Marine Rapid Environmental Assessment (MREA) was developed in the late 1990s to collect synoptic oceanographic data relevant for nowcasts, forecasts and derived applications (Robinson and Sellschopp, 2002). Situational sea awareness relies on observational and modeling information on the dynamical state of the sea in order to ensure safer and more efficient operations. MREA contributes to situational sea awareness by advancing the synoptic data acquisition for state estimation and forecasting. Opportunity observations, such as ARGO profilers, are normally not abundant enough to resolve the local scales of interest especially from a synoptic point of view. Thus MREA is one of the optimal experimental strategies to collect definitive evidence on ocean mesoscales for improving knowledge and forecasts.

The ocean is eventful and intermittent, and several processes generally contribute to its space–time variability. For its physical state variables, the space scales that MREA methodology targets are from a few hundred meters to several kilometers and temporal scales range from hours to days. Assuming a timescale of several days, the dominant variability is the large-scale general circulation and its mesoscale and submesoscale components (Thomas et al., 2008). At shorter timescales internal waves, upper mixed layer daily cycles and tides dominate the energy spectrum (Talley et al., 2011). MREA observations are necessary to resolve specific time and space scales of interest, thus increasing the usefulness of the observations for nowcasts and forecasts.

The MREA methodology has three main components: (1) the observational strategy and ocean state estimation; (2) the nowcast and forecasting studies; (3) the forecast/analysis validation and reanalysis. In this paper we concentrate on the observational strategy and the objective mapping of the resulting circulation structures.

MREA has been implemented globally, in several regions, and adaptive sampling has also been developed (Lermusiaux, 2007, Frolov et al., 2014). Intermittency and multiscale processes have led to the concept of nested forecasts, which make use of optimized sampling networks to increase forecast accuracy. However, it is not yet clear if a regular grid (“lawnmower”) survey, which is a “classical” strategy for synoptic ocean sampling, would be less efficient than adaptive path-planned surveys to map the synoptic variability with unknown field correlation scales.

We applied MREA to a little studied region of the world's ocean, the Gulf of Taranto in the northern Ionian Sea (Fig. 1). We use the classical, regular grid sampling strategy and explore the basic water mass properties as well as the geostrophic circulation. The insertion of these data in the large-scale operational model of the Mediterranean Forecasting System (Oddo et al., 2014) and in the nested high-resolution model for the Gulf of Taranto are shown in a companion paper in this issue (Federico et al., 2016).

Here we analyze four surveys carried out in the area of the Gulf of Taranto from 1 to 10 October 2014 with the Italian Navy Survey Vessel Galatea and the RV Cerruti. A new multiscale sampling strategy was used to measure the temperature and salinity structure of the flow field from the open ocean to the shelf–coastal scales of the Gulf of Taranto (northwestern Ionian Sea in the Mediterranean Sea) and the coastal–harbor scales of Mar Grande (Fig. 1). The novelty of this data collection experiment is related to the different resolution of the stations carried out in the different areas under the strict constraint of synoptic time coverage (3–4 days in the ocean).

The sampling strategy and the temperature and salinity data enables us to estimate the water masses in the area and construct dynamic height and the derived geostrophic circulation using objective analysis mapping techniques. This is the first time that the circulation has been mapped for the whole Gulf of Taranto with increasing resolution from the coasts to the open ocean and with synoptic timescale resolution. The usage of the MREA14 observations to assess model performances is given in two companion papers (Federico et al., 2016; Gaeta et al., 2016)

The paper is organized as follows: Section 2 describes the data collection methodology and Sect. 3 the water mass analysis. Section 4 presents the dynamic height and the geostrophic circulation and Sect. 5 discusses the results.

The Gulf of Taranto is a deep, semi-enclosed ocean area in southern Italy encircled by two peninsulas, Salento and Calabria (Fig. 1). It is open to the northern Ionian Sea, and a deep trench of more than 2000 m connects it to the eastern Mediterranean Sea. The continental shelf area, considered as the area from the coasts to the 200 m depth contour, occupies only 10 % of the total Gulf area. The shelf is wider on the Salento than the Calabria side and a 7.5 km wide sheltered elliptical embayment, called the Mar Grande, opens in the northeastern part of the Gulf (Fig. 1).

