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  • 2015-2019  (114)
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  • 1
    Type of Medium: Image
    Pages: 156 Seiten , Illustrationen
    ISBN: 3000572430 , 9783000572432
    DDC: 550
    Language: German
    Location Call Number Limitation Availability
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  • 2
    Publication Date: 2023-01-13
    Description: A polar-adjusted version of the regional climate model COSMO-CLM was used at a horizontal resolution of about 15km (0.125°; C15) to simulate and analyse extreme 10m wind speeds from 1979-2016 in winter (Nov to Apr) in the Arctic and around Greenland. Based on daily maximum 10m wind speeds, extreme indices (95% percentiles, average strong gale and hurricane days per winter) and return levels up to the 20year return period are calculated and compared with state-of-the-art reanalysis data sets (ERA-Interim, ASR version 1 and 2) and with the satellite product CCMP version 2. The return levels were calculated by the 'peaks-over-threshold' (POT) method which fits a General Pareto distribution (GPD) to extreme values exceeding a specified high threshold (for this study the local 95% and 99% percentiles). The model comparison was based on the overlapping winter period 2000-2012 (Nov to Apr). Before the analysis all data sets were interpolated onto the rotated model grid of C15 (450x350 grid boxes, with the rotated pole located at 100°W and 0°N).
    Keywords: Arctic; File content; File format; File name; File size; pan-Arctic; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 10 data points
    Location Call Number Limitation Availability
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  • 3
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    PANGAEA
    In:  Supplement to: Zakharova, Elena A; Fleury, Sara; Guerreiro, Kévin; Willmes, Sascha; Rémy, Frédérique; Kouraev, Alexei V; Heinemann, Günther (2015): Sea ice leads detection using SARAL/AltiKa altimeter. Marine Geodesy, 38(sup1), 522-533, https://doi.org/10.1080/01490419.2015.1019655
    Publication Date: 2023-01-13
    Description: Sea ice leads play an essential role in ocean-ice-atmosphere exchange, in ocean circulation, geochemistry, and in ice dynamics. Their precise detection is crucial for altimetric estimations of sea ice thickness and volume. This study evaluates the performance of the SARAL/AltiKa (Satellite with ARgos and ALtiKa) altimeter to detect leads and to monitor their spatio-temporal dynamics. We show that a pulse peakiness parameter (PP) used to detect leads by Envisat RA-2 and ERS-1,-2 altimeters is not suitable because of saturation of AltiKa return echoes over the leads. The signal saturation results in loss of 6–10% of PP data over sea ice. We propose a different parameter—maximal power of waveform—and define the threshold to discriminate the leads. Our algorithm can be applied from December until May. It detects well the leads of small and medium size from 200 m to 3–4 km. So the combination of the high-resolution altimetric estimates with low-resolution thermal infra-red or radiometric lead fraction products could enhance the capability of remote sensing to monitor sea ice fracturing.
