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  • Elsevier  (5,804,762)
  • PANGAEA  (422,881)
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  • 1
    Publication Date: 2024-06-05
    Description: Upwelling systems are significant sources of atmospheric nitrous oxide (N₂O). The Benguela Upwelling System is one of the most productive regions worldwide and a temporally variable source of N₂O. Strong O₂ depletions above the shelf are favoring periodically OMZ formations. We aimed to assess underlying N₂O production and consumption processes on different temporal and spatial scales during austral winter in the Benguela Upwelling System, when O₂⁻deficiency in the water column is relatively low. The fieldwork took place during the cruise M157 (August 4ᵗʰ – September 16ᵗʰ 2019) onboard the R/V METEOR. This expedition included four close-coastal regions around Walvis Bay at 23°S, which presented the lowest O₂ concentrations near the seafloor and thus may provide hotspots of N₂O production. Seawater was collected in 10 L free-flow bottles by using a rosette system equipped with conductivity-temperature-depth (CTD) sensors (SBE 911plus, Seabird-electronics, USA).Seawater samples were collected from 10 L free-flow bottles bubble-free, filled into 200 mL serum bottles and immediately fixed with saturated mercury chloride (HgCl₂). Concentrations of dissolved N₂O were measured by a purge and trap system using a dynamic headspace (Sabbaghzadeh et al., 2021). The N₂O gas saturation (N₂Oₛₐₜ in %) was calculated from the concentration ratio between the seawater sample and seawater equilibrated with the atmosphere. ∆N₂O (N₂O saturation disequilibrium in nmol L⁻¹) was calculated as the difference between the measured N₂O concentration and the atmospheric equilibrium N₂O concentration using Bunsen solubility coefficient (Weiss and Price, 1980). AOU (apparent oxygen utilization in µmol L⁻¹) expresses the O₂ consumption by microbial respiration and was calculated as the difference between the equilibrated O₂ and observed O₂ concentration with the same physico-chemical properties (Weiss and Price, 1980).
    Keywords: apparent oxygen utilization; Benguela Upwelling System; BUSUC 1; Calculated according to Weiss and Price (1980); CTD, Sea-Bird SBE 911plus; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Event label; Field observation; Gas chromatography, Agilent 7820B, coupled with a flame ionization detector and an Electron Capture Detector; LATITUDE; LONGITUDE; M157; M157_14-2; M157_16-3; M157_17-2; M157_2-8; Measured according to Sabbaghzadeh et al. (2021); Meteor (1986); Namibia; nitrous oxide; Nitrous oxide, dissolved; Nitrous oxide, dissolved, disequilibrium; Nitrous oxide, dry air; Nitrous oxide saturation; Oxygen, apparent utilization; oxygen minimum zone; Partial pressure of nitrous oxide in wet air; Sample code/label; Station label
    Type: Dataset
    Format: text/tab-separated-values, 332 data points
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  • 2
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    PANGAEA
    In:  Instituto de Geofísica, Universidad Nacional Autónoma De México
    Publication Date: 2024-06-05
    Keywords: Air temperature at 2 m height; BARO; Barometer; Baseline Surface Radiation Network; BSRN; DATE/TIME; Diffuse radiation; Diffuse radiation, maximum; Diffuse radiation, minimum; Diffuse radiation, standard deviation; Direct radiation; Direct radiation, maximum; Direct radiation, minimum; Direct radiation, standard deviation; HEIGHT above ground; Humidity, relative; HYGRO; Hygrometer; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave downward radiation, standard deviation; Long-wave upward radiation; Long-wave upward radiation, maximum; Long-wave upward radiation, minimum; Long-wave upward radiation, standard deviation; Mexico; Monitoring station; MONS; Pyranometer, Kipp & Zonen, CMP22, SN 160484, WRMC No. 83003; Pyranometer, Kipp & Zonen, CMP22, SN 160486, WRMC No. 83001; Pyrgeometer, Kipp & Zonen, CGR4, SN 140084, WRMC No. 83004; Pyrheliometer, Kipp & Zonen, CHP 1, SN 140189, WRMC No. 83002; SEL; Selegua, Mexico Solarimetric Station; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave downward (GLOBAL) radiation, standard deviation; Short-wave upward (REFLEX) radiation; Short-wave upward (REFLEX) radiation, maximum; Short-wave upward (REFLEX) radiation, minimum; Short-wave upward (REFLEX) radiation, standard deviation; Station pressure; Thermometer; UV-Biometer, Solar Light 501A, SN 19489, WRMC No. 83007
    Type: Dataset
    Format: text/tab-separated-values, 1163609 data points
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  • 3
    Publication Date: 2024-06-05
    Keywords: air temperature; B1; B3; Climate change; DATE/TIME; Date/Time local; Event label; hydrology; IT25_B1; IT25_B3; IT25_P2; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; P2; precipitation; Quality code; relative humidity; S3; S4; snow height; Soil Moisture and Temperature Sensor, Campbell Scientific, CS655; soil temperature; soil water content; soil water potential; Solar radiation; Temperature, soil; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 10435248 data points
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  • 4
    Publication Date: 2024-06-05
    Keywords: air temperature; B1; B3; Climate change; DATE/TIME; Date/Time local; Event label; hydrology; IT25_B1; IT25_B3; IT25_P2; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; P2; precipitation; Quality code; relative humidity; S3; S4; snow height; Soil Moisture and Temperature Sensor, Campbell Scientific, CS655; soil temperature; soil water content; Soil water content, volumetric; soil water potential; Solar radiation; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 10428948 data points
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  • 5
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    PANGAEA
    In:  Université de La Réunion
    Publication Date: 2024-06-05
    Keywords: Air temperature at 2 m height; BARO; Barometer; Baseline Surface Radiation Network; BSRN; DATE/TIME; Diffuse radiation; Diffuse radiation, maximum; Diffuse radiation, minimum; Diffuse radiation, standard deviation; Direct radiation; Direct radiation, maximum; Direct radiation, minimum; Direct radiation, standard deviation; HEIGHT above ground; Humidity, relative; HYGRO; Hygrometer; Long-wave downward radiation; Long-wave downward radiation, maximum; Long-wave downward radiation, minimum; Long-wave downward radiation, standard deviation; Monitoring station; MONS; Pyranometer, Kipp & Zonen, CMP22, SN 120360, WRMC No. 82002; Pyranometer, Kipp & Zonen, CMP22, SN 140114, WRMC No. 82001; Pyrgeometer, Kipp & Zonen, CGR4, SN 150123, WRMC No. 82004; Pyrheliometer, Kipp & Zonen, CH1, SN 040374, WRMC No. 82003; Reunion; Reunion Island, University; RUN; Short-wave downward (GLOBAL) radiation; Short-wave downward (GLOBAL) radiation, maximum; Short-wave downward (GLOBAL) radiation, minimum; Short-wave downward (GLOBAL) radiation, standard deviation; Station pressure; Thermometer
    Type: Dataset
    Format: text/tab-separated-values, 820716 data points
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  • 6
    Publication Date: 2024-06-05
    Description: Upwelling systems are significant sources of atmospheric nitrous oxide (N₂O). The Benguela Upwelling System is one of the most productive regions worldwide and a temporally variable source of N₂O. Strong O₂ depletions above the shelf are favoring periodically OMZ formations. We aimed to assess underlying N₂O production and consumption processes on different temporal and spatial scales during austral winter in the Benguela Upwelling System, when O₂-deficiency in the water column is relatively low. The fieldwork took place during the cruise M157 (August 4ᵗʰ – September 16ᵗʰ 2019) onboard the R/V METEOR. This expedition included four close-coastal regions around Walvis Bay at 23°S, which presented the lowest O₂ concentrations near the seafloor and thus may provide hotspots of N₂O production. Seawater was collected in 10 L free-flow bottles by using a rosette system equipped with conductivity-temperature-depth (CTD) sensors (SBE 911plus, Seabird-electronics, USA). Concentrations of inorganic nutrients (PO₄³⁻, NH₄⁺, NO₃⁻, NO₂⁻, and SiO₂) were measured colorimetrically according to Grasshoff et al. (1999) by means of a continuous segmented flow analyzer (SEAL Analytical, QuAAtro39). To determine the water mass fractions along the sampling transects, vertical profiles were collected using a free-falling microstructure profiler (MSS90L, Sea & Sun Technology). Temperature, dissolved oxygen, and salinity were measured with a CTD system consisting of a SeaBird 911+ probe, mounted on a sampling rosette.
    Keywords: Ammonium; Benguela Upwelling System; BUSUC 1; Continuous Segmented Flow Analyzer, SEAL Analytical, QuAAtro39; CTD, Sea-Bird SBE 911plus; CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Event label; Field observation; LATITUDE; LONGITUDE; M157; M157_10-7; M157_11-4; M157_12-2; M157_14-2; M157_16-25; M157_16-3; M157_16-6; M157_17-16; M157_17-2; M157_24-1; M157_25-1; M157_2-8; M157_28-1; M157_2-9; M157_36-2; M157_41-14; M157_42-2; M157_43-2; M157_43-6; M157_9-2; Meteor (1986); Microstructure profiler, Sea & Sun Technology, MSS90L; Namibia; Nitrate; Nitrite; nutrients; Oxygen; oxygen minimum zone; PCTD-RO; Phosphate; PumpCTD/Rosette; Salinity; Sample code/label; Silicate; Station label; Temperature, water; Water mass; water mass fraction
    Type: Dataset
    Format: text/tab-separated-values, 1660 data points
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  • 7
    Publication Date: 2024-06-05
    Keywords: air temperature; Anemometer; B1; B3; Climate change; DATE/TIME; Date/Time local; Event label; hydrology; IT25_B1; IT25_B3; IT25_P2; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; P2; precipitation; Quality code; relative humidity; S3; S4; snow height; soil temperature; soil water content; soil water potential; Solar radiation; wind direction; Wind direction; wind speed; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 7286649 data points
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  • 8
    Publication Date: 2024-06-05
    Keywords: air temperature; B1; B3; Climate change; DATE/TIME; Date/Time local; Dielectric Water Potential Sensor; Event label; hydrology; IT25_B1; IT25_B3; IT25_P2; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; P2; precipitation; Quality code; relative humidity; S3; S4; snow height; soil temperature; soil water content; soil water potential; Soil water potential; Solar radiation; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 6842757 data points
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  • 9
    Publication Date: 2024-06-05
    Keywords: air temperature; B1; B3; Climate change; DATE/TIME; Date/Time local; Event label; hydrology; IT25_B1; IT25_B3; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; precipitation; Quality code; relative humidity; S3; S4; snow height; Snow height; soil temperature; soil water content; soil water potential; Solar radiation; Ultrasonic Distance Sensor; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 3290463 data points
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  • 10
    Publication Date: 2024-06-05
    Description: This is a compilation of all short-wave and long-wave radiation datasets from Reunion Island that were and are published in the frame of BSRN. New data will be added regularly. The data are subject to the data release guidelines of BSRN (https://bsrn.awi.de/data/conditions-of-data-release/).
    Keywords: Baseline Surface Radiation Network; BSRN; Monitoring station; MONS; Reunion; Reunion Island, University; RUN
    Type: Dataset
    Format: application/zip, 59 datasets
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  • 11
    Publication Date: 2024-06-05
    Description: Core MVSEIS08-TG-2 was acquired during the Euromargin-MVSEIS08 cruise on the Moroccan margin, south of the Gulf of Cadiz. This site was targeted to discuss the possibility of the saline upper branch of the Mediterranean Outflow Water (MOW) diverging southwards along the Moroccan margin, in addition to the well-established northern MOW current. The southern TG-2 is a 2.12 m gravity core. Contourite records cover the time interval from the last deglaciation starting at HS1 (18.6 ka) to the Holocene. New datasets of current velocity (sortable silt) and water mass origin (O and C isotopes of benthic foraminifera) define the intensity of the MOW as contourite deposits, together with element ratios (XRF-scan) and physical properties of the sediment. O and C isotopes of planktonic foraminifera, foraminiferal species and sea surface temperature (simmax.28 transfer function) were used to determine millennial climate changes during the last deglaciation.
    Keywords: contourites; Gulf of Cadiz; Heinrich Event 1; Mediterranean outflow; MOW; sortable silt; SST (foraminifera); stable oxygen and carbon isotopes; Younger Dryas
    Type: Dataset
    Format: application/zip, 9 datasets
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  • 12
    Publication Date: 2024-06-05
    Description: Core GC-01A was collected during the Contouriber-I cruise in the northern Gulf of Cadiz, bathed by the well-established northern Mediterranean Outflow Water (MOW) current along the Iberian margin; this site was a contrasting reference site to discuss the possibility of the saline upper branch of the MOW diverging southwards along the Moroccan margin (core MVSEIS08-TG-2). GC-01A-TC (0.87 m) and GC-01A-PC (5.21 m) consist of trigger and gravity cores from the same site, with both sections connected by a time gap at 8-9 ka. The contourite records cover the time interval from the Last Glacial Maximum (22 ka) through the last deglaciation to the Holocene (GC-01A). New datasets of current velocity (sortable silt) and water mass origin (O and C isotopes of benthic foraminifera) define the intensity of the MOW as contourite deposits, together with element ratios (XRF-scan) and physical properties of the sediment. O and C isotopes of planktonic foraminifera, foraminiferal species and sea surface temperature (simmax.28 transfer function) were used to determine millennial climate changes during the last deglaciation.
    Keywords: contourites; Gulf of Cadiz; Heinrich Event 1; Mediterranean outflow; MOW; sortable silt; SST (foraminifera); stable oxygen and carbon isotopes; Younger Dryas
    Type: Dataset
    Format: application/zip, 9 datasets
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  • 13
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    PANGAEA
    In:  Supplement to: Bergen, James A; Truax III, S; de Kaenel, Eric P; Blair, Stacie A; Browning, Emily L; Lundquist, J; Boesiger, Todd; Bolivar, M; Clark, K (2019): BP Gulf of Mexico Neogene Astronomically-tuned Time Scale (BP GNATTS). Bulletin of the Geological Society of America, 131(11-12), 1871-1888, https://doi.org/10.1130/B35062.1
    Publication Date: 2024-06-05
    Description: This paper introduces an integrated Neogene microfossil biostratigraphic chart developed within post-merger BP for the Gulf of Mexico Basin and is the first published industrial framework "fully-tuned" to orbital periodicities. Astronomical-tuning was accomplished through a 15-year research program on ODP Leg 154 sediments (offshore NE Brazil) with sampling resolution for calcareous nannofossils and planktonic foraminifera about 20 k.y. and 40 k.y. (thousand year), respectively. This framework extends from the Late Oligocene (25.05 Ma) to Recent at an average Chart Horizon resolution for the Neogene of 144 k.y., approximately double that of published Gulf of Mexico biostratigraphic charts and a five-fold increase over the highest resolution global calcareous microfossil biozonation. Such resolution approximates that of 4th to 5th order parasequences and is a critical component in the verification of seismic correlations between mini-basins in the deep-water Gulf of Mexico. Its utility in global time-scale construction and correlation has been proven, in part, by application of the scheme in full to internal research for the Oligocene-Miocene boundary interval on the Global Boundary Stratotype Section and Point (GSSP) in northern Italy and offshore wells in the eastern Mediterranean Sea. This step change in Neogene resolution, now at the level of cyclostratigraphy (the orbital periodicity of eccentricity) and the magnetostratigraphic chron, demonstrates the potential for calcareous microfossil biostratigraphy to more consistently reinforce correlations of these time scale parameters. The integration of microfossil disciplines, consistent taxonomies, and rigorous analytical methodologies are all critical to obtaining and reproducing this new level of biostratigraphic resolution.
    Keywords: Ocean Drilling Program; ODP
    Type: Dataset
    Format: application/zip, 21 datasets
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  • 14
    Publication Date: 2024-06-05
    Description: This dataset shows benthic and planktic foraminiferal data, planktic foraminiferal fragmentation and coarse fraction values. The sampled sediment record PS93/016, consisting of the Giant box core (GKG) PS93/016-4 (81.217°N, 7.341°W, 1549.8 m water depth) and the Kastenlot core (KAL) PS93/016-6 (81.217°N, 7.341°W, 1548.3 m water depth), was obtained during the expedition PS93.1 (2015) (Stein, 2016) of RV Polarstern at the NE Greenland continental margin in the northwestern Fram Strait. The presented data covers the last ca. 195 ka. In representative splits of the 100-250 µm size fraction, planktic and benthic foraminifers were determined and counted. Fragmentation of planktic foraminifers was calculated by counting fragments which were no more determinable to species level. Thereby, the equation from Pfuhl and Shackleton (2004) was used which includes a fragment-divisor of 3. Aim of the study was to reconstruct glacial advance and retreat, changes in sea-ice and export of freshwater in the western Fram Strait and, to a certain extent, in the Arctic Ocean.
    Keywords: AGE; Arctic; ARK-XXIX/2.1; Coarse fraction; DEPTH, sediment/rock; ECHONEG; Environmental and Climate History Off NorthEast Greenland; Event label; Foraminifera; Foraminifera, benthic; Foraminifera, planktic; Fragmentation index, planktic foraminifera; Fram Strait; Giant box corer; GKG; Greenland; KAL; Kasten corer; North Greenland Sea; Polarstern; PS93/016-4; PS93/016-6; PS93.1; Size fraction 〉 0.063 mm, sand; Size fraction 〉 1 mm, gravel
    Type: Dataset
    Format: text/tab-separated-values, 2438 data points
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  • 15
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    PANGAEA
    In:  Supplement to: Tambutté, Eric; Venn, Alexander A; Holcomb, Michael; Segonds, Natacha; Techer, Nathalie; Zoccola, Didier; Allemand, Denis; Tambutté, Sylvie (2015): Morphological plasticity of the coral skeleton under CO2-driven seawater acidification. Nature Communications, 6, 7368, https://doi.org/10.1038/ncomms8368
    Publication Date: 2024-06-05
    Description: Ocean acidification causes corals to calcify at reduced rates, but current understanding of the underlying processes is limited. Here, we conduct a mechanistic study into how seawater acidification alters skeletal growth of the coral Stylophora pistillata. Reductions in colony calcification rates are manifested as increases in skeletal porosity at lower pH, while linear extension of skeletons remains unchanged. Inspection of the microstructure of skeletons and measurements of pH at the site of calcification indicate that dissolution is not responsible for changes in skeletal porosity. Instead, changes occur by enlargement of corallite-calyxes and thinning of associated skeletal elements, constituting a modification in skeleton architecture. We also detect increases in the organic matrix protein content of skeletons formed under lower pH. Overall, our study reveals that seawater acidification not only causes decreases in calcification, but can also cause morphological change of the coral skeleton to a more porous and potentially fragile phenotype.
    Keywords: Acid-base regulation; Alkalinity, total; Alkalinity, total, standard deviation; Animalia; Aragonite saturation state; Aragonite saturation state, standard deviation; Area, standard error; Area in square milimeter; Benthic animals; Benthos; Bicarbonate ion; Bicarbonate ion, standard deviation; Biomass/Abundance/Elemental composition; Calcification/Dissolution; Calcification rate, standard error; Calcification rate of calcium carbonate; Calcifying fluid, pH; Calcifying fluid, pH, standard error; Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Chlorophyll a; Chlorophyll a, per protein; Chlorophyll a, standard error; Chlorophyll a per cell; Chlorophyll c2; Chlorophyll c2, per protein; Chlorophyll c2, standard error; Chlorophyll c2 per cell; Cnidaria; Coast and continental shelf; Corallite, per skeleton surface area; Corallite, per skeleton surface area, standard error; Density, skeletal bulk; Density, skeletal bulk, standard error; Figure; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Laboratory experiment; Linear extension; Linear extension, standard error; Mediterranean Sea; OA-ICC; Ocean Acidification International Coordination Centre; Organic matrix protein, per skeleton; Organic matrix protein, per skeleton, standard error; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); pH; pH, standard deviation; pH, standard error; Photosynthesis rate, oxygen, per protein; Photosynthesis rate of oxygen; Photosynthesis rate of oxygen, per symbiont cell; Photosynthesis rate of oxygen, standard error; Porosity; Porosity, standard error; Potentiometric; Potentiometric titration; Primary production/Photosynthesis; Protein per surface area; Proteins, standard error; Respiration; Respiration rate, oxygen; Respiration rate, oxygen, per protein; Respiration rate, oxygen, standard error; Salinity; Single species; Species; Stylophora pistillata; Symbiont cell density; Symbiont cell density, standard error; Table; Temperature, water; Treatment; Tropical
    Type: Dataset
    Format: text/tab-separated-values, 464 data points
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  • 16
    Publication Date: 2024-06-05
    Description: This dataset shows measured stable isotopes of N. pachyderma & C. neoteretis (without vital effect correction). The sampled sediment record PS93/016, consisting of the Giant box core (GKG) PS93/016-4 (81.217°N, 7.341°W, 1549.8 m water depth) and the Kastenlot core (KAL) PS93/016-6 (81.217°N, 7.341°W, 1548.3 m water depth), was obtained during the expedition PS93.1 (2015) (Stein, 2016) of RV Polarstern at the NE Greenland continental margin in the northwestern Fram Strait. The presented data covers the last ca. 195 ka. For stable oxygen and carbon isotope analyses, ca. 30 specimens of the planktic foraminifer Neogloboquadrina pachyderma or the benthic foraminifer species Cassidulina neoteretis from the 100–250 μm fraction were used. Results are expressed in the δ notation referring to the PDB (Pee Dee Belemnite) standard while using NBS 19. Aim of the study was to reconstruct glacial advance and retreat, changes in sea-ice and export of freshwater in the western Fram Strait and, to a certain extent, in the Arctic Ocean.
