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(a) Drifter trajectories in the Adriatic Sea for the period August 1990–March 2007. (b) The mean flow obtained by averaging the drifter data in circular bins of 10 km radius. Selected geographical names and two sections in the vicinity of the Otranto Channel are shown, as well as the three squared areas considered for the transit time pdfs shown in Figs. 2 and 4.
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Statistics of transit and residence times in the Adriatic Sea surface, a semi-enclosed basin of the Mediterranean, are estimated from drifter data and Lagrangian numerical simulations. The results obtained from the drifters are generally underestimated given their short operating lifetimes (half life of ∼40 days) compared to the transit and residen...
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... from the global scale ( Niiler et al., 2003) to marginal seas ( Falco et al., 2000;Lacorata et al., 2001) and selected coastal areas ( Haza et al., 2010;Veneziani et al., 2007, Choukroun et al., 2010. In particu- lar, the Adriatic Sea, a semi-enclosed basin of the Mediter- ranean, connected to the Ionian Sea through the Otranto Channel ( Fig. 1), has been extensively studied over the last decades with surface drifters. The mean surface circulation, its seasonal and mesoscale variability, and the role of the wind-forcing have been investigated by Poulain (2001) and Ursella et al. (2006). Adriatic surface transport properties have been studied by Falco et al. (2000). Relative ...
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... mean surface flow in the Adriatic Sea was computed by averaging all the drifter velocities in circular bins of 10 km radius organized on a uniform grid with 10 km cell size (Fig. 1). Only bins with at least 5 observations were con- sidered for the pseudo-Eulerian statistics. Similar drifter ve- locity averages were computed by Poulain (1999Poulain ( , 2001) and Ursella et al. (2006). A Lagrangian statistical model, whose parameters are de- rived from the data, was used to generate numerical parti- cles. It is ...
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... sections were chosen in the Otranto Channel (Fig. 1b). The western one corresponds to the outflow and connects the Italian coast at point (341.1 km, −70.8 km) to point (365.9 km, −43.2 km) where the speed of the mean flow derived from the drifters is equal to zero. The sec- ond section on the eastern side connects the Albanian coast (371.3 km, 13.82 km) point to point (350 km, −30 km) ...
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... mean surface circulation in the Adriatic Sea estimated from the real drifters ( Fig. 1) shows the basin-scale cyclonic circulation with Ionian waters entering on the eastern flank of the Otranto Channel and flowing northwestward off Al- bania and Croatia as the Eastern Adriatic Current (EAC). The basin-scale circulation is actually composed of 3 cy- clonic cells with waters from the EAC splitting and cross- ing the basin ...
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... Along the Italian coast, a strong coastal cur- rent, the Western Adriatic Current (WAC), flows towards the southeast and the surface waters eventually exit on the west- ern side of the Otranto Channel. Example pdfs of the transit times of numerical particles between areas selected in the northern, central and south- ern Adriatic (see squares in Fig. 1b) and the western Otranto Channel are shown in Fig. 2. In total, more than 1800 tracks connect the selected areas to the Otranto section, including all the sub-tracks of the same numerical particle as long as the 6 h observations are localized in the departure squared area. The distributions have mean values of about 210, 168 and 121 ...
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... us now consider the transit times of surface drifters and numerical particles after their entrance in the eastern Otranto Channel. The transit time pdfs to reach the selected squared areas depicted in Fig. 1b are shown in Fig. 4. Compared to the distributions considered before (Fig. 2) the number of tracks is considerably reduced: only 175 (62) numerical (real) tracks reach the square in the southern Adriatic. Mean transit times for numerical particles range in 129-225 days, compared to 27-62 for real ...
