Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration

Authors

Kelsey L. Ruckert, The Pennsylvania State University
Gary Shaffer, University of Magallanes & University of Copenhagen
David Pollard, The Pennsylvania State University
Yawen Guan, The Pennsylvania State University
Tony E. Wong, The Pennsylvania State University
Chris E. Forest, The Pennsylvania State University
Klaus Keller, The Pennsylvania State University & Carnegie Mellon UniversityFollow

Document Type

Article

Date of this Version

1-12-2017

Citation

2017 Ruckert et al.

Comments

Ruckert KL, Shaffer G, Pollard D, Guan Y, Wong TE, Forest CE, et al. (2017) Assessing the Impact of Retreat Mechanisms in a Simple Antarctic Ice Sheet Model Using Bayesian Calibration. PLoS ONE 12(1): e0170052. doi:10.1371/journal.pone.0170052

Abstract

The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.