The Green Edge project was designed to investigate the onset, life and fate of a phytoplankton spring bloom (PSB) in the Arctic Ocean. The lengthening of the ice-free period and the warming of seawater, amongst other factors, have induced major changes in arctic ocean biology over the last decades. Because the PSB is at the base of the Arctic Ocean food chain, it is crucial to understand how changes in the arctic environment will affect it. Green Edge was a large multidisciplinary collaborative project bringing researchers and technicians from 28 different institutions in seven countries, together aiming at understanding these changes and their impacts on the future. The fieldwork for the Green Edge project took place over two years (2015 and 2016) and was carried out from both an ice camp and a research vessel in the Baffin Bay, Canadian arctic. Here, we describe the data set obtained during the research cruise, which took place aboard the Canadian Coast Guard Ship (CCGS) Amundsen in spring 2016. 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: water temperature, salinity,air temperature,visibility (code). Research vessel:"Mikhail Somov".

Datawell Waverider data collected at southern part of full-scale wave test site at EMEC (Orkney, UK), in year 2017. Data was processed using Datawell W@ves21 software, no QC had been applied. Location: Billia Croo; Resolution of data: 1.28 Hz; Sample period (s): 1800; Number of data records: 17520; Pings (readings) per Ens: 2304; Mode: Integrated parameters. 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 dataset represents data primary processing of zoobenthos samples taken in the Chaun Bay of the East Siberian Sea in October 2020 during an expedition onboard R/V "Akademik Oparin" 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 mooring was deployed on 15 September 2017 from Norwegian Research Vessel Lance at 80.6°N and 7.26°E (depth of 730 m) in the Yermak Pass over the Yermak Plateau north of Svalbard. It comprised 3 instruments: an upward-looking RDI 75kHz, a Long Ranger Acoustic Doppler Current Profiler (ADCP) at 340 m with 16 m vertical resolution (25 bins of 16 m each) and a 2-hour sampling time; a Seabird SBE37 measuring temperature, salinity and pressure at 348 m with 10-minute sampling time; and an Aquadopp current meter at 645 m with a 2-hour sampling time. The mooring was retrieved on the 19 July 2020 by Norwegian Icebreaker K.V. Svalbard. The present dataset features: The ADCP 50-hour high pass filtered velocities and  the Aquadopp 50-hour high pass filtered velocities. 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.

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.

This data set includes CTD-O2 and LADCP data from the 46 stations occupied in Storfjorden and Storfjordrenna during the STEP cruise in July 2016 onboard R/V l'Atalante. Hydrographic data are provided in the form of Seabird ascii format (cnv), with two files per station (up and down). The data set also includes  underway S-ADCP data provided in netcdf format. Sensor metadata: CTD data were collected with a Seabird SBE 911+ probe. Dissolved oxygen data were collected with a Seabird SBE43 probe attached to the rosette. Additional data include fluorescence (Chelsea Aqua3) and transmission (WET labs C-Star).LADCP data were collected with a pair of  300 kHz Workhorse Sentinel from RD Instruments mounted on the rosette. Shipborne-ADCP data were collected with the  150 kHz Ocean Surveyor (RD Instruments)  mounted on the hull of R/V L'Atalante. 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.

Gridded fields of salinity for 50 N - 67 N, 41,5 W - 61,5 W geographic region (yearly, 2000 - 2016), 29 standard depths

Surface Ocean CO2 Atlas (SOCAT)

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.