Optimising the Design of Ensembles to Support Science and Society (ODESSS)

This project will build the foundational techniques needed to understand what is necessary from an ensemble of computer model simulations to provide robust reliable knowledge. Such knowledge includes progress in scientific understanding of complex multi-component systems on the one hand, and guidance for societal decisions on the other. The focus is on climate models, where ensembles are a core element of research activities. The research will involve, indeed requires, integrating expertise across a range of disciplines.

Global climate models (GCMs) are complex, high-dimensional, discretized systems. They use the latest computer technology to solve a large number of simultaneous differential equations. Different disciplines and researchers view them in radically different ways and hence have very different perspectives on how to explore their errors and uncertainties. For physicists they are interpreted as representing physical understanding so the term "model error" encompasses their failure to effectively represent what we know about physical processes. For nonlinear dynamicists they are high dimensional systems of nonlinear equations so there is an expectation that profoundly different results could potentially arise as a consequence of uncertainty in both initial conditions and model formulation (model errors) of even the smallest degree. For risk analysts and forecasters they are generators of timeseries from which errors can be judged by relation to historic observations, although physicists and statisticians might be concerned that the extrapolatory nature of the climate change problem undermines such an assessment. For "users" such as adaptation planners and policy makers they provide climate projections which represent the starting point for their own work; any uncertainties provided by the scientists are assumed to be reliable estimates of the best current knowledge. This variety of perspectives leads to many different ways of interpreting the errors and uncertainties, and creates conflicting demands on the models themselves.

Projection uncertainties are typically quantified from ensembles of simulations which come in a variety of shapes and sizes. These ensembles are used to explore the impact of initial condition uncertainty (ICU - the consequence of not knowing the current state of the climate system when trying to make simulations of the future) and of model uncertainty (MU - the consequence of our models being different from reality). Today's ensembles (and models) have been built under the constraint of limited computational capacity so their designs start from the question: "what's the best we can do with today's technology?" By contrast, one of the unique and innovative aspects of this project is that its starting point is "what type and size of ensembles are necessary to provide the information we want?" It will develop designs for "aspirational ensembles" i.e. ensembles that are necessary to answer a particular set of questions without regard to current computational limitations. From this foundation it will evaluate the best way to approach the trade-offs necessary in building practical ensembles which DO allow for current computational limitations.

The approach taken will be twofold. First will be to use low-dimensional nonlinear systems to study the consequences of nonlinearity for ensemble design in climate like situations. Second will be to build a collaborative, collective picture of the demands and constraints on model ensembles from a wide range of different disciplinary and national perspectives.

The project will provide a solid foundation for future ensemble designs and will inform the widely debated computational conflict between uncertainty exploration, resolution and complexity. The latter two of these are much studied but there has been no comprehensive assessment of former. This project will fill that gap from the perspective of what is needed rather than what is available.