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ExSamples: Generating samples of extreme winters to support climate adaptation

Wed 26th January 2022, Online

Speakers:  David Sexton (Met Office) and Nick Leach (University of Oxford)

Abstract: Recent extreme weather in the UK highlights the need to understand the potential for more extreme events in the present–day, and how such events may change with global warming. We present a methodology for more efficiently sampling extremes in future climate projections. As a proof–of–concept, we use the UK’s most recent set of national Climate Projections (UKCP18). UKCP18 includes a 15–member perturbed parameter ensemble (PPE) of coupled global simulations, providing a range of climate projections incorporating uncertainty in both internal variability and forced response. However, this ensemble is too small to adequately sample extremes with return periods over 100 years, which are of interest to policy–makers and adaptation planners. To better understand the statistics of these events, we use distributed computing to run three ~1000–member initial–condition ensembles with the atmosphere–only HadAM4 model at 60km resolution on volunteers’ computers, taking boundary conditions from future extreme winters within the UKCP18 ensemble. This experimental setup allows us to explore the uncertainty surrounding these rare extreme events in UKCP18 more completely, which includes determining whether they were genuinely exceptional events, or if they could have been even greater extremes.

We find that every UKCP18 extreme winter is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. The ensemble contains physically, spatially and temporally coherent information for several extremes that would only be expected to be sampled by a UKCP18 PPE of over 500 members, which would be prohibitively expensive with current supercomputing resource. The most extreme winters simulated lie above those for UKCP18 by 0.85K for daily maximum temperature and 37% of the present–day average for UK precipitation. Therefore, the ensembles form a prototype product for use in impacts studies that require large samples.