    Type: Dataset
    Format: application/zip, 1.3 MBytes
    Location Call Number Limitation Availability
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  • 4
    Publication Date: 2023-10-28
    Keywords: ANT-Land_2012; Atka Bay; Date/Time of event; Depth, bottom/max; DEPTH, ice/snow; Depth, top/min; Event label; Hardness description; Latitude of event; Location; Longitude of event; Magnifying glass and grid-card; NEUMAYER III; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SNOW; Snow/ice sample; Snow grain size, maximum; Snow grain size, minimum; Snow type; SP01_ATKA03-1; SP01_ATKA03-2; SP01_ATKA03-3; SP01_ATKA03-4; SP01_ATKA03-5; SP01_ATKA03-6; SP01_ATKA11-1; SP01_ATKA21-1; SP01_ATKA21-2; SP01_ATKA21-3; SP01_ATKA21-4; SP01_ATKA21-5; SP01_SNOW01-1; SP01_SNOW03-1; SP01_SNOW04-1; SP02_ATKA07-1; SP02_ATKA07-2; SP02_ATKA11-1; SP02_ATKA11-2; SP02_ATKA16-1; SP02_ATKA21-1; SP02_ATKA24-1; SP02_SNOW03-1; SP02_SNOW03-2; SP03_ATKA07-1; SP03_ATKA11-1; SP03_ATKA21-1; SP03_ATKA24-1; SP03_SNOW01-1; SP03_SNOW02-1; SP04_ATKA16-1; SP04_ATKA21-1; SP05_ATKA16-1; SP05_ATKA21-1; SP06_ATKA21-1; SPP1158
    Type: Dataset
    Format: text/tab-separated-values, 828 data points
    Location Call Number Limitation Availability
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  • 5
    Publication Date: 2023-10-28
    Keywords: ANT-Land_2012; Atka Bay; Date/Time of event; Density, snow; Depth, bottom/max; DEPTH, ice/snow; Depth, top/min; Event label; Latitude of event; Location; Longitude of event; NEUMAYER III; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SNOW; Snow/ice sample; SP01_ATKA03-1; SP01_ATKA03-2; SP01_ATKA03-3; SP01_ATKA03-4; SP01_ATKA03-5; SP01_ATKA03-6; SP01_ATKA11-1; SP01_ATKA21-1; SP01_ATKA21-2; SP01_ATKA21-3; SP01_ATKA24-1; SP01_SNOW01-1; SP01_SNOW03-1; SP01_SNOW04-1; SP02_ATKA07-1; SP02_ATKA07-2; SP02_ATKA11-1; SP02_ATKA11-2; SP02_ATKA16-1; SP02_ATKA24-1; SP02_SNOW03-1; SP02_SNOW03-2; SP03_ATKA07-1; SP03_ATKA11-1; SP03_SNOW01-1; SP03_SNOW02-1; SP04_ATKA16-1; SP04_ATKA21-1; SP05_ATKA16-1; SP05_ATKA21-1; SP06_ATKA21-1; SP07_ATKA16-1; SPP1158; Volumetry with snow tube
    Type: Dataset
    Format: text/tab-separated-values, 240 data points
    Location Call Number Limitation Availability
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  • 6
    Publication Date: 2023-10-28
    Keywords: ANT-Land_2012; Atka Bay; Date/Time of event; DEPTH, ice/snow; Digital thermometer, Testo, 110; Event label; Latitude of event; Location; Longitude of event; NEUMAYER III; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SNOW; Snow/ice sample; SP01_ATKA03-1; SP01_ATKA03-2; SP01_ATKA03-3; SP01_ATKA03-4; SP01_ATKA03-5; SP01_ATKA03-6; SP01_ATKA11-1; SP01_ATKA21-1; SP01_ATKA21-2; SP01_ATKA21-3; SP01_ATKA21-4; SP01_ATKA21-5; SP01_ATKA24-1; SP01_SNOW01-1; SP01_SNOW03-1; SP01_SNOW04-1; SP02_ATKA07-1; SP02_ATKA07-2; SP02_ATKA11-1; SP02_ATKA11-2; SP02_ATKA11-3; SP02_ATKA16-1; SP02_ATKA21-1; SP02_ATKA24-1; SP02_SNOW02-1; SP02_SNOW03-1; SP02_SNOW03-2; SP03_ATKA07-1; SP03_ATKA11-1; SP03_ATKA11-2; SP03_ATKA21-1; SP03_ATKA24-1; SP03_SNOW01-1; SP03_SNOW02-1; SP04_ATKA16-1; SP04_ATKA21-1; SP05_ATKA16-1; SP05_ATKA21-1; SP05_ATKA24-1; SP06_ATKA21-1; SP07_ATKA16-1; SPP1158; Temperature, ice/snow
    Type: Dataset
    Format: text/tab-separated-values, 760 data points
    Location Call Number Limitation Availability
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  • 7
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    PANGAEA
    In:  Supplement to: Paul, Stephan; Willmes, Sascha; Heinemann, Günther (2015): Long-term coastal-polynya dynamics in the Southern Weddell Sea from MODIS thermal-infrared imagery. The Cryosphere, 9(6), 2027-2041, https://doi.org/10.5194/tc-9-2027-2015
