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25 results for “Greenland fjords”
CTD Profiles from the R/V Sanna cruise to Northwest Greenland fjords, August 11-31, 2016
<p>Greenland fjords are the gateway connecting the Greenland Ice Sheet to the coastal ocean. The rapidly increasing discharge of meltwater and ice from the Greenland Ice Sheet has complex hydrodynamic and biogeochemically impacts on the coastal marine ecosystem around Greenland, although data is still limited for most fjord systems. Therefore, six different fjord systems in Northwest Greenland were investigated in August 2016 during an interdisciplinary research cruise aboard the R/V Sanna that On aimed to describe the physical, chemical and biological variability from glaciers to the shelf. This data set consists of 55 CTD profiles that were obtained with a Seabird 19plusV2 conductivity, temperature, depth CTD) instrument. In addition to measuring pressure, conductivity, and temperature, the CTD recorded chlorophyll-a fluorescence, photosynthetically available radiation (PAR), and dissolved oxygen. The instrument was factory calibrated before the cruise. The CTD recorded all variables at 4 Hz and the raw data were processed using Seabird standard workflow to produce 0.5 m binned profiles using the downcast data only. This dataset compiles all CTD variables measured from all profiles into a single netCDF file. </p> <p>We would like to thank the captain and crew of R/V Sanna for excellent collaboration. The cruise was funded by the Danish Center for Marine Research and by the EU Horizon2020 funded project INTAROS.</p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 17:09 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 17:09 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_417-419 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 12:41 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 12:41 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_493-497 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
Carbonate Chemistry and δ18O-H2O from East and West Greenland fjords, August 2018 and 2016
<p>Greenland’s fjords and coastal waters are highly productive and sustain important fisheries, but retreating glaciers and increasing meltwater supply are changing fjord circulation and biogeochemistry, which may threaten the future productivity of these unique ecosystems. The freshening of Greenland fjords caused by unprecedented melting of the Greenland Ice Sheet has the potential to alter carbonate chemistry in coastal waters, which would influence CO<sub>2</sub> uptake as well as have biological consequences from acidification. However, few studies to date explore the current acidification state in Greenland coastal waters. Here we present the first-ever large-scale measurements of carbonate system parameters and δ<sup>18</sup>O measurements in 16 Greenlandic fjords and by combining datasets from two August cruises. HDMS Lauge Koch and RV Sanna cruises sampled on the East and West coast of Greenland in August 2018 and 2016 respectively. This dataset consists of 52 carbonate chemistry sample sites where dissolved inorganic carbon (DIC), total alkalinity (TA), and δ<sup>18</sup>O-H<sub>2</sub>O were measured throughout the water column. Water samples were collected in Niskin bottles and were transferred directly into triplicate 12 ml exetainers with a gas tight Tygon tubes, allowing overflow of at least 3 times the volume of the exetainer. Triplicate exetainers were collected for both DIC and TA at each depth. Samples were preserved with HgCl<sub>2</sub> (saturated solution) to a final concentration of 0.02%. TA was measured on an Apollo SciTech AS-ALK2 total alkalinity titrator based on the Gran titration procedure for samples in West Greenland. While samples for East Greenland were measured on automatic titrator (Metrohm 888 Titrando), and a combined Metrohm glass electrode (Unitrode). DIC samples were analyzed on Apollo SciTech's AS-C3 analyzer for both cruises, using a sample volume of 0.5 ml. Routine analysis of Certified Reference Materials (provided by A. G. Dickson, Scripps Institution of Oceanography) verified that the accuracy of DIC and TA measurements. Coalescing this dataset therefore provides the first-ever analysis of wide-scale Greenland Fjord carbonate chemistry.</p> <p> </p> <p>Environmental variables recorded by CTD instruments are available for cruises from the West and East coasts respectively at the following DOIs: <a href="https://doi.org/10.5281/zenodo.4062024">https://doi.org/10.5281/zenodo.4062024</a> and <a href="https://doi.org/10.5281/zenodo.5572329">https://doi.org/10.5281/zenodo.5572329</a>.</p> <p> </p> <p>We would like to thank the crew on board the HDMS Lauge Koch and RV Sanna for their collaboration. The Cruises were funded by Danish Centre for Marine Science (Grants: 2016-05 and 2017-06) and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic. This dataset is a contribution the project FACE-IT (The Future of Arctic Coastal Ecosystems – Identifying Transitions in Fjord Systems and Adjacent Coastal Areas). FACE-IT has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 869154.</p>
