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Generating samples of extreme winters to support climate adaptation
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  • Nicholas James Leach,
  • Peter A. G. Watson,
  • Sarah Sparrow,
  • David C H Wallom,
  • David Sexton
Nicholas James Leach
University of Oxford

Corresponding Author:[email protected]

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Peter A. G. Watson
School of Geographical Sciences
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Sarah Sparrow
University of Oxford
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David C H Wallom
University of Oxford
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David Sexton
UK Met Office
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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. 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. 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 simulated lie above those for UKCP18 by 0.85K for daily maximum temperature and 37% of the present-day average for precipitation (UK winter means).
Jun 2022Published in Weather and Climate Extremes volume 36 on pages 100419. 10.1016/j.wace.2022.100419