We use the Coupled Ocean-Atmosphere-Wave-Sediment Transport Modeling System [2] that utilises the Model Coupling Toolkit to exchange prognostic variables between the circulation model ROMS and the wave model SWAN. As part of the system, the wave and circulation models run with nested and refined grids to provide increased spatial resolution, scaling down to solve nearshore wave-driven flows, all within selected regions of a larger, coarser-scale coastal modelling system. Bathymetry was built using a combination of data from EMODnet [3] and specific high-resolution sources provided by local authorities. Copernicus products have driven these high-resolution simulations.

Field campaigns have been used to validate results, displaying agreements between modelled outputs and in-situ observations. Therefore, the model provides results that will be used to develop new forecast capabilities, such as predicting erosion and flooding, simulating rip currents, tracking the floating debris, and knowing the flushing times.

Finally, we look ahead to the future of the development and maintenance of the operational prediction systems because their harmonisation and integration with the existing ocean knowledge will increase the availability of credible scientific evidence to inform industry, help to reduce the impact of human activities on the ocean and improve environmental management.

We would like to acknowledge financial support from EuroSea Project (GA862626), an EU Innovation Action funded through Horizon 2020.

[1] EuroSea Project (https://eurosea.eu/)

[2] Warner, J.C., Armstrong, B., He, R., and Zambon, J.B., 2010, Development of a Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modelling system: Ocean Modeling, v. 35, no. 3, p. 230-244.

[3] EMODnet (https://www.emodnet-bathymetry.eu/)

How to cite: Liste, M., Mestres, M., Samper, Y., Espino, M., Sánchez-Arcilla, A., García-Sotillo, M., and Álvarez-Fanjul, E.: High-resolution Forecasting for Harbour-Beach Interactions. A Mediterranean Application., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10298, https://doi.org/10.5194/egusphere-egu22-10298, 2022.

The Regional Ocean Modelling System (ROMS) coupled via Model Coupling Toolkit (MCT) with the Los Alamos sea ice model (CICE) is being prepared. The years 2016-2019 were successfully simulated on a 2.3 x 2.3 km grid with 30 sigma levels and atmospheric forcing delivered externally from data produced by Weather Research and Forecast Model (WRF). For long-term hindcast simulations it is assumed to use the other sources of atmospheric data such as reanalysis.

The latest work was focused on the creation of a new high resolution grid. Bathymetry with a resolution of 500 m was used, which was then filtered to meet the slope factor and so-called Haney number criteria. In this way, a grid with 40 sigma layers and a 2700x3200 horizontal number of nodes (which corresponds to a resolution of 0.25 NM) was created. In addition, interpolation of forcing data was introduced to save disk space. Relevant pieces of code have been added to both ROMS and CICE that made possibility of delivering atmospheric data on 600x640 grid which is almost 23 times smaller than model grid.

In the future it is planned to work on the CICE model for improving its compatibility with satellite data. In particular, it will be interesting to compare the ice deformations available from the model output with those calculated from consecutive satellite images. Another important aspect of the CICE model appears to have the correct fast ice representation in simulations. In the case of the Baltic Sea, a particularly important area for this phenomenon is the Bay of Bothnia. Preliminary simulations show that CICE underestimates the area covered by fast ice and thus the problem need to be better studied.

Calculations were carried out at the Academic Computer Centre in Gdańsk. 

How to cite: Muzyka, M., Przyborska, A., Eskandari, S., and Jakacki, J.: Adaptation and implementation of the coupled Regional Ocean Modelling System (ROMS) and the Los Alamos sea ice model (CICE) for Baltic Sea, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4359, https://doi.org/10.5194/egusphere-egu22-4359, 2022.

