K Béranger
email: karine.beranger@ujf-grenoble.fr

JOUINI ET AL.: THE SICILY CHANNEL SURFACE CIRCULATION

Keywords: Sicily Channel, Circulation Variability, Neural Clustering

The Sicily Channel surface circulation is investigated by analyzing the outputs of a highresolution ocean model MED12 forced during 46 years by the ARPERA atmospheric fields.

Applying a neural network classifier, we show that the surface circulation in the Sicily Channel can be decomposed into 8 modes characterizing the major patterns of that circulation, particularly the Algerian Current separation at the entrance to the Sicily Channel, the features of the Atlantic Tunisian Current and of the Atlantic Ionian Stream. These modes reflect the variability of the circulation in space and time at seasonal and interannual scales. Some modes preferably occur in winter whilst others are characteristic of summer. The mode sequence presents an inter-annual variability in good agreement with observations. The topography of the Sicily Channel sill plays a major role in steering the circulation. In particular the summer upwelling along the southern coast of Sicily, which is present in several modes, could be explained by a large-scale density forcing. A combination of barotropic/baroclinic double Kelvin waves generated on both sides of the sill provides a mechanism for explaining the complexity of the surface circulation advecting the surface waters from the Western Mediterranean towards the Eastern Mediterranean, the most salient features of which are the Atlantic Tunisian Current, the Atlantic Ionian Stream and the Tyrrhenian Sicilian Current which is a new feature highlighted by the present study.

Introduction

The Sicily Channel (SC here-after), which is characterized by a sill of about 400 m depth, controls the water exchanges between the Western Mediterranean and the Eastern Mediterranean, and thus plays a major role in constraining the Mediterranean Sea circulation. The large scale circulation of the Mediterranean Sea is forced by air-sea fluxes of energy and mass, which determine the formation of dense water masses generating a thermohaline circulation [START_REF] Lacombe | Variable bottom water in the western Mediterranean[END_REF][START_REF] Béthoux | chap. Deep-water in the western Mediterranean Sea, yearly climatic signature and enigmatic spreading[END_REF][START_REF] Béthoux | Functioning of the Mediterranean Sea: past and present changes related to freshwater input and climate changes[END_REF]Millot and Taupier-Letage, 2005]. The circulation in the SC can be described as a two-layer exchange of inflowing low-density water in the top layer, and of outflowing higher-density water in the bottom layer. The low-density water coming from the Western Mediterranean occupies the top 150 m surface layer and is mainly composed of Atlantic Water (AW). The higher-density water coming from the Eastern Mediterranean occupies the 150-400 m depth zone and is mainly composed of water circulating at intermediate depths in the Eastern Mediterranean, in particular the Levantine Intermediate Water (LIW). The transport of these two main flows is highly variable at seasonal and inter-annual scales [START_REF] Astraldi | Hydrographic characteristics and interannual variability of water masses in the central Mediterranean: a sensitivity test for long-term changes in the Mediterranean Sea[END_REF][START_REF] Béranger | Seasonal variability of water transports through the Straits of Gibraltar, Sicily and Corsica, derived from a high resolution model of the Mediterranean circulation[END_REF].

The circulation of the surface layer in the SC has been synthesized from the data of many oceanographic campaigns and by modelling studies; it can be schematized as follows (Figure 1, Table 1). The Algerian Current (AC) advects the AW eastwards along the Algerian slope and splits into two branches at the entrance to the SC, owing to a topographic effect caused by the Sicily sill [START_REF] Herbaut | Separation of a coastal current at a strait level : case of the strait of Sicily[END_REF]. A first branch of the AW stream penetrates into the Tyrrhenian Sea forming the Bifurcation Tyrrhenian Current D R A F T April 15, 2016, 7:06pm D R A F T X -7

(BTC, as denoted by [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF]), which flows along the northern coast of Sicily, while the second branch crosses the SC and enters the Eastern Mediterranean running along the coast of Tunisia [START_REF] Manzella | Atlantic water in the Strait of Sicily[END_REF] forming the Atlantic Tunisian Current (ATC, as denoted by [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF]). In several high-resolution models, the BTC generally flows eastwards along the northern coast of Sicily [START_REF] Onken | Data-driven simulations of synoptic circulation and transports in the Tunisia-Sardinia-Sicily region[END_REF][START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF][START_REF] Napolitano | Modeling the baroclinic circulation in the area of the Sicily channel: The role of stratification and energy diagnostics[END_REF][START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF], while in some others [START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF]Sorgente et al., 2003;[START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF], the BTC presents a secondary bifurcation near 11 • E flowing north-westwards, which forms a cyclonic circulation denoted as the South Eastern Sardinian Gyre (SESG) by [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF]. We also observe an additional AW stream entering the Eastern Mediterranean and flowing along the southern coast of Sicily forming the Atlantic Ionian Stream (AIS, as denoted by [START_REF] Robinson | The Atlantic Ionian Stream[END_REF]).

This description is in agreement with recent observations made with surface drifters released in the SC [START_REF] Poulain | Surface circulation in the central Mediterranean Sea as deduced from Lagrangian drifters in the 1990s[END_REF][START_REF] Gerin | Surface circulation in the Eastern Mediterranean using Lagrangian drifters[END_REF], infra-red remotesensing studies [START_REF] Hamad | The surface circulation in the eastern basin of the Mediterranean Sea[END_REF][START_REF] Taupier-Letage | On the Use of Thermal Images for Circulation Studies: Applications to the Eastern Mediterranean Basin, Remote Sensing of the European Seas[END_REF] and very high-horizontalresolution modelling studies at (1/32 • ) [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF][START_REF] Olita | Impact of SLA assimilation in the Sicily Channel Regional Model: model skills and mesoscale features[END_REF] resolving the first radius of deformation of the Mediterranean Sea. It is important to emphasize that very few drifters have been launched at the western entrance to the channel (south of the Tyrrhenian Sea), most of the floats being released in the SC [START_REF] Poulain | Surface circulation in the central Mediterranean Sea as deduced from Lagrangian drifters in the 1990s[END_REF][START_REF] Gerin | Surface circulation in the Eastern Mediterranean using Lagrangian drifters[END_REF]. Consequently, although the circulation at the eastern exit to the SC is well known, it is poorly documented for the western entrance to the SC [START_REF] Ismail | Water masses exchanged through the Sicily Channel: evidence for the presence of new water masses on the Tunisian side of the Channel, Deep-Sea Research I[END_REF].

