Atmospheric Waves and Regional Weather Extremes

大气波和区域极端天气

• 批准号: RGPIN-2020-05783
• 负责人: White, Rachel
• 金额: $ 2.55万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=740825
• 关键词: Atmospheric; Waves; Regional; Weather; Extremes

Literature Review and Recent Progress Heatwaves, cold snaps, flooding and other extreme weather can have a devastating impact on humanity, with heightened suffering and mortality, as well as economic losses. For example, the 2010 Russian heatwave caused ~55,000 deaths, a 25% reduction in annual crop yield, and ~US$15 billion in economic losses. Clearly, skillful predictions of such events with lead-times of weeks to months can provide huge benefits to humankind; unfortunately, our present abilities to do this are extremely limited. Large-scale atmospheric waves are associated with many such events. Recently, I have shown that such waves are connected with particular patterns of atmospheric flow: atmospheric waveguides. These waveguides may provide a source of probabilistic seasonal to sub-seasonal (S2S) predictability. Objectives and HQP My overarching long-term research objective is to increase understanding and predictability, and reduce the impacts of extratropical extreme weather events, by determining how the probability of such events is influenced by large-scale atmospheric flow and associated compounding factors. Within the scope of this Discovery Grant research program progress towards this goal will be reached through a series of studies led by 9 MSc and PhD HQP under my mentorship. I will actively recruit HQP from under-represented and disadvantaged groups, and consider HQP applications from a holistic perspective. The work is organized into four short-term objectives: 1. Investigation of connections between extreme events and large-scale atmospheric flow and related predictability in present-day and future climates; 2. Quantifying effects of compounding factors on the probability and prediction of extreme events in present-day and future climates; 3. Quantifying and understanding prediction skill and biases in state-of-the-art S2S dynamical forecasts of extreme events; and 4. Developing and validating empirical and hybrid empirical-dynamical S2S probabilistic extreme events forecasts. Methodology This work will be accomplished using quantitative statistical and machine-learning analysis on gridded observational data, as well as ensembles of climate model and forecast simulations. Causal effect networks will be used to establish causal links, allowing a deeper investigation of the underlying dynamics of our climate system. Established causal links will then be used to develop empirical probabilistic S2S forecasts. Impact Significantly, this research program will forward our understanding of the atmospheric dynamics associated with extreme events, allowing a deeper understanding of model biases, and how to mitigate them. A hybrid empirical-dynamical early warning system will be developed for extreme events over Canada to provide more accurate probabilistic forecasts of extreme events at longer lead times than is currently available. Collaboration with end-users will help provide targeted and tailored information to maximise impact.

热浪、寒流、洪水和其他极端天气可能对人类造成毁灭性影响，加剧人类的痛苦和死亡率，以及经济损失。例如，2010年俄罗斯热浪造成约55，000人死亡，农作物年产量减少25%，经济损失约150亿美元。显然，对这些提前数周到数月的事件进行巧妙的预测可以为人类带来巨大的利益;不幸的是，我们目前的能力非常有限。大尺度大气波与许多这样的事件有关。最近，我证明了这种波与大气流动的特定模式有关：大气波导。这些波导可以提供概率性季节到亚季节（S2 S）可预测性的来源。目标和HQP我的首要长期研究目标是增加理解和可预测性，并通过确定此类事件的概率如何受到大规模大气流动和相关复合因素的影响来减少极端天气事件的影响。在这个发现补助金研究计划的范围内，朝着这一目标的进展将通过一系列的研究，由9个硕士和博士HQP在我的指导下领导。我会积极从代表性不足和弱势群体中招募HQP，并从整体角度考虑HQP申请。这项工作分为四个短期目标：1。研究极端事件和大尺度大气流动之间的联系以及当前和未来气候的相关可预测性; 2.量化复合因素对当前和未来气候中极端事件的概率和预测的影响; 3.量化和理解极端事件的最新S2 S动态预报中的预测技巧和偏差; 4.开发和验证经验和混合动力学S2 S概率极端事件预报。这项工作将利用对网格观测数据的定量统计和机器学习分析以及气候模型和预报模拟的集合来完成。因果关系网络将用于建立因果联系，从而能够更深入地调查我们气候系统的潜在动态。建立因果关系，然后将用于开发经验概率S2 S预测。重要的是，这项研究计划将促进我们对与极端事件相关的大气动力学的理解，从而更深入地了解模型偏差以及如何减轻这些偏差。将为加拿大的极端事件开发一个气象-动力混合预警系统，以便在比目前更长的时间内提供更准确的极端事件概率预报。与最终用户的合作将有助于提供有针对性和量身定制的信息，以最大限度地发挥影响。