Coastal areas in the North Sea and more specifically the German Bight are subject to continuously developing activities such as, among others, wind farming, transportation, river regulations and transport. The resulting environmental changes interact with those caused by the local natural variability and interaction with the open ocean. Coastal areas are representatives of the biogeochemically most active regions, incorporating interactions between land and ocean, sediment dynamics and morphodynamics. The small temporal and spatial scales of processes are challenging for the provision of sufficient and credible high-resolution 4D observations. Therefore, the mix between modelling and observations is considered as the most efficient tool to develop up-to-date coastal products, such as predictions and estimates of coastal and estuarine states, and scientific support for activities and decision making. Thereby, one major research direction is to shorten the gap between regional ocean and coastal/estuarine modelling and to ensure a seamless interface between CMEMS and regional operational predictions. This is demonstrated in our  REST-COAST applications for the German Bight and its estuaries where we develop flexible interfaces beneficial for the CMEMS framework and coastal forecasting systems. This development is transferable to other European coastal areas and contributes to harmonizing various similar, and nor well inter-linked, activities. The downscaled model is based on the SCHISM unstructured-grid model coupled to the wind wave model WWM. The performance  of the German Bight circulation model is assessed against in-situ observations and CMEMS regional products.

How to cite: Jacob, B., Koch, W., and Staneva, J.: Downscaling of an unstructured-grid model for the German Bight, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8181, https://doi.org/10.5194/egusphere-egu22-8181, 2022.

Freshwater pulses, during which the freshwater discharge by rivers exceeds three times its long-yearly average value for no longer than one month, are common features in many estuaries around the world. The goal of this study is to develop a tool to describe the salinity response to freshwater pulses. To this end, a new model is presented, which generalises a number of assumptions often used in studies of estuarine adjustment, but retains their idealised character. This model is applied to observed freshwater pulses in the Guadalquivir Estuary (Spain) and the effect of different assumptions is quantified. Results show that it is important to adequately simulate the vertical salinity structure during a freshwater pulse. Assuming the depth-averaged salinity at the estuary mouth to be fixed during freshwater pulses ignores important feedback mechanisms between river discharge and stratification at the mouth. Prescribing instead the salinity at the bottom only circumvents this problem.
More precisely comparing simulated and observed salt intrusion lengths shows that the idealised model captures the essence of the estuarine salinity response. Overall, the results indicate that the model can be used be used as a tool to quantify the dependence of the estuarine salinity response to different parameters. 

How to cite: Biemond, B., de Swart, H. E., Dijkstra, H. A., and Dìez-Minguito, M.: Idealised modelling of freshwater pulses, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2685, https://doi.org/10.5194/egusphere-egu22-2685, 2022.

By applying an unsupervised neuronal network (NN) to sea velocity profiles and wind data, it was possible to determine the main wind-driven circulation patterns in the Toulon bay. In addition, the NN outputs were utilized to perform a conditional averaging to High-Frequency radar surface current data (HFR) and the atmospheric AROME model, in order to understand the connectivity between the inner Toulon bay circulation features and the offshore marine-atmospheric conditions. For instance, upwelling scenarios are observed under strong westerly winds, whereas the downwelling is present under easterly wind conditions. Additionally, a barotropic system is observed when weak-mid wind blows for long time periods, and first baroclinic modes occur under strong wind events. Up to date, few studies have presented a clear connectivity between semi-enclosed bays and the offshore conditions, particularly in the northwestern Mediterranean Sea. Thus, this methodology presents great advantages when trying to study the interaction between semi-enclosed bays and the open sea by means of a combination of several in situ meteo-marine information.

How to cite: Caceres Euse, A., Bourg, N., and Molcard, A.: A methodology to understand the wind-driven circulation in semi-enclosed bays and its connectivity with the open sea at the southern coast of France, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12401, https://doi.org/10.5194/egusphere-egu22-12401, 2022.

This modeling study investigates how density gradient, wind and tide control water exchanges through the Strait of Hormuz in the Persian Gulf. The 3D model simulates the intraseasonal and interannual variability of the volume transports. Model results reveal a two-layer transport through the Strait of Hormuz mainly due to density gradients between the Persian Gulf and the Indian Ocean. Both wind and tides affect the exchange flow, however the tidal impacts dominate those from winds. Earlier estimates of the annually-averaged volume transports amounted to approximately 0.2 Sv. With the high-resolution model used in this study, volume transports increase by more than 2.5 times and reaching about 0.6 Sv. The dominant wind in the Persian Gulf is the northwesterly wind, which oppose the inflow from the Indian Ocean. A model experiment without wind confirms that annual mean inflow rates increase. On the other hand, the monthly net-transport (inflow rate - outflow rate) correlates with the wind magnitude when the model is run with complete forcing. Winds mostly affect extreme (maximum) daily flow rates but the flow rates driven by tides typically fluctuate around their annual mean values. Finally, this study reveals the seasonal cycle of the volume exchange with stronger exchange in early winter and summer than in spring and fall.