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Besides, a large part of these studies shows that the circulation in the SC region is subject to seasonal variations, as for example a more pronounced ATC in winter than in summer [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF] or the occurrence of an upwelling on the southern coast of Sicily in summer [START_REF] Robinson | The Atlantic Ionian Stream[END_REF][START_REF] Lermusiaux | Features of dominant mesoscale variability, circulation patterns and dynamics in the Strait of Sicily, Deep-Sea Research I, 48[END_REF][START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF]. For the 2009-2011 period, [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF] using their very high-resolution model, showed several bifurcations of the ATC and of the BTC (Figure 1). The ATC can split near Lampedusa Island and form the Bifurcation Atlantic Tunisian Current (BATC) or the Atlantic Libyan Current (ALC). And in summer, they demonstrated the presence of the Westward Northern Sicilian Current (WNSC) in the Tyrrhenian Sea. But these descriptions are still controversial, owing to the presence of important mesoscale structures like eddies or plumes [START_REF] Sammari | Hydrological characteristics in the Tunisia -Sardinia -Sicily area during spring 1995[END_REF][START_REF] Lermusiaux | Estimation and study of mesoscale variability in the Strait of Sicily[END_REF][START_REF] Ciappa | Surface circulation pattern in the Sicily Channel and Ionian Sea as revealed by MODIS chlorophyll images from 2003 to 2007[END_REF] (see the Figure 1 in [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF], for a review) which may have an impact on a large part of the AW transport [START_REF] Onken | Data-driven simulations of synoptic circulation and transports in the Tunisia-Sardinia-Sicily region[END_REF][START_REF] Napolitano | Modeling the baroclinic circulation in the area of the Sicily channel: The role of stratification and energy diagnostics[END_REF][START_REF] Fernandez | Mesoscale, seasonal and interannual variability in the Mediterranean Sea using a numerical ocean model[END_REF][START_REF] Ismail | Water masses exchanged through the Sicily Channel: evidence for the presence of new water masses on the Tunisian side of the Channel, Deep-Sea Research I[END_REF]. [START_REF] Molcard | Numerical investigation of the Sicily Channel dynamics: density currents and water mass advection[END_REF] argued that the mean paths of AW are not sensitive to the magnitude of the density gradient between the Western Mediterranean and the Eastern Mediterranean, but a modulation of the paths of the different bifurcation branches was found in the sensitivity experiments of [START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF] and in the seasonal analysis of Vetrano et al. [2004] based on an inverse model applied to in situ observations. Moreover, [START_REF] Poulain | Surface circulation in the central Mediterranean Sea as deduced from Lagrangian drifters in the 1990s[END_REF] utilized the wind forcing to explain the seasonal variations of the circulation, while [START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF] invoked non-linear effects for determining the surface bifurcation process.

Recent observations show that these circulation outlines can be modulated at decadal scales by well marked events such as the Eastern Mediterranean Transient in the 1990's [START_REF] Roether | Transient Eastern Mediterranean deep waters in response to the massive dense-water output of the Aegean Sea in the 1990s[END_REF], the recent deep water renewal called the Western Mediterranean Transition after 2005 [START_REF] Schroeder | An extensive western Mediterranean deep water renewal between 2004 and 2006[END_REF], and the Adriatic-Ionian Bimodal Oscillating System (BiOS) evidenced by [START_REF] Gacic | Can internal processes sustain reversals of the ocean upper circulation? The Ionian Sea example[END_REF].

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The aim of the present paper is to provide a revised description and interpretation of the AW circulation in the Sicily Channel and to identify the different AW paths and mesoscale patterns by analyzing a 46-year high-resolution simulation of this region forced by realistic atmospheric fluxes and winds. Some major questions arise about the functioning of the Sicily Channel. Is the current regime stable? Can we define specific regimes related to the seasonal or inter-annual variations of the current patterns? What are the possible mechanisms responsible for such regimes? In order to answer these questions, we decided to use a high-performance clustering method to extract, in the statistical sense, the most pertinent channel circulation situations from the multi-decadal numerical simulation of the Mediterranean Sea [Beuvier et al., 2012a]. As done in previous works, with success [START_REF] Liu | Current Patterns on the West Florida Shelf from Joint Self-Organizing Map Analyses of HF Radar and ADCP Data[END_REF][START_REF] Mihanovic | Surface current patterns in the northern Adriatic extracted from high-frequency radar data using self-organizing map analysis[END_REF], we computed the different modes of the circulation using a neural network clustering method, the so-called Self Organized Map (SOM) [START_REF] Kohonen | How to make large self-organizing maps for nonvectorial data[END_REF].

The paper is articulated as follows: the numerical model experiment and the clustering methodology for analyzing the model outputs are detailed in Section 2 and Section 3, respectively. The main characteristics of the different circulation regimes are presented in Section 4. The reality of a new branch of the sea-surface circulation is discussed in Section 5 and an explanation of the origin of its generation is investigated using conceptual models.

Summary and conclusions are presented in Section 6. Acronyms used in the following can be found in Tables 1 and2.

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The MED12 simulation

We use the ocean free-surface general circulation model NEMO [Madec and The-NEMO-Team, 2008] in a regional configuration of the Mediterranean Sea called MED12 [Lebeaupin Brossier et al., 2011;Beuvier et al., 2012b] developed in the framework of the national MORCE and SiMED projects. For this study, we use 46 years of the long-term simulation of Beuvier et al. [2012a] forced during the 1958-2013 period by the atmospheric fields of ARPERA [START_REF] Herrmann | Relevance of ERA40 dynamical downscaling for modeling deep convection in the Mediterranean Sea[END_REF].

The MED12 configuration (see Beuvier et al. [2012b] for details) covers the whole Mediterranean Sea plus a buffer zone including a part of the near Atlantic Ocean. The horizontal resolution is about 7 km and we use 50 stretched z-levels on the vertical. A Laplacian operator is applied along iso-neutral surfaces for tracers with a horizontal eddy diffusivity coefficient of 60 m 2 .s -1 while a biharmonic operator is applied for the momentum with a horizontal viscosity coefficient of -1.25 10 10 m 4 .s -2 . The TVD (Total Variance Dissipation) scheme is used for the tracer advection and the EEN (Energy and ENstrophy conservative) scheme is used for the momentum advection [START_REF] Barnier | Impact of partial steps and momentum advection schemes in a global ocean circulation model at eddy-permitting resolution[END_REF]. The vertical diffusivity for tracers and momentum is modeled with the standard turbulent kinetic energy closure scheme of NEMO [START_REF] Blanke | Variability of the Tropical Atlantic Ocean Simulated by a General Circulation Model with Two Different Mixed-Layer Physics[END_REF] with a vertical diffusivity coefficient increased to 10 m 2 .s -1 in case of vertical static instabilities. A time-step of 12 minutes is used. No-slip conditions are applied at the lateral boundaries, while at the sea bed, a bottom friction is applied taking into account a 2D field of the mean tidal energy computed from the tidal model of [START_REF] Lyard | Modelling the global ocean tides: modern insights from FES2004[END_REF]. In order to represent the inter-annual variability of the exchanges through the Strait of Gibraltar, the Atlantic Ocean is modelled as a buffer zone where the 3D temperature and salinity of MED12 are relaxed towards the climatological fields of Levitus et al. [2005] on which the monthly anomalies of the ENSEMBLES global oceanic reanalysis [START_REF] Daget | Ensemble estimation of background-error variances in a three-dimensional variational data assimilation system for the global ocean[END_REF] are superimposed. And in this buffer zone, the modelled sea-surface height is relaxed to a sea-surface height climatology built from the global ocean reanalyses of [START_REF] Ferry | Mercator Global Eddy Permitting Ocean Reanalysis GLORYS1V1: Description and Results[END_REF].

The river runoff is provided by the monthly climatology of [START_REF] Ludwig | River discharges of water and nutrients to the Mediterranean and Black Sea: Major drivers for ecosystem changes during past and future decades ?[END_REF] and is distributed in the main Mediterranean river mouths and as coastal runoff. The Black Sea freshwater input is applied as a river, using the estimates of [START_REF] Stanev | Regional sea level response to global climatic change: Black Sea examples[END_REF].

