The ocean’s meso- and submeso-scales (1-100 km, days to weeks) host features like filaments and eddies that have a key structuring effect on phytoplankton distribution, but that due to their ephemeral nature, are challenging to observe. This problem is exacerbated in regions with heavy cloud coverage and/or difficult access like the Southern Ocean, where observations of phytoplankton distribution by satellite are sparse, manned campaigns costly, and automated devices limited by power consumption. Here, we address this issue by considering high-resolution in-situ data from 18 bio-logging devices deployed on southern elephant seals (Mirounga leonina) in the Kerguelen Islands between 2018 and 2020. These devices have submesoscale-resolving capabilities of light profiles due to the high spatio-temporal frequency of the animals’ dives (on average 1.1 +-0.6 km between consecutive dives, up to 60 dives per day), but observations of fluorescence are much coarser due to power constraints. Furthermore, the chlorophyll a concentrations derived from the (uncalibrated) bio-logging devices’ fluorescence sensors lack a common benchmark to properly qualify the data and allow comparisons of observations. By proposing a method based on functional data analysis, we show that a reliable predictor of chlorophyll a concentration can be constructed from light profiles (14 686 in our study) and matchups with satellite ocean-color data, thus enabling effective (1) homogenization then calibration of the bio-logging devices’ fluorescence data and (2) filling of the spatial gaps in coarse-grained fluorescence sampling. The developed method improves the spatial resolution of the chlorophyll a field description from ~30 km to ~12 km. These results open the way to empirical study of the coupling between physical forcing and biological response at submesoscale in the Southern Ocean, especially useful in the context of upcoming high-resolution ocean-circulation satellite missions like SWOT. Important Note: This submission has been initially submitted to SEA scieNtific Open data Edition (SEANOE) publication service and received the recorded DOI. The metadata elements have been further processed (refined) in EMODnet Ingestion Service in order to conform with the Data Submission Service specifications.

MARLEY (Monitoring deep-seA coRaL EcosYstems) is a deep seafloor observing system dedicated to the monitoring of cold-water coral habitats. The system is deployed in the Lampaul canyon off Brittany, France since August 2021 and maintained each year during the ChEReef-Obs cruises. The study site is a coral garden dominated by Madrepora oculata, located on a sedimented platform at 780 m depth. MARLEY is equipped with a CTD SBE 37-SIP, an oxygen optode Aanderaa (4330 or 4831), an ADCP Teledyne RDI Workhorse 300kHz, a turbidity sensor Wetlabs ECO NTU (sensitivity: 0-1000 NTU), a sediment trap Technicap PPS 4/3 – 24 bottles and a camera module. The camera module, which can be moved from up to 30 m from the main station, is equipped with a camera AXIS Q1786, two flash lights and a fluorometer & scattering meter SEA-BIRD ECO FLNTU. All sensors are controlled and synchronised by the Communication and Storage Front-end - 2nd generation (COSTOF2), which is also managing data storage. Optical sensors are protected from fouling by electrochlorination (20 seconds, each 6 hours). The oxygen optode is calibrated each year prior to deployment. These datasets provide raw data from the oxygen optode Aandera 4831, the CTD Seabird SBE37, the Wetlabs ECO NTU and the SEAR-BIRD ECO FLNTU covering the period 28/08/2021 to 19/01/2022, with a frequency of 15 minutes. Data from Wetlabs ECO NTU include raw counts and Nephelometric Turbidity Unit (NTU) derived from manufacturer’s calibration with Scale Factor = 0.0611 and Dark Counts = 50. Data form SEABIRD ECO FLNTU include raw counts at 695 nm (Chlorophyll) and 700 nm (Turbidity). Chlorophyll concentration (µg/l) is derived from manufacturer’s calibration with Scale Factor = 0.0180 and Dark Counts = 48. Nephelometric Turbidity Unit (NTU) is derived from manufacturer’s calibration with Scale Factor = 0.0481 and Dark Counts = 50. Important Note: This submission has been initially submitted to SEA scieNtific Open data Edition (SEANOE) publication service and received the recorded DOI. The metadata elements have been further processed (refined) in EMODnet Ingestion Service in order to conform with the Data Submission Service specifications.

The tasks of the expedition: - conducting complex hydrometeorological observations in the spring-summer period in the Arctic basin of the Arctic Ocean; - obtaining data on the morphometry of the ice cover, the physical and mechanical properties of ice; - study of hydrometeorological and oceanographic conditions.

Global phytoplankton production monthly maps for 2017 are produced using an artificial neural network to perform a generalized nonlinear regression of PP on several predictive variables, including latitude, longitude, day length, MLD, SST, PBopt computed according to Behrenfeld and Falkowski (1997), PAR and CHL(0 m). More details about this model can be found in Scardi (2001). Behrenfeld, M. J., Falkowski, P. G. (1997), Photosynthetic rates derived from satellite-based chlorophyll concentration, Limnology & Oceanography, 42(1), 1–20. Scardi, M. (2001), Advances in neural network modeling of phytoplankton primary production, Ecological Modelling, 146, 33–45.