    Keywords: AGE; Arctic; ARK-XXIX/2.1; Cassidulina neoteretis, δ13C; Cassidulina neoteretis, δ18O; DEPTH, sediment/rock; ECHONEG; Environmental and Climate History Off NorthEast Greenland; Event label; Fram Strait; Giant box corer; GKG; Greenland; KAL; Kasten corer; Neogloboquadrina pachyderma, δ13C; Neogloboquadrina pachyderma, δ18O; North Greenland Sea; Polarstern; PS93/016-4; PS93/016-6; PS93.1; Stable isotopes
    Type: Dataset
    Format: text/tab-separated-values, 1507 data points
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  • 17
    Publication Date: 2024-06-05
    Description: The high surface productivity triggered by nutrient-rich Benguela upwelled waters results in significant enrichment of organic carbon in the sub-surface waters due to enhanced mineralization in the water column and benthic fluxes. Hence, microbial O2-consuming processes are promoted, driving oxygen depletion that favors trace gases i.e. methane (CH4) and nitrous oxide (N2O) production at relatively shallow depths. During upwelling, gas-rich subsurface waters are also transported towards the surface waters, enhancing trace gas sea-air fluxes. We investigate the variability of these fluxes on seasonal and shorter timescales to understand the intensity of the Benguela upwelling system in gas emissions. The data might serve as a base for projections under a changing climate. The fieldwork took place during the cruise M157 (August 4th – September 16th, 2019) onboard the R/V METEOR, which encompassed close-coastal and open ocean regions between Mindelo (Cape Verde) and Walvis Bay. The main transect lines around 18, 23 and 25°S represents the Angola-Benguela frontal zone, Walvis Bay and Lüderitz upwelling cells respectively, which are suggested to represent some regional hotspots of trace gas emissions to the atmosphere, in particular in the vicinity of the upwelling cells. The partial pressures of CH4, N2O, and CO2 as well as oxygen saturation in surface water were determined using IOW's self-built Mobile Equilibrator Sensor System (MESS). The system was described in details elsewhere (Sabbaghzadeh et al., 2021) but in brief, it consists of a custom-built equilibrator (combined shower-head/bubble type) with a water flow rate of about 5 l min-1 and an airflow rate of ~ 4 l min-1, which is linked to two off-axis integrated cavity output laser spectrometers (oa-ICOS, Los Gatos Instruments) for the detection of CH4 / CO2 and N2O / CO. Seawater was supplied by a pump installed at a water depth of about 6 m in the moon pool on board of RV METEOR. oa-ICOS sensors combine a highly specific infrared band laser with a set of reflective mirrors and achieve an effective absorption path length of several kilometers. This enables the detection of the trace gases with high accuracy. Three standard gases, provided by the central calibration lab of the European Integrated Carbon Observation System Research Infrastructure (ICOS RI) were used to calibrate the sensors almost daily throughout the entire expedition. To estimate sea-air gas fluxes, the atmospheric concentration of trace gases was also measured at several positions during the cruise using a tube with the inlet positioned to minimize ship contamination. All other ancillary parameters out of the MESS system were synchronized with D-ship data with a simultaneous data reduction to one-minute intervals.
    Keywords: Benguela Upwelling System; BUSUC 1; Carbon dioxide, dry air; Carbon monoxide, dry air; CT; DATE/TIME; EVAR; M157; M157-track; Meteor (1986); Methane, dry air; Namibia; Nitrous oxide, dry air; oxygen deficient zones; Ship speed; The Benguela Upwelling System under climate change – Effects of VARiability in physical forcing on carbon and oxygen budgets; Threshold; trace gases; Underway cruise track measurements; Wind direction, relative; Wind speed, relative
    Type: Dataset
    Format: text/tab-separated-values, 260 data points
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  • 18
    Publication Date: 2024-06-05
    Description: This publication comprises the following meteorological parameters: air temperature, relative humidity, soil temperature (at 2 cm, 5 cm, and 20 cm depths), soil water content (at 2 cm, 5 cm, and 20 cm depths), soil water potential (at 5 cm and 20 cm depths), precipitation, snow height, solar radiation, wind speed and wind direction. All parameters were collected by five automatic weather stations ranging from 983 m a.s.l. to 2705 m a.s.l. within the IT25 LT(S)ER site Matschertal – Val Mazia in Northern Italy. The automatic weather stations were installed and are operated by Eurac Research. The dataset covers the period from 2017 to 2022 and was collected for monitoring purposes within the LT(S)ER project. The data includes both raw time series and processed time series (cleaned data), including information on data treatments.
    Keywords: air temperature; Climate change; hydrology; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; precipitation; relative humidity; snow height; soil temperature; soil water content; soil water potential; Solar radiation; wind direction; wind speed
    Type: Dataset
    Format: application/zip, 8 datasets
    Location Call Number Expected Availability
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  • 19
    Publication Date: 2024-06-05
    Description: The high surface productivity triggered by nutrient-rich Benguela upwelled waters results in significant enrichment of organic carbon in the sub-surface waters due to enhanced mineralization in the water column and benthic fluxes. Hence, microbial O2-consuming processes are promoted, driving oxygen depletion that favors trace gases i.e. methane (CH4) and nitrous oxide (N2O) production at relatively shallow depths. During upwelling, gas-rich subsurface waters are also transported towards the surface waters, enhancing trace gas sea-air fluxes. We investigate the variability of these fluxes on seasonal and shorter timescales to understand the intensity of the Benguela upwelling system in gas emissions. The data might serve as a base for projections under a changing climate. The fieldwork took place during the cruise M157 (August 4th – September 16th, 2019) onboard the R/V METEOR, which encompassed close-coastal and open ocean regions between Mindelo (Cape Verde) and Walvis Bay. The main transect lines around 18, 23 and 25°S represents the Angola-Benguela frontal zone, Walvis Bay and Lüderitz upwelling cells respectively, which are suggested to represent some regional hotspots of trace gas emissions to the atmosphere, in particular in the vicinity of the upwelling cells. To explore further, nearly 300 discrete water samples were collected from the Niskin bottles at different stations for determination of the concentrations of CH4, N2O, and total inorganic carbon (CT). Analysis for CH4 and N2O was performed using an in-house designed purge and trap system with a dynamic headspace. In brief, a subsample of the water is purged with an inert ultrapure carrier gas of Helium, and the gases are focused on a cryo-trap operated at about -120°C. The volatile compounds are desorbed by rapid heating and analyzed by a gas chromatograph (Agilent 7890 B), equipped with a Flame Ionization Detector for CH4 and an Electron Capture Detector for N2O measurements, respectively. Samples for CT were taken to investigate the carbonate system. CT was measured using an automated Infra-Red Inorganic Carbon Analyzer (AIRICA) system (Marianda e.K., 24145 Kiel) from discrete 250 ml samples. In brief, a subsample is drawn into a volume-calibrated syringe and injected into a purge vessel, where the discrete sample is acidified. All species of the inorganic carbon system are converted to CO2, which is purged from the water using a carrier gas that streams through the acidified probe. Then the gas flows through a Peltier cooler and a NAFION dryer to be dried. The concentration of CO2 is then measured by an infrared detector (LICOR 7000), which integrates the peak of the purged sample. The integrated signal is directly proportional to the carbon released, allowing the CT concentration to be calculated with high precision. Certified reference material (CRM) of known CT-concentration is used for standardization and to account for drift of the sensor response.
    Keywords: Benguela Upwelling System; BUSUC 1; Carbon dioxide; CTD/Rosette; CTD-RO; DEPTH, water; EVAR; Event label; Gas chromatography, Agilent 7820B, coupled with a flame ionization detector and an Electron Capture Detector; Infrared detector LICOR 7000; LATITUDE; LONGITUDE; M157; M157_10-7; M157_11-4; M157_12-2; M157_14-2; M157_15-14; M157_16-3; M157_17-2; M157_24-1; M157_25-1; M157_26-2; M157_27-1; M157_2-8; M157_28-1; M157_31-1; M157_34-4; M157_36-2; M157_38-2; M157_39-2; M157_40-2; M157_41-14; M157_42-2; M157_43-2; M157_44-2; M157_45-2; M157_46-3; M157_49-3; M157_6-1; M157_8-2; M157_9-2; Meteor (1986); Methane; Namibia; Nitrous oxide; oxygen deficient zones; Station label; The Benguela Upwelling System under climate change – Effects of VARiability in physical forcing on carbon and oxygen budgets; trace gases
    Type: Dataset
    Format: text/tab-separated-values, 1370 data points
    Location Call Number Expected Availability
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  • 20
    Publication Date: 2024-06-05
    Description: The high surface productivity triggered by nutrient-rich Benguela upwelled waters results in significant enrichment of organic carbon in the sub-surface waters due to enhanced mineralization in the water column and benthic fluxes. Hence, microbial O2-consuming processes are promoted, driving oxygen depletion that favors trace gases i.e. methane (CH4) and nitrous oxide (N2O) production at relatively shallow depths. During upwelling, gas-rich subsurface waters are also transported towards the surface waters, enhancing trace gas sea-air fluxes. We investigate the variability of these fluxes on seasonal and shorter timescales to understand the intensity of the Benguela upwelling system in gas emissions. The data might serve as a base for projections under a changing climate. The fieldwork took place during the cruise M157 (August 4th – September 16th, 2019) onboard the R/V METEOR, which encompassed close-coastal and open ocean regions between Mindelo (Cape Verde) and Walvis Bay. The main transect lines around 18, 23 and 25°S represents the Angola-Benguela frontal zone, Walvis Bay and Lüderitz upwelling cells respectively, which are suggested to represent some regional hotspots of trace gas emissions to the atmosphere, in particular in the vicinity of the upwelling cells. The partial pressures of CH4, N2O, and CO2 as well as oxygen saturation in surface water were determined using IOW's self-built Mobile Equilibrator Sensor System (MESS). The system was described in details elsewhere (Sabbaghzadeh et al., 2021) but in brief, it consists of a custom-built equilibrator (combined shower-head/bubble type) with a water flow rate of about 5 l min-1 and an airflow rate of ~ 4 l min-1, which is linked to two off-axis integrated cavity output laser spectrometers (oa-ICOS, Los Gatos Instruments) for the detection of CH4 / CO2 and N2O / CO. Seawater was supplied by a pump installed at a water depth of about 6 m in the moon pool on board of RV METEOR. oa-ICOS sensors combine a highly specific infrared band laser with a set of reflective mirrors and achieve an effective absorption path length of several kilometers. This enables the detection of the trace gases with high accuracy. Three standard gases, provided by the central calibration lab of the European Integrated Carbon Observation System Research Infrastructure (ICOS RI) were used to calibrate the sensors almost daily throughout the entire expedition. To estimate sea-air gas fluxes, the atmospheric concentration of trace gases was also measured at several positions during the cruise using a tube with the inlet positioned to minimize ship contamination. All other ancillary parameters out of the MESS system were synchronized with D-ship data with a simultaneous data reduction to one-minute intervals.
    Keywords: Benguela Upwelling System; BUSUC 1; Carbon dioxide; Carbon dioxide, dry air; Carbon dioxide, equilibrium; Carbon dioxide, partial pressure; Carbon dioxide saturation; Carbon monoxide; Carbon monoxide, dissolved, equilibrium; Carbon monoxide, dry air; Course over ground; CT; Date; DATE/TIME; EVAR; Flow rate; Humidity, relative; LATITUDE; LONGITUDE; Long-wave downward radiation; M157; M157-track; Meteor (1986); Methane; Methane, dissolved, equilibrium; Methane, dry air; Methane saturation; Namibia; Nitrous oxide; Nitrous oxide, dissolved, equilibrium; Nitrous oxide, dry air; Nitrous oxide saturation; oxygen deficient zones; Pressure, atmospheric; Salinity; Ship speed; Short-wave downward (GLOBAL) radiation; Speed; Temperature, air; Temperature, water; The Benguela Upwelling System under climate change – Effects of VARiability in physical forcing on carbon and oxygen budgets; trace gases; Ultraviolet radiation; Underway cruise track measurements; Visibility; Wind direction, relative; Wind direction, true; Wind speed, relative; Wind speed, true
    Type: Dataset
    Format: text/tab-separated-values, 1080901 data points
    Location Call Number Expected Availability
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  • 21
    Publication Date: 2024-06-05
    Description: The stable silicon isotopic composition of siliceous sponge skeletal elements, spicules, is a potential archive of past dissolved silicon (silicic acid, or DSi) concentrations in bottom waters. Several field-based studies have shown that there is a non-linear relationship between the concentration of ambient DSi and both the isotopic composition (denoted by d30Si) of spicules and apparent isotopic fractionation by sponges during growth. There is considerable scatter in the calibration, with some studies highlighting variation within an individual sponge, and between individuals in both monospecific and more diverse populations. When reconstructing past DSi, it is only possible to differentiate spicules by their morphology, which in many cases will not be taxonomically diagnostic. However, there has yet to be a systematic study of core top and downcore d30Si measurements from different spicule types. Here we address that gap using spicules extracted from two shallow sediment cores from the Schultz Massif Seamount between the Norwegian and Greenland Seas collected on R/V G.O.Sars expedition GS2016109A. Sediments were sliced at 1cm intervals, washed and dried, and spicules hand-picked out and sorted by morphological type. The spicules were dissolved and analysed for silicon isotopic composition using a multi-collector inductively coupled plasma mass spectrometer (MC-ICP-MS).
    Keywords: Arctic; BC; Box corer; Core; Deep-sea Sponge Grounds Ecosystems of the North Atlantic; Depth, bathymetric; DEPTH, sediment/rock; Event label; G. O. Sars (2003); GS16A-202; GS2016109A; GS2016109A-09-BC-01; GS2016109A-10-BC-02; ICY-LAB; Isotope CYcling in the LABrador Sea; LATITUDE; LONGITUDE; Multi-Collector ICP-MS (MC-ICP-MS); Sample ID; Schultz Bank; silicon isotope; spicule; sponge; SponGES; Sponge spiculae, δ30Si; Sponge spicule type
    Type: Dataset
    Format: text/tab-separated-values, 475 data points
    Location Call Number Expected Availability
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  • 22
    Publication Date: 2024-06-05
    Description: Raw multibeam bathymetry data were collected aboard RV METEOR during cruise M191 using a Kongsberg EM 122 multibeam echosounder. The expedition took place during 16.07.2923 – 05.08.2023 from Algeciras (Spain) to Piraeus (Greece) in the Mediterranean. The main objective of M191 was to extensively map the seafloor (with multibeam echo sounder, sediment echo sounder, and towed magnetometer) and sample (by chain bag dredging) unexplored volcanic structures along the Sicilian Channel Rift Zone (Pantelleria-, Malta- and Linosa-Graben) and the Capo-Granitola-Sciacca Fault Zone (CGSFZ). Data were recorded in the Italian and Greek EEZ. Sound velocity profiles (SVP) were applied on the data for calibration. Please see environmental data and the cruise report for details. The data are unprocessed and can therefore contain incorrect depth measurements (artifacts) if not further processed. Note that refraction errors may occur when no proper SVP is applied. Acquisition and provision of the data are part of the DAM Underway Project and published according to the FAIR principles.
    Keywords: Bathymetry; Binary Object; Binary Object (File Size); Binary Object (Media Type); Comment; DAM_Underway; DAM Underway Research Data; Data file recording distance; Data file recording duration; DATE/TIME; ELEVATION; EM122; EM122 multibeam echosounder; Event label; Extracted from file; Extracted with MB-System; File content; Kongsberg datagram raw file name; LATITUDE; LONGITUDE; M191; M191_0_Underway-3; Meteor (1986); Multibeam; Number of pings; Ship speed; Start of data file, depth; Start of data file, heading; Start of data file recording, date/time; Start of data file recording, latitude; Start of data file recording, longitude; Stop of data file, depth; Stop of data file, heading; Stop of data file recording, date/time; Stop of data file recording, latitude; Stop of data file recording, longitude; SUAVE
    Type: Dataset
    Format: text/tab-separated-values, 10424 data points
    Location Call Number Expected Availability
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  • 23
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    PANGAEA
    In:  Supplement to: Mendoza, Irene; Peres, Carlos Augusto; Morellato, Leonor Patricia C (2016): Continental-scale patterns and climatic drivers of fruiting phenology: A quantitative Neotropical review. Global and Planetary Change, https://doi.org/10.1016/j.gloplacha.2016.12.001
    Publication Date: 2024-06-05
    Description: Changes in the life cycle of organisms (i.e. phenology) are one of the most widely used early-warning indicators of climate change, yet this remains poorly understood throughout the tropics. We exhaustively reviewed any published and unpublished study on fruiting phenology carried out at the community level in the American tropics and subtropics (latitudinal range: 26°N?26°S) to (1) provide a comprehensive overview of the current status of fruiting phenology research throughout the Neotropics; (2) unravel the climatic factors that have been widely reported as drivers of fruiting phenology; and (3) provide a preliminary assessment of the potential phenological responses of plants under future climatic scenarios. Despite the large number of phenological datasets uncovered (218), our review shows that their geographic distribution is very uneven and insufficient for the large surface of the Neotropics (~ 1 dataset per ~ 78,000 km2). Phenological research is concentrated in few areas with many studies (state of São Paulo, Brazil, and Costa Rica), whereas vast regions elsewhere are entirely unstudied. Sampling effort in fruiting phenology studies was generally low: the majority of datasets targeted fewer than 100 plant species (71%), lasted 2 years or less (72%), and only 10.4% monitored 〉 15 individuals per species. We uncovered only 10 sites with ten or more years of phenological monitoring. The ratio of numbers of species sampled to overall estimates of plant species richness was wholly insufficient for highly diverse vegetation types such as tropical rainforests, seasonal forest and cerrado, and only slightly more robust for less diverse vegetation types, such as deserts, arid shrublands and open grassy savannas. Most plausible drivers of phenology extracted from these datasets were environmental (78.5%), whereas biotic drivers were rare (6%). Among climatic factors, rainfall was explicitly included in 73.4% of cases, followed by air temperature (19.3%). Other environmental cues such as water level (6%), solar radiation or photoperiod (3.2%), and ENSO events (1.4%) were rarely addressed. In addition, drivers were analyzed statistically in only 38% of datasets and techniques were basically correlative, with only 4.8% of studies including any consideration of the inherently autocorrelated character of phenological time series. Fruiting peaks were significantly more often reported during the rainy season both in rainforests and cerrado woodlands, which is at odds with the relatively aseasonal character of the former vegetation type. Given that climatic models predict harsh future conditions for the tropics, we urgently need to determine the magnitude of changes in plant reproductive phenology and distinguish those from cyclical oscillations. Long-term monitoring and herbarium data are therefore key for detecting these trends. Our review shows that the unevenness in geographic distribution of studies, and diversity of sampling methods, vegetation types, and research motivation hinder the emergence of clear general phenological patterns and drivers for the Neotropics. We therefore call for prioritizing research in unexplored areas, and improving the quantitative component and statistical design of reproductive phenology studies to enhance our predictions of climate change impacts on tropical plants and animals.
    Keywords: Area/locality; Biome; Code; Country; Duration; Feces; Frequency; Herbarium; Herbs; Identification; Individuals; Latin_America; LATITUDE; Liana; LONGITUDE; Number of species; Number of trap; Observation; Peak of fruiting; Plant, others; Reference/source; Shrubs; Surface of trap; Trees; Uniform resource locator/link to reference; Vegetation type
    Type: Dataset
    Format: text/tab-separated-values, 4889 data points
    Location Call Number Expected Availability
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  • 24
    Publication Date: 2024-06-05
    Description: Raw multibeam bathymetry data were collected aboard RV METEOR during cruise M191 using a Kongsberg EM 122 multibeam echosounder. The expedition took place during 16.07.2923 – 05.08.2023 from Algeciras (Spain) to Piraeus (Greece) in the Mediterranean. The main objective of M191 was to extensively map the seafloor (with multibeam echo sounder, sediment echo sounder, and towed magnetometer) and sample (by chain bag dredging) unexplored volcanic structures along the Sicilian Channel Rift Zone (Pantelleria-, Malta- and Linosa-Graben) and the Capo-Granitola-Sciacca Fault Zone (CGSFZ). Data were recorded in the Italian and Greek EEZ. Sound velocity profiles (SVP) were applied on the data for calibration. Please see environmental data and the cruise report for details. The data are unprocessed and can therefore contain incorrect depth measurements (artifacts) if not further processed. Note that refraction errors may occur when no proper SVP is applied. Acquisition and provision of the data are part of the DAM Underway Project and published according to the FAIR principles.
    Keywords: Bathymetry; Binary Object; Binary Object (File Size); Binary Object (Media Type); Comment; DAM_Underway; DAM Underway Research Data; Data file recording distance; Data file recording duration; DATE/TIME; ELEVATION; EM122; EM122 multibeam echosounder; Event label; Extracted from file; Extracted with MB-System; File content; Kongsberg datagram raw file name; LATITUDE; LONGITUDE; M191; M191_0_Underway-3; Meteor (1986); Multibeam; Number of pings; Ship speed; Start of data file, depth; Start of data file, heading; Start of data file recording, date/time; Start of data file recording, latitude; Start of data file recording, longitude; Stop of data file, depth; Stop of data file, heading; Stop of data file recording, date/time; Stop of data file recording, latitude; Stop of data file recording, longitude; SUAVE
    Type: Dataset
    Format: text/tab-separated-values, 828 data points
    Location Call Number Expected Availability
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  • 25
    Publication Date: 2024-06-05
    Description: In this study, we used functional genomics to examine the molecular response of OA exposed red king crab. We leveraged juveniles that were exposed to (and tolerated) three carbonate chemistry treatments from hatching to the first crab stage (C1), thus capturing transcriptional differences among crab that are reared in historically ambient conditions along the Bering Sea shelf (pH 8.0), and those acclimated to a moderately (pH 7.8) and severely (pH 7.5) acidified environments that are projected to occur in surface and bottom waters by the end of this century [15]. Using RNA-Seq, a high-throughput sequencing approach that measures gene-activity, our study provides a snap-shot of system-wide changes in energy allocation due to acidification exposure by identifying genes, their functions, and biological processes that differ in OA-reared crab [56]. Libraries were constructed from at least 13 individuals per treatment, rather than pools of individuals which can obscure genotypedependent variation. Importantly, since the crab used in this experiment were quite tolerant of OA conditions, the molecular mechanisms and pathways described here may be potentially critical to survival in an acidified environment.
