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Arctic PASSION - Polar Monthly Mean Ice Surface Temperature (AP-MMIST) for the time period 1982 to 2024

The Arctic PASSION Polar Monthly Mean IST data set (AP-MMIST) is a combined surface temperature product covering open ocean, marginal ice zone and closed sea ice areas, represented by Sea Surface Temperatures (SST), Marginal Ice Zone Temperatures (MIZT) and sea Ice Surface Temperatures (IST). Beside ocean and sea ice the data set also includes surface temperatures from the Greenland and Antarctic ice sheets. AP-MMIST has been jointly developed and produced by Arctic PASSION WP-1 and the Sea Ice Thematic Assembly Centre (Sea Ice TAC) under the Copernicus Climate Change Service (C3S - service contract: 2022/C3S2_312b_MOi_SC1). The AP-MMIST is a monthly averaged temperature product based on the C3S daily IST CDR and ICDR level 3 data. The daily mean C3S IST data set is a resampled and averaged daily mean IST product using Global Area Coverage - Advanced Very High-Resolution Radiometer (AVHRR) IST level 2 data as input. The level 2 and 3 CDR and ICDR data records are described in Algorithm Theoretical Baseline Document (Eastwood et al., 2023). The surface temperature retrieval algorithm used to produce the basic level 2 product is a traditional split window algorithm using two Thermal InfraRed (TIR) channels to compensate for atmosphere and angular emissivity dependency. This is described in the Algorithm Theoretical Baseline Document (Eastwood et al., 2023). The level 1 TIR input data set is the full data record from the AVHRR on-board NOAA satellite platforms since 1982, as well as AVHRR records on-board Metop satellites since 2006. The product output format is NetCDF with standard attributes, following CF convention to the degree possible. The monthly data are divided into 2 monthly files, one for each hemisphere, SH and NH.

High resolution age model from sedimentary collected along the southwestern Svalbard margin

This dataset includes updated versions of high-resolution age models derived from six sedimentary cores collected from the southwestern Svalbard margin. The dataset presented here represents a refinement of a previous version (Caricchi et al., 2020; 2022), achieved through correlation of the stratigraphic trends of the ARM/k parameter with the GICC05modelext timescale and the NGRIP record (Rasmussen et al., 2014). Additional refinement was obtained from newly acquired and recalibrated radiometric data, as well as from improved lithological constraints. The dataset enables the calculation of sedimentation rates during glacial and interglacial periods and during short-lived, widespread meltwater pulses and Heinrich-like events, thereby allowing the reconstruction of ice-sheet instability and meltwater events along the Svalbard–Barents Sea margin over the last 60,000 years.

Schwerpunktprogramm (SPP) 1158: Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; Bereich Infrastruktur - Antarktisforschung mit vergleichenden Untersuchungen in arktischen Eisgebieten, Einflusss von Umweltveränderungen auf antarktisches Phytoplankton untersucht mit Hilfe eines synergistischen multi- und hyper-spektralen Satellitendatenansatzes