From a large-scale point of view, the mean circulation in the area can be assessed by taking the current fields from a reanalysis product (Pinardi et al., 2015) that does not contain the MREA data. The surface circulation (Fig. 2) is anticyclonic in October 2014, while in June 2014 it is cyclonic. This opposite circulation pattern is probably connected to the different Western Adriatic Coastal Current (WACC; Guarnieri et al., 2013), Northern Ionian Sea outflow–inflow system in the 2 months and the local atmospheric forcing.. One of the major aims of the MREA experiment was to verify the October circulation shown in Fig. 2.

The Gulf of Taranto is a deep semi-enclosed sea with lateral water exchanges with the Ionian Sea. The seasonally different circulation of the Gulf of Taranto described above may lead to changes in the inflow–outflow structure. In the anticyclonic case, it is likely that vertically stratified water masses enter the Gulf from the western side (Calabria in Fig. 1) and exit from the Salento side (eastern side, see Fig. 1). We argue that Mar Grande could have both lateral and vertical exchanges, as classified by Cessi et al. (2014). The MREA experiment partly clarified these questions.

Very few CTD observations in the past have been reported in the Gulf of Taranto and none with a synoptic coverage. Our goal was mainly to carry out the first survey of the thermohaline properties of the area with synoptic coverage at three different scales: large, shelf and harbor scale (Mar Grande). Based on the large-scale flow structure in Fig. 2, the four surveys were planned and implemented following the schemes presented in Fig. 3 and described in Table 1.

The large-scale surveys (LS1 and LS2) were carried out over 3 days, a quasi-synoptic timescale, in an area which is on average 800 m deep. The stations were repeated in LS2 in order to understand large-scale temperature and salinity changes on a weekly basis. The mean station distance between stations was 16 km, which is about the Rossby radius of deformation for the eastern Mediterranean (Hecht et al., 1988). This spacing was chosen as a good compromise between the horizontal resolution and the time needed to cover the area synoptically.

The shelf-scale survey, CS1, was carried out in the northeastern Gulf of Taranto, an extended shelf area of the Gulf (Fig. 1). The mean distance between the stations was 5 km and the mean depth of the area was 400 m. The MG1 survey covers the shelf area of the Mar Grande which is a heavily human impacted harbor area. The distance between stations in the Mar Grande is ∼ 1 km and the mean depth was 15 m.

All measurements were carried out with Idronaut CTD 316Plus on board of the RV Galatea for LS1, CS1 and LS2 and the RV Cerruti for MG1.

A dynamic height at 10 m with respect to a reference level of 100 m was computed from the density profiles (Fig. 14). This shallow reference level was chosen in order to capture part of the connections between the open ocean and the shelf.

The dynamic height shows the large-scale anticyclonic gyre of the Gulf of Taranto in a similar position to that found in the October 2014 reanalysis picture in Fig. 2. The periphery of the anticyclone was characterized by the cyclonic eddy corresponding to C3 of Fig. 10 in LS1 and by an eddy-like upwelling area in LS2, located in the area of the cyclonic eddy C4 in Fig. 10. Between LS1 and LS2, the anticyclone changed noticeably in shape and intensity, strengthening from LS1 to LS2. These changes are likely due to dynamical instabilities of the anticyclone rim current that modulate the upwelling at the periphery.

Figure 15 shows the geostrophic velocities computed from the dynamic height. The LS1 surface currents, turning clockwise around the anticyclonic gyre, are characterized by jets, i.e., intensified rim current segments. In LS1 one of these jets developed between the anticyclone and the eastern side cyclonic eddy, corresponding to C3 in Fig. 10. At LS2 + CS1, the gyre underwent considerable changes in the jets strength because the rim current was meandering, which is a manifestation of baroclinic–barotropic instability and eddy growth. On the northeastern side of the anticyclonic gyre, the cyclonic eddy C4 of Fig. 10 emerged as a circulation structure in Fig. 15. The rim current of the gyre meandered around this cyclonic and a new jet developed, with velocities of the order of 40 cm s-1. This cyclonic eddy was much smaller than the LS1 C3 eddy, about 5–10 km in diameter, probably generated by a frontogenesis/cyclogenesis event along the anticyclonic rim current. The submesoscale field was mapped in the Mediterranean Sea (Bouffard et al., 2012) for the Balearic northern current. Here for the first time we found the growth of a small-scale submesoscale eddy at the border of a large, subbasin-scale gyre as a result of the meandering of its rim current. Notwithstanding the different generation mechanisms in different parts of the basin, we believe this is an evidence of pervasive submesoscale dynamics in the Mediterranean Sea.