The MED12 model is forced by the daily fields of the momentum, freshwater and heat fluxes of the atmospheric forcing ARPERA, which is a dynamical downscaling of ECMWF products above the European-Mediterranean domain performed with the ARPEGE-Climat model [START_REF] Herrmann | Relevance of ERA40 dynamical downscaling for modeling deep convection in the Mediterranean Sea[END_REF]. The simulated period extends from 1 October 1958 to 31 December 2013. For the period 1958-2001, the driven fields of ARPERA come from the coarse resolution ERA40 reanalysis [START_REF] Simmons | Seasonal variability in the Central Mediterranean Sea circulation[END_REF],

whereas from 2002 to 2013 the driven fields come from the higher resolution ECMWF analyses [START_REF] Richardson | Changes to the operational forecasting system[END_REF] after being downgraded to the ERA40 resolution to ensure consistency between the two successive periods. This method has already been evaluated

in [START_REF] Beuvier | Modelling the Mediterranean Sea interannual variability over the last 40 years: focus on the EMT[END_REF]. In the formulation of the surface temperature condition, a relax-

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ation of the model sea-surface temperature (SST here-after) is applied to the ERA40 SST with a constant coefficient of -40 W.m -2 .K -1 . In the formulation of the surface salinity distribution, no salinity damping is applied [START_REF] Beuvier | Modelling the Mediterranean Sea interannual variability over the last 40 years: focus on the EMT[END_REF].

The first 7 years of the simulation (1958)(1959)(1960)(1961)(1962)(1963)(1964)(1965) were considered as a spi-nup period and were not used in the clustering method. In the following, the 46-year study period extends from 1966 to 2011.

The clustering method

As the surface circulation in the SC is highly variable, we try to identify the principal modes of the circulation by analyzing the long-term numerical simulation of Beuvier et al.

[2012a] allowing some representativity of the inter-annual variations of the Mediterranean general circulation [Palmieri et al., 2015;[START_REF] Ayache | Modelling of the anthropogenic tritium transient and its decay product helium-3 in the Mediterranean Sea using a highresolution regional model[END_REF]. We apply an efficient clustering method (SOM + HAC here-after) performed in two steps (Figure 2). First, a Self Organising Map algorithm (SOM here-after) is applied to the daily outputs of the simulation and gives different typical situations of the SC surface circulation. Then, a Hierarchical Ascendant Classification algorithm (HAC here-after) is applied to these situations in order to aggregate them into a small number of classes facilitating their interpretation as physical processes. The SOM is well adapted to the problem we solve.

It has a higher discriminant power to separate the different classes than the Empirical Orthogonal Functions (EOF) [START_REF] Lorenz | Empirical Orthogonal Functions and Statistical Weather Prediction[END_REF] or the K-Means algorithm [START_REF] Badran | chap. Self-Organizing Maps and Unsupervised Classification[END_REF] due the topological constrain among the different classes. Besides the SOM is able to deal with non-linear aspects of the phenomenon that we analyze, contrary to EOF, which mainly deals with linear aspects of the phenomena.

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The SOM method

After several trials using different combinations of the daily MED12 outputs, we found that the SC surface circulation can be statistically represented by the two components of the horizontal current vector at 30-m depth taking on a sub-grid of MED12 (Figure 3). The reference vectors W j on the map aim at approximating the density of the data set D. They are computed through an iterative learning process by minimizing a specific non-linear cost function as in the K-Means algorithm [START_REF] Badran | chap. Self-Organizing Maps and Unsupervised Classification[END_REF].

Following our previous study [START_REF] Jouini | Reconstruction of satellite chlorophyll images under heavy cloud coverage using a neural classification method[END_REF], we applied the SOM training process (available on the web site http://www.cis.hut.fi/projects/somtoolbox/download/) in order to summarize the information contained in this data set. We then produced a small number of reference vectors characterizing it. Each reference vector, represents a set of vectors of the simulation that are close to each other, and corresponds to a typical surface circulation regime of the SC.

The number of neurons determines the granularity of the mapping, which in turn is responsible for the accuracy and the SOM map's generalization capabilities. The different neurons of the topological map are connected and thus determine a topological (neighbourhood) relationship between the different neurons. Close neurons on the maps correspond to reference vectors that are quite similar, while very distant neurons correspond to reference vectors that are very different. The topological map (Figure 4a) was learned according to the procedure described in [START_REF] Niang | Automatic Neural Classification of Ocean Colour Reflectance Spectra at the Top of the Atmosphere with Introduction of Expert Knowledge[END_REF] and [START_REF] Niang | Decoding aerosol typology and optical thickness over the Mediterranean from SeaWifs images using neural methodology[END_REF].

The number of neurons was determined empirically from solutions of similar problems and then adjusted, as described in [START_REF] Badran | chap. Self-Organizing Maps and Unsupervised Classification[END_REF]. In the present study, we used a SOM map of m = 6 × 7 = 42 neurons, which corresponds to a quite large number of reference vectors.

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The HAC method

With the SOM algorithm we have thus clustered the 16801 initial circulation situations into m = 42 typical circulation regimes providing a highly discriminating representation of the circulation of the study area, which is difficult to interpret in terms of physical processes. We then applied a HAC method (Figure 2) in order to cluster the 42 circulation regimes into a small number of classes, which synthesizes the geophysical information embedded in the data, such as spatial, seasonal or inter-annual specificities. This corresponds to the second step of our HAC + SOM algorithm.

The HAC algorithm is a bottom-up hierarchical classification [START_REF] Jain | Algorithms for clustering Data[END_REF][START_REF] Jain | Data clustering: a review[END_REF]. This method iteratively computes a partition hierarchy [START_REF] Badran | chap. Self-Organizing Maps and Unsupervised Classification[END_REF]. From the initial partition containing the neuron groups of the SOM map, two neurons of the same neighbourhood are regrouped at each iteration. These two subsets are selected by measuring their similarity according to the Ward criteria. The iterations continue until all the subsets of the partition are regrouped together.

The corresponding circulation regimes of the 42 neurons were aggregated into 8 significant classes (for simplification, only 4 classes are schematized in Figure 2). The resulting clustering of the 176-dimension vectors associated with the neurons of the topological map is given in Figure 4b. Classes 1 and 2 are characterized by two neurons, classes 3 and 4 by four neurons, Class 8 by six neurons, and, classes 5, 6 and 7 by eight neurons.

We note that the SOM + HAC algorithm is very coherent, since the classes represent clusters whose neurons are contiguous on the topological map (Figure 4a). The number of classes ( 8) was selected because it presents the most significant discriminative partition with respect to the full dendrogram of the HAC according to two statistical indices

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used as metrics for evaluating the clustering quality. Indeed, we varied the number of classes between 1 and 12 and computed for each one the Davies-Bouldin index [START_REF] Davies | A cluster separation measure[END_REF] and the gap index [Tibshirani et al., 2001]. The first one is an index that takes into account not only the intra-class homogeneity by comparing the distance between each situation vector and its reference vector, but also the heterogeneity between the different classes. The second one compares the within-class dispersion with that expected under an appropriate null dispersion. The number of 8 classes was selected by minimizing the Davies-Bouldin index and maximizing the gap index. classes 2 to 6. The most striking feature according to this classification is the evidence of a new current, flowing westwards along the northern coast of Sicily, that is flowing in the opposite direction of the BTC (Figure 5). This current is present in classes 3, 4, and 6 to 8, with velocity values varying between 0.03 and 0.08 m.s -1 at 30 m depth (Figure 7).

It feeds the AIS with Tyrrhenian Sea water composed of AW characterized by a higher salinity by 0.3 than the AW coming from the AC (Figures 6c,d,f,g,h). Another important feature is the like-disappearance of the AIS in Class 1 and Class 7, with very low velocities of 0.04 m.s -1 in the 30-m depth AIS core (Figure 7a,m).

In this simulation and according to the classification, the ATC and the SESG are permanent features of the circulation, whereas the AIS may disappear in some classes (Class 1 and Class 7). A new feature appears in five classes (classes 3, 4, 6, 7 and 8): a westward coastal current north of Sicily, denoted the Tyrrhenian Sicilian Current (TSC) here-after, which advects surface Tyrrhenian Sea water characterized by a higher salinity as compared to the AW salinity outflowing from the Algerian Basin into the Ionian Basin. The TSC appears to be an important feature of the circulation as well as the BTC, which is simulated in almost all classes except Class 3.