Klimamodelle sagen voraus, dass sich in naher Zukunft im Antarktischen Ozean signifikant die Temperatur und der PH-Wert ändern werden, bedingt durch den Anstieg der Konzentrationen troposphärischer Treibhausgase und vor allem durch den erhöhten Kohlenstoffdioxidausstoß aus fossilen Brennstoffen. Solche Änderungen wirken sich auf die Zusammensetzung des Phytoplanktons aus und damit auch auf die Stoffkreisläufe wichtiger Elemente (Kohlenstoff, Stickstoff, usw.). Ziel dieses interdisziplinären Projektes ist die genauere Bestimmung der räumlichen und zeitlichen Variabilität der Biomasse von unterschiedlichen Phytoplanktontypen im Antarktischen Ozean. Einerseits wird hiermit das Verständnis der Rolle des antarktischen Phytoplanktons für das Ökosystem vertieft und andererseits deren Beitrag für den globalen Kohlenstoffzyklus genauer quantifiziert. Durch die einzigartige Kombination von Satellitendaten zweier unterschiedlicher Instrumententypen soll die Konzentration verschiedener Phytoplankton-Typen im Antarktischen Ozean zum ersten Mal mit umfassender zeitlicher und räumlicher Abdeckung bestimmt werden. Die Gesamtbiomasse wird durch eine an die Antarktis angepasste Prozessierung mit Hilfe multispektraler Satellitenmessdaten berechnet. Der Anteil wesentlicher Phytoplanktontypen an der Gesamtbiomasse wird anhand der Auswertung charakteristischer Absorptionsstrukturen von hyperspektralen Messdaten (PhytoDOAS-Methode) ermittelt. Somit soll ein synergetisches Produkt aus sich ergänzenden Informationen multi- und hyperspektraler Satelliteninstrumente entwickelt werden, das auf ähnliche Satelliteninstrumente, deren Messungen in naher Zukunft starten, übertragbar sein wird. Damit kann dann ein Datensatz über die Verteilung von Phytoplanktontypen über Dekaden erstellt werden. Mit dem im Projekt entstehenden Datensatz über die Verteilung der Phytoplanktontypen soll deren Variabilität und Korrelation mit sich ändernden Umweltfaktoren im Antarktischen Ozean in den vergangenen untersucht werden. Darüber hinaus soll unser Datensatz genutzt werden, zur Verbesserung und Evaluierung eines Ökosystem-Models, welches die Biogeographie verschiedener Phytoplanktontypen durch Parametrisierung physiologischer Eigenschaften an ein Ozeanzirkulatonsmodell errechnet. Mit Hilfe des Langzeitdatensatz und dem damit verbundenen Wissen über die Variabilität der Phytoplanktontypen, wird ein Fundament geschaffen, um den Einfluss der Klimaveränderungen im Antarktischen Ozean zu bemessen.

Temperature measurements from SIMBA-type sea ice mass balance buoy 2022T97

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2022T97 (original name NPOL_0803) installed on drifting sea ice in the Arctic Ocean during the expedition Kronprins Haakon AO22 in 2022. Data is available between 2022-08-06 10:38:00 and 2022-11-22 03:02:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project Arctic Passion.

Multibeam bathymetry processed data (dataset compilation) of RV POLARSTERN & RV MARIA S. MERIAN during 44 cruises from 1984 to 2024, Fram Strait, Greenland Sea, Arctic Ocean

The Long-Term Ecological Research observatory HAUSGARTEN was established by the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung in the Fram Strait in summer 1999 to detect and track the impact of large-scale environmental changes on the marine ecosystem in the transition zone between the northern North Atlantic and the central Arctic Ocean. In this area, bathymetric data have been recorded with multibeam echosounders during 44 research expeditions on RV Polarstern and RV Maria S. Merian since 1984. From these data, a digital elevation model was generated and geostatistical analyses were performed to calculate geospatial derivatives and quantitative terrain descriptors for subsequent terrain analyses and habitat mapping. The dataset covers an area from 78°N to 81°N and 6°W to 12°E. To create the data product, archive data was used from seven different multibeam echosounders in various raw data formats. This data has been processed and cleaned with CARIS HIPS & SIPS, including sound velocity correction for datasets from 1999 and newer. Older datasets are calculated with a static sound velocity of 1500 m/s. Soundings where exported for gridding with Generic Mapping Tools (GMT) nearneighbor. The resulting Digital Elevation Model (DEM) is in the WGS84/Arctic Polar Stereographic (EPSG:3995) projection with a cell size of 100m x 100m. The hillshade was computed with a combination of slope and synthetic illumination with a vertical exaggeration of 10. Slope inclination was calculated with GDAL tool Slope with the formula of Zevenbergen and Thorne (1987) in degree. Terrain Ruggedness Index (TRI) was computed with the QGIS tool Ruggedness index following the approach of Riley et al. (1999) in meters. For the Bathymetric Position Indices (BPI), focal statistics have been calculated with the GRASS tool "r.neighbors" and the QGIS raster calculator following the concept of the Topographic Position Index (Weiss, 2001) with a circular reference area of 99 cells (broad) and 9 cells (fine). The additional coverage polygon layer gives and overview on the used datasets and their corresponding metadata. The map gives an overview on the LTER HAUSGARTEN area and the HAUSGARTEN 2024 DEM.

Clay and heavy minerals and total organic carbon in Arctic Ocean surface sediments – Data tables to distribution maps of Stein (2008)

Total organic carbon (TOC) and mineral assemblages are key data sets determined to characterize marine sediments in terms of sediment provenances, processes, and depositional environments. In a comprehensive review and synthesis (Stein, 2008), such data were compiled for Arctic Ocean surface sediments and shown in nine selected distribution maps: four maps of clay minerals (illite, smectite, chlorite, and kaolinite), four maps of heavy minerals (amphibole, clinopyroxene, epidote, and garnet), and one TOC map. The data used to produce these maps, are represented in the three tables of this data report. For details in background information and methodology see primary source literature cited here as well as the Stein (2008) synthesis.

