Published April 5, 2023 | Version v3
Dataset Open

Alpine ice sheet glacial cycle simulations continuous variables

  • 1. ETH Zurich
  • 1. ETH Zurich
  • 2. University of Freiburg

Description

These data contain a subset of time-dependent glacier model output variables.

Reference:

  • Seguinot, J., Ivy-Ochs, S., Jouvet, G., Huss, M., Funk, M., and Preusser, F.: Modelling last glacial cycle ice dynamics in the Alps, The Cryosphere, 12, 3265-3285, doi:10.5194/tc-12-3265-2018, 2018.

File names:

alpcyc.{1km|2km}.{epic|grip|md01}.{cp|pp}.{ex.100a|ex.1ka|ts.10a}.nc
  • Horizontal resolution:
    • 1km: 1 km horizontal resolution
    • 2km: 2 km horizontal resolution
  • Temperature forcing:
    • epic: EPICA ice core temperature forcing
    • grip: GRIP ice core temperature forcing
    • md01: MD01-2444 core temperature forcing
  • Precipitation forcing:
    • cp: constant precipitation
    • pp: palaeo-precipitation reduction
  • Variable types:
    • ex.100a: spatial diagnostics every hundred years
    • ex.1ka: spatial diagnostics every thousand years
    • ts.10a: scalar time-series every ten years

Data format: The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (*.ex.*.nc) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:

gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif

The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. The band number equals 120 minus the age in ka. Band information can be displayed with:

gdalinfo NETCDF:filename.nc:variable

Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata. Also see aggregated variables.

Changelog:

  • Version 3:
    • Add spatial diagnostics every hundred years (*.ex.100a.nc)
  • Version 2:
    • Add age coordinate in kiloyears (ka) before present.
    • Replace NCO by Xarray workflow (no effect on the results).
  • Version 1:
    • Initial version.

Notes

This work was supported by the Swiss National Science Foundation (SNSF) grants 200020-169558 and 200021-153179/1, and the Swiss National Supercomputing Centre (CSCS) grants s573 and sm13.

Files

Files (10.5 GB)

Name Size Download all
md5:892f00d59ea5f4685ac8acd63f6bd0d6
4.0 GB Download
md5:d3ec428c8a145e21be223ae337246102
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1.0 GB Download
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md5:1a108e8bcbbec4d149c9c2591a7077ab
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md5:55250e9d57f0c984e3f450839c3bf9cc
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md5:d7d2090a53468a76c901775d293ea920
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md5:1f4a8efc1123955c6f2469cf47433c2e
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md5:98269d4eedc2893339b6f23142401a1e
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md5:c0938071bbaa0cbd6c67b024846c1935
96.9 MB Download
md5:d117d6bd49382a84370f3688c63cf1a5
2.8 MB Download

Additional details

Related works

Is supplement to
10.5194/tc-12-3265-2018 (DOI)
References
10.5281/zenodo.1423160 (DOI)