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  • Arctic ice management; Climate modelling; File content; File format; File name; File size; Geoengineering; Sea ice; sea ice modelling; Seamless Sea Ice Prediction; SSIP; Uniform resource locator/link to file  (1)
  • Southern Ocean  (1)
  • 1
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    PANGAEA
    In:  Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven | Supplement to: Zampieri, Lorenzo; Goessling, Helge (2019): Sea ice targeted geoengineering can delay Arctic sea ice decline but not global warming. Earth's Future, 7, https://doi.org/10.1029/2019EF001230
    Publication Date: 2023-03-16
    Description: To counteract global warming, a geoengineering approach that aims at intervening in the Arctic ice-albedo feedback has been proposed (Desch et al., (2017)). A large number of wind-driven pumps shall spread seawater on the surface in winter to enhance ice growth, allowing more ice to survive the summer melt. We test this idea with a coupled climate model by modifying the surface exchange processes such that the physical effect of the pumps is simulated. This database contains a selection of fields from CMIP5 type RCP 8.5 ensemble climate projections with the AWI Climate Model (AWI-CM). The data are stored as netCDF and include the following variables: Monthly averaged time series of pan-Arctic sea ice extent and volume from 1850 to 2100. These are divided into a "Historical" simulation (1850 to 1999; 1 ensemble member), a "Control" simulation (2000 to 2100; 4 ensemble members) and a "Geoengineering" simulation (2020 to 2100; 4 ensemble members). Monthly averaged 2D fields of 2m temperature, total cloud cover, net solar radiation energy flux and total precipitation for the "Control", "Geoengineering" and "Extreme Geoengineering" simulations. The data are averaged over two periods: 2021 to 2060 and 2061 to 2100. Further details about the data can be found in the publication associated with the database. For practical reasons, the full climate model output is stored at DKRZ and will be made available only upon request to the authors. This dataset has been created with the financial support of the Federal Ministry of Education and Research of Germany in the framework of the research group Seamless Sea Ice Prediction (SSIP; grant 01LN1701A).
    Keywords: Arctic ice management; Climate modelling; File content; File format; File name; File size; Geoengineering; Sea ice; sea ice modelling; Seamless Sea Ice Prediction; SSIP; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 60 data points
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  • 2
    Publication Date: 2021-09-24
    Description: Coupled subseasonal forecast systems with dynamical sea ice have the potential of providing important predictive information in polar regions. Here, we evaluate the ability of operational ensemble prediction systems to predict the location of the sea ice edge in Antarctica. Compared to the Arctic, Antarctica shows on average a 30% lower skill, with only one system remaining more skillful than a climatological benchmark up to ∼30 days ahead. Skill tends to be highest in the west Antarctic sector during the early freezing season. Most of the systems tend to overestimate the sea ice edge extent and fail to capture the onset of the melting season. All the forecast systems exhibit large initial errors. We conclude that subseasonal sea ice predictions could provide marginal support for decision-making only in selected seasons and regions of the Southern Ocean. However, major progress is possible through investments in model development, forecast initialization and calibration.
    Keywords: 551.343 ; sea ice prediction ; sea ice edge ; Antarctica ; Southern Ocean ; S2S time scale
    Language: English
    Type: map
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