CTD Profiles from the HDMS Lauge Koch cruise to East Greenland fjords, August 2018
<p>Greenland fjords are currently undergoing large ecosystem changes due to unprecedented melting of the Greenland Ice Sheet (GrIS). The rapidly increasing discharge of meltwater and ice not only influences circulation patterns and stratification of the water column, but it also introduces large fluxes of allochthonous carbon and nutrients into the Greenland coastal environment, as well as transports large quantities of inorganic particles and suspended sediments which could limit light availability to primary producers. However, data is still limited for most Greenland fjord systems and the east coast of Greenland is especially understudied. During this cruise we investigated 3 different fjord systems of East Greenland in August 2018 aboard the HDMS Lauge Koch. We aimed to describe the physical, chemical and biological variability from glaciers to the shelf. This data set consists of 84 CTD profiles that were obtained with a Seabird SBE25 conductivity, temperature, depth (CTD) instrument. In addition to measuring pressure, conductivity, and temperature, the CTD recorded chlorophyll-a fluorescence, photosynthetically available radiation (PAR), dissolved oxygen, turbidity and pH. The instrument was factory calibrated before the cruise. The CTD recorded variables at 16 Hz and the raw data were processed using Seabird standard workflow to produce 0.1 m binned profiles using the downcast data only. This dataset compiles all CTD variables measured from all profiles into a single comma separated CSV file. </p> <p>We would like to thank the captain and crew of HDMS Lauge Koch for excellent collaboration. The cruise was funded by the Danish Center for Marine Research and by the EU Horizon2020 funded project INTAROS (grant no. 727890) and the Danish Cooperation for Environment in the Arctic.</p> <p> </p> <p> </p> <p> </p>
Linked collectors and determiners for: Phytoplankton community composition in the water column of East Greenland fjords, August 2022.
Natural history specimen data linked to collectors and determiners held within, "Phytoplankton community composition in the water column of East Greenland fjords, August 2022". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="http://bionomia.net/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef">https://bionomia.net/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef">https://gbif.org/dataset/a00e14eb-3398-4c40-87f6-41e78081e7ef</a>. Formatted as a Frictionless Data package.
Fig. 4. A, B. Trackway S3 in Sauropodomorph dinosaur trackways from the Fleming Fjord Formation of East Greenland: Evidence for Late Triassic sauropods
Fig. 4. A, B. Trackway S3 (Evazoum sp.). This bipedal trackway can possibly be attributed to a non-sauropod sauropodomorph trackmaker. Two smaller tridactyl trackways can be identified in close association with trackway S3. C, D. Detail of the best preserved pes impression RP1 (total depth represented by the color scale is 58 mm). Photogrammetric orthophoto (A), depth-color images (B, C), interpretative drawing (D).
Fig. 3. Trackway S2 in Sauropodomorph dinosaur trackways from the Fleming Fjord Formation of East Greenland: Evidence for Late Triassic sauropods
Fig. 3. Trackway S2 (Eosauropus sp.), which probably represents the same trackmaker species, or even the same individual, as trackway S1; trackmaker is moving towards the southwest. A. Photogrammetric orthophoto. B. Depth-color image. C. Interpretative drawing. Abbreviations: LM, left manus; LP, left pes; RM, right manus; RP, right pes.
Fig. 2. A–C. Trackway S1 in Sauropodomorph dinosaur trackways from the Fleming Fjord Formation of East Greenland: Evidence for Late Triassic sauropods
Fig. 2. A–C. Trackway S1 (Eosauropus sp.), here attributed to a sauropod trackmaker based on pedal synapomorphies; trackmaker is moving towards the south-west. Two consequtive pes impressions of a tridactyl Grallator trackway can be seen left to the S1 trackway. D, E. Detail of representative pes-manus set RP1/RM1. F, G. Detail of representative pes-manus set RP2/RM2. Photogrammetric orthophoto (A), depth-color images (B, D, F), interpretative drawings (C, E, G). Abbreviations: LM, left manus; LP, left pes; RM, right manus; RP, right pes.
Fig. 1. A in Sauropodomorph dinosaur trackways from the Fleming Fjord Formation of East Greenland: Evidence for Late Triassic sauropods
Fig. 1. A. Location of the "Track Mountain" locality (star) on a ridge on the northeastern slope of Wood Bjerg in the Late Triassic sediments at the west side of Carsberg Fjord. B. Location of Jameson Land (A) in central East Greenland. C. Photograph of the "Track Mountain" locality showing the approximate location of trackways S1, S2, and S3 (view towards the east).
Linked collectors and determiners for: Downward flux of phytoplankton cells in East Greenland fjords, August 2022.