Seasonal and inter-annual variabilities

The seasonality of the classes is shown in Figure 8. several hydrographical observations showing a low-salinity signature along the Tunisian side during the whole year [START_REF] Sammari | Hydrological characteristics in the Tunisia -Sardinia -Sicily area during spring 1995[END_REF][START_REF] Ismail | Water masses exchanged through the Sicily Channel: evidence for the presence of new water masses on the Tunisian side of the Channel, Deep-Sea Research I[END_REF]. Classes 7 and 8 occur from spring to the end of summer with a well developed TSC. Class 7 is characterized by a high-velocity ATC whereas Class 8 is characterized by a high-velocity AIS.

Inter-annual variations are investigated by analyzing the temporal occurrence of the classes during the 46 years of the experiment (Figure 9a). Each month of the 46-year period under study is affected to a class which corresponds to the daily class which is the most frequently encountered during that month. 1999 and 2003 (i.e. in Class 7), which confirms the fact that the MCC is not a permanent structure of the circulation, as shown by [START_REF] Bonnano | Variability of water mass properties in the Strait of Sicily in summer period of 1998-2013[END_REF]. The present simulation is also in agreement with the results of previous numerical studies. For example, Class 5 corresponds well to the mean circulation of the month of December of the years 2008-2010 reported by [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF] and to the mean circulation of January 2001 reported by [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF].

Transport variabilities

The mean eastward surface transports through the SC associated with each class are shown in Figure 10a. The values correspond to the total transport of surface water crossing eastwards the SC (section SS in Figure 3). The median values (red line in Figure 10a), as well as the mean values (red square in Figure 10a), range from about 0.7 to 1.3 Sv (1 3), while minimum and maximum values range from 0.1 to 3.0 Sv. It highlights the fact that the stronger surface inflows into the Eastern Mediterranean occur in classes 1, 2 and 5, associated with high-velocity ATC, ALC, and BATC, and mainly corresponding to autumn-winter seasons, whereas the weaker surface inflows occur in classes 3, 4 and 8 associated with a low-velocity ATC and a high-velocity AIS, and for the most part corresponding to summer-fall seasons.

D R A F T April 15, 2016, 7:06pm D R A F T sverdrup (Sv) = 10 6 m 3 .s -1 ) (Table
The net transport through the SC according to each class is very small (Figure 10b,

Table 3). The distribution of minimum and maximum of the net transport (Figure 10b) for each class is different from that of the net surface inflow (Figure 10a). There are negative (towards the west for classes 5, 6 and 8) and positive (towards the east for other classes) net transports, but the variance of the net transport (Figure 10b) looks like the variance of the surface transport (Figure 10a). For example, the amplitudes of the variance are very large for Class 1 and Class 5 for both the surface and the net transports, whereas it is two times lower for Class 3 and Class 4 for both the surface and the net transports. July 2010 to December 2011. A relationship between these alternations of net transport regimes (Figure 9b) and the alternation of Class 7 with classes 6 or 8 in summer-spring (Figure 9a) is highlighted in Table 4. As an example, for the period from 1992 to 1997, the circulation is largely interpreted as situations of classes 5, 6 and 8 (Figure 9a), which all correspond to negative net transports (Table 3). This period is mainly characterized by situations for which Class 8 is present in summer, Class 5 in autumn and winter, and Class 6 in spring (Figure 9a). And for the following period, from 1998 to 2006, for which the net transport is positive (Figure 9b), the circulation is dominated by the situations of classes 1, 3, 4 and 7 (Figure 9a), which all correspond to positive net transports (Table 3). This period is mainly characterized by situations for which Class 7 is present a large part of the year (between February and October).

5. Evidence of a new current: the Tyrrhenian Sicilian Current (TSC)

Description of the TSC

All the classes show the presence of well identified currents flowing in the SC which have already been described in the scientific literature; i.e. the AC, the ATC and the AIS (Figure 1). These currents are of an irregular distribution according to the class.

The definite presence in classes 7 and 8, and 3 and 4, and slightly less definite in Class 6, of a westward coastal current flowing along the northern coast of Sicily (TSC) before entering the Eastern Mediterranean has never been mentioned as a well defined feature of the circulation. [START_REF] Napolitano | Modeling the baroclinic circulation in the area of the Sicily channel: The role of stratification and energy diagnostics[END_REF], using numerical experiments run during a summer situation (their Figure 7B), first mentioned it and reported it as a possible effect of open boundary conditions introduced into their high-resolution model. Moreover, the TSC is depicted in Figure 5 of [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF]; it was obtained with a basin-scale

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model of similar resolution to that of [START_REF] Napolitano | Modeling the baroclinic circulation in the area of the Sicily channel: The role of stratification and energy diagnostics[END_REF]. It is also shown in Figure 8 of [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF], who used a very-high-resolution ocean model (1/32 • ). These authors described an anti-cyclonic circulation in summer which they called the WNSC, but this current did not feed the AIS in their simulation; it turned anti-cyclonically into the northern part of the Tyrrhenian Sea.

In order to characterize the TSC, we looked at the SST associated with each class, as well as the corresponding sea-surface height anomaly (Figure 11). A low SST occurring along the southern coast of Sicily is the signature of a coastal upwelling. This upwelling, which is quasi-permanent during the summer months [START_REF] Kostianoy | Variability of the Sicilian upwelling, Oceanic Fronts and related Phenomena, Konstantin Federov Memorial Symposium[END_REF], often extends up to the eastern and northern coasts of Sicily in our simulation, in particular for classes 3,4,7 and 8 (Figures 11c,11d,11g,11h), denoting an upward motion of deep water. This is characterized by a negative sea-surface height anomaly around the Sicily coastline.

This kind of signature, characterized by a low-SST water along the southern and western coasts of Sicily, is confirmed by the lower temperature observed from satellite infrared images, in July 2005 and 2010 (Figure 12), and is also found in the low sea-surface height anomaly values in AVISO products (Figure 13e) along the southern and northern coasts of Sicily. Our results are also confirmed by another companion simulation run with a 1/36 •resolution basin-scale model MED36 [START_REF] Arsouze | Sensibility analysis of the Western Mediterranean Transition inferred by four companion simulations[END_REF] forced by the same atmospheric forcing as MED12. The SSTs of MED12 (Figure 13a) and MED36 (Figure 13b The TSC therefore appears as a new fact in the SC circulation scheme and it must be taken into account in the interpretation of the characteristics of the surface water flowing eastwards through the SC.

Origin of the TSC

In our model, the TSC, which flows westwards along the northern coast of Sicily, occurs at the same time as the Sicilian upwelling develops along the southern coast of Sicily, mainly during the summer period. This upwelling is described in the scientific literature as a wind-induced upwelling, since the winds are mostly blowing from the northwest following the loop of the Mistral [Zavatarelli and Mellor , 1995]. In order to verify if the wind is a good candidate for driving the upwelling, we computed the correlation coefficient between the time-series of the component of the wind stress parallel to the southern coast of Sicily and the SST off the coast for several time periods of Class 8 for which the upwelling is well defined (Figure 11h). As an example, Figure 13f shows that the wind stress during the 5-day period (9 to 13 July 2005) preceding the upwelling was very low and unable to generate an upwelling. Moreover, the low value of the correlation coefficient (< 0.1) shows that there is no direct link between the northwesterly winds and the SST and consequently with the upwelling.

