Understanding the seasonal dynamics of plankton in the Atlantic Ocean is the first step towards the proper assessment of marine ecosystem health and productivity. This study examines the response of the total chlorophyll-a concentration to physical drivers observable by remote sensing in the Atlantic Ocean using a combination of multivariate Principal Component Analysis (PCA) and deep learning models. The results show that the Sea Surface Salinity (SSS), Absolute Dynamic Topography (ADT), and Sea Surface Temperature (SST) are found to be the predominant drivers of physical variability across the ocean, with distinct spatial patterns. The clustering of the principal components identifies regions characterised by distinct physical processes. Based on these clusters, we devised a Transformer Encoder model to predict chlorophyll-a concentrations in three distinct regions. The model outperformed climatological baselines, especially in the temperate and tropical regions, though accuracy varied seasonally, with higher accuracy in winter months and increased complexity in summer due to more dynamic oceanographic conditions. A SHAP-based sensitivity analysis showed that ADT and SSS dominate chlorophyll-a variability, particularly during summer months, while SST, mixed layer depth (MLD), ocean currents (OC) and wind stress (WS) exert secondary but regionally and seasonally distinct influences. The study highlights the necessity to account for both seasonal and regional differences in predictive modelling, and it underscores the importance of continuing to develop spatio-temporal models to improve forecasting accuracy for marine ecosystem management and conservation.

Mediterranean Sea surface temperature (SST) has warmed consistently over the past century, with a clear intensification in recent decades. Using multiple observational SST products spanning more than 170 years, we jointly analyse the low-frequency SST, T ( t ), and its temporal derivative, d T /d t , to characterize the interplay between historical multidecadal modulation and long-term warming in Mediterranean SST evolution. During the satellite era (1982–2025), the basin-mean SST exhibits a linear warming trend of 0.34 ± 0.06 °C/decade and pronounced warm-season amplification. After removing the component linearly associated with global mean SST, an Atlantic Multidecadal Variability-like modulation remains evident in Mediterranean residual variability, whereas multidecadal reversals become less apparent in the total SST record. Change-point and phase-space diagnostics indicate an early twentieth-century shift toward higher low-frequency SSTs and a late twentieth-century shift toward sustained positive warming rates, placing recent decades in a warmer and faster-warming region of the ( T ( t ), d T /d t ) phase space. Overall, the observational record suggests that long-term warming is becoming increasingly dominant in shaping Mediterranean SST variability.

Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. However, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patterns of trends in six fundamental components of upper ocean physics. We analyze a data-driven reconstruction of the ocean state covering the 1993-2018 period. We found that including temporal derivatives in the state vector enhances the description of the ocean's dynamical system. Once Pacific oscillations are properly accounted for, averaged surface warming appears >60% faster, and a deep reshaping of the seascape is revealed. A clustering of the trend patterns identifies the main factors that drive observed trends in chlorophyll a concentration. This data-driven approach provides a wider framework for empirical climate modeling.

The Surface Water and Ocean Topography (SWOT) mission provides unprecedented high-resolution observations of sea-surface height. However, their direct use in ocean circulation studies is complicated by the presence of small-scale unbalanced motion signals and instrumental noise, which hinder accurate estimation of geostrophic velocities. To address these limitations, we developed an adaptive convolutional neural network (CNN)-based filtering technique that refines SWOT-derived sea-level observations. The network includes multi-head attention layers to exploit information on concurrent wind fields and standard altimetry interpolation errors. We train the model with a custom loss function that accounts for the differences between geostrophic velocities computed from SWOT sea-surface topography and simultaneous in-situ drifter velocities. We compare our method to existing filtering techniques, including a U-Net-based model and a variational noise-reduction filter. Our adaptive-filtering CNN produces accurate velocity estimates while preserving small-scale features and achieving a substantial noise reduction in the spectral domain. By combining satellite and in-situ data with machine learning, this work demonstrates the potential of an adaptive CNN-based filtering approach to enhance the accuracy and reliability of SWOT-derived sea-level and velocity estimates, providing a valuable tool for global oceanographic applications.

