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From 6 to 8 November 2011, a baroclinic wave moved from North Atlantic to Balearic Sea, produced a cut-off, at all altitudes, and developed into a Tropical-Like Cyclone (TLC) characterized by a deep-warm core. This TLC led to a mean sea level pressure minimum of about 987 hPa, 10 m wind speeds higher than 30 m/s around the eye, and very intense rainfall, especially in the Gulf of Lion and the surrounding areas, close to the mountain chains (floods in Genoa and Elba Island).To explore in details the effect of the sea surface temperature on the TLC development, we employed the coupled modeling system COAWST, which consists of the following models: ROMS for the hydrodynamic part, WRF for the meteorological part and SWAN for the surface wave modeling, using a 5 km horizontal grid over all Mediterranean Sea.COAWST was used with different configurations: Stand Alone (SA) approach using only the atmospheric part, atmosphere-ocean coupled mode (AO), and fully coupled version including also surface waves (AOW). Comparing the three runs, the effects of different simulations on the TLC trajectory are significant only in the later stage of the cyclone lifetime. On the other hand, wind intensity is higher in the SA case w.r.t. both coupled runs. When compared to case AO, winds are about 1 m/s larger, even though the spatial distribution is very similar (possibly because of the lower SST produced by case AO). Case AOW produces less intense winds than SA and AO case in the areas where the wave is most developed (differences are about 2-4 m/s), while winds are more intense nearby the cyclone’s eye. Moreover, the inclusion of the wave model (AOW) has implications in the water column, by increasing the mixing in the ocean, changing the depth of the ocean mixed layer along the track of the TLC, increasing the surface drag and the net heat fluxes from ocean to atmosphere, so that eventually SST in AOW run is colder than in AO.It is observed that SST of the SA case is overestimated compared to the coupled cases, and in particular the best performances are observed using the fully coupled case, with the wave motion implementation. The best description of the SST impacts the cyclone intensity and the amount of precipitation at catchment scale. The vertical profiles show that wave induced mixing modifies the mixed layer structure and cools the water column, removing much of the SST (and Ocean Heat Content) anomaly present in the mixed layer.The date chosen for the run initialization appears important: an earlier initial condition allows to properly simulate the evolution of the cyclone from the cyclogenesis between the inclusion and setting-up of air sea interaction effect, through the coupled models.Warming SST in the Mediterranean Sea induced by climate change might increase TLC frequency and/or intensity, potentially becoming more harmful for coastal populations and infrastractures. Fully coupled AOW models might be better suited for studying such aspects. Funding from the STO Office of Chief Scientist (907EUR30) is gratefully acknowledged.

Climate change and its consequences on coastal erosion, flooding and water quality are becoming a major concern for a significant percentage of littorals in the world. This issue is particularly challenging for gentle-sloping sandy coasts which are vulnerable to slow and continuous changes related to rising sea-levels and to extreme storm surge and wave events.Here we present a multidisciplinary research combining satellite image with machine learning and GIS spatial analysis tools to analyze coastal erosion risk in the Venice shoreline over the period 2015-2019. Firstly, an advanced image preprocessing was performed on satellite images (e.g. co-registration, colors normalization) to prepare the input dataset. Secondly, different supervised and unsupervised machine learning classification methods were tested to accurately define shoreline position by recognizing land-sea areas in each image and the Digital Shoreline Analysis System (DSAS) tool in ArcGis was applied to evaluate the net shoreline movement overtime. Finally, a GIS-based Bayesian Network (BN) approach was developed, to evaluate the probability and uncertainty of coastal erosion risks, and the cascading effects on water quality variation, against multiple ‘what-if’ scenarios related to extreme sea levels and wave conditions under climate change for the period 2040-2050.According to the spatial resolution of the available data for the case study of Venice (Veneto Region-Italy), the proposed BN-model was trained and validated by considering atmospheric, oceanographic and water quality parameters over the 2015-2019 timeframe, allowing to capture local-scale coastal progression and related driving forces.Results showed general shoreline stability in the considered reference timeframe. However, the high presence of anthropogenic structures (e.g. jetties, breakwaters) induces the formation of well-delimited hotspots of erosion/accretion. Future trends from the BN-based scenario analysis, according to RCP8.5 scenario within the 2040-2050 period, showed that, even if in minor extent, water quality parameters (i.e. suspended matter, diffuse attenuation) will increase. On the other hand, shoreline evolution trend will face a decreasing probability of the stable class, which in turn will increase instability.Despite constraints posed by the spatial resolution of the available data for the investigated case, the outcomes of the performed assessment represent valuable information to support adaptive policy pathways in the context of Integrated Coastal Zone Management and Disaster Risk Reduction in the Venice coastal area.

