Data accompanying the article "Atmospheric-river-induced foehn events drain glaciers on Novaya Zemlya"

doi: 10.4121/10753234-8bf5-4f8a-b427-2eec0b3af060.v1
The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/10753234-8bf5-4f8a-b427-2eec0b3af060
Datacite citation style:
Haacker, Jan; Bert Wouters; Fettweis, X. (Xavier); Glissenaar, I.A.; Box, J.E. (2024): Data accompanying the article "Atmospheric-river-induced foehn events drain glaciers on Novaya Zemlya". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/10753234-8bf5-4f8a-b427-2eec0b3af060.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset

The glacier volume of the High Russian Arctic ice cap on Novaya Zemlya has been declining since the Little Ice Age. However, in the recent period, the loss of glacial ice has accelerated. We studied the glacier mass loss drivers by comparing the output of the regional atmospheric model MAR with observations from CryoSat-2, GRACE/GRACE-FO, and ICESat, and reanalysis results from ERA5 and MERRA2. We found that additional moisture import marjorly drives the mass loss increase; with foehn winds steering the transported energy to the lee-slope glacier surface. Here, we publish time series of modeled surface mass fluxes and moisture transport from reanalysis from 1980 to 2022, glacier mass change time series from observational data for different periods between 2002 and 2022, and the CryoSat-2 derived glacier surface elevation trends from 2011 to 2022 at a 500-by-500 m resolution. This dataset contributes to the reproducibility of the results, and can be compared to future datasets or used for further research.


The source data of CryoSat-2 derived products were provided by the European Space Agency (ESA).

history
  • 2024-05-17 first online, published, posted
publisher
4TU.ResearchData
format
spreadsheet/csv, geospatial/netCDF4
funding
  • NWO 16.Vidi.171.063 (grant code 16.Vidi.171.063) [more info...] Nederlandse Organisatie voor Wetenschappelijk Onderzoek
organizations
TU Delft, Faculty of Civil Engineering & Geosciences, Department of Geoscience and Remote Sensing

DATA

files (8)