Direct in situ measurements of ocean currents are still quite limited and, due to its small magnitude, measurements of the vertical velocity remain one of the biggest challenges in oceanography. Vertical velocities are therefore generally inferred indirectly, and a common approach to diagnose them is to use the quasi-geostrophic omega equation. In the framework of the European Space Agency World Ocean Circulation project, a new high-resolution (1/10°) data-driven dataset of 3D ocean currents, including the vertical component, has been developed: the WOC-NATL3D dataset. The product domain extends over a wide portion of the North Atlantic Ocean from the surface down to 1500 m depth, and the dataset covers the period between 2010 and 2019. This entire domain holds immense importance for fishery activities and is identified as a key area within international conventions for the conservation of fishing resources, such as tuna and tuna-like fishes under ICCAT (International Commission for the Conservation of Atlantic Tunas). To generate this product, a diabatic quasi-geostrophic diagnostic model is applied to data-driven 3D temperature and salinity fields obtained through a deep learning technique, along with ERA5 fluxes and empirical estimates of the horizontal Ekman currents based on input provided by the European Copernicus Marine Service. The assessment of WOC-NATL3D currents is performed by direct validation of the total horizontal velocities with independent drifter estimates at various depths (0, 15 and 1000 m) and by comparing them with existing reanalyses that are obtained through the assimilation of observations into ocean general circulation numerical models. Our estimates of the ageostrophic components of the flow improve the total horizontal velocity reconstruction, being more accurate and closer-to-observations than model reanalyses in the upper layers, also providing an indirect proof of the reliability of the resulting vertical velocities.

This study evaluates the potential impact of the Copernicus Imaging Microwave Radiometer (CIMR) mission on the sea surface temperature (SST) products of the Mediterranean Sea. Currently, infrared (IR) radiometers provide accurate, high-resolution SST measurements, but they are limited by their inability to see through clouds. Passive microwave (PMW) radiometers, on the other hand, offer monitoring capabilities in almost all weather conditions but typically at lower spatial resolutions. The CIMR mission represents a notable advance in microwave remote sensing of SSTs, as it will ensure a ≤15 km spatial resolution in the recovered SST field. Using an observing system simulation experiment (OSSE), this study evaluates the effect of inserting synthetic CIMR observations into the Copernicus Mediterranean SST analysis system, which is based on an optimal interpolation (OI) algorithm. The OSSE was conducted using data for the year 2017, including daily SST and salinity outputs from a Mediterranean Sea model, hourly precipitation rates from the IMERG, and wind and cloud cover data from ERA5. The results suggest that the improved spatial resolution and accuracy of the CIMR could potentially improve SST retrievals in the Mediterranean Sea, offering better insights for climate and environmental monitoring in semi-closed basins. Including CIMR data in the OI algorithm reduced the mean error and root mean square error (RMSE) of the SST analysis, especially under conditions of low IR coverage. The greatest improvements were found to occur in July, corresponding to coastal upwelling and Atlantic inflow into the Alboran Sea. Improvements ranged from 16% to 29%, with an overall improvement of 26% for the full year of 2017. In conclusion, this preliminary study indicates that Copernicus Mediterranean Sea HR SST products could benefit from the inclusion of the CIMR in the current IR sensor constellation.

In this work, we address the super-resolution problem of satellite-derived sea surface temperature (SST) using deep generative models. Although standard gap-filling techniques are effective in producing spatially complete datasets, they inherently smooth out fine-scale features that may be critical for a better understanding of the ocean dynamics. We investigate the use of deep learning models as Autoencoders (AEs) and generative models as Conditional-Generative Adversarial Networks (C-GANs), to reconstruct small-scale structures lost during interpolation. Our supervised – model free – training is based on SST observations of the Mediterranean Sea, with a focus on learning the conditional distribution of high-resolution fields given their low-resolution counterparts. We apply a tiling and merging strategy to deal with limited observational coverage and to ensure spatial continuity. Quantitative evaluations based on mean squared error metrics, spectral analysis, and gradient statistics show that while the AE reduces reconstruction error, it fails to recover high-frequency variability. In contrast, the C-GAN effectively restores the statistical properties of the true SST field at the cost of increasing the pointwise discrepancy with the ground truth observation. Our results highlight the potential of deep generative models to enhance the physical and statistical realism of gap-filled satellite data in oceanographic applications.