This implies that other mechanisms must be sought. We therefore investigated the possibility that the upwelling is density-driven. Previous works [START_REF] Herbaut | Separation of a coastal current at a strait level : case of the strait of Sicily[END_REF][START_REF] Onken | Seasonal variability of flow instabilities in the Strait of Sicily[END_REF][START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF][START_REF] Molcard | Numerical investigation of the Sicily Channel dynamics: density currents and water mass advection[END_REF][START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF] showed the importance of coastal topographic waves for explaining the circulation D R A F T April 15, 2016, 7:06pm D R A F T in the SC. In particular, [START_REF] Herbaut | Separation of a coastal current at a strait level : case of the strait of Sicily[END_REF] showed that a boundary coastal jet flowing from left to right along a straight coast (AC in Figure 14a) can split into two branches when encountering a topographic shallow channel situated in the middle of the coast. One branch enters the channel and flows along its left coast as a boundary coastal jet (ATC in Figure 14a); the other crosses the channel, guided by a double Kelvin wave trapped by the channel topography, and then continues to flow along the coastline (BTC in Figure 14a). In these process studies and the associated numerical experiments, the boundary coastal jet representing the AC was generated by a density gradient in the left part of channel coast (Figure 14b).

Molcard et al.

[2002] employing a similar reasoning but dealing with a realistic coastline and topography and conducting several numerical sensitivity experiments were able to

show that an AIS can be generated by a density gradient between the Western Mediterranean and the Ionian Basin. The velocities of the ATC and of the AIS vary according to the density gradient through the channel [START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF]. These two process studies provide a rational framework for explaining the ATC, the AIS and the BTC. [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF], comparing idealized and realistic configurations, showed that the meandering of the AIS appeared to be associated with the continental slope of the Adventure Bank, as the realistic simulation of [START_REF] Pierini | Modeling the Oceanic Circulation in the Area of the Strait of Sicily: The Remotely Forced Dynamics[END_REF] has shown. According to a high-resolution modelling using data assimilation techniques, [START_REF] Onken | Data-driven simulations of synoptic circulation and transports in the Tunisia-Sardinia-Sicily region[END_REF] argued that the AIS is triggered by both topography and LIW outflow.

Therefore, it appears that a good candidate for driving the Sicilian upwelling is the presence of a density gradient between the Tyrrhenian Sea and the Ionian Basin, which induces a baroclinic coastal boundary jet [START_REF] Molcard | Numerical investigation of the Sicily Channel dynamics: density currents and water mass advection[END_REF] in the form of a baroclinic D R A F T April 15, 2016, 7:06pm D R A F T Kelvin wave, which propagates from the Ionian Basin towards the Tyrrhenian Sea, according to the scheme in Figure 14c. Schematically this baroclinic Kelvin wave of very long wave-length is associated with a deep current, which transports dense Ionian water into the Tyrrhenian Sea and a surface current in the opposite direction transporting Tyrrhenian Sea surface water into the Ionian Basin. This motion can be interpreted in terms of conservation of mass and relative potential vorticity in a two-layer system [START_REF] Hsieh | Rossby adjustment problem in a rotating stratified channel, with and without topography[END_REF]. The dense Ionian water flows at depth along the coast towards the Tyrrhenian Sea.

This water is trapped by the coast around the western tip of Sicily and propagates along the northern coast of Sicily. During its progression, this deep Ionian water uplifts the two-layer fluid interface, generating an upwelling along the coast of Sicily (Figure 14d)

with a positive vorticity in the lower layer and a negative one in the upper layer, the sum of these two vorticities being constant. In order to satisfy the continuity equation, the low-density Tyrrhenian Sea water (compared to the denser Ionian water) flows in the opposite direction and enters the Ionian Basin forming the TSC in the Tyrrhenian Sea and the AIS in the eastern part of the SC.

Mechanisms driving the TSC

The simulated results (Figure 5 and Figure 7) are qualitatively in agreement with the density driven forcing described in Section 5.2. In the numerical model, the Kelvin waves found in the idealized scheme of depth. Figure 15b redraws these monthly values in the line of Figure 9 while the 46-year seasonal cycle of this difference is reported in Figure 15c.

As an example in Class 3, which mainly occurs in summer-autumn (Figure 8), the density gradient between the Ionian Basin and the Tyrrhenian Sea is large and the vertical stratification in the Sicily Channel is characterized by strong isopycnal slopes (Figure 7e).

These slopes are the signature of the advection of the dense Ionian water into the Western Mediterranean associated with coastal trapped waves propagating towards the Tyrrhenian Sea. The Ionian water isopycns are uplifted to the surface at the coast. Consequently, the sea surface temperature is cooled along the southern Sicily coast as seen in Figure 11c showing a density-driven coastal upwelling. These coastal trapped waves when arriving in the Tyrrhenian Sea generate the TSC as explained in Section 5.2 and evidenced by the The forcing mechanism of this westward current along the southern Sicily is enhanced by the Ionian water spreading through the SC towards the Tyrrhenian Sea. Figure 15a confirms that the density gradient which exists between the Ionian Basin and the Tyrrhenian Sea at intermediate depths, is at the origin of the westward sub-surface current of Ionian water (schematized by ρ 3 in Figure 14d). Classes 3, 4, 7, 8 occurred mainly in spring-summer and Class 6 in spring-summer, when the density gradient is larger than in autumn-winter for which classes 1, 2, 5 occurred. A correlation is suspected between the inter-annual variability of this density gradient (Figure 15b) and the alternation of Class 7 and Class 8 (Figure 9a), high gradient values corresponding to Class 7 and low values to Class 8, in agreement with the results presented in Figure 9b, in which Class 7

(high gradients values) corresponds to a net westward transport and Class 8 (low gradient values) to a net eastward transport.

Summary and Conclusions

We analyzed the surface circulation in the Sicily Channel provided by a 46-year run of the high-resolution model MED12. The aim was to evidence the most characteristic modes of the circulation. For that, we used a high-performance clustering method, the SOM, which is a non-parametric neural method widely used in many research fields. We This new description of the surface circulation, which was evidenced in the analysis of the outputs of MED12 and MED36, is also present in other models, such as that of [START_REF] Sorgente | Numerical simulation and decomposition of kinetic energy in the Central Mediterranean: insight on mesoscale circulation and energy conversion[END_REF] and in good agreement with satellite SST observations for different years.

Moreover, the model results can be interpreted through a theoretical framework involving coastal topographic baroclinic waves, in the light of the works of [START_REF] Herbaut | Separation of a coastal current at a strait level : case of the strait of Sicily[END_REF] and [START_REF] Molcard | Numerical investigation of the Sicily Channel dynamics: density currents and water mass advection[END_REF]. The forcing of these motions is the density gradient which exists between the Ionian Basin and the Tyrrhenian Sea The seasonal variations of this density gradient are correlated with the occurrences of classes presented in Figure 9. Classes 3, 4, 6, 7, and 8 occur in spring-summer when the density gradient is large while classes Crest (MCC) is drawn as an anti-cyclone (as in [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF] and in Lermusiaux

and [START_REF] Lermusiaux | Features of dominant mesoscale variability, circulation patterns and dynamics in the Strait of Sicily, Deep-Sea Research I, 48[END_REF]). The new feature highlighted by this study, the Tyrrhenian Sicilian Current (TSC), is added in red because it is preferably simulated in summer. Corresponding acronyms are in Table 1. The bathymetry is drawn using a grey scale to highlight the shallow continental shelves in the Central Mediterranean (m). L is Lampedusa Island. This corresponds to a sub-grid increment of ∆x ≈ 15 km along the x-axis in the W-E direction and to a sub-grid increment of ∆y ≈ 25 km along the y-axis in the N-S direction.

