Amundsen Sea MITgcm model output forced with Pacific Pacemaker Ensemble, 1920-2013
This dataset provides model output for 20th-century ice-ocean simulations in the Amundsen Sea, Antarctica. The simulations are performed with the MITgcm model at 1/10 degree resolution, including components for the ocean, sea ice, and ice shelf thermodynamics. Atmospheric forcing is provided by the...
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| Format: | Dataset |
| Language: | English |
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NERC EDS UK Polar Data Centre
2022
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| Online Access: | https://dx.doi.org/10.5285/a4ea4d64-169a-4981-a64d-c2604b52522e https://data.bas.ac.uk/full-record.php?id=GB/NERC/BAS/PDC/01606 |
| Summary: | This dataset provides model output for 20th-century ice-ocean simulations in the Amundsen Sea, Antarctica. The simulations are performed with the MITgcm model at 1/10 degree resolution, including components for the ocean, sea ice, and ice shelf thermodynamics. Atmospheric forcing is provided by the CESM Pacific Pacemaker Ensemble, using 20 members from 1920-2013. An additional simulation is forced with the ERA5 atmospheric reanalysis from 1920-2013. The simulations were completed in 2021 by Kaitlin Naughten at the British Antarctic Survey (Polar Oceans team). Supported by UKRI Fund for International Collaboration NE/S011994/1. : Simulations were performed with the Massachusetts Institute of Technology general circulation model (MITgcm) version 67s, over the Amundsen Sea region (140 W:80 W, 76 S:62 S). Resolution is 1/10° in the horizontal, with 50 levels in the vertical. Bathymetry and ice shelf topography were generated using BedMachine version 2. Initial and boundary conditions were derived from the World Ocean Atlas 2018 for temperature and salinity, and the Biogeochemical Southern Ocean State Estimate iteration 122 for all other variables. Atmospheric forcing is given by the 20-member Pacific Pacemaker Ensemble for 1920-2013, as well as an additional simulation forced by the ERA5 atmospheric reanalysis for 1979-2019. The raw MITgcm output was converted to NetCDF using the xmitgcm python package, and then processed into timeseries, Hovmoellers, and trends using the mitgcm_python library (https://github.com/knaughten/mitgcm_python). : Values which are identically zero or masked represent the land and ice shelf mask. |
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