How to cite: Hosseini, S. T., Stanev, E., Pein, J., Jacob, B., and Schrum, C.: Quantifying the roles of tide, wind, and density gradient on volume transports in the Persian Gulf, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13451, https://doi.org/10.5194/egusphere-egu22-13451, 2022.

The forecasting of physical and biogeochemical variables has always proven to be a challenge in marginal and coastal seas. Over the years, data assimilation has played a significant role in improving model accuracy for operational forecasting. In this study, we assess the impact of assimilating satellite sea surface temperature and chlorophyll data, and in-situ profile temperatures in an operational forecast model with the aim to improve the forecast of ocean variables in the North and Baltic Seas. For this purpose, we use the data assimilation software PDAF coupled to the biogeochemical ocean model HBM-ERGOM, which is used operationally at the BSH, and perform data assimilation using an ensemble Kalman filter. We conduct data assimilation experiments for a one-year period from October 2018 to September 2019. The study will discuss and quantify the effects of the data assimilation on the oceanographic and biogeochemical variables in the model and on the coupled interaction of ocean physics and biogeochemistry.

How to cite: Sathyanarayanan, A., Li, X., van der Lee, E., Marki, A., Lorkowski, I., and Nerger, L.: Influence of temperature and chlorophyll data assimilation on a biogeochemical ocean model for the North and Baltic Seas, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11341, https://doi.org/10.5194/egusphere-egu22-11341, 2022.

Severe ocean surface waves generated by wind (hereafter waves) have a strong impact on socio-economic activities such as navigation, harbours, oil exploitation, and coastal infrastructure. The South Atlantic monitoring remains behind regarding high-resolution wave products that can support the understanding and impacts of extreme wave events over the region. In this work, we present a high-resolution wave hindcast for the Southwestern South Atlantic (SWSA) evaluated under extreme conditions. Such a product can be used by several sectors to contribute to a more predictive and open data ocean, engaging the goals proposed by the UN Ocean Decade. The hindcast is produced using the WAM model forced by 1-hourly ERA5 surface winds. Three horizontal grids are used for downscaling, to keep a smooth resolution increase: a Global grid (0.25°), an intermediate grid that covers the Eastern coast of South America (0.1°), and a finer grid, focusing on the SWSA (0.05°). The spectral domain is discretized into 30 logarithmically spaced frequency bins and the wave propagating directions are set with a resolution of 24°. Sensibility runs are performed to obtain the more suitable configuration to represent the extreme wave climate in the region. The physics parameterization for the input and open ocean dissipation are tested between Jansen and Ardhuin formulations. The analyses showed that Ardhuin's parameterization (ST4) with Betamax of 1.60 performed better in comparison with buoys and satellite measurements during storm conditions. Moreover, the sea ice inclusion improved the wave height and wave direction in the coastal region, particularly on the southern Brazilian coast. Including depth refraction in both intermediate and finer grids also played an important role in the wave direction, improving the wave model performance against in situ data. We also present the wave hindcast evaluation against buoy and satellite data from 2017 to 2021, focusing on extreme wave events. Furthermore, significant wave height and wind speed are assimilated and the benefits of data assimilation in predicting extreme waves in the region are evaluated.

How to cite: Gramcianinov, C., Behrens, A., Staneva, J., Ricker, M., Wiese, A., de Camargo, R., and da Silva Dias, P.: Numerical modelling of extreme wave events in the Southwestern South Atlantic, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12277, https://doi.org/10.5194/egusphere-egu22-12277, 2022.