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.

全球最近的极端天气凸显了了解当今更多极端事件的可能性以及此类事件如何随着全球变暖而变化的必要性。我们提出了一种在未来气候预测中更有效地采样极端值的方法。作为概念验证，我们研究了英国最新的一组国家气候预测 (UKCP18)。UKCP18 包括一个 15 成员的全球耦合模拟扰动参数集合 (PPE)，提供了一系列气候预测，其中包括内部变率和强制响应的不确定性。然而，这个集合太小，无法充分采样具有非常高的回归期的极端值，这对政策制定者和适应规划者很感兴趣。为了更好地了解这些事件的统计数据，我们使用分布式计算在志愿者计算机上以 60 公里分辨率运行三个 1000 名成员的初始条件集合，其中只有大气的 HadAM4 模型，从 UKCP18 集合中三个不同的未来极端冬季获取边界条件。我们发现每个冬季极端的幅度都在我们的集合中被捕获，并且三个集合中的两个被限制在边界条件下产生极端。我们的集合包含几个极端情况，预计这些极端情况只能由超过 500 名成员的 UKCP18 PPE 进行采样，这对于当前的超级计算资源来说过于昂贵。我们模拟的最极端冬季分别比 UKCP18 内的冬季高出 0.85 K 和英国冬季每日最高气温和降水平均值的 37%。因此，