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\begin{document}

\title{Generating samples of extreme winters to support climate adaptation}



\author[1]{Nicholas James Leach}%
\author[2]{Peter A. G. Watson}%
\author[1]{Sarah Sparrow}%
\author[1]{David C H Wallom}%
\author[3]{David Sexton}%
\affil[1]{University of Oxford}%
\affil[2]{School of Geographical Sciences}%
\affil[3]{UK Met Office}%


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  \date{May 18, 2024}


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\begin{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 \textasciitilde{}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).%
\end{abstract}%



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