Abstract
How much evidence would it take to convince climate skeptics that they are wrong? I explore this question within an empirical Bayesian framework. I consider a group of stylized skeptics and examine how these individuals rationally update their beliefs in the face of ongoing climate change. I find that available evidence in the form of instrumental climate data tends to overwhelm all but the most extreme priors. Most skeptics form updated beliefs about climate sensitivity that correspond closely to estimates from the scientific literature. However, belief convergence is a nonlinear function of prior strength and it becomes increasingly difficult to convince the remaining pool of dissenters. I discuss the necessary conditions for consensus formation under Bayesian learning and show that apparent deviations from the Bayesian ideal can still be accommodated within the same conceptual framework. I argue that a generalized Bayesian model provides a bridge between competing theories of climate skepticism as a social phenomenon.
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Notes
It should be said that there is an important literature on Bayesian learning in IAMs that originates with Kelly and Kolstad (1999). But I am unaware of any IAM studies that explicitly try to model learning by climate skeptics.
In terms of tangentially related empirical work, Kaufmann et al. (2017) shows that spatial heterogeneity in local climate change effects and temperatures can partially explain persistent skepticism in different regions of the United States. Moore (2017) does not deal with skeptics per se, but characterizes learning about climate as a (potentially) Bayesian process where individuals make inferences based on local weather shocks. This builds off of earlier work by Deryugina (2013), who finds that longer spells of abnormal local weather patterns are consistent with Bayesian updating about climate beliefs.
Another group of researchers beginning with Stern and Kaufmann (2000), has argued that the instrumental temperature record contains a stochastic trend that is imparted by, and therefore cointegrates with, the time-series data of radiative forcings. The reader is referred to Estrada and Perron (2013) and Hillebrand et al. (2020) for helpful overviews of this debate.
So-called conjugate priors are a prominent exception and belong to the same distribution family as the resulting posterior. However, conjugacy places strong restrictions on the questions that can asked of the data.
Anthropogenic forcings such as CO2, CH4, and N2O all follow very similar trends over time. Any empirical model that does not constrain these forcings in some way will therefore struggle to correctly attribute warming between them.
Volcanic aerosols are an exception because they impart only a transitory level of forcing. This explains why V OLC may be included as a separate component in the regression equation (Estrada et al. 2013a).
It is worth noting that a number of studies which provide climate sensitivity estimates via time-series methods — e.g., Kaufmann et al. (2006), Mills (2009), and Estrada and Perron (2012)—do so under the assumption that F2× = 4.37 Wm− 2. This outdated figure appears to be based on early calculations by Hansen et al. (1988). The climate sensitivity estimates of these studies may consequently be regarded as inflated.
The choice of normally distributed priors should have little bearing on the generality of the results. An exception might occur if I assumed a bounded prior, like a triangular or uniform distribution. Because these bounded distributions assign zero weight to outcomes beyond a specific interval, no amount of data can shift the posterior beyond that interval. This idea, that a Bayesian posterior can converge on a particular outcome only if the prior allocates some (infinitesimal) weight to it, is known colloquially as Cromwell’s rule (Jackman 2009).
This is the default prior suggested by Goodrich et al. (2020), which they refer to as “weakly informative.”
HadCRUT5 (Morice et al. 2020) was released during the late revision stages of the manuscript. Among other things, this updated version of the HadCRUT temperature record adopts a similar approach to interpolating coverage gaps as in CW14.
As a corollary, concerns over the use of the full historical dataset would only hold sway in cases where priors already incorporate information that has been obtained from applying the same model on a sub-sample of the dataset. In that case, we would need to exclude the sub-sample from the analysis to derive a valid posterior that avoids double counting.
Temperature predictions for RCPs 2.6 and 4.5—depicting respective CO2 stabilisation scenarios—are included in Fig. 4 for reference purposes only.
This is not to say that people fail to update rationally, or even heuristically, in a Bayesian manner. For further discussion in the context of climate, see Lewandowsky et al. (2019).
While the precise theoretical development differs from the framework presented here, I would note the closely related concept of Bayesian networks (Pearl and Russell 2000). Cook and Lewandowsky (2016) use a Bayesian network approach in an experimental setting to document (rational) belief polarization after individuals are presented with evidence about climate change. Mistrust of climate scientists is a primary source of the polarization in their study.
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Acknowledgements
The author would like to thank Øivind A. Nilsen, Jonas Andersson, Gunnar Eskeland, and Christopher Christopher Costello for their early encouragement and support. Various seminar participants and, especially, several anonymous reviewers provided extremely helpful feedback that substantially improved the final manuscript.
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McDermott, G.R. Skeptic priors and climate consensus. Climatic Change 166, 7 (2021). https://doi.org/10.1007/s10584-021-03089-x
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DOI: https://doi.org/10.1007/s10584-021-03089-x