Assessing the impact of retreat mechanisms in a simple antarctic ice sheet model using bayesian calibration

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dc.contributor.authorRuckert, Kelsey L.
dc.contributor.authorShaffer, Gary
dc.contributor.authorPollard, David
dc.contributor.authorGuan, Yaguen
dc.contributor.authorWong, Tony E.
dc.contributor.authorForest, Chris E.
dc.contributor.authorKeller, Klaus
dc.contributor.editorAñel, Juan A.
dc.date.accessioned2017-08-11T15:20:05Z
dc.date.available2017-08-11T15:20:05Z
dc.date.issued2017-01-12
dc.descriptionAny 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. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., The authors have declared that no competing interests exist.es_ES
dc.description.abstractThe 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.es_ES
dc.description.sponsorshipEste trabajo fue apoyado por: 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.es_ES
dc.identifier.citationRuckert 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.es_ES
dc.identifier.issn1932-6203
dc.identifier.otherDOI:10.1371/journal.pone.0170052
dc.identifier.urihttp://hdl.handle.net/20.500.11894/914
dc.identifier.urihttps://github.com/scrim-network/Ruckertetal_DAIS_2016
dc.language.isoenes_ES
dc.publisherPublic Library of Sciencees_ES
dc.relation.ispartofPlos Onees_ES
dc.rights© 2017 Ruckert et al. Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.es_ES
dc.subjectCienciaes_ES
dc.subjectCalibración bayesianaes_ES
dc.subjectAntarctic ice sheetes_ES
dc.subjectHoja de hielo antárticoes_ES
dc.titleAssessing the impact of retreat mechanisms in a simple antarctic ice sheet model using bayesian calibrationes_ES
dc.typeArtículoes_ES

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