Data underlying the publication: Regional barystatic sea-level trends and uncertainties 1993-2016

Datacite citation style:
Machado Lima de Camargo, Carolina; Hermans, Tim; Riva, Riccardo; Slangen, Aimee (2021): Data underlying the publication: Regional barystatic sea-level trends and uncertainties 1993-2016. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/16778794.v1
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Dataset
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time coverage
1993-2016
licence
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Regional barystatic sea-level change trend and uncertainties, from 2003-2016 and 1993-2016.

Attention: This dataset is linked to a submitted manuscript, and has not undergo peer review yet!

Barystatic sea-level change (also known as ocean mass change) is driven by the exchange of freshwater between the land and the ocean, such as melting of continental ice from glaciers and ice sheets, and variations in land water storage.

Here, we use a range of estimates for the individual freshwater sources, which are used to compute regional patterns (fingerprints) of barystatic sea-level change.

We then compute the trend (rate of sea-level change), and quantify three types of uncertainties of these regional barystatic sea-level change fields:
1. Intrinsic uncertainty: related to the observational error;
2. Temporal uncertainty: related to the temporal variability in the time series;
3. Spatial-structural: related to the location/distribution of the mass change sources;

The methods used to obtain this dataset, as well as the results, are presented in the manuscript "Trends and Uncertainties of Regional Barystatic Sea-level Change in the Satellite Altimetry Era", submitted to the journal Earth System Dynamics

Code for exploring this dataset can be found on: https://github.com/carocamargo/barystatic_SL

history
  • 2021-10-29 first online, published, posted
publisher
4TU.ResearchData
format
netcdf; pickle; txt; pdf
funding
  • ALWO.2017.002
organizations
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems
TU Delft, Faculty of Civil Engineering and Geosciences, Department of Geoscience and Remote Sensing

DATA

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