Zampieri, Lorenzo; Goessling, Helge (2019): Sea ice targeted geoengineering simulation with the AWI Climate Model [dataset]. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA, https://doi.org/10.1594/PANGAEA.906077, Supplement to: Zampieri, L; Goessling, H (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
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Abstract:
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).
Further details:
Desch, Steven J; Smith, Nathan; Groppi, Christopher; Vargas, Perry; Jackson, Rebecca; Kalyaan, Anusha; Nguyen, Peter; Probst, Luke; Rubin, Mark E; Singleton, Heather; Spacek, Alexander; Truitt, Amanda; Zaw, Pye Pye; Hartnett, Hilairy E (2017): Arctic ice management. Earth's Future, 5(1), 107-127, https://doi.org/10.1002/2016EF000410
Project(s):
Seamless Sea Ice Prediction (SSIP)
Funding:
Federal Ministry of Education and Research (BMBF), grant/award no. 01LN1701A: Seamless Sea Ice Prediction
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | File content | Content | Zampieri, Lorenzo | |||
| 2 | File name | File name | Zampieri, Lorenzo | |||
| 3 | File format | File format | Zampieri, Lorenzo | |||
| 4 | File size | File size | kByte | Zampieri, Lorenzo | ||
| 5 | Uniform resource locator/link to file | URL file | Zampieri, Lorenzo |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Size:
60 data points