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  • 1
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    Fram Strait sea ice export variability over the last 80 years (2016)
    PANGAEA
    In:  Supplement to: Smedsrud, Lars Henrik; Halvorsen, Mari H; Stroeve, Julienne C; Zhang, Rong; Kloster, Kjell (2017): Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years. The Cryosphere, 11(1), 65-79, https://doi.org/10.5194/tc-11-65-2017
    Details
    Publication Date: 2023-01-13
    Description: A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880?000 km**2, representing 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with several years having annual ice exports that exceeded 1 million km**2. This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual ice export of about +6 % per decade, with spring and summer showing larger changes in ice export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased ice export during winter will generally result in new ice growth and contributes to thinning inside the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice?albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice concentration. We find a general moderate influence between export anomalies and the following September sea ice extent, explaining 18?% of the variance between 1935 and 2014, but with higher values since 2004.
    Keywords: DATE/TIME; Fram Strait; Fram-Strait_Sea-ice; Sea ice area, flux, at given time interval
    Type: Dataset
    Format: text/tab-separated-values, 960 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 2
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    Shipborne visual observations of Arctic sea ice during Arctic Sunrise cruise ArcSun1209 (2018)
    PANGAEA
    Details
    Publication Date: 2023-02-12
    Keywords: ArcSun1209; ArcSun1209-track; Arctic Sunrise; cruise_10; CT; DATE/TIME; Ice concentration; Investigator; LATITUDE; LONGITUDE; Number; Underway cruise track measurements
    Type: Dataset
    Format: text/tab-separated-values, 159 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 3
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    Snow height on sea ice, meteorological conditions and drift of sea ice from autonomous measurements from buoy 2018S82, deployed during RV Araon 2018 (2021)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Details
    Publication Date: 2024-01-22
    Description: Snow height was measured by the Snow Buoy 2018S82, an autonomous platform, installed on drifting sea ice in the Arctic Ocean during RV Araon 2018. The resulting time series describes the evolution of snow height as a function of place and time between 19 August 2018 and 24 August 2019 in sample intervals of 1 hour. The Snow Buoy consists of four independent acoustic range finder measurements representing the area (approx. 10 m**2) around the buoy. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy.
    Keywords: 2018S82; autonomous platform; AWI_SeaIce; buoy; BUOY_SNOW; Current sea ice maps for Arctic and Antarctic; DATE/TIME; drift; Global positioning system, time since last fix; LATITUDE; LONGITUDE; meereisportal.de; MIDO; Multidisciplinary Ice-based Distributed Observatory; Pressure, atmospheric; Quality flag, air temperature; Quality flag, atmospheric pressure; Quality flag, position; Quality flag, snow height; Quality flag, technical temperature; Sea Ice Physics @ AWI; Snow buoy; snow depth; Snow height; Temperature, air; Temperature, technical
    Type: Dataset
    Format: text/tab-separated-values, 141952 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 4
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    Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2018S83, deployed during the RV ARAON cruise 2018 in Chukchi Sea (2019)
    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven
    Details
    Publication Date: 2024-01-31
    Description: Snow height was measured by the Snow Depth Buoy 2018S83, an autonomous platform, drifting on Arctic sea ice, deployed during the RV ARAON cruise 2018 in Chukchi Sea. The resulting time series describes the evolution of snow depth as a function of place and time between 21 August 2018 and 14 September 2018 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and an internal ice temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses.
    Keywords: 2018S83; Arctic Ocean; autonomous platform; buoy; BUOY_SNOW; DATE/TIME; drift; LATITUDE; LONGITUDE; MIDO; Multidisciplinary Ice-based Distributed Observatory; Pressure, atmospheric; Snow buoy; snow depth; Snow height; Temperature, air; Temperature, technical
    Type: Dataset
    Format: text/tab-separated-values, 4645 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 5
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    Brightness temperature measured by UoM SBR radiometer at 19, 37 and 89 GHz from September 2019 to September 2020 during the MOSAiC expedition (2023)
    PANGAEA
    Details
    Publication Date: 2024-06-12
    Description: This dataset contains ground-based radiometer observations from the University of Manitoba Surface Based Radiometer (UoM SBR) at 19, 37 and 89 GHz taken during Leg 1 to Leg 5 of the MOSAiC campaign (October 2019 - September 2020). Included are I) calibrated brightness temperatures, II) quality controlled calibrated brightness temperatures, resampled to 1 minute temporal resolution. Details about the data format, usage and the instrument can be found in the file Data_manual.pdf.
    Keywords: Arctic; Arctic Ocean; Binary Object; Binary Object (File Size); brightness temperature; Comment; Cruise/expedition; DATE/TIME; File content; Frequency; IceSense; LATITUDE; LONGITUDE; Microwave Radiometer; Mosaic; MOSAiC; MOSAiC20192020; MRA; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Polarstern; PS122/1; PS122/1_4-23; PS122/1_5-63; PS122/1_5-66; PS122/1_5-76; PS122/1_5-77; PS122/1_6-127; PS122/1_6-128; PS122/1_6-2; PS122/1_7-1; PS122/1_7-7; PS122/2; PS122/2_14-163; PS122/2_14-316; PS122/2_24-105; PS122/2_24-29; PS122/3; PS122/3_28-4; PS122/5; PS122/5_58-86; radiometer; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice
    Type: Dataset
    Format: text/tab-separated-values, 87 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 6
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    Brightness temperature raw data measured by UoM SBR radiometer at 19, 37 and 89 GHz from September 2019 to September 2020 during the MOSAiC expedition (2023)
    PANGAEA
    Details
    Publication Date: 2024-06-12
    Description: This dataset contains ground-based radiometer observations from the University of Manitoba Surface Based Radiometer (UoM SBR) at 19, 37 and 89 GHz taken during Leg 1 to Leg 5 of the MOSAiC campaign (October 2019 - September 2020). Included are the raw observations and quick-look, uncalibrated quick-look brightness temperatures. In addition, images, automatically taken from a camera mounted on the instrument are provided. Details about the data format, usage and the instrument can be found in the file Data_manual.pdf.
