Towards the probabilistic Earth-system simulator: a vision for the future of climate and weather prediction

There is no more challenging problem in computational science than that of estimating, as accurately as science and technology allows, the future evolution of Earth's climate; nor indeed is there a problem whose solution has such importance and urgency. Historically, the simulation tools needed to predict climate have been developed, somewhat independently, at a number of weather and climate institutes around the world. While these simulators are individually deterministic, it is often assumed that the resulting diversity provides a useful quantification of uncertainty in global or regional predictions. However, this notion is not well founded theoretically and corresponding multi-simulator estimates of uncertainty can be prone to systemic failure. Separate to this, individual institutes are now facing considerable challenges in finding the human and computational resources needed to develop more accurate weather and climate simulators with higher resolution and full Earth-system complexity. A new approach, originally designed to improve reliability in ensemble-based numerical weather prediction, is introduced to help solve these two rather different problems. Using stochastic mathematics, this approach recognizes uncertainty explicitly in the parametrized representation of unresolved climatic processes. Stochastic parametrization is shown to be more consistent with the underlying equations of motion and, moreover, provides more skilful estimates of uncertainty when compared with estimates from traditional multi-simulator ensembles, on time-scales where verification data exist. Stochastic parametrization can also help reduce long-term biases which have bedevilled numerical simulations of climate from the earliest days to the present. As a result, it is suggested that the need to maintain a large gene pool of quasi-independent deterministic simulators may be obviated by the development of probabilistic Earth-system simulators. Consistent with the conclusions of the World Summit on Climate Modelling, this in turn implies that individual institutes will be able to pool human and computational resources in developing future-generation simulators, thus benefitting from economies of scale; the establishment of the Airbus consortium provides a useful analogy here. As a further stimulus for such evolution, discussion is given to a potential new synergy between the development of dynamical cores, and stochastic processing hardware. However, it is concluded that the traditional challenge in numerical weather prediction, of reducing deterministic measures of forecast error, may increasingly become an obstacle to the seamless development of probabilistic weather and climate simulators, paradoxical as that may appear at first sight. Indeed, going further, it is argued that it may be time to consider focusing operational weather forecast development entirely on high-resolution ensemble prediction systems. Finally, by considering the exceptionally challenging problem of quantifying cloud feedback in climate change, it is argued that the development of the probabilistic Earth-system simulator may actually provide a route to reducing uncertainty in climate prediction. Copyright (c) 2012 Royal Meteorological Society