The advent of open science and the United Nations Decade of Ocean Science for Sustainable Development are revolutionizing the ocean-data-sharing landscape for an efficient and transparent ocean information and knowledge generation. This blue revolution raised awareness on the importance of metadata and community standards to activate interoperability of the digital assets (data and services) and guarantee that data-driven science preserves provenance, lineage and quality information for its replicability. Historical data are frequently not compliant with these criteria, lacking metadata information that was not retained, crucial at the time of data generation and further ingestion into marine data infrastructures. The present data review is an example attempt to fill this gap through a thorough data reprocessing starting from the original raw data and operational log sheets. The data gathered using XBT (eXpendable BathyThermograph) probes during several monitoring activities in the Tyrrhenian and Ligurian seas between 1999 and 2019 have first been formatted and standardized according to the latest community best practices and all available metadata have been inserted, including calibration information never applied, uncertainty specification and bias correction from Cheng et al. (2014). Secondly, a new automatic quality control (QC) procedure has been developed and a new interpolation scheme applied. The reprocessed (REP) dataset has been compared to the data version, presently available from the SeaDataNet (SDN) data access portal, processed according to the pioneering work of Manzella et al. (2003) conducted in the framework of the European Union Mediterranean Forecasting System Pilot Project (Pinardi et al., 2003). The comparison between REP and SDN datasets has the objective to highlight the main differences derived from the new data processing process. The maximum discrepancy among the REP and SDN data versions always resides within the surface layer (REP profiles are warmer than SDN ones) until 150 m depth generally when the thermocline settles (from June to November). The overall bias and root mean square difference are equal to 0.002 and 0.041 °C, respectively. Such differences are mainly due to the new interpolation technique (Barker and McDougall, 2020) and the application of the calibration correction in the REP dataset. The REP dataset (Reseghetti et al., 2024; https://doi.org/10.13127/rep_xbt_1999_2019.2) is available and accessible through the INGV (Istituto Nazionale di Geofisica e Vulcanologia, Bologna) ERDDAP (Environmental Research Division's Data Access Program) server, which allows for machine-to-machine data access in compliance with the FAIR (findable, accessible, interoperable and reusable) principles (Wilkinson et al., 2016).

We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.

SOURCE utility for reprocessing, calibration, and evaluation is a software designed for web applications that permits to calibrate and validate ocean models within a selected spatial domain using in-situ observations. Nowadays, in-situ observations can be freely accessed online through several marine data portals together with the metadata information about the data provenance and its quality. Metadata information and compliance with modern data standards allow the user to select and filter the data according to the level of quality required for the intended use and application. However, the available data sets might still contain anomalous data, bad data flagged as good, due to several reasons, i.e., the general quality assurance procedures adopted by the data infrastructure, the selected data type, the timeliness of delivery, etc. In order to provide accurate model skill scores, the SOURCE utility performs a secondary quality check, or re-processing, of observations through gross check tests and a recursive statistical quality control. This first and basic SOURCE implementation uses Near Real Time moored temperature and salinity observations distributed by the Copernicus Marine Environment and Monitoring Service (CMEMS) and two model products from Istituto Nazionale di Geofisica e Vulcanologia (INGV), the first an analysis and the second a reanalysis, distributed during CMEMS phase I for the Mediterranean Sea. The SOURCE tool is freely available to the scientific community through the ZENODO open access repository, consistent with the open science principles and for that it has been designed to be relocatable, to manage multiple model outputs, and different data types. Moreover, its observation reprocessing module provides the possibility to characterize temperature and salinity variability at each mooring site and continuously monitor the ocean state. Highest quality mooring time series at 90 sites and the corresponding model values have been obtained and used to compute model skill scores. The SOURCE output also includes mooring climatologies, trends, Probability Density Functions and averages at different time scales. Model skill scores and site statistics can be used to visually inspect both model and sensor performance in Near Real Time at the single site or at the basin scale. The SOURCE utility uptake allows the interested user to adapt it to its specific purpose or domain, including for example additional parameters and statistics for early warning applications.

The action of propeller-induced jets on the seabed of ports can cause erosion and the deposition of sediment around the port basin, potentially significantly impacting the bottom topography over the medium and long term. If such dynamics are constantly repeated for long periods, a drastic reduction in ships' clearance can result through accretion, or the stability and duration of structures can be threatened through erosion. These sediment-related processes present port management authorities with problems, both in terms of navigational safety and the optimization of management and maintenance activities of the port's bottom and infrastructure. In this study, which is based on integrated numerical modeling, we examine the hydrodynamics and the related bottom sediment erosion and accumulation patterns induced by the action of vessel propellers in the passenger port of Genoa, Italy. The proposed new methodology offers a state-of-the-art science-based tool that can be used to optimize and efficiently plan port management and seabed maintenance.

