Reliable Climate Projections: The Final Frontier for Stochastic Parametrisation

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.

The following groups will benefit from this research

Operational forecasting centres

The new stochastic schemes I produce will be included into two operational models during the project: the European Centre for Medium Range Weather Forecasts and the UK Met Office. The new suite of stochastic parametrisation schemes will improve the skill of forecasts on a range of timescales. In particular, the reliability of weather and seasonal forecasts will improve, which will make them more useful for end users. This will directly benefit the modelling centres, as it increases the economic value of their forecasts. The schemes will also be suitable for use in climate models, where they have the potential to improve probabilistic climate projection. It is anticipated that other modelling groups worldwide will readily adopt the schemes.

Policy makers

Using a complete set of physically motivated stochastic parametrisation schemes to represent model uncertainty has the potential to improve probabilistic climate projection compared to the perturbed parameter or multi-model approaches currently used. The new schemes will improve the reliability of the projection for a given forcing scenario. This will directly benefit policy makers, who can make better-informed decisions. This will increase the effectiveness of policy, and facilitate planning to mitigate and adapt to climate change.

Forecast users

Improving operational probabilistic weather and seasonal forecasts improves our ability as a society to prepare, both for severe weather events such as storms, and for seasonal warnings of drought or flooding. A better-calibrated early warning system allows both companies and members of the public to plan and react. This research will therefore benefit society and especially those sectors of society that use weather and climate models for quantitative decision-making. Examples include:

- Industry
Forecast users in industry will benefit economically from the improved forecasts on a range of timescales. For example, the UK rail network can decide whether to invest in new 'slab tracks' that are very costly, but more resistant to buckling in hot weather, given that the number of hot days is predicted to increase due to changes in climate. On a shorter timescale, forecasts of extreme wind could inform a wind farm manager of the need to shut down the wind turbines to prevent damage in a storm. In both cases, if the forecasts used to inform these decisions are unreliable, businesses may make decisions that are costly to them (e.g. shutting down a turbine), while the benefits of making that decision are never realised (e.g. the storm does not materialise). Similarly, improved forecasts will directly increase the economic performance of these industries, and thereby improve the economic competitiveness of the United Kingdom.

- Charity sector
Improved forecasts on seasonal timescales have many important humanitarian applications, including the likelihood of floods or droughts for agricultural planning, and the projections of the prevalence of infectious diseases in a given season. The charity sector is showing an interest in using seasonal forecast information to plan relief programs in regions at risk: improved reliability of seasonal forecasts will improve the efficacy of these projects.

- Public
Improved weather forecasts benefit the public, as the information can be integrated into personal plans for hours or days ahead. This increased confidence in forecasts will in return be beneficial to the forecasting centres. The public will also benefit through improved decision-making by industries that provide public services, such as water companies, transport providers, and local government. Longer term, the health and life of the general public will benefit from an increased resilience to climate change, enabled by good policy decisions made today.