    Keywords: Arctic; Binary Object; Binary Object (File Size); brightness temperature; DATE/TIME; Event label; IceSense; LATITUDE; LONGITUDE; Microwave Radiometer; MOSAiC; MOSAiC20192020; MOSAiC expedition; MRA; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Passive microwave radiometer; PMRA; Polarstern; PS122/1; PS122/1_10-130; PS122/1_1-303; PS122/1_4-23; PS122/1_5-66; PS122/1_5-77; PS122/1_6-1; PS122/1_6-128; PS122/1_7-1; PS122/1_7-7; radiometer; Remote Sensing of the Seasonal Evolution of Climate-relevant Sea Ice Properties; Sea ice
    Type: Dataset
    Format: text/tab-separated-values, 148 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 7
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    Hydroacoustic backscatter recorded by buoy 2020AZFP1 in the central Arctic Ocean during Sep 2020 - May 2021 (2023)
    PANGAEA
    Details
    Publication Date: 2024-06-13
    Description: This dataset contains hydroacoustic measurements collected by an autonomous ice-tethered bio-physical observatory during its drift across the Central Arctic Ocean from the end of MOSAiC in September 2020 to the next spring in May 2021. The measurements were performed by an Acoustic Zooplankton and Fish Profiler (AZFP, ASL) with factory calibrations. Data are provided as volume backscatter (Sv, in dB re 1 m⁻¹). We provide data for the frequencies 67, 125, 200 and 455 kHz. These files contain the quality-controlled raw data. The data were cleaned, processed and analysed in the paper Sea-ice decline makes zooplankton stay deeper for longer by Flores, Veyssiere et al. (submitted to Nature Cliimate Change), which contains a detailed description of the instrument and the measurement settings. The format can be readily ingested in the hydroacoustic data processing application EchoView.
    Keywords: 2020AZFP1; Acoustical Zoological Fish Profiler; Arctic_PASSION; Arctic Ocean; Arctic PASSION; AZFP; Binary Object; central Arctic Ocean; DATE/TIME; ECOLIGHT; Ecosystem functions controlled by sea ice and light in a changing Arctic; File content; FRAM; FRontiers in Arctic marine Monitoring; Hydroacoustic backscatter; LATITUDE; LONGITUDE; MIDO; Mosaic; MOSAiC; MOSAIC_PO; MOSAiC20192020; Multidisciplinary drifting Observatory for the Study of Arctic Climate; Multidisciplinary Ice-based Distributed Observatory; Pan-Arctic observing System of Systems: Implementing Observations for societal Needs; Polarstern; PS122/5; PS122/5_58-163; vertical migration; Zooplankton
    Type: Dataset
    Format: text/tab-separated-values, 8 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 8
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    Arctic Sea Ice in CMIP6 (2020)
    Wiley
    In:  EPIC3Geophysical Research Letters, Wiley, ISSN: 0094-8276
    Details
    Publication Date: 2020-05-04
    Description: We examine CMIP6 simulations of Arctic sea‐ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea‐ice area, capturing the observational estimate within the multi‐model ensemble spread. The CMIP6 multi‐model ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea‐ice area to a given amount of anthropogenic CO2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea‐ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea‐ice free (sea‐ice area 〈 1 million km2) in September for the first time before the year 2050 in each of the four emission scenarios SSP1‐1.9, SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 examined here.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
    Format: application/pdf
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  • 9
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    Altimetry for the future: Building on 25 years of progress (2021)
    In:  EPIC3Advances in Space Research, ISSN: 02731177
    Details
    Publication Date: 2021-04-14
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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  • 10
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    Surface-based Ku- and Ka-band polarimetric radar for sea ice studies (2020)
    In:  EPIC3The Cryosphere, 14(12), pp. 4405-4426, ISSN: 1994-0424
    Details
    Publication Date: 2020-12-05
    Description: To improve our understanding of how snow properties influence sea ice thickness retrievals from presently operational and upcoming satellite radar altimeter missions, as well as to investigate the potential for combining dual frequencies to simultaneously map snow depth and sea ice thickness, a new, surface-based, fully polarimetric Ku- and Ka-band radar (KuKa radar) was built and deployed during the 2019–2020 year-long MOSAiC international Arctic drift expedition. This instrument, built to operate both as an altimeter (stare mode) and as a scatterometer (scan mode), provided the first in situ Ku- and Ka-band dual-frequency radar observations from autumn freeze-up through midwinter and covering newly formed ice in leads and first-year and second-year ice floes. Data gathered in the altimeter mode will be used to investigate the potential for estimating snow depth as the difference between dominant radar scattering horizons in the Ka- and Ku-band data. In the scatterometer mode, the Ku- and Ka-band radars operated under a wide range of azimuth and incidence angles, continuously assessing changes in the polarimetric radar backscatter and derived polarimetric parameters, as snow properties varied under varying atmospheric conditions. These observations allow for characterizing radar backscatter responses to changes in atmospheric and surface geophysical conditions. In this paper, we describe the KuKa radar, illustrate examples of its data and demonstrate their potential for these investigations.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
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