Over the past 30 years, the Gulf of Lions in the northwestern Mediterranean Sea has undergone various environmental changes that have impacted marine ecosystems. At the beginning of the 21st century, shifts were observed in small pelagic fish and zooplankton communities. However, little is known about the impact of these changes on phytoplankton communities, especially pico- and nanophytoplankton (hereafter referred as PicoNano), which play a crucial role in the northwestern Mediterranean Sea. One major limitation is the lack of data on these size fractions during the observed shifts. In this study, we show that vertical profiles of temperature and salinity can serve to infer PicoNano assemblages from the thermohaline properties measured in a coastal area, the Bay of Marseille (BoM). We use functional data analysis and clustering methods to identify recurring vertical thermohaline structures over 30 years of temperature and salinity data (1994–2024), measured at low and high frequencies. To address the issue of truncated vertical profiles, we employ a reconstruction method based on functional principal component analysis. Using flow cytometry data collected over the past 11 years (2014–2024), we characterize PicoNano assemblages and the nutrient concentrations in the surface waters associated with each recurring thermohaline structure. We identify three thermohaline structures associated with the most oligotrophic conditions observed in the BoM, all involving a thermally stratified water column. The occurrence of these stratified conditions does not show any significant trend over the past 27 years. However, vertical structures indicative of Rhône River intrusions have been less frequently observed since 2004, while mixed water columns are more frequently highly homogeneous over the same period. We show that highly homogeneous water columns are associated with different PicoNano assemblages compared to mixed but less homogeneous water columns. Overall, these results suggest that thermohaline conditions associated with larger nanoeukaryotes have become less frequent since 2004. This work highlights that easily measurable variables, such as vertical profiles of temperature and salinity, can provide valuable insights into more complex variables – such as nutrient concentrations and PicoNano assemblages – when direct observations are unavailable, a situation commonly encountered in long-term and high-frequency monitoring.
Ocean monitoring is a crucial component of Earth's observation systems. These systems have evolved to achieve a global scope and an exceptionally high level of complexity, encompassing not only observation programs but also data centres for collection and storage, as well as computational facilities where predictions and re-analyses of oceanic conditions are generated using coupled ocean-atmosphere circulation models. The products generated by the different components of the oceanic observation systems are of paramount importance for the knowledge of the environmental state of the oceans, their variability ranges, the impact of natural and anthropogenic stressors (such as climate change) and, in summary, the appropriate governance of the sea and the sustainable use of its resources. Given the diverse nature of the data (collected through a wide range of technologies, instruments, and institutions across multiple countries), it can be challenging to maintain a comprehensive overview of existing observation programs and datasets. MonGOOS (Mediterranean Oceanographic Network for the Global Ocean Observing System) serves as a GOOS (Global Ocean Observing System) Regional Alliance (GRA). Through its Observations Working Group (OBSWG), MonGOOS has conducted an extensive consultation among Mediterranean institutions to compile information on ongoing observation programs and the institutions operating within this basin. The results of this consultation, obtained through the collaboration of a very large number of scientists and technicians from a wide range of institutions, are reflected in this community report. The objectives of this communication are: i. To update our knowledge of existing observation capabilities in the Mediterranean Sea.ii. To identify contact persons for strengthening the current MonGOOS network in order to keep this information up to date in the future.iii. To describe the present strengths and weaknesses of the Mediterranean Sea's observation system.iv. To support European ocean observation programs such as the Copernicus Marine Service and the European Marine Observation and Data Network (EMODnet). The consultation phase began in July 2023 and continued until December 2025. It included a survey addressed to 90 researchers from 59 institutions across 18 countries as well as contacts with several EuroGOOS task teams, IOC (Intergovernmental Oceanographic Commission) sea level monitoring, as well as OceanOPS, for the compilation of metadata. During this consultation phase, the list of researchers addressed increased to 128. Finally, metadata from observation activities were obtained from 74 institutions belonging to 16 countries, 4 companies and the European Joint Research Centre (JRC). The description of all the observation capabilities in the Mediterranean Sea is not closed and should be considered as an ongoing effort to be reviewed periodically and more systematically in the future. However, we believe that this objective has been accomplished to a high degree of detail and inclusivity. The present compilation includes most of the currently active and operational observing systems, including 80 Argo floats and 16 surface drifters, 58 high-frequency radars (HFR), 220 tide gauges and 88 tsunami alerting devices, 9 periodic or sustained glider lines, 5 FerryBox lines, 11 wave buoys, 36 subsurface or deep moorings, 46 meteorological and oceanographic surface buoys, 5 seabed platforms or cabled observatories, 13 periodic transects or repeated cruises, and 17 oceanographic stations visited on a regular basis (time-series stations). The present survey revealed a strong asymmetry in the distribution of these platforms, reflecting a clear discrepancy in the capabilities of different Mediterranean countries. It also shows the difficulty for maintaining long-term observations, the fragility of some of these programs, and the need for national programs with structural and sustainable funds that are not only subject to competitive financial support.