In this work, we analyze the geomagnetic field measurements collected from 2017 to 2020 at the Italian observatories of Lampedusa and Duronia (an island and inland site, respectively) for investigating a possible signature of the tidal sea water level changes on the local magnetic variations. We obtain the following main results: (a) evidence of the geomagnetic power spectral peaks at the solar and lunar tidal main frequencies at both sites is found; (b) by using a robust fit procedure, we find that the geomagnetic field variations at Lampedusa are strongly influenced by the lunar tidal variations in the sea level, while at Duronia, the main effects on the geomagnetic field variations are associated with diurnal solar ionospheric tides; (c) a single-station induction arrows (SSIAs) investigation reveals different behaviors between Lampedusa and Duronia. Specifically, Lampedusa shows that the induction arrows in different frequency ranges point toward different directions with different amplitudes, probably related to the surrounding regions with different water depths, while Duronia shows a persistent coast effect, with the induction arrows pointing toward the Adriatic sea; and (d) a Superposed Epoch Analysis reveals, only for Lampedusa, a close relationship between SSIAs with a frequency of >2 mHz (<1.3 mHz) and the sea level variations driven by the astronomical O1 tide, indicating an amplitude intensification of ∼4×10−3 (∼5×10−3) and an azimuthal angle increment of ∼3∘( ∼9∘), in correspondence to a 1 cm sea level increase.

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities. In 2023, the sea surface temperature (SST) and upper 2000 m ocean heat content (OHC) reached record highs. The 0–2000 m OHC in 2023 exceeded that of 2022 by 15 ± 10 ZJ (1 Zetta Joules = 1021 Joules) (updated IAP/CAS data); 9 ± 5 ZJ (NCEI/NOAA data). The Tropical Atlantic Ocean, the Mediterranean Sea, and southern oceans recorded their highest OHC observed since the 1950s. Associated with the onset of a strong El Niño, the global SST reached its record high in 2023 with an annual mean of ∼0.23°C higher than 2022 and an astounding > 0.3°C above 2022 values for the second half of 2023. The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

It is well established that most of the plastic pollution found in the oceans is transported via rivers. Unfortunately, the main processes contributing to plastic and debris displacement through riparian systems is still poorly understood. The Marine Litter Drifter project from the Arno River aims at using modern consumer software and hardware technologies to track the movements of real anthropogenic marine debris (AMD) from rivers. The innovative “Marine Litter Trackers” (MLT) were utilized as they are reliable, robust, self-powered and they present almost no maintenance costs. Furthermore, they can be built not only by those trained in the field but also by those with no specific expertise, including high school students, simply by following the instructions. Five dispersion experiments were successfully conducted from April 2021 to December 2021, using different types of trackers in different seasons and weather conditions. The maximum distance tracked was 2845 km for a period of 94 days. The activity at sea was integrated by use of Lagrangian numerical models that also assisted in planning the deployments and the recovery of drifters. The observed tracking data in turn were used for calibration and validation, recursively improving their quality. The dynamics of marine litter (ML) dispersion in the Tyrrhenian Sea is also discussed, along with the potential for open-source approaches including the “citizen science” perspective for both improving big data collection and educating/awareness-raising on AMD issues.

Joint Meeting of IQuOD, GTSPP, SOOP, and XBT Science Groups What: More than 50 international experts of ocean observations, data quality control, and data management came together, under the umbrella of the International Oceanographic Data and Information Exchange (IODE) of the Intergovernmental Oceanographic Commission (IOC) of UNESCO, to explore future collaborations and synergies for the efficient ocean in situ data and products provision (https://oceanexpert.org/event/4431). When: 11–15 November 2024 Where: Bologna (Italy)

Global ocean warming continued unabated in 2025 in response to increased greenhouse gas concentrations and recent reductions in sulfate aerosols, reflecting the long-term accumulation of heat within the climate system, with conditions evolving toward La Niña during the year. In 2025, global upper 2000 m ocean heat content (OHC) increased by ∼23 ± 8 ZJ relative to 2024 according to IAP/CAS estimates. CIGAR-RT, and Copernicus Marine data confirm the continued ocean heat gain. Regionally, about 33% of the global ocean area ranked among its historical (1958–2025) top three warmest conditions, while about 57% fell within the top five, including the tropical and South Atlantic Ocean, Mediterranean Sea, North Indian Ocean, and Southern Oceans, underscoring the broad ocean warming across basins. Multiple datasets consistently indicate ocean warming, as measured by 0–2000 m OHC, increased from 0.14 ± 0.03 W m−2 (10 yr)−1 during 1960–2025 to 0.32 ± 0.14 W m−2 (10 yr)−1 during 2005–2025 (IAP/CAS), the latter being consistent with EEI (Earth’s Energy Imbalance) estimates within uncertainties. In contrast, the global annual mean sea surface temperature (SST) in 2025 was 0.49°C above the 1981–2010 baseline and 0.12 ± 0.03°C lower than in 2024 (IAP/CAS; similar in CMA-SST, FY3 MWRI SST, ERSSTv5 and Copernicus Marine data), consistent with the development of La Niña conditions, but still ranking as the third-warmest year on record.