We carried out a multiscale sampling experiment in the Gulf of Taranto. Our experiment provided the first synoptic evidence of the large-scale circulation structure and associated mesoscale variability. We used a classical sampling strategy, with a regularly spaced station network in three different subareas of the Gulf. In the open ocean, the LS1 and LS2 sampling was carried out at 15 km resolution over 3 days, while in the northeastern shelf area the station spacing was about 5 km and the 24 stations were carried out in 1 day. Finally the Mar Grande harbor scale was sampled at 1 km resolution and the sampling required 12 h.

The water mass analysis for LS1, LS2 and CS1 showed that the vertical thermohaline structure is dominated by four water mass types: the first one belonging to the surface, down to 30 m, characterized by relatively fresh and warm waters; the second one characterizing the thermocline; the third recognizable as MLIW with subsurface temperature and salinity maxima; and the fourth a deep water mass probably of Adriatic origin. The water column is highly stratified (Brunt–Väisälä frequency of the order of 10 cycles h-1) and the seasonal thermocline is located between 30 and 100 m. Furthermore a precipitation event occurred between LS1 and LS2 which lowered the surface salinity of 0.1 PSU, concomitantly changing the mixed layer temperatures of 0.5 ∘C due to a large wind event (Fig. 7).

The mapping of the temperature and salinity structures was carried out by objective analysis, calibrated for the specific survey sampling scheme. Starting with the mixed layer mapping, it emerged that large-scale frontal structures changed between LS1 and CS1 + LS2 cruises due to the growth and decay of small-scale, mixed layer intensified cyclonic eddies. The middle thermocline circulation, temperature and salinity structure of the Gulf of Taranto is dominated by an anticyclonic large-scale gyre. Its periphery is dominated by upwelling as shown by the low temperature and high salinity waters, especially on the northeastern side of the Gulf of Taranto. The gyre rim current was hydrodynamically unstable, generating frontogenesis and rim current segment intensifications. Two cyclonic eddy centers (C3 and C4) grew and decayed between LS1 and LS2. One of them could be classified as submesoscale, due to its small size, captured only by the finer sampling scheme of the CS1 survey.

The mapping of temperature and salinity fields in the Mar Grande suggests several density compensating fronts and generally low stratification, presumably connected to the estuarine vertical circulation.

The instability of gyre rim currents and/or large mesoscale eddy field borders has been studied in the past (Mc Williams et al., 1983; Pinardi et al., 1987; Staneva et al., 2001) and more recently for submesoscale generating fronts (Hamlington et al., 2014). The instabilities of rim currents connected to temperature frontal structures generate eddies, which are due to cyclogenetic processes such as mixed baroclinic–barotropic instabilities. In our case the observations show that instabilities occur in a week-long time frame and most importantly modulate the upwelling phenomena at the open-ocean–shelf-area interface, a mechanism that could be very important to support good environmental conditions in the near coastal regions. Numerical modeling studies have now started to understand the vorticity and energy dynamics of the flow field observed in this experiment.

In conclusion, regular sampling networks can capture most of the significant ocean variability if adequately calibrated for station resolution from the open ocean to the shelf and coastal–harbor scale. More observations will be required to map the seasonal variability of the anticyclonic gyre, the structure of the upwelling areas and their influence on the coastal ecosystem and the submesoscale flow field captured for the first time in this experiment.

This paper's MREA sampling methodology could be also used to collect data in order to respond to environmental emergencies, such as oil spills or other pollutant dispersal. If the location of the source of pollution is known, the CS1 sampling strategy could be carried out in 1 day, and forecasting models could have the initial condition adjusted to the measured fields through data assimilation, improving the forecast skill. Thus this paper has also put the basis for a protocol of in situ data collection that could support emergency management at sea.

The CTD data are available at the following link: http://mrea.sincem.unibo.it/index.php/experiments/mrea14. The objective analysis codes can be made available at any time upon request to nadia.pinardi@unibo.it.