Dataset: Climate Variability and West Antarctic Ice Sheet Collapse

Zenodo Data Repository

Authors:
Javier Blasco¹, Jan Swierczek-Jereczek²·³, Marisa Montoya²·³, Jorge Alvarez-Solas³, Alexander Robinson¹

Affiliations:
¹ Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany
² Department of Earth Physics and Astrophysics, Complutense University of Madrid, Madrid, Spain
³ Geosciences Institute, CSIC–UCM, Madrid, Spain

Model: Yelmo ice-sheet model v1.14
Domain: Antarctic Ice Sheet (AIS), 16 km resolution
Simulation period: 1990–3500 CE
Associated manuscript: Manuscript in preparation.

Overview

This dataset contains 1D timeseries statistics and 2D spatial snapshots from an ensemble of Antarctic ice-sheet simulations designed to quantify the role of climate variability on the timing and magnitude of West Antarctic Ice Sheet (WAIS) collapse. The ensemble applies five levels of deterministic atmospheric warming (ΔT = 4, 6, 8, 10, 12 K) combined with stochastic climate variability drawn from historical reanalysis (ORAS5/ERA5, 1990–2019). A parallel set of no-variability (NCV) runs provides the deterministic baseline for each scenario.

A sensitivity experiment isolating the contributions of oceanic (OCN) and atmospheric (ATM) variability components is also included for the ΔT = 10 K scenario.

Directory structure

1D_data/    — CSV timeseries (statistics across 100-member ensemble)
2D_data/    — NetCDF spatial snapshots at selected years

1D_data — CSV files

All CSV files cover the period 2020–3500 CE. Years are stored as floating-point values (e.g. 2020.0).

Ensemble statistics (with climate variability)

Columns: year, median, q25, q75, min, max

• {region} ∈ {wais, eais, ais}
• {dT} ∈ {dT4, dT6, dT8, dT10, dT12}

Example: slc_wais_dT10.csv — WAIS sea-level contribution for ΔT = 10 K, ensemble spread.

No-variability deterministic runs (NCV baseline)

Columns: year, slc (SLC files) or year, area (area files)

SLR acceleration distribution

Per-member peak sea-level rise rate within the 2500–3500 CE window.

Columns: member_id, peak_year, peak_rate_mm_yr

Forcing timeseries

NCV deterministic forcing — forcing_atm_ncv.csv, forcing_ocn_ncv.csv
Columns: year, dT4, dT6, dT8, dT10, dT12
Atmospheric (ΔT_atm, K) and oceanic (ΔT_ocn, K) forcing anomalies for each dT scenario.

Ensemble variability forcing — forcing_atm_var_dT{X}.csv, forcing_ocn_var_dT{X}.csv
Columns: year, median, q25, q75, min, max
Per-timestep statistics of the stochastic variability component across the 100-member ensemble.

Individual noise trajectories — forcing_var_members.csv
Columns: year, atm_m1, ..., atm_m10, ocn_m1, ..., ocn_m10
First 10 ensemble members' variability trajectories (used for spaghetti plots).

WAIS volume flux (dVidt) — for Figures 3 and 4

Units: dV/dt in mm SLR / yr (conversion factor: −0.00247 m³/yr ice → mm SLR/yr)

2D_data — NetCDF files

All 2D files use the ANT-16KM polar stereographic grid.
Dimensions: x (316), y (316) — grid spacing 16 km.
Coordinates: xc, yc in km.

Figure 3: ice thickness anomaly and grounding at NCV peak year

Files: fig3_{dT}.nc — one file per scenario (dT6, dT8, dT10, dT12)

Global attributes: scenario, peak_year (year of NCV peak dV/dt used as snapshot time), snap_year.

Figure 4: OCN/ATM sensitivity at fixed target years (ΔT = 10 K)

Files: fig4_dT10_yr{year}.nc — one file per target year (2800, 2900, 2950, 3000)

Global attributes: dT, snap_year.

Note on grounded fraction: Values represent the fraction of ensemble members (0–100%) for which a given grid cell is grounded at the snapshot year. Values of 100% (shown as grey in figures) indicate that all members remain grounded — the ice sheet has not yet retreated at that location.

Ensemble design

License

Creative Commons Attribution 4.0 International (CC BY 4.0)
Please cite the associated manuscript when using this data.