Satellite measurements indicate that the Barents and Kara Seas have experienced some of the most significant sea ice declines and increases in sea surface temperature in the Arctic. Understanding the processes driving these changes is of utmost importance to assess the implications of climate change in the Arctic and globally. Given the observational limitations in these high-latitude areas, coupled models and reanalysis products play a key role. However, it is essential to determine how reliably these products represent the observed sea ice decline and mixed-layer warming. Models are often evaluated on a pan-Arctic scale, but the model skill might differ when considering sub-regions. This study examines two coupled regional climate models (HIRHAM–NAOSIM 2.2 and RASM), two regional ocean reanalyses (RARE 1.15.2 and TOPAZ 4B), one global ocean reanalysis (ORAS5), and OSI SAF satellite observations over the period 1991–2020. We focus on spring (March–May) and autumn (August–October) sea ice area and sea ice concentration, the edge of the sea ice, and the temperature of the mixed layer. We also examine correlations between sea ice and mixed-layer temperature anomalies and five climate indices to assess how the datasets respond to large-scale climate variability. The results show a statistically significant decrease in sea ice area and sea ice concentration in the satellite measurements and in all reanalyses. However, HIRHAM–NAOSIM shows only a near-significant decline in the Barents Sea, RASM shows no long-term change, and none of the two models shows a significant trend in the Kara Sea. Additionally, the reanalyses show a more pronounced warming of the mixed layer than the coupled models, particularly in autumn and in the Kara Sea. ORAS5 and TOPAZ agree most closely with the observed sea ice, partly because assimilated observations constrain their historical states, whereas RARE reproduces the decreasing trends but overestimates sea ice and shows anomalous mixed-layer temperature behaviour in the Barents Sea. The reanalyses also represent the relationships with the climate indices more consistently than the coupled models, particularly for the Atlantic Multidecadal Oscillation and the Northern Hemisphere ocean heat content. Among the coupled models, HIRHAM–NAOSIM represents changes in the Barents Sea better than RASM, but in the Kara Sea, where none of the models captures the decreasing trends, RASM has a more realistic mean sea-ice state. Although the coupled models reproduce interannual variability, the results indicate that in particular the effects of large-scale changes of the ocean circulation are not realistically reproduced in the coupled models.
Typically, diatoms, haptophytes, and cryptophytes dominate phytoplankton biomass and abundance in the Southern Ocean, determining bloom dynamics and carbon transfer and responding to physical factors such as sea ice concentration and mixing. However, phytoplankton community composition remains understudied in many regions of the Southern Ocean. Here, we present pigment-based phytoplankton community-composition data from a summer cruise to the northwest Weddell Sea continental shelf. Our pigment and diagnostic pigment analysis results show a predominance of fucoxanthin and 19'-hexanolyoxyfucoxanthin, i.e., diatoms and haptophytes, throughout various oceanic regimes and water column depths, consistent with community compositions from similar cold continental shelf regions. These are rare pigment-based community composition data from this key region for deep-water formation and carbon transport. Taken together, the relative biomass of the three dominant phytoplankton groups (39.7 % diatoms, 30.2 % haptophytes, and 14.9 % cryptophytes) help to explain (R2= 0.37–0.82) the magnitude of carbon export to 150 m. While additional measurements are needed to verify these relationships, these findings highlight the need to consider frequently overlooked phytoplankton types, alongside diatoms, in Southern Ocean models of carbon export due to the key, but differing, roles these groups play in production-export dynamics.
This study evaluates the impact of riverine freshwater fluxes on the Atlantic Ocean. The river discharge has been estimated at the outlet of 18 major rivers flowing into the Atlantic by solving the water mass balance equation at the river basin scale. In this approach, water storage changes are evaluated with satellite gravimetry measurements. In contrast, atmospheric fluxes (i.e., precipitation, evapotranspiration) are assessed with atmospheric reanalyses, in situ measurements from a global network of rain gauges, or a global hydrological model. The river discharge estimated with the water mass balance is consistent with independent river gauge measurements across all South American rivers, in particular the Amazon, where the annual and monthly climatology can be estimated with an error of less than 5 % when compared with in situ measurements. Larger discrepancies are observed for other basins, likely due to uncertainties in the precipitation and evapotranspiration fluxes. When climatological estimates of the river discharge are replaced by the water mass balance approach in ocean model simulations, a decrease in the sea surface salinity bias is observed at the outlet of the Amazon and across the whole Atlantic. The water mass balance approach can bring new observational constraints on freshwater fluxes flowing from the continent to the ocean, but is not universally reliable due to potential biases in atmospheric flux estimates. When combined with in situ measurements or hydrological model predictions, the water mass balance approach can enhance freshwater flux quantification, leading to improved ocean model simulations near major river outlets. In particular, the enhanced river runoff dataset significantly reduces salinity biases, with associated changes in vertical stratification, upper ocean circulation and basin scale meridional transports.
