The Future of Extreme European Winter Weather

欧洲极端冬季天气的未来

• 批准号: NE/S014713/1
• 负责人: Peter Watson
• 金额: $ 66.6万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS014713%2F1
• 关键词: Future; Extreme; European; Winter; Weather

Every time there is an extreme weather event, there is much speculation about the role played by climate change. Questions that are particularly relevant for Europeans include how is climate change affecting the occurrence of flooding brought about by severe storminess, like that seen in the 2013/14 winter, or storms Emma and Friederike in 2017/18? How is it affecting cold air outbreaks like the "Beast from the East" in 2018? Until now, our capacity for studying the impact of climate change on weather extremes has been limited. This is because we could not run atmospheric models that can successfully simulate weather events like these for long enough to be able to study climate change's effect - this requires thousands of years of simulation time. But I have recently led development of a system that can achieve this, using distributed computing and an atmospheric model with a fine enough grid spacing to be able to simulate extreme storms and anticyclones realistically. I will exploit this system to show how climate change is affecting extreme weather systems, with a particular focus on winter storms in Europe. This will done by seeing how the model simulations respond to specified changes in greenhouse gas levels and other atmospheric constituents and changes in the oceans and sea ice associated with climate change. This allows an exquisite level of control over the climate change scenario being simulated, and makes it possible to simulate a wide range of futures.However, it is not sufficient to run a single climate model and just report its best estimate of climate change's effect. It also needs to be shown what is the range of possible outcomes. This is necessary to ensure that decision-makers can prepare for the worst possible outcomes whilst not wasting resources on adapting to scenarios that are very unlikely. It is also very important to understand the physical mechanisms behind climate change's impact, so that we can make better judgements about how much to trust the model simulations.I will investigate the main sources of uncertainty in how climate change will affect weather extremes in a given future scenario: 1. Uncertainty about how to best model the atmosphere.2. Uncertainty about how atmospheric dynamics will change, for example the shift in the mean latitude of the jet stream, or changes in the frequency of blocking events (when stable, high-pressure systems form and divert storms to the north and south).3. Uncertainty in the future factors that will influence the atmosphere, namely ocean temperatures, ice cover and the atmospheric chemical composition (including man-made greenhouse gases and aerosols).To understand the mechanisms behind how climate change is affecting Europe's weather, I will first separate the changes in man-made greenhouse gas and aerosol levels in the atmosphere from the oceanic changes, then further divide the latter into changes in different regions (such as the North Atlantic, the Arctic, the tropics etc.). Experiments will be done to see the effect of each change separately. This will help to answer questions such as how are North Atlantic storms affected by warming of the ocean waters there, which may invigorate storms by evaporating more water, providing more fuel for their growth? Are changes more due to remote influences of the tropics and Arctic? There has been much speculation in the media about the melting of sea ice in the Arctic being a driver of European extreme weather, for instance, but is still very scientifically controversial, and its impact on extreme weather events has not been studied in this way before.Overall, this research will give us much greater confidence and understanding about how climate change will be felt through extreme European winter weather, informing governments and industries about the difference that reducing greenhouse gas emissions will make, and helping decision-makers to plan for the future.