In recent years, the study of extreme ocean waves has gained considerable interest and several theoretical approaches have been developed for their statistical prediction. However, a full understanding of the main mechanisms responsible for the occurrence of extreme waves has not yet been reached in the relatively common case of a crossing sea, where a local wind sea system coexists with a system of swell. In this context, we investigate how the space-time extreme-value statistics of realistic crossing sea states differs from the statistics of the corresponding short-crested wind sea and long-crested swell partitions during tropical cyclone Kong Rey (2018) in the Northwestern Pacific Ocean (Yellow Sea and East China Sea). The investigation is carried out using an ensemble of numerical simulations obtained from a phase-resolving wave model based on the high-order spectral method (HOSM) and focuses on the maximum sea surface elevation (crest height). The reliability of the numerical model outputs has been assessed with space-time measurements of the 3D sea surface elevation field collected from a fixed offshore platform in the area of interest. Our results highlight the different roles that linear and nonlinear effects have in the formation of extreme waves for different combinations of wind sea and swell systems.

Sea surface wave breaking is the dominant process that results in dissipation of ocean surface wave energy. During the breaking process, wave energy is converted into turbulent kinetic energy, and if significantly energetic, entrains air which facilitates air-sea gas transfer and scatters light to create the signature whitecap. Exploiting the broadband scattering of light by the surface whitecaps, this study uses a fixed stereo video system to detect and track individual air-entraining surface breaking waves at wind speeds of up to 16 m/s. The sea surface foam (whitecap) from a breaking event is detected in grayscale images using a brightness thresholding technique based on the image pixel intensity histogram. The movement of individual whitecaps is estimated with optical flow and is used to track whitecaps between consecutive frames. Once breaking events have been tracked through their lifetime, fundamental properties of the whitecap such as the time-evolving foam area [m2], breaking speed [m/s], average crest length [m] and foam area growth and decay timescales [s] are extracted and subsequently aggregated into whitecap statistics. The geometric, kinematic and dynamic quantities obtained for individual whitecaps via this tracking method are used in conjunction with the volume-time-integral method developed in Callaghan et al 2016 to estimate the energy dissipated by each individual whitecap and to then develop an empirical frequency-dependent whitecap energy dissipation source term.

Reliable wave forecasts and hindcasts, together with long-term statistical analysis of extreme conditions, are of utmost importance for monitoring marine areas. Indeed, there is general consensus that high-quality predictions of extreme events during marine storms can substantially contribute to avoiding or minimizing human and material damage, especially in busy waterways such as the Mediterranean and Black Seas. So far, however, the wave climate characterization (average and anomaly relative to the average) has focused on the bulk characterization of the significant wave height Hs, and it has lacked a description of the individual waves, such as the maximum ones that may occur at a given location in the sea. To fill this gap, we provide the intensity and geographical distribution of the maximum waves in the Mediterranean and Black Seas over 27 years (1993-2019), by representing the average annual (1993-2018) and anomaly for 2019 relative to the average of the 99th percentile of the expected maximum wave height Hm and crest height Cm. The analysis combines wave model hindcasts available through CMEMS model setup and the wave model WAVEWATCH III®, both forced with ECMWF ERA5 reanalysis winds. Results show that in 2019 maximum waves were smaller than usual in the Black Sea (anomalies of Hm up to -1.5 m), while in the Mediterranean Sea a markedly positive anomaly (+2.5 m for Hm) was found in the southern part of the basin. The peculiar 2019 configuration seems to be caused by a widespread atmospheric stability over the Black Sea and by depressions that rapidly passed over the Mediterranean Sea.

Over the past decade, model reanalysis data products have found widespread application in many areas of research and have often been used for the assessment of the past and present atmospheric climate. They produce reliable fields at high temporal resolution (1 hour), albeit generally at low-to-mid spatial resolution (0.25°-1.00°). On the other hand, climatological analyses, quite often down-scaled (up to few km) to represent conditions also in enclosed basins, lack the actual historical sequence of events and are often provided at poor temporal resolution (6 hours or daily).In this context, we investigated the possibility of using climate model data to scale ERA5 reanalysis wind (25-km and 1-hour resolution data) to assess the Mediterranean Sea wind and wave climate. We propose a statistical strategy to fuse ERA5 wind speeds over the sea with the past and future wind speeds produced by the COSMO-CLM (8-km and daily-mean data) climatological model. In the method, the probability density function of the ERA5 wind speed at each grid point is adjusted to match that of COSMO-CLM using a histogram equalization strategy. In this way, past ERA5 data are corrected to account for the COSMO-CLM wind distribution, while ERA5 scaled wind sequence can be also projected in the future with COSMO-CLM scenarios. Comparison with past observations of wind and waves confirms the validity of the adopted method.We have tested this strategy for the assessment of the changing wind and, after WAVEWATCH III model runs, also the wave climate in the northern Adriatic Sea, especially in front of Venice and the MOSE barriers. In general, this data fusion strategy may be applied to produce a scaled wind dataset in enclosed basins and improve past and scenario wave modeling applications based on any reanalysis wind data.

