Statistical emulation of a perturbed basal melt ensemble of an ice sheet model to better quantify Antarctic sea level rise uncertainties

Abstract

Abstract. Antarctic ice shelves are vulnerable to warming ocean temperatures, and have already begun thinning in response to increased basal melt rates. Sea level is therefore expected to rise due to Antarctic contributions, but uncertainties in its amount and timing remain largely unquantified. In particular, there is substantial uncertainty in future basal melt rates arising from multi-model differences in thermal forcing and how melt rates depend on that thermal forcing. To facilitate uncertainty quantification in sea level rise projections, we build, validate, and demonstrate projections from a computationally efficient statistical emulator of a high resolution (4 km) Antarctic ice sheet model, the Community Ice Sheet Model version 2.1. The emulator is trained to a large (500-member) ensemble of 200-year-long 4-km resolution transient ice sheet simulations, whereby regional basal melt rates are perturbed by idealized (yet physically informed) trajectories. The main advantage of our emulation approach is that by sampling a wide range of possible basal melt trajectories, the emulator can be used to (1) produce probabilistic sea level rise projections over much larger Monte Carlo ensembles than are possible by direct numerical simulation alone, thereby providing better statistical characterization of uncertainties, and (2) predict the simulated ice sheet response under differing assumptions about basal melt characteristics as new oceanographic studies are published, without having to run additional numerical ice sheet simulations. As a proof-of-concept, we propagate uncertainties about future basal melt rate trajectories, derived from regional ocean models, to generate probabilistic sea level rise estimates for 100 and 200 years into the future.

Global warming levels control time of emergence of Antarctic ice loss

“…Statistical emulation has been used in a number of studies investigating ice sheet sea level contribution and regional sea level change 6,9,18,19,58 . In this case, the statistical emulator is a regression model that is based on Gaussian Processes (GP).…”

“…The process-based ice sheet model simulations using the standard RCP2.6 and RCP8.5 forcings are then used to train a statistical emulator that employs Gaussian Process (GP) Regression (Fig 1). While previous studies have employed similar statistical techniques for determining the probabilistic sea level contribution of the ice sheet or ice sheet catchment for a given time period 9,18,19 , here we also consider the temporal evolution of the sea level contribution. In particular, the GP regression uses the 4 ice sheet model parameters, and a combination of i) the direct effect, ii) the cumulative effect, and iii) the committed effect of global warming as independent variables.…”