    Keywords: Alkalinity, total; Alkalinity, total, standard deviation; Animalia; Aragonite saturation state; Aragonite saturation state, standard deviation; Arthropoda; Benthic animals; Benthos; Bicarbonate ion; Bicarbonate ion, standard deviation; Calcite saturation state; Calcite saturation state, standard deviation; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbon, inorganic, dissolved, standard deviation; Carbonate ion; Carbonate ion, standard deviation; Carbonate system computation flag; Carbon dioxide; Coast and continental shelf; Containers and aquaria (20-1000 L or 〈 1 m**2); Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Gene expression (incl. proteomics); Identification; Laboratory experiment; North Pacific; OA-ICC; Ocean Acidification International Coordination Centre; Paralithodes camtschaticus; Partial pressure of carbon dioxide, standard deviation; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); pH; pH, standard deviation; Salinity; Salinity, standard deviation; Single species; Species, unique identification; Species, unique identification (Semantic URI); Species, unique identification (URI); Temperate; Temperature, water; Temperature, water, standard deviation; Treatment; Type of study
    Type: Dataset
    Format: text/tab-separated-values, 495 data points
    Location Call Number Expected Availability
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  • 26
    Publication Date: 2024-06-05
    Description: Upwelling systems are significant sources of atmospheric nitrous oxide (N₂O). The Benguela Upwelling System is one of the most productive regions worldwide and a temporally variable source of N₂O. Strong O₂ depletions above the shelf are favoring periodically OMZ formations. We aimed to assess underlying N₂O production and consumption processes on different temporal and spatial scales during austral winter in the Benguela Upwelling System, when O₂-deficiency in the water column is relatively low. The fieldwork took place during the cruise M157 (August 4th – September 16th 2019) onboard the R/V METEOR. This expedition included four close-coastal regions around Walvis Bay at 23°S, which presented the lowest O₂ concentrations near the seafloor and thus may provide hotspots of N₂O production. Seawater was collected in 10 L free-flow bottles by using a rosette system equipped with conductivity-temperature-depth (CTD) sensors (SBE 911plus, Seabird-electronics, USA). Incubation experiments were performed using stable isotope ¹⁵N-tracers. Seawater samples for ¹⁵N-tracer incubations and natural abundance N₂O analysis were collected from 10 L free-flow bottles and filled bubble-free into 125 mL serum bottles. The samples for natural abundance N₂O analysis were immediately fixed with saturated HgCl₂ and stored in the dark. To perform the incubation, we added ¹⁵N-labeled NO₂-, NO₃⁻ and NH₄⁺ to estimate the in-situ N₂O production rates and associated reactions. To determine a single rate, the bottles were sacrificed after tracer addition, and within the time interval of 12 h, 24 h and 48 h by adding HgCl₂. Rates were calculated based on a linear regression over time. Total N₂O and natural abundance isotopologues of N₂O were analyzed by using an isotope ratio mass spectrometer (IRMS, Delta V Plus, Thermo Scientific). NO₂- production was additionally analyzed by transforming ¹⁵NO₂- to ¹⁵N₂O following the azide method after McIlvin & Altabet (2005) and the nitrogen isotope ratio of N₂O was measured by an IRMS. N₂ production was determined via an IRMS (Flash-EA-ConfloIV-DELTA V Advanced, Thermo Scientific) by injecting headspace from exetainers. The N₂O yield per nitrite produced and the N₂O yield during denitrification was calculated. Samples for natural abundance N₂O was sampled and measured in triplicates and is shown as an average with standard deviation (SD). In order to estimate the contribution of different N₂O producing pathways by major biological processes and the extent of N₂O reduction to N₂, the dual-isotope mapping approach was applied to natural abundance isotopologues of N₂O, which uses the relative position of background-subtracted N₂O samples in a δ¹⁵Nˢᴾ-N₂O vs. δ¹⁸O-N₂O diagram (Yu et al., 2020; Lewicka-Szczebak et al., 2020).
    Keywords: 15N-tracer; Ammonium, oxidation rate; Ammonium, oxidation rate, limit of detection; Ammonium, oxidation rate, standard error; ammonium oxidation; Anammox rate; Anammox rate, standard error; Benguela Upwelling System; BUSUC 1; Calculated; CTD/Rosette; CTD-RO; DATE/TIME; Denitrification; Denitrification rate, standard error; DEPTH, water; Event label; Field observation; Gas Chromatograph (GC), Manufacturer unknown, custom built; coupled with Isotope Ratio Mass Spectrometer (IRMS), Thermo Scientific, Delta V Plus; Isotope Ratio Mass Spectrometer (IRMS), Thermo Scientific, Delta V Advantage [Conflo IV interface]; LATITUDE; LONGITUDE; M157; M157_14-14; M157_16-25; M157_17-16; M157_2-9; Meteor (1986); N2O production rates; Namibia; Nitrate, reduction rate; Nitrate, reduction rate, limit of detection; nitrate reduction; nitrification; Nitrous oxide, limit of detection; Nitrous oxide, yield; Nitrous oxide production; Nitrous oxide production, standard error; oxygen minimum zone; Sample code/label; Site preference, N2O; Site preference, N2O, standard deviation; Stable isotope; Station label; δ15N, nitrous oxide; δ15N, nitrous oxide, standard deviation; δ15N-alpha, nitrous oxide; δ15N-alpha, nitrous oxide, standard deviation; δ15Nbeta, nitrous oxide; δ15Nbeta, nitrous oxide, standard deviation; δ18O, nitrous oxide; δ18O, nitrous oxide, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 801 data points
    Location Call Number Expected Availability
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  • 27
    Publication Date: 2024-06-05
    Keywords: air temperature; B1; B2; B3; Climate change; DATE/TIME; Date/Time local; Event label; hydrology; IT25_B1; IT25_B2; IT25_B3; IT25_P2; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; P2; precipitation; Precipitation; Quality code; relative humidity; S4; snow height; soil temperature; soil water content; soil water potential; Solar radiation; Weighing rain gauge, Ott Pluvio²; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 3333368 data points
    Location Call Number Expected Availability
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  • 28
    Publication Date: 2024-06-05
    Description: Upwelling systems are significant sources of atmospheric nitrous oxide (N₂O). The Benguela Upwelling System is one of the most productive regions worldwide and a temporally variable source of N₂O. Strong O₂ depletions above the shelf are favoring periodically OMZ formations. We aimed to assess underlying N₂O production and consumption processes on different temporal and spatial scales during austral winter in the Benguela Upwelling System, when O₂-deficiency in the water column is relatively low. The fieldwork took place during the cruise M157 (August 4th – September 16th 2019) onboard the R/V METEOR. This expedition included four close-coastal regions around Walvis Bay at 23°S, which presented the lowest O₂ concentrations near the seafloor and thus may provide hotspots of N₂O production. Seawater was collected in 10 L free-flow bottles by using a rosette system equipped with conductivity-temperature-depth (CTD) sensors (SBE 911plus, Seabird-electronics, USA). Incubation experiments were performed using stable isotope ¹⁵N-tracers. Seawater samples for ¹⁵N-tracer incubations and natural abundance N₂O analysis were collected from 10 L free-flow bottles and filled bubble-free into 125 mL serum bottles. The samples for natural abundance N₂O analysis were immediately fixed with saturated HgCl₂ and stored in the dark. To perform the incubation, we added ¹⁵N-labeled NO₂-, NO₃⁻ and NH₄⁺ to estimate the in-situ N₂O production rates and associated reactions. To determine a single rate, the bottles were sacrificed after tracer addition, and within the time interval of 12 h, 24 h and 48 h by adding HgCl₂. Rates were calculated based on a linear regression over time. Total N₂O and natural abundance isotopologues of N₂O were analyzed by using an isotope ratio mass spectrometer (IRMS, Delta V Plus, Thermo Scientific). NO₂- production was additionally analyzed by transforming ¹⁵NO₂- to ¹⁵N₂O following the azide method after McIlvin & Altabet (2005) and the nitrogen isotope ratio of N₂O was measured by an IRMS. N₂ production was determined via an IRMS (Flash-EA-ConfloIV-DELTA V Advanced, Thermo Scientific) by injecting headspace from exetainers. The N₂O yield per nitrite produced and the N₂O yield during denitrification was calculated. Samples for natural abundance N₂O was sampled and measured in triplicates and is shown as an average with standard deviation (SD). In order to estimate the contribution of different N₂O producing pathways by major biological processes and the extent of N₂O reduction to N₂, the dual-isotope mapping approach was applied to natural abundance isotopologues of N₂O, which uses the relative position of background-subtracted N₂O samples in a δ¹⁵Nˢᴾ-N₂O vs. δ¹⁸O-N₂O diagram (Yu et al., 2020; Lewicka-Szczebak et al., 2020).
    Keywords: 15N-tracer; Ammonium, oxidation rate; Ammonium, oxidation rate, limit of detection; Ammonium, oxidation rate, standard error; ammonium oxidation; Anammox rate; Anammox rate, standard error; Benguela Upwelling System; BUSUC 1; Calculated; CTD/Rosette; CTD-RO; DATE/TIME; Denitrification; Denitrification rate, standard error; DEPTH, water; Event label; Field observation; Gas Chromatograph (GC), Manufacturer unknown, custom built; coupled with Isotope Ratio Mass Spectrometer (IRMS), Thermo Scientific, Delta V Plus; Isotope Ratio Mass Spectrometer (IRMS), Thermo Scientific, Delta V Advantage [Conflo IV interface]; LATITUDE; LONGITUDE; M157; M157_14-14; M157_16-25; M157_17-16; M157_2-9; Meteor (1986); N2O production rates; Namibia; Nitrate, reduction rate; Nitrate, reduction rate, limit of detection; Nitrate, reduction rate, standard error; nitrate reduction; nitrification; Nitrous oxide, limit of detection; Nitrous oxide, yield; Nitrous oxide production; Nitrous oxide production, standard error; oxygen minimum zone; Sample code/label; Site preference, N2O; Site preference, N2O, standard deviation; Stable isotope; Station label; δ15N, nitrous oxide; δ15N, nitrous oxide, standard deviation; δ15N-alpha, nitrous oxide; δ15N-alpha, nitrous oxide, standard deviation; δ15Nbeta, nitrous oxide; δ15Nbeta, nitrous oxide, standard deviation; δ18O, nitrous oxide; δ18O, nitrous oxide, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 801 data points
    Location Call Number Expected Availability
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  • 29
    Publication Date: 2024-06-05
    Description: To understand the foraging and pH sensing behavior of juvenile crab, and how this interacts with their nutritional status, we exposed recently settled second instar juveniles to either ambient pH or reduced pH for 42-d, crossed with either a 'maintenance'- or low-quantity 'challenge' diet treatment. After the experimental exposure period, we introduced crab into foraging and sensing pH behavior experiments. In the foraging experiment, we placed crab in a behavior arena with unidirectional flow, where we measured the food discovery time and time allocation of activities in 300-s trials for all individual crab. This dataset is included in the OA-ICC data compilation maintained in the framework of the IAEA Ocean Acidification International Coordination Centre (see https://oa-icc.ipsl.fr). Original data were provided by the author of the related paper (see Related to) to the OA-ICC data curator. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2024) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2024-05-06.
    Keywords: Alkalinity, total; Animalia; Arachidonic acid, per dry mass; Aragonite saturation state; Arthropoda; Behaviour; Benthic animals; Benthos; Bicarbonate ion; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Charleston_OA; Coast and continental shelf; Diet; Docosahexaenoic acid; Eicosapentaenoic acid; EXP; Experiment; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Identification; Laboratory experiment; Lipids, total; Metacarcinus magister; North Pacific; OA-ICC; Ocean Acidification International Coordination Centre; Other; Other studied parameter or process; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); pH; Potentiometric titration; Salinity; Sample ID; Single species; Species, unique identification; Species, unique identification (Semantic URI); Species, unique identification (URI); Spectrophotometric; Temperate; Temperature, water; Time in days; Time in seconds; Tissue, dry mass; Tissue type; Treatment; Type of study; Width
    Type: Dataset
    Format: text/tab-separated-values, 3606 data points
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  • 30
    Publication Date: 2024-06-05
    Keywords: air temperature; B1; B3; Climate change; DATE/TIME; Date/Time local; Event label; Humidity, relative; hydrology; IT25_B1; IT25_B3; IT25_P2; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; P2; precipitation; Quality code; relative humidity; S3; S4; snow height; soil temperature; soil water content; soil water potential; Solar radiation; Temperature, air; Temperature and humidity sensor; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 7256866 data points
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  • 31
    Publication Date: 2024-06-05
    Keywords: air temperature; B2; B3; Climate change; DATE/TIME; Date/Time local; Event label; hydrology; IT25_B2; IT25_B3; IT25_S3; IT25_S4; Latitude of event; Longitude of event; long-term ecological monitoring; Long-term Socio-Ecological Research (LTSER) site Matschertal/Val di Mazia; LTER site; LTSER_Matsch; meteorology; Monitoring station; MONS; Optional event label; precipitation; PYRA; Pyranometer; Quality code; relative humidity; S3; S4; Short-wave downward (GLOBAL) radiation; snow height; soil temperature; soil water content; soil water potential; Solar radiation; wind direction; wind speed
    Type: Dataset
    Format: text/tab-separated-values, 3179137 data points
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  • 32
    Publication Date: 2024-06-05
    Description: The dataset contains temperature, salinity, oxygen saturation, chlorophyll a and turbidity data from the AWIPEV underwater observatory from the year 2022 in a temporal resolution of 1 hour. The cabled observatory is located in 12m water depth and comprises single or multiple sensors for a specific parameter (see https://www.awi.de/en/science/biosciences/shelf-sea-system-ecology/main-research-focus/cosyna/underwater-node-spitsbergen.html). For a detailed description of the data see associated metadatafile metadata_svulobs_2022_hydrography.pdf
    Keywords: AWIPEV; AWIPEV_based; AWIPEV_UW-Observatory; Centre for Scientific Diving; Chlorophyll a; Chlorophyll a, confidence value; Coastal Observing System for Northern and Arctic Seas; COSYNA; CSD; DATE/TIME; Helmholtz-Zentrum Geesthacht, Institute of Coastal Research; HZG; Kongsfjorden, Spitsbergen, Arctic; Modular Observation Solutions for Earth Systems; MOSES; OBSE; Observation; Oxygen saturation; Oxygen saturation, confidence value; Salinity; Salinity, confidence value; See further details: Metadata for the AWIPEV underwater observatory; Temperature, water; Temperature, water, confidence value; Turbidity, confidence value; Turbidity (Formazin Turbidity Unit)
    Type: Dataset
    Format: text/tab-separated-values, 78906 data points
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  • 33
    Publication Date: 2024-06-05
    Description: Highlights: • Acartia hudsonica shows strong seasonality in thermal tolerance. • The observed seasonal differences in are consistent with pheno-typic plasticity not adaptation. • Body size in A. hudsonica is negatively correlated to environmental and developmental temperature. Abstract: Seasonal changes in environmental conditions require substantial physiological responses for population persistence. Phenotypic plasticity is a common mechanism to tolerate these changes, but for organisms with short generation times rapid adaptation may also be a contributing factor. Here, we used a common garden design (11 °C and 18 °C) to disentangle the impacts of adaptation from phenotypic plasticity on thermal tolerance of the calanoid copepod Acartia hudsonica collected throughout spring and summer of a single year. Acartia hudsonica were collected from five time points across the season and thermal tolerance was determined using critical thermal maximum followed by additional measurements after one generation of common garden. As sea surface temperature increased through the season, field collected individuals showed corresponding increases in thermal tolerance but decreases in body size. Despite different thermal tolerances of wild collections, after one generation of common garden animals did not differ in within thermal treatments. Instead, there was evidence of phenotypic plasticity where higher temperatures were tolerated by the 18 °C versus the 11 °C treatment animals across all collections. Despite persisting differences between collections due to either adaptation or parental effects, acclimation also had significant effects on body size, with the warm treatment resulting in smaller individuals, consistent with the temperature size rule. Therefore, the differences in thermal tolerance and body size observed in field collected A. hudsonica were predominantly driven by plasticity rather than adaptation. However, the observed decrease in body size suggests that nutrient availability for higher trophic levels and ecosystem functioning could be impacted if temperatures consistently increase with no change in copepod abundance. This is the first record of A. hudsonica in the Baltic Sea known to the authors.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
    Format: text
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  • 34
    Publication Date: 2024-06-05
    Description: Abundant mineral resources in the deep sea are prospected for mining for the global metal market. Seafloor massive sulphide (SMS) deposits along the Mid-Atlantic Ridge are one of the potential sources for these metals. The extraction of SMS deposits will expose adjacent marine ecosystems to suspended particle plumes charged with elevated concentrations of heavy metals and other potentially toxic compounds. Up to date there is no information about the impact of mining activities on deep-sea benthic ecosystems such as abundant deep-sea sponge grounds in the North Atlantic Ocean. Sponge grounds play a major role in benthic-pelagic coupling and represent an important habitat for a diversity of vertebrates, invertebrates and microorganisms. To simulate the effects of mining plumes on benthic life in the deep sea, we exposed Geodia barretti, a dominant sponge species in the North Atlantic Ocean, and an associated brittle star species from the genus Ophiura spp. to a field-relevant concentration of 30 mg L−1 suspended particles of crushed SMS deposits. Three weeks of exposure to suspended particles of crushed SMS resulted in a tenfold higher rate of tissue necrosis in sponges. All brittle stars in the experiment perished within ten days of exposure. SMS particles were evidently accumulated in the sponge's mesohyl and concentrations of iron and copper were 10 times elevated in SMS exposed individuals. Oxygen consumption and clearance rates were significantly retarded after the exposure to SMS particles, hampering the physiological performance of G. barretti. These adverse effects of crushed SMS deposits on G. barretti and its associated brittle star species potentially cascade in disruptions of benthic-pelagic coupling processes in the deep sea. More elaborate studies are advisable to identify threshold levels, management concepts and mitigation measures to minimize the impact of deep-sea mining plumes on benthic life.
    Type: Article , PeerReviewed , info:eu-repo/semantics/article
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  • 35
    Publication Date: 2024-06-04
    Description: Data presented here were collected between January 2021 to December 2021 within the research unit DynaCom (Spatial community ecology in highly dynamic landscapes: From island biogeography to metaecosystems, https://uol.de/dynacom/ ) of the Universities of Oldenburg, Göttingen, and Münster, the iDiv Leipzig and the Nationalpark Niedersächsisches Wattenmeer. Experimental islands and saltmarsh enclosed plots were created in the back barrier tidal flat and in the saltmarsh zone of the island of Spiekeroog. Meteorological data were collected near the experimental setup, with a locally installed weather station located approximately 500m north of the southern shoreline. The weather station system used here was a ClimaSensor US 4.920x.00.00x that was pre-calibrated by the manufacturer (Adolf Thies GmbH & Co. KG, D-Göttingen). Data were recorded and saved within the Meteo-Online (V4.5.0.20253) software in a sampling interval of 1 min, with an averaging time of 10 s. Date and time were given in UTC and the position was derived from the internal GPS system. Data handling was performed according to Zielinski et al. (2018): Post-processing of collected data was done using MATLAB (R2018a). Quality control was performed by (a) erasing data covering maintenance activities, (b) removing outliers, defined as data exhibiting changes of more than two standard deviations within one time step, and (c) visually checks.
    Keywords: BEFmate; biodiversity - ecosystem functioning; DynaCom; experimental islands; FOR 2716: Spatial community ecology in highly dynamic landscapes: from island biogeography to metaecosystems; Metacommunity; meteorology; salt marsh; Spiekeroog
    Type: Dataset
    Format: application/zip, 12 datasets
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  • 36
    Publication Date: 2024-06-04
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: Airborne laser scanning; Arctic; Freeboard; Helicopter; IceSense; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: application/zip, 64 datasets
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  • 37
    Publication Date: 2024-06-04
    Description: This data data set is the taxonomically harmonized pollen data from records 2831 sites. 1032 sites are located in North America, 1075 sites in Europe, 488 sites in Asia, 150 sites in South America, 54 in Africa and 32 in the Indopacific. Most of the data where retrieved from the Neotoma Paleoecology Database (https://www.neotomadb.org/), with additional data from Cao et al. (2020; https://doi.org/10.5194/essd-12-119-2020), Cao et al. (2013, https://doi.org/10.1016/j.revpalbo.2013.02.003) and our own collection for the Asian sector. The ages of the samples refer to the newly established LegacyAge 1.0 framework (https://doi.pangaea.de/10.1594/PANGAEA.933132). The 10,110 original pollen taxa names and notations were harmonized to 1002 taxa names. We present the table with the harmonization approach crossreferencing the original taxa with the harmonized taxa name. The harmonised pollen data are presented as counts (when available) and as percentage values. We complement the data publication by providing the source information on the references (most data are related to Neotoma) as a table linked to each Dataset ID. The data set and site IDs are from Neotoma if the data sets are derived from the Neotoma repository. In case of our own data collection efforts (Cao et al. (2020), Cao et al. (2013) and our own data) we used the already published PANGAEA event names in case they are related to the data or created own site names with referencing to geographical regions similar to the Neotoma data naming principle.
    Keywords: AWI_Envi; fossil pollen; Neotoma; paleoecology; Polar Terrestrial Environmental Systems @ AWI; taxonomically harmonized
    Type: Dataset
    Format: application/zip, 14 datasets
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  • 38
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191002_01; 20191020_01; 20191029_01; 20191105_01; 20191112_01; 20191112_02; 20191119_01; 20191130_01; 20191206_01; 20191224_01; 20191225_01; 20191228_01; 20191230_01; 20200107_01; 20200107_02; 20200108_01; 20200108_03; 20200108_04; 20200116_01; 20200116_02; 20200121_01; 20200123_01; 20200123_02; 20200125_01; 20200128_01; 20200202_01; 20200204_01; 20200209_01; 20200212_01; 20200217_01; 20200217_02; 20200227_01; 20200321_01; 20200321_02; 20200423_01; Airborne laser scanning; Arctic; Arctic Ocean; HELI; Helicopter; IceSense; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_1_2_45_2019092801; PS122_4_44_27_2020061101; PS122_4_44_65_2020061502; PS122_4_44_78_2020061601; PS122_4_45_112_2020070401; PS122_4_45_36_2020063001; PS122_4_45_37_2020063002; PS122_4_46_36_2020070701; PS122_4_46_39_2020070703; PS122_4_46_97_2020071101; PS122_4_47_96_2020071701; PS122_4_48_69_2020072201; PS122_4_50_32_2020080601; PS122_4_50_45_2020080701; PS122/1; PS122/1_10-78; PS122/1_2-167; PS122/1_2-45; PS122/1_2-57; PS122/1_5-9; PS122/1_6-11; PS122/1_7-24; PS122/1_7-25; PS122/1_8-23; PS122/1_9-98; PS122/2; PS122/2_17-101; PS122/2_17-98; PS122/2_17-99; PS122/2_18-7; PS122/2_19-44; PS122/2_19-45; PS122/2_19-46; PS122/2_19-51; PS122/2_19-52; PS122/2_19-53; PS122/2_20-52; PS122/2_20-53; PS122/2_21-122; PS122/2_21-41; PS122/2_21-77; PS122/2_21-78; PS122/2_22-16; PS122/2_22-97; PS122/2_23-109; PS122/2_23-14; PS122/2_24-31; PS122/2_25-7; PS122/2_25-8; PS122/3; PS122/3_29-49; PS122/3_32-42; PS122/3_32-70; PS122/3_32-71; PS122/3_33-17; PS122/3_35-48; PS122/3_35-49; PS122/3_37-63; PS122/3_37-66; PS122/3_39-109; PS122/4; PS122/4_44-27; PS122/4_44-65; PS122/4_44-78; PS122/4_45-112; PS122/4_45-36; PS122/4_45-37; PS122/4_46-36; PS122/4_46-39; PS122/4_46-97; PS122/4_47-96; PS122/4_48-69; PS122/4_50-32; PS122/4_50-45; PS122/5; PS122/5_59-139; PS122/5_61-190; PS122/5_61-62; PS122/5_61-63; PS122/5_62-166; PS122/5_62-67; PS122/5_63-118; PS122/5_63-3; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: application/zip, 64 datasets
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  • 39
    Publication Date: 2024-06-04
    Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea-ice draft; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 90 datasets
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  • 40
    Publication Date: 2024-06-04
    Description: This data set is a higher-processing-level version of Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Hutter et al., 2022; doi:10.1594/PANGAEA.950339), where the individual 30-second segments of the small scale grid flights have been combined into merged grids. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (https://hdl.handle.net/10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & https://hdl.handle.net/10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The merged data are stored in netCDF and geotiff format. The data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate. The merged grids include all data variables of the gridded 30-s segments: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. Also the calculated elevation offset correction term is provided for each flight as a csv file.
    Keywords: 20191002_01; 20191020_01; 20191112_02; 20191119_01; 20191130_01; 20191224_01; 20191225_01; 20191228_01; 20200107_01; 20200108_01; 20200108_03; 20200108_04; 20200116_01; 20200121_01; 20200123_02; 20200128_01; 20200204_01; 20200212_01; 20200217_02; 20200227_01; 20200321_01; 20200423_01; Airborne laser scanning; Arctic Ocean; Freeboard; HELI; Helicopter; IceSense; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_44_78_2020061601; PS122_4_45_112_2020070401; PS122_4_45_36_2020063001; PS122_4_46_36_2020070701; PS122_4_47_96_2020071701; PS122_4_48_69_2020072201; PS122/1; PS122/1_2-167; PS122/1_2-57; PS122/1_7-25; PS122/1_8-23; PS122/1_9-98; PS122/2; PS122/2_17-101; PS122/2_17-98; PS122/2_17-99; PS122/2_19-44; PS122/2_19-46; PS122/2_19-52; PS122/2_19-53; PS122/2_20-52; PS122/2_21-41; PS122/2_21-78; PS122/2_22-16; PS122/2_23-14; PS122/2_24-31; PS122/2_25-8; PS122/3; PS122/3_29-49; PS122/3_32-42; PS122/3_32-70; PS122/3_35-49; PS122/3_37-63; PS122/3_39-109; PS122/4; PS122/4_44-78; PS122/4_45-112; PS122/4_45-36; PS122/4_46-36; PS122/4_47-96; PS122/4_48-69; PS122/5; PS122/5_61-190; PS122/5_61-62; PS122/5_62-166; PS122/5_62-67; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: application/zip, 35 datasets
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  • 41
    Publication Date: 2024-06-04
    Description: pH values were obtained using a SBE18 pH sensor (Seabird) mounted on the remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. The values were derived from the sensor voltages using the same calibration during the entire expedition.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; pH; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 93 datasets
    Location Call Number Expected Availability
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  • 42
    Publication Date: 2024-06-04
    Description: Fluorometric data on chlorophyll a concentration, Fluorescent Dissolved Organic Matter (FDOM) concentration, and optical backscatter were measured by a triplet fluorometer (ECO-Puck BBFL2SSC, Wetlabs) attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 93 datasets
    Location Call Number Expected Availability
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  • 43
    Publication Date: 2024-06-04
    Description: Absorbance and spectral absorption coefficient (SAC) parameters as measured by a VIPER G2 spectral transmissometer (TriOS) mounted in the sensor skid of a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration. The path length was 250 mm and the wavelength range 360-750 nm. More technical details can be found here: https://www.trios.de/en/viper.html.
    Keywords: Arctic Ocean; attenuation coefficient; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 92 datasets
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  • 44
    Publication Date: 2024-06-04
    Description: Nitrate and UV-absorbance spectra were measured by a SUNA V2 UV-spectrometer (Satlantic) mounted in the sensor skid of a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_48-213; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-5; PS122/5_61-200; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 71 datasets
    Location Call Number Expected Availability
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  • 45
    Publication Date: 2024-06-04
    Description: Videos as recorded by a HD-zoom camera (Bowtech Surveyor WAHD) with a 10:1 optical zoom attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 142 datasets
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  • 46
    Publication Date: 2024-06-04
    Description: Water/ice velocity data and instrument status from a Nortek Aquadopp Profiler 2MHz acoustic doppler current profiler (ADCP) attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. The Aquadopp System Integrator Manual by Nortek AS can be found here: https://sensor.awi.de/rest/sensors/onlineResources/getOnlineResourcesFile/1764/system-integrator-manual_Mar2016.pdf
    Keywords: ADCP; Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 184 datasets
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  • 47
    Publication Date: 2024-06-04
    Description: Firn cores OH-7 and OH-11 were retrieved from Plateau Laclavere, a small ice cap on the northernmost end of the Antarctic Peninsula, at about 1130 m above sea level (a.s.l.). OH-7 was drilled in January 2014 to a depth of 15.31 m using a mechanical 9 cm diameter drilling device (Rufli auger). OH-11 was drilled in January 2015 to a depth of 20.44 m. Firn core LP-01 was recovered from Plateau Louis Phillipe, which is located approximately 40 km south of Plateau Laclavere, at about 1390 m a.s.l. The core was drilled in January 2016 to a depth of 21.38 m. Cores OH-11 and LP-01 were obtained using a portable solar-powered and electrically operated ice-core drill (Backpack Drill; icedrill.ch AG). Subsamples for stable water isotope analysis were obtained from the three cores at 5 cm resolution. Stable water isotope measurements of OH-7 and LP-01 were performed at the ISOLAB Stable Isotope Facility of the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI) in Potsdam, Germany, in summer 2017 and autumn 2018, respectively, using cavity ring-down spectrometers L2130-i and L2140-i (Picarro Inc.) coupled to an auto-sampler (L2130-i: PAL HTC-xt, CTC Analytics AG; L2140-i: Picarro Autosampler, Picarro Inc.). Stable water isotope measurements of OH-11 were conducted at the Stable Isotope Laboratory of the Universidad Nacional Andrés Bello (UNAB) in Viña del Mar, Chile, in autumn 2015 with an off-axis integrated cavity output spectrometer (TLWIA 45EP; Los Gatos Research). The three cores have not been dated yet. The data has been used in combination with data on the stable water isotope composition of three other firn cores from the same study area (doi:10.1594/PANGAEA.871083; doi:10.1594/PANGAEA.939718) to identify common isotopic patterns and to investigate their spatial and temporal variability.
    Keywords: Antarctic Peninsula; Firn chemistry; firn core; proxies; stable water isotopes
    Type: Dataset
    Format: application/zip, 3 datasets
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  • 48
    Publication Date: 2024-06-04
    Description: Conductivity, temperature, and pressure were measured by a Glider Payload CTD (SBE GPCTD, Seabird). Oxygen frequency was measured by an oxygen optode (SBE 43F DO, Seabird). Both instruments were mounted in the sensor skid of a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020. Data use manufacturer calibration. The Gibbs SeaWater (GSW) Oceanographic Toolbox of TEOS-10 was used to derive other hydrographic data. The conversion from oxygen frequency to dissolved oxygen concentration was performed using the OOI L2 data product DOCONCF (Vardaro, 2014).
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; GPCTD; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-17; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-27; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-176; PS122/4_46-177; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 84 datasets
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  • 49
    Publication Date: 2024-06-04
    Description: The distance between a remotely operated vehicle (ROV) and the sea-ice underside was measured by a single-beam upward-looking acoustic sonar altimeter (Tritech PA500) attached to the ROV during the ARTofMELT2023 expedition in May and June 2023. Sea-ice draft was derived by subtracting the distance to the sea-ice underside from the ROV depth, uncorrected for ROV attitude (pitch, roll). An offset between the depth reference (ROV bumper bars) and the altimeter of 0.105 m is accounted for in the presented data.
    Keywords: ARTofMELT; ARTofMELT2023; Atmospheric rivers and the onset of sea ice melt 2023; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 18 datasets
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  • 50
    Publication Date: 2024-06-04
    Description: pH values were obtained using a SBE18 pH sensor (Seabird) mounted on the remotely operated vehicle (ROV) during the ARTofMELT2023 expedition in May and June 2023. The values were derived from the sensor voltages using the same calibration during the entire expedition.
    Keywords: ARTofMELT; ARTofMELT2023; Atmospheric rivers and the onset of sea ice melt 2023; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 19 datasets
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  • 51
    Publication Date: 2024-06-04
    Description: These bundled biogeochemical data of sediment core EN20001, from Lake Khamra (59.99095° N, 112.98345° E), in SW Yakutia consist of four datasets: (1) Radiocarbon age dating of bulk sediments from sediment core EN20001 from Lake Khamra, measured at AWI MICADAS; (2) Element composition of the sediment core EN20001 from Lake Khamra, measured at the Bundesanstalt für Geowissenschaften und Rohstoffe (BGR); (3) TOC and TN of the sediment core EN20001 from Lake Khamra, measured in the sediment laboratory at AWI, Potsdam; (4) Pollen and non-pollen palynomorphs of the sediment core EN20001 from Lake Khamra, measured at AWI, Potsdam. This study was additionally supported by a short-term grant (not numbered) from AWI Graduate School (POLMAR), and PhD Completion Scholarship (not numbered) provided by University of Potsdam.
    Keywords: AWI_Envi; Boreal; Lake sediment; Lake sediment core; lake sediment proxies; Land cover; non-pollen palynomorphs; Polar Terrestrial Environmental Systems @ AWI; Pollen; pollen analysis; pollen and spores; radiocarbon dating; Russia; sakha; Sakha Republic; Siberia; subarctic; TN; TOC; Vegetation; XRF; XRF core scanner data; Yakutia
    Type: Dataset
    Format: application/zip, 4 datasets
    Location Call Number Expected Availability
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  • 52
    Publication Date: 2024-06-04
    Description: Conductivity, temperature, and pressure were measured by a Glider Payload CTD (SBE GPCTD, Seabird). Oxygen frequency was measured by an oxygen optode (SBE 43F DO, Seabird). Both instruments were mounted in the sensor skid of a remotely operated vehicle (ROV) during the ARTofMELT2023 expedition in May and June 2023. Data use manufacturer calibration. The Gibbs SeaWater (GSW) Oceanographic Toolbox of TEOS-10 was used to derive other hydrographic data. The conversion from oxygen frequency to dissolved oxygen concentration was performed using the OOI L2 data product DOCONCF (Vardaro, 2014).
    Keywords: ARTofMELT; ARTofMELT2023; Atmospheric rivers and the onset of sea ice melt 2023; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 19 datasets
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  • 53
    Publication Date: 2024-06-04
    Description: The open source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high resolution and a design that limits wind disturbance. Here, movies and images of falling precipitation particles are provided for station Ny-Ålesund from July 2022 to December 2023. For further details on the VISSS Sensor see Maahn et al. (2024).
    Keywords: AC3; Arctic Amplification; In-situ; Ny-Ålesund; snowfall; snowflake
    Type: Dataset
    Format: application/zip, 523 datasets
    Location Call Number Expected Availability
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  • 54
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Checkley, David M; Dickson, Andrew G; Takahashi, Motomitsu; Radich, J Adam; Eisenkolb, Nadine; Asch, Rebecca (2009): Elevated CO2 enhances otolith growth in young fish. Science, 324(5935), 1683, https://doi.org/10.1126/science.1169806
    Publication Date: 2024-06-04
    Description: A large fraction of the carbon dioxide added to the atmosphere by human activity enters the sea, causing ocean acidification. We show that otoliths (aragonite ear bones) of young fish grown under high CO2 (low pH) conditions are larger than normal, contrary to expectation. We hypothesize that CO2 moves freely through the epithelium around the otoliths in young fish, accelerating otolith growth while the local pH is controlled. This is the converse of the effect commonly reported for structural biominerals.
    Keywords: Alkalinity, total; Animalia; Aragonite saturation state; Atractoscion nobilis; Atractoscion nobilis, dry mass; Atractoscion nobilis, larval age; Atractoscion nobilis, orientation; Atractoscion nobilis, otolith area; Behaviour; Bicarbonate ion; Biomass/Abundance/Elemental composition; Bottles or small containers/Aquaria (〈20 L); Calcite saturation state; Calculated using CO2SYS; Calculated using seacarb after Nisumaa et al. (2010); Carbon, inorganic, dissolved; Carbonate ion; Carbonate system computation flag; Carbon dioxide; Checkley_etal_09; Chordata; EPOCA; EUR-OCEANS; European network of excellence for Ocean Ecosystems Analysis; European Project on Ocean Acidification; EXP; Experiment; Experimental treatment; Fugacity of carbon dioxide (water) at sea surface temperature (wet air); Growth/Morphology; Image analysis NIH ImageJ; Laboratory experiment; Laboratory strains; Light:Dark cycle; Measured; Nekton; Not applicable; OA-ICC; Ocean Acidification International Coordination Centre; Otolith; Partial pressure of carbon dioxide (water) at sea surface temperature (wet air); Pelagos; pH; Salinity; Single species; Temperature, water
    Type: Dataset
    Format: text/tab-separated-values, 4392 data points
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  • 55
    facet.materialart.
    Unknown
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Publication Date: 2024-06-04
    Description: Raw data acquired by GPS1 position sensors on board research aircraft Polar 6 during the campaign P6_244_ANT_23_24 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 DAVIS SHIP data base (https://dship.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.
    Type: Dataset
    Format: application/zip, 16 datasets
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  • 56
    Publication Date: 2024-06-04
    Description: Upward-looking still images as acquired by a photo camera (Tiger Shark, Imenco) with internal flash and 4 x zoom attached to a remotely operated vehicle (ROV) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition between November 2019 and September 2020.
    Keywords: Arctic Ocean; AWI_SeaIce; BEAST; FRAM; FRontiers in Arctic marine Monitoring; MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-113; PS122/1_5-62; PS122/1_6-118; PS122/1_6-16; PS122/1_6-31; PS122/1_7-18; PS122/1_7-55; PS122/1_8-125; PS122/1_9-22; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-70; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; PS122/3; PS122/3_29-14; PS122/3_29-65; PS122/3_30-69; PS122/3_31-75; PS122/3_32-11; PS122/3_32-33; PS122/3_32-34; PS122/3_32-78; PS122/3_33-83; PS122/3_34-20; PS122/3_35-32; PS122/3_35-95; PS122/3_36-112; PS122/3_36-125; PS122/3_36-24; PS122/3_37-108; PS122/3_37-19; PS122/3_37-20; PS122/3_38-50; PS122/3_38-85; PS122/3_38-91; PS122/3_39-111; PS122/3_39-152; PS122/3_39-20; PS122/3_39-77; PS122/4; PS122/4_44-162; PS122/4_44-191; PS122/4_44-206; PS122/4_45-129; PS122/4_45-149; PS122/4_45-61; PS122/4_46-172; PS122/4_46-174; PS122/4_46-175; PS122/4_46-37; PS122/4_47-135; PS122/4_47-31; PS122/4_48-213; PS122/4_48-4; PS122/4_49-105; PS122/5; PS122/5_59-269; PS122/5_59-369; PS122/5_60-165; PS122/5_60-166; PS122/5_60-167; PS122/5_60-28; PS122/5_60-5; PS122/5_61-156; PS122/5_61-200; PS122/5_61-35; PS122/5_62-103; PS122/5_62-165; PS122/5_62-65; Remotely operated sensor platform BEAST; Remotely operated vehicle (ROV); Sea ice; Sea Ice Physics @ AWI
    Type: Dataset
    Format: application/zip, 88 datasets
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  • 57
    facet.materialart.
    Unknown
    PANGAEA
    In:  Supplement to: Böger, Horst; Kowalczyk, Gotthard (1993): Stratigraphische, sedimentologische und paläoökologische Untersuchungen im Mesozoikum der Depressão Periférica in Rio Grande do Sul, Brasilien. Berichte-Reports, Geologisch-Paläontologisches Institut der Universität Kiel, 63, 72 pp, https://doi.org/10.2312/reports-gpi.1993.63
    Publication Date: 2024-06-04
    Description: Stratigraphy, sedimentology and paleoecology of Mesozoic continental sequences in the Depressao periferica, Rio Grande do Sui, Brazil, are subject of a DFG (German Research Foundation) research project. Results of the first two years period of activities in which the Geologicai-Paleontological Institutes of the Universities of Kiel and Frankfurt/M. in collaboration with the Departamento de Geociencias, University of Santa Maria in Camobi, RS, were involved are reported here. A second phase of field activities is planned for the time period from fall 1993 to the spring of 1995. The stratigraphic boundaries of the investigation are the underlying sediments of the Permian Passa Dais-Series and the overlying basalts of the Serra Geral Formation, covering the time span of 235 Ma to 133 Ma. A subordinate, chronostratigraphic system encompassing the sediments of this time period has yet to be established and extensive hiatuses are to be expected. Correlations with the lschigualasto Formation in NW-Argentina support the assumption that the upper Santa Maria Formation (Aiemoa member) falls in the mid Carnian. This is the only reasonable certain chronostratigraphic date from the Mesozoic of the Depressao periferica established at the present time. The classical tetrapod sites of the Triassic Santa Maria Formation all fall within the Alemoa-Member, the sediments of which were deposited under in part evaporitic conditions on playa mud flats. Evidence points to isochronic sedimentation and discounts the possibility of a diachronic genesis. The Santa Maria Formation and the underlying Sanga do Cabral Formation are placed together in the Rio do Rasta Subgroup as a genetic unit in accordance with the original definition, which conflicts with present day usage of the names. The Rio do Rasto Subgroup pinches out west of Sao Francisco do Assis and east of the Taquarl river. The entire Rio do Rasto Formation is enclosed in eolic sediments, indicating an extensive sedimentation complex arising from a persistently subsiding playa areal within the Botucatu desert. Beyond the range of the Rio do Rasto Subgroup, it is difficult or impossible to distinguish between the eolic sediments of the older, underlying Rosario do Sui Formation and the overlying, younger Botucatu Sandstone Member. As such, the entire paleogeographically and genetically uniform sedimentation complex is compiled together under the term Botucatu Group. The Sanga do Cabral Formation is characterized by an abundance of detritic micas (muscovite and biotite). K/ Ar dating have indicated a preliminary age for muscovite of 418 ± 8 Ma and 423.5 ± 9.7 Ma. Presumably, they originated from volcanites, subvolcanites and pyroclastics of the Camaqua Group (Brasiliano molasse). As such, the Precambrian/ lower Paleozoic Escudo Sui in Rio Grande do Sui was exposed and eroded to the level found today at the time of deposition of the Sanga do Cabral Formation.
    Keywords: Area/locality; GIK/IfG; Institute for Geosciences, Christian Albrechts University, Kiel; LATITUDE; LONGITUDE; Outcrop ID; Stratigraphy
    Type: Dataset
    Format: text/tab-separated-values, 841 data points
    Location Call Number Expected Availability
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  • 58
    Publication Date: 2024-06-04
    Description: ASCII file with Data from the SeaBird Glider-Payload CTD (GPCTD) of the following format: 1 Header line: [SPOT.ON general serial format version 1] followed by datalines: [2016.10.01T08.22.49.317 | 1.54, -2.2081,-0.00001, 3199.84] [Timestamp | Pressure(db), Temperature(°C), Conductivity(S/m), DissolvedOxygenFrequency(Hz)]
    Keywords: Arctic Ocean; BEAST; DATE/TIME; Event label; GPCTD; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_18-10; PS122/2_18-19; PS122/2_18-89; PS122/2_19-115; PS122/2_19-27; PS122/2_20-101; PS122/2_20-23; PS122/2_21-125; PS122/2_21-36; PS122/2_22-107; PS122/2_22-45; PS122/2_23-116; PS122/2_23-29; PS122/2_24-97; PS122/2_25-104; PS122/2_25-44; Remotely operated sensor platform BEAST; Sea-bird SBE Glider Payload CTD; Uniform resource locator/link to raw data file
    Type: Dataset
    Format: text/tab-separated-values, 17 data points
    Location Call Number Expected Availability
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  • 59
    facet.materialart.
    Unknown
    PANGAEA
    In:  Research School of Earth Sciences, The Australian National University, Canberra | Supplement to: Willmes, Malte; McMorrow, Linda; Kinsley, Les; Armstrong, R; Aubert, Maxime; Eggins, Stephen M; Falguères, Christophe; Maureille, Bruno; Moffat, Ian; Grün, R (2014): The IRHUM (Isotopic Reconstruction of Human Migration) database - bioavailable strontium isotope ratios for geochemical fingerprinting in France. Earth System Science Data, 6(1), 117-122, https://doi.org/10.5194/essd-6-117-2014
    Publication Date: 2024-06-04
    Description: The dataset consists of 87Sr/86Sr isotope ratios of plant samples and soil leachates covering the major geologic regions of France. In addition to the isotope data it provides the spatial context for each sample, including background geology, field observations and soil descriptions. The dataset can be used to create Sr isoscapes for France, which can be applied in a wide range of fields including archaeology, ecology, soil, food, and forensic sciences.
