Micro- and Macroparametric Uncertainty in Climate Change Prediction: A Large Ensemble Perspective

Abstract Earth system models (ESMs) are widely used to make projections of the future behavior of Earth’s climate in the context of anthropogenic climate change. Setting aside uncertainties stemming from the design and implementation of the model, there, nevertheless, remain substantial uncertainties with such projections. Two important ones arise from uncertainties in (i) the initial conditions and (ii) the values of parameters within the model. Here, we systematically investigate the latter: the consequences of parametric uncertainty, as might be explored by perturbed parameter ensembles. Utilizing a low-dimensional system with key characteristics of a climate model, we examine two types of parametric uncertainty through a large ensemble approach. The first, microparametric uncertainty, is akin to microinitial condition uncertainty and explores a situation where one knows the relevant parameter values well but not perfectly. The second, macroparametric uncertainty, explores the situation where there may be substantial uncertainty in parameter values. We also investigate how they interact with each other and with microinitial condition uncertainty. In general, we find that microparametric uncertainty can lead to a much broader range of states than in initial condition ensembles, with the resulting standard deviations being over 2.5–3.5 times higher for slow- and fast-mixing variables alike. Additionally, we show that the scale of the effect may be even larger with macroparametric uncertainty. Finally, we discuss the implications for ensemble design and interpretation and particularly how these results indicate the need for more complex ensemble designs when making projections of climate change within ESMs. Significance Statement This study presents a systematic discussion of the sources and consequences of parametric uncertainty in climate models. Two types of parametric uncertainty are found depending on the source and magnitude of uncertainty: a micro- and macrouncertainty, with the latter being subdivided into two types. While parametric ensembles are generally found to quantify a broader range of plausible states and behavior than initial condition ensembles, the results are varied and dependent on multiple factors, such as the number of parameters perturbed and the output variable of interest. Together, these results shed some light on how to better design informative climate model ensembles, particularly given the computational power demands of running such models.