Reliable prediction of oceanic waves during severe marine storms has always been foremost for offshore platform design, coastal activities, and navigation safety. Indeed, many damaging accidents and casualties during storms were ascribed to the impact with abnormal and unexpected waves. However, predicting extreme wave occurrence is a challenging task, at first, because of their inherent randomness, and because the observation of large ocean waves, of primary importance to assess theoretical and numerical models, is limited by the costs and risks of deployment during severe open-ocean sea-state conditions.In the context of the EU-based Copernicus Marine Environment Monitoring Service (CMEMS) evolution, the LATEMAR project (https://www.mercator-ocean.fr/en/portfolio/latemar/) aimed at improving the modelling of large wave events during marine storms. Indeed, at present, operational systems only provide average and peak wave parameters, with no information on individual waves whatsoever. However, developments of the state-of-the-art third-generation wave models demonstrated that using the directional wave spectrum moments into theoretical statistical models for wave extremes, forecasters are able to accurately infer the expected shape and likelihood of the maximum waves during storms.The main purpose of the activity is therefore to provide the wave models WAM and WAVEWATCH III with common procedures to explicitly estimate the maximum wave heights for each sea state. LATEMAR achieved this goal by: performing an extensive assessment of the model maximum waves using field observations collected from an oceanographic tower; comparing WAM and WAVEWATCH III maximum wave estimates in the Mediterranean Sea; investigating the sensitivity of the maximum waves on the main sea state parameters. All model developments and evaluations resulting from this research project will be directly applicable to the wave model forecasting systems to expand their catalogue.

Over the past decade, reanalysis data products have found widespread application in many areas of research and have often been used for the assessment of the past and present climate. They produce reliable atmospheric fields at high temporal resolution, albeit at low-to-mid spatial resolution. On the other hand, climatological analyses, quite often down-scaled to represent conditions also in enclosed basins, lack the historical sequence of stormy events and are often provided at poor temporal resolution.In this context, we investigated the possibility of using the ERA5 reanalysis 10-m wind (25-km and 1-hour resolution data) to assess the Mediterranean Sea wind climate (past and scenario). We propose a statistical strategy to relate ERA5 wind speeds over the sea to the past and future wind speeds produced by the COSMO-CLM (8-km and 6-hour resolution data) climatological model. In particular, the probability density function of the ERA5 wind speed at each grid point is adjusted to match that of COSMO-CLM. In this way, past ERA5 winds are corrected to account for the COSMO-CLM energy, while ERA5 scaled wind sequence can be projected in the future with COSMO-CLM scenario energy. Comparison with past observations confirms the validity of the adopted method.In the Venezia2021 project, we have applied this strategy for the assessment of the changing wind and, after WAVEWATCH III model runs, also the wave climate in the Northern Adriatic Sea, especially in front of Venice and the MOSE barriers, under two IPCC (RCP 4.5 and 8.5) scenarios.In general, this strategy may be applied to produce a scaled wind dataset in enclosed basins and improve past wave modeling applications based on any reanalysis wind data.

On 12 November 2019, an exceptional flood event occurred in Venice, second only to the one that occurred on 4 November 1966. The maximum recorded sea level value of 189 cm above local datum resulted in the flooding of more than 85% of the pedestrian surface of the historical city. Moreover, with four extremely high tides since 11 November 2019, this has been the worst week for flooding in Venice ever since 1872, when official statistics were first produced. The event that struck Venice and the northern Adriatic Sea on 12 November 2019, although having certain conditions seemingly typical of the events that cause exceptional high waters, also had some peculiar characteristics not observed before and therefore it requires an in-depth analysis. Several factors made this event exceptional: an in-phase timing of the peak of the storm surge and the astronomical tide; an anomalously high monthly mean sea level in the Adriatic Sea induced by a steady low-pressure and wind systems over the Mediterranean Sea associated with large-scale low-frequency atmospheric dynamics; a deep low-pressure system over the central-southern Tyrrhenian Sea that generated strong sirocco (south-easterly) winds along the main axis of the Adriatic Sea pushing the waters towards north; a fast-moving local depression - and the associated wind perturbation - travelling in the north-westward direction along the Italian coast that may have forced long ocean waves (e.g., edge wave); and very strong winds (100 km h-1 on average, with gusts reaching 110 km h-1) over the Lagoon of Venice which led to a further rise in water levels and damage to the historic city. In this study, a large set of available observations and the high-resolution numerical simulations are used to quantify the influence of these drivers on the peak flood event and to investigate the peculiar weather and sea conditions over the Mediterranean Sea during the Venice floods of November 2019.

