欧洲复合风雨极端事件：从气候到金融风险的不确定因素分析

该博士项目旨在研究欧洲西部因复合风力和降雨极端天气导致的未来财务损失的不确定性问题。通过运用机器学习与多变量极值统计方法，该项目旨在建立一套分析框架，以揭示气候预测与天气事件如何转化为财务风险，并研究影响损失估算的潜在因素。

Job description: Details: Background: Europe’s most damaging weather rarely occurs as a single hazard. Winter windstorms can coincide with heavy rainfall, severe events can cluster within weeks, and a single weather system can affect multiple countries. These “compound extremes” can cause financial losses far greater than individual hazards, yet we have limited understanding of how uncertain these losses are in a changing climate. New climate ensembles and machine-learning methods make it possible to explore rare and interconnected extremes, but uncertainty enters at every stage: from climate models and natural variability, through rare-event statistics, to translating hazards into financial losses. A key challenge is to identify which uncertainties dominate future loss estimates, how they propagate through the climate-to-loss chain, and which can be constrained using observations and improved modelling. This research will support more robust estimates of climate-related financial risk. PhD project: How uncertain are future financial losses from compound wind and rainfall extremes, what drives this uncertainty, and which uncertainties can be reduced? This PhD will develop a framework to understand how uncertainty propagates from climate projections and regional weather extremes to financial losses. The aim is not simply to estimate how compound events will change, but to determine why future loss estimates are uncertain, how different sources interact, and which matter most for financial risk. The student will investigate uncertainty in the frequency, intensity and temporal clustering of compound wind and rainfall events across Western Europe, focusing on poorly sampled joint tails where small differences in hazard dependence and return periods can produce large differences in estimated losses. Large climate-model ensembles, multivariate extreme-value statistics and machine-learning emulators will be combined to explore rare events at the scale needed to quantify these uncertainties. A key innovation will be to decompose uncertainty across the climate-to-loss chain, distinguishing contributions from climate-model structure and parameters, internal variability, future scenarios, statistical estimation of rare compound events, spatial and temporal aggregation, and the translation of hazards into financial losses. Sensitivity and uncertainty-propagation methods will identify which sources dominate decision-relevant loss metrics. The project will also assess where uncertainty can be constrained using observations and historical events, distinguishing reducible uncertainties arising from limitations in models or data from fundamentally irreducible uncertainty. Working with Aon Impact Forecasting, the student will test these advances across the UK, France, Germany, Belgium and the Netherlands, developing transferable methods for other compound hazards and regions. Results will support robust catastrophe-risk estimates and climate risk decisions across Europe. Applicant Profile: We welcome students with a strong background in physics, meteorology, mathematics, statistics, and/or any related experience in quantitative disciplines who are excited by applying scientific and computational methods to real-world climate risk and decision-making. Strong Python programming skills are highly desirable and will be valuable throughout the project. Experience with statistics, machine learning, climate science, or catastrophic modeling would be advantageous, but no prior knowledge is required. Training will be provided in the relevant climate, statistical, and risk-modelling concepts. The project would particularly suit a student who enjoys working across disciplines and is interested in understanding not only what future climate risks might be, but also why estimates of those risks are uncertain and how scientific evidence can improve them. The project will not involve developing or running climate models; instead, the student will work primarily with publicly available climate-model outputs, applying advanced statistical, computational and machine-learning approaches to understand uncertainties in compound weather extremes and their impacts.

资助说明： 该项目属于 UNRISK CDT 项目的一部分，该项目提供 15-18 项由全额资助的 NERC 学生奖学金，涵盖完整的大学学费；按标准 UKRI 标准发放的个人津贴；6000 英镑的个人研究与培训费用；5000 英镑（每名学生）的群体级培训费用；以及用于特殊项目的“灵活基金”。 国际申请者需承担申请学生签证及国际健康附加费相关的费用（IHS） 申请面向英国及国际学生开放。根据 UKRI 规定，国际学生的录取名额有限。 有关更多信息可访问 [会员链接] 的 UNRISK 网站。