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Air bubbles in the upper ocean are generated mainly by waves breaking at the air–sea interface. As such, after the waves break, entrained air bubbles evolve in the form of plumes in the turbulent flow, exchange gas with the surrounding water, and may eventually rise to the surface. To shed light on the short-term response of entrained bubbles in different stormy conditions and to assess the link between bubble plume penetration depth, mechanical and thermal forcings, and the air–sea transfer velocity of CO2, a field experiment was conducted from an oceanographic research tower in the north Adriatic Sea. Air bubble plumes were observed using high-resolution echosounder data from an upward-looking 1000 kHz sonar. The backscatter signal strength was sampled at a high resolution, 0.5 s in time and 2.5 cm along the vertical direction. Time series profiles of the bubble plume depth were established using a variable threshold procedure applied to the backscatter strength. The data show the occurrence of bubbles organized into vertical plume-like structures, drawn downwards by wave-generated turbulence and other near-surface circulations, and reaching the seabed at 17 m depth under strong forcing. We verify that bubble plumes adapt rapidly to wind and wave conditions and have depths that scale approximately linearly with wind speed. Scaling with the wind–wave Reynolds number is also proposed to account for the sea-state severity in the plume depth prediction. Results show a correlation between measured bubble depths and theoretical air–sea CO2 transfer velocity parametrized with wind-only and wind/wave formulations. Further, our measurements corroborate previous results suggesting that the sinking of newly formed cold-water masses helps bring bubbles to greater depths than those reached in stable conditions for the water column. The temperature difference between air and sea seems sufficient for describing this intensification at the leading order of magnitude. The results presented in this study are relevant for air–sea interaction studies and pave the way for progress in CO2 gas exchange formulations.

The Mediterranean Sea is a primary source of food, ecosystem services and economic activities and one of the most active cyclogenetic regions in the world, where the influence of orographic and morphological features of the relatively small basin plays an important role. Together with the explosive cyclogenesis, tropical-like cyclones (also called Mediterranean Hurricanes or Medicanes) are among the strongest types of storms that can be found in the Mediterranean basin, occurring predominantly in the Ionian, Balearic and Tyrrhenian sub-basins. Similarly to tropical cyclones (Hurricanes or Typhoons), these cyclonic structures are characterized by strong rotating and translating wind fields, which often lead to a combination of remotely generated swell waves and locally generated wind waves, often referred to as crossing sea states. Despite the well-known potential of Medicanes to cause significant damage near islands and coastal zones, which is predicted to intensify as a result of climate change, to date the characterization of maximum individual waves generated during these events is still lacking. In this study, we carry out the first analysis of the large-scale geographical distribution of wave maxima within the wave fields generated during three recent Medicane events using the WAVEWATCH III® spectral wave model forced by ERA5 reanalysis winds, also investigating the influence of crossing sea states on the maximum wave amplitudes with novel statistical formulations developed for such conditions. Our results show that, as in the case of tropical cyclones, several regions of the cyclone field are characterized by crossing sea states, whose role in the formation of the maximum individual waves occurring near the eye of the storm was found to be confined. Furthermore, extreme wave predictions accounting for the local crossing conditions yield differences up to 5% compared to standard statistical distributions.

The study of extreme ocean waves has gained considerable interest in recent years, due to their importance for offshore design and navigation safety, and several theoretical approaches have been developed for their statistical description. However, in the case of crossing seas, where two or more wave systems of different characteristics are present, a full understanding of the main physical mechanisms responsible for the occurrence of very high individual waves is still lacking. As a consequence, the prediction of extremes in such conditions currently relies on integrated parameters of the total sea state, such as the spectral wave steepness. In this study, to gain further insight into the role of the crossing wind sea and swell wave systems in producing extreme individual waves, we investigate realistic sea states during typhoon Kong-rey (2018) using an ensemble of numerical simulations obtained from a phase-resolving wave model based on the High-Order Spectral (HOS) method. The reliability of the numerical fields is assessed for the first time with stereo wave measurements of the sea surface elevation field collected from an offshore platform in the area of interest. We show that, in specific conditions, space-time extreme crest heights in crossing seas can be larger than in unimodal seas due to second-order bound wave interactions between the wind sea and the swell. To improve existing prediction capabilities, we propose a novel formulation for the wave steepness in crossing seas, which includes nonlinear effects up to the second order and accounts for the spectral parameters of the interacting wave systems.