Our study focuses on absolute dynamic topography (ADT) and sea surface temperature (SST) mapping from satellite observations, with the primary objective of improving the satellite-derived ADT (and derived geostrophic currents) spatial resolution. Retrieving consistent high-resolution ADT and SST information from space is challenging, due to instrument limitations, sampling constraints, and degradations introduced by the interpolation algorithms used to obtain gap-free (L4) analyses. To address these issues, we developed and tested different deep learning methodologies, specifically convolutional neural network (CNN) models that were originally proposed for single-image super resolution. Building upon recent findings, we conduct an Observing System Simulation Experiment (OSSE) relying on Copernicus numerical model outputs (with respective temporal and spatial resolutions of 1 d and 1/24°), and we present a strategy for further refinements. Previous OSSEs combined low-resolution L4 satellite equivalent ADTs with high-resolution “perfectly known” SSTs to derive high-resolution sea surface dynamical features. Here, we introduce realistic SST L4 processing errors and modify the network to concurrently predict high-resolution SST and ADT from synthetic, satellite equivalent L4 products. This modification allows us to evaluate the potential enhancement in the ADT and SST mapping while integrating dynamical constraints through tailored, physics-informed loss functions. The neural networks are thus trained using OSSE data and subsequently applied to the Copernicus Marine Service satellite-derived ADTs and SSTs, allowing us to reconstruct super-resolved ADTs and geostrophic currents at the same spatiotemporal resolution of the model outputs employed for the OSSE. A 12-year-long time series of super-resolved geostrophic currents (2008–2019) is thus presented and validated against in situ-measured currents from drogued drifting buoys and via spectral analyses. This study suggests that CNNs are beneficial for improving standard altimetry mapping: they generally sharpen the ADT gradients, with consequent correction of the surface currents direction and intensities with respect to the altimeter-derived products. Our investigation is focused on the Mediterranean Sea, quite a challenging region due to its small Rossby deformation radius (around 10 km).

The contribution of the ocean biological carbon pump to the export of organic carbon at depth has predominantly been assessed by considering sinking particulate matter and vertically migrating organisms. Despite growing recognition of the importance of dynamical pathways that export carbon through upper-ocean mixing and advection, observation-based estimates of their global impact are still lacking. Here, we quantify the values and uncertainties of the export driven by the physical injection pump (PIP) and its interannual variability by leveraging a 4D data-driven time series (1997-2018) of particulate organic carbon concentration (POC) and ocean circulation, as well as 3D fields of climatological dissolved organic carbon (DOC). Vertical diffusion dominates our POC export estimates, but remains the most uncertain process. Assuming maximal diffusivity estimates that are consistent with observations, POC and DOC export amount to 0.37 Pg C yr⁻¹ and 0.48 Pg C yr⁻¹, respectively. The contribution from entrainment and advection is strongly modulated by the POC annual cycle, revealing the critical coupling between biological production and upper-layer mixing in driving the net annual export. Observed interannual signals correlate with a linear combination of El Niño-Southern Oscillation and Southern Annular Mode indices, suggesting that the PIP is connected to intermediate- and mode-water formation dynamics in the Southern Ocean.

Monitoring the ocean's four-dimensional state is essential for marine ecosystem preservation. Artificial Intelligence (AI) algorithms represent promising tools to merge satellite and in situ measurements, improving reconstructions of ocean interior dynamics. Here, we describe 4DMED-bionet, an AI-based model developed under the European Space Agency 4DMED-Sea project, designed to infer subsurface properties from surface observations. Combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers, the model reconstructs 4D fields of temperature, salinity, density and chlorophyll-a in the Mediterranean Sea. The algorithm includes a physics-informed loss function that imposes constraints on density predictions, improving its accuracy without degrading other outputs. 4DMED-bionet outperforms different deep learning models, providing a high-quality 4D dataset, available at https://doi.org/10.25423/CMCC/4DMEDSEA_BIOPHYS_REP_3D. This dataset includes 4D geostrophic velocities derived from reconstructed physical tracers and surface geostrophic currents. Scientific analysis of 4D data is ongoing, aiming to better understand the processes that couple phytoplankton responses with 3D physical dynamic.

