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.

As part of the STeP project (STorfjorden Polynya multidisciplinary study), two moorings, M1 and M2, were deployed in Storfjorden (Svalbard) on July 14, 2016 from the French R/V L’Atalante and were recovered one year later, on September 28, 2017, from the French R/V Pourquoi-Pas?. The two moorings, deployed a few hundred meters apart at 78°N and 20°E at a depth of 100m, documented the formation of dense Brine-enriched Shelf Water (BSW).  The moorings included both physical oceanography (PO) and biogeochemistry sensors. The present dataset is composed of PO data only: the 3 components of the currents, backscatter, salinity, temperature and dissolved oxygen. PO sensors on M1, spanning the whole water column, included 6 Seabird SBE37 microcat (CTD),  15 RBR solo (T), and 1 RBR duet (TD) for hydrography, while currents were monitored with a RDI WH 300kHz upward looking ADCP and 1 Nortek Aquadopp underneath. PO sensors on the shorter M2 mooring included 1 Seabird SBE63 (CTD-O2), 1 RBR solo (T) and 1 RBR duo (TD). Data have been calibrated and validated and the different steps of this processing are discussed in the technical report provided with the dataset. Two netcdf4 files are provided for M1: one for hydrography (STEP2016_M1_hydrography.nc), the other one (STEP2016_M1_current.nc)  for currents and backscatter. Only one netcdf4 files (STEP2016_M2_hydrography.nc) is provided for the shorter M2. Temperature and salinity data from SBE sensors have been interpolated on a common time grid with a 20’ time step. Likewise temperature data from RBR are provided on a 30” time grid. A merged SBE-RBR dataset has also been built for increased vertical resolution, providing temperature every 20’. ADCP data are provided on a 100’ time grid. The user is referred to the technical report provided with the dataset for further information on the different fields. 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.

We gathered ocean profiles during the first two floes of the N-ICE2015 ice camp north of Svalbard with IAOOS ocean profilers. Between January and March 2015, four ocean profilers were deployed: two below a full IAOOS platform (500 m long cable) during floe 1, two on an 800 m long instrumented line in a tent-covered testing-hole during floe1 and floe 2. The ocean profilers, from French manufacturer NKE (PROVOR SPI), carried a Seabird SBE41CP CTD (Conductivity, Temperature, Depth) with an Aanderaa 4330 optode for dissolved oxygen (DO). The profilers were set to perform two profiles a day from 500 m upward (800 m from testing hole) starting at 6 am and 6 pm. They provided the first winter data in the region with a total of 138 profiles during floe 1 (January 15- February 21) with 62, 50, and 26 profiles for IAOOS7, IAOOS8, and IAOOS 9, respectively and 16 profiles during floe 2 (February 24 - March 19- IAOOS 11 from testing hole). Following quality control, we retain all the temperature profiles and remove 1% of the salinity profiles. Finally, the accuracy is estimated to be 0.002°C in temperature, and 0.02 g/kg in salinity. Several profiles are missing or incomplete because of high drift speeds (> 0.4 m s-1) impeding the ascent of the profiler. There are no bottle DO measurements during Floe 1 to calibrate the DO data. DO accuracy is estimated comparing the deep values of DO concentration (rather stable at 500m) between the three profilers. A difference of 3 µmol L-1 is observed between IAOOS 8 and 9, and IAOOS 7. An offset of 3 µmol L-1 is then applied to the oxygen data from IAOOS7 and the accuracy of the data is estimated to be at ±3 µmol L-1. The vertical resolution of the processed CTD data is 1 dbar in the upper 400 dbars, 5 dbars from 400 to 550 dbars and 10 dbars from 550 to 850 dbars. The vertical resolution in dissolved oxygen is 2 dbars over all depths. 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.

This dataset is included the following parameters: Meteorological: air tTemperature,humidity,pressure,wind speed,wind direction. Hydrology: temperature,salinity,density. Hydrochemistry: oxygen,phosphate,silicate,oxygen,pH,alkalinity.

Measurements data on "North Pole-36" station.This dataset is included the following hydrology parameters:temperature,salinity,density. Meteorological parameters: wind direction,wind speed,air temperature,visibility,air pressure,humidity,cloud cover,cloud cover of the form,ice:type,ice:form,ice:concentration.

This dataset is included the following parameters: water temperature, salinity,air temperature,visibility (code). Research vessel:"Mikhail Somov".