IntroductionIn hospitalized patients with COVID-19, bloodstream infections (BSI) are associated with high mortality and high antibiotic resistance rates. The aim of this study was to describe BSI etiology, antimicrobial resistance profile and risk factors in a sample of patients deceased with COVID-19 from the Italian National COVID-19 surveillance.MethodsHospital charts of patients who developed BSI during hospitalization were reviewed to describe the causative microorganisms and their antimicrobial susceptibility profiles. Risk factors were analyzed in univariate and multivariate analyses.ResultsThe study included 73 patients (71.2% male, median age 70): 40 of them (54.8%) received antibiotics and 30 (41.1%) systemic steroids within 48 h after admission; 53 (72.6%) were admitted to intensive care unit. Early steroid use was associated with a significantly shorter interval between admission and BSI occurrence. Among 107 isolated microorganisms, the most frequent were Enterococcus spp., Candida spp., Acinetobacter baumannii, and Klebsiella pneumoniae. Median time from admission to BSI was shorter for Staphylococcus aureus compared to all other bacteria (8 vs. 24 days, p = 0.003), and longer for Enterococcus spp., compared to all other bacteria (26 vs. 18 days, p = 0.009). Susceptibility tests showed a high rate of resistance, with 37.6% of the bacterial isolates resistant to key antibiotics. Resistance was associated with geographical area [adjusted odds ratio (AOR) for Central/South Italy compared to North Italy: 6.775, p = 0.002], and with early use of systemic steroids (AOR 6.971, p = 0.018).ConclusionsIn patients deceased with COVID-19, a large proportion of BSI are caused by antibiotic-resistant bacteria. Early steroid use may facilitate this occurrence.

Accurate wind-wave forecasting in enclosed and semi-enclosed basins is a challenging task, demanding primarily for high-resolution wind forcing at regional scale. This is generally obtained with dynamical downscaling from a low-to-mid resolution atmospheric model. In this context, a new wave forecasting system for the marginal Adriatic Sea is herein presented aimed at proposing an alternative strategy for accurate wind-wave forecasting in (semi-) enclosed basins that does not require an ad-hoc regional atmospheric model. The system is based on the state-of-the-art WAVEWATCH III® spectral wave model forced by the global IFS-ECMWF forecast. At first, wind speed is quantile-corrected to account for the systematic underestimation over the Adriatic Sea. Then, the significant wave height in the target region and for regimes associated with marine storms is calibrated following standard procedure. Wind and wave observations from different sources are used for calibration and validation of the wave forecasts, which achieve satisfactory scores. We also compare results with those of other forecasting systems in the area, highlighting the importance of the wind forcing accuracy and the wave model calibration. Doing so, we discuss the challenges that characterise (semi-) enclosed environments in order to propose effective solutions for them and future developments.

Measurement(s) atmospheric wind direction • atmospheric wind speed • wave Technology Type(s) weather station • acoustic AWAC system • waverider buoy • Camera Device Sample Characteristic - Environment coastal sea water Sample Characteristic - Location Adriatic Sea Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.13415780

Abstract Background A history of previous cardiac disease increases the maternal mortality risk by as much as 100%. There is no consensus on the absolute contraindications to vaginal delivery in valvular heart disease, but central regional anesthesia is traditionally considered contraindicated in patients with severe aortic stenosis. Case presentation A 29-year-old primigravid woman with severe aortic stenosis was admitted to the obstetrics department for programmed labor induction. With epidural anesthesia and mini-invasive hemodynamic monitoring labor and operative vaginal delivery were well tolerated, and hemodynamic stability was always maintained. Conclusions Epidural analgesia and oxytocin induction are possible for the labor management of parturients with severe aortic stenosis given that continuous non-invasive followed by invasive hemodynamic monitoring can be provided and given the absence of any obstetric or cardiologic contraindications and the strong will of the patient.