每次发生极端天气事件，都会有很多人猜测气候变化所起的作用。与欧洲人特别相关的问题包括，气候变化如何影响2013/14年度冬季的严重暴风雨或2017/18年度的艾玛和弗里德里克风暴带来的洪水的发生？它对2018年的冷空气爆发有何影响，比如《来自东方的野兽》？到目前为止，我们研究气候变化对极端天气的影响的能力一直有限。这是因为我们不能运行大气模型来成功地模拟这样的天气事件足够长的时间来研究气候变化的影响--这需要数千年的模拟时间。但我最近领导了一个可以实现这一目标的系统的开发，使用分布式计算和一个具有足够精细的网格间距的大气模型，能够真实地模拟极端风暴和反气旋。我将利用这个系统来展示气候变化如何影响极端天气系统，特别是欧洲的冬季风暴。这将通过观察模型模拟如何响应温室气体水平和其他大气成分的特定变化，以及与气候变化相关的海洋和海冰的变化来实现。这允许对正在模拟的气候变化情景进行精确的控制，并使模拟广泛的未来成为可能。然而，运行单一的气候模型并仅报告其对气候变化影响的最佳估计是不够的。还需要向人们展示可能的结果范围是什么。这是必要的，以确保决策者能够为可能出现的最坏结果做好准备，同时不会浪费资源来适应不太可能出现的情况。了解气候变化影响背后的物理机制也是非常重要的，这样我们才能更好地判断模型模拟的可信度。我将调查在给定的未来情景中，气候变化将如何影响极端天气的主要不确定性来源：1.关于如何最好地模拟大气的不确定性。关于大气动力学将如何变化的不确定性，例如急流平均纬度的变化，或阻塞事件频率的变化(当稳定的高压系统形成并将风暴转向北方和南方时)。未来影响大气的因素，即海洋温度、冰盖和大气化学成分(包括人造温室气体和气溶胶)存在不确定性。为了了解气候变化如何影响欧洲天气背后的机制，我将首先将大气中人为温室气体和气溶胶水平的变化与海洋变化分开，然后进一步将后者划分为不同地区(如北大西洋、北极、热带等)的变化。将分别进行实验，以观察每种变化的效果。这将有助于回答诸如北大西洋风暴是如何受到那里海水变暖的影响的问题，那里的海水变暖可能会通过蒸发更多的水来增强风暴的活力，为风暴的生长提供更多的燃料？这些变化是否更多地归因于热带和北极的遥远影响？例如，关于北极海冰融化是欧洲极端天气的驱动因素，媒体有很多猜测，但在科学上仍然存在很大争议，而且它对极端天气事件的影响以前没有被以这种方式研究过。总体而言，这项研究将给我们更大的信心和理解，让我们更有信心和理解气候变化将如何通过欧洲极端冬季天气来感受，让政府和行业了解减少温室气体排放将产生的影响，并帮助决策者规划未来。

项目成果

Generating samples of extreme winters to support climate adaptation

生成极端冬季样本以支持气候适应

• DOI: 10.1002/essoar.10508424.1
• 发表时间: 2021
• 作者: Leach N

Using Deep Learning for an Analysis of Atmospheric Rivers in a High-Resolution Large Ensemble Climate Data Set

使用深度学习分析高分辨率大型集合气候数据集中的大气河流

• DOI: 10.1029/2022ms003495
• 发表时间: 2023
• 期刊: Journal of Advances in Modeling Earth Systems
• 影响因子: 6.8
• 作者: Higgins T

Deep Learning for Downscaling Tropical Cyclone Rainfall to Hazard-Relevant Spatial Scales

• DOI: 10.1029/2022jd038163
• 发表时间: 2023-05-27
• 期刊: JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
• 影响因子: 4.4
• 作者: Vosper，Emily;Watson，Peter;Mitchell，Dann
• 通讯作者: Mitchell，Dann

Larger Spatial Footprint of Wintertime Total Precipitation Extremes in a Warmer Climate

• DOI: 10.1029/2020gl091990
• 发表时间: 2021-04-28
• 期刊: GEOPHYSICAL RESEARCH LETTERS
• 影响因子: 5.2
• 作者: Bevacqua, Emanuele;Shepherd, Theodore G.;Mitchell, Dann
• 通讯作者: Mitchell, Dann

Machine learning emulation of a local-scale UK climate model

• DOI: 10.48550/arxiv.2211.16116
• 发表时间: 2022-11
• 期刊: ArXiv
• 作者: Henry Addison;E. Kendon;Suman V. Ravuri;L. Aitchison;Peter A. G. Watson