Model Uncertainty

Optimal representing model uncertainty is a key challenge in climate modelling. Explicit representation of sub-gridscale uncertainties has been shown to provide a wealth of benefits to forecasts; reducing model biases, improving skill and fundamentally giving better estimates of forecast uncertainty along with the forecast. We study the impact of different methods of model uncertainty representation, particularly stochastic physics, and are in the process of developing new methods.

Inexact computing

We study the use of inexact hardware in numerical weather and climate models. Inexact hardware is promising a reduction of computational cost and power consumption of supercomputers and could be a shortcut to higher resolution forecasts with higher forecast accuracy. 


We study the predictability of the climate system on multiple timescales. We have a particular focus on the seasonal scale, looking at the skill of retrospective hindcasts across multiple domains and their potential use for societally beneficial applications.

Making use of forecasts

We are engaging with a broad spectrum of users to evaluate the potential for using forecasts to inform decision-making. We work across a wide range of timescales, from weeks to seasons ahead.

Seamless Predictions

Numerical weather and climate predictions exist for a range of time scales from days to weeks, seasons, year and decades. While these forecasts are based on similar global circulation models of the coupled atmosphere-ocean-sea-ice-land system, the forecasts are often not "seamless", that is they don't cover all these time ranges. Instead and mostly for convenience, forecasts for different time ranges are produced separately. This approach leaves users of the forecasts with the problem of bringing the different products together if their horizon of interest spans different forecast ranges. We actively research how to best combine and merge different forecast products into a seamless prediction.