Abstract Background Rapid neuromuscular block reversal at the end of major abdominal surgery is recommended to avoid any postoperative residual block. To date, no study has evaluated sugammadex performance after rocuronium administration in patients undergoing liver transplantation. This is a randomized controlled trial with the primary objective of assessing the neuromuscular transmission recovery time obtained with sugammadex versus neostigmine after rocuronium induced neuromuscular blockade in patients undergoing orthotopic liver transplantation. Methods The TOF-Watch SX®, calibrated and linked to a portable computer equipped with TOF-Watch SX Monitor Software®, was used to monitor and record intraoperative neuromuscular block maintained with a continuous infusion of rocuronium. Anaesthetic management was standardized as per our institution’s internal protocol. At the end of surgery, neuromuscular moderate block reversal was obtained by administration of 2 mg/kg of sugammadex or 50 mcg/kg of neostigmine (plus 10 mcg/kg of atropine). Results Data from 41 patients undergoing liver transplantation were analysed. In this population, recovery from neuromuscular block was faster following sugammadex administration than neostigmine administration, with mean times±SD of 9.4 ± 4.6 min and 34.6 ± 24.9 min, respectively (p

Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support demonstrate greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled during support searching remains unclear. To fill this gap, here we tested whether simulation-based machine learning methods can capture differences in movement patterns nested in actual kinematical data. We compared machine learning classifiers with the aim of generating models that learn to discriminate between circumnutation patterns related to the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features at the level of the junction underneath the tendrils that seems to be a superior indicator for discerning the presence/absence of the support by the plant. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.

A climatology of the wind waves in the Mediterranean Sea is presented. The climate patterns, their spatio-temporal variability and change are based on a 40-year (1980–2019) wave hindcast, obtained by combining the ERA5 reanalysis wind forcing with the state-of-the-art WAVEWATCH III spectral wave model and verified against satellite altimetry. Results are presented for the typical (50th percentile) and extreme (99th percentile) significant wave height and, for the first time at the regional Mediterranean Sea scale, for the typical and extreme expected maximum individual wave height of sea states. The climate variability of wind waves is evaluated at seasonal scale by proposing and adopting a definition of seasons for the Mediterranean Sea states that is based on the satellite altimetry wave observations of stormy (winter) and calm (summer) months. The results, initially presented for the four seasons and then for winter and summer only, show the regions of the basin where largest waves occur and those with the largest temporal variability. A possible relationship with the atmospheric parameter anomalies and with teleconnection patterns (through climate indices) that motivates such variability is investigated, with results suggesting that the Scandinavian index variability is the most correlated to the Mediterranean Sea wind-wave variability, especially for typical winter sea states. Finally, a trend analysis shows that the Mediterranean Sea typical and extreme significant and maximum individual wave heights are decreasing during summer and increasing during winter.

In oil and gas production, it is essential to monitor some performance indicators that are related to the composition of the extracted mixture, such as the liquid and gas content of the flow. These indicators cannot be directly measured and must be inferred with other measurements by using soft sensor approaches that model the target quantity. For the purpose of production monitoring, point estimation alone is not enough, and a confidence interval is required in order to assess the uncertainty in the provided measure. Decisions based on these estimations can have a large impact on production costs; therefore, providing a quantification of uncertainty can help operators make the most correct choices. This paper focuses on the estimation of the performance indicator called the water-in-liquid ratio by using data-driven tools: firstly, anomaly detection techniques are employed to find data that can alter the performance of the subsequent model; then, different machine learning models, such as Gaussian processes, random forests, linear local forests, and neural networks, are tested and employed to perform uncertainty-aware predictions on data coming from an industrial tool, the multiphase flow meter, which collects multiple signals from the flow mixture. The reported results show the differences between the discussed approaches and the advantages of the uncertainty estimation; in particular, they show that methods such as the Gaussian process and linear local forest are capable of reaching competitive performance in terms of both RMSE (1.9–2.1) and estimated uncertainty (1.6–2.6).

