Midlatitude storm tracks and their impacts in a changing climate

Background

Midlatitude (or extratropical) storm tracks are a dominant force in shaping day-to-day weather variability across the mid-latitudes, often driving some of the largest insured losses over Europe due to their associated strong winds and heavy precipitation. Accurate projections of these storms are therefore critical for improving preparedness and assessing associated risks. Projecting changes of extratropical storm tracks and their associated hazards into the future relies on global circulation models that represent atmospheric interactions through complex mathematical equations. However, these models are subject to various sources of uncertainty that require rigorous quantification to assess the reliability of their projections (Little et al 2023, Priestley and Catto 2022). Such quantification is essential for policymakers to make informed climate-related decisions.

PhD Opportunity

This project aims to quantify the uncertainty in projections of the extratropical storm tracks and the extreme wind and precipitation associated with them, including compound or co-occurring extremes (Owen et al 2021). During the project we will develop and apply uncertainty quantification (UQ) techniques, specifically uncertainty and sensitivity analyses (Saltelli 2008), to the output of computational models used for predicting extratropical storms and their associated hazards. Simple models will be developed to explain the storm track and hazard timeseries projections. Sources of uncertainty in the models will include climate model variability, internal variability, and changing relationships between the input factors. Uncertainty analysis deals with propagating uncertainty through the model and characterising the probability distribution of its outputs. Sensitivity analysis identifies the most influential input parameters, helping to reduce model complexity by focusing on the most impactful inputs. However, performing these analyses requires many model runs, which may not be affordable if each run is time-consuming. To address this, the project will employ statistical emulators, such as Gaussian processes (Rasmussen and Williams 2005), that provide fast approximations of the full model based on a limited number of simulations, enabling efficient UQ for storm track projections and their impacts.

By improving the uncertainty quantification for storm track projections, the project will highlight areas of importance for climate model development, as well as providing more confident projections of regional weather hazard changes to improve resilience and help with disaster risk reduction.

Applicant Profile

Students with a strong quantitative background and an interest in understanding future climate risks from extreme weather events.

Other information

References:
Little, A.S., Priestley, M.D.K. & Catto, J.L. (2023). Future increased risk from extratropical windstorms in northern Europe. Nat Commun 14, 4434. https://doi.org/10.1038/s41467-023-40102-6
Owen L E, Catto J L, Stephenson D B and Dunstone N J (2021) Compound precipitation and wind extremes over Europe and their relationship to extratropical cyclones Weather and Climate Extremes, 33, https://doi.org/10.1016/j.wace.2021.100342.
Priestley, M. D. K. and Catto, J. L.: Future changes in the extratropical storm tracks and cyclone intensity, wind speed, and structure, Weather Clim. Dynam., 3, 337–360, https://doi.org/10.5194/wcd-3-337-2022
Rasmussen, C. E., & Williams, C. K. I. (2005). Gaussian processes for machine learning (adaptive computation and machine learning). The MIT Press.