    Publication Date: 2023-10-28
    Description: Based upon high-resolution thermal-infrared Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite imagery in combination with ERA-Interim atmospheric reanalysis data, we derived long-term polynya parameters such as polynya area, thin-ice thickness distribution and ice-production rates from daily cloud-cover corrected thin-ice thickness composites. Our study is based on a thirteen year investigation period (2002-2014) for the austral winter (1 April to 30 September) in the Antarctic Southern Weddell Sea. The focus lies on coastal polynyas which are important hot spots for new-ice formation, bottom-water formation and heat/moisture release into the atmosphere. MODIS has the capability to resolve even very narrow coastal polynyas. Its major disadvantage is the sensor limitation due to cloud cover. We make use of a newly developed and adapted spatial feature reconstruction scheme to account for cloud-covered areas. We find the sea-ice areas in front of Ronne and Brunt Ice Shelf to be the most active with an annual average polynya area of 3018 ± 1298 and 3516 ± 1420 km2 as well as an accumulated volume ice production of 31 ± 13 and 31 ± 12 km**3, respectively. For the remaining four regions, estimates amount to 421 ± 294 km**2 and 4 ± 3 km**3 (Antarctic Peninsula), 1148 ± 432 km**2 and 12 ± 5 km**3 (Iceberg A23A), 901 ± 703 km**2 and 10 ± 8 km**3 (Filchner Ice Shelf) as well as 499 ± 277 km**2 and 5 ± 2 km**3 (Coats Land). Our findings are discussed in comparison to recent studies based on coupled sea-ice/ocean models and passive-microwave satellite imagery, each investigating different parts of the Southern Weddell Sea.
    Keywords: File format; File name; File size; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; South_Weddell_Sea; SPP1158; Uniform resource locator/link to file; Weddell Sea
    Type: Dataset
    Format: text/tab-separated-values, 52 data points
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  • 8
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    PANGAEA
    In:  Supplement to: Paul, Stephan; Willmes, Sascha; Hoppmann, Mario; Hunkeler, Priska A; Wesche, Christine; Nicolaus, Marcel; Heinemann, Günther; Timmermann, Ralph (2015): The impact of early-summer snow properties on Antarctic landfast sea-ice X-band backscatter. Annals of Glaciology, 56(69), 263-273, https://doi.org/10.3189/2015AoG69A715
    Publication Date: 2023-10-28
    Description: Up to now, snow cover on Antarctic sea ice and its impact on radar backscatter, particularly after the onset of freeze/thaw processes, are not well understood. Here we present a combined analysis of in situ observations of snow properties from the landfast sea ice in Atka Bay, Antarctica, and high-resolution TerraSAR-X backscatter data, for the transition from austral spring (November 2012) to summer (January 2013). The physical changes in the seasonal snow cover during that time are reflected in the evolution of TerraSAR-X backscatter. We are able to explain 76-93% of the spatio-temporal variability of the TerraSAR-X backscatter signal with up to four snowpack parameters with a root-mean-squared error of 0.87-1.62 dB, using a simple multiple linear model. Over the complete study, and especially after the onset of early-melt processes and freeze/thaw cycles, the majority of variability in the backscatter is influenced by changes in snow/ice interface temperature, snow depth and top-layer grain size. This suggests it may be possible to retrieve snow physical properties over Antarctic sea ice from X-band SAR backscatter.
