Micro and macro parametric uncertainty in climate change prediction:a large ensemble perspective

Earth system models (ESMs) are widely used to make projections of the future behaviour of the 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 (a) the initial conditions and (b) 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. Utilising 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, micro-parametric uncertainty, is akin to micro-initial condition uncertainty and explores a situation where one knows the relevant parameter values well but not perfectly. The second, macro-parametric uncertainty, explores the situation where there may be substantial uncertainty in parameter values. We also investigate how they interact with each other and with micro-initial condition uncertainty. In general, we find that micro-parametric 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 macro-parametric 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.