The black line represents the area [33.5-39.5 • N;7-15 through the SC for each class (see Figure 9). drawn as an anti-cyclone (as in [START_REF] Béranger | The dynamics of the Sicily Strait: A comprehensive study from observations and models[END_REF] and in [START_REF] Lermusiaux | Features of dominant mesoscale variability, circulation patterns and dynamics in the Strait of Sicily, Deep-Sea Research I, 48[END_REF]).

The new feature highlighted by this study, the Tyrrhenian Sicilian Current (TSC), is added in red because it is preferably simulated in summer. Corresponding acronyms are in Table 1. The bathymetry is drawn using a grey scale to highlight the shallow continental shelves in the Central Mediterranean (m). L is Lampedusa Island.

  Figure captions

  Figures

  The initial conditions in the Mediterranean domain are provided by the monthly mean potential temperature and salinity 3D fields from the MEDATLAS-II climatology [The-D R A F T April 15, 2016, 7:06pm D R A F T X -11 MEDAR-Group, 2002]. These fields are smoothed by a low-pass filtering with a 10-year time-window using the MEDATLAS data covering the 1955-1965 period. The simulation then starts in October 1958 with an ocean at rest.

  Figure 7. Table1shows the current and gyre acronyms.

  The maximum net transports correspond mainly to Class 2 (0.050 Sv) while the minimum transports correspond to Class 6 (-0.090 Sv). Positive values are then for Class 4 (0.040 Sv) and for classes 1, 3 and 7 (<0.035 Sv). Negative values are then for Class 8 (-0.020 Sv) and Class 5 (-0.040 Sv).In the inter-annual time-series of the monthly net transport (Figure9b), there were several major changes between 1966 and 2011 in the net transport regimes, with alternatively positive (eastwards, in the range 0 to 0.032 Sv) and negative (westwards, in the range -0.043 to 0 Sv) values. Four periods show a positive net transport: from October 1967 to October 1972, from March 1986 to April 1988, from August 1998 to March 2007, and from D R A F T April 15, 2016, 7:06pm D R A F T

  ) in July 2005 (Class 7) compare very well, as do their temperatures at 30-m depth (Figure 13b, d). In MED36, there is a TSC (somewhat narrower than in MED12) feeding the AIS when the upwelling is well developed. The good agreement of the modelled surface results D R A F T April 15, 2016, 7:06pm D R A F T (Figure 13a, b) with the satellite observations (Figures 12a and 13e) confirms the realism of the models and consequently of the TSC at 30-m depth (Figure 13c, d).

  Figure 14d are represented by coastal trapped waves modified by the topography. Figure 15a presents the variability of the density difference between the Ionian Basin and the Tyrrhenian Sea, obtained by averaging the monthly density in the two boxes drawn in Figure 3 in the sub-surface layer between 50 and 150 m D R A F T April 15, 2016, 7:06pm D R A F T

  negative sea surface height in Figure11c. Classes 4, 6, 7 and 8 present a similar behavior as this of Class 3(Figures 7g,k,m,o).Class 5 mainly occurs in autumn-winter (Figure8) when the density gradient between the Ionian Basin and the Tyrrhenian Sea is small (Figure15). It is characterized by flatter isopycnal slopes in the Sicily Channel (Figure7i). The coastal trapped waves are less energetics and consequently the associated transport of Ionian water into the Western Mediterranean is weaker. These waves are not enough energetics to force the TSC when arriving in the Tyrrhenian Sea. Classes 1 and 2 present a similar behavior as this of Class 5. These behaviors fit the theoretical schemes shown in Figure 14. Besides, in all the classes, one can observe a slow westward current of dense Ionian water close to the Sicilian shelf, whose maximum velocities 0.012 m.s -1 is encountered at 110 m depth above the D R A F T April 15, 2016, 7:06pm D R A F T X -27 Adventure Bank. It is up to 50 m depth in Class 5 and reaches 10 m depth in Class 3 with a maximum of 0.010 m.s -1 .

  decomposed the circulation into 8 major classes, whose the shortest duration can last several weeks, and the longest, several months. Current and salinity maps according to each class confirmed the AW spreading into three main well known branches, the ATC, the BTC and the AIS. A new branch of the SC circulation has been also highlighted by D R A F T April 15, 2016, 7:06pm D R A F T these maps, the so-called TSC which streams along the northern coast of Sicily and brings Tyrrhenian Sea surface water into the Eastern Mediterranean. The 8 modes present a well-marked seasonality. Some of them occur mainly in summer (Classes 3, 4, 7 and 8) or in spring (Classes 6 and 7), some others in winter (Classes 1 and 5) and in autumn (Classes 2 and 3). The major features characterizing the modes are the relative importance of the ATC versus the AIS, or the presence of the TSC versus the BTC, in most of the classes, the all-year-round presence of the SESG. In particular, several classes show the quasi absence of the AIS (Classes 1 and 7). Some classes (Classes 1 and 2) appeared only at an inter-annual scale. The classes are associated with well marked hydrographical features. Most of the summer classes 8, 7, 4, 3 are associated with a strong upwelling along the southern coast of Sicily. The net transport through the Sicily Channel varies at an interannual scale of about 10 years. A class alternation is associated with this net transport variation, Classes 1 to 4 and 7 corresponding to positive net transport, and classes 5, 6 and 8 corresponding to negative net transport. Correlation between the component of the wind parallel to the coast and the SST off the southern coast of Sicily is very weak, showing that the wind does not play a role in forcing this upwelling. This interpretation is re-enforced by the fact that the upwelling bypasses the western tip of Sicily, reaching the northern coast of Sicily, as seen in negative sea-surface height anomaly contours around Sicily and the presence of low SST on the northern coast of Sicily. This is in agreement with a density-driven upwelling associated with the propagation of Ionian Basin water trapped along the coast as a coastal baroclinic topographic wave responsible for the advection of Ionian Basin water into the Tyrrhenian Sea. Besides the Mistral-like wind, which quite often blows in the SC region in summer, D R A F T April 15, 2016, 7:06pm D R A F T has a large spatial structure with respect to the western tip of Sicily. It generates an Ekman transport, which favours upwelling along the southern coast and downwelling along the northern coast of Sicily, showing that such a wind cannot generate an upwelling on both sides of the coast, as can be seen in the satellite observations and in Class 8 for the model results. The TSC and the upwelling are consequently the signature of the density forcing of the circulation in the SC. All these facts lead us to conclude that the surface circulation and the hydrographical structures of the SC are mainly density-driven. We have evidence of the importance of the TSC, which is present in most of the classes. The TSC and the AIS are strongly linked, since they are driven by the entry of the dense Ionian Basin water at intermediate depth (50-150 m) into the Tyrrhenian Sea, which uplifts the interface separating the surface water and the deeper water, generating the southern Sicily coastal upwelling and also the upwelling occurring at the eastern tip of Sicily and along the northern coast of Sicily.

Figure Captions Figure 1 .

 Captions1 Figure Captions

Figure 2 .Figure 3 .

 23 Figure 2. Schematic representation of the two steps of the clustering method SOM

  . The red lines (TYR) north and (SSI) south of Sicily represent the vertical sections in Figure 7. The 2 blue boxes, T-box in the Tyrrhenian Sea and I-box in the Ionian Basin, are used for calculation in Figure 15.

Figure 4 .

 4 Figure 4. (a: upper panel) The topological map shows the distances between neurons

Figure 5 .Figure 6 .

 56 Figure 5. Maps of currents (modulus and vectors in m.s -1 ) at 30-m depth for the 8

Figure 7 .