Citations
... One of the key advantages of Lagrangian analysis lies in its ability to capture the inherent variability and complexity of ocean currents. This approach takes into account the individual paths of water parcels, thus considering the effects of mesoscale eddies, coastal jets, upwelling, and other localized flow phenomena (Poulain and Niiler, 1989;Swenson and Niiler, 1996;Blanke and Raynaud, 1997;Dever et al., 1998;LaCasce, 2008;Alberto et al., 2011;Watson et al., 2011;Mora et al., 2012;Van Sebille et al., 2012;Poulain and Hariri, 2013;Hariri et al., 2015;Hariri, 2020;Hariri, 2022;Van Sebille et al., 2018). These fine-scale processes exert a significant influence on connectivity patterns, thereby shaping the distribution of marine organisms, the dispersal of larvae, and the transport of contaminants (Dong and McWilliams, 2007;Dong et al., 2009;Mitarai et al., 2009). ...
... It has provided a more nuanced understanding of the mechanisms driving population dynamics, species distributions, and the spread of contaminants in the marine environment. The integration of Lagrangian analysis with remote sensing data and numerical models has further enhanced our ability to quantify and predict connectivity patterns in the ocean (Poulain and Niiler, 1989;Swenson and Niiler, 1996;Blanke and Raynaud, 1997;Dever et al., 1998;LaCasce, 2008;Mora et al., 2012;Poulain and Hariri, 2013;Hariri et al., 2015;Van Sebille et al., 2018). Overall, Lagrangian methods have facilitated the study of connectivity in oceanography, enabling us to unravel the intricate interplay between physical processes and ecological dynamics (Drouet et al., 2021;Ser-Giacomi et al., 2021;Wang et al., 2019). ...
This study explores the impact of sub-mesoscale structures and vertical advection on the connectivity properties of the Baltic Sea using a Lagrangian approach. High-resolution flow fields from the General Estuarine Transport Model (GETM) were employed to compute Lagrangian trajectories, focusing on the influence of fine-scale structures on connectivity estimates. Six river mouths in the Baltic Sea served as initial positions for numerical particles, and trajectories were generated using flow fields with varying horizontal resolutions: 3D trajectories with 250m resolution as well as 2D trajectories with 250m and 1km resolutions. Several Lagrangian indices, such as mean transit time, arrival depths, and probability density functions of transit times, were analyzed to unravel the complex circulation of the Baltic Sea and highlight the substantial impact of sub-mesoscale structures on numerical trajectories. Results indicate that in 2D simulations, particles exhibit faster movement on the eastern side of the Gotland Basin in high-resolution compared to coarse-resolution simulations. This difference is attributed to the stronger coastal current in high-resolution compared to coarse-resolution simulations. Additionally, the study investigates the influence of vertical advection on numerical particle motion within the Baltic Sea, considering the difference between 3D and 2D trajectories. Findings reveal that denser water in the eastern and south-eastern areas significantly affects particle dispersion in 3D simulations, resulting in increased transit times. Conversely, regions in the North-western part of the basin accelerate particle movement in 3D compared to the 2D simulations. Finally, we calculated the average residence time of numerical particles exiting the Baltic Sea through the Danish strait. Results show an average surface layer residence time of approximately 790 days over an eight-year integration period, highlighting the relatively slow water circulation in the semi-enclosed Baltic Sea basin. This prolonged residence time emphasizes the potential for the accumulation of pollutants. Overall, the study underscores the pivotal role of fine-scale structures in shaping the connectivity of the Baltic Sea, with implications for understanding and managing environmental challenges in this unique marine ecosystem.
... Since its value is fairly small, direct observations of U are subject to large RMS errors so the error in such direct observations exceeds the mean value (see e.g., Notarstefano et al., 2008). However, estimates of the residence time of drifters in the Sea yield an average value of under 200 days (Poulain & Hariri, 2013). In the 800 km long Sea this residence time implies a mean speed of about 0.04 ms −1 . ...
Plain Language Summary
Differences in ocean water salinity were used for over a century to quantify the horizontal fluxes in and out of evaporative, motionless, basins such as the Mediterranean Sea. In the present study we develop simple expressions based on analytic models that extend the century‐old approach to ocean currents where the water is constantly moving rather than remaining stagnant. The models developed here are combined with long‐term data of sea surface salinity along two currents—the salty Irminger Current that flows around the southern tip of Greenland and the flow of fresh snow‐melt water from the Po river into the Adriatic Sea. The models and climatological data used here yield quantitative estimates of two basic parameters: (a) the rate at which a high‐salinity current detrains salt to the surrounding ocean. (b) The balance between the slow downstream propagation and eddy (turbulent) exchange coefficient. The models developed in this study can be applied to other currents and regions of the world ocean.
