Generating samples of extreme winters to support climate adaptation

Recent extreme weather across the globe 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 examine 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 very high return periods, 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 three distinct future extreme winters within the UKCP18 ensemble. We find that the magnitude of each winter extreme is captured within our ensembles, and that two of the three ensembles are conditioned towards producing extremes by the boundary conditions. Our ensembles contain 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 we simulate exceed those within UKCP18 by 0.85 K and 37% of the present-day average for UK winter means of daily maximum temperature and precipitation respectively. As such, our ensembles contain a rich set of multivariate, spatio-temporally and physically coherent samples of extreme winters with wide-ranging potential applications.

Automated summary

Recent extreme weather events around the world highlight the importance of studying present and future extreme events under global warming. This study presents a methodology for efficiently sampling extreme events in future climate projections. By examining the UK's national Climate Projections, the researchers find that the current ensemble of climate projections is too small to adequately capture extreme events with very high return periods. To address this issue, they use distributed computing to run additional ensembles and find that these ensembles contain extreme events that would require a much larger ensemble size to sample with current computing resources. The study suggests that these ensembles provide valuable and comprehensive samples of extreme events for various applications.