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

Eirik Myrvoll-Nilsen, Sigrunn Holbek Sørbye, Hege-Beate Fredriksen, Haavard Rue, Martin Rypdal

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)329-345
Number of pages17
JournalEarth System Dynamics
Issue number2
StatePublished - Apr 8 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This research has been supported by the European Union Horizon 2020 research and innovation program (grant no. 820970).


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