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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 2
Earth Syst. Dynam., 11, 329–345, 2020
https://doi.org/10.5194/esd-11-329-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Dynam., 11, 329–345, 2020
https://doi.org/10.5194/esd-11-329-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 08 Apr 2020

Research article | 08 Apr 2020

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

Eirik Myrvoll-Nilsen1, Sigrunn Holbek Sørbye1, Hege-Beate Fredriksen1, Håvard Rue2, and Martin Rypdal1 Eirik Myrvoll-Nilsen et al.
  • 1Department of Mathematics and Statistics, UiT The Arctic University of Norway, 9037 Tromsø, Norway
  • 2CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

Abstract. 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.

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This paper presents efficient Bayesian methods for linear response models of global mean surface temperature that take into account long-range dependence. We apply the methods to the instrumental temperature record and historical model runs in the CMIP5 ensemble to provide estimates of the transient climate response and temperature projections under the Representative Concentration Pathways.
This paper presents efficient Bayesian methods for linear response models of global mean surface...
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