Glacial cycles simulation of the Antarctic Ice Sheet with PISM – Part 2: Parameter ensemble analysis

• Main Authors: Albrecht, Torsten, Winkelmann, Ricarda, Levermann, Anders
• Format: Text
• Language: English
• Published: 2019
• Subjects: Antarctic; Briggs; Pollard; The Antarctic; Antarc*; Ice Sheet
• Online Access: https://doi.org/10.5194/tc-2019-70; https://www.the-cryosphere-discuss.net/tc-2019-70/

The Parallel Ice Sheet Model (PISM) is applied to the Antarctic Ice Sheet over the last two glacial cycles (≈ 210,000 years) with a resolution of 16 km. A Large Ensemble of 256 model runs is analyzed in which four relevant model parameters have been systematically varied using full-factorial parameter sampling. Parameters and plausible parameter ranges have been identified in a companion paper (Albrecht et al., 2019) and are associated with ice dynamics, climatic forcing, basal sliding and bed deformation and represent distinct classes of model uncertainties. The model is calibrated against both modern and geologic data, including reconstructed grounding line locations, elevation-age data, ice thickness and surface velocities as well as uplift rates. An aggregated score is computed for each ensemble member that measures the overall model-data misfit, including measurement uncertainty in terms of a Gaussian error model (Briggs and Tarasov, 2013). The statistical method used to analyze the ensemble simulation results follows closely the simple averaging method described in Pollard et al. (2016). This analysis further constrains relevant model and boundary parameters by revealing clusters of best fit parameter combinations. The ensemble of reconstructed histories of Antarctic Ice Sheet volumes provides a score-weighted likely range of sea-level contributions since the Last Glacial Maximum of 9.4 ± 4.1 m (or 6.5 ± 2.0 × 10 6 km 3 ), which is at the upper range of previous studies. The last deglaciation occurs in all ensemble simulations after around 12,000 years before present, and hence after the Meltwater Pulse-1A. Our Large Ensemble analysis also provides well-defined parametric uncertainty bounds and a probabilistic range of present-day states that can be used for PISM projections of future sea-level contributions from the Antarctic Ice Sheet.