Ice and runoff samples collected and analysed during the RV Sanna GLICE cruise (August 2022)

Samples collected from an underway Towfish during RV Sanna cruise GLICE (August 2022) and either analysed at sea or returned preserved to GEOMAR for analysis. Sensor data refers to in-line data matching the underway samples.

We defined the interfaces between the air/snow, snow/ice, and ice/ocean and calculated the ocean heat flux for two SIMBA recordings (SIMBA2015a and SIMBA_2015f) of repeated temperature profiles at 6h interval and 2cm vertical resolution, during N-ICE 2015 experiment floe1. The snow/ice interface is derived from the sharp contrast in the diffusivity proxy values between both media. The snow/ice interface does not change except for slush formation associated with flooding events. The air/snow interface is calculated using simultaneous information from the vertical gradient of the temperature and the standard deviation over 24, 48, and 72 h period. Snow accumulation of more than 10 centimeters happened at different time for the 2 SIMBA. The ice/ocean interface is estimated from temperature profiles alone since the winter sea-ice remains colder than the ocean. The ocean just below the ice is at or just above the freezing temperature (estimated from a near surface conductivity-temperature-depth (CTD) sensor see Koenig et al. [2016]). The method detects (1) the first sensor, downward of the snow/ice interface, with a temperature above the ocean freezing temperature and (2) the last sensor in the ice with a temperature below the mean ocean temperature by at least twice the ocean temperature standard deviation in that profile. The ice/ocean interface is then defined as half way between the last sensor in the ice and the first sensor in the ocean. Note it take 3-4 days for the deployment hole to refreeze. Then the ice thickness remains constant up to 20 February when floe1 breaks. Simba_2015f stops working and SIMBA_2015a features basal melt events corresponding to temperature changes in the ocean. The consistency of the 3 interfaces estimate is validated with the thermal diffusivity proxy and the vertical and temporal derivatives of temperature. The ocean heat flux is derived from the latent heat flux which is directly proportional to the change in time of the ice/ocean interface depth and the conductive heat flux in the lower portion of the ice estimated 6 cm above the ice/ocean interface. The ocean heat flux values for SIMBA_2015a and SIMBA2015f range from -50 to 350 W/m2, and -50 to 150 W/m2 respectively, while the basal melt events associated with ocean temperature increase stand out in SIMBA_2015a.   The SIMBA data are available through the Norwegian Polar Institute’s data center (https://data.npolar.no/dataset/6ed9a8ca-95b0-43be-bedf-8176bf56da80) and the method of interface detection is thoroughly described in Provost et al. (2017). Note that all time series have been smoothed with a 36-h running mean.   Provost, C., N. Sennechael, J. Miguet, P. Itkin, A. Rosel, Z. Koenig, N. Villacieros-Robineau, and M. A. Granskog (2017), Observations of flooding and snow-ice formation in a thinner Arctic sea-ice regime during the N-ICE2015 campaign: Influence of basal ice melt and storms, J. Geophys. Res. Oceans, 122, 7115–7134, doi:10.1002/2016JC012011. 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.

In the Northwest Atlantic, Pandalus borealis (northern shrimp) serve as key mid-trophic consumers and prey for higher-trophic predators, including commercially important fish species. However, the impact of changing environmental conditions on trophic interactions and lipid storage in sub-Arctic ecosystems is not well understood. We employed biochemical tracers (fatty acids and stable isotopes) to investigate the trophic ecology and stage-specific nutritional condition of P. borealis across spatial and seasonal scales. A total of 68 different fatty acids (FAs) were identified in P. borealis tissues (i.e., muscle and eggs). The relative abundances of these FAs varied among sex, tissues, seasons, and fishing areas. Results revealed that P. borealis primarily fed on diatoms and zooplankton, with opportunistic feeding on sinking phytodetritus. Lipid composition showed strong seasonality, with storage triacylglycerols being the predominant lipid class. Ovigerous females exhibited the highest lipid concentrations and essential fatty acids, emphasizing the ecological importance of eggs as high-quality lipid sources. Additionally, total lipid content in eggs increased from spring to summer, highlighting vulnerability to shifts in seasonal primary production. This study underscores the significant seasonal variability in the nutritional status of P. borealis and the need to understand lipid dynamics to assess population resilience to environmental changes. 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.