Atmospheric Waves and Regional Weather Extremes

热浪、寒流、洪水和其他极端天气会对人类造成毁灭性的影响，增加苦难和死亡率，并造成经济损失。例如，2010年俄罗斯的热浪造成约55,000人死亡，作物年产量减少25%，经济损失约150亿美元。显然，对这类提前数周至数月的事件进行熟练的预测，可以为人类带来巨大的利益；不幸的是，我们目前在这方面的能力非常有限。大尺度大气波与许多这样的事件有关。最近，我已经证明了这种波与大气流动的特定模式有关：大气波导。这些波导可以提供季节到次季节（S2S）可预测性的概率来源。我的总体长期研究目标是通过确定温带极端天气事件的概率如何受到大尺度大气流动和相关复合因素的影响，来增加对这些事件的了解和可预测性，并减少其影响。在这个发现基金研究项目的范围内，将通过我指导的9名HQP硕士和博士领导的一系列研究来实现这一目标。我会积极从弱势及弱势群体中招募优秀人才，并从整体角度考虑优秀人才的申请。这项工作分为四个短期目标：研究极端事件与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. 复合因子对当前和未来气候极端事件的概率和预测的量化影响；3. 量化和理解最先进的S2S极端事件动态预报的预测技巧和偏差和4。开发和验证经验和混合经验-动力S2S概率极端事件预测。这项工作将通过对网格观测数据的定量统计和机器学习分析，以及气候模式和预测模拟的集合来完成。因果效应网络将用于建立因果关系，从而对气候系统的潜在动态进行更深入的调查。建立的因果关系将用于开发经验概率S2S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，使我们能够更深入地了解模式偏差，以及如何减轻它们。将为加拿大的极端事件开发一种混合经验-动态早期预警系统，以提供比目前更准确的极端事件概率预测，提前时间更长。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。

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.