Interocean exchanges and the spreading of Antarctic Intermediate Water south of Africa

International audienceArgo hydrographic profiles collected from 2004 to 2011 in the southeast Atlantic sector of the Southern Ocean are used in combination with hydrographic transects to describe the characteristics of Antarctic Intermediate Water (AAIW) in the region. Making use of the recently developed ANDRO velocity data set, we estimate the evolution of the dynamical properties of different AAIW varieties along their pathways within the isoneutral layer (27.1 < gn < 27.6). Three different regional varieties of intermediate water converge in the southeast Atlantic: Atlantic AAIW (A-AAIW, characterized by S ≤ 34.2), Indian AAIW (I-AAIW, S ≥ 34.3), and a previously unknown variety that we named Indo-Atlantic intermediate water (IA-AAIW, 34.2 < S < 34.3). South of Africa, the I-AAIW flowing within the Agulhas Current separates into two branches. One branch retroflects following the Agulhas Return Current (13.4 Sv) and proceeds back to the Indian Ocean. The other one separates from the Agulhas Current, and flows into the southeast Atlantic via the Cape Basin within mesoscale eddies (13.5 Sv). A-AAIW enters the domain between the Subtropical Front and the Subantarctic Front (36 Sv). Part of this water (28 Sv) flows eastward into the Indian Ocean, while 10 Sv are injected into the Cape Basin and mix with I-AAIW giving rise to the new IA-AAIW variety. The latter separates into two branches, both transporting 7.4 Sv. One flows northwestward and subducts along the Northern Subtropical Front, while the other moves eastward to contribute a sizable volume of fresh and oxygenated water to the Indian Ocean

Evidence of atmosphere–sea ice–ocean coupling in the Terra Nova Bay polynya (Ross Sea—Antarctica)

A rare long time series of hydrographic profiles and moored current meter data, collected from 1995 to 2008 in Terra Nova Bay polynya, are used in combination with meteorological data, acquired by an Automatic Weather Station, and remote sensing data from a Special Sensor Microwave/Imager. The behaviour of Terra Nova Bay coastal polynya in terms of air–ice–sea interactions and the consequent High Salinity Shelf Water production are detailed. The katabatic regime that characterizes Terra Nova Bay polynya is investigated and different types of events are distinguished on the bases of their duration and intensity. The more frequent katabatic events take place during the winter season from April to October, blowing on average 1–7 h, with speed between 25 and 56 m s−1 and they abruptly end in just a few hours. The link between the persistence of the wind and the opening of the polynya is showed. In particular, an increase of the open water percentage in correspondence with each katabatic event of long duration is detected. Terra Nova Bay polynya appears characterized by two different periods of activity during the winter season. A period characterized by a considerable sea-ice free area and by an increase in salinity along the water column (from July to November), which is preceded (from March to June) and followed (from December to February) by a period in which the polynya is still open but the salinity of the water column decreases. While the period between July and November appears related to a maximum efficiency of Terra Nova Bay polynya in the sea-ice production, the period from March to June marks a “partial” functioning of the polynya. During March–June, the polynya is partially free of ice and consequently the brine is released but, at this time of year, it is merely increasing the salinity of the upper layer of the ocean, reducing the stratification, but not causing High Salinity Shelf Water to be formed

COORDINATION INTERNATIONALE DES RÉSEAUX D'OBSERVATION IN SITU MET-OCEAN

International audienceFor the last 20 years, OceanOPS (formerly JCOMMOPS) has been providing vital services in coordinating, monitoring, and integrating data and metadata, across an expanding network of met-ocean observing communities. OceanOPS monitors and reports on the status of the GOOS to support effi cient operations, to ensure the transmission and timely exchange of high-quality metadata, and to assist free and unrestricted data delivery to users. OceanOPS tracks over 100,000 observations a day coming from profi ling fl oats, moored/drifting buoys, ocean time series reference stations, gliders, research vessels, ships of opportunity, sea level gauges and HF radars, and provides monitoring services and support to emerging networks, regional systems, and third-party projects to help the observing system implementation. In the context of OceanOPS 5-year Strategic Plan, the GOOS 2030 Strategy and implementation plan, the new earth system approach of the WMO, the UN Ocean Decade, and in close collaboration with European initiatives, OceanOPS ensures and promotes metadata standardization, integration, and interoperability across and within the global ocean observing networks, as well as develops web tools and metrics to analyse trends and to assess the current and future state of the GOOS.Au cours des 20 dernières années, OceanOPS (anciennement JCOMMOPS) a fourni des services vitaux en coordonnant, surveillant et intégrant les données et les métadonnées, à travers un réseau en expansion de communautés d'observation méta-océaniques. OceanOPS surveille et rend compte de l'état du GOOS afin de soutenir l'efficacité des opérations, d'assurer la transmission et l'échange en temps voulu de métadonnées de haute qualité, et d'aider à la livraison gratuite et sans restriction des données aux utilisateurs. OceanOPS suit plus de 100 000 observations par jour provenant de flotteurs professionnels, de bouées fixes/dérivantes, de stations de référence de séries chronologiques océaniques, de planeurs, de navires de recherche, de navires d'opportunité, de jauges de niveau de la mer et de radars HF, et fournit des services de surveillance et un soutien aux réseaux émergents, aux systèmes régionaux et aux projets tiers afin de faciliter la mise en œuvre du système d'observation. Dans le contexte du plan stratégique quinquennal de l'OceanOPS, de la stratégie et du plan de mise en œuvre du GOOS 2030, de la nouvelle approche du système terrestre de l'OMM, de la Décennie des Nations Unies pour l'océan, et en étroite collaboration avec les initiatives européennes, l'OceanOPS assure et promeut la normalisation, l'intégration et l'interopérabilité des métadonnées à travers et au sein des réseaux mondiaux d'observation de l'océan, et développe des outils Web et des mesures pour analyser les tendances et évaluer l'état actuel et futur du GOOS

Untargeted lipidomics reveals specific lipid profiles in COVID-19 patients with different severity from Campania region (Italy)

COVID-19 infection evokes various systemic alterations that push patients not only towards severe acute respiratory syndrome but causes an important metabolic dysregulation with following multi-organ alteration and potentially poor outcome. To discover novel potential biomarkers able to predict disease's severity and patient's outcome, in this study we applied untargeted lipidomics, by a reversed phase ultra-high performance liquid chromatography-trapped ion mobility mass spectrometry platform (RP-UHPLC-TIMS-MS), on blood samples collected at hospital admission in an Italian cohort of COVID-19 patients (45 mild, 54 severe, 21 controls). In a subset of patients, we also collected a second blood sample in correspondence of clinical phenotype modification (longitudinal population). Plasma lipid profiles revealed several lipids significantly modified in COVID-19 patients with respect to controls and able to discern between mild and severe clinical phenotype. Severe patients were characterized by a progressive decrease in the levels of LPCs, LPC-Os, PC-Os, and, on the contrary, an increase in overall TGs, PEs, and Ceramides. A machine learning model was built by using both the entire dataset and with a restricted lipid panel dataset, delivering comparable results in predicting severity (AUC= 0.777, CI: 0.639-0.904) and outcome (AUC= 0.789, CI: 0.658-0.910). Finally, re-building the model with 25 longitudinal (t1) samples, this resulted in 21 patients correctly classified. In conclusion, this study highlights specific lipid profiles that could be used monitor the possible trajectory of COVID-19 patients at hospital admission, which could be used in targeted approaches