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  • Copernicus  (3)
  • Berlin : Technische Universität Berlin  (1)
  • 1
    Keywords: Forschungsbericht
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (47 Seiten, 1,77 MB) , Illustrationen, Diagramme
    Language: German
    Note: Förderkennzeichen BMWi 0324029C , Verbundnummer 01169010 , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden
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  • 2
    Publication Date: 2020-07-10
    Description: The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt-onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from European Remote Sensing Satellite (ERS-1/2), Quick Scatterometer (QSCAT) and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: A weak backscatter rise indicating the initial warming and destructive metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise indicating the onset of thaw-freeze cycles (snowmelt). Results show large interannual variability with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end-December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band (13.4 GHz signal frequency) derived pre-melt and snowmelt onset dates are earlier by 20 and 18 days, respectively, than ERS and ASCAT C-band (5.6 GHz) derived dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 14 and 6 days than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the seasonal evolution of snow temperature profiles may affect different microwave bands with different penetration depths. These suggest that future multi-frequency active/passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column of thick snow on perennial Antarctic sea ice.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 3
    Publication Date: 2021-03-25
    Description: Landfast sea ice (fast ice) attached to Antarctic (near-)coastal elements is a critical component of the local physical and ecological systems. Through its direct coupling with the atmosphere and ocean, fast-ice properties are also a potential indicator of processes related to a changing climate. However, in situ fast-ice observations in Antarctica are extremely sparse because of logistical challenges and harsh environmental conditions. Since 2010, a monitoring program observing the seasonal evolution of fast ice in Atka Bay has been conducted as part of the Antarctic Fast Ice Network (AFIN). The bay is located on the northeastern edge of Ekström Ice Shelf in the eastern Weddell Sea, close to the German wintering station Neumayer III. A number of sampling sites have been regularly revisited each year between annual ice formation and breakup to obtain a continuous record of sea-ice and sub-ice platelet-layer thickness, as well as snow depth and freeboard across the bay. Here, we present the time series of these measurements over the last 9 years. Combining them with observations from the nearby Neumayer III meteorological observatory as well as auxiliary satellite images enables us to relate the seasonal and interannual fast-ice cycle to the factors that influence their evolution. On average, the annual consolidated fast-ice thickness at the end of the growth season is about 2 m, with a loose platelet layer of 4 m thickness beneath and 0.70 m thick snow on top. Results highlight the predominately seasonal character of the fast-ice regime in Atka Bay without a significant interannual trend in any of the observed variables over the 9-year observation period. Also, no changes are evident when comparing with sporadic measurements in the 1980s and 1990s. It is shown that strong easterly winds in the area govern the year-round snow distribution and also trigger the breakup of fast ice in the bay during summer months. Due to the substantial snow accumulation on the fast ice, a characteristic feature is frequent negative freeboard, associated flooding of the snow–ice interface, and a likely subsequent snow ice formation. The buoyant platelet layer beneath negates the snow weight to some extent, but snow thermodynamics is identified as the main driver of the energy and mass budgets for the fast-ice cover in Atka Bay. The new knowledge of the seasonal and interannual variability of fast-ice properties from the present study helps to improve our understanding of interactions between atmosphere, fast ice, ocean, and ice shelves in one of the key regions of Antarctica and calls for intensified multidisciplinary studies in this region.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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  • 4
    Publication Date: 2024-05-29
    Description: Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 m SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 m and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98-114 m.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
    Format: application/pdf
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