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

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...

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Bibliographic Details
Published in:Earth system dynamics 2020-04, Vol.11 (2), p.329-345
Main Authors: Myrvoll-Nilsen, Eirik, Sørbye, Sigrunn Holbek, Fredriksen, Hege-Beate, Rue, Håvard, Rypdal, Martin
Format: Article
Language:English
Subjects:
Air pollution
Analysis
Anthropogenic climate changes
Anthropogenic factors
Bayesian analysis
Bayesian theory
Climate change
Climate models
Climate sensitivity
Energy balance
Energy balance models
Estimates
Global temperature changes
Global temperatures
Greenhouse effect
Greenhouse gases
Heat
Historic temperatures
History
Intercomparison
Matematikk og Naturvitenskap: 400
Matematikk: 410
Mathematical models
Mathematics and natural science: 400
Mathematics: 410
Noise
Parameter estimation
Probability theory
Radiative forcing
Response functions
Risk assessment
Scaling
Statistics
Surface temperature
Temperature dependence
Temperature effects
Temperature variability
Time
Time series
VDP
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Summary: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.
ISSN:2190-4987
2190-4979
2190-4987
DOI:10.5194/esd-11-329-2020