In this paper we introduce a new high-resolution (1/10°) data-driven dataset of 3D ocean currents developed by the National Research Council of Italy in the framework of the European Space Agency World Ocean Circulation project: the WOC-NATL3D dataset. The product domain extends over a wide portion of the North Atlantic Ocean from the surface down to 1500 m depth, and the dataset covers the period between 2010 and 2019. To generate this product, a diabatic quasi-geostrophic diagnostic model is applied to data-driven 3D temperature and salinity fields obtained through a deep learning technique, along with ERA5 fluxes and empirical estimates of the horizontal Ekman currents based on input provided by the European Copernicus Marine Service. The assessment of WOC-NATL3D currents is performed by direct validation of the total horizontal velocities with independent drifter estimates at various depths (0, 15 and 1000 m) and by comparing them with existing reanalyses that are obtained through the assimilation of observations into ocean general circulation numerical models. Our estimates of the ageostrophic components of the flow improve the total horizontal velocity reconstruction, being more accurate and closer to observations than model reanalyses in the upper layers, also providing an indirect proof of the reliability of the resulting vertical velocities. The reconstructed WOC-NATL3D currents are freely available at https://doi.org/10.12770/0aa7daac-43e6-42f3-9f95-ef7da46bc702 (Buongiorno Nardelli, 2022).

We present a study on the ocean surface currents reconstruction by merging Level-4 (L4, gap-free) altimeter-derived geostrophic currents and satellite sea surface temperature. Building upon past studies on the multi-variate reconstruction of geostrophic currents from satellite observations, we regionalized and optimized an algorithm to improve the altimeter-derived surface circulation estimates in the North Atlantic Ocean. A ten-year-long time series (2010–2019) is presented and validated by means of in situ observations. The newly optimized algorithm allowed us to improve the currents estimate along the main axis of the Gulf Stream and in correspondence of well-known upwelling areas in the North Eastern Atlantic, with percentage improvements of around 15% compared to standard operational altimetry products.

Sea surface temperature (SST) is one of the essential variables of the Earth's climate system. Being at the air–sea interface, SST modulates heat fluxes in and out of the ocean, provides insight into several upper and interior ocean dynamical processes, and is a fundamental indicator of climate variability potentially impacting the health of marine ecosystems. Its accurate estimation and regular monitoring from space is therefore crucial. However, even if satellite infrared/microwave measurements provide much better coverage than what is achievable from in situ platforms, they cannot sense the sea surface under cloudy and rainy conditions. Large gaps are present even in merged multi-sensor satellite products, and different statistical strategies, mostly based on optimal interpolation (OI) algorithms, have thus been proposed to obtain gap-free (L4) images. These techniques, however, filter out the signals below the space–time decorrelation scales considered, significantly smoothing most of the small mesoscale and submesoscale features. Here, deep learning models, originally designed for single-image super resolution (SR), are applied to enhance the effective resolution of SST products and the accuracy of SST gradients. SR schemes include a set of computer vision techniques leveraging convolutional neural networks to retrieve high-resolution data from low-resolution images. A dilated convolutional multi-scale learning network, which includes an adaptive residual strategy and implements a channel attention mechanism, is used to reconstruct features in SST data at 1/100° spatial resolution starting from 1/16° data over the Mediterranean Sea. The application of this technique shows an improvement in the high-resolution reconstruction, capturing small-scale features and providing a root-mean-squared-difference improvement of 0.02 °C with respect to the L3 ground-truth data.