Natural history specimen data linked to collectors and determiners held within, "Downward flux of phytoplankton cells in East Greenland fjords, August 2022". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/61137d30-c560-4309-b278-b2e5aebce92f">https://bionomia.net/dataset/61137d30-c560-4309-b278-b2e5aebce92f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/61137d30-c560-4309-b278-b2e5aebce92f">https://gbif.org/dataset/61137d30-c560-4309-b278-b2e5aebce92f</a>. Formatted as a Frictionless Data package.
A 10 m land mask for the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems in southwest Greenland
<p>This dataset consists of a 10 m land mask of the Nuup Kangerlua, Kobbefjord, and Ameralik fjord systems in southwest Greenland. The land mask is based on two Sentinel-2 MSI images of the area (tiles WDS and WES) that were acquired at high tide on 30 July 2017. Land was masked by applying a threshold of 0.1 Wm<sup>-2</sup> to the short-wave infrared (SWIR) band at 1614 nm. Mountain lakes and shaded coastal regions were masked using the TanDEM-X digital elevation model. Some inland waters were masked manually. </p> <p>Land and water were assigned values of 0 and 1, respectively, and are provided in geotiff format projected to UTM Zone 22 (WGS 84).</p> <p>The boundaries of the mask area extend from 63.90°N, 51.999°W in the southwest to 64.01°N, 51.302°W in the northeast.</p>
Glacial meltwater determines the balance between autotrophic and heterotrophic processes in a Greenland fjord
<p>Raw data for the summer CTD transect, the annual CTD mooring, data from the seasonal sediment trap, data from the oxygen and 14C incubations and nutrients data presented in the paper</p>
Hydroacoustic data from Inglefield Bredning Fjord, Northwest Greenland (Oct 2022 - Jul 2023): Part B
Open the record for dataset details and reuse information.
Hydroacoustic data from Inglefield Bredning Fjord, Northwest Greenland (Aug 2022 - Jul 2023): Part A
Open the record for dataset details and reuse information.
Underwater-sound records in glacier fjords (Inglefield Bredning, Baffin Bay, NW Greenland, Denmark), 19-28 July 2019
<p>Acoustic data (.wav) recorded by 2 hydrophones suspended from boats in Inglefield Bredning and Bowdoin fjords (Baffin Bay, NW Greenland, Denmark) in July 2019 for underwater soundscape documentation (narwhal vocalizations and environmental sources). </p> <p>*******************</p> <p>First set-up had a hydrophone AQH-020 by AquaSound Inc. (20Hz – 20kHz) connected to Amplifier Aquafeeler III (SQE-1001B, 50dB gain) by AquaSound Inc. and a recorder PCM-M10 by Sony (44.1 kHz, 16 bit, auto-mode). Recording depth was about 6.6 m, except a record collected on July 27, 2019 at 15:56:33 (depth was about 0.5 m).</p> <p>Channels: 2 (but records are only at “Left”/1 channel; the “Right”/2 is electric noise).</p> <p>File name: sony.YYMMDDhhmmss.wav</p> <p>Note that the strongest regularly-spaced impulsive sounds in two files (sony.190719130533.WAV, sony.190719132214.WAV) are seemingly not due to a whale nearby, but due to repetitive impacts of the hydrophone with a ballast-rope in strong current. This issue was fixed by adjusting the rope length, after which the sound was gone.</p> <p>*******************</p> <p>Second set-up had a hydrophone SoundTrap SD3000 by Ocean Instruments NZ (20Hz – 60kHz), integrated with amplifier and recorder, sampling at 96 kHz, 16 bit. Signal-to-pressure conversion constant was 176.2 dB for this particular device (ID number 5146, at High-Gain mode). Recording depth was about 10.8 m, except records collected on July 20, 2019 between 00:44:47 and 08:44:47 (depth was about 1 m). </p> <p>Channels: 1</p> <p>File name format: 5146.YYMMDDhhmmss.wav</p> <p>*******************</p> <p>Coordinates for each record by each set-up are shown below.