 7 Figure 7. Vertical sections of density and currents along (left panels) south of Sicily (red

Figure 7 (

 7 Figure 7 (continued). for the classes 5 to 8.

Figure 8 .

 8 Figure 8. The x-axis represents the numbered classes, while the y-axis indicates the

Figure 9 .

 9 Figure 9. (a: upper panel) Time variability of the 8 classes: The x-axis represents the

Figure 10 .

 10 Figure 10. Box-and-whisker plots of the eastward (a: upper panel) and the net (b: bot-

Figure 11 .

 11 Figure 11. Sea-surface temperature ( • C) for the 8 classes of Figure 5 and the associated

Figure 12 .

 12 Figure 12. NOAA/AVHRR thermal images in 13 July 2005 (a: left panel) and in 22

Figure 13 .

 13 Figure 13. Snapshots of sea-surface temperature ( • C) and currents (m.s -1 ) (top panel)

Figure 14 .

 14 Figure 14. Schematic functioning of the surface currents in the Sicily Channel. The thin

Figure 15 .

 15 Figure 15. (a-top) Time-series of the density gradient between the Ionian Basin and the

Table 4 .Figure 1 .

 41 Figure 1. Scheme of the Atlantic Water circulation in the Sicily Channel (SC) area mainly

.Figure 2 .Figure 3 .

 23 Figure 2. Schematic representation of the two steps of the clustering method SOM + HAC.

Figure 4 .Figure 5 .Figure 6 .Figure 7 .Figure 8 .

 45678 Figure 4. (a: upper panel) The topological map shows the distances between neurons (ranged

Figure 11 .Figure 12 .Figure 13 .Figure 14 .

 11121314 Figure 11. Sea-surface temperature ( • C) for the 8 classes of Figure 5 and the associated

  ATC in Class 4, which never occurs in winter, is more surprising, but is in agreement with

		Classes 3, 4, 7 and 8, characterized
	by a high TSC signature, mainly occur in summer. Classes 1, 5, and 6, characterized
	by a high ATC signature, mainly occur in winter. Seasonal variations are not a reliable
	indicator for explaining the very low AIS velocity (the quasi-absence of the AIS) in classes
	1 and 7. The relatively high winter signature of the ATC in classes 1, 2, 5 and 7 is
	qualitatively in agreement with previous studies in winter. The relatively high-velocity
	D R A F T	April 15, 2016, 7:06pm	D R A F T

  and 8 with a decadal period cannot directly be related to the BiOS phenomenon, since this phenomenon is poorly reproduced by the numerical model. In fact the simulated circulations in the northern Ionian Basin (not shown), are cyclonic in both classes (with deeper sea surface height in Class 7) and do not correspond to the BiOS.The high-velocity ATC signature of Class 1 is in agreement with observations in November 1985 reported by[START_REF] Manzella | Atlantic water in the Strait of Sicily[END_REF]. The ABV, MCC and the high-velocity AIS and low-velocity ATC of Class 8 are in good agreement with the southern Sicily upwelling summer situation documented in the literature[Zavatarelli and Mellor , 1995; Robinson

	Class 7 occurs (in particular in summer) from 1967 to 1972, from 1985 to 1988, from 1998
	to 2000, from 2002 to 2006, and in 2011. Class 5 predominates from November to January
	with some alternation with Class 1, while Class 6 predominates during March-April. On
	the contrary, Class 1 and Class 7 generally occur in the same years. Class 6 is less common
	or even absent when Class 7 is present. Classes 1 and 2 do not occur every year. Classes
	3 and 4 look like classes of transition from a dynamical summer state represented by
	classes 7 or 8 to a winter state represented by Class 5. That alternation of the classes 7
	D R A F T	April 15, 2016, 7:06pm	D R A F T

The confidence of that class for a given month is expressed as a percentage which is the number of occurrence days of the class with respect to the number of days in the month. Most of the class confidences are larger than 66%, showing the efficiency of the classification. Classes 7 and 8 are more or less exclusive of each other (Table

4

). The alternation of these two classes shows a strong inter-annual variability with a pseudo-period of about a decade. Class 8 is present (in particular in summer) from

1973 to 1984, from 1990 to 1997, and from 2007 to 2010. et al., 1999]

and observed in 1996 by

[START_REF] Lermusiaux | Features of dominant mesoscale variability, circulation patterns and dynamics in the Strait of Sicily, Deep-Sea Research I, 48[END_REF]

. The Class-8 features in July 2008 are also in agreement with the high-velocity AIS signature, and the ALC signature owing to the ATC moving over the Tunisian continental slope reported by

[START_REF] Placenti | Water masses and nutrient distribution in the Gulf of Syrte and between Sicily and Libya[END_REF]

. During the period 1998 to 2011, the MCC is not present in 1998,

  • E] covered by the maps of classes inFigures 5, 6 and 11. The black line between Tunisia and Sicily (SS) represents the SC section used for transport calculation reported in Table3, and in Figures9b and 10

Table Captions Table 1 .

 Captions1 Acronyms of Mediterranean oceanic features in the Sicily Channel.

Table 2 .

 2 Acronyms used in the text.

Table 3 .

 3 Values of the mean and median eastward and net transports (in sverdrups, Sv)

Table 1 .

 1 Acronyms of Mediterranean oceanic features in the Sicily Channel.

	Acronym	Name	Notes
	ABV	Adventure Bank Vortex	cyclonic
	AC	Algerian Current
	AIS	Atlantic Ionian Stream
	ALC	Atlantic Libyan Current
	ATC	Atlantic Tunisian Current
	AW	Atlantic Water
	BATC Bifurcation Atlantic Tunisian Current
	BTC	Bifurcation Tyrrhenian Current
	ISV	Ionian Shelf break Vortex	cyclonic
	LIW	Levantine Intermediate Water
	MCC	Maltese Channel Crest	anti-cylonic
	MG	Medina Gyre	cyclonic
	MRV	Messina Rise Vortex	cyclonic
	SESG	South Eastern Sardinia Gyre	cyclonic
	TSC	Tyrrhenian Sicilian Current
	WNSC Westward Northern Sicilian Current
	PV	Pantelleria Vortex	cyclonic

Acknowledgments.

This work was supported by the following French and European agencies: Mercator Ocean (www.mercator-ocean.fr) and the DGA for the SiMED project, and the GIS for the MORCE project. Atmospheric forcing data sets were made available by CNRM. This work was granted access to the HPC resources of IDRIS of the CNRS under project 010227 made by GENCI. The financial support of J. Beuvier's Ph.D was provided by Météo-France. We thank Michel Déqué of Météo-France for running the ARPERA simulation with ARPEGE-Climat. The realization of the GLORYS1 global ocean reanalysis had the benefit of the grants that the GMMC, and INSU-CNRS and the support of the European Union via the MyOcean project. We thank Bruno Levier of Mercator Ocean for gathering the bathymetry products. We acknowledge the GEBCO-08 Grid, version 20081212, http ://www.gebco.net, for providing the GEBCO-08 bathymetry. The altime-D R A F T April 15, 2016, 7:06pm D R A F T

0.035 0.050 0.020 0.040 -0.040 -0.090 0.010 -0.020 Table 3.

