Collaborative Research: Fingerprinting Energy and Water Cycle Extremes through a Scale-Interaction Lens

极端天气事件，如热浪、冷空气爆发（CAO）、干旱和强降水，通常会造成北方中西部或南方大平原地区的破坏。 这些极端情况通常嵌入在大尺度气象模式（LMP）中。 例如，干旱可能发生在高压中心，这是起源于北太平洋并延伸到美国大陆的高潮和低谷模式的一部分。 LMPs可能反过来产生或影响行星尺度气候模式（PCM），如厄尔尼诺/南方涛动（ENSO）事件和北极涛动（AO）。因此，要理解和预测极端天气事件，或者预测气候变化导致的极端天气事件频率和强度的变化，必须从多尺度的角度来考虑这些事件，认识到区域、大陆和行星尺度的相互作用。这里所做的工作旨在开发一套全面的多尺度指标，或“指纹”，用于描述导致区域极端天气事件的大气过程。 一旦指纹被开发出来，它们就被用来确定大规模过程导致或强烈影响区域极端现象的动力机制。 该研究还使用指纹和随后的动力学分析来评估气候模式模拟区域极端事件及其与LMP和PCM的关联的现实性。 一种分析技术使用两层准地转模式中的位势高度趋势方程来确定“最佳模式”，即最佳形状的波型，以从平均流中提取能量，在几天内实现快速增长。 主要研究人员（PI）及其合作者先前的工作使用这种方法表明，行星尺度的环流异常可以改变平均状态，从而促进促进CAO的最佳模式的增长。 额外的先前工作链接的长期减少夏季赤道到极点的温度对比减少在北美的锋面天气系统，这反过来又促进干燥和温暖的夏天在美国。这项工作是社会以及科学的兴趣，鉴于极端天气造成的破坏。 该项目支持一名研究生和一名博士后研究员，从而培养该研究领域未来的劳动力。 PI在格鲁吉亚技术与教育中心整合科学，数学和计算（CEISMC），进行K-12在亚特兰大地区的学校推广，并组织夏季研讨会集中在代表性不足的学生。佛罗里达州立大学的PI与本科生合作，对大陆尺度的CAO进行长期预测，这些预测已被农业、能源和水利部门的利益相关者使用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Extreme weather events such as heat waves, cold air outbreaks (CAOs), droughts, and heavy precipitation commonly cause disruptions over regions the size of the northern midwest or the southern great plains. These extremes are generally embedded in large-scale meterological patterns (LMPs). For instance a drought might occur within a high-pressure center which is part of a pattern of highs and lows originating over the North Pacific and extending across the continental US. The LMPs may in turn be generated or influenced by planetary-scale climate modes (PCMs) such as El Nino/Southern Oscillation (ENSO) events and the Arctc Oscillation (AO). Thus efforts to understand and predict extreme weather events, or to anticipate changes in their frequency and intensity due to climate change, must consider the events from a multi-scale perspective that recognizes the interplay of regional, continental, and planetary scales that contribute to them.Work performed here seeks to develop a comprehensive suite of multi-scale metrics, or "fingerprints", for characterizing the atmospheric processes responsible for regional extreme weather events. Once the fingerprints are developed they are used to determine the dynamical mechanisms through which larger-scale processes lead to, or strongly influence, the regional extremes. The research also uses the fingerprints and subsequent dynamical analysis to assess the realism of climate model simulations of regional extremes and their associations with LMPs and PCMs. One analysis technique uses the the tendency equation for geopotential height in a two-layer quasi-geostrophic model to identify "optimal modes", meaning wave patterns which are optimally shaped to extract energy from the mean flow to achieve rapid growth over a few days. Prior work by the Principle Investigators (PIs) and their collaborators used this approach to show that planetary-scale circulation anomalies can alter the mean state in a way that promotes the growth of an optimal mode that promotes CAOs. Additional prior work links the long-term reduction in the summertime equator-to-pole temperature contrast to a reduction in frontal weather systems over North America, which in turn promotes dryer and warmer summers in the US.The work is of societal as well as scientific interest given the disruptions caused by extreme weather. The project supports a graduate student and a postdoctoral research fellow, thereby developing the future workforce in this research area. The PI at Georgia Tech works with the Center for Education Integrating Science, Mathematics and Computing (CEISMC), which conducts K-12 outreach in Atlanta-area schools and organizes summer workshops focused on underrepresented students. The PI at Florida State University works with undergraduate students to produce long-range forecasts of continental-scale CAOs, which have been used by stakeholders from the agriculture, energy, and water sectors.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.