Surfactant layers with viscoelastic properties floating on the water surface damp short gravity-capillary waves. Inspired by the known virtue of fish oil to still angry seas, a laboratory study has been made on wind wave generation and on the interaction between wind-waves, paddle-waves and airflow in a tank containing a thin fish oil film uniformly spread on the water surface. According to the Marangoni resonance-type damping mechanism, for oily surfaces the energy dissipation process is quite selective in wavenumbers, but its effects are not, since it spreads (although to a lesser extent) towards longer and shorter waves via nonlinear interactions and modification of the airflow profile. With a thin layer of oil on the surface, it is rather peculiar that in the wind-only condition (no paddle waves) the wave field does not grow from the rest condition. This equilibrium was altered by paddle (longer) waves, the generation and evolution of short waves (in clean water and with oil) being modified by their interaction with the orbital velocity of the longer waves and their effect on the airflow. Paddle waves did grow under the action of wind, how much being similar in clean and oily water conditions, a fact we ascribe to the similar distortion of the wind profile in the two cases. We have also found that wind-supported stress on the oily water surface was able to generate a surface current, whose magnitude turns out to be comparable to the one in clean water. Our results expand previous investigations on the same topic. We stress the benefit of experiments with surfactants to explore in detail the physics at, and the exchanges across, the wavy and no-wavy air-water interface.

Global Navigation Satellite Systems (GNSS) and modern motion-sensor packages allow the measurement of ocean surface waves with low-cost drifters. Drifting along or across current gradients provides unique measurements of wave-current interactions. In this study, we investigate the response of several combinations of GNSS receiver, motion-sensor package and hull design in order to define a prototype surface kinematic buoy (SKIB) that is particularly optimized for measuring wave-current interactions, including relatively short wave components (relative frequency around 1 Hz) that are important for air-sea interactions and remote sensing applications. The comparison with existing Datawell Directional Waverider and SWIFT buoys, as well as stereo-video imagery demonstrates the accuracy of SKIB. The use of low-cost accelerometers and a spherical ribbed and skirted hull design provide acceptable heave spectra, while velocity estimates from GNSS receivers yield a mean direction and directional spread. Using a low-power acquisition board allows autonomous deployments over several months with data transmitted by satellite. The capability to measure current-induced wave variations is illustrated with data acquired in a macro-tidal coastal environment.

We propose a new satellite mission that uses a near-nadir Ka-band Doppler radar to measure surface currents, ice drift and ocean waves at spatial scales of 40 km and more, with snapshots at least every day for latitudes 75 to 82, and every few days otherwise. The use of incidence angles at 6 and 12 degrees allows a measurement of the directional wave spectrum which yields accurate corrections of the wave-induced bias in the current measurements. The instrument principle, algorithm for current velocity and mission performance are presented here. The proposed instrument can reveal features on tropical ocean and marginal ice zone dynamics that are inaccessible to other measurement systems, as well as a global monitoring of the ocean mesoscale that surpasses the capability of today’s nadir altimeters. Measuring ocean wave properties facilitates many applications, from wave-current interactions and air-sea fluxes to the transport and convergence of marine plastic debris and assessment of marine and coastal hazards.

In this paper the Self-Organizing Map (SOM) technique to assess the multivariate sea wave climate at a site is analyzed and discussed with the aim of a more complete representation which includes the most severe sea states that otherwise would be missed by the standard SOM. Indeed, it is commonly recognized, and herein confirmed, that SOM is a good regressor of a sample where the density of events is high (e.g. for low/moderate and frequent sea states), while SOM fails where the density is low (e.g. for severe and rare sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period, and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979–2008. Three different strategies derived by the standard SOM have been tested in order to widen the range of applicability to extreme events. The first strategy contemplates a pre-processing of the input dataset with the Maximum Dissimilarity Algorithm; the second and the third strategies focus on the post-processing of SOM outputs, resulting in a two-steps SOM, where the first step is the standard SOM applied to the original dataset, and the second step is an additional SOM on the events exceeding a threshold (either taking all the events over the threshold or only the peaks of storms). Results suggest that post-processing strategies are more effective than the pre-processing one in representing the extreme wave climate, both in the time series and probability density spaces. In addition, a complete graphical representation of the outcomes of two-steps SOM as double-sided maps is proposed.