    Keywords: Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158
    Type: Dataset
    Format: application/zip, 3 datasets
    Location Call Number Limitation Availability
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  • 9
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    In:  Supplement to: Reiser, Fabian; Willmes, Sascha; Hausmann, Ute; Heinemann, Günther (2019): Predominant Sea Ice Fracture Zones Around Antarctica and Their Relation to Bathymetric Features. Geophysical Research Letters, 46(21), 12117-12124, https://doi.org/10.1029/2019GL084624
    Publication Date: 2023-10-28
    Description: The dataset shows the relative lead frequencies around Antarctica for the months April-September, 2003-2018. The dataset is based on the MOD/MYD 29 Ice Surface Temperatures (NSIDC, Riggs and Hall (2015), see doi:10.5067/MODIS/MYD29.006 and doi:10.5067/MODIS/MOD29.006). By applying the algorithms introduced by Willmes and Heinemann (2015, doi:10.3189/2015AoG69A615; 2016, doi:10.3390/rs8010004) leads are identified in the swaths and subsquently combined to daily composites. Only clear-sky pixels are regarded. For the Weddell-Sea model results from a coupled ocean/sea-ice model (NEMO-LIM 3.6) are compared with the lead frequencies to further study the interaction of sea ice and the ocean. For further reading see Reiser et al. (2019): Predominant sea-ice fracture zones around Antarctica and their relation to bathymetric features.
    Keywords: Antarctic sea-ice leads; Bathymetry; Event label; File content; File format; File name; File size; MODIS; ocean currents; pan-Antarctica; Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; SPP1158; Tides; Uniform resource locator/link to file; Weddell_Sea
    Type: Dataset
    Format: text/tab-separated-values, 15 data points
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  • 10
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    PANGAEA
    In:  Supplement to: Preußer, Andreas; Heinemann, Günther; Willmes, Sascha; Paul, Stephan (2015): Multi-Decadal variability of polynya characteristics and ice production in the North Water Polynya by means of passive microwave and thermal infrared satellite imagery. Remote Sensing, 7(12), 15844-15867, https://doi.org/10.3390/rs71215807
    Publication Date: 2023-09-29
    Description: The North Water (NOW) Polynya is a regularly-forming area of open-water and thin-ice, located between northwestern Greenland and Ellesmere Island (Canada) at the northern tip of Baffin Bay. Due to its large spatial extent, it is of high importance for a variety of physical and biological processes, especially in wintertime. Here, we present a long-term remote sensing study for the winter seasons 1978/1979 to 2014/2015. Polynya characteristics are inferred from (1) sea ice concentrations and brightness temperatures from passive microwave satellite sensors (Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager/Sounder (SSM/I-SSMIS)) and (2) thin-ice thickness distributions, which are calculated using MODIS ice-surface temperatures and European Center for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis data in a 1D thermodynamic energy-balance model. Daily ice production rates are retrieved for each winter season from 2002/2003 to 2014/2015, assuming that all heat loss at the ice surface is balanced by ice growth. Two different cloud-cover correction schemes are applied on daily polynya area and ice production values to account for cloud gaps in the MODIS composites. Our results indicate that the NOW polynya experienced significant seasonal changes over the last three decades considering the overall frequency of polynya occurrences, as well as their spatial extent. In the 1980s, there were prolonged periods of a more or less closed ice cover in northern Baffin Bay in winter. This changed towards an average opening on more than 85% of the days between November and March during the last decade. Noticeably, the sea ice cover in the NOW polynya region shows signs of a later-appearing fall freeze-up, starting in the late 1990s. Different methods to obtain daily polynya area using passive microwave AMSR-E/AMSR2 data and SSM/I-SSMIS data were applied. A comparison with MODIS data (thin-ice thickness 〈 20 cm) shows that the wintertime polynya area estimates derived by MODIS are about 30 to 40% higher than those derived using the polynya signature simulation method (PSSM) with AMSR-E data. In turn, the difference in polynya area between PSSM and a sea ice concentration (SIC) threshold of 70% is fairly low (approximately 10%) when applied to AMSR-E data. For the coarse-resolution SSM/I-SSMIS data, this difference is much larger, particularly in November and December. Instead of a sea ice concentration threshold, the PSSM method should be used for SSM/I-SSMIS data. Depending on the type of cloud-cover correction, the calculated ice production based on MODIS data reaches an average value of 264.4 ± 65.1 km**3 to 275.7 ± 67.4 km**3 (2002/2003 to 2014/2015) and shows a high interannual variability. Our achieved long-term results underline the major importance of the NOW polynya considering its influence on Arctic ice production and associated atmosphere/ocean processes.
    Type: Dataset
    Format: application/zip, 62 datasets
    Location Call Number Limitation Availability
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