| Summary: | Data files are available at: https://arcticdata.io/data/10.18739/A2G737525/ The potential contribution of ice sheets remains the largest source of uncertainty in projecting sea-level due to the limited predictive skill of numerical ice sheet models, yet effective planning for coming sea level rise necessitates that predictions are credible and accompanied by a defensible assessment of uncertainty. Characterization of the likelihood of upper-end contributions are particularly important for developing adaptation strategies. While the use of large ensembles of simulations allows these kinds probabilistic assessments, there is no guarantee that simulations are aligned with observations. Here, we show that calibrating an ensemble of model simulations on observations reduces uncertainties in projecting 21st century mass loss from the Greenland Ice Sheet relative to a plausible a priori distribution of model configurations. We find that jointly conditioning on surface speeds and cumulative mass loss reduces the projected 2100 median contribution and 5--95th percentile by 16-30% and 38-56, respectively, compared to the un-calibrated ensemble, resulting in calibrated sea-level contributions ranging from 4 to 30 centimeters at the year 2100. This data set contains several products: - Surrogate model training data. ~1000 surface speed realizations in netCDF format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io) - Trained emulators. 50 trained emulators in HDF5 format, prepared with PyTorch (www.pytorch.org) - Posterior parameter distributions. 50 posterior distributions in CSV format. - Time series of projected mass change. Time series of projected mass change from 2008 until 2100 in CSV format, prepared with the Parallel Ice Sheet Model (PISM, www.pism.io), for both the ensemble using the Prior and the Posterior (calibrated) parameter distribution. 500 realizations for each Representative Concentration Pathway (RCP) scenario 2.6, 4.5, and 8.5.
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