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dc.contributor.authorMyrvoll-Nilsen, Eirik
dc.contributor.authorSørbye, Sigrunn Holbek
dc.contributor.authorFredriksen, Hege-Beate
dc.contributor.authorRue, Haavard
dc.contributor.authorRypdal, Martin
dc.date.accessioned2020-04-09T08:24:06Z
dc.date.available2020-03-02T11:26:20Z
dc.date.available2020-04-09T08:24:06Z
dc.date.issued2020-04-08
dc.date.submitted2019-10-29
dc.identifier.citationMyrvoll-Nilsen, E., Sørbye, S. H., Fredriksen, H.-B., Rue, H., & Rypdal, M. (2020). Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling. Earth System Dynamics, 11(2), 329–345. doi:10.5194/esd-11-329-2020
dc.identifier.issn2190-4987
dc.identifier.doi10.5194/esd-11-329-2020
dc.identifier.doi10.5194/esd-2019-66
dc.identifier.urihttp://hdl.handle.net/10754/661837
dc.description.abstractReliable quantification of the global mean surface temperature (GMST) response to radiative forcing is essential for assessing the risk of dangerous anthropogenic climate change. We present the statistical foundations for an observation-based approach using a stochastic linear response model that is consistent with the long-range temporal dependence observed in global temperature variability. We have incorporated the model in a latent Gaussian modeling framework, which allows for the use of integrated nested Laplace approximations (INLAs) to perform full Bayesian analysis. As examples of applications, we estimate the GMST response to forcing from historical data and compute temperature trajectories under the Representative Concentration Pathways (RCPs) for future greenhouse gas forcing. For historic runs in the Model Intercomparison Project Phase 5 (CMIP5) ensemble, we estimate response functions and demonstrate that one can infer the transient climate response (TCR) from the instrumental temperature record. We illustrate the effect of long-range dependence by comparing the results with those obtained from one-box and two-box energy balance models. The software developed to perform the given analyses is publicly available as the R package INLA.climate.
dc.description.sponsorshipThis research has been supported by the European Union Horizon 2020 research and innovation program (grant no. 820970).
dc.publisherCopernicus GmbH
dc.relation.urlhttps://www.earth-syst-dynam.net/11/329/2020/
dc.rightsArchived with thanks to Earth System Dynamics. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleStatistical estimation of global surface temperature response to forcing under the assumption of temporal scaling
dc.typeArticle
dc.contributor.departmentStatistics Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalEarth System Dynamics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
dc.identifier.volume11
dc.identifier.issue2
dc.identifier.pages329-345
kaust.personRue, Haavard
dc.date.accepted2020-03-01
dc.relation.issupplementedbygithub:eirikmn/INLA.climate
refterms.dateFOA2020-03-02T11:26:43Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Software]</i> <br/> Title: eirikmn/INLA.climate: Repository for the INLA.climate R-package. Publication Date: 2019-01-29. github: <a href="https://github.com/eirikmn/INLA.climate" >eirikmn/INLA.climate</a> Handle: <a href="http://hdl.handle.net/10754/667392" >10754/667392</a></a></li></ul>


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Archived with thanks to Earth System Dynamics. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
Except where otherwise noted, this item's license is described as Archived with thanks to Earth System Dynamics. © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License.
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