    Keywords: Age, comment; Age, maximum/old; Age, minimum/young; Area/locality; Comment; ELEVATION; Environment; Event label; F06(F)-001; F06(F)-002; F06(F)-004; F06(F)-006; F06(F)-008; F06(F)-012; F06(F)-016; F06(F)-018; F06(F)-021; F06(F)-023; F06(F)-024; F06(F)-025; F06(F)-027; F06(F)-033; F06(F)-034; F06(F)-035; F06(F)-037; F06(F)-038; F06(F)-040; F06-002; F06-003; F06-005; F06-007; F06-008; F06-009; F06-010; F06-011; F06-012; F06-014; F06-015; F06-016; F06-017; F06-019; F06-020; F06-023; F06-024; F06-026; F06-027; F06-029; F06-030; F06-031; F06-032; F06-033; F06-036; F06-037; F06-038; F06-040; F06-042; F06-043; F06-044; F06-045; F06-046; F06-047; F06-050; F06-051; F06-054; F06-055; F06-056; F06-057; F06-058; F06-059; F06-061; F06-062; F09-001; F09-002; F09-003; F09-004; F09-005; F09-006; F09-007; F09-008; F09-009; F09-010; F09-011; F09-012; F09-013; F09-014; F09-015; F09-016; F09-017; F09-018; F09-019; F09-020; F09-021; F09-022; F09-023; F09-024; F09-025; F09-026; F09-027; F09-028; F09-029; F09-030; F09-031; F09-032; F09-033; F09-034; F09-035; F09-036; F09-037; F09-038; F09-039; F09-040; F09-041; F09-042; F09-043; F09-044; F09-045; F09-046; F09-047; F09-048; F09-049; F09-050; F09-051; F09-052; F09-053; F09-054; F09-055; F09-056; F09-057; F09-058; F09-060; F09-061; F09-062; F09-063; F09-064; F09-065; F09-066; F09-067; F09-068; F09-069; F09-070; F09-071; F09-072; F09-073; F09-074; F09-075; F09-076; F09-077; F09-078; F09-079; F09-080; F09-081; F09-082; F09-083; F09-084; F09-085; F09-086; F09-087; F09-088; F09-089; F09-090; F09-091; F09-092; F09-093; F09-094; F09-095; F09-096; F09-097; F09-098; F09-099; F09-100; F09-101; F09-102; F09-103; F09-104; F09-105; F09-106; F09-107; F09-108; F09-109; F09-110; F09-111; F09-112; F09-113; F09-114; F09-115; F09-116; F09-117; F09-118; F11-001; F11-002; F11-003; F11-004; F11-005; F11-006; F11-007; F11-008; F11-009; F11-010; F11-011; F11-012; F11-013; F11-014; F11-015; F11-016; F11-017; F11-018; F11-019; F11-020; F11-021; F11-022; F11-023; F11-024; F11-025; F11-026; F11-027; F11-028; F11-029; F11-030; F11-031; F11-032; F11-033; F11-034; F11-035; F11-036; F11-037; F11-038; F11-039; F11-040; F11-041; F11-042; F11-043; F11-044; F11-045; F11-046; F11-047; F11-048; F11-049; F11-050; F11-051; F11-052; F11-053; F11-054; F11-055; F11-056; F11-057; F11-058; F11-059; F11-060; F11-061; F11-062; F11-063; F11-064; F11-065; F11-066; F11-067; F11-068; F11-069; F11-070; F11-071; F11-072; F11-073; F11-074; F11-075; F11-076; F11-077; F11-078; F11-079; F11-080; F11-081; F11-082; F11-083; F11-084; F11-085; F11-086; F11-087; F11-088; F11-089; F11-090; F11-091; F11-092; F11-093; F11-094; F11-095; F11-096; F11-097; F11-099; F11-100; F11-101; F11-102; F11-103; F11-104; F11-105; F11-106; F11-107; F11-108; F11-109; F11-110; F11-111; F11-112; F11-113; F11-114; F11-115; F11-116; F11-117; F11-118; F11-119; F11-120; F11-121; F11-122; F11-123; F11-124; F11-125; F11-126; F11-127; F11-128; F11-129; F11-130; F11-131; F11-132; F11-133; F11-134; F11-135; F11-136; F11-137; F11-138; F11-139; F11-140; F11-141; F11-142; F11-143; F11-144; F11-145; F11-146; F11-147; F11-148; F11-149; F11-150; F11-151; F11-152; F11-153; F11-154; F11-155; F11-156; F11-157; F11-158; F11-159; F11-160; F11-161; F11-162; F11-163; F11-164; F11-165; F11-166; F11-167; F11-168; F11-169; F11-170; F11-171; F11-172; F11-173; F11-174; F11-175; F11-176; F11-178; F11-179; F11-180; F11-181; F11-182; F11-183; F11-184; F11-185; F11-186; F11-187; F11-188; F11-189; F11-190; F11-191; F11-192; F11-193; F11-194; F11-195; F11-196; F11-197; F11-198; F12-001; F12-002; F12-003; F12-004; F12-005; F12-006; F12-007; F12-008; F12-009; F12-010; F12-011; F12-012; F12-013; F12-014; F12-015; F12-016; F12-017; F12-018; F12-019; F12-020; F12-021; F12-022; F12-023; F12-024; F12-025; F12-026; F12-027; F12-028; F12-029; F12-030; F12-031; F12-032; F12-033; F12-034; F12-035; F12-036; F12-037; F12-038; F12-039; F12-040; F12-041; F12-042; F12-044; F12-045; F12-046; F12-047; F12-048; F12-049; F12-050; F12-051; F12-052; F12-053; F12-054; F12-055; F12-056; F12-057; F12-058; F12-060; F12-061; F12-062; F12-063; F12-064; F12-065; F12-066; F12-067; F12-068; F12-069; F12-070; F12-071; F12-072; F12-073; F12-074; F12-075; F12-076; F12-077; F12-078; F12-079; F12-080; F12-081; F12-082; F12-083; F12-084; F12-085; F12-086; F12-087; F12-088; F12-089; F12-090; F12-091; F12-092; F12-093; F12-094; F12-095; F12-096; F12-097; F12-098; F12-099; F12-100; F12-101; F12-102; F12-103; F12-104; F12-105; F12-106; F12-107; F12-108; F12-109; F12-110; F12-111; F12-112; F12-113; F12-114; F12-115; F12-116; F12-117; F12-118; F12-119; F12-120; F12-121; F12-122; F12-123; F12-124; F12-125; F12-126; F12-127; F12-128; F12-129; F12-130; F12-131; F12-132; F12-133; F12-134; F12-135; F12-136; F12-137; F12-138; F12-139; F12-140; F12-141; F12-142; F12-143; F12-144; F12-145; F12-146; F12-147; F12-148; F12-149; F12-150; F12-151; F12-153; F12-154; F12-155; F12-156; F12-157; F12-158; F12-159; F12-160; F12-161; F12-162; F12-163; F12-164; F12-165; F12-166; F12-167; F12-168; F12-169; F12-170; F12-171; F12-172; F12-173; F12-174; F12-175; F12-176; F12-177; F12-178; F12-179; F12-180; F12-181; F12-182; F12-183; F12-184; F12-185; F12-186; F12-187; F12-188; F12-189; F12-190; F12-191; F12-192; F12-193; F12-194; F12-195; F12-196; F12-197; F12-198; F12-199; F12-200; F12-201; F12-202; F12-203; F12-204; F12-205; F12-206; F12-207; F12-208; F12-209; F12-210; F12-211; F12-212; F12-213; F12-214; F12-215; F12-216; F12-217; F12-218; F12-219; F12-220; F12-221; F12-222; F12-223; F12-224; F12-225; F12-226; F12-227; F12-228; F12-229; F12-230; F12-231; F12-232; F12-233; F12-234; F12-235; F12-236; F12-237; F12-238; F13-001; F13-002; F13-003; F13-004; F13-005; F13-006; F13-007; F13-008; F13-009; F13-010; F13-011; F13-012; F13-013; F13-014; F13-015; F13-016; F13-017; F13-018; F13-019; F13-020; F13-021; F13-022; F13-023; F13-024; F13-025; F13-026; F13-027; F13-028; F13-029; F13-030; F13-031; F13-032; F13-033; F13-034; F13-035; F13-036; F13-037; F13-038; F13-039; F13-040; F13-042; F13-043; F13-044; F13-045; F13-046; F13-047; F13-048; F13-049; F13-051; F13-052; F13-053; F13-054; F13-055; F13-056; F13-057; F13-058; F13-059; F13-060; F13-061; F13-062; F13-063; F13-064; F13-065; F13-066; F13-067; F13-068; F13-069; F13-070; F13-071; F13-072; F13-073; F13-074; F13-075; F13-076; F13-077; F13-078; F13-079; F13-080; F13-081; F13-082; F13-084; F13-085; F13-086; F13-087; F13-088; F13-089; F13-090; F13-092; F13-093; F13-094; F13-095; F13-096; F13-097; F13-098; F13-099; F13-100; F13-101; F13-102; F13-103; F13-104; F13-105; F13-106; F13-107; F13-108; F13-109; F13-110; F13-111; F13-112; F13-113; F13-114; F13-115; F13-116; F13-117; F13-118; F13-119; F13-120; F13-121; F13-122; F13-123; F13-124; F13-125; F13-126; F13-127; F13-129; F13-130; F13-131; F13-132; F13-133; F13-134; F13-135; F13-136; F13-137; F13-138; F13-139; F13-140; F13-141; F13-142; F13-143; F13-144; F13-145; F13-146; F13-147; F13-148; F13-149; F13-150; F13-151; F13-152; F13-153; F13-154; F13-155; F13-156; F13-157; F13-158; F13-159; F13-160; F13-161; F13-162; F13-163; F13-164; F13-165; F13-166; F13-167; F13-168; F13-169; F13-170; F13-171; F13-172; F13-173; F13-174; F13-175; F13-176; F13-177; F13-178; F13-179; F13-180; F13-181; F13-182; F13-183; F13-184; F13-185; F13-186; F13-187; F13-188; F13-189; F13-190; F13-191; F13-192; F13-193; F13-194; F13-195; F13-196; F13-197; F13-198; F13-199; F13-200; F13-201; F13-202; F13-203; F13-204; F13-205; F13-206; F13-207; F13-208; F13-209; F13-210; F13-211; F13-212; F13-213; F13-214; F13-215; F13-216; F13-217; F13-218; F13-219; F13-220; F13-221; F13-222; F13-223; F13-224; F13-225; F13-226; F13-227; F13-228; France; HAND; Latitude of event; Lithologic unit/sequence; Longitude of event; Name; Observation; Outcrop ID; Rock type; Sample comment; Sample type; Sampling by hand; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, error
    Type: Dataset
    Format: text/tab-separated-values, 15675 data points
    Location Call Number Expected Availability
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  • 60
    Publication Date: 2024-06-04
    Description: The data set contains daily files of atmospheric radiation measured during zenith (mwr00) and boundary layer (mwrBL00) mode by the HATPRO microwave radiometer (see Rose et al., 2005) onboard the Polarstern during cruise PS122 (MOSAiC expedition). The data covers the range October 2019 to October 2020. The atmospheric radiation measurements are given as brightness temperatures in seven K band (22.24 - 31.4 GHz) and seven V band (51.26 - 58 GHz) channels. The elevation scans have been perfomed approximately every 30 minutes while zenith measurements (elevation angle at 90 degrees) fill the remaining time. The brightness temperatures are provided for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity measurements at the instrument location as well as quality flags characterizing the instrument and retrieval performance.
    Keywords: AC3; Arctic; Arctic Amplification; Arctic Ocean; ATMOBS; Atmospheric Observatory; Binary Object; Binary Object (File Size); brightness temperatures; Comment; DATE/TIME; Event label; Hatpro; LATITUDE; LONGITUDE; microwave radiometer; Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Other event; Polarstern; PS122; PS122/1; PS122/1_1-38; PS122/2; PS122/2_14-18; PS122/3; PS122/3_28-6; PS122/4; PS122/4_43-11; PS122/4_43-145; PS122/5; PS122/5_58-3; remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 1392 data points
    Location Call Number Expected Availability
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  • 61
    Publication Date: 2024-06-04
    Description: The data set contains daily files of atmospheric radiation measured by the MiRAC-P (or LHUMPRO-243-340) microwave radiometer (see Mech et al., 2019) onboard the Polarstern during cruise PS122 (MOSAiC expedition). The data covers the range October 2019 to October 2020. The atmospheric radiation measurements are given as brightness temperatures in six double side band averaged G band (183.31 +/- 0.6 to 183.31 +/- 7.5 GHz) and two higher frequency (243 and 340 GHz) channels. The brightness temperatures are provided for all available times so that it is up to the user to decide whether or not to use the values if quality flags are set. Additionally included are temperature, pressure and humidity measurements at the instrument location as well as quality flags characterizing the instrument and retrieval performance.
    Keywords: AC3; Arctic; Arctic Amplification; Arctic Ocean; ATMOBS; Atmospheric Observatory; Binary Object; Binary Object (File Size); brightness temperature; DATE/TIME; Event label; LATITUDE; LONGITUDE; microwave radiometer; Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Other event; Polarstern; PS122; PS122/1; PS122/1_1-38; PS122/2; PS122/2_14-18; PS122/3; PS122/3_28-6; PS122/4; PS122/4_43-11; PS122/4_43-145; PS122/5; PS122/5_58-3; remote sensing
    Type: Dataset
    Format: text/tab-separated-values, 346 data points
    Location Call Number Expected Availability
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  • 62
    Publication Date: 2024-06-04
    Description: The provided dataset contains surface water samples from lakes and ponds, streams and inflows in the Lucky Lake catchment (lake center coordinates: 72°17'56.1N; 126°10'29.7E) in the south of Kurungnakh Island, Lena River Delta, Russia. It includes concentrations of dissolved organic carbon (DOC) as well as stable isotopes of oxygen (δ¹⁸O) and hydrogen (δD). Samples of this dataset were collected during the Russian-German LENA expeditions in July and August 2013, June to September 2014, and in July 2016. For DOC measurements, we used the Shimadzu TOC-VCPH high-temperature catalytic combustion. Stable isotopes of oxygen (δ¹⁸O) and hydrogen (δD) were measured with a Finnigan MAT Delta-S mass spectrometer at the ISOLAB Isotope Facility AWI Potsdam.
    Keywords: aquatic carbon cycle; Arctic lakes; AWI_Envi; AWI_Perma; AWI Arctic Land Expedition; Carbon, organic, dissolved; DATE/TIME; DEPTH, water; Deuterium excess; Event label; KUR16_W_13; KUR16_W_14; KUR16_W_15; KUR16_W_16; KUR16_W_23; LATITUDE; LD13_A_01; LD13_A_02; LD13_A_04; LD13_A_05; LD13_A_06; LD13_A_07; LD13_A_08; LD13_A_09; LD13_A_10; LD13_A_11; LD13_A_12; LD13_A_13; LD13_A_14; LD13_A_15; LD13_A_35; LD13_A_36; LD13_A_37; LD13_A_38; LD13_A_39; LD13_A_40; LD13_A_41; LD13_A_42; LD13_A_43; LD13_A_44; LD13_A_51; LD13_A_52; LD13_A_53; LD13_A_54; LD13_A_55; LD13_A_56; LD13_A_57; LD13_A_58; LD13_A_61; LD13_A_62; LD13_S_01; LD13_S_03; LD13_S_06; LD13_S_09; LD13_S_10; LD13_S_15; LD13_S_27; LD13_S_30; LD13_S_37; LD13_S_40; LD13_S_41; LD13_S_42; LD13_S_49; LD13_S_52; LD13_S_53; LD14_45; LD14_46; LD14_47; LD14_48; LD14_49; LD14_50; LD14_A_01; LD14_A_02; LD14_A_03; LD14_A_04; LD14_A_05; LD14_A_06; LD14_A_07; LD14_A_08; LD14_A_09; LD14_A_10; LD14_A_11; LD14_A_13; LD14_A_14; LD14_A_39; LD14_A_41; LD14_A_68; LD14_A_69; LD14_A_70; LD14_A_71; LD14_A_72; LD14_A_73; LD14_A_74; LD14_A_75; LD14_A_76; LD14_A_77; LD14_A_78; LD14_A_79; LD14_A_81; LD14_A_83; LD14_B_01; LD14_B_21; LD14_B_22; LD14_T_13; LD14_T_14; LD14_T_15; LD14_T_16; LD14_T_18; LD14_T_19; LD14_T_20; LD14_T_21; LD14_T_22; LD14_T_24; LD14_T_25; LD14_T_27; Lena2013; Lena2016_spring, Lena2016_summer; Lena Delta, Siberia, Russia; Lena River Delta, Russia; LONGITUDE; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); MULT; Multiple investigations; Permafrost; Permafrost Research; PETA-CARB; Polar Terrestrial Environmental Systems @ AWI; Rapid Permafrost Thaw in a Warming Arctic and Impacts on the Soil Organic Carbon Pool; RU-Land_2013_Lena; RU-Land_2014_Lena; RU-Land_2016_Lena; Shimadzu TOC-VCPH total organic carbon analyzer SN H51304730370CS (ISOLAB); Siberia; thermokarst lakes; δ18O, standard deviation; δ18O, water; δ Deuterium, standard deviation; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 599 data points
    Location Call Number Expected Availability
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  • 63
    Publication Date: 2024-06-04
    Description: The dataset comprises stable water isotopes and conductitities of a lead case study during leg 5 of the MOSAiC campaign. Samples have been taken from different water and ice types for this lead case study. Discrete water samples were taken using a peristaltic pump (Masterflex E/S Portable Sampler, Masterflex, USA) through a 2 m long PTFE tube (L/S Pump Tubing, Masterflex, USA). Water samples for measurement of stable water isotopes (δ18O, δD,) were collected in 50-mL glass screw-cap narrow-neck vials (VWR international LLC, Germany). Snow on the sea ice was sampled with a polyethylene shovel (GL Science Inc., Tokyo, Japan) and placed into a polyethylene zip-loc bag. Ice in the lead was collected and a 0.25 m ' 0.25 m ice block was cut with a hand saw and placed into a zip-lock bag. Ice temperature at the surface was measured with a needle-type temperature sensor (Testo 110 NTC, Brandt Instruments, Inc., USA). Two ice cores from the bottom of a melt pond were collected, using an ice corer with an inner diameter of 0.09 m (Mark II coring system, KOVACS Enterprises, Inc., USA). The cores were cut with a stainless steel saw into 0.1 m thick sections and stored in plastic bags for subsequent salinity and δ18O measurements. Snow and ice samples were immediately placed in a cooler box along with refrigerants to keep their temperature low and to minimize brine drainage. Onboard Polarstern, ice samples were transferred into ice melting bags (Smart bags PA, AAK 5L, GL Sciences Inc., Japan) and melted in the dark at +4°C. After the ice melted, the meltwater was placed in a 30-mL glass screw-cap vial for later stable water isotope measurement and into a 100-mL polypropylene bottle (I-Boy, AS ONE Corporation, Japan) for later salinity measurement. These samples were stored at +4°C in the dark until analysis. Under-ice water samples (from about 10 m depth) were collected via R/V Polarstern's underway water sampling system during leg 5. Samples were placed into 250-mL glass vials (Duran Co. Ltd, Germany) for later δ18O and salinity measurements. Salinity of collected samples was determined with a same conductivity sensor used on sea ice (Cond 315i, WTW GmbH, Germany). Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (hdl:10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): hdl:10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 and hdl:10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; AWI_Envi; AWI_Perma; Calculated after Dansgaard (1964); Chamber for gas sampling; CHAMGAS; Comment; Conductivity sensor Cond 315i, WTW GmbH, Germany; DATE/TIME; DEPTH, ice/snow; DEPTH, water; Deuterium excess; Event label; freshwater; IC; Ice corer; Latitude of event; leads; Leg 5; Longitude of event; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Permafrost Research; Polarstern; Polar Terrestrial Environmental Systems @ AWI; PS122/5; PS122/5_59-343; PS122/5_59-389; PS122/5_59-392; PS122/5_59-446; PS122/5_59-447; PS122/5_60-130; PS122/5_60-133; PS122/5_60-146; PS122/5_60-16; PS122/5_60-260; PS122/5_60-61; PS122/5_61-126; PS122/5_61-205; PS122/5_61-206; PS122/5_62-117; PS122/5_62-120; PS122/5_62-35; PS122/5_62-40; PS122/5_62-42; PS122/5_62-5; Salinity; Sample code/label; Sample ID; Sample type; Sea ice; snow; SNOW; Snow/ice sample; Station label; Water sample; WS; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 838 data points