... To address the problem of the lack of data in some areas, some authors (Serra et al., 2010;Andrello et al., 2013;Rossi et al., 2014;Berline et al., 2014;Dubois et al., 2016) used Lagrangian models to study transport and dispersion of marine organisms and/or larvae, between MPAs in the entire basin. For instance, in the Adriatic Sea, the connectivity was studied using drifter data together with virtual trajectories simulated by Lagrangian models (Poulain and Hariri, 2013;Carlson et al., 2016). Zambianchi et al. (2017), used a Lagrangian model, built on real drifter data, to estimate the probability of debris particles to reach different subareas of the Mediterranean basin. ...
... 743by combining drifter measurements and numerical models to obtain estimates 744 of the surface water transit time.Poulain and Hariri (2013) integrated drifter 745 data with a statistical advection-dispersion model of the Adriatic surface cir-746 culation obtaining particles transit time through the Otranto Channel rang-747 ing between 260 days for the Northern sub-basin to 185 days for the Southern 748 one. In a recent study, Hariri (2020) followed a similar approach using a par-749 ticle tracking model (PTM) integrating the surface mean flow field obtained 750 by the MITgcm ocean model implemented for the Adriatic Sea. ...
Understanding the water circulation in oceans and coastal seas is among the key topics of oceanographic and climate research. Hydrodynamic studies form the basis for many oceanographic subjects, whether sediment transport, morphology, water quality, ecological and climate changes are being investigated. Hydrodynamic modelling of oceans and coastal seas has become a fundamental tool for describing the dynamics of marine environments, revealing the human impact on the sea and promoting sustainable development of marine resources. By complementing - through data assimilation - more and more diffuse and integrated global and regional observing systems (composed of coastal gauges, moorings, buoys, satellites, drifters), hydrodynamic models provide a deterministic 4D view of the ocean state. In this context, the semi-enclosed Adriatic Sea represents a natural long-standing laboratory for hydrodynamic modelling. The peculiar historical, morphological and oceanographic characteristics of this basin and its complex coastline stimulated over decades the development and application of several ocean and coastal models. In this work, we review different aspects of hydrodynamic modelling covered by the literature, highlighting the wide variety of model applications carried out in the Adriatic Sea which could serve as examples for semi-enclosed, marginal and coastal seas worldwide. Within the scope of the review, we find that although significant progress has been made over the last few decades, most of the modelling studies underrate the importance of a detailed representation of the land-coastal-sea fluxes. We list a set of recommendations that can be used as guidelines for model implementation to broaden the applicability of hydrodynamic models in future studies. Finally, we discuss the remaining questions that still need to be further explored.
... To test our framework in a realistic geophysical setting, we exploit state-of-the-art gridded velocity fields of the ocean as modeled by a high-resolution hydrodynamic model and as measured by satellite altimetry (see Methods IV D). By doing so, we illustrate numerically our approach for velocity fields widely exploited in oceanography while considering different effective resolutions: altimetry originates from remote-sensing observations but resolves only the upper mesoscale [36]; the high-resolution model is eddy-resolving, spanning the full mesoscale and possibly the upper submesoscale [37]. Note that while we focus on a few examples of specific regions in the following, our analyses and conclusions would also hold to other similar structures that are found elsewhere in the surface global ocean. ...