Values of the mean and median eastward and net transports (in sverdrups, Sv)

through the SC for each class (see Figure 10).

month 1966 1967/ 1973/ 1985/ 1989 1990/ 1998/ 2001 2002/ 2007/ 2011 1972 1984 1988 1997 2000 2006


M Hijazi

M Rieu
email: rieu@emse.fr

V Stambouli

G Tournier

J-P Viricelle

C Pijolat

Selective ammonia gas sensor based on SnO 2 -APTES modification

Keywords: SnO2, surface functionalization, APTES, gas sensors, selectivity, NH3, acetone

In the present work, SnO 2 sensors were produced by thick film fabrication. In order to deal with well-known lack of selectivity of SnO 2 sensors, functionalization was performed. Silanization by 3-aminopropyltriethoxysilane (APTES) in liquid phase was used as an intermediate step, followed by functionalization with molecules bearing acyl chloride with different end functional groups molecules, for example ester group. Modified sensors with APTES and ester end functional group were successfully characterized by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR). The SnO 2 sensor modified with ester end group was found to be sensitive and selective to ammonia gas at 100°C. Working at low temperature is also one of the advantages of these sensors as well as the selectivity with respect to other gases like acetone.

Introduction

Molecularly modified metal oxide gas sensors have shown to be promising devices for selective gas sensor related to disease diagnosis [1]. Those sensors can be used to detect the gas emanated from the human body for breath analysis application. Tin dioxide sensors have lack of selectivity and work at high temperature (350-500°C). The need of selective sensors with high sensitivity in presence of humidity at low gases concentration pushes the research to modify SnO 2 sensing element in order to change its interactions with gas. The modification with organic functional groups with different polarities change the sensor response to specific gases depending on their polarity [2]. A functionalization based on 3-aminopropyltriethoxysilane (APTES) combined with methyl adipoyl chloride was investigated. Another objective is to reduce the power consumption by decreasing the operating temperature.

In the present work we focus on the change in selectivity and sensitivity of SnO 2 sensors after functionalization with ester end functional group film. The sensors were firstly functionalized by APTES followed by covalent attachment of ester end functional group molecule. Functionalization was characterized with FTIR analysis. Then, detection performances of resulting sensors were investigated in regards of ammonia detection.

Experimental

SnO 2 thick films are deposited on alumina substrate by screen-printing technology. In this survey, a semiautomatic Aurel C890 machine was used; the procedure for preparing the SnO 2 ink and sensor fabrication parameters has been described elsewhere [3]. The SnO 2 powder (Prolabo Company) is first mixed with a solvent and an organic binder. An oxide film with a thickness of 40 microns is then deposited on an alpha-alumina substrate (38×5×0.4 mm 3 ) provided on one face with a screen-printed platinum heater and on the other face with two gold electrodes deposited by reactive sputtering. The SnO 2 material is finally annealed for 10 hours at 700°C in air. The SnO 2 particles sizes were found to be between 10 nm and 500 nm.

The functionalization of SnO 2 sensor leads to covalent attachment of ester end functional groups. Hydroxyl groups present on the surface of SnO 2 allow the condensation of 3-aminopropyltriethoxysilane (ACROS Organics). The terminal amine groups of APTES allow the coupling reaction with molecules bearing acyl chloride group. Silanization in liquid phase has been described elsewhere [4]. In a first step, SnO 2 sensors were immersed in 50 mM APTES dissolved in 95 % absolute ethanol and 5 % of deionized water for 5 hours under stirring at room temperature. To remove the unbounded APTES molecules, the sensors were rinsed with absolute ethanol and dried under N 2 flow. In a second step, for ester end group grafting, APTES modified SnO 2 sensors were immersed in a solution of 10 mM of methyl adipoyl chloride (96 %, Alfa Aesar) and 5 µL of triethylamine (Fluka) in 5 mL of chloroform as solvent for 17 hours under stirring. The sensors were then rinsed with chloroform and dried under N 2 flow.

Modified molecular layers were characterized by attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), the sample being placed face-down on the diamond crystal, and a force being applied by pressure tip. FTIR spectra were recorded in a wavelength range from 400 to 4000 cm -1 . The scanning resolution was 2 cm -1 . The entire ATR-FTIR spectrums were collected using a Golden Gate Diamond ATR accessory (Bruker Vertex 70).

Results and Discussions

Figure 1 shows the ATR-FTIR spectra of SnO 2 film (a) after functionalization by APTES (b) and APTESester (c). The most significant absorption bands are found between 800 and 1800 cm -1 for functionalized SnO 2 . The peak at 938 cm 1 is attributed to Sn-O-Si bond in stretching mode [5]. The wide band between 978 cm -1 and 1178 cm -1 is attributed to siloxane groups (Si-O-Si) from polymerized APTES. The -NH 2 and -NH 3 + vibrational signals found at 1570 cm -1 and 1496 cm -1 (figure 1b) respectively, confirms the presence of amine end functional group of APTES after silanization. These two peaks disappeared and two another appear at 1547 cm -1 and 1645 cm -1 (figure 1c) after ester modification which correspond to carbonyl stretch mode and N-H bending mode of amide respectively. Additional broad peak between ~ 3000 and ~ 3600 cm -1 correspond to N-H stretch of amide. The stretching peak of carbon double bounded to oxygen of ester group is found at 1734 cm -1 . The zone between ~1950 and 2380 cm -1 is not exploitable; it corresponds to CO 2 gas in ambient air. As a conclusion, FTIR analysis allows to confirm that functionalization was successfully achieved on SnO 2 films. Resulting sensors performances were investigated in a test bench allowing to generate various NH 3 concentrations, but also acetone with controlled humidity. Figure 2 shows the response (conductance variation) of SnO 2 -APTES-ester sensor compared to pure SnO 2 sensor at 100°C under humid air (26 % of relative humidity) upon exposure to NH 3 gas. The ester modified sensor gives increase in conductance to NH 3 , while no significant response for the non-modified sensor with noisy base line is observed. Figure 3a, shows the relative responses versus NH 3 concentration and points out a nearly constant sensitivity of ester modified sensor in the range 0-50 ppm. Relative response is defined as (G-G0/G0), where G0 is the base line conductivity under air and G is the conductivity after 20 minutes of gas injection (NH 3 ). Sensitivity at ppb level has to be investigated. Concerning selectivity, figure 3b demonstrates that the SnO 2 -APTES-ester sensor doesn't have any response to acetone (0-50 ppm) that could interfere in the detection of NH 3 gas. Regarding detection mechanism, assembled polar molecules on a surface of metal oxide semiconductor surface can form a dipole layer, which can create a depletion layer on metal oxide surface and adjusts the work function of semiconductor [6]. The ester end group (COOCH 3 ) is electron withdrawing group. The positive pole of NH 3 molecule, near the molecular layer surface, generates changes in the layer's dipole moment, which implies modifications in the conductance of the whole film.

Conclusion

SnO 2 thick films produced by screen printing were successfully functionalized. The modifications were done by attachement of APTES on SnO 2 film followed by coupling reaction with acyl chloride molecules having ester end functional group. Modified films were characterized by ATR-FTIR. Ester modified sensor was found to be sensitive and selective to NH 3 gas at 100 °C in humid air. The selectivity was tested with respect to acetone gas. Sensors performances at ppb level have to be examined as well as the detailed sensing mechanism.

Fig. 1 .

 1 Fig. 1. ATR-FTIR spectra of SnO2 (a), SnO2-APTES (b), and SnO2-APTES-ester (c) films.

Fig. 2 .

 2 Fig. 2. Response of SnO2-APTES-Ester and SnO2 sensor upon exposure to NH3 gas in air containing 26 % of relative humidity at 20 °C. The sensor operating temperature is 100 °C.

Fig. 3 .

 3 Fig. 3. a) Relative response of SnO2-APTES-Ester and SnO2 sensors versus NH3 concentrations; b) Response of SnO2-APTES-Ester sensor upon exposure to acetone gas. The sensors operating temperature is 100 °C, in air containing 26 % of relative humidity at 20 °C.