Probabilistic projections of the Amery Ice Shelf catchment, Antarctica, under high ice-shelf basal melt conditions

• Main Authors: Jantre, Sanket, Hoffman, Matthew J., Urban, Nathan M., Hillebrand, Trevor, Perego, Mauro, Price, Stephen, Jakeman, John D.
• Format: Text
• Language: English
• Published: 2024
• Subjects: Amery Ice Shelf; Antarc*; Antarctica; East Antarctica; Ice Sheet; Ice Shelf; Lambert Glacier
• Online Access: https://doi.org/10.5194/egusphere-2024-1677; https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1677/

Antarctica's Lambert Glacier drains about one-sixth of the ice from the East Antarctica Ice Sheet and is considered stable due to the strong buttressing provided by the Amery Ice Shelf. While previous projections of the sea-level contribution from this sector of the ice sheet have predicted significant mass loss only with near complete removal of the ice shelf, the ocean warming necessary for this was deemed unlikely. Recent climate projections through 2300 indicate that sufficient ocean warming is a distinct possibility after 2100. This work explores the impact of parametric uncertainty on projections of the Lambert-Amery system's (hereafter "Amery sector") response to abrupt ocean warming through Bayesian calibration of a perturbed-parameter ice-sheet model ensemble. We address the computational cost of uncertainty quantification for ice-sheet model projections via statistical emulation, which employs surrogate models for fast and inexpensive parameter space exploration while retaining critical features of the high-fidelity simulations. To this end, we build Gaussian process (GP) emulators from simulations of the Amery sector at medium resolution (4–20 km mesh) using the MPAS-Albany Land Ice (MALI) model. We consider six input parameters that control basal friction, ice stiffness, calving, and ice-shelf basal melting. From these, we generate 200 perturbed input parameter initializations using space-filling Sobol sampling. For our end-to-end probabilistic modeling workflow, we first train emulators on the simulation ensemble then calibrate the input parameters using observations of the mass balance, grounding line movement, and calving front movement with priors assigned via expert knowledge. Next, we use MALI to project a subset of simulations to 2300 using ocean and atmosphere forcings from a climate model for both low and high greenhouse gas emissions scenarios. From these simulation outputs, we build multivariate emulators by combining GP regression with principal component dimension reduction to ...