Subpolar Mode Water (SPMW) is an important water mass originating in the eastern North Atlantic. Its formation, subject to modification through oceanic interior mixing, can directly influence the volume of water contributing to the Atlantic meridional overturning circulation. Utilizing observation-based data sets spanning from 1993 to 2018, we estimated the formation rates and volume of SPMW within isopycnal layers and examined its temporal variability. Two complementary approaches were used to estimate the formation rate: a thermodynamic approach focusing on the air-sea interactions and a kinematic approach involving volume transport from the mixed layer to the ocean's interior, including the entrainment/detrainment of the mixed layer itself. This is the first time that thermodynamic and kinematic approaches are applied to observation-based data in the North Atlantic. Our results suggest a substantial role of diapycnal mixing in diluting the dense waters formed by air-sea fluxes toward the range of SPMW densities. The study reveals a complex interplay of processes, with entrainment being the primary driver of subduction/obduction rates, while advection contributes to the overall small-scale dynamics. Variations in the volume and location of SPMW formation are observed from year to year. Notably, when SPMW forms extensively in lighter isopycnal layers, the volume occupied by denser isopycnals decreases and vice versa. We attributed this compensation effect to a propagation signal, where formation in the lightest isopycnal bins influences the formation in denser isopycnal bins with a delay of a few years, emphasizing the circulation's role in shaping the SPMW distribution. We present an analysis of one important type of water mass, the Subpolar Mode Water (SPMW) located in the North Atlantic Ocean. We looked at data collected over a period of 26 years from 1993 to 2018, to understand how SPMW forms and how its volume changes over time. We used two different methods to calculate how much SPMW forms each year to have the best possible picture. One method looked at air-water interactions, and the other at how the water moves from the top layer of the ocean to deeper parts. This is the first time these methods are applied to observation-based data in the North Atlantic. Our findings highlight the important role of water mixing in changing the density of SPMW. We also found that one of the main reasons how the SPMW leaves the top layer of the ocean is through the shoaling/deepening of the mixed layer itself over time, modulated horizontally by the small-scale dynamics of the ocean. Additionally, the volume of SPMW and location where it forms can vary from year to year. Notably, when SPMW forms in larger amounts in the upper part of the ocean, there is less SPMW in the denser water. Subduction of water below the mixed layer is driven by entrainment with advection modulating the small-scale dynamics Volume and formation of Subpolar Mode Water changes every year in size and location The difference between the formation rate from the thermodynamic and kinematic approaches suggests a large role of diapycnal mixing

The Harmony satellite mission was recently approved as the next European Space Agency (ESA) Earth Explorer 10. The mission science objectives cover several applications related to solid earth, the cryosphere, upper-ocean dynamics and air–sea interactions. The mission consists of a constellation of two satellites, flying with the Copernicus Sentinel 1 (C or D) spacecraft, each hosting a C-band receive-only radar and a thermal infrared (TIR) payload. From an ocean dynamics/air–sea interaction perspective, the mission will provide the unique opportunity to observe simultaneously the signature of submesoscale upper-ocean processes via synthetic aperture radar and TIR imagery. The TIR imager is based on microbolometer technology and its acquisitions will rely on four channels: three narrow-band channels yielding observations at a ≃1 km spatial sampling distance (SSD) and a panchromatic (PAN, 8–12 μm) channel characterized by a ≃300 m SSD. Our study investigates the potential of Harmony in retrieving spatial features related to sea surface temperature (SST) gradients from the high-resolution PAN channel, relying on top-of-atmosphere (TOA) observations. Compared to a standard SST gradient retrieval, our approach does not require atmospheric correction, thus avoiding uncertainties due to inter-channel co-registration and radiometric consistency, with the possibility of exploiting the higher resolution of the PAN channel. The investigations were carried out simulating the future Harmony TOA radiances (TARs), as well as relying on existing state-of-the-art level 1 satellite products. Our approach enables the correct description of SST features at the sea surface avoiding the generation of spurious features due to atmospheric correction and/or instrumental issues. In addition, analyses based on existing satellite products suggest that the clear-sky TOA observations, in a typical mid-latitude scene, allow the reconstruction of up to 85% of the gradient magnitudes found at the sea-surface level. The methodology is less efficient in tropical areas, suffering from smoothing effects due to the high concentrations of water vapor.