One of the most promising techniques for the analysis of Spatio-Temporal ocean wave fields is stereo vision. Indeed, the reconstruction accuracy and resolution typically outperform other approaches like radars, satellites, etc. However, it is computationally expensive so its application is typically restricted to the analysis of short pre-recorded sequences. What prevents such methodology from being truly real-time is the final 3D surface estimation from a scattered, non-equispaced point cloud. Recently, we studied a novel approach exploiting the temporal dependence of subsequent frames to iteratively update the wave spectrum over time. Albeit substantially faster, the unpredictable convergence time of the optimization involved still prevents its usage as a continuously running remote sensing infrastructure. In this work, we build upon the same idea, but investigating the feasibility of a fully data-driven Machine Learning (ML) approach. We designed a novel Convolutional Neural Network that learns how to produce an accurate surface from the scattered elevation data of three subsequent frames. The key idea is to embed the linear dispersion relation into the model itself to physically relate the sparse points observed at different times. Assuming that the scattered data are uniformly distributed in the spatial domain, this has the same effect of increasing the sample density of each single frame. Experiments demonstrate how the proposed technique, even if trained with purely synthetic data, can produce accurate and physically consistent surfaces at five frames per second on a modern PC.

Abstract Pleural effusion (PLEFF), mostly caused by volume overload, congestive heart failure, and pleuropulmonary infection, is a common condition in critical care patients. Thoracic ultrasound (TUS) helps clinicians not only to visualize pleural effusion, but also to distinguish between the different types. Furthermore, TUS is essential during thoracentesis and chest tube drainage as it increases safety and decreases life-threatening complications. It is crucial not only during needle or tube drainage insertion, but also to monitor the volume of the drained PLEFF. Moreover, TUS can help diagnose co-existing lung diseases, often with a higher specificity and sensitivity than chest radiography and without the need for X-ray exposure. We review data regarding the diagnosis and management of pleural effusion, paying particular attention to the impact of ultrasound. Technical data concerning thoracentesis and chest tube drainage are also provided.

Abstract Intraoperative transesophageal echocardiography is a minimally invasive monitoring tool that can provide real-time visual information on ventricular function and hemodynamic volume status in patients undergoing liver transplantation. The American Association for the Study of Liver Diseases states that transesophageal echocardiography should be used in all liver transplant candidates in order to assess chamber sizes, hypertrophy, systolic and diastolic function, valvular function, and left ventricle outflow tract obstruction. However, intraoperative transesophageal echocardiography can be used to “visualize” other organs too; thanks to its proximity and access to multiple acoustic windows: liver, lung, spleen, and kidney. Although only limited scientific evidence exists promoting this comprehensive use, we describe the feasibility of TEE in the setting of liver transplantation: it is a highly valuable tool, not only as a cardiovascular monitoring, but also as a tool to evaluate lungs and pleural spaces, to assess hepatic vein blood flow and inferior vena cava anastomosis and patency, i.e., in cases of modified surgical techniques. The aim of this case series is to add our own experience of TEE as a comprehensive intraoperative monitoring tool in the field of orthotopic liver transplantation (and major liver resection) to the literature.

Small-bore pleural drainage device insertion has become a first-line therapy for the treatment of pleural effusions (PLEFF) in the intensive care unit; however, no data are available regarding the performance of resident doctors in the execution of this procedure. Our aim was to assess the prevalence of complications related to ultrasound-guided percutaneous small-bore pleural drain insertion by resident doctors. In this single-center observational study, the primary outcome was the occurrence of complications. Secondary outcomes studied were as follows: estimation of PLEFF size by ultrasound and postprocedure changes in PaO 2 /FiO 2 ratio. In all, 87 pleural drains were inserted in 88 attempts. Of these, 16 were positioned by the senior intensivist following a failed attempt by the resident, giving a total of 71 successful placements performed by residents. In 13 cases (14.8%), difficulties were encountered in advancing the catheter over the guidewire. In 16 cases (18.4%), the drain was positioned by a senior intensivist after a failed attempt by a resident. In 8 cases (9.2%), the final chest X-ray revealed a kink in the catheter. A pneumothorax was identified in 21.8% of cases with a mean size (±SD) of just 10 mm (±6; maximum size: 20 mm). The mean size of PLEFF was 57.4 mm (±19.9), corresponding to 1148 mL (±430) according to Balik’s formula. Ultrasound-guided placement of a small-bore pleural drain by resident doctors is a safe procedure, although it is associated with a rather high incidence of irrelevant pneumothoraces.