    Location Call Number Expected Availability
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  • 64
    Publication Date: 2024-06-04
    Description: Snow samples were collected from several locations on the main MOSAiC ice floe on weekly basis. Snow samples for measurement of stable water isotopes (δ18O, δD,) were collected in three different layers (top, middle, bottom) using a metal density cutter. At first, samples were stored in sealed plastic bags and the air was squeezed out before closing the bags. At later stages of the expedition, samples were stored in plastic cups with lids. Later the samples were thawed completely at room temperature and poured into 20 ml glass vials and sealed with parafilm tape and stored at 4°C. Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (https://hdl.handle.net/10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): https://hdl.handle.net/10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 https://hdl.handle.net/10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c. employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; Calculated after Dansgaard (1964); Comment; DATE/TIME; DEPTH, water; Deuterium excess; Event label; Height, relative, from ice/snow line, maximum; Height, relative, from ice/snow line, minimum; IC; Ice corer; isotopes; Layer description; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-35; PS122/1_10-38; PS122/1_10-5; PS122/1_11-23; PS122/1_4-10; PS122/1_5-5; PS122/1_5-92; PS122/1_5-93; PS122/1_5-95; PS122/1_6-10; PS122/1_6-136; PS122/1_6-140; PS122/1_6-34; PS122/1_6-6; PS122/1_6-61; PS122/1_7-105; PS122/1_7-106; PS122/1_7-12; PS122/1_7-89; PS122/1_8-110; PS122/1_8-24; PS122/1_8-33; PS122/1_8-79; PS122/1_9-23; PS122/1_9-31; PS122/1_9-39; PS122/1_9-65; PS122/2; PS122/2_16-9; PS122/2_17-109; PS122/2_17-16; PS122/2_18-17; PS122/2_18-66; PS122/2_19-144; PS122/2_19-28; PS122/2_19-9; PS122/2_19-92; PS122/2_20-36; PS122/2_20-4; PS122/2_20-80; PS122/2_20-83; PS122/2_21-14; PS122/2_21-15; PS122/2_21-96; PS122/2_22-5; PS122/2_22-6; PS122/2_22-73; PS122/2_23-2; PS122/2_23-34; PS122/2_23-73; PS122/2_23-74; PS122/2_23-9; PS122/2_24-14; PS122/2_24-15; PS122/2_24-35; PS122/2_24-86; PS122/2_25-22; PS122/2_25-80; PS122/2_25-81; PS122/3; PS122/3_29-28; PS122/3_29-29; PS122/3_29-9; PS122/3_30-17; PS122/3_30-25; PS122/3_31-44; PS122/3_31-55; PS122/3_31-64; PS122/3_31-65; PS122/3_31-91; PS122/3_32-5; PS122/3_32-88; PS122/3_32-92; PS122/3_33-53; PS122/3_33-65; PS122/3_33-66; PS122/3_34-34; PS122/3_34-45; PS122/3_35-23; PS122/3_35-53; PS122/3_35-56; PS122/3_36-14; PS122/3_36-178; PS122/3_36-35; PS122/3_36-99; PS122/3_37-129; PS122/3_37-41; PS122/3_37-57; PS122/3_38-1; PS122/3_38-141; PS122/3_38-4; PS122/3_38-51; PS122/3_39-46; PS122/3_39-48; PS122/3_39-88; PS122/3_39-92; PS122/4; PS122/4_44-157; PS122/4_44-215; PS122/4_44-216; PS122/4_44-47; PS122/4_46-32; PS122/4_46-50; PS122/4_47-23; PS122/4_48-142; PS122/4_48-143; PS122/4_48-144; PS122/4_48-145; PS122/4_48-146; PS122/5; PS122/5_59-204; PS122/5_59-313; PS122/5_60-2; PS122/5_60-91; PS122/5_61-25; PS122/5_62-124; PS122/5_62-44; Sample code/label; Sample ID; Sample type; snow; SNOWPIT; Snow pit; Snow sampler metal; SSM; Station label; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 2717 data points
    Location Call Number Expected Availability
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  • 65
    Publication Date: 2024-06-04
    Description: Underway seawater samples have been taken from underneath the research vessel Polarstern through a pipe installed on the ship. The valve had been open for about 2 minutes before collecting the samples to avoid possible contaminations. Water samples for measurement of stable water isotopes (δ18O, δD,) were collected in narrow-mouth low-density polyethylene 20- or 30-mL plastic bottles (VWR international LLC, Germany), sealed with Parafilm M and stored at +4 °C from the end of the expedition until the measurement. Average daily salinity values were extracted from dship portal (https://dship.awi.de/). Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (https://hdl.handle.net/10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): https://hdl.handle.net/10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 https://hdl.handle.net/10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; Calculated after Dansgaard (1964); Comment; DATE/TIME; DEPTH, water; Deuterium excess; Event label; isotopes; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); MOSAiC; MOSAiC20192020; MOSAiC expedition; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Polarstern; PS122/1; PS122/1_10-111; PS122/1_10-2; PS122/1_10-25; PS122/1_10-32; PS122/1_10-52; PS122/1_10-77; PS122/1_10-93; PS122/1_11-15; PS122/1_11-28; PS122/1_11-4; PS122/1_11-42; PS122/1_7-111; PS122/1_7-95; PS122/1_8-100; PS122/1_8-119; PS122/1_8-15; PS122/1_8-26; PS122/1_8-4; PS122/1_8-84; PS122/1_9-103; PS122/1_9-15; PS122/1_9-29; PS122/1_9-45; PS122/1_9-56; PS122/1_9-92; PS122/2; PS122/2_15-6; PS122/2_15-8; PS122/2_16-14; PS122/2_16-29; PS122/2_16-37; PS122/2_16-53; PS122/2_16-61; PS122/2_16-8; PS122/2_17-17; PS122/2_17-2; PS122/2_17-25; PS122/2_17-42; PS122/2_17-67; PS122/2_17-75; PS122/2_17-97; PS122/2_18-15; PS122/2_18-2; PS122/2_18-23; PS122/2_18-39; PS122/2_18-58; PS122/2_18-75; PS122/2_18-90; PS122/2_19-114; PS122/2_19-17; PS122/2_19-2; PS122/2_19-33; PS122/2_19-58; PS122/2_19-80; PS122/2_19-90; PS122/2_20-1; PS122/2_20-111; PS122/2_20-13; PS122/2_20-29; PS122/2_20-50; PS122/2_20-75; PS122/2_20-99; PS122/2_21-11; PS122/2_21-112; PS122/2_21-123; PS122/2_21-50; PS122/2_21-69; PS122/2_21-85; PS122/2_22-14; PS122/2_22-2; PS122/2_22-31; PS122/2_22-64; PS122/2_22-84; PS122/2_22-95; PS122/2_23-1; PS122/2_23-11; PS122/2_23-33; PS122/2_23-49; PS122/2_23-66; PS122/2_23-86; PS122/2_24-20; PS122/2_24-30; PS122/2_24-41; PS122/2_24-48; PS122/2_24-5; PS122/2_24-71; PS122/2_24-82; PS122/2_25-101; PS122/2_25-29; PS122/2_25-43; PS122/2_25-5; PS122/2_25-56; PS122/2_25-75; PS122/2_25-88; PS122/3; PS122/3_29-21; PS122/3_29-36; PS122/3_29-51; PS122/3_29-6; PS122/3_29-60; PS122/3_29-7; PS122/3_29-81; PS122/3_30-18; PS122/3_30-23; PS122/3_30-35; PS122/3_30-52; PS122/3_30-6; PS122/3_30-66; PS122/3_30-83; PS122/3_31-13; PS122/3_31-16; PS122/3_31-28; PS122/3_31-46; PS122/3_31-54; PS122/3_31-60; PS122/3_31-78; PS122/3_32-1; PS122/3_32-21; PS122/3_32-37; PS122/3_32-48; PS122/3_32-60; PS122/3_32-72; PS122/3_33-15; PS122/3_33-35; PS122/3_33-50; PS122/3_33-64; PS122/3_33-79; PS122/3_33-8; PS122/3_33-93; PS122/3_34-1; PS122/3_34-12; PS122/3_34-26; PS122/3_34-35; PS122/3_34-47; PS122/3_34-61; PS122/3_34-74; PS122/3_35-103; PS122/3_35-17; PS122/3_35-3; PS122/3_35-35; PS122/3_35-52; PS122/3_35-75; PS122/3_35-89; PS122/3_36-1; PS122/3_36-110; PS122/3_36-13; PS122/3_36-135; PS122/3_36-34; PS122/3_36-55; PS122/3_36-72; PS122/3_37-112; PS122/3_37-12; PS122/3_37-2; PS122/3_37-23; PS122/3_37-42; PS122/3_37-67; PS122/3_37-90; PS122/3_38-113; PS122/3_38-22; PS122/3_38-26; PS122/3_38-37; PS122/3_38-48; PS122/3_38-67; PS122/3_38-88; PS122/3_39-1; PS122/3_39-14; PS122/3_39-29; PS122/3_39-49; PS122/3_39-68; PS122/3_39-76; PS122/3_39-85; PS122/3_40-1; PS122/3_40-13; PS122/3_40-22; PS122/3_40-30; PS122/3_40-35; PS122/3_40-45; PS122/3_40-7; PS122/3_41-13; PS122/3_41-20; PS122/3_41-27; PS122/3_41-38; PS122/3_41-4; PS122/3_41-42; PS122/3_41-48; PS122/3_42-1; PS122/3_42-12; PS122/3_42-21; PS122/3_42-27; PS122/3_42-33; PS122/3_42-43; PS122/3_42-52; PS122/3_42-58; PS122/3_42-6; PS122/3_42-67; PS122/4; PS122/4_44-123; PS122/4_44-131; PS122/4_44-146; PS122/4_44-160; PS122/4_44-175; PS122/4_44-19; PS122/4_44-194; PS122/4_44-28; PS122/4_44-34; PS122/4_44-54; PS122/4_44-64; PS122/4_44-80; PS122/4_44-85; PS122/4_44-96; PS122/4_45-111; PS122/4_45-133; PS122/4_45-14; PS122/4_45-6; PS122/4_45-65; PS122/4_45-90; PS122/4_46-124; PS122/4_46-22; PS122/4_46-5; PS122/4_46-51; PS122/4_46-67; PS122/4_46-96; PS122/4_47-105; PS122/4_47-118; PS122/4_47-24; PS122/4_47-37; PS122/4_47-5; PS122/4_47-70; PS122/4_47-88; PS122/4_48-119; PS122/4_48-149; PS122/4_48-156; PS122/4_48-18; PS122/4_48-48; PS122/4_48-68; PS122/4_49-23; PS122/4_49-24; PS122/4_49-28; PS122/4_49-58; PS122/4_49-67; PS122/4_49-82; PS122/4_49-96; PS122/4_50-12; PS122/4_50-22; PS122/4_50-3; PS122/4_50-34; PS122/4_50-44; PS122/4_50-53; PS122/4_50-59; PS122/4_50-62; PS122/5; PS122/5_59-11; PS122/5_59-133; PS122/5_59-151; PS122/5_59-167; PS122/5_59-179; PS122/5_59-199; PS122/5_59-220; PS122/5_59-247; PS122/5_59-26; PS122/5_59-266; PS122/5_59-287; PS122/5_59-323; PS122/5_59-341; PS122/5_59-36; PS122/5_59-361; PS122/5_59-379; PS122/5_59-4; PS122/5_59-59; PS122/5_59-65; PS122/5_60-11; PS122/5_60-116; PS122/5_60-140; PS122/5_60-169; PS122/5_60-35; PS122/5_60-50; PS122/5_60-73; PS122/5_61-106; PS122/5_61-141; PS122/5_61-184; PS122/5_61-208; PS122/5_61-244; PS122/5_61-3; PS122/5_61-34; PS122/5_62-111; PS122/5_62-133; PS122/5_62-150; PS122/5_62-171; PS122/5_62-63; PS122/5_62-8; PS122/5_62-86; PS122/5_63-115; PS122/5_63-123; PS122/5_63-134; PS122/5_63-144; PS122/5_63-27; PS122/5_63-52; PS122/5_63-6; PS122/5_63-66; PS122/5_63-7; PS122/5_63-78; PS122/5_63-93; Salinity; Sample code/label; Sample ID; Sample type; seawater; see abstract; Station label; Surface water sample; SWS; Tap; TAP; Water sample; WS; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 2375 data points
    Location Call Number Expected Availability
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  • 66
    Publication Date: 2024-06-04
    Description: Helicopter position (latitude, longitude, altitude) and attitude (pitch, roll, true heading) were measured by an inertial measurement unit (IMU-57) as part of the combined global navigation satellite system (GNSS) and inertial navigation system (INS) Applanix AP60-Air (hdl:10013/sensor.a9fee346-91e7-4eed-9f2f-89f1368e53a0). The IMU received input signal from two AV39 GNSS antennae installed on the forward and aft cowlings on top of the main cabin of the helicopter. The IMU was mounted in the rear cargo compartment on a sensor plate together with the airborne laser scanner and the sensor plate was connected with dampeners to the helicopter airframe. The helicopter flights in this data set include surveys where the airborne laser scanner was operated along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. They are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle pattern, or transect flights. The position and attitude data were collected to aid the processing of data from the instruments onboard like the airborne laser scanner (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), the infrared camera (Thielke et al., 2022; doi:10.1594/PANGAEA.941017), and the RGB camera (Neckel et al., 2022; doi:10.1594/PANGAEA.949433). The GPS/INS data was post-processed using Applanix software POSPac Mobile Mapping Suite (MMS) 8.3 and resulted in the 200 Hz precise point positioning (PPP) solution. The post-processed positions correspond to the location of the IMU in the aircraft reference frame in the cargo compartment of the helicopter. For a set of high latitude flights, the post-processing failed due to the low signal-to-noise of the horizontal component of the Earth's rotation rate. In this case only the 10 Hz real time navigation (RTNav) solution is provided. The positioning and altitude error for the real time can be metres, while the post-processed 200 Hz solution has an accuracy of decimetres. The challenging nature of GNSS (limited satellite visibility, ionospheric interference) and inertial navigation means that the quality of the INS/GPS is degraded even after post-processing compared to lower latitude data.
    Keywords: 20191002_01; 20191020_01; 20191029_01; 20191105_01; 20191112_01; 20191112_02; 20191119_01; 20191130_01; 20191206_01; 20191224_01; 20191225_01; 20191228_01; 20191230_01; 20200107_01; 20200107_02; 20200108_01; 20200108_03; 20200108_04; 20200116_01; 20200116_02; 20200121_01; 20200123_01; 20200123_02; 20200125_01; 20200128_01; 20200202_01; 20200204_01; 20200209_01; 20200212_01; 20200217_01; 20200217_02; 20200227_01; 20200321_01; 20200321_02; 20200423_01; airborne; Arctic; Arctic Ocean; Binary Object; Binary Object (File Size); DATE/TIME; Event label; Flight number; GPS; HELI; Helicopter; IceSense; INS; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_1_2_45_2019092801; PS122_4_44_27_2020061101; PS122_4_44_65_2020061502; PS122_4_44_78_2020061601; PS122_4_45_112_2020070401; PS122_4_45_36_2020063001; PS122_4_45_37_2020063002; PS122_4_46_36_2020070701; PS122_4_46_39_2020070703; PS122_4_46_97_2020071101; PS122_4_47_96_2020071701; PS122_4_48_69_2020072201; PS122_4_50_32_2020080601; PS122_4_50_45_2020080701; PS122/1; PS122/1_10-78; PS122/1_2-167; PS122/1_2-45; PS122/1_2-57; PS122/1_5-9; PS122/1_6-11; PS122/1_7-24; PS122/1_7-25; PS122/1_8-23; PS122/1_9-98; PS122/2; PS122/2_17-101; PS122/2_17-98; PS122/2_17-99; PS122/2_18-7; PS122/2_19-44; PS122/2_19-45; PS122/2_19-46; PS122/2_19-51; PS122/2_19-52; PS122/2_19-53; PS122/2_20-52; PS122/2_20-53; PS122/2_21-122; PS122/2_21-41; PS122/2_21-77; PS122/2_21-78; PS122/2_22-16; PS122/2_22-97; PS122/2_23-109; PS122/2_23-14; PS122/2_24-31; PS122/2_25-7; PS122/2_25-8; PS122/3; PS122/3_29-49; PS122/3_32-42; PS122/3_32-70; PS122/3_32-71; PS122/3_33-17; PS122/3_35-48; PS122/3_35-49; PS122/3_37-63; PS122/3_37-66; PS122/3_39-109; PS122/4; PS122/4_44-27; PS122/4_44-65; PS122/4_44-78; PS122/4_45-112; PS122/4_45-36; PS122/4_45-37; PS122/4_46-36; PS122/4_46-39; PS122/4_46-97; PS122/4_47-96; PS122/4_48-69; PS122/4_50-32; PS122/4_50-45; PS122/5; PS122/5_59-139; PS122/5_61-190; PS122/5_61-62; PS122/5_61-63; PS122/5_62-166; PS122/5_62-67; PS122/5_63-118; PS122/5_63-3; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties
    Type: Dataset
    Format: text/tab-separated-values, 134 data points
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  • 67
    Publication Date: 2024-06-04
    Description: The dataset compiles water isotope measurements of 66 lakes, sampled in Central and Eastern Yakutia during a summer field campaign in August and September 2021 (RU-Land_2021_Yakutia). Additionally, there are isotope data of a single rain event, received during the campaign. The investigated lakes are located in four different study areas in the Sakha Republic, Russia: in the mountainous region of the Verkhoyansk Range within the Oymyakonsky and Tomponsky District (EN21401 - EN21415), and in three lowland regions of Central Yakutia within the Churapchinsky, Tattinsky and the Megino-Kangalassky District (Event EN21416 - EN21467). One lake (EN21160) is centrally located in the city of Yakutsk, the capital of the Sakha Republic. Baisheva et al. (2022) gives an overview of the lakes studied and the corresponding hydrochemistry. Surface water samples (0 – 0.5 m) for measurement of stable water isotopes (δ18O, δD) have been taken for all lakes. If the lakes were deeper than five meters (≥ 5 m), water samples of the middle and bottom water (MW, BW) of the lake were taken, too. Where it was available and reachable, there are also water isotope data from in- or outflow (IF, OF). For two greater lakes, there are one or even more depth profiles composed of several isotope samples from different depths (EN21112, EN21116, EN21124¸ EN21160; numbers at the end indicate different sampling depths). There were two different methods of sampling: Either water for isotope measurements was directly sampled from the lake into 30 ml narrow-mouth PE bottles, filled to the top and closed tightly. Otherwise water samples were taken with an UWITEC water sampler (2 L), filled into a larger sample container (2 L Whirl-Pak®) and subsampled in 30 ml narrow-mouth PE bottles as soon as possible afterwards. The single rain event was sampled on the 22nd of August 2021 at one of the field camp sites, close to the lake EN21427. A dry, clean plastic container was placed outside for receiving the rain. The subsampling was done immediately after the event by rinsing it into 30 ml narrow-mouth PE bottles, filled to the top and closed tightly. All samples were stored cool and dark as soon as possible until analysis. All data were collected and processed by scientists from the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Germany, the University of Potsdam, Germany, and the North-Eastern Federal University of Yakutsk (NEFU), Russia. Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at Alfred Wegener Institute in Potsdam (hdl:10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): hdl:10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 and hdl:10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values are given in per mill (‰) vs. Vienna Standard Mean Ocean Water (VSMOW) as the standard. N indicates the number of measurements per sample. If N 〉 1, the mean isotope value of the sample was calculated from the individual measurement results. The standard deviation includes all measurements of the individual sample, which is generally better than the external (or machine) error. The external errors of long-term standard measurements for hydrogen and oxygen are better than 0.8‰ and 0.10‰, respectively (Meyer et al., 2000). The second order parameter d excess was computed according to: d excess = δD – 8 * δ18O (Dansgaard, 1964). For the calculation of d excess, the respective mean values were used.
