Estimating unprecedented extremes in UK summer daily rainfall. (6th January 2022) - Kent, Chris; Dunstone, Nick; Tucker, Simon; Scaife, Adam A; Brown, Simon; Kendon, Elizabeth J; Smith, Doug; McLean, Lynsay; Greenwood, Shirley
Abstract: The UNSEEN (unprecedented simulated extremes using ensemble) method involves using a large ensemble of climate model simulations to increase the sample size of rare events. Here we extend UNSEEN to focus on intense summertime daily rainfall, estimating plausible rainfall extremes in the current climate. To address modelling limitations simulations from two climate models were used; an initialised 25 km global model that uses parameterised convection, and a dynamically downscaled 2.2 km model that uses explicit convection. In terms of the statistical characteristics that govern very rare return periods, the models are not significantly different from the observations across much of the UK. Our analysis provides more precise estimates of 1000 year return levels for extreme daily rainfall, reducing sampling uncertainty by 70%–90% compared to using observations alone. This framework enables observed daily storm profiles to be adjusted to more statistically robust estimates of extreme rainfall. For a damaging storm in July 2007 which led to surface water flooding, we estimate physically plausible increases in the total daily rainfall of 50%–100%. For much of the UK the annual chance of record-breaking daily summertime rainfall is estimated to be around 1% per year in the present-day climate. Analysis of the dynamical states in our UNSEEN events indicates that heavy daily rainfall is associated with a southward displaced and meandering North Atlantic jet stream, increasing the advection of warm moist air from across Southern Europe and the Mediterranean, and intensifying extratropical storms. This work represents an advancement in the use of climate modelling for estimating present-day climate hazards and outlines a framework for applying UNSEEN at higher spatial and temporal resolutions.