The study of connectivity patterns in networks has brought novel insights across diverse fields ranging from neurosciences to epidemic spreading or climate. In this context, betweenness centrality has demonstrated to be a very effective measure to identify nodes that act as focus of congestion, or bottlenecks, in the network. However, there is not a way to define betweenness outside the network framework. By analytically linking dynamical systems and network theory, we provide a trajectory-based formulation of betweenness, called Lagrangian betweenness, as a function of Lyapunov exponents. This extends the concept of betweenness beyond the context of network theory relating hyperbolic points and heteroclinic connections in any dynamical system to the structural bottlenecks of the network associated with it. Using modeled and observational velocity fields, we show that such bottlenecks are present and surprisingly persistent in the oceanic circulation across different spatio-temporal scales and we illustrate the role of these areas in driving fluid transport over vast oceanic regions. Analyzing plankton abundance data from the Kuroshio region of the Pacific Ocean, we find significant spatial correlations between measures of diversity and betweenness, suggesting promise for ecological applications. The motion of the ocean transports microorganisms, pollutants, and other particles, but these are challenging to track. Here the authors present a Lagrangian form of Betweenness Centrality which identifies bottlenecks in dynamical systems and fluid flows as well as an interpretation of diversity hotspots.
... Consequently, concentrations of nutrients in the NAS are generally higher than in other parts of the Mediterranean (Krom et al., 2014). Given that the mean residence time of water in the Adriatic Sea as a whole is estimated to be on the order of 5-6 months (Poulain & Hariri, 2013), the residence time of riverine inputs of nutrients is likely to be less than 5 months. ...
The Northern Adriatic Sea (NAS) and Chesapeake Bay (CB) are examples of semienclosed coastal ecosystems in which primary production is dominated by phytoplankton. We update an earlier comparative analysis of phytoplankton dynamics in these two systems by comparing spatial and temporal dynamics of nutrients, chlorophyll‐a biomass, community composition, and primary production. New nutrient concentrations (e.g., nitrate and nitrite) decrease with distance from riverine sources and with increasing salinity. Phosphorus is generally considered to be the principal limiting nutrient of phytoplankton growth rate in the NAS, while CB generally varies between P‐limited growth in spring and N‐limited growth in summer. Both systems exhibit clear annual cycles of phytoplankton biomass and productivity, with substantial interannual variability driven by variations in riverine inputs of nutrient‐rich water. Both are characterized by a temporal separation between seasonal peaks in phytoplankton biomass (spring in both systems, fall in the NAS) and productivity (summer in both systems). Despite comparable nitrogen loads, phytoplankton biomass and production are much higher in CB than in the NAS, and CB is classified as eutrophic to hypertrophic over most of its extent, while the NAS is classified as oligotrophic to eutrophic depending on distance from riverine inputs of nutrients. This contrast is due primarily to the large volume of the NAS relative to the riverine input of nutrient‐rich water compared with CB, and to inputs of oligotrophic water to the NAS from the southern Adriatic. Both systems have experienced reductions in nutrient loading in recent years, with associated reductions in chlorophyll‐a and productivity in the NAS, but no clear changes in CB. Future comparisons of phytoplankton dynamics in these systems will need to consider ongoing efforts to reduce anthropogenic nutrient inputs concurrent with climate‐driven changes in water temperature and hydrological. Such analyses will be dependent on sustained long‐term monitoring of phytoplankton productivity and environmental parameters that impact plankton dynamics.
... In the Mediterranean area, some authors (Serra et al., 2010;Andrello et al., 2013;Rossi et al., 2014;Berline et al., 2014;Dubois et al., 2016) used Lagrangian models to study transport and dispersion of marine organisms and/or larvae between MPAs of in the entire basin. Some authors (Poulain and Hariri, 2013;Carlson et al., 2016) studied connectivity by means of transit and residence time analyses (Buffoni et al., 1997) inside the Adriatic basin, using drifter data along with virtual trajectories simulated by Lagrangian models. Zambianchi et al. (2017), used a Lagrangian model built on real drifter data to estimate the probability of debris particles reaching different subareas of the Mediterranean basin. ...
... The Green function describes the probability that a particle, starting from its initial position r, at time t, reaches a position r' at time t' taking a time t' -t. The first moment of this pdf is the mean transit time or mean age and is a common statistical proxy used in the Lagrangian framework (e.g., Poulain and Hariri, 2013) to study connectivity. In this work, considering that the available data are real surface drifter trajectories, transport takes place in a two-dimensional domain. ...