    Keywords: AWI_Envi; AWI Arctic Land Expedition; Calculated after Dansgaard (1964); Central Yakutia; Churapchinsky District; Comment; DATE/TIME; Depth, bathymetric; DEPTH, water; Deuterium excess; d excess; ELEVATION; EN21160; EN21401; EN21402; EN21403; EN21404; EN21405; EN21406; EN21407; EN21408; EN21409; EN21410; EN21411; EN21412; EN21413; EN21414; EN21415; EN21416; EN21417; EN21418; EN21419; EN21420; EN21421; EN21422; EN21423; EN21424; EN21425; EN21426; EN21427; EN21428; EN21429; EN21430; EN21431; EN21432; EN21433; EN21434; EN21435; EN21436; EN21437; EN21438; EN21439; EN21440; EN21441; EN21442; EN21443; EN21444; EN21445; EN21446; EN21447; EN21448; EN21449; EN21450; EN21451; EN21452; EN21453; EN21454; EN21455; EN21456; EN21457; EN21458; EN21459; EN21462; EN21463; EN21464; EN21465; EN21466; EN21467; Event label; lake; LAKE; Landform; LATITUDE; Location; LONGITUDE; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Megino-Kangalassky District; Number of observations; Oymyakonsky District; Polar Terrestrial Environmental Systems @ AWI; RU-Land_2021_Yakutia; Russia; Sample ID; Sampling lake; Siberia; stabe isotopes; stable oxygen and hydrogen isotopes; Tattinsky District; thermokarst lakes; Tomponsky District; Type; water isotopes; Yakutia; Yakutsk; δ18O, water; δ18O, water, standard deviation; δ Deuterium, water; δ Deuterium, water, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 1480 data points
    Location Call Number Expected Availability
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  • 68
    Publication Date: 2024-06-04
    Description: The dataset comprises the main geochemical characteristics of purified lake sediment samples from Lake Bolshoye Shchuchye, in the Polar Ural based on EDS and stable isotope data. Moreover, core segment (column A), composite depth (in cm; column B); calibrated age (in cal ka BP; column C) are given. Details on coring and age model are given in Lenz et al. (2021) Energy-Dispersive X-ray Spectroscopy (EDS) was carried out with a scanning electron microscope (SEM) at the German Research Centre for Geosciences (GFZ Potsdam, Germany) to assess contamination of all diatom samples (following Chapligin et al., 2012). Three replicate analyses were carried out with an excited-area size with a radius of ~200 μm at an acceleration voltage of 20.0 kV. All detectable elements were normalized to 100% weight. The results were expressed as weight percentages (in %) and displayed as oxides: SiO2 content (%); Al2O3 content (%); Na2O content (%); MgO content (%); K2O content (%); CaO content (%); MnO content (%); FeO content (%): Total sum (%) of the purified sediment sample (columns D to L). Details are given in Meyer et al. (2022) The diatom oxygen isotope composition (δ18Odiatom) from lacustrine sediments helps tracing the hydrological and climate dynamics in individual lake catchments. The oxygen isotope data has been generated in the ISOLAB Facility Potsdam including all d18Odiatom values (all in ‰ vs. VSMOW). The measured δ18O values (δ18Omeas), the standard deviation (SD) and number of replicates (N) are given (columns M to O), as well as the calculated contamination (ccont; in %) and δ18O values corrected for contamination (δ18Ocorr) (columns P to Q). The details of the contamination correction and isotope analytics are given in Meyer et al. (2022)
    Keywords: AGE; Aluminium oxide; biogenic silica; Calcium oxide; chironomid-inferred temperature reconstructions; Climate change; Co1321; Contamination; Core; Corrected; DEPTH, sediment/rock; Diatom; Diatoms, δ18O; Diatoms, δ18O, standard deviation; hydrological fluctuations; Iron oxide, FeO; Isotope ratio mass spectrometry; Lake Bolshoye Shchuchye, Polar Urals, Russia; Lake sediment; Magnesium oxide; Manganese oxide; oxygen isotopes; Paleolimnological Transect; PCUWI; Piston corer, UWITEC; PLOT; Potassium oxide; Replicates; Scanning electron microscope (SEM) equipped with electron-dispersive x-ray spectroscopy (EDX); Silicon dioxide; Sodium oxide; Total; δ18O, adjusted/corrected
    Type: Dataset
    Format: text/tab-separated-values, 720 data points
    Location Call Number Expected Availability
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  • 69
    Publication Date: 2024-06-04
    Description: Seawater samples have been taken from the station Ocean City on the main MOSAiC ice floe on legs 1, 2, and 3. Water samples for measurement of stable water isotopes (δ18O, δD) were collected in 50-mL glass screw-cap narrow-neck vials (VWR international LLC, Germany), sealed with Parafilm M and stored at +4 °C from the end of the expedition until the measurement. Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (https://hdl.handle.net/10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): https://hdl.handle.net/10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 https://hdl.handle.net/10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; Calculated after Dansgaard (1964); CTD/Rosette; CTD-RO; DATE/TIME; DEPTH, water; Deuterium excess; Event label; isotopes; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-45; PS122/1_11-40; PS122/1_2-68; PS122/1_4-37; PS122/1_5-46; PS122/1_6-38; PS122/1_7-40; PS122/1_8-16; PS122/1_9-28; PS122/2; PS122/2_18-16; PS122/2_19-4; PS122/2_20-17; PS122/2_21-101; PS122/2_21-114; PS122/2_21-128; PS122/2_21-26; PS122/2_22-18; PS122/2_22-3; PS122/2_22-71; PS122/2_23-17; PS122/2_23-4; PS122/2_23-70; PS122/2_24-47; PS122/2_25-4; PS122/3; PS122/3_29-74; PS122/3_29-8; PS122/3_30-38; PS122/3_30-9; PS122/3_31-18; PS122/3_31-81; PS122/3_32-12; PS122/3_32-77; PS122/3_33-82; PS122/3_34-17; PS122/3_34-76; PS122/3_35-25; PS122/3_35-92; PS122/3_36-115; PS122/3_36-19; PS122/3_37-116; PS122/3_37-15; PS122/3_38-100; PS122/3_38-31; PS122/3_39-16; Sample code/label; Sample ID; Sample type; seawater; Station label; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 762 data points
    Location Call Number Expected Availability
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  • 70
    Publication Date: 2024-06-04
    Description: Snow samples of the upper 10 cm were taken between 14 May and 3 August 2018 next to the EastGRIP deep drilling site in northeast Greenland situated in the accumulation zone of the Greenland Ice Sheet. All samples were measured for their stable water isotope composition (δ18O, δD, d-excess). Samples were taken at 30 positions along a 39 m long transect. The first 20 samples had a spacing of 1 m and the remaining 10 samples had a spacing of 2 m. Sampling was performed every third day for three depth intervals (0-1 cm, 1-4 cm, 4-10 cm) with two one-week periods with daily sampling. The first period was from 8 to 14 June with a sampling of six depth intervals (0-1 cm, 1-2 cm, 2-4 cm, 4-6 cm, 6-8 cm, 8-10 cm) at ten locations (4 m spacing). The second period was from 24 to 30 July with a sampling of six depth intervals (0-0.5 cm, 0.5-1 cm, 1-2 cm, 2-4 cm, 4-6 cm, 6-10 cm) at 25 positions (position 1 - 10 with 1 m spacing, afterwards 2 m spacing). The depth indication in the data set always refers to the mean of each sampling interval. All samples were airtightly stored in high-purity sampling bags (®Whirl-Paks) and kept frozen until measurement. In the same area, a photogrammetry structure-from-motion approach was performed to generate digital elevation models for each day (Zuhr et al., 2022: https://doi.org/10.5194/tc-15-4873-2021, https://doi.org/10.1594/PANGAEA.936082, https://doi.org/10.1594/PANGAEA.923418). Thus, every sampling position has a depth indication, relative to the snow height of the entire transect. About 70 % of the samples were measured in the ISOLAB Facility at the Alfred Wegener Institute Helmholtz-Center for Polar and Marine Research, Potsdam, Germany. The samples were measured with an L2140-i CRDS device from Picarro Inc. with a high-throughput vaporizer. All data were corrected for memory and instrumental drift and calibrated on the VSMOW-SLAP scale following van Geldern and Barth (2012) using the calibration algorithm described in Münch et al. (2016). The mean measurement uncertainty for δ18O and δD derived from an independent quality control standard was 0.09 and 0.8 ‰, respectively. The ISOLAB Facility metadata is part of the sensor web: Sensor (2022): Metadata for laboratory ISOLAB Facility - Stable Isotope Laboratory Potsdam at Current Version. hdl:10013/sensor.ddc92f54-4c63-492d-81c7-696260694001 Sensor (2022): Metadata for Isotopic Water Liquid Analyzer for the online determination of the hydrogen and oxygen isotopic composition in water samples using Cavity Ring-Down Spectroscopy (CRDS) L2140-i: https://sensor.awi.de?urn=laboratory:isolab_facility_potsdam:picarro_crds_l2140i_p About 30 % of the samples were measured in the Stable Isotope Laboratory of the institute for Physics of Ice, Climate and Earth, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark. The vaporisation of the sample is performed using a high throughput, low volume vaporiser (Picarro-A0212 – discontinued model as of 2016). The raw isotope measurements are calibrated on the VSMOW-SLAP scale using a two fixed-point calibration similar to Gkinis et al. (2011) and following the IAEA recommended procedures. The mean measurement uncertainty for δ18O and δD derived from a quality control standard was 0.04 and 0.33 ‰, respectively. The detailed sample and data handling are described in Gkinis et al. (2021). All measurements from both laboratories are reported on the international VSMOW-SLAP (VSMOW and SLAP refer to the International Atomic Energy reference water materials and stand for Vienna Mean Ocean Water and Standard Light Antarctic Precipitation) isotope scale after careful calibration using local standards calibrated against the provided international reference materials.
    Keywords: AWI_SPACE; AWI Arctic Land Expedition; DATE/TIME; Day of the year; DEPTH, ice/snow; Depth, relative; Depth layer, ice/snow; Deuterium excess; Deuterium excess, standard deviation; EGRIP_2018_snow; GL-Land_2018_EGRIP; Greenland; POINT DISTANCE from start; Position; Sample code/label; Signals from the Surface Snow: Post-Depositional Processes Controlling the Ice Core Isotopic Fingerprint; SNOWISO; Snow pits/firn core/ice core; Space-time structure of climate change @ AWI; stable water isotopes; surface snow; δ18O, water; δ18O, water, standard deviation; δ Deuterium, water; δ Deuterium, water, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 40393 data points
    Location Call Number Expected Availability
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  • 71
    Publication Date: 2024-06-04
    Description: Sea ice cores were collected from the Biogeochemistry (BGC) team at different stations located on the main ice floe of MOSAiC expedition. Flat sea ice on the floe was categorized into three types based on the age: sea ice which grew during the same winter referred to as First-Year-Ice (FYI); sea ice which had survived one or more summer melting periods referred to as Second-Year-Ice (SYI). Sea ice cores were collected using a Kovacs Mark II 9 cm diameter corer. The core was extracted and placed in an aluminum holder equipped with a metric ruler. Using a standard Kovacs ice thickness gauge, the freeboard was taken and the length of the core was measured. The snow on top of the sea ice was brushed off the top of the cores to minimize the snow affecting the ice surface. Onboard RV Polarstern, the cores were cut in 10 cm sections using a handsaw at 4° C (leg 1) or an electric saw at -20° C (legs 2 and 3). Each section was transferred into a gas-tight TedlarTM bag. The closed bags were carefully degassed with a vacuum pump (NKF Neuberger, type N035). Melting occurred within 12 to 15 hours in a water bath in the dark. After shaking the melted ice within the TedlarTM bags, discrete sampling started by first rising the melt water carefully through a Tygon tube connected with the opened valves of the gas tide bags and then into prepared sample vials. Here we present the data from samples collected at Main Core Site (MCS) at the Dark Sector (DS). Oxygen and hydrogen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (https://hdl.handle.net/10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): https://hdl.handle.net/10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 https://hdl.handle.net/10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O and δD values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard. The second order parameter d excess was computed according to: d excess = δD-8 δ18O (Dansgaard, 1964).
    Keywords: Arctic Ocean; Calculated after Dansgaard (1964); DATE/TIME; DEPTH, ice/snow; Deuterium excess; Event label; IC; Ice corer; Mass spectrometer Finnigan MAT Delta-S (ISOLAB); Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_10-16; PS122/1_10-19; PS122/1_5-3; PS122/1_5-78; PS122/1_5-81; PS122/1_6-10; PS122/1_7-6; PS122/1_7-9; PS122/1_8-2; PS122/1_9-30; PS122/1_9-6; PS122/2; PS122/2_17-3; PS122/2_19-7; PS122/2_20-5; PS122/2_21-13; PS122/2_22-7; PS122/2_23-3; PS122/3; PS122/3_32-63; PS122/3_33-18; PS122/3_35-11; PS122/3_36-21; PS122/3_36-4; PS122/3_38-16; PS122/3_38-24; PS122/3_39-18; PS122/3_39-7; Salinity; Sample code/label; Sample ID; Sample type; Sea ice; Station label; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 2179 data points
    Location Call Number Expected Availability
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  • 72
    Publication Date: 2024-06-04
    Description: Second-year sea-ice thickness, draft, salinity, temperature, and density were measured during near-weekly surveys at the main second-year ice coring site (MCS-SYI) during the MOSAiC expedition (legs 1 to 3) and new second-year ice coring site leg 4, since the earlier site was not accessible any longer. The ice cores were extracted either with a 9-cm (Mark II) or 7.25-cm (Mark III) internal diameter ice corers (Kovacs Enterprise, US). This data set includes data from 18 coring site visits and were performed from 28 October 2019 to 20 July 2020 at coring locations within 50 m to each other in the MOSAiC Central Observatory. During each coring event, ice temperature was measured in situ from a separate temperature core, using Testo 720 thermometers in drill holes with a length of half-core-diameter at 5-cm vertical resolution. Ice bulk practical salinity was measured from melted core sections at 5-cm resolution using a YSI 30 conductivity meter. Ice density was measured using the hydrostatic weighing method (Pustogvar and Kulyakhtin, 2016) from a density core in the freezer laboratory onboard Polarstern at the temperature of –15°C. Relative volumes of brine and gas were estimated from ice salinity, temperature and density using Cox and Weeks (1983) for cold ice and Leppäranta and Manninen (1988) for ice warmer than –2°C. The data contains the event label (1), time (2), and global coordinates (3,4) of each coring measurement and sample IDs (13, 15). Each salinity core has its manually measured ice thickness (5), ice draft (6), core length (7), and mean snow height (22). Each core section has the total length of its top (8) and bottom (9) measured in situ, as well estimated depth of section top (10), bottom (11), and middle (12). The depth estimates assume that the total length of all core sections is equal to the measured ice thickness. Each core section has the value of its practical salinity (14), isotopic values (16, 17, 18) (Meyer et al., 2000), as well as sea ice temperature (19) and ice density (20) interpolated to the depth of salinity measurements. The global coordinates of coring sites were measured directly. When it was not possible, coordinates of the nearby temperature buoy 2019T62 (legs 1-3) or 2019T61 (leg 4) were used. Ice mass balance buoy 2019T62 installation is described in doi:10.1594/PANGAEA.940231, ice mass balance buoy 2020T61 installation is described in doi: 10.1594/PANGAEA.926580. Brine volume (21) fraction estimates are presented only for fraction values from 0 to 30%. Each core section also has comments (23) describing if the sample is from a new coring site or has any other special characteristics. Macronutrients from the salinity core will be published in a subsequent version of this data set.
    Keywords: Arctic; Arctic Ocean; Arctic Research Icebreaker Consortium: A strategy for meeting the needs for marine-based research in the Arctic; ARICE; Calculated; Comment; Core length; cores; DATE/TIME; density; Density, ice; Depth, adjusted; Depth, adjusted bottom; Depth, adjusted top; Depth, ice/snow, bottom/maximum; Depth, ice/snow, top/minimum; Deuterium excess; Ecological monitoring; Event label; HAVOC; Hydrostatic weighing; IC; Ice corer; ICEGAUGE; Ice thickness gauge; Isotopic liquid water analyzer; LATITUDE; LONGITUDE; MOSAiC; MOSAiC_ECO; MOSAiC_ICE; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Physical properties; Polarstern; PS122/1; PS122/1_10-16; PS122/1_5-78; PS122/1_6-36; PS122/1_7-53; PS122/1_7-9; PS122/1_9-11; PS122/2; PS122/2_20-5; PS122/2_22-7; PS122/2_25-15; PS122/3; PS122/3_33-18; PS122/3_35-4; PS122/3_36-4; PS122/3_38-16; PS122/3_39-18; PS122/4; PS122/4_45-29; PS122/4_46-20; PS122/4_47-18; PS122/4_48-25; Ridges - Safe HAVens for ice-associated Flora and Fauna in a Seasonally ice-covered Arctic OCean; Salinity; Temperature; Salinometer, inductive; Sample ID; Sea ice; Sea ice draft; Sea ice salinity; Sea ice thickness; Snow height; Tape measure; Temperature, ice/snow; Thermometer; time-series; Volume, brine; δ18O, water; δ Deuterium, water
    Type: Dataset
    Format: text/tab-separated-values, 9395 data points
    Location Call Number Expected Availability
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  • 73
    Publication Date: 2024-06-04
    Description: During the MOSAiC expedition 2019-2020 atmospheric thermodynamic profile measurements have been conducted from a meteorological (Met) Tower on the sea ice, as well as via collocated radiosondes that were launched approximately every six hours from aboard Polarstern. While the radiosondes lack the lowermost 10 m above the sea ice, the Met Tower profile can be used to fill this gap (observations at 0, 2, 6 and 10 meters). This is a blended data product that merges the Met Tower profile (data version 3.4, doi:10.18739/A2PV6B83F) in the minute of the radiosonde launch with the radiosonde profile aloft (data version 3, doi:10.1594/PANGAEA.943870). Parameters included are temperature (T), relative humidity (RH), wind speed and -direction, and air pressure. The aim of this product is two-fold: (1) To provide comprehensive atmospheric profiles for each radiosonde launch, that additionally retain the lowermost meters of the atmospheric boundary layer above the sea ice and (2) to remove potential unrealistic T/RH values from the radiosonde profiles that can emerge in the lowermost 100 m due to the influence of the ship on the measurement. Examples for the latter are occasional warm anomalies due to the heat island effect of the ship, or elevated, vertically confined peaks that can arise from the ship's exhaust plume. The potential effect of the exhaust plume on the T profile is estimated by comparing the radiosonde at 30 m height to the concurring Polarstern meteorological observation (doi:10.1594/PANGAEA.935263 - doi:10.1594/PANGAEA.935267). Given the geometrical constellation of the Polarstern observation towards the bow of the ship and the sounding launch platform at the aft of the ship, and depending on the wind direction relative to the ship, it can be assumed that at least one of the T measurements is less impacted from the ship exhaust than the other, and is retained. In a next step, the 10 - 30 m height segment in T and RH is filled with a linear interpolation between the Met Tower at 10 m and the radiosonde observation at 30 m. When identified, remaining T/RH peaks in the lowermost 100 m of the profile are removed and filled with a linear interpolation from below to above the peak. T/RH flags are provided to indicate where the profiles have been manipulated from the original data, and to indicate the reason for missing data in the profile. Compared to the original profiles, this blended product adds value and quality control in the lowest 100 m, which makes it better suitable, for example, for boundary layer analyses.
