Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation

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Reliable climate projections are needed by governments and industry to make decisions in response to climate change. For example, the governmental National Flood Resilience Review, published in September 2016, used risks calculated from climate projections to evaluate how to best protect the country from extreme weather and flood events. By weighing up the cost of investment against the predicted risks, the government decided to invest in temporary protective flood barriers.However, it is likely that our current climate projections do not accurately represent the possibility of different climate outcomes. On seasonal timescales, where we can test the skilfulness of our predictions, we can see that our current models are unable to reliably predict the likelihood of different events. The shortcomings observed in seasonal forecasts will also infect climate prediction. This limits the usefulness of current climate models for decision-making.I propose to develop a new technique for representing uncertainty in climate models. This will improve our ability to predict the likelihood of different climate outcomes. The key aim is to improve the reliability of climate projections, and therefore their usefulness to decision-makers and end users.In particular, I will target uncertainty in climate prediction due to the limitations of the climate model. The process of representing the atmosphere, oceans and land-surface and their many interactions in a piece of computer code is a large source of uncertainty. Some processes in the atmosphere take place on too small a scale to be explicitly represented on the grid used by a computer model, where grid points are typically 10-100 km apart. Instead, these processes are accounted for using "parametrisation schemes".In the case of weather and seasonal forecasting, random numbers are widely used as a tool to help represent these small-scale processes in the model. These so called "stochastic" parametrisation schemes improve the consistency of the model with the underlying physical processes. The introduction of stochastic parametrisation schemes produced a step-change improvement in weather and seasonal forecasts.However, current stochastic schemes do not directly target the physical source of uncertainty. Instead they simply target the impact of the uncertainty on the forecast. While this is sufficient for short-range applications, these schemes are not mathematically rigorous, and do not necessarily conserve moisture, mass or energy over longer time periods. This is very problematic for climate prediction.My research will provide a set of new, physically motivated stochastic parametrisation schemes. By targeting uncertainty at the source, these schemes will be useful for climate projections as well as on weather and seasonal timescales.I will develop this set of new stochastic schemes by comparing a typical forecast model to a high-resolution atmospheric simulation. My project partners across Europe will provide these state-of-the-art simulations in which small-scale processes are explicitly modelled. In the forecast model, these small-scales must be represented through parametrisation schemes. By comparing the two, I will identify processes that are better represented in a probabilistic manner instead of in the traditional deterministic manner.I will initially evaluate my new schemes in weather and seasonal forecasts. This provides a way to validate the representation of fast timescale processes in the forecast model. Accurate representation of these processes is necessary for realistic climate simulation, due to the non-linear nature of the atmosphere. Having validated my new approach in this way, I will produce a probabilistic climate change projection where, for the first time, model uncertainty is accounted for in a complete and physically consistent manner.