@article{4aba1166e3f04bd2b0aa45efd6b1c40c,
title = "The role of internal climate variability in projecting Antarctica{\textquoteright}s contribution to future sea-level rise",
abstract = "Retreat of the Antarctic ice sheet (AIS) is likely to be a major contributor to future sea-level rise (SLR). Current projections of SLR due to ice-sheet mass loss remain highly uncertain. Better understanding of how ice sheets respond to future climate forcing and variability is essential for assessing long-term risk of SLR. However, predictability of future climate is limited by uncertainties from emission scenarios, model structural differences, and internal climate variability (ICV) that is inherently generated within the fully coupled climate system. Among those uncertainties, the impact of ICV on the AIS changes has not been explicitly assessed. Here we quantify the effects of ICV on the AIS evolutions by employing climate fields from two large-ensemble experiments using the Community Earth System Model to force a three-dimensional ice-sheet model. We find that ICV of climate fields among ensemble members leads to significantly different AIS responses, and that most of the effect is due to atmospheric variability compared to oceanic. Our results show that ICV can cause about 0.08 m differences of AIS contribution to SLR by 2100 compared to the ensemble-mean AIS contribution of 0.38–0.45 m. Moreover, using ensemble-mean climate forcing fields as the forcing in an ice-sheet model significantly delays retreat of the West AIS for up to 20 years and significantly underestimates the AIS contribution to SLR by 0.07–0.11 m in 2100 and up to 0.34 m in the 2250{\textquoteright}s. This study highlights the need to take internal climate variability into account in assessing uncertainty associated with the AIS contribution in sea-level rise projections.",
author = "Tsai, {Chii Yun} and Forest, {Chris E.} and David Pollard",
note = "Funding Information: This study was sponsored by the National Science Foundation (NSF) under grant DMS-1418090 and supported by the Penn State Center for Climate Risk Management (CLIMA). For SFK LE experiment, we thank Ryan Sriver for providing data so that the authors could extend the previous SFK LE to 2300. For the extension of SFK LE simulations, we acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR{\textquoteright}s Computational and Information Systems Laboratory (CISL), sponsored by the NSF. For the data from NCAR LE, we acknowledge the CESM Large Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone. For the ice sheet model simulations using two LEs, we thank Network for Sustainable Climate Risk Management (SCRiM), supported by the National Science Foundation under NSF cooperative agreement GEO-1240507, for providing computing resources and data storage. For the CMIP5 results, we acknowledge the World Climate Research Programme{\textquoteright}s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table in Appendix) for producing and making available their model output. The ice sheet model used in this study, the SFK LE data set, input files and scripts are available from the authors upon request (ceforest@psu.edu). The NCAR LE data sets can be downloaded at Earth System Grid ( https://www.earthsystemgrid.org ). Funding Information: This study was sponsored by the National Science Foundation (NSF) under grant DMS-1418090?and?supported by the Penn State Center for Climate Risk Management (CLIMA). For SFK LE experiment, we thank Ryan Sriver for providing data so that the authors could extend the previous SFK LE to 2300. For the extension of SFK LE simulations, we acknowledge high-performance computing support from Yellowstone (ark:/85065/d7wd3xhc) provided by NCAR?s Computational and Information Systems Laboratory (CISL), sponsored by the NSF. For the data from NCAR LE, we acknowledge the CESM Large Ensemble Community Project and supercomputing resources provided by NSF/CISL/Yellowstone. For the ice sheet model simulations using two LEs, we thank Network for Sustainable Climate Risk Management (SCRiM), supported by the National Science Foundation under NSF cooperative agreement GEO-1240507, for providing computing resources and data storage. For the CMIP5 results, we acknowledge the World Climate Research Programme?s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table?4 in Appendix) for producing and making available their model output. The ice sheet model used in this study, the SFK LE data set, input files and scripts are available from the authors upon request (ceforest@psu.edu). The NCAR LE data sets can be downloaded at Earth System Grid (https://www.earthsystemgrid.org). Publisher Copyright: {\textcopyright} 2020, The Author(s).",
year = "2020",
month = oct,
day = "1",
doi = "10.1007/s00382-020-05354-8",
language = "English (US)",
volume = "55",
pages = "1875--1892",
journal = "Climate Dynamics",
issn = "0930-7575",
publisher = "Springer Verlag",
number = "7-8",
}