    Keywords: Arctic Ocean; boundary layer; DATE/TIME; Event label; FLUX_TOWER; Flux tower; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; North Greenland Sea; Other event; Polarstern; Profile; PS122/1; PS122/1_10-103; PS122/1_10-105; PS122/1_10-106; PS122/1_10-107; PS122/1_10-108; PS122/1_10-134; PS122/1_10-135; PS122/1_10-21; PS122/1_10-22; PS122/1_10-23; PS122/1_10-24; PS122/1_10-28; PS122/1_10-29; PS122/1_10-3; PS122/1_10-30; PS122/1_10-31; PS122/1_10-4; PS122/1_10-53; PS122/1_10-54; PS122/1_10-56; PS122/1_10-57; PS122/1_10-73; PS122/1_10-74; PS122/1_10-75; PS122/1_10-76; PS122/1_10-94; PS122/1_10-95; PS122/1_10-99; PS122/1_11-10; PS122/1_11-29; PS122/1_11-30; PS122/1_11-31; PS122/1_11-32; PS122/1_11-33; PS122/1_11-43; PS122/1_11-44; PS122/1_11-45; PS122/1_11-46; PS122/1_11-5; PS122/1_11-6; PS122/1_11-7; PS122/1_11-8; PS122/1_11-9; PS122/1_1-299; PS122/1_2-10; PS122/1_2-100; PS122/1_2-101; PS122/1_2-102; PS122/1_2-103; PS122/1_2-104; PS122/1_2-105; PS122/1_2-106; PS122/1_2-107; PS122/1_2-11; PS122/1_2-110; PS122/1_2-111; PS122/1_2-112; PS122/1_2-113; PS122/1_2-115; PS122/1_2-116; PS122/1_2-117; PS122/1_2-118; PS122/1_2-119; PS122/1_2-12; PS122/1_2-120; PS122/1_2-121; PS122/1_2-122; PS122/1_2-123; PS122/1_2-127; PS122/1_2-135; PS122/1_2-136; PS122/1_2-137; PS122/1_2-139; PS122/1_2-141; PS122/1_2-142; PS122/1_2-143; PS122/1_2-144; PS122/1_2-145; PS122/1_2-146; PS122/1_2-147; PS122/1_2-148; PS122/1_2-149; PS122/1_2-150; PS122/1_2-16; PS122/1_2-160; PS122/1_2-161; PS122/1_2-162; PS122/1_2-163; PS122/1_2-17; PS122/1_2-171; PS122/1_2-172; PS122/1_2-173; PS122/1_2-174; PS122/1_2-179; PS122/1_2-180; PS122/1_2-181; PS122/1_2-182; PS122/1_2-184; PS122/1_2-185; PS122/1_2-186; PS122/1_2-187; PS122/1_2-188; PS122/1_2-189; PS122/1_2-190; PS122/1_2-191; PS122/1_2-192; PS122/1_2-193; PS122/1_2-20; PS122/1_2-204; PS122/1_2-205; PS122/1_2-21; PS122/1_2-27; PS122/1_2-28; PS122/1_2-29; PS122/1_2-31; PS122/1_2-32; PS122/1_2-33; PS122/1_2-34; PS122/1_2-36; PS122/1_2-37; PS122/1_2-38; PS122/1_2-39; PS122/1_2-4; PS122/1_2-41; PS122/1_2-42; PS122/1_2-43; PS122/1_2-44; PS122/1_2-49; PS122/1_2-5; PS122/1_2-51; PS122/1_2-52; PS122/1_2-53; PS122/1_2-54; PS122/1_2-55; PS122/1_2-56; PS122/1_2-59; PS122/1_2-6; PS122/1_2-60; PS122/1_2-61; PS122/1_2-62; PS122/1_2-69; PS122/1_2-7; PS122/1_2-70; PS122/1_2-71; PS122/1_2-72; PS122/1_2-73; PS122/1_2-74; PS122/1_2-75; PS122/1_2-76; PS122/1_2-77; PS122/1_2-78; PS122/1_2-79; PS122/1_2-80; PS122/1_2-81; PS122/1_2-82; PS122/1_2-83; PS122/1_2-86; PS122/1_2-87; PS122/1_2-88; PS122/1_2-9; PS122/1_2-91; PS122/1_2-92; PS122/1_2-93; PS122/1_2-94; PS122/1_4-19; PS122/1_4-20; PS122/1_4-21; PS122/1_4-22; PS122/1_4-30; PS122/1_4-31; PS122/1_4-32; PS122/1_4-33; PS122/1_4-35; PS122/1_4-36; PS122/1_4-4; PS122/1_4-5; PS122/1_4-6; PS122/1_4-7; PS122/1_4-8; PS122/1_4-9; PS122/1_5-10; PS122/1_5-11; PS122/1_5-12; PS122/1_5-13; PS122/1_5-20; PS122/1_5-21; PS122/1_5-22; PS122/1_5-23; PS122/1_5-31; PS122/1_5-32; PS122/1_5-33; PS122/1_5-34; PS122/1_5-36; PS122/1_5-37; PS122/1_5-38; PS122/1_5-39; PS122/1_5-49; PS122/1_5-50; PS122/1_5-51; PS122/1_5-52; PS122/1_5-6; PS122/1_5-7; PS122/1_5-72; PS122/1_5-73; PS122/1_5-74; PS122/1_5-75; PS122/1_5-79; PS122/1_5-80; PS122/1_6-112; PS122/1_6-113; PS122/1_6-114; PS122/1_6-115; PS122/1_6-12; PS122/1_6-125; PS122/1_6-126; PS122/1_6-13; PS122/1_6-14; PS122/1_6-15; PS122/1_6-24; PS122/1_6-25; PS122/1_6-26; PS122/1_6-27; PS122/1_6-3; PS122/1_6-53; PS122/1_6-54; PS122/1_6-55; PS122/1_6-56; PS122/1_6-71; PS122/1_6-72; PS122/1_6-73; PS122/1_6-74; PS122/1_6-82; PS122/1_6-83; PS122/1_6-84; PS122/1_6-85; PS122/1_7-100; PS122/1_7-101; PS122/1_7-102; PS122/1_7-107; PS122/1_7-108; PS122/1_7-109; PS122/1_7-110; PS122/1_7-113; PS122/1_7-114; PS122/1_7-13; PS122/1_7-14; PS122/1_7-26; PS122/1_7-27; PS122/1_7-29; PS122/1_7-30; PS122/1_7-43; PS122/1_7-44; PS122/1_7-45; PS122/1_7-46; PS122/1_7-63; PS122/1_7-64; PS122/1_7-65; PS122/1_7-66; PS122/1_7-83; PS122/1_7-84; PS122/1_7-85; PS122/1_7-86; PS122/1_7-99; PS122/1_8-101; PS122/1_8-11; PS122/1_8-115; PS122/1_8-116; PS122/1_8-117; PS122/1_8-118; PS122/1_8-12; PS122/1_8-120; PS122/1_8-121; PS122/1_8-13; PS122/1_8-14; PS122/1_8-39; PS122/1_8-40; PS122/1_8-41; PS122/1_8-42; PS122/1_8-5; PS122/1_8-6; PS122/1_8-63; PS122/1_8-64; PS122/1_8-65; PS122/1_8-66; PS122/1_8-80; PS122/1_8-81; PS122/1_8-82; PS122/1_8-83; PS122/1_8-95; PS122/1_8-96; PS122/1_9-100; PS122/1_9-101; PS122/1_9-102; PS122/1_9-105; PS122/1_9-106; PS122/1_9-13; PS122/1_9-14; PS122/1_9-18; PS122/1_9-19; PS122/1_9-20; PS122/1_9-21; PS122/1_9-41; PS122/1_9-42; PS122/1_9-43; PS122/1_9-44; PS122/1_9-57; PS122/1_9-58; PS122/1_9-59; PS122/1_9-60; PS122/1_9-77; PS122/1_9-78; PS122/1_9-79; PS122/1_9-80; PS122/1_9-88; PS122/1_9-89; PS122/1_9-90; PS122/1_9-91; PS122/1_99-46; PS122/1_99-47; PS122/1_9-99; PS122/2; PS122/2_14-119; PS122/2_15-1; PS122/2_15-13; PS122/2_15-2; PS122/2_15-3; PS122/2_15-4; PS122/2_15-5; PS122/2_15-7; PS122/2_16-10; PS122/2_16-11; PS122/2_16-13; PS122/2_16-16; PS122/2_16-17; PS122/2_16-18; PS122/2_16-19; PS122/2_16-2; PS122/2_16-3; PS122/2_16-31; PS122/2_16-32; PS122/2_16-33; PS122/2_16-4; PS122/2_16-40; PS122/2_16-41; PS122/2_16-42; PS122/2_16-43; PS122/2_16-5; PS122/2_16-57; PS122/2_16-58; PS122/2_16-59; PS122/2_16-6; PS122/2_16-67; PS122/2_16-68; PS122/2_16-69; PS122/2_16-7; PS122/2_16-70; PS122/2_16-76; PS122/2_17-10; PS122/2_17-102; PS122/2_17-104; PS122/2_17-105; PS122/2_17-11; PS122/2_17-110; PS122/2_17-12; PS122/2_17-21; PS122/2_17-22; PS122/2_17-23; PS122/2_17-24; PS122/2_17-35; PS122/2_17-36; PS122/2_17-37; PS122/2_17-38; PS122/2_17-55; PS122/2_17-56; PS122/2_17-57; PS122/2_17-58; PS122/2_17-71; PS122/2_17-72; PS122/2_17-73; PS122/2_17-74; PS122/2_17-92; PS122/2_17-93; PS122/2_17-94; PS122/2_17-95; PS122/2_18-100; PS122/2_18-11; PS122/2_18-12; PS122/2_18-13; PS122/2_18-20; PS122/2_18-21; PS122/2_18-22; PS122/2_18-28; PS122/2_18-29; PS122/2_18-30; PS122/2_18-31; PS122/2_18-48; PS122/2_18-49; PS122/2_18-50; PS122/2_18-51; PS122/2_18-67; PS122/2_18-68; PS122/2_18-69; PS122/2_18-70; PS122/2_18-85; PS122/2_18-86; PS122/2_18-87; PS122/2_18-88; PS122/2_18-94; PS122/2_18-95; PS122/2_18-96; PS122/2_18-97; PS122/2_19-10; PS122/2_19-100; PS122/2_19-11; PS122/2_19-12; PS122/2_19-124; PS122/2_19-125; PS122/2_19-126; PS122/2_19-127; PS122/2_19-143; PS122/2_19-22; PS122/2_19-23; PS122/2_19-25; PS122/2_19-47; PS122/2_19-48; PS122/2_19-49; PS122/2_19-50; PS122/2_19-71; PS122/2_19-72; PS122/2_19-73; PS122/2_19-74; PS122/2_19-84; PS122/2_19-85; PS122/2_19-86; PS122/2_19-87; PS122/2_19-97; PS122/2_19-98; PS122/2_19-99; PS122/2_20-10; PS122/2_20-103; PS122/2_20-104; PS122/2_20-105; PS122/2_20-106; PS122/2_20-119; PS122/2_20-120; PS122/2_20-121; PS122/2_20-122; PS122/2_20-135; PS122/2_20-19; PS122/2_20-20; PS122/2_20-21; PS122/2_20-22; PS122/2_20-37; PS122/2_20-38; PS122/2_20-39; PS122/2_20-40; PS122/2_20-66; PS122/2_20-67; PS122/2_20-68; PS122/2_20-69; PS122/2_20-8; PS122/2_20-84; PS122/2_20-85; PS122/2_20-86; PS122/2_20-87; PS122/2_20-9; PS122/2_21-106; PS122/2_21-107; PS122/2_21-108; PS122/2_21-109; PS122/2_21-115; PS122/2_21-116; PS122/2_21-117; PS122/2_21-132; PS122/2_21-133; PS122/2_21-134; PS122/2_21-135; PS122/2_21-136; PS122/2_21-21; PS122/2_21-22; PS122/2_21-23; PS122/2_21-37; PS122/2_21-38; PS122/2_21-39; PS122/2_21-40; PS122/2_21-57; PS122/2_21-58; PS122/2_21-59; PS122/2_21-60; PS122/2_21-79; PS122/2_21-80; PS122/2_21-81; PS122/2_21-82; PS122/2_22-10; PS122/2_22-102; PS122/2_22-103; PS122/2_22-104; PS122/2_22-105; PS122/2_22-11; PS122/2_22-111; PS122/2_22-20; PS122/2_22-21; PS122/2_22-22; PS122/2_22-23; PS122/2_22-38; PS122/2_22-39; PS122/2_22-41; PS122/2_22-57; PS122/2_22-58; PS122/2_22-59; PS122/2_22-60; PS122/2_22-78; PS122/2_22-79; PS122/2_22-80; PS122/2_22-81; PS122/2_22-86; PS122/2_22-87; PS122/2_22-88; PS122/2_22-89; PS122/2_22-9; PS122/2_23-101; PS122/2_23-102; PS122/2_23-103; PS122/2_23-104; PS122/2_23-117; PS122/2_23-118; PS122/2_23-119; PS122/2_23-
    Type: Dataset
    Format: text/tab-separated-values, 3036 data points
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  • 74
    Publication Date: 2024-06-04
    Description: Melt ponds water sampling for biogeochemical parameters such as dissolved inorganic carbon (DIC), total alkalinity (TA), oxygen isotopes were examined from August to September 2020. To obtain discrete water samples from the melt ponds and leads, we checked the vertical structure and depth of the meltwater layer from the same hole used for the RINKO Profiler by attaching a conductivity sensor (Cond 315i, WTW GmbH, Germany) to a 2-m-long ruler and inserting the ruler into the lead water until the salinity measured with the Cond 315i increased at the meltwater–seawater interface (Nomura et al., 2024) . Water was pumped up with a peristaltic pump through a 2-m-long PTFE tube (L/S Pump Tubing, Masterflex, USA) at depths corresponding to meltwater (surface), the interface between meltwater and seawater (interface), and seawater (bottom). Salinity was measured at each depth by attaching a Cond 315i conductivity sensor to the bottom of the ruler. The tube intake was likewise attached to the bottom of the ruler. Seawater was subsampled into a 250-mL glass vial (Duran Co., Ltd., Germany) for measurement of dissolved inorganic carbon (DIC) and total alkalinity (TA) and a 50-mL glass, screw-cap, narrow-neck vial (VWR international LLC, Germany) for measurement of the oxygen isotopic ratio (δ18O) of the water. Immediately after subsampling for measurement of DIC and TA, a 6.0% (wt.) mercuric chloride (HgCl2) solution (100 µL) was added to stop biological activity. Samples for DIC and TA were stored at +4°C on the R/V Polarstern. Samples for δ18O were stored at room temperature (20°C). During the discrete water sampling, the CO2 concentration in the water column was measured directly on site by passing the water through an equilibrator Liqui-Cel® (G542, S/N: 132462, 3M Company, USA) connected to an infrared gas analyzer (LI-8100A, LI-COR Inc., USA). The analyzer was calibrated with standard gases containing 0.0, 299.3, and 501.3 ppm CO2 before MOSAiC Leg 5. RMS (root means square) noise at 370 ppm with 1 sec signal averaging is 〈1 ppm (https://www.licor.com/env/products/soil-flux/LI-8100a). The equilibrator was connected in the loop for water sampling (vide supra), and a 2-m-long ruler was inserted into the water and kept at that depth until the CO2 was equilibrated with air (about 1 minute) by monitoring the CO2 values. The CO2 concentration was measured at each depth (i.e., surface, interface, and bottom). At the ROV lead sites, vertical CO2 measurements were made every 0.05 m for detailed profiles. The DIC of water was determined by coulometry (Johnson et al., 1985; Johnson, 1992) using a home-made CO2 extraction system (Ono et al., 1998) and a coulometer (CM5012, UIC, Inc., Binghamton, NY, USA). The TA of water was determined by titration (Dickson et al., 2007) using a TA analyzer (ATT-05, Kimoto Electric Co., Ltd., Japan). Both DIC and TA measurements were calibrated with reference seawater materials (Batch AR, AU, and AV; KANSO Technos Co., Ltd., Osaka, Japan) traceable to the Certified Reference Material distributed by Prof. A. G. Dickson (Scripps Institution of Oceanography, La Jolla, CA, USA). Oxygen isotope analyses were carried out at the ISOLAB Facility at AWI Potsdam (hdl:10013/sensor.ddc92f54-4c63-492d-81c7-696260694001) with mass spectrometers (DELTA-S Finnigan MAT, USA): hdl:10013/sensor.af148dea-fe65-4c87-9744-50dc4c81f7c9 and hdl:10013/sensor.62e86761-9fae-4f12-9c10-9b245028ea4c employing the equilibration method (details in Meyer et al., 2000). δ18O values were given in per mil (‰) vs. Vienna standard mean ocean water (V-SMOW) as the standard.
    Keywords: Alkalinity, total; Arctic Ocean; Carbon, inorganic, dissolved; Carbonate chemistry; Carbon dioxide; Chamber for gas sampling; CHAMGAS; Conductivity sensor Cond 315i, WTW GmbH, Germany; Coulometry; DATE/TIME; DEPTH, water; Equilibrator, 3M, Liqui-Cel [G542, S/N: 132462]; followed by Infrared gas analyzer, LI-COR Inc., LI-8100A; Event label; LATITUDE; lead; LONGITUDE; Mass spectrometer, Finnigan, Delta-S; melt pond; melt water; Mosaic; MOSAiC; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; oxygen isotope; Polarstern; PS122/5; PS122/5_59-200; PS122/5_59-202; PS122/5_59-203; PS122/5_59-207; PS122/5_59-208; PS122/5_59-209; PS122/5_59-210; PS122/5_59-211; PS122/5_59-212; PS122/5_59-213; PS122/5_59-214; PS122/5_59-215; PS122/5_59-343; PS122/5_60-130; PS122/5_60-146; PS122/5_60-61; PS122/5_62-33; Salinity; Sample type; Site; Temperature, water; Titration; Water sample; WS; δ18O, water
    Type: Dataset
    Format: text/tab-separated-values, 204 data points
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  • 75
    Publication Date: 2024-06-04
    Description: Passive acoustic monitoring (PAM) data were collected by recorder AU0302 of type AURAL (Autonomous Underwater Recorder for Acoustic Listening (AURAL; Model 3, Multi-Électronique) at 79.1669° N, 6.3327° E, mooring F4-OZA2, in Fram Strait. During a deployment period from July 2020 to July 2022, passive acoustic data were collected from July 2020 to May 2021 (recording period) by AU0302 as part of the Frontiers in Arctic Marine Monitoring (FRAM) observatory in Fram Strait. The recorder was moored at 300 m depth and scheduled to record at a duty cycle of 10 min per 1 h at a sample rate of 32,000 Hz. Due to a firmware bug, the set duty cycle changed on 2021-01-02 to continuous recording of files with 10 min duration. Further details about the data acquisition and processing of this data set can be found in the accompanying metadata file (see Additional metadata) as well as the data processing report (see Data Processing Report). Passive acoustic data archived here represent data processing Level 1+, according to the standards defined in the associated Standard Operation Procedure (SOP) Glossary (Thomisch et al. 2023a). Further information on data processing with regard to data preparation and standardization can be found in the associated SOP Part 1: Data preparation and standardization (Thomisch et al. 2023b).
    Keywords: Arctic Ocean; ATWAICE; Audio file; Audio file (File Size); Autonomous Underwater Recorder for Acoustic Listening, Multi-Électronique, AURAL Model 3; DATE/TIME; DEPTH, water; F4-OZA-2; FRAM; Fram Strait; FRontiers in Arctic marine Monitoring; GPF 18-1_33; Maria S. Merian; MOOR; Mooring; MSM93; MSM93_24-2; MSM93_24-2,PS131_22-1; North Greenland Sea; Polarstern; PS131; PS131_22-1
    Type: Dataset
    Format: text/tab-separated-values, 300 data points
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  • 76
    Publication Date: 2024-06-04
    Description: Gypsum samples collected in two outcrops located in the Polemi basin, Cyprus, have been analyzed for their strontium isotopic composition. Respective methods are described in the original publication. Individual Strontium sources comprise variable Sr isotopic compositions, allowing some measure of the relative proportions of Sr derived from Atlantic seawater and continental sources including rivers and the Parathethys.
    Keywords: Cyprus; Date of determination; Event label; Messinian Salinity Crisis; Multi-collector ICP-MS (MC-ICP-MS), Neptune Plus, Thermo; OUTCROP; Outcrop 1; Outcrop 2; Outcrop ID; Outcrop sample; Paleoceanography; paleohumidity; Polemi_basin_gypsum_outcrop_1; Polemi_basin_gypsum_outcrop_2; Sample ID; Sample type; Seawater δ18O; Sr isotopes; Strontium-87/Strontium-86 ratio; Strontium-87/Strontium-86 ratio, standard error; Time point, descriptive; triple oxygen isotopes
    Type: Dataset
    Format: text/tab-separated-values, 90 data points
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  • 77
    Publication Date: 2024-06-04
    Description: Gypsum samples collected in two outcrops located in the Polemi basin, Cyprus, have been analyzed for their triple oxygen and hydrogen isotopic composition. Respective methods are described in the original publication. The oxygen and hydrogen data provided here is derived from the crystal bound H2O within the Gypsum (CaSO4*2H2O), which allows calculating paleo-water isotopic compositions of the mother brine from which this gypsum precipitated during the Messinian Salinity Crisis. Because the isotopic composition of the mother brine is affected by the local hydroclimate at that time, such analyses allow reconstructing paleo-hydroclimate. One aim of the study was to reconstruct paleo-relative humidity of the Messinian Salinity Crisis. For this purpose, the triple oxygen and hydrogen isotope data was fitted to an appropriate isotope model that is based on the Craig and Gordon formula, which provides absolute paleo-relative humidity estimates. Model input and output data are summarized in Table S2.
    Keywords: Cyprus; Date of determination; Deuterium excess; Deuterium excess, standard deviation; Event label; Isotope ratio mass spectrometer (IRMS), Thermo Fisher, MAT253; Messinian Salinity Crisis; OUTCROP; Outcrop 1; Outcrop 2; Outcrop ID; Outcrop sample; Oxygen-17 excess; Oxygen-17 excess, standard deviation; Paleoceanography; paleohumidity; Polemi_basin_gypsum_outcrop_1; Polemi_basin_gypsum_outcrop_2; Sample code/label; Sample ID; Seawater δ18O; Sr isotopes; Time point, descriptive; triple oxygen isotopes; δ17O, water; δ17O, water, standard deviation; δ18O, water; δ18O, water, standard deviation; δ Deuterium; δ Deuterium, standard deviation
    Type: Dataset
    Format: text/tab-separated-values, 672 data points
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  • 78
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-06-04
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 33069 data points
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  • 79
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-06-04
    Keywords: ALTITUDE; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Monitoring station; MONS; Pressure, at given altitude; Radiosonde, Meisei, iMS; SYO; Syowa; Temperature, air; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 26585 data points
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  • 80
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-06-04
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 248711 data points
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  • 81
    facet.materialart.
    Unknown
    PANGAEA
    In:  Japan Meteorological Agency, Tokyo
    Publication Date: 2024-06-04
    Keywords: Anemometer; BARO; Barometer; Baseline Surface Radiation Network; BSRN; Cosmonauts Sea; DATE/TIME; Dew/frost point; Horizontal visibility; HYGRO; Hygrometer; Monitoring station; MONS; Pressure, atmospheric; SYO; Syowa; Temperature, air; Thermometer; Visibility sensor; Wind direction; Wind speed
    Type: Dataset
    Format: text/tab-separated-values, 267726 data points
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  • 82
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200108_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_19-46; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 18 data points
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  • 83
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191112_02; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_7-25; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
    Location Call Number Expected Availability
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  • 84
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191130_01; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_9-98; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 12 data points
    Location Call Number Expected Availability
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  • 85
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_46_97_2020071101; PS122/4; PS122/4_46-97; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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  • 86
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191230_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_18-7; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 38 data points
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  • 87
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191112_01; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_7-24; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 28 data points
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  • 88
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20191119_01; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_8-23; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 24 data points
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  • 89
    Publication Date: 2024-06-04
    Description: This data set is a higher-processing-level version of Geolocated sea-ice or snow surface elevation point clouds from helicopter-borne laser scanner during the MOSAiC expedition, version 1 (Jutila et al., 2022; doi:10.1594/PANGAEA.950509), where the surface elevation point cloud has been converted to freeboard using automatic open water detection scheme and projected onto a regular 0.5-meter grid. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The gridded data are stored in 30-second along-track segments in netCDF format. For the small scale grid flights, the data are drift corrected using the position and heading data of RV Polarstern and elevation offset corrected using overlapping segments to overcome degraded GPS altitude data 〉85°N. Open water points are identified to derive a freeboard estimate from the surface elevations. For the flights with degraded GPS altitude quality, we provide only a freeboard estimate (grid pattern flights) or no freeboard (transects). The gridded 30-s segments include as data variables: surface elevation, freeboard (estimate), freeboard uncertainty, estimated sea surface height, surface reflectance, echo width, and number of points used in the interpolation. In addition, list of detected open water points and an overview figure of each flight is provided.
    Keywords: 20191112_02; Airborne laser scanning; Arctic; Binary Object; DATE/TIME; Flight number; Freeboard; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_7-25; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 50 data points
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  • 90
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200123_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_21-78; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 22 data points
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  • 91
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200128_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_22-16; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 38 data points
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  • 92
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200212_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/2; PS122/2_24-31; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 42 data points
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  • 93
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200321_02; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_32-71; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 34 data points
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  • 94
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_45_37_2020063002; PS122/4; PS122/4_45-37; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 95
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_48_69_2020072201; PS122/4; PS122/4_48-69; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 54 data points
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  • 96
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: 20200321_01; Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_32-70; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 48 data points
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  • 97
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122_4_45_36_2020063001; PS122/4; PS122/4_45-36; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 98
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/3; PS122/3_37-66; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 32 data points
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  • 99
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/5; PS122/5_59-139; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 18 data points
    Location Call Number Expected Availability
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  • 100
    Publication Date: 2024-06-04
    Description: This data set provides high-resolution geolocated point clouds of sea-ice or snow surface elevation for mapping temporal and spatial evolution of sea-ice conditions such as freeboard, roughness, or the size and spatial distributions of surface features. The surface elevation data are referenced to the DTU21 mean sea surface height and are not corrected for sea-ice drift during acquisition. The data were collected using a near-infrared, line-scanning Riegl VQ-580 airborne laser scanner (hdl:10013/sensor.7ebb63c3-dc3b-4f0f-9ca5-f1c6e5462a31 & hdl:10013/sensor.7a931b33-72ca-46d0-b623-156836ac9550) mounted in a helicopter along the MOSAiC drift from the north of the Laptev Sea, across the central Arctic Ocean, and towards the Fram Strait from September 2019 to October 2020. The flights are both small scale, ~5x5 km grid patterns mainly over the central observatory, and large scale, few tens of km away from RV Polarstern, triangle patterns, or transects. The point cloud data are stored in 5-min along-track segments in a custom binary format, for which we provide a python-based parsing tool in awi-als-toolbox (https://github.com/awi-als-toolbox/awi-als-toolbox), together with corresponding metadata json and line-shot quicklook png files. The point cloud data includes as variables: surface elevation (referenced to DTU mean sea surface height), surface reflectance, and echo width. The degraded GPS altitude data 〉85°N may cause undulations in the along-track surface elevations, which are not corrected for in this data product.
    Keywords: Airborne laser scanning; Arctic; Arctic Ocean; Binary Object; DATE/TIME; Flight number; HELI; Helicopter; IceSense; LATITUDE; LONGITUDE; MOSAiC; MOSAiC20192020; MOSAIC-HELI; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/5; PS122/5_61-190; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice; Surface Elevation
    Type: Dataset
    Format: text/tab-separated-values, 54 data points
    Location Call Number Expected Availability
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