</p> <p> </p> <p>Geographic position of each measurement with <strong>SoundTrap</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 12:55:17, 77.474752, -68.660610 </p> <p>19 July 2019, 13:19:53, 77.488215, -68.597227 </p> <p>19 July 2019, 14:19:53, 77.485103, -68.574985 </p> <p>19 July 2019, 16:19:41, 77.523033, -68.403958 </p> <p>19 July 2019, 19:06:57, 77.618543, -68.564536 </p> <p> </p> <p>20 July 2019, 00:44:47, 77.548649, -68.550752 </p> <p>20 July 2019, 13:02:59, 77.527026, -68.534285</p> <p>20 July 2019, 14:30:26, 77.487211, -68.483834</p> <p>20 July 2019, 15:10:13, 77.495954, -68.658084 </p> <p> </p> <p>*******************</p> <p>Geographic position of each measurement with <strong>Sony-AquaSound</strong> is as the following:</p> <p>Date, Record Start Time(UTC), lon, lat,</p> <p> </p> <p>19 July 2019, 13:05:33, 77.474752, -68.660610 </p> <p>19 July 2019, 13:22:14, 77.488215, -68.597227 </p> <p>19 July 2019, 16:11:54, 77.523033, -68.403958 </p> <p>19 July 2019, 19:09:11, 77.618543, -68.564536</p> <p> </p> <p>20 July 2019, 13:04:42, 77.527026, -68.534285</p> <p>20 July 2019, 14:01:42, 77.505033, -68.553648</p> <p>20 July 2019, 15:11:10, 77.495954, -68.658084 </p> <p> </p> <p>21 July 2019, 22:17:04, 77.675334, -68.636040 </p> <p>21 July 2019, 22:19:53, 77.671904, -68.639090</p> <p> </p> <p>22 July 2019, 00:12:34, 77.525553, -68.442136 </p> <p> </p> <p>27 July 2019, 13:28:58, 77.617588, -68.597946</p> <p>27 July 2019, 14:13:59, 77.667788, -68.643976 </p> <p>27 July 2019, 15:56:33, 77.665487, -68.778195 </p> <p>27 July 2019, 17:03:04, 77.672426, -68.658150 </p> <p>27 July 2019, 17:22:16, 77.669874, -68.657587 </p> <p>27 July 2019, 17:59:39, 77.676941, -68.664817 </p> <p>27 July 2019, 19:44:32, 77.668669, -68.656365 </p> <p>27 July 2019, 21:57:07, 77.628218, -68.637347</p> <p>27 July 2019, 23:07:12, 77.625571, -68.616875</p> <p> </p> <p>28 July 2019, 00:02:41, 77.619299, -68.595757</p>
Data for: Low mercury concentration in a Greenland glacial fjord attributed to oceanic sources
<p>Total mercury (THg) and methyl mercury (MeHg) concentrations measured in southeast Greenland between 10 August and 20 August 2021. The majority of samples were collected in Sermilik Fjord and on the continental shelf to the west of the fjord mouth (identified by CTD cast number; see below for corresponding data citation). A small number of samples were collected by hand from surface waters in Apuseeq Fjord, in a lake and river near the village of Tiilerilaaq (formerly known as Tiniteqilaq), and in a river near Tasiilaq. All samples are identified by the date, time, and lat/lon of collection.</p> <p>For further details about the sample collection and analysis, please see the methods section of the associated publication.</p> <p>Sermilik Fjord and continental shelf CTD data are available via the NSF Arctic Data Center (doi:10.18739/A2HD7NT9K).</p>
Observational data and numerical experimental files for subglacial discharge plume in Bowdoin Fjord, northwestern Greenland
<p>The dataset includes observational data and all the files used in the numerical experiments. Additionally, this includes the codes to generate the figures and analysis. Please see Ohashi et al. (2019) for details (<a href="https://www.ocean-sci-discuss.net/os-2019-33/">https://www.ocean-sci-discuss.net/os-2019-33/</a>).</p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 20 August 2018 at 16:56 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 20 August 2018. The UAV survey commenced at 16:56 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_814-817 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
3D mesh model and raw images of a drifting iceberg in Dickson Fjord (NE Greenland) on 21 August 2018 at 21:31 UTC
<p>This dataset consists of low-altitude aerial imagery that was acquired by a DJI Phantom 3 Standard unoccupied aerial vehicle (UAV) in Dickson Fjord in northeast Greenland on 21 August 2018. The UAV survey commenced at 21:31 UTC. These images were processed in Agisoft PhotoScan Pro (v1.4; Linux Ubuntu). During the image alignment step in PhotoScan, the ‘High’ accuracy setting and key point and tie point limits of 60000 and 0 were used. Generic and reference preselection were disabled. Gradual selection was used to remove tie points that exceeded thresholds for the projection accuracy, reconstruction uncertainty, and reprojection error and the lens parameters were computed. Reference data from images DJI_767-773 were used to scale the sparse point cloud. The dense point cloud was then computed using the ‘High’ setting, followed by the textured mesh. The mesh model was exported in .obj and .pdf formats. </p> <p>A complete file list is provided in the README file that accompanies this dataset.</p> <p>This dataset is discussed in:</p> <p>Carlson et al. Quantifying iceberg deterioration using UAV imagery and Structure from Motion photogrammetry software. Submitted to <em>Remote Sensing.</em></p>
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