Generating samples of extreme winters to support climate adaptation
Generating samples of extreme winters to support climate adaptation
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生成极端冬季样本以支持气候适应
DOI：
10.1002/essoar.10508424.1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Leach N
中科院分区：
文献类型：
--
作者：
Leach N
最近全球各地的极端天气地球仪突出表明，需要了解当今发生更多极端事件的可能性，以及这些事件如何随着全球变暖而变化。我们提出了一种在未来气候预测中更有效地采样极端情况的方法。作为概念验证，我们研究了英国最新的国家气候预测（UKCP18）。UKCP18包括一个由15个成员组成的耦合全球模拟扰动参数集合（PPE），提供了一系列的气候预测，包括内部变率和强迫响应的不确定性。然而，这一总体太小，无法充分采样决策者和适应规划者感兴趣的重现期很高的极端情况。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的计算机上运行三个1000个成员的初始条件集合，其中只有大气HadAM4模型的分辨率为60公里，从UKCP18集合中的三个不同的未来极端冬季中获取边界条件。我们发现，每个冬季极端的幅度被捕获在我们的合奏，和三个合奏中的两个条件对生产极端的边界条件。我们的集成包含几个极端，预计只有超过500名成员的UKCP 18 PPE才能进行采样，这对于当前的超级计算资源来说是非常昂贵的。我们模拟的最极端的冬天超过UKCP18内的0.85 K和37%的英国冬季平均日最高气温和降水量分别为现今平均值。因此，我们的合奏包含一组丰富的多元，时空和物理相干的极端冬季样本，具有广泛的潜在应用。
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.
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