The self-organizing map (SOM) technique is considered and extended to assess the extremes of a multivariate sea wave climate at a site. The main purpose is to obtain a more complete representation of the sea states, including the most severe states that otherwise would be missed by a SOM. Indeed, it is commonly recognized, and herein confirmed, that a SOM is a good regressor of a sample if the frequency of events is high (e.g., for low/moderate sea states), while a SOM fails if the frequency is low (e.g., for the most severe 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 SOM have been tested in order to capture the most extreme events. The first contemplates a pre-processing of the input data set aimed at reducing redundancies; the second, based on the post-processing of SOM outputs, consists in a two-step SOM where the first step is applied to the original data set, and the second step is applied on the events exceeding a given threshold. A complete graphical representation of the outcomes of a two-step SOM is proposed. Results suggest that the post-processing strategy is more effective than the pre-processing one in order to represent the wave climate extremes. An application of the proposed two-step approach is also provided, showing that a proper representation of the extreme wave climate leads to enhanced quantification of, for instance, the alongshore component of the wave energy flux in shallow water. Finally, the third strategy focuses on the peaks of the storms.

The oceanographic campaign CARPET2014 (Characterizing Adriatic Region Preconditionig EvenTs), (30 January–4 February 2014) collected the very first turbulence data in the Gulf of Trieste (northern Adriatic Sea) under moderate wind (average wind speed 10 m s−1) and heat flux (net negative heat flux ranging from 150 to 400 W m−2). Observations consisted of 38 CTD (Conductivity, Temperature, Depth) casts and 478 microstructure profiles (grouped into 145 ensembles) with three sets of yoyo casts, each lasting for about 12 consecutive hours. Averaging closely repeated casts, such as the ensembles, can lead to a smearing effect when in the presence of a vertical density structure with strong interfaces that can move up or down between subsequent casts under the influence of tides and internal waves. In order to minimize the smearing effect of such displacements on mean quantities, we developed an algorithm to realign successive microstructure profiles to produce sharper and more meaningful mean profiles of measured turbulence parameters. During the campaign, the water column in the gulf evolved from well-mixed to stratified conditions due to Adriatic waters intruding at the bottom along the gulf's south-eastern coast. We show that during the warm and relatively dry winter, the water column in the Gulf of Trieste, even under moderate wind forcing, was not completely mixed due to the influence of bottom waters intruding from the open sea. Inside the gulf, two types of water intrusions were found during yoyo casts: one coming from the northern coast of the Adriatic Sea (i.e. cooler, fresher and more turbid) and one coming from the open sea in front of the Po Delta (i.e. warmer, saltier and less turbid). The two intrusions had different impacts on turbulence kinetic energy dissipation rate profiles. The former, with high turbidity, acted as a barrier to wind-driven turbulence, while the latter, with low sediment concentrations and a smaller vertical density gradient, was not able to suppress downward penetration of turbulence from the surface.

There is general consensus that accurate model predictions of extreme wave events during marine storms can substantially contribute to avoiding or minimizing human losses and material damage. Reliable wave forecasts and hindcasts, together with statistical analysis of extreme conditions, are then of utmost importance for monitoring marine areas. In this study, we perform an analysis of the limitations of the available short-term/range extreme-value distributions suitable for space-time maximum wave and crest heights. In particular, we propose an improvement of the theoretical distributions by including upper bounds on the maximum heights that waves may reach. The modification of the space-time probability distributions and its impact for extreme-value assessment is discussed in the paper. We show that unbounded space-time distributions are still effective provided that the surface area included in the analysis has sides smaller than O(102 m). For wider surfaces, the use of the bounded distributions is consistent with the expected saturation of maximum heights that ocean waves attain.

Measuring systems are becoming increasingly sophisticated in order to tackle the challenges of modern industrial problems. In particular, the Multiphase Flow Meter (MPFM) combines different sensors and data fusion techniques to estimate quantities that are difficult to be measured like the water or gas content of a multiphase flow, coming from an oil well. The evaluation of the flow composition is essential for the well productivity prediction and management, and for this reason, the quantification of the meter measurement quality is crucial. While instrument complexity is increasing, demands for confidence levels in the provided measures are becoming increasingly more common. In this work, we propose an Anomaly Detection approach, based on unsupervised Machine Learning algorithms, that enables the metrology system to detect outliers and to provide a statistical level of confidence in the measures. The proposed approach, called AD4MPFM (Anomaly Detection for Multiphase Flow Meters), is designed for embedded implementation and for multivariate time-series data streams. The approach is validated both on real and synthetic data.

