# A Fast Particle-Based Approach for Calibrating a 3-D Model of the Antarctic Ice Sheet

## TL;DR

This paper introduces a fast, parallelized particle-based calibration method for complex 3-D Antarctic ice sheet models, enabling detailed uncertainty analysis and improved sea level rise projections.

## Contribution

It presents a novel, computationally efficient sequential Monte Carlo approach that allows for calibration of high-dimensional ice sheet models using modern high performance computing.

## Key findings

- Accurate posterior distributions of model parameters obtained.
- Significant reduction in computational time compared to traditional methods.
- Differences in sea level projections when including more uncertain parameters.

## Abstract

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters.