Climate Variability and West Antarctic Ice Sheet Collapse

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, Germany² Department of Earth Physics and Astrophysics, Complutense University of Madrid, Madrid, Spain³ Geosciences Institute, CSIC–UCM, Madrid, Spain Model: Yelmo ice-sheet model v1.14Domain: Antarctic Ice Sheet (AIS), 16 km resolutionSimulation period: 1990–3500 CEAssociated 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 File pattern Variable Region Unit slc_{region}_{dT}.csv Sea-level contribution (SLC) WAIS / EAIS / AIS m ag_{region}_{dT}.csv Grounded-ice area WAIS / EAIS / AIS 10⁶ km² {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) File pattern Variable Region Unit slc_{region}_{dT}_ncv.csv Sea-level contribution WAIS / EAIS / AIS m ag_{region}_{dT}_ncv.csv Grounded-ice area WAIS / EAIS / AIS 10⁶ km² 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 File pattern Region slr_accel_dist_{region}_{dT}.csv WAIS / EAIS / AIS Forcing timeseries NCV deterministic forcing — forcing_atm_ncv.csv, forcing_ocn_ncv.csvColumns: year, dT4, dT6, dT8, dT10, dT12Atmospheric (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}.csvColumns: year, median, q25, q75, min, maxPer-timestep statistics of the stochastic variability component across the 100-member ensemble. Individual noise trajectories — forcing_var_members.csvColumns: year, atm_m1, ..., atm_m10, ocn_m1, ..., ocn_m10First 10 ensemble members' variability trajectories (used for spaghetti plots). WAIS volume flux (dVidt) — for Figures 3 and 4 File Description Columns dvidt_wais_ncv.csv NCV dV/dt for all dT year, dT4, dT6, dT8, dT10, dT12 dvidt_wais_envelope_{dT}.csv Per-timestep envelope max across ensemble year, dv_max dvidt_wais_peaks_{dT}.csv Per-member peak rate and year member_id, peak_year, peak_rate dvidt_wais_envelope_ocn_dT10.csv OCN-only ensemble envelope year, dv_max dvidt_wais_envelope_atm_dT10.csv ATM-only ensemble envelope year, dv_max dvidt_wais_peaks_ocn_dT10.csv OCN-only per-member peak years member_id, peak_year dvidt_wais_peaks_atm_dT10.csv ATM-only per-member peak years member_id, peak_year 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) Variable Description Unit mean_dH_anom Ensemble mean ice thickness anomaly minus NCV (H̄ᵥ − H_nv) m std_dH Ensemble standard deviation of ice thickness anomaly m mean_fgrnd Ensemble mean grounded fraction % fgrnd_ncv NCV grounded fraction (binary 0/1) — 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) Variable Description Unit mean_fgrnd_atm_ocn ATM+OCN ensemble mean grounded fraction % mean_fgrnd_ocn OCN-only ensemble mean grounded fraction % mean_fgrnd_atm ATM-only ensemble mean grounded fraction % fgrnd_ncv NCV grounded fraction (binary 0/1) — 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 Parameter Value Ensemble size 100 members per scenario Variability source Stochastic resampling from ORAS5/ERA5 1990–2019 Forcing scenarios ΔT_atm = 4, 6, 8, 10, 12 K (1pctCO2-equivalent ramp) Ice-sheet model Yelmo v1.14, 16 km Antarctic domain Ocean forcing ESM-derived thermal forcing (ORAS5 base + variability) Sensitivity experiment OCN-only and ATM-only variability at ΔT = 10 K (100 members each) License Creative Commons Attribution 4.0 International (CC BY 4.0)Please cite the associated manuscript when using this data.