<div> <p><span data-contrast="none">Producing comprehensive information about the ocean has become a top priority to monitor and predict the ocean and climate change.</span><span data-contrast="none"> Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is developed thought the Copernicus Marine Service MultiOBservation Thematic Assembly (</span><span data-contrast="auto">MOB TAC). Recent advances in data fusion techniques and use of machine-learning approach open the possibility of producing estimators of ocean physic and biogeochemistry (BGC) operationally, using input data from diverse sensors, satellites and in-situ programs.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":160,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">MOB TAC provides the following multi observations products at global scale: </span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":60,"335559740":259}"> </span></p> </div> <div> <p><span data-contrast="auto">Blue ocean</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":60,"335559740":259}"> </span></p> </div> <div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">3D temperature, salinity, geopotential height and geostrophic current fields, both in near-real-time (NRT) and as long time series (REP=Reprocessing) in delayed-mode;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">2D sea surface salinity and sea surface density fields, both in NRT and as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">2D total surface and near-surface currents, both in NRT and as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="4" data-aria-level="1"><span data-contrast="auto">3D Vertical velocity fields as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="5" data-aria-level="1"><span data-contrast="auto">L2Q and L4 sea surface salinity from SMOS in REP and NRT (only L2Q)</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> </ul> </div> </div> <div> <div> <p><span data-contrast="auto">Green ocean</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559685":0,"335559739":200,"335559740":276}"> </span></p> </div> <div> <ul> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="1" data-aria-level="1"><span data-contrast="auto">2D surface carbon data sets of FCO2, pCO2, DIC, Alkalinity, saturation states of surface waters with respect to calcite and aragonite as REP;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="2" data-aria-level="1"><span data-contrast="auto">Nutrient and Carbon vertical distribution (including Nitrates, Phosphates, Silicates, pH, pCO2, Alkalinity, DIC) profiles as REP and NRT;</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> <li data-leveltext="" data-font="Wingdings" data-listid="1" data-list-defn-props="{"335552541":1,"335559684":-2,"335559685":720,"335559991":360,"469769226":"Wingdings","469769242":[9642],"469777803":"left","469777804":"","469777815":"hybridMultilevel"}" aria-setsize="-1" data-aria-posinset="3" data-aria-level="1"><span data-contrast="auto">3D Particulate Organic Carbon (POC), particulate backscattering coefficient (bbp) and Chlorophyll a (Chl-a) fields as REP.</span><span data-ccp-props="{"201341983":0,"335551550":6,"335551620":6,"335559739":200,"335559740":276}"> </span></li> </ul> </div> <div> <p><span data-contrast="auto">Parallel to its portfolio, MOB TAC has and will further develop specific expertise about the integration of multiple satellites and in-situ based observations coming from the other CMEMS TACs and projects. </span><span data-contrast="none">Furthermore, MOB TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor and the global ocean carbon sink. </span></p> </div> </div>

The larvae of the European eel travel an extensive distance of approximately 5,000 km from the spawning area in the Sargasso Sea to the European coasts. We here study the larval drift with focus on the effects of mesoscale processes, analyzing data from a targeted survey and modeling possible drift trajectories. The survey covered the initial distribution of larvae in the Subtropical Convergence Zone (STCZ), which is characterized by complex patterns of oceanic fronts and mesoscale eddies. During March–April 2014, sampling was carried out along north–south transects. Hydrography was described using vertical CTD casts and UCTD profiles, and larval distributions assessed from hauls of a large ring net. Patterns in water mass distribution and particle dispersion dynamics were analyzed by reconstruction and diagnosis of mesoscale dynamics, combining satellite observations and Argo profiles. Lagrangian drift trajectories of eel larvae were subsequently simulated starting from a data-driven high-resolution 3D reconstruction of the modeled flow. We found the area of larval distribution delimited by frontal zones, defined by the combined effects of marked longitudinal salinity gradients and large-scale zonal temperature variations. Modeled patterns of eel larvae dispersion were predominantly influenced by the current shear and eddy strain, and while the direction was mainly westward, a significant dispersal was also observed in northeastward directions. Such almost isotropic transport of European eels is supported by historical data on larval size distribution, and results challenge common interpretations of eel larval drift, which propose an initial westward advection of the entire population to the Gulf Stream along the offshore edge of the Antilles current.

Subsurface ocean measurements are extremely sparse and irregularly distributed, narrowing our ability to describe deep ocean processes and thus also limiting our understanding of the role of ocean and marine ecosystems in the Earth system. To overcome these observational limitations, neural networks combining remotely-sensed surface measurements and in situ vertical profiles are increasingly being used to retrieve high-quality three-dimensional estimates of the ocean state. This study proposes a convolutional neural network (CNN) architecture for the reconstruction of vertical profiles of temperature and salinity starting from surface observation-based data. The model is trained on satellite and in situ data collected between 2005 and 2020 in the Atlantic Ocean. Rather than using spatially gridded in situ observations, we use directly measured vertical profiles. Different combinations of surface variables are analyzed and compared in order to determine the most effective inputs for the CNN. Furthermore, the relative importance of each of these variables in the vertical reconstruction is assessed using Shapley values, originally developed in the framework of cooperative game theory. The model performance is shown to be superior to current state-of-the-art methods and the same approach can easily be extended to other basins or to the global ocean.