ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for matematikk og statistikk
  • Artikler, rapporter og annet (matematikk og statistikk)
  • Vis innførsel
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for matematikk og statistikk
  • Artikler, rapporter og annet (matematikk og statistikk)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling

Permanent lenke
https://hdl.handle.net/10037/19431
DOI
https://doi.org/10.5194/esd-11-329-2020
Thumbnail
Åpne
article.pdf (2.610Mb)
Publisert versjon (PDF)
Dato
2020-04-08
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Forfatter
Myrvoll-Nilsen, Eirik; Sørbye, Sigrunn Holbek; Fredriksen, Hege-Beate; Rue, Håvard; Rypdal, Martin Wibe
Sammendrag
Reliable 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.
Forlag
Copernicus Publications
Sitering
Myrvoll-Nilsen E, Sørbye SH, Fredriksen H, Rue H, Rypdal MW. Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling. Earth System Dynamics. 2020;11:329-345
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (matematikk og statistikk) [357]
Copyright 2020 The Author(s)

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring