Synthesizing long-term sea level rise projections – the MAGICC sea level model v2.0

dc.bibliographicCitation.firstPage2495eng
dc.bibliographicCitation.issue6eng
dc.bibliographicCitation.lastPage2524eng
dc.bibliographicCitation.volume10
dc.contributor.authorNauels, Alexander
dc.contributor.authorMeinshausen, Malte
dc.contributor.authorMengel, Matthias
dc.contributor.authorLorbacher, Katja
dc.contributor.authorWigley, Tom M.L.
dc.date.accessioned2018-11-22T17:16:56Z
dc.date.available2019-06-28T10:35:07Z
dc.date.issued2017
dc.description.abstractSea level rise (SLR) is one of the major impacts of global warming; it will threaten coastal populations, infrastructure, and ecosystems around the globe in coming centuries. Well-constrained sea level projections are needed to estimate future losses from SLR and benefits of climate protection and adaptation. Process-based models that are designed to resolve the underlying physics of individual sea level drivers form the basis for state-of-the-art sea level projections. However, associated computational costs allow for only a small number of simulations based on selected scenarios that often vary for different sea level components. This approach does not sufficiently support sea level impact science and climate policy analysis, which require a sea level projection methodology that is flexible with regard to the climate scenario yet comprehensive and bound by the physical constraints provided by process-based models. To fill this gap, we present a sea level model that emulates global-mean long-term process-based model projections for all major sea level components. Thermal expansion estimates are calculated with the hemispheric upwelling-diffusion ocean component of the simple carbon-cycle climate model MAGICC, which has been updated and calibrated against CMIP5 ocean temperature profiles and thermal expansion data. Global glacier contributions are estimated based on a parameterization constrained by transient and equilibrium process-based projections. Sea level contribution estimates for Greenland and Antarctic ice sheets are derived from surface mass balance and solid ice discharge parameterizations reproducing current output from ice-sheet models. The land water storage component replicates recent hydrological modeling results. For 2100, we project 0.35 to 0.56m (66% range) total SLR based on the RCP2.6 scenario, 0.45 to 0.67m for RCP4.5, 0.46 to 0.71m for RCP6.0, and 0.65 to 0.97m for RCP8.5. These projections lie within the range of the latest IPCC SLR estimates. SLR projections for 2300 yield median responses of 1.02m for RCP2.6, 1.76m for RCP4.5, 2.38m for RCP6.0, and 4.73m for RCP8.5. The MAGICC sea level model provides a flexible and efficient platform for the analysis of major scenario, model, and climate uncertainties underlying long-term SLR projections. It can be used as a tool to directly investigate the SLR implications of different mitigation pathways and may also serve as input for regional SLR assessments via component-wise sea level pattern scaling.eng
dc.description.versionpublishedVersioneng
dc.formatapplication/pdf
dc.identifier.urihttps://doi.org/10.34657/388
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/3803
dc.language.isoengeng
dc.publisherMünchen : European Geopyhsical Unioneng
dc.relation.doihttps://doi.org/10.5194/gmd-10-2495-2017
dc.relation.ispartofseriesGeoscientific Model Development, Volume 10, Issue 6, Page 2495-2524eng
dc.rights.licenseCC BY 3.0 Unportedeng
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/eng
dc.subjectClimate conditionseng
dc.subjecthydrological modelingeng
dc.subjectice sheeteng
dc.subjectmass balanceeng
dc.subjectparameterizationeng
dc.subjectpredictioneng
dc.subjectsea level changeeng
dc.subjectsoftwareeng
dc.subjectuncertainty analysiseng
dc.subject.ddc500eng
dc.titleSynthesizing long-term sea level rise projections – the MAGICC sea level model v2.0eng
dc.typearticleeng
dc.typeTexteng
dcterms.bibliographicCitation.journalTitleGeoscientific Model Developmenteng
tib.accessRightsopenAccesseng
wgl.contributorPIKeng
wgl.subjectUmweltwissenschafteneng
wgl.typeZeitschriftenartikeleng
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