@article{7a9ebf34b5fd4099922b57bd1b99fe9b,
title = "Assessing the impact of retreat mechanisms in a simple antarctic ice sheet model using Bayesian calibration",
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.",
author = "Ruckert, {Kelsey L.} and Gary Shaffer and David Pollard and Yawen Guan and Wong, {Tony E.} and Forest, {Chris E.} and Klaus Keller",
note = "Funding Information: We thank A. Bakker, G. Garner, and R. Fuller for assistance on speeding up the DAIS model and for testing the reproducibility of the code. Additionally, we thank P. Oddo and P. Applegate for Latin hypercube sampling assistance. Also, we thank Varada Vaidya for producing future forcings. We thank M. Haran, D. Diaz, and B. Forest for valuable inputs. This work was partially supported by the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507 and the Penn State Center for Climate Risk Management. G. Shaffer was supported in part by Chilean ICM grant NC120066. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or other funding entities. We also acknowledge the World Climate Research Programme{\textquoteright}s Working Group on Coupled Modelling and thank the climate modeling groups that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5; http://cmip-pcmdi.llnl.gov/cmip5/ ), which supplied the climate model output used in this paper (listed in ). The US Department of Energy{\textquoteright}s Program for Climate Model Diagnosis and Intercomparison in partnership with the Global Organization for Earth System Science Portals, provides coordinating support for CMIP5. Data and codes are available through the corresponding author and will be available via Github at https://github.com/scrim-network/Ruckertetal_DAIS_2016 upon publication. Publisher Copyright: {\textcopyright} 2017 Ruckert et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",
year = "2017",
month = jan,
doi = "10.1371/journal.pone.0170052",
language = "English (US)",
volume = "12",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "1",
}