The surface connection between the Ionian Sea (central Mediterranean Sea) and the surrounding areas is studied by looking at the statistical properties of 1632 near-surface Lagrangian trajectories. The choice of the area is due to the key role in the dynamics of the Mediterranean Sea and to the geographical distribution of data. The Lagrangian drifter data were taken from the OGS Mediterranean drifter database, which gathers drifter data collected in the Mediterranean Sea from various institutions and countries between 1986 and 2016. The database has proved to be sufficiently complete, but the spatial and temporal data coverage are less satisfactory in the case of selection of a specific study area and temporal range. The strategy used in this work aims to limiting the problem of data coverage by choosing many target boxes around the study area, choice based on drifter trajectories, current patterns and areas of interest. The pseudo-eulerian analysis obtained from all trajectories passing through the Ionian Sea show the main dynamic structure present in the central Mediterranean Sea (e.g. Atlantic Ionian Stream, Mid Ionian Jet, etc.).On the other hand, the data density decreases progressively toward the eastern and western sectors of the Mediterranean. The highest connection was observed with the Strait of Sicily, Eastern Ionian and Adriatic Sea, with connection percentages 30%, 25% and 16% respectively. The transit times between the Ionian Sea and these target boxes are about 20–50 days. The Ionian Sea is characterized by phenomena of inversion of the basin surface circulation, from cyclonic to anti-cyclonic, over a ten-year timescale, the so-called Adriatic-Ionian Bimodal Oscillating System (BiOS, e.g. Gačić et al., 2010; Gačić et al., 2011). An application of the “target box methodology” has been used to describe how the cyclic variability of the dynamic surface currents generates an equally cyclical fluctuation of the connectivity between the Ionian Sea and the surrounding areas during the BiOS phases.
... In another study by [2], the statistics of transit and residence times in the Adriatic Sea surface were estimated from drifter data and Lagrangian numerical simulations. They found that the results obtained for the drifters were generally underestimated given their short operating lifetimes (half-life of 40 days) compared to the transit and residence times, so they implemented numerical particles whose trajectories were integrated over a longer time period (750 days) with a statistical advection-dispersion model of the Adriatic surface circulation. ...
... The residence time is defined as the average time spent by a tracer particle in the basin. The normalized population in the basin, C(t), and its residence time, T, in the Lagrangian framework are [2]: ...
... In general, the mean flow maps ( Figure 1) generated by synthetic trajectories in 2007 and 2008 confirm most of the results obtained previously from drifter observations [2] and hydrographic data. ...
This paper describes the near-surface transport properties and Lagrangian statistics in the Adriatic semi-enclosed basin using synthetic drifters. Lagrangian transport models were used to simulate synthetic trajectories from the mean flow fields obtained by the Massachusetts Institute of Technology general circulation model (MITgcm), implemented in the Adriatic from October 2006 until December 2008. In particular, the surface circulation properties in two contrasting years (2007 had a mild winter and cold fall, while 2008 had a normal winter and hot summer) are compared here. In addition, the Lagrangian statistics for the entire Adriatic Basin after removing the Eulerian mean circulation for numerical particles were calculated. The results indicate that the numerical particles were slower in this simulation when compared with the real drifters. This is because of the reduced energetic flow field generated by the MIT general circulation model during the selected years. The numerical results showed that the balanced effects of the wind-driven recirculation in the northernmost area(which would be a sea response to the Bora wind field) and the Po River discharge cause the residence times to be similar during the two selected years (182 and 185 days in 2007 and 2008, respectively). Furthermore, the mean angular momentum, diffusivity, and Lagrangian velocity covariance values are smaller than in the real drifter observations, while the maximum Lagrangian integral time scale is the same.
... As the water mass characteristics in the Otranto Strait (between Adriatic and Ionian Seas) are predominantly determined by the BiOS, the tracing of a substantial salinity increase or decrease in the Adriatic may reflect the time needed to traverse the whole basin. Indeed, there were several estimates that favored the observed correlations: (i) Mosetti (1983) computed water transport through the Otranto Strait and estimated the Adriatic turnover time at 3.7 years; (ii) the Adriatic decay time was estimated at approximately 2.6 years in the 1974-1976period (Vilibi� c and Orli� c, 2002, taking into account three cruises with the dissolved oxygen sampled throughout the basin (essential for proper estimates of the water mass fraction) and applying a simple box model; (iii) a shorter residence time of 150-168 days has been estimated from surface drifter data (Poulain and Hariri, 2013), but the deep branch of the Adriatic thermohaline circulation, one of whose sources is in the northern Adriatic, was not considered, while (iv) Frani� c (2005) and Frani� c and Petrinec (2006) estimated the Adriatic turnover time at approximately 3.4 years using 90Sr radionuclide concentrations that have been carried out in the coastal Adriatic since 1963. These estimates qualitatively matched the correlation between the annual NAd salinity and the BiOS index, revealing the BiOS as the dominant driver of the salinity changes in the NAd, which had not been previously considered in the literature. ...
In situ thermohaline variables measured monthly or bimonthly in the shallow northern Adriatic at prescribed stations between 1979 and 2017 were correlated with the local (river runoff, precipitation, net heat flux) and remote atmospheric (various hemispheric and regional indices) and oceanic (the Adriatic-Ionian Bimodal Oscillating System, BiOS) drivers acting on monthly to annual timescales. The highest correlation between the temperature and the local drivers was obtained in winter along the western part of the study area, while the rivers and precipitation mostly shaped the summer and December salinity values. The net heat fluxes and river discharges preceding the dense water formation (DWF) by 1–2 months and up to 3 months were found to be important for the February temperature and salinity variability, respectively. No significant correlations were found between the February thermohaline variables and the monthly BiOS index for the phase lags up to −11 months, while the correlation was the largest between the yearly averaged BiOS index and the yearly averaged salinity at phase lags of −2 to −4 years. The bottom yearly averaged salinity has also been highly correlated with the Mediterranean Oscillation index at phase lags of −1 and −3 years, while the yearly averaged surface temperature was influenced by the East Atlantic and East Atlantic/West Russia patterns. Acting independently on different timescales, both the local and remote processes were found to drive the thermohaline variability in the northern Adriatic, thus providing an option for a forecast of the DWF and the thermohaline circulation in the Adriatic-Ionian basin.
... Residence time is a useful indicator in Lagrangian particle-tracking simulations for evaluating the time that pollutant particles need to exit from an enclosed region or a basin (Liu et al., 2011;Patgaonkar et al., 2012;Poulain and Hariri, 2013;Li and Yao, 2015;Carlson et al., 2017). Such information is important for assessing the ecosystem health and its sensitivity to pollution threats. ...
... Prevenios et al. (2017) monitored beach stranded litter in four selected beaches of Corfu Island and reported that the residence time, before litter being washed offshore from the beaches, ranged from 20 to 50 days. Similarly, in the Adriatic Sea, the half-life time of particles, defined as a measure of dissipativity of the basin, has been found approximately 40 days by both drifting experiments and numerical modeling (Poulain and Hariri, 2013;Liubartseva et al., 2016). The qualitative connectivity graphical network, produced by averaging over years the percentages in connectivity plots of Fig. 10, pictured a clear south-to-north interconnection of litter, being stronger between adjacent subregions (Fig. 11, red lines). ...
A Lagrangian particle tracking model coupled to a circulation was used to explore the transport, residence timeand connectivity offloating litter that originated from theΕastern Ionian Sea during 2011–2014. At the end ofsimulations, on average 26% of litter was retained within the coastal waters of the Eastern Ionian Sea, whereas58% was washed into offshore waters without formulating permanent accumulation areas, as the basin-widesurface circulation was characterized by considerable interannual variability. The inflow of litter into theAdriatic and Eastern Mediterranean Seas was moderate, ranging between 9% and 20%, and the beached litterwas on average 9.2%, mostly located in the northern subregions. The average residence time of litter particlesranged between 20 and 80 days, implying their temporary retention before drifting offshore. Connectivitypatterns depicted an exchange of litter mainly between adjacent subareas and with a northward direction.