Cell densities of the Arctic diatom Thalassiosira gravida in response to temperature, photoperiod and microbiome presence

In March 2023, cell densities of the Arctic diatom Thalassiosira gravida (isolated from the Central Arctic Ocean) were determined to calculate its growth rates at different temperatures and photoperiods in the presence and absence of its natural microbiome. Therefore, a full-factorial experimental design was chosen with two levels of temperature (9°C; 13.5°C) and photoperiod (16h; 24h), to which axenic and xenic diatom cultures were acclimated for one week in climate cabinets prior to the start of the actual growth experiment at a light intensity of 50 µmol photons m-2 s-1. With an initial cell density of 1500 cells/ml, axenic and xenic diatoms were grown under the respective experimental conditions until a cell density of approximately 15000 cells/ml was reached. Cell densities were determined microscopically using an inverted light microscope, following the procedure described in detail in Giesler et al. (2023, 10.3389/fmars.2023.1244639).

Master tracks in different resolutions during POLAR 5 campaign P5-256_COMPEX-EC_2025

Raw data acquired by GPS1 position sensors on board research aircraft Polar 5 during the campaign P5-256_COMPEX-EC_2025 were processed to receive a validated master track which can be used as reference of further expedition data. Novatel FlexPak6 GPS receiver was used as navigation sensors during the campaign. Data were downloaded from AWI Datamanagement System (https://dms.awi.de) with a resolution of 1 sec. Processed data are provided as a master track with 1 sec resolution and a generalized track with a reduced set of the most significant positions of the master track. A detailed report on processing is also available for each flight.

Nitrogen isotope and biomarker data during Late Paleocene–early Eocene

This dataset compiles raw measurements generated to investigate perturbations of the marine nitrogen cycle during the Paleocene–Eocene Thermal Maximum (PETM). It includes abundances of isoprenoidal GDGTs (isoGDGTs) and crenarchaeol mass accumulation rates, (ii) chromatographic peak areas of bacteriohopanetetrol (BHT) and BHT-x, and (iii) the nitrogen isotopic composition of bulk sediments (bulk sediment δ¹⁵N). Samples were collected from multiple ocean basins and regions: the Central Arctic Ocean (IODP 302–M0004), East Tasman Plateau in the Southwest Pacific (ODP Site 1172), Central Northern Caucasus (Kheu River), the New Jersey Shelf/Atlantic Coastal Plain (ODP 174AX Ancora), the Côte d'Ivoire–Ghana Transform Margin in the equatorial Atlantic (ODP 959), the Southeast Newfoundland Ridge in the central North Atlantic (IODP 1403), Fur Island, Denmark (Fur Formation), and the Tarim Basin, western China (Qimugen Formation). Lipid biomarker data were obtained using liquid chromatography coupled to mass spectrometry, and bulk nitrogen isotope data were measured by elemental analysis coupled to isotope-ratio mass spectrometry.

Temperature and heating-induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2022T97

Temperature and heating-induced temperature difference profiles were measured through the atmosphere, sea ice, and ocean using a SIMBA-type sea ice mass balance buoy equipped with a several meter long thermistor chain. The present dataset was recorded by SIMBA 2022T97 (original name NPOL_0803) installed on drifting sea ice in the Arctic Ocean during the expedition Kronprins Haakon AO22 in 2022. Data is available between 2022-08-06 10:38:00 and 2022-11-22 03:02:00. The thermistor chain was Variable 5 m long and included 241 sensors with a regular spacing of 2 cm. The resulting time series includes the evolution of temperature and temperature differences at 30 s and 120 s during a heating cycle of 120 s as a function of location, depth and time. The sampling intervals were usually between hourly and daily, but were most frequently configured to 6 hours for temperature, and 24 hours for temperature differences. In addition to temperatures and geographic location, barometric pressure, ~1 m air temperature, instrument tilt, and compass heading were measured. The present dataset was processed as follows: obvious inconsistencies (missing values) and unrealistic values of GPS position have been removed. This instrument was deployed as part of the project Arctic Passion.

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