We perform a critical analysis of the present approach in wave modeling and of the related results. While acknowledging the good quality of the best present forecasts, we point out the limitations that appear when we focus on the corresponding spectra. Apart from the meteorological input, these are traced back to the spectral approach at the base of the present operational models, and the consequent approximations involved in properly modeling the various physical processes at work. Future alternatives are discussed. We then focus our attention on how, given the situation, to deal today with the estimate of the maximum wave heights, both in the long term and for a specific situation. For this, and within the above limits, a more precise evaluation of the wave spectrum is shown to be a mandatory condition.

Abstract Background Chest tube positioning is an invasive procedure associated with potentially serious injuries. In the last few years, we have been running a project directed at developing a practical simulator of a surgical procedure taught on our medical training program. The phantom model reconstructs the pleural anatomy, visible by lung ultrasound, used for the assessed performance of the Seldinger technique. The aim of the present study was to investigate the validity of this simulation technology for assessing residents in anesthesia and intensive care medicine; specifically, their skill in positioning a US-guided chest tube drain was tested using the simulator device. The second aim of the paper was to evaluate the learning curve of our residents over their 5-year study course and validate the phantom scoring system. Methods This was a prospective, single-blinded observational study. Participants were recruited from residents in anesthesia and intensive care medicine and divided into two groups: ‘Novice’ and ‘Expert,’ based on the course year attended (years 1, 2, and 3 vs. years 4 and 5, respectively). We asked them to position a chest tube drain in a phantom model, guided by ultrasound, to drain a simulated pleural effusion. Each subject performed two tests that simulated pleural effusions of 4 and 2 cm, respectively. Every step of the maneuver was constantly monitored and the performance scored by the investigators. We then performed a Spearman correlation analysis to evaluate the effect of experience level on the performance of the two groups of residents. Results Thirty-one residents were included in this study: 20 in the Novice group and 11 in the Expert group. The mean performance rating score was 0.75 ± 4.38 for the Novice Group and 5.91 ± 3.75 for the Expert group (p = 0.0026). The Spearman correlation analysis examining the relationship between year of residency and performance rating score confirmed a positive correlation (r = 0.58, p = 0.0006). Post-test trend analysis revealed a statistically significant linear trend for skill growth across time, i.e., course year (p = 0.0022). Conclusions Our simulated procedure using a phantom model of lung anatomy can accurately and reliably be used to assess the skill levels of operators in their ability to drain pleural effusion.

In November 2011, an Atlantic depression affected the Mediterranean basin, eventually evolving into a Tropical-Like Cyclone (TLC or Mediterranean Hurricane, usually designated as Medicane). In the region affected by the Medicane, mean sea level pressures down to 990 hPa, wind speeds of hurricane intensity close to the eye (around 115 km/h) and intense rainfall in the prefrontal zone were reported. The intensity of this event, together with its long permanence over the sea, suggested its suitability as a paradigmatic case for investigating the sensitivity of a numerical modeling system to different configurations, air-sea interface parameterizations and coupling approaches. Toward this aim, a set of numerical experiments with different parameterization schemes and levels of coupling complexity was carried out within the Coupled Ocean Atmosphere Wave Sediment Transport System (COAWST), which allows the description of air-sea dynamics by coupling an atmospheric model (WRF), an ocean circulation model (ROMS), and a wave model (SWAN). The sensitivity to different initialization times and Planetary Boundary Layer (PBL) parameterizations was firstly investigated by running a set of WRF standalone (atmospheric-only) simulations. In order to better understand the effect of coupling on the TLC formation, intensification and trajectory, different configurations of atmosphere-ocean coupling were subsequently tested, eventually including the full coupling among atmosphere, ocean and waves, also changing the PBL parameterization and the formulation of the surface roughness. Results show a strong sensitivity of both the trajectory and the intensity of this TLC to the initial conditions, while the tracks and intensities provided by the coupled modeling approaches explored in this study do not introduce drastic modifications with respect to those resulting from a fine-tuned standalone atmospheric run, though they provide by definition a better physical and energetic consistency. Nevertheless; the use of different schemes for the calculation of the surface roughness from wave motion, which reflects the description of air-sea interface processes, can significantly affect the results in the fully coupled runs.