Abstract
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.
Highlights
Despite decades of research and development regarding global circulation models (GCMs) and Earth system models (ESMs), the discrepancies between models remain substantial, even as we describe physical processes with increasing accuracy and resolution
These studies focus on the equilibrium climate sensitivity (ECS) as an essential metric of the climate response, as have numerous paleoclimate studies (Hansen et al, 2013; von der Heydt and Ashwin, 2017; Köhler et al, 2017)
This paper presents a Bayesian formulation to analyze a linear temperature response model to radiative forcing, incorporating long-range temporal dependence using a scaleinvariant response function
Summary
Despite decades of research and development regarding global circulation models (GCMs) and Earth system models (ESMs), the discrepancies between models remain substantial, even as we describe physical processes with increasing accuracy and resolution. The scaling exponent β (defined from the PSD in Eq 7) relates to the so-called Hurst exponent of the fGn via the formula β = 2H − 1 Based on this Rypdal and Rypdal (2014) proposed a fractional linear response model in the form of Eq (1), in which the parsimonious expression in Eq (8) replaces the linear combination of exponential functions in Eq (6). On timescales up to approximately 103 years, the model provides an accurate description of both forced and unforced surface temperature fluctuations (Rypdal and Rypdal, 2014; Rypdal et al, 2015), and the millennial-scale climate sensitivity in the estimated fractional linear response model correlates strongly with ECS over the ensemble of models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Rypdal et al, 2018a).
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