Theoretical foundations of emergent constraints: relationships between climate sensitivity and global temperature variability in conceptual models

Abstract

There is as yet no theoretical framework to guide the search for emergent constraints. As a result, there are significant risks that indiscriminate data-mining of the multidimensional outputs from GCMs could lead to spurious correlations and less than robust constraints on future changes. To mitigate against this risk, Cox et al (hereafter CHW18) proposed a theory-motivated emergent constraint, using the one-box Hasselmann model to identify a linear relationship between ECS and a metric of global temperature variability involving both temperature standard deviation and autocorrelation ($\Psi$). A number of doubts have been raised about this approach, some concerning the theory and the application of the one-box model to understand relationships in complex GCMs which are known to have more than the single characteristic timescale. We illustrate theory driven testing of emergent constraints using this as an example, namely we demonstrate that the linear $\Psi$-ECS proportionality is not an artifact of the one-box model and rigorously features to a good approximation in more realistic, yet still analytically soluble conceptual models, namely the two-box and diffusion models. Each of the conceptual models predict different power spectra with only the diffusion model's pink spectrum being compatible with observations and the complex CMIP5 GCMs. We also show that the theoretically predicted $\Psi$-ECS relationship exists in the \texttt{piControl} as well as \texttt{historical} CMIP5 experiments and that the differing